相关申请的交叉引用Cross-references to related applications
本申请主张在2022年8月3日在中国提交的中国专利申请No.202210926618.5的优先权，其全部内容通过引用包含于此。This application claims priority from Chinese Patent Application No. 202210926618.5 filed in China on August 3, 2022, the entire content of which is incorporated herein by reference.
本申请属于通信技术领域，具体涉及一种AI网络模型的性能监督方法、装置和通信设备。This application belongs to the field of communication technology, and specifically relates to a performance supervision method, device and communication equipment for an AI network model.
在相关技术中，可以采用人工智能(Artificial Intelligence，AI)网络模型来对无线通信网络中的终端进行定位。In related technologies, artificial intelligence (Artificial Intelligence, AI) network models can be used to locate terminals in wireless communication networks.
其中，无线网络环境的改变会影响AI网络模型的输入信息，干扰AI网络模型的输出结果，从而可能导致AI网络模型的定位精度无法满足终端的定位精度需求。Among them, changes in the wireless network environment will affect the input information of the AI network model and interfere with the output results of the AI network model, which may cause the positioning accuracy of the AI network model to fail to meet the positioning accuracy requirements of the terminal.
发明内容Contents of the invention
本申请实施例提供一种AI网络模型的性能监督方法、装置和通信设备，能够对AI网络模型的性能进行监督，从而及时发现AI网络模型的性能较低的情况。Embodiments of the present application provide a performance supervision method, device and communication equipment for an AI network model, which can supervise the performance of the AI network model, thereby promptly discovering situations where the performance of the AI network model is low.
第一方面，提供了一种AI网络模型的性能监督方法，该方法包括：The first aspect provides a performance supervision method for AI network models, which includes:
终端获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；The terminal obtains first information, the first information is used to determine the performance of a target AI network model, and the target AI network model is used to locate the terminal;
所述终端向网络侧设备发送所述第一信息，或者，所述终端根据所述第一信息确定所述目标AI网络模型的性能。The terminal sends the first information to a network side device, or the terminal determines the performance of the target AI network model based on the first information.
第二方面，提供了一种AI网络模型的性能监督装置，应用于终端，该装置包括：In the second aspect, a performance supervision device of an AI network model is provided, which is applied to a terminal. The device includes:
获取模块，用于获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；An acquisition module, configured to acquire first information, the first information being used to determine the performance of a target AI network model, and the target AI network model being used to position the terminal;
第一发送模块或者第一确定模块，所述第一发送模块用于向网络侧设备发送所述第一信息，所述第一确定模块用于根据所述第一信息确定所述目标AI网络模型的性能。A first sending module or a first determination module. The first sending module is used to send the first information to a network side device. The first determination module is used to determine the target AI network model according to the first information. performance.
第三方面，提供了一种AI网络模型的性能监督方法，包括：In the third aspect, a performance supervision method for AI network models is provided, including:
网络侧设备接收来自终端的第一信息，并根据所述第一信息确定目标AI网络模型的性能，其中，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；The network side device receives the first information from the terminal, and determines the performance of the target AI network model based on the first information, wherein the first information is used to determine the performance of the target AI network model, and the target AI network model is To locate the terminal;
或者，or,
所述网络侧设备接收来自终端的第五指示信息，其中，所述第五指示信息用于指示所述目标AI网络模型的性能。The network side device receives fifth indication information from the terminal, wherein the fifth indication information is used to indicate that the Describe the performance of the target AI network model.
第四方面，提供了一种AI网络模型的性能监督装置，应用于网络侧设备，所述装置包括：In the fourth aspect, a performance supervision device of an AI network model is provided, which is applied to network-side equipment. The device includes:
第三接收模块和第二确定模块，所述第三接收模块用于接收来自终端的第一信息，所述第二确定模块，用于根据所述第一信息确定目标AI网络模型的性能，其中，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；A third receiving module and a second determining module, the third receiving module is used to receive the first information from the terminal, and the second determining module is used to determine the performance of the target AI network model according to the first information, wherein , the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal;
或者，or,
第四接收模块，用于接收来自终端的第五指示信息，其中，所述第五指示信息用于指示所述目标AI网络模型的性能。The fourth receiving module is configured to receive fifth indication information from the terminal, where the fifth indication information is used to indicate the performance of the target AI network model.
第五方面，提供了一种通信设备，该通信设备包括处理器和存储器，所述存储器存储可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时实现如第一方面或第三方面所述的方法的步骤。In a fifth aspect, a communication device is provided. The communication device includes a processor and a memory. The memory stores a program or instructions that can be run on the processor. The program or instructions are implemented when executed by the processor. The steps of the method described in the first aspect or the third aspect.
第六方面，提供了一种通信设备，包括处理器及通信接口，其中，在所述通信设备为终端时，所述通信接口用于获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；所述通信接口还用于向网络侧设备发送所述第一信息或者所述处理器用于根据所述第一信息确定所述目标AI网络模型的性能；或者，In a sixth aspect, a communication device is provided, including a processor and a communication interface, wherein when the communication device is a terminal, the communication interface is used to obtain first information, and the first information is used to determine the target AI The performance of the network model, the target AI network model is used to locate the terminal; the communication interface is also used to send the first information to the network side device or the processor is used to determine based on the first information The performance of the target AI network model; or,
在所述通信设备为网络侧设备时，所述通信接口用于接收来自终端的第一信息，所述处理器用于根据所述第一信息确定目标AI网络模型的性能，其中，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；或者，所述通信接口用于接收来自终端的第五指示信息，其中，所述第五指示信息用于指示所述目标AI网络模型的性能。When the communication device is a network-side device, the communication interface is used to receive first information from a terminal, and the processor is used to determine the performance of the target AI network model according to the first information, wherein the first The information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal; or the communication interface is used to receive fifth indication information from the terminal, wherein the fifth indication The information is used to indicate the performance of the target AI network model.
第七方面，提供了一种通信系统，包括：终端和网络侧设备，所述终端可用于执行如第一方面所述的AI网络模型的性能监督方法的步骤，所述网络侧设备可用于执行如第三方面所述的AI网络模型的性能监督方法的步骤。In a seventh aspect, a communication system is provided, including: a terminal and a network side device. The terminal can be used to perform the steps of the performance supervision method of the AI network model as described in the first aspect. The network side device can be used to perform The steps of the performance supervision method of the AI network model as described in the third aspect.
第八方面，提供了一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面所述的方法的步骤，或者实现如第三方面所述的方法的步骤。In an eighth aspect, a readable storage medium is provided. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method are implemented as described in the first aspect. The steps of the method described in the third aspect.
第九方面，提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面所述的方法，或实现如第三方面所述的方法。In a ninth aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the method described in the first aspect. , or implement the method as described in the third aspect.
第十方面，提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如第一方面所述的AI网络模型的性能监督方法的步骤，或者所述计算机程序/程序产品被至少一个处理器执行以实现如第三方面所述的AI网络模型的性能监督方法的步骤。In a tenth aspect, a computer program/program product is provided, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement the method as described in the first aspect The steps of the performance supervision method of the AI network model, or the computer program/program product is executed by at least one processor to implement the steps of the performance supervision method of the AI network model as described in the third aspect.
在本申请实施例中，终端获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；所述终端向网络侧设备发送所述第一信息，或者，所述终端根据所述第一信息确定所述目标AI网络模型的性能。终端能够获取用于辅助确定目标AI网络模型的性能的第一信息，并向网络侧设备上报该第一信息，以使网络侧设备根据该第一信息来判断目标AI网络模型的性能，或者直接由终端根据该第一信息来判断目标AI网络模型的性能。这样，能够及时地发现目标AI网络模型的性能不能够满足定位需求的情况，进而能够根据该目标AI网络模型的性能不能够满足定位需求的结果来采取适当的措施，例如：更新目标AI网络模型、采用其他定位方式来对终端进行定位等。降低了因目标AI网络模型的定位性能不能够满足定位需求，而继续按照该目标AI网络模型的定位结果来执行相关的无线通信时，造成的无线通信性能低甚至出错的概率。In this embodiment of the present application, the terminal obtains first information, the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal; the terminal transmits information to the network side device Send the first information, or the terminal determines the performance of the target AI network model based on the first information. The terminal can obtain the first information used to assist in determining the performance of the target AI network model, and report the first information to the network side device, so that the network side device determines the performance of the target AI network model based on the first information, or directly The terminal determines the performance of the target AI network model based on the first information. In this way, it can be discovered in time that the performance of the target AI network model cannot meet the positioning requirements, and then appropriate measures can be taken based on the result that the performance of the target AI network model cannot meet the positioning requirements, such as updating the target AI network model. , use other positioning methods to locate the terminal, etc. This reduces the probability of low wireless communication performance or even errors when the positioning performance of the target AI network model cannot meet the positioning requirements and the wireless communication continues to be performed based on the positioning results of the target AI network model.
图1是本申请实施例能够应用的一种无线通信系统的结构示意图；Figure 1 is a schematic structural diagram of a wireless communication system to which embodiments of the present application can be applied;
图2是本申请实施例提供的一种AI网络模型的性能监督方法的流程图；Figure 2 is a flow chart of a performance supervision method for an AI network model provided by an embodiment of the present application;
图3是本申请实施例提供的另一种AI网络模型的性能监督方法的流程图；Figure 3 is a flow chart of another performance supervision method for an AI network model provided by an embodiment of the present application;
图4是本申请实施例提供的一种AI网络模型的性能监督装置的结构示意图；Figure 4 is a schematic structural diagram of a performance monitoring device for an AI network model provided by an embodiment of the present application;
图5是本申请实施例提供的另一种AI网络模型的性能监督装置的结构示意图；Figure 5 is a schematic structural diagram of another performance monitoring device for an AI network model provided by an embodiment of the present application;
图6是本申请实施例提供的另一种AI网络模型的性能监督装置的结构示意图；Figure 6 is a schematic structural diagram of another performance monitoring device for an AI network model provided by an embodiment of the present application;
图7是本申请实施例提供的一种通信设备的结构示意图；Figure 7 is a schematic structural diagram of a communication device provided by an embodiment of the present application;
图8是本申请实施例提供的一种终端的硬件结构示意图；Figure 8 is a schematic diagram of the hardware structure of a terminal provided by an embodiment of the present application;
图9是本申请实施例提供的一种网络侧设备的硬件结构示意图。Figure 9 is a schematic diagram of the hardware structure of a network side device provided by an embodiment of the present application.
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art fall within the scope of protection of this application.
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述特定的顺序或先后次序。应该理解这样使用的术语在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”所区别的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和/或”表示所连接对象的至少其中之一，字符“/”一般表示前后关联对象是一种“或”的关系。The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and that "first" and "second" are distinguished objects It is usually one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.
值得指出的是，本申请实施例所描述的技术不限于长期演进型(Long Term Evolution，LTE)/LTE的演进(LTE-Advanced，LTE-A)系统，还可用于其他无线通信系统，诸如码分多址(Code Division Multiple Access，CDMA)、时分多址(Time Division Multiple Access，TDMA)、频分多址(Frequency Division Multiple Access，FDMA)、正交频分多址(Orthogonal Frequency Division Multiple Access，OFDMA)、单载波频分多址(Single-carrier Frequency Division Multiple Access，SC-FDMA)和其他系统。本申请实施例中的术语“系统”和“网络”常被可互换地使用，所描述的技术既可用于以上提及的系统和无线电技术，也可用于其他系统和无线电技术。以下描述出于示例目的描述了新空口(New Radio，NR)系统，并且在以下大部分描述中使用NR术语，但是这些技术也可应用于NR系统应用以外的应用，如第6代(6th Generation，6G)通信系统。It is worth pointing out that the technology described in the embodiments of this application is not limited to Long Term Evolution (LTE)/LTE Evolution (LTE-Advanced, LTE-A) systems, and can also be used in other wireless communication systems, such as code Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access, OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only for the above-mentioned systems and radio technologies, but also for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in much of the following description, but these techniques can also be applied to applications other than NR system applications, such as 6thgeneration Generation, 6G) communication system.
图1示出本申请实施例可应用的一种无线通信系统的框图。无线通信系统包括终端11和网络侧设备12。其中，终端11可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(Ultra-Mobile Personal Computer，UMPC)、移动上网装置(Mobile Internet Device，MID)、增强现实(Augmented Reality，AR)/虚拟现实(Virtual Reality，VR)设备、机器人、可穿戴式设备(Wearable Device)、车载设备(Vehicle UE，VUE)、行人终端(Pedestrian UE，PUE)、智能家居(具有无线通信功能的家居设备，如冰箱、电视、洗衣机或者家具等)、游戏机、个人计算机(Personal Computer，PC)、柜员机或者自助机等终端侧设备，可穿戴式设备包括：智能手表、智能手环、智能耳机、智能眼镜、智能首饰(智能手镯、智能手链、智能戒指、智能项链、智能脚镯、智能脚链等)、智能腕带、智能服装等。需要说明的是，在本申请实施例并不限定终端11的具体类型。网络侧设备12可以包括接入网设备或核心网设备，其中，接入网设备也可以称为无线接入网设备、无线接入网(Radio Access Network,RAN)、无线接入网功能或无线接入网单元。接入网设备可以包括基站、无线局域网(Wireless Local Area Networks，WLAN)接入点或无线保真(Wireless Fidelity，WiFi)节点等，基站可被称为节点B、演进节点B(Evolved Node B，eNB)、接入点、基收发机站(Base Transceiver Station，BTS)、无线电基站、无线电收发机、基本服务集(Basic Service Set，BSS)、扩展服务集(Extended Service Set，ESS)、家用B节点、家用演进型B节点、发送接收点(Transmitting Receiving Point，TRP)或所述领域中其他某个合适的术语，只要达到相同的技术效果，所述基站不限于特定技术词汇，需要说明的是，在本申请实施例中仅以NR系统中的基站为例进行介绍，并不限定基站的具体类型。Figure 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network side device 12. Among them, the terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, or a super mobile personal computer. (Ultra-Mobile Personal Computer, UMPC), Mobile Internet Device (MID), Augmented Reality (AR)/Virtual Reality (VR) equipment, robots, wearable devices (Wearable Device) , vehicle UE (VUE), pedestrian terminal (Pedestrian UE, PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers (Personal Computer, PC), teller machines or self-service machines and other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, Smart anklets, etc.), smart wristbands, smart clothing, etc. It should be noted that the embodiment of the present application does not limit the specific type of the terminal 11. The network side equipment 12 may include access network equipment or core network equipment, where the access network equipment may also be called wireless access network equipment, radio access network (Radio Access Network, RAN), radio access network function or wireless access network unit. Access network equipment can include base stations, Wireless Local Area Networks (WLAN) access points or Wireless Fidelity (WiFi) nodes, etc. The base station can be called Node B, Evolved Node B (Evolved Node B). eNB), access point, base transceiver station (Base Transceiver Station, BTS), radio base station, radio transceiver, basic service set (Basic Service Set, BSS), extended service set (Extended Service Set, ESS), home B Node, home evolved B node, Transmitting Receiving Point (TRP) or some other suitable term in the field, as long as the same technical effect is achieved, the base station is not limited to specific technical terms. It needs to be explained that , in the embodiment of this application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.
人工智能目前在各个领域获得了广泛的应用。AI网络模型有多种实现方式，例如神经网络、决策树、支持向量机、贝叶斯分类器等。本申请以神经网络为例进行说明，但是并不限定AI网络模型的具体类型。Artificial intelligence is currently widely used in various fields. There are many ways to implement AI network models, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application takes a neural network as an example for explanation, but does not limit the specific type of AI network model.
一般而言，根据需要解决的问题的不同类型，所选取的AI算法和采用的网络模型也有所差别。借助AI网络模型提升5G网络性能的主要方法是通过基于神经网络的算法和模型增强或者替代目前已有的算法或处理模块。在特定场景下，基于神经网络的算法和模型可以取得比基于确定性算法更好的性能。比较常用的神经网络包括深度神经网络、卷积神经网络和循环神经网络等。Generally speaking, depending on the type of problem that needs to be solved, the AI algorithm selected and the network model used are also different. The main way to improve 5G network performance with the help of AI network models is to enhance or replace existing algorithms or processing modules with neural network-based algorithms and models. In specific scenarios, neural network-based algorithms and models can achieve better performance than deterministic-based algorithms. The more commonly used neural networks include deep neural networks, convolutional neural networks, and recurrent neural networks.
本申请实施例能够对用于终端定位的目标AI网络模型的性能进行分析，其中，目标AI网络模型能够根据终端的无线通信信息来对该终端进行定位，该目标AI网络模型的具体功能和工作原理与相关技术中的定位AI网络模型相似，在此不再赘述。The embodiments of the present application can analyze the performance of the target AI network model used for terminal positioning. The target AI network model can locate the terminal according to the wireless communication information of the terminal. The specific functions and work of the target AI network model The principle is similar to the positioning AI network model in related technologies, and will not be described again here.
下面结合附图，通过一些实施例及其应用场景对本申请实施例提供的AI网络模型的性能监督方法、AI网络模型的性能监督装置及终端等进行详细地说明。The performance supervision method of the AI network model, the performance supervision device and terminal of the AI network model provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through some embodiments and application scenarios.
请参阅图2，本申请实施例提供的一种AI网络模型的性能监督方法，其执行主体是终端，该终端可以是如图1所示实施例中的终端11，或者是如图1所示实施例中列举的终端11之外的其他终端，在此不作具体限定，如图2所示，该终端执行的AI网络模型的性能监督方法可以包括以下步骤：Please refer to Figure 2. An embodiment of the present application provides a performance supervision method for an AI network model. The execution subject is a terminal. The terminal can be the terminal 11 in the embodiment shown in Figure 1, or the method shown in Figure 1. Terminals other than the terminal 11 listed in the embodiment are not specifically limited here. As shown in Figure 2, the performance supervision method of the AI network model executed by the terminal may include the following steps:
步骤201、终端获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位。Step 201: The terminal obtains first information, the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal.
在实施中，目标AI网络模型的输入信息可以包括：终端的历史位置信息、波束到达角、波束离开角、波束到达时间差等信息，该目标AI网络模型的输出信息可以包括终端的位置信息。其中，该终端的位置信息可以作为无线通信的依据，例如：作为波束选择的依据、作为TRP切换的依据、作为传输功率控制的依据等等，在此不作具体限定。In implementation, the input information of the target AI network model may include: historical location information of the terminal, beam arrival angle, beam departure angle, beam arrival time difference and other information, and the output information of the target AI network model may include the location information of the terminal. The location information of the terminal can be used as a basis for wireless communication, for example, as a basis for beam selection, as a basis for TRP switching, as a basis for transmission power control, etc., which are not specifically limited here.
当然，该终端的位置信息还可以作为某些应用功能的数据基础，例如：导航等，在此对目标AI网络模型输出的终端位置信息的用途不作具体限定。为了便于说明，本申请实施例中，以根据目标AI网络模型输出的终端位置信息进行无线通信为例进行举例说明，在此不构成具体限定。Of course, the location information of the terminal can also be used as the data basis for certain application functions, such as navigation. The use of the terminal location information output by the target AI network model is not specifically limited here. For ease of explanation, in the embodiment of the present application, wireless communication based on the terminal location information output by the target AI network model is used as an example for illustration, and no specific limitation is constituted here.
值得提出的是，在实际应用中，终端所处的位置(如：室内或室外)的变化，以及无线通信环境的变化(如：终端与基站之间有无遮挡等)，会影响上述目标AI网络模型的输入信息，从而干扰目标AI网络模型的输出结果。此时，可能存在某些场景下，目标AI网络模型的输出结果的精确度较低，且不满足终端的定位需求的情况，例如：在目标AI网络模型的输出结果的精确度较低时，若继续按照该定位结果进行波束选择，则选择的波束可能不是最优的波束。It is worth mentioning that in practical applications, changes in the location of the terminal (such as indoor or outdoor) and changes in the wireless communication environment (such as whether there is any obstruction between the terminal and the base station, etc.) will affect the above target AI The input information of the network model interferes with the output results of the target AI network model. At this time, there may be some scenarios where the accuracy of the output result of the target AI network model is low and does not meet the positioning requirements of the terminal. For example: when the accuracy of the output result of the target AI network model is low, If you continue to perform beam selection based on the positioning result, the selected beam may not be the optimal beam.
本步骤中，终端可以通过测量或者与其他通信设备进行信息交互等方式中的至少一种来获取上述第一信息，该第一信息可以用来确定目标AI网络模型得出的定位结果的准确度或者确定目标AI网络模型与终端的无线环境的匹配程度等信息，其中，目标AI网络模型与终端的无线环境的匹配程度越低，则目标AI网络模型得出的所述终端的定位结果的可靠性也越低。In this step, the terminal can obtain the above-mentioned first information by at least one of measurement or information exchange with other communication devices. The first information can be used to determine the accuracy of the positioning result obtained by the target AI network model. Or determine information such as the matching degree between the target AI network model and the wireless environment of the terminal. The lower the matching degree between the target AI network model and the wireless environment of the terminal, the more reliable the positioning result of the terminal obtained by the target AI network model will be. The sex is also lower.
步骤202、所述终端向网络侧设备发送所述第一信息，或者，所述终端根据所述第一信息确定所述目标AI网络模型的性能。Step 202: The terminal sends the first information to the network side device, or the terminal determines the performance of the target AI network model based on the first information.
在一种实现方式中，终端可以将第一信息发送给网络侧设备，由网络侧设备根据该第一信息来确定目标AI网络模型的性能，例如：根据第一信息确定目标AI网络模型在终端当前的无线环境下有效(即目标AI网络模型得出的定位结果能够满足终端的定位精度需求，或者目标AI网络模型得出的定位结果的可靠性较高)或者失效(即目标AI网络模型得出的定位结果不能够满足终端的定位精度需求，或者目标AI网络模型得出的定位结果的可靠性较低)。In an implementation manner, the terminal may send the first information to the network side device, and the network side device may A piece of information to determine the performance of the target AI network model, for example: determine based on the first information that the target AI network model is valid in the current wireless environment of the terminal (that is, the positioning result obtained by the target AI network model can meet the positioning accuracy requirements of the terminal, or The positioning results obtained by the target AI network model are more reliable) or invalid (that is, the positioning results obtained by the target AI network model cannot meet the positioning accuracy requirements of the terminal, or the positioning results obtained by the target AI network model are not reliable. lower).
其中，若确定目标AI网络模型的失效，则网络侧设备可以采取相应措施，来降低终端继续按照目标AI网络模型得出的定位结果来进行无线通信时，造成的通信性能降低甚至通信失败的概率。例如：通知终端目标AI网络模型失效；或者，通知终端停止按照目标AI网络模型得出的定位结果进行无线通信相关的操作；或者，更新目标AI网络模型，并向终端下发更新后的目标AI网络模型。Among them, if it is determined that the target AI network model is invalid, the network side device can take corresponding measures to reduce the probability of communication performance degradation or even communication failure when the terminal continues to perform wireless communications according to the positioning results obtained by the target AI network model. . For example: notify the terminal that the target AI network model is invalid; or notify the terminal to stop performing wireless communication-related operations based on the positioning results obtained by the target AI network model; or update the target AI network model and send the updated target AI to the terminal. network model.
在一种实现方式中，终端可以根据该第一信息来确定目标AI网络模型的性能。本实现方式与网络侧设备根据该第一信息来确定目标AI网络模型的性能的实现方式相似，不同之处在于，本实现方式中，执行根据该第一信息来确定目标AI网络模型的性能的主体为终端，在此不再赘述。In one implementation, the terminal can determine the performance of the target AI network model based on the first information. This implementation is similar to the implementation in which the network side device determines the performance of the target AI network model based on the first information. The difference is that in this implementation, the process of determining the performance of the target AI network model based on the first information is performed. The main body is the terminal, which will not be described again here.
作为一种可选的实施方式，所述第一信息包括以下至少一项：As an optional implementation, the first information includes at least one of the following:
在M个连续时间单位内，基于所述目标AI网络模型确定的所述终端的位置信息和运动状态信息，M为正整数；Within M continuous time units, the location information and motion status information of the terminal determined based on the target AI network model, M is a positive integer;
采用第一方式确定的所述终端的位置信息，所述第一方式不包括所述目标AI网络模型对应的定位方式；The location information of the terminal is determined using a first method that does not include the positioning method corresponding to the target AI network model;
基于所述目标AI网络模型确定的所述终端的位置信息的误差或者置信度；The error or confidence of the location information of the terminal determined based on the target AI network model;
所述终端的信道测量信息的变化幅度或变化率；The change amplitude or rate of change of the terminal’s channel measurement information;
采用第二方式确定的所述终端与定位参考单元(Positioning Reference Unit，PRU)或传输接收节点TRP的距离信息，所述第二方式不包括所述目标AI网络模型对应的方式；The distance information between the terminal and the positioning reference unit (Positioning Reference Unit, PRU) or the transmission receiving node TRP is determined in a second way, and the second way does not include the way corresponding to the target AI network model;
采用所述第二方式确定的所述终端与其他终端的距离信息；The distance information between the terminal and other terminals determined using the second method;
基于所述目标AI网络模型确定的所述其他终端的位置信息；The location information of the other terminals determined based on the target AI network model;
视距传播(Line of Sight，LOS)或非视距传播(Non Line of Sight，NLOS)指示信息；Line of Sight (LOS) or Non-Line of Sight (NLOS) indication information;
所述其他终端的标识信息；The identification information of the other terminals;
所述PRU或TRP的标识信息。The identification information of the PRU or TRP.
选项一，终端可以在M个连续时间单位内，基于所述目标AI网络模型确定的所述终端的位置信息和运动状态信息，其中，可以周期性地采用目标AI网络模型确定终端的位置信息，或者周期性地检测上述第一信息，此时，上述时间单位可以包括一个周期内的时间。时间单位可以包括：正交频分复用(Orthogonal Frequency Division Multiplex，OFDM)符号、时隙、子帧、参考信号周期、毫秒、秒、分钟、小时、天等，在此不作具体限定。Option one, the terminal can determine the location information and motion status information of the terminal based on the target AI network model within M consecutive time units, wherein the target AI network model can be periodically used to determine the location information of the terminal, Or the first information is detected periodically. In this case, the time unit may include time within a cycle. The time unit may include: Orthogonal Frequency Division Multiplex (OFDM) symbols, time slots, subframes, reference signal periods, milliseconds, seconds, minutes, hours, days, etc., which are not specifically limited here.
在实施中，可以基于目标AI网络模型分别确定所述M个连续时间单位中的每一个时间单位时，所述终端的位置信息，且还可以基于其他方式(如采用运动传感器检测得到终端的运动状态信息)来确定所述终端在所述M个连续时间单位内的运动状态信息，例如：运动速度、运动距离等。In implementation, the location information of the terminal in each of the M consecutive time units can be determined based on the target AI network model, and the terminal location information can also be obtained based on other methods (such as using motion sensor detection). (movement status information of the terminal) to determine the movement status information of the terminal within the M consecutive time units, such as: movement speed, movement distance, etc.
其中，基于所述目标AI网络模型确定的所述终端分别在M个连续时间单位的位置信息，与所述终端在M个连续时间单位内的运动状态信息，可以进行相互校验，若两者不匹配，则可以确定目标AI网络模型失效。Among them, the position information of the terminal in M continuous time units determined based on the target AI network model and the motion status information of the terminal in M continuous time units can be verified with each other. If the two If there is no match, it can be determined that the target AI network model is invalid.
可选地，所述终端根据所述第一信息确定所述目标AI网络模型的性能，包括：在第一距离与第二距离之间的差异大于或者等于第一阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一距离为基于所述运动状态信息确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，所述第二距离为基于所述目标AI网络模型确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，M大于1。Optionally, the terminal determines the performance of the target AI network model based on the first information, including: when the difference between the first distance and the second distance is greater than or equal to a first threshold, determining the The target AI network model is invalid, wherein the first distance is the distance between the position of the terminal in the first time unit and the position of the terminal in the Mth time unit determined based on the motion state information, and the The second distance is the distance between the position of the terminal in the first time unit and the position of the terminal in the Mth time unit determined based on the target AI network model, and M is greater than 1.
例如：假设所述运动状态信息为所述终端在所述M个连续时间单位内的运动速度，则若满足公式v|t1-tM|＞T1+|P1-PM|，则确定目标AI网络模型失效，其中，v表示终端在所述M个连续时间单位内的运动速度，t1表示所述M个连续时间单位中的第一个时间单位，tM表示所述M个连续时间单位中的第M个时间单位，T1表示第一阈值，P1表示基于所述目标AI网络模型确定的在所述M个连续时间单位中的第一个时间单位时的所述终端的位置，PM表示基于所述目标AI网络模型确定的在所述M个连续时间单位中的第M个时间单位时的所述终端的位置。|P1-PM|表示求P1与PM的距离，如欧式距离。For example: assuming that the motion status information is the motion speed of the terminal within the M continuous time units, then if the formula v|t1 -tM |>T1 +|P1 -PM | is satisfied, then Determine that the target AI network model is invalid, where v represents the movement speed of the terminal within the M continuous time units, t1 represents the first time unit among the M continuous time units, and tM represents the M consecutive time units. The Mth time unit among the continuous time units, T1 represents the first threshold, and P1 represents the terminal at the first time unit among the M consecutive time units determined based on the target AI network model. The position of PM represents the position of the terminal at the Mth time unit among the M consecutive time units determined based on the target AI network model. |P1 -PM | means finding the distance between P1 and PM , such as the Euclidean distance.
值得提出的是，在实施中，还可以在第一距离与第二距离之间的差异小于上述第一阈值的情况下，确定所述目标AI网络模型有效。It is worth mentioning that in implementation, the target AI network model may also be determined to be valid when the difference between the first distance and the second distance is less than the above-mentioned first threshold.
选项二，采用出了目标AI网络模型之外的第一方式确定的所述终端的位置信息，可以对基于所述目标AI网络模型确定的所述终端的位置信息进行校验，在两者差异较大时，可以确定所述目标AI网络模型失效。Option two, using the location information of the terminal determined by a first method other than the target AI network model, the location information of the terminal determined based on the target AI network model can be verified. The difference between the two When it is larger, it can be determined that the target AI network model is invalid.
其中，上述第一方式可以包括：基于所述终端的历史位置信息分析所述终端当前的位置信息、基于传感器对所述终端的感知信息估计的所述终端的位置信息等，在此不作一一穷举。Wherein, the above-mentioned first method may include: analyzing the current location information of the terminal based on the historical location information of the terminal, estimating the location information of the terminal based on the sensor's perception information of the terminal, etc., which will not be discussed one by one here. Exhaustive.
可选地，在采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第二阈值的情况下，确定所述目标AI网络模型失效。Optionally, when the distance between the location of the terminal determined using the first method and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold, determine the The target AI network model is invalid.
其中，采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离，可以表示：采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的空间距离，或者采用所述第一方式确定的所述终端的位置信息与基于所述目标AI网络模型确定的所述终端的位置信息之间的差异度。Wherein, the distance between the position of the terminal determined using the first method and the position of the terminal determined based on the target AI network model may represent: the distance of the terminal determined using the first method. The spatial distance between the location and the location of the terminal determined based on the target AI network model, or the location information of the terminal determined using the first method and the location of the terminal determined based on the target AI network model. The degree of difference between location information.
值得提出的是，在实施中，还可以在采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离小于上述第二阈值的情况下，确定所述目标AI网络模型有效。It is worth mentioning that in implementation, the location of the terminal determined using the first method and the location based on If the distance between the terminal positions determined by the target AI network model is less than the above-mentioned second threshold, the target AI network model is determined to be valid.
此外，上述第一方式可以包括一种或者至少两种方式，且每一种方式可以确定所述终端的一个或者至少两个位置，此时，可以基于全部第一方式确定的至少两个位置的均值或加权均值来校验基于所述目标AI网络模型确定的所述终端的位置的准确度，从而能够基于目标AI网络模型确定的所述终端的位置的准确度的高低来确定目标AI网络模型性能好坏。In addition, the above-mentioned first method may include one or at least two methods, and each method may determine one or at least two positions of the terminal. In this case, the at least two positions determined based on all the first methods may be used. The average or weighted average is used to verify the accuracy of the location of the terminal determined based on the target AI network model, so that the target AI network model can be determined based on the accuracy of the location of the terminal determined by the target AI network model. Performance is good or bad.
可选地，在第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第三阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一位置为采用所述第一方式确定的所述终端的N个位置的均值或加权均值，N为大于1的整数；Optionally, when the distance between the first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, it is determined that the target AI network model is invalid, wherein, The first position is the average or weighted average of the N positions of the terminal determined using the first method, and N is an integer greater than 1;
例如：在满足公式的情况下，确定所述目标AI网络模型失效，其中，P(n)表示采用所述第一方式确定的所述终端的N个位置中的第n个，n为小于或者等于N的正整数，P表示基于所述目标AI网络模型确定的所述终端的位置，T2表示上述第三阈值。For example: when satisfying the formula In the case of , it is determined that the target AI network model is invalid, where P(n) represents the nth one of the N positions of the terminal determined using the first method, and n is a positive integer less than or equal to N. , P represents the position of the terminal determined based on the target AI network model, and T2 represents the above-mentioned third threshold.
值得提出的是，在实施中，还可以在第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离小于上述第三阈值的情况下，确定所述目标AI网络模型有效。It is worth mentioning that in implementation, the target AI may also be determined when the distance between the first location and the location of the terminal determined based on the target AI network model is less than the above-mentioned third threshold. The network model works.
选项三，基于所述目标AI网络模型确定的所述终端的位置信息的误差或者置信度，可以直观地反映基于所述目标AI网络模型确定的所述终端的位置信息的可靠性程度，从而在基于所述目标AI网络模型确定的所述终端的位置信息的误差较大或者置信度较低的情况下，可以确定目标AI网络模型失效。与之相对应的，在基于所述目标AI网络模型确定的所述终端的位置信息的误差较小或者置信度较高的情况下，可以确定目标AI网络模型有效。Option three: the error or confidence of the terminal's location information determined based on the target AI network model can intuitively reflect the reliability of the terminal's location information determined based on the target AI network model, thereby in If the error in the location information of the terminal determined based on the target AI network model is large or the confidence level is low, it may be determined that the target AI network model is invalid. Correspondingly, when the error of the location information of the terminal determined based on the target AI network model is small or the confidence level is high, it can be determined that the target AI network model is valid.
其中，终端的位置通常是连续变化的，可以将终端的位置划分为至少两个区间，以确定终端的位置分别在所述至少两个区间内的误差程度或置信度。Wherein, the position of the terminal usually changes continuously, and the position of the terminal can be divided into at least two intervals to determine the degree of error or confidence that the position of the terminal is within the at least two intervals.
可选地，在所述基于所述目标AI网络模型确定的所述终端的位置信息的最小误差大于或者等于第九阈值的情况下，确定所述目标AI网络模型失效。Optionally, in the case where the minimum error of the location information of the terminal determined based on the target AI network model is greater than or equal to a ninth threshold, it is determined that the target AI network model is invalid.
其中，目标AI网络模型可以确定终端的位置信息位于至少两个可能的位置或位置区间的误差，若基于目标AI网络模型确定的终端位于至少两个可能的位置或位置区间的最小误差都大于或等于第九阈值，则可以确定所述目标AI网络模型的误差过大，从而确定目标AI网络模型失效。Among them, the target AI network model can determine the error that the terminal's position information is located in at least two possible positions or position intervals. If the minimum error of the terminal located in at least two possible positions or position intervals determined based on the target AI network model is greater than or If equal to the ninth threshold, it can be determined that the error of the target AI network model is too large, thereby determining that the target AI network model is invalid.
当然，在基于目标AI网络模型确定的终端位于至少两个可能的位置或位置区间的至少一个误差小于第九阈值时，可以确定所述目标AI网络模型的误差较小，从而确定目标AI网络模型有效。Of course, when the terminal determined based on the target AI network model is located in at least two possible locations or location intervals, When one less error is less than the ninth threshold, it can be determined that the error of the target AI network model is small, thereby determining that the target AI network model is effective.
可选地，在所述基于所述目标AI网络模型确定的所述终端的位置信息的最大置信度小于或者等于第十阈值的情况下，确定所述目标AI网络模型失效。Optionally, in the case where the maximum confidence of the location information of the terminal determined based on the target AI network model is less than or equal to a tenth threshold, it is determined that the target AI network model is invalid.
其中，上述终端的位置信息的置信度与上可选实施方式中终端的位置信息的误差负相关，即终端的位置信息的置信度越高，则该终端的位置信息的误差越小。The confidence of the terminal's location information is negatively correlated with the error of the terminal's location information in the above optional embodiment. That is, the higher the confidence of the terminal's location information, the smaller the error of the terminal's location information.
例如：基于目标AI网络模型的中间结果(如根据信道状态信息估计终端的位置)，并对位置标签进行量化，如量化间隔为2m，则0-2m的位置区间量化为1m，2-4m的位置区间量化为3m等等，此时，对终端进行位置估计的问题，便可以转化为对终端位置进行分类的问题，即确定终端位置位于各个位置标签对应的位置区间内的置信度，这样，可以将目标AI网络模型最后一层函数(softmax)输出的结果作为置信度。此时，如果目标AI网络模型最后一层softmax输出的全部位置标签对应的置信度中的最大值小于某一阈值，则可以确定目标AI网络模型失效。For example: based on the intermediate results of the target AI network model (such as estimating the location of the terminal based on channel state information), and quantizing the location label, if the quantization interval is 2m, then the location interval of 0-2m is quantized to 1m, and the location interval of 2-4m is quantized. The location interval is quantified as 3m and so on. At this time, the problem of position estimation of the terminal can be transformed into the problem of classifying the terminal location, that is, determining the confidence that the terminal location is within the location interval corresponding to each location label. In this way, The output result of the last layer function (softmax) of the target AI network model can be used as the confidence level. At this time, if the maximum value of the confidence levels corresponding to all location labels output by the softmax layer of the last layer of the target AI network model is less than a certain threshold, it can be determined that the target AI network model is invalid.
选项四，所述终端的信道测量信息的变化幅度或变化率。Option four: the change amplitude or rate of change of the terminal's channel measurement information.
其中，上述终端的信道测量信息可以反映终端所处的无线通信环境的变化情况，且目标AI网络模型可以根据终端的无线通信信息来确定终端的位置信息，则终端所处的无线通信环境的变化情况直接关系到目标AI网络模型输出的所述终端的位置信息的准确度。Among them, the channel measurement information of the terminal can reflect changes in the wireless communication environment where the terminal is located, and the target AI network model can determine the location information of the terminal based on the wireless communication information of the terminal. Then the changes in the wireless communication environment where the terminal is located The situation is directly related to the accuracy of the location information of the terminal output by the target AI network model.
其中，上述终端的信道测量信息可以包括以下至少一项：Wherein, the channel measurement information of the above-mentioned terminal may include at least one of the following:
信道状态信息，所述信道状态信息包括：时域信道状态信息、频域信道状态信息、空域信道状态信息、时延多普勒域信道状态信息以及功率时延谱中的至少一项；Channel state information, the channel state information includes: at least one of time domain channel state information, frequency domain channel state information, air domain channel state information, delay Doppler domain channel state information, and power delay spectrum;
信道特征信息，所述信道特征信息包括：多普勒频域、首径时延、首径功率、首径相位、首径角度、最大H径的时延、最大H径的功率、最大H径的相位以及最大H径的角度中的至少一项，H为大于或者等于1的整数；Channel characteristic information, which includes: Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path At least one of the phase and the angle of the maximum H diameter, H is an integer greater than or equal to 1;
信道质量信息，所述信道质量信息包括：参考信号接收功率(Reference Signal Received Power，RSRP)、参考信号接收质量(Reference Signal Received Quality，RSRQ)、信噪比(signal-to-noise ratio，SNR)以及信号与干扰加噪声比(signal-to-noise and interference ratio，SINR)中的至少一项。Channel quality information, which includes: Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Signal-to-noise ratio (SNR) And at least one of the signal-to-noise and interference ratio (SINR).
上述终端的信道测量信息的变化幅度或变化率越剧烈，可以表示终端在相邻的时间单位或连续的M个单位时间内的位置变化较大。具体地，终端的信道测量信息的变化幅度或变化率可以反映终端所处的无线网络环境的变化情况，而由于终端运动的连续性，在相邻两个时间或连续一段时间M内终端的信道测量信息应该是相似的，如果终端在相邻两个时间或连续一段时间M内测量得到的信道测量信息的变化过于剧烈，则可以表示这段时间内可能有其他因素干扰了终端所处的无线通信环境，例如：有车辆遮挡了终端的无线链路而引发终端的信道测量信息的变化幅度或变化率越剧烈。同理，这段时间内输入所述目标AI网络模型的信息的置信度较低，且目标AI网络模型输出的所述终端的位置信息的置信度较低，从而可以确定目标AI网络模型失效。The more drastic the change amplitude or change rate of the channel measurement information of the terminal is, it can mean that the position of the terminal changes greatly in adjacent time units or in M consecutive units of time. Specifically, the change amplitude or change rate of the terminal's channel measurement information can reflect changes in the wireless network environment in which the terminal is located, and due to the continuity of the terminal's movement, the channel of the terminal in two adjacent times or a continuous period of time M The measurement information should be similar. If the channel measurement information measured by the terminal at two adjacent times or within a continuous period of time M changes too drastically, it can indicate that there may be other factors interfering with the wireless network where the terminal is located during this period. The communication environment, for example, if a vehicle blocks the terminal's wireless link, causing the terminal's channel measurement information to change in amplitude or rate, the more drastic the change will be. Similarly, the confidence of the information input to the target AI network model during this period is low, and the location information of the terminal output by the target AI network model is The confidence level is low, thereby determining that the target AI network model is invalid.
在实施中，还可以将基于所述目标AI网络模型确定的所述终端的位置信息的置信度设置为信道测量信息变化率相关的函数，这样，基于该信道测量信息变化率便可以计算得到基于所述目标AI网络模型确定的所述终端的位置信息的置信度，从而可以基于该置信度较高来确定目标AI网络模型有效，或者基于该置信度较低来确定目标AI网络模型失效。In an implementation, the confidence of the location information of the terminal determined based on the target AI network model can also be set as a function related to the change rate of the channel measurement information. In this way, the change rate of the channel measurement information can be calculated based on The confidence of the terminal's location information determined by the target AI network model can determine that the target AI network model is valid based on the higher confidence, or determine that the target AI network model is invalid based on the lower confidence.
例如：在满足|RSRP1-RSRPM|＞T4的情况下，确定目标AI网络模型失效，其中，RSRP1表示所述终端在所述连续的M个时间单位中的第一个时间单位检测到的RSRP；RSRPM表示所述终端在所述连续的M个时间单位中的第M个时间单位检测到的RSRP，T4表示上述第四阈值。For example: when |RSRP1 -RSRPM |>T4 is satisfied, it is determined that the target AI network model is invalid, where RSRP1 represents the first time unit detection of the terminal in the M consecutive time units. RSRP; RSRPM represents the RSRP detected by the terminal in the M-th time unit among the M consecutive time units, and T4 represents the above-mentioned fourth threshold.
选项五，采用第二方式确定的所述终端与定位参考单元(Positioning Reference Unit，PRU)或传输接收节点(Transmission Reception Point，TRP)的距离信息，其中，第二方式可以包括基于旁链路(sidelink)通信的距离检测，基于蓝牙(bluetooth)通信的距离检测，以及其他方式的距离检测(ranging)，在此不作具体限定。Option five, use the distance information between the terminal and the positioning reference unit (Positioning Reference Unit, PRU) or transmission reception node (Transmission Reception Point, TRP) determined in the second method, where the second method may include based on the side link ( Distance detection based on sidelink communication, distance detection based on Bluetooth communication, and other methods of distance detection (ranging) are not specifically limited here.
其中，PRU和TRP的地面真实(Ground-truth)位置是已知的，通过建立终端与TRP/PRU的关联关系，可以辅助估计终端当前位置估计的准确性。Among them, the ground-truth positions of the PRU and TRP are known. By establishing the association between the terminal and the TRP/PRU, the accuracy of the terminal's current position estimation can be assisted to estimate.
此外，上述PRU/TRP的数量可以是一个或至少两个，且可以通过在第一信息中将PRU/TRP的标识信息与各个PRU/TRP的位置信息进行关联，以此来区分每一个PRU/TRP的真实位置(如地面真实(Ground-truth)位置)。In addition, the number of the above-mentioned PRU/TRP may be one or at least two, and each PRU/TRP may be distinguished by associating the identification information of the PRU/TRP with the location information of each PRU/TRP in the first information. The true location of the TRP (such as the ground-truth location).
可选地，在第三距离与第四距离之间的差异大于或者等于第五阈值的情况下，确定所述目标AI网络模型失效，其中，所述第三距离为采用所述第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离，所述第四距离为基于所述目标AI网络模型确定的所述终端的位置与所述PRU或TRP的位置之间的距离Optionally, when the difference between the third distance and the fourth distance is greater than or equal to the fifth threshold, it is determined that the target AI network model is invalid, wherein the third distance is determined using the second method The distance between the terminal and the positioning reference unit PRU or the transmission and reception node TRP, the fourth distance is the distance between the position of the terminal determined based on the target AI network model and the position of the PRU or TRP
例如：可以通过基于旁链路(sidelink)通信或蓝牙(bluetooth)通信的距离检测方式来检测终端与PRU/TRP的第三距离，然后基于目标AI网络模型确定终端的位置信息，并确定该位置信息对应的位置与PRU/TRP的位置之间的第四距离，然后利用第三距离来验证第四距离的准确程度，若满足|P1-P0|＞D+T5，则可以确定第四距离的误差较大，从而确定目标AI网络模型失效，其中，P1表示基于目标AI网络模型确定的所述终端的位置，P0表示PRU/TRP的真实位置，D表示上述第三距离，T5表示上述第五阈值。For example: the third distance between the terminal and the PRU/TRP can be detected through distance detection based on sidelink communication or bluetooth communication, and then the location information of the terminal is determined based on the target AI network model, and the location is determined The fourth distance between the position corresponding to the information and the position of the PRU/TRP, and then use the third distance to verify the accuracy of the fourth distance. If |P1 -P0 |＞D+T5 is satisfied, the third distance can be determined. The errors of the four distances are large, thus determining that the target AI network model is invalid, where P1 represents the position of the terminal determined based on the target AI network model, P0 represents the true position of the PRU/TRP, and D represents the above third distance, T5 represents the fifth threshold value mentioned above.
当然，在第三距离与第四距离之间的差异小于上述第五阈值的情况下，可以确定目标AI网络模型有效。Of course, in the case where the difference between the third distance and the fourth distance is less than the above-mentioned fifth threshold, it can be determined that the target AI network model is valid.
选项六，采用所述第二方式确定的所述终端与其他终端的距离信息。Option six: Use the distance information between the terminal and other terminals determined in the second method.
与上述选项五相似的，本实施方式中，可以基于两个终端之间的相对位置关系来验证所述目标AI网络模型的准确度。Similar to the above option five, in this embodiment, the accuracy of the target AI network model can be verified based on the relative position relationship between the two terminals.
其中，上述其他终端的位置可以基于上述目标AI网络模型来确定，或者采用其他方式确定，例如：对于位置固定的其他终端，可以获取该其他终端的固定位置，为了便于说明，本申请实施例中，以其他终端的位置也是采用上述目标AI网络模型来确定的为例进行举例说明。Wherein, the positions of the other terminals can be determined based on the target AI network model, or determined in other ways. For example, for other terminals with fixed positions, the fixed positions of the other terminals can be obtained. For convenience of explanation, in the embodiments of this application , taking the position of other terminals also determined using the above target AI network model as an example to illustrate.
此外，上述其他终端的数量可以是一个或至少两个，且可以通过在第一信息中将其他终端的标识信息与各个其他终端的位置信息进行关联，以此来区分每一个其他终端的位置信息。In addition, the number of the above-mentioned other terminals may be one or at least two, and the location information of each other terminal may be distinguished by associating the identification information of the other terminals with the location information of each other terminal in the first information. .
可选地，在第五距离与第六距离之间的差异大于或者等于第六阈值的情况下，确定所述目标AI网络模型失效，其中，所述第五距离为采用所述第二方式确定的所述终端与其他终端的距离，所述第六距离为基于所述目标AI网络模型确定的所述终端的位置与基于所述目标AI网络模型确定的所述其他终端的位置之间的距离Optionally, when the difference between the fifth distance and the sixth distance is greater than or equal to the sixth threshold, it is determined that the target AI network model is invalid, wherein the fifth distance is determined using the second method The distance between the terminal and other terminals, the sixth distance is the distance between the location of the terminal determined based on the target AI network model and the location of the other terminal determined based on the target AI network model
其中，第二方式与上述选项五中的第二方式的含义相同，在此不再赘述。The second method has the same meaning as the second method in option five above, and will not be described again here.
上述终端以及其他终端的位置均采用所述目标AI网络模型来确定，这样，基于目标AI网络模型分别确定两个终端的位置之后，便可以根据这两个位置计算得到上述第六距离。例如：若满足|P1-P2|＞D+T6，则表示第六距离与第五距离差异过大，即确定目标AI网络模型确定的所述终端以及其他终端的位置的误差较大，从而可以判断目标AI网络模型失效，其中，P1表示基于目标AI网络模型确定的所述终端的位置，P2表示基于目标AI网络模型确定的所述其他终端的位置，D表示上述第五距离，T6表示上述第六阈值。The positions of the above terminal and other terminals are determined using the target AI network model. In this way, after the positions of the two terminals are determined based on the target AI network model, the sixth distance can be calculated based on the two positions. For example: if |P1 -P2 |>D+T6 is satisfied, it means that the difference between the sixth distance and the fifth distance is too large, that is, the error in determining the positions of the terminal and other terminals determined by the target AI network model is large. , so that it can be judged that the target AI network model is invalid, where P1 represents the location of the terminal determined based on the target AI network model, P2 represents the location of the other terminal determined based on the target AI network model, and D represents the above-mentioned fifth The distance, T6 represents the sixth threshold above.
选项七，上述基于所述目标AI网络模型确定的所述其他终端的位置信息，可以用来验证目标AI网络模型的定位准确性，例如：对于位置已知的其他终端，可以将其已知的位置与基于目标AI网络模型确定的该其他终端的位置进行比较，如果两者距离较大，则可以确定目标AI网络模型的定位准确性较低，从而判断目标AI网络模型失效。Option seven, the location information of the other terminals determined based on the target AI network model can be used to verify the positioning accuracy of the target AI network model. For example, for other terminals whose locations are known, their known locations can be The position is compared with the position of the other terminal determined based on the target AI network model. If the distance between the two is large, it can be determined that the positioning accuracy of the target AI network model is low, thereby determining that the target AI network model is invalid.
选项八，视距传播(Line of Sight，LOS)或非视距传播(Non Line of Sight，NLOS)指示信息。Option eight, Line of Sight (LOS) or Non-Line of Sight (NLOS) indication information.
在实施中，可能存在有的AI网络模型只能够在LOS场景下取得较高的定位准确度，有的AI网络模型只能够在NLOS场景下取得较高的定位准确度。这样，可以根据终端所处无线通信环境为LOS场景来选择在LOS场景下能够取得较高的定位准确度的AI网络模型，根据终端所处无线通信环境为NLOS场景来选择在NLOS场景下能够取得较高的定位准确度的AI网络模型。During implementation, there may be some AI network models that can only achieve higher positioning accuracy in LOS scenarios, and some AI network models that can only achieve higher positioning accuracy in NLOS scenarios. In this way, the AI network model that can achieve higher positioning accuracy in the LOS scenario can be selected based on the LOS scenario where the wireless communication environment of the terminal is located, and the AI network model that can achieve higher positioning accuracy in the NLOS scenario can be selected based on the wireless communication environment of the terminal being located in the NLOS scenario. AI network model with higher positioning accuracy.
可选地，在所述目标AI网络模型为LOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的LOS占比小于或者等于第七阈值的情况下，确定所述目标AI网络模型失效；和/或，在所述目标AI网络模型为NLOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的NLOS占比小于或者等于第八阈值的情况下，确定所述目标AI网络模型失效。Optionally, in the case where the target AI network model is a network model used in a LOS scenario, and the LOS or NLOS indication information indicates that the LOS proportion of the terminal is less than or equal to a seventh threshold, the target is determined The AI network model fails; and/or, when the target AI network model is a network model used in an NLOS scenario, and the LOS or NLOS indication information indicates that the NLOS proportion of the terminal is less than or equal to the eighth threshold. , determining that the target AI network model is invalid.
其中，所述LOS指示信息可以指示所述终端所处的无线通信环境中LOS路径的占比，所述NLOS指示信息可以指示所述终端所处的无线通信环境中NLOS路径的占比。其中，若LOS路径的占比小于或者等于第七阈值，则表示终端所处的无线通信环境更倾向于NLOS场景，此时采用适用于NLOS场景的定位AI网络模型能够取得更好的定位效果，而采用适用于LOS场景的定位AI网络模型时的定位效果较差；若NLOS路径的占比小于或者等于第八阈值，则表示终端所处的无线通信环境更倾向于LOS场景，此时采用适用于LOS场景的定位AI网络模型能够取得更好的定位效果，而采用适用于NLOS场景的定位AI网络模型时的定位效果较差。Wherein, the LOS indication information may indicate the proportion of LOS paths in the wireless communication environment where the terminal is located, The NLOS indication information may indicate the proportion of NLOS paths in the wireless communication environment where the terminal is located. Among them, if the proportion of LOS paths is less than or equal to the seventh threshold, it means that the wireless communication environment in which the terminal is located is more inclined to the NLOS scenario. At this time, the positioning AI network model suitable for the NLOS scenario can achieve better positioning results. The positioning effect is poor when using the positioning AI network model suitable for LOS scenarios; if the proportion of NLOS paths is less than or equal to the eighth threshold, it means that the wireless communication environment in which the terminal is located is more inclined to the LOS scenario. In this case, the applicable The positioning AI network model suitable for LOS scenarios can achieve better positioning results, while the positioning effect when using the positioning AI network model suitable for NLOS scenarios is poor.
本实施方式中，可以根据所述终端采用的目标AI网络模型所适用的LOS/NLOS场景，与所述终端所处的无线通信环境为LOS/NLOS场景是否匹配，来确定目标AI网络模型的性能。In this embodiment, the performance of the target AI network model can be determined based on whether the LOS/NLOS scenario applicable to the target AI network model adopted by the terminal matches the LOS/NLOS scenario of the wireless communication environment in which the terminal is located. .
需要说明的是，上述第一阈值、第二阈值、第三阈值、第四阈值、第五阈值、第六阈值、第七阈值、第八阈值以及第九阈值中的至少一项，可以是终端根据定位精度需求或业务场景等确定的误差阈值，或者是协议中约定的误差阈值，或者是网络侧设备指示的误差阈值，在此不作具体限定。It should be noted that at least one of the first threshold, the second threshold, the third threshold, the fourth threshold, the fifth threshold, the sixth threshold, the seventh threshold, the eighth threshold and the ninth threshold may be a terminal The error threshold is determined based on positioning accuracy requirements or business scenarios, or it is the error threshold agreed in the protocol, or the error threshold indicated by the network side device, which is not specifically limited here.
作为一种可选的实施方式，在所述终端获取第一信息之前，所述方法还包括：As an optional implementation, before the terminal obtains the first information, the method further includes:
所述终端接收来自所述网络侧设备的第二指示信息或第三指示信息，其中，所述第二指示信息用于指示所述终端测量和/或上报所述第一信息，所述第三指示信息用于指示所述终端对所述目标AI网络模型进行性能监督。The terminal receives second indication information or third indication information from the network side device, wherein the second indication information is used to instruct the terminal to measure and/or report the first information, and the third The instruction information is used to instruct the terminal to perform performance supervision on the target AI network model.
在一种可能的实现方式中，终端接收来自网络侧设备的第二指示信息时，终端可以按照第二指示信息的指示来测量第一信息和/或向所述网络侧设备上报第一信息。例如：终端按照第二指示信息测量得到上述第一信息；或者，终端按照第二指示信息将预先存储或接收的第一信息发送给网络侧设备；或者，终端按照第二指示信息测量得到上述第一信息，并将该第一信息发送给网络侧设备；或者，终端按照第二指示信息测量上述第一信息，并基于其他指示信息或条件的触发来确定由终端根据该第一信息来判断目标AI网络模型的性能。其中，在终端向网络侧设备上报第一信息时，网络侧设备可以基于接收的第一信息来确定目标AI网络模型的性能。In a possible implementation, when the terminal receives the second indication information from the network side device, the terminal may measure the first information and/or report the first information to the network side device according to the instructions of the second indication information. For example: the terminal measures and obtains the above-mentioned first information according to the second instruction information; or the terminal sends the pre-stored or received first information to the network side device according to the second instruction information; or the terminal obtains the above-mentioned third information according to the second instruction information. information, and sends the first information to the network side device; or, the terminal measures the above-mentioned first information according to the second instruction information, and determines based on the triggering of other instruction information or conditions, and the terminal determines the target based on the first information. Performance of AI network models. Wherein, when the terminal reports the first information to the network side device, the network side device can determine the performance of the target AI network model based on the received first information.
在一种可能的实施方式中，终端接收来自网络侧设备的第三指示信息时，终端可以按照第三指示信息的指示，根据第一信息来确定目标AI网络模型的性能。In a possible implementation, when the terminal receives the third indication information from the network side device, the terminal can determine the performance of the target AI network model based on the first information according to the instructions of the third indication information.
本实施方式下，可以基于网络侧设备的指示信息来触发对目标AI网络模型的性能监督过程。In this embodiment, the performance supervision process of the target AI network model can be triggered based on the instruction information of the network side device.
可选地，所述第三指示信息包括第一性能监督方法的标识信息，所述第三指示信息用于指示所述终端按照所述第一性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the third instruction information includes identification information of the first performance supervision method, and the third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model according to the first performance supervision method. .
这样，网络侧设备可以通过第三指示信息向终端推荐性能监督方法，例如：根据上述第一阈值或第二阈值或第三阈值等来监督目标AI网络模型的性能。In this way, the network side device can recommend a performance supervision method to the terminal through the third indication information, for example: supervising the performance of the target AI network model based on the above-mentioned first threshold, second threshold, or third threshold.
可选地，所述第三指示信息还包括第二信息，所述第二信息用于辅助所述目标AI网络模型的性能监督。Optionally, the third indication information also includes second information, the second information is used to assist the target AI network Performance supervision of network models.
其中，上述第二信息可以包括用于辅助所述目标AI网络模型的性能监督的阈值，例如：第一阈值、第二阈值、第三阈值、第四阈值、第五阈值、第六阈值、第七阈值、第八阈值以及第九阈值中的至少一项，和/或，第二信息还可以包括用于辅助所述目标AI网络模型的性能监督的位置信息，例如：PRU/TRP的位置信息，或者其他终端的位置信息。Wherein, the above-mentioned second information may include thresholds used to assist performance supervision of the target AI network model, such as: first threshold, second threshold, third threshold, fourth threshold, fifth threshold, sixth threshold, third threshold. At least one of the seventh threshold, the eighth threshold, and the ninth threshold, and/or the second information may also include location information used to assist performance supervision of the target AI network model, for example: location information of PRU/TRP , or the location information of other terminals.
这样，网络侧设备可以通过上述第三指示信息，动态的配置目标AI网络模型的允许误差程度，和/或，网络侧设备可以通过上述第三指示信息为终端判断目标AI网络模型的性能提供数据支持。In this way, the network side device can dynamically configure the allowable error degree of the target AI network model through the above third indication information, and/or the network side device can provide data for the terminal to judge the performance of the target AI network model through the above third indication information. support.
作为一种可选的实施方式，在所述终端接收来自所述网络侧设备的第二指示信息或第三指示信息之前，所述方法还包括：As an optional implementation manner, before the terminal receives the second indication information or the third indication information from the network side device, the method further includes:
所述终端向所述网络侧设备发送第一请求信息，所述第一请求信息用于请求对所述目标AI网络模型进行性能监督。The terminal sends first request information to the network side device, where the first request information is used to request performance supervision of the target AI network model.
其中，第一请求信息可以请求网络侧设备对所述目标AI网络模型进行性能监督，这样，网络侧设备可以基于接收的第一请求信息，向终端发送第二指示信息，该第二指示信息可以指示终端测量并向网络侧设备上报第一信息；或者，第一请求信息可以请求网络侧设备允许所述终端对所述目标AI网络模型进行性能监督，这样，网络侧设备可以基于接收的第一请求信息，向终端发送第三指示信息，该第三指示信息可以指示终端根据测量的第一信息来确定目标AI网络模型的性能；或者，第一请求信息可以请求对所述目标AI网络模型进行性能监督，且由网络侧设备决定由网络侧设备或终端来对所述目标AI网络模型进行性能监督，这样，网络侧设备可以基于接收的第一请求信息，向终端发送第二指示信息或第三指示信息。The first request information may request the network side device to perform performance supervision on the target AI network model. In this way, the network side device may send second instruction information to the terminal based on the received first request information. The second instruction information may Instruct the terminal to measure and report the first information to the network-side device; or, the first request information may request the network-side device to allow the terminal to perform performance supervision of the target AI network model, so that the network-side device may perform performance supervision on the target AI network model based on the received first request information. Request information to send third instruction information to the terminal. The third instruction information may instruct the terminal to determine the performance of the target AI network model based on the measured first information; or the first request information may request that the target AI network model be processed. Performance supervision, and the network side device decides to perform performance supervision on the target AI network model by the network side device or the terminal. In this way, the network side device can send the second instruction information or the third instruction information to the terminal based on the received first request information. Three instructions.
本实施方式下，可以由终端触发对目标AI网络模型的性能监督过程。In this implementation mode, the terminal can trigger the performance supervision process of the target AI network model.
可选地，所述第一请求信息包括第二性能监督方法的标识信息，所述第一请求信息用于请求按照所述第二性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the first request information includes identification information of the second performance supervision method, and the first request information is used to request performance supervision of the target AI network model according to the second performance supervision method.
本实施方式下，终端可以通过第一请求信息向网络侧设备推荐性能监督方法，例如：根据上述第一阈值或第二阈值或第三阈值等来监督目标AI网络模型的性能。In this embodiment, the terminal can recommend a performance supervision method to the network side device through the first request information, for example, supervising the performance of the target AI network model based on the above-mentioned first threshold, second threshold, or third threshold.
作为一种可选的实施方式，在所述终端向网络侧设备发送所述第一信息之后，所述方法还包括：As an optional implementation manner, after the terminal sends the first information to the network side device, the method further includes:
所述终端接收来自所述网络侧设备的第四指示信息，所述第四指示信息用于指示所述目标AI网络模型的性能。The terminal receives fourth indication information from the network side device, where the fourth indication information is used to indicate the performance of the target AI network model.
本实施方式下，终端在确定目标AI网络模型的性能之后，可以将该性能结果上报给网络侧设备，以使网络侧设备获知目标AI网络模型的性能，从而据此采取相应的措施，例如：在目标AI网络模型失效时，可以重新训练目标AI网络模型，并向终端下发重新训练后的目标AI网络模型。In this embodiment, after determining the performance of the target AI network model, the terminal can report the performance results to the network side device, so that the network side device can learn the performance of the target AI network model and take corresponding measures accordingly, such as: When the target AI network model fails, the target AI network model can be retrained and the retrained target AI network model can be delivered to the terminal.
可选地，在所述第五指示信息指示所述目标AI网络模型失效的情况下，所述第五指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fifth indication information indicates that the target AI network model is invalid, the fifth indication The indication information also indicates the cause of the failure of the target AI network model.
其中，目标AI网络模型失效的原因可以与根据所述第一信息确定所述目标AI网络模型的性能的方法相对应，例如：目标AI网络模型失效的原因可以包括：第一距离与第二距离之间的差异大于或者等于第一阈值、采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第二阈值、第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第三阈值等等，在此不做赘述。The reason for the failure of the target AI network model may correspond to the method of determining the performance of the target AI network model based on the first information. For example, the reason for the failure of the target AI network model may include: the first distance and the second distance. The difference between them is greater than or equal to the first threshold, the distance between the location of the terminal determined using the first method and the location of the terminal determined based on the target AI network model is greater than or equal to the second threshold, The distance between the first position and the position of the terminal determined based on the target AI network model is greater than or equal to the third threshold, etc., which will not be described again here.
本实施方式下，终端可以通过第五指示信息告知网络侧设备目标AI网络模型失效的原因，此时，网络侧设备可以根据该原因来采取相应的措施，或者在调整目标AI网络模型时，能够根据该原因来确定如何调整目标AI网络模型。In this embodiment, the terminal can inform the network side device of the reason for the failure of the target AI network model through the fifth instruction information. At this time, the network side device can take corresponding measures based on the reason, or when adjusting the target AI network model, it can Use this reason to determine how to adjust the target AI network model.
在本申请实施例中，终端获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；所述终端向网络侧设备发送所述第一信息，或者，所述终端根据所述第一信息确定所述目标AI网络模型的性能。终端能够获取用于辅助确定目标AI网络模型的性能的第一信息，并向网络侧设备上报该第一信息，以使网络侧设备根据该第一信息来判断目标AI网络模型的性能，或者直接由终端根据该第一信息来判断目标AI网络模型的性能。这样，能够及时地发现目标AI网络模型的性能不能够满足定位需求的情况，进而能够根据该目标AI网络模型的性能不能够满足定位需求的结果来采取适当的措施，例如：更新目标AI网络模型、采用其他定位方式来对终端进行定位等。降低了因目标AI网络模型的定位性能不能够满足定位需求，而继续按照该目标AI网络模型的定位结果来执行相关的无线通信时，造成的无线通信性能低甚至出错的概率。In this embodiment of the present application, the terminal obtains first information, the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal; the terminal transmits information to the network side device Send the first information, or the terminal determines the performance of the target AI network model based on the first information. The terminal can obtain the first information used to assist in determining the performance of the target AI network model, and report the first information to the network side device, so that the network side device determines the performance of the target AI network model based on the first information, or directly The terminal determines the performance of the target AI network model based on the first information. In this way, it can be discovered in time that the performance of the target AI network model cannot meet the positioning requirements, and then appropriate measures can be taken based on the result that the performance of the target AI network model cannot meet the positioning requirements, such as updating the target AI network model. , use other positioning methods to locate the terminal, etc. This reduces the probability of low wireless communication performance or even errors when the positioning performance of the target AI network model cannot meet the positioning requirements and the wireless communication continues to be performed based on the positioning results of the target AI network model.
请参阅图3，本申请实施例提供的另一种AI网络模型的性能监督方法，其执行主体是网络侧设备，该网络侧设备可以是如图1所示实施例中的网络侧设备12，或者是如图1所示实施例中列举的网络侧设备12之外的其他网络侧设备，在此不作具体限定，如图3所示，该网络侧设备执行的AI网络模型的性能监督方法可以包括以下步骤：Please refer to Figure 3. Another performance supervision method for an AI network model provided by an embodiment of the present application is executed by a network-side device. The network-side device may be the network-side device 12 in the embodiment shown in Figure 1. Or other network side devices other than the network side device 12 listed in the embodiment as shown in Figure 1, which are not specifically limited here. As shown in Figure 3, the performance supervision method of the AI network model executed by the network side device can be Includes the following steps:
步骤301、网络侧设备接收来自终端的第一信息，并根据所述第一信息确定目标AI网络模型的性能，其中，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位。Step 301: The network side device receives the first information from the terminal, and determines the performance of the target AI network model based on the first information, where the first information is used to determine the performance of the target AI network model, and the target AI The network model is used to locate the terminal.
步骤302、所述网络侧设备接收来自终端的第五指示信息，其中，所述第五指示信息用于指示所述目标AI网络模型的性能。Step 302: The network side device receives fifth indication information from the terminal, where the fifth indication information is used to indicate the performance of the target AI network model.
需要说明的是，如图3所示实施例中，以网络侧设备执行的AI网络模型的性能监督方法包括上述步骤301和步骤302为例进行举例说明，在实施中，网络侧设备执行的AI网络模型的性能监督方法可以仅包括上述步骤301和步骤302中的一个，其具体过程可以参考如图2所示终端侧方法实施例中的说明，在此不作具体限定。It should be noted that in the embodiment shown in Figure 3, the performance supervision method of the AI network model executed by the network side device includes the above steps 301 and 302 as an example. In the implementation, the AI executed by the network side device The performance supervision method of the network model may include only one of the above-mentioned steps 301 and 302. For the specific process, please refer to the description in the terminal-side method embodiment as shown in Figure 2, which is not specifically limited here.
可选地，所述第一信息包括以下至少一项：Optionally, the first information includes at least one of the following:
在M个连续时间单位内，基于所述目标AI网络模型确定的所述终端的位置信息和运动状态信息，M为正整数；Within M continuous time units, the location information and motion status information of the terminal determined based on the target AI network model, M is a positive integer;
采用第一方式确定的所述终端的位置信息，所述第一方式不包括所述目标AI网络模型对应的定位方式；The location information of the terminal is determined using a first method that does not include the positioning method corresponding to the target AI network model;
基于所述目标AI网络模型确定的所述终端的位置信息的误差或者置信度；The error or confidence of the location information of the terminal determined based on the target AI network model;
所述终端的信道测量信息的变化幅度或变化率；The change amplitude or rate of change of the terminal’s channel measurement information;
采用第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离信息，所述第二方式不包括所述目标AI网络模型对应的方式；The distance information between the terminal and the positioning reference unit PRU or the transmission and reception node TRP is determined in a second way, and the second way does not include a way corresponding to the target AI network model;
采用所述第二方式确定的所述终端与其他终端的距离信息；The distance information between the terminal and other terminals determined using the second method;
基于所述目标AI网络模型确定的所述其他终端的位置信息；The location information of the other terminals determined based on the target AI network model;
视距传播LOS或非视距传播NLOS指示信息；Line-of-sight propagation LOS or non-line-of-sight propagation NLOS indication information;
所述其他终端的标识信息；The identification information of the other terminals;
所述PRU或TRP的标识信息。The identification information of the PRU or TRP.
可选地，所述网络侧设备根据所述第一信息确定所述目标AI网络模型的性能，包括以下至少一项：Optionally, the network side device determines the performance of the target AI network model based on the first information, including at least one of the following:
在第一距离与第二距离之间的差异大于或者等于第一阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一距离为基于所述运动状态信息确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，所述第二距离为基于所述目标AI网络模型确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，M大于1；When the difference between the first distance and the second distance is greater than or equal to a first threshold, it is determined that the target AI network model is invalid, wherein the first distance is the terminal determined based on the motion state information. The distance between the position of the terminal in the first time unit and the position of the terminal in the Mth time unit. The second distance is the position of the terminal in the first time unit and the position determined based on the target AI network model. The distance between the positions of the terminals in the Mth time unit, M is greater than 1;
在采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第二阈值的情况下，确定所述目标AI网络模型失效；When the distance between the location of the terminal determined using the first method and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold, determine the target AI network model Failure;
在第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第三阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一位置为采用所述第一方式确定的所述终端的N个位置的均值或加权均值，N为大于1的整数；When the distance between the first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, it is determined that the target AI network model is invalid, wherein the first The position is the average or weighted average of the N positions of the terminal determined using the first method, and N is an integer greater than 1;
在所述终端测量的信道状态信息的变化幅度或变化率大于或者等于第四阈值的情况下，确定所述目标AI网络模型失效；When the change amplitude or change rate of the channel state information measured by the terminal is greater than or equal to the fourth threshold, it is determined that the target AI network model is invalid;
在第三距离与第四距离之间的差异大于或者等于第五阈值的情况下，确定所述目标AI网络模型失效，其中，所述第三距离为采用所述第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离，所述第四距离为基于所述目标AI网络模型确定的所述终端的位置与所述PRU或TRP的位置之间的距离；When the difference between the third distance and the fourth distance is greater than or equal to the fifth threshold, it is determined that the target AI network model is invalid, wherein the third distance is the terminal determined by the second method. The distance from the positioning reference unit PRU or the transmission reception node TRP, the fourth distance is the distance between the position of the terminal determined based on the target AI network model and the position of the PRU or TRP;
在第五距离与第六距离之间的差异大于或者等于第六阈值的情况下，确定所述目标AI网络模型失效，其中，所述第五距离为采用所述第二方式确定的所述终端与其他终端的距离，所述第六距离为基于所述目标AI网络模型确定的所述终端的位置与基于所述目标AI网络模型确定的所述其他终端的位置之间的距离；When the difference between the fifth distance and the sixth distance is greater than or equal to the sixth threshold, it is determined that the target AI network model is invalid, wherein the fifth distance is the terminal determined by the second method. The distance from other terminals, the sixth distance is the distance between the location of the terminal determined based on the target AI network model and the location of the other terminal determined based on the target AI network model;
在所述目标AI网络模型为LOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的LOS占比小于或者等于第七阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in a LOS scenario, and the LOS or NLOS indication information indicates that the LOS proportion of the terminal is less than or equal to the seventh threshold, it is determined that the target AI network model is invalid. ;
在所述目标AI网络模型为NLOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的NLOS占比小于或者等于第八阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in an NLOS scenario, and the LOS or NLOS indication information indicates that the NLOS proportion of the terminal is less than or equal to the eighth threshold, it is determined that the target AI network model is invalid. ;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最小误差大于或者等于第九阈值的情况下，确定所述目标AI网络模型失效；In the case where the minimum error of the location information of the terminal determined based on the target AI network model is greater than or equal to the ninth threshold, it is determined that the target AI network model is invalid;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最大置信度小于或者等于第十阈值的情况下，确定所述目标AI网络模型失效。When the maximum confidence of the location information of the terminal determined based on the target AI network model is less than or equal to the tenth threshold, it is determined that the target AI network model is invalid.
可选地，所述终端的信道测量信息包括以下至少一项：Optionally, the channel measurement information of the terminal includes at least one of the following:
信道状态信息，所述信道状态信息包括：时域信道状态信息、频域信道状态信息、空域信道状态信息、时延多普勒域信道状态信息以及功率时延谱中的至少一项；Channel state information, the channel state information includes: at least one of time domain channel state information, frequency domain channel state information, air domain channel state information, delay Doppler domain channel state information, and power delay spectrum;
信道特征信息，所述信道特征信息包括：多普勒频域、首径时延、首径功率、首径相位、首径角度、最大H径的时延、最大H径的功率、最大H径的相位以及最大H径的角度中的至少一项，H为大于或者等于1的整数；Channel characteristic information, which includes: Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path At least one of the phase and the angle of the maximum H diameter, H is an integer greater than or equal to 1;
信道质量信息，所述信道质量信息包括：参考信号接收功率RSRP、参考信号接收质量RSRQ、信噪比SNR以及信号与干扰加噪声比SINR中的至少一项。Channel quality information, the channel quality information includes: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference plus noise ratio SINR.
可选地，在所述网络侧设备接收来自终端的第一信息或者接收来自终端的第五指示信息之前，所述方法还包括：Optionally, before the network side device receives the first information from the terminal or receives the fifth indication information from the terminal, the method further includes:
所述网络侧设备向所述终端发送第二指示信息或第三指示信息，其中，所述第二指示信息用于指示所述终端测量和/或上报所述第一信息，所述第三指示信息用于指示所述终端对所述目标AI网络模型进行性能监督。The network side device sends second indication information or third indication information to the terminal, where the second indication information is used to instruct the terminal to measure and/or report the first information, and the third indication The information is used to instruct the terminal to perform performance supervision on the target AI network model.
可选地，所述第三指示信息包括第一性能监督方法的标识信息，所述第三指示信息用于指示所述终端按照所述第一性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the third instruction information includes identification information of the first performance supervision method, and the third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model according to the first performance supervision method. .
可选地，所述第三指示信息还包括第二信息，所述第二信息用于辅助所述目标AI网络模型的性能监督。Optionally, the third indication information also includes second information, and the second information is used to assist performance supervision of the target AI network model.
可选地，在所述网络侧设备向所述终端发送第二指示信息或第三指示信息之前，所述方法还包括：Optionally, before the network side device sends the second indication information or the third indication information to the terminal, the method further includes:
所述网络侧设备接收来自所述终端的第一请求信息，所述第一请求信息用于请求对所述目标AI网络模型进行性能监督。The network side device receives first request information from the terminal, where the first request information is used to request performance supervision of the target AI network model.
可选地，所述第一请求信息包括第二性能监督方法的标识信息，所述第一请求信息用于请求按照所述第二性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the first request information includes identification information of the second performance supervision method, and the first request information is used to request performance supervision of the target AI network model according to the second performance supervision method.
可选地，在所述网络侧设备接收来自终端的第一信息之后，所述方法还包括：Optionally, after the network side device receives the first information from the terminal, the method further includes:
所述网络侧设备向所述终端发送第四指示信息，所述第四指示信息用于指示所述目标AI网络模型的性能。The network side device sends fourth indication information to the terminal, where the fourth indication information is used to indicate the target Performance of AI network models.
可选地，在所述第四指示信息指示所述目标AI网络模型失效的情况下，所述第四指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fourth indication information indicates that the target AI network model is invalid, the fourth indication information also indicates the reason why the target AI network model is invalid.
可选地，在所述第五指示信息指示所述目标AI网络模型失效的情况下，所述第五指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fifth indication information indicates that the target AI network model is invalid, the fifth indication information also indicates the reason why the target AI network model is invalid.
本申请实施例提供的网络侧设备执行的AI网络模型的性能监督方法，与终端执行的AI网络模型的性能监督方法相对应，且终端和网络侧设备分别执行各自的AI网络模型的性能监督方法中的步骤，能够及时发现目标AI网络模型的定位性能较低的情况，从而采取适当的措施来降低因目标AI网络模型的定位性能较低而造成的无线通信性能和应用体验较低的概率。The performance supervision method of the AI network model executed by the network side device provided by the embodiment of the present application corresponds to the performance supervision method of the AI network model executed by the terminal, and the terminal and the network side device respectively execute the performance supervision method of their respective AI network models. The steps in the above can promptly detect the low positioning performance of the target AI network model, and then take appropriate measures to reduce the probability of low wireless communication performance and application experience caused by the low positioning performance of the target AI network model.
本申请实施例提供的AI网络模型的性能监督方法，执行主体可以为AI网络模型的性能监督装置。本申请实施例中以AI网络模型的性能监督装置执行AI网络模型的性能监督方法为例，说明本申请实施例提供的AI网络模型的性能监督装置。For the performance supervision method of the AI network model provided by the embodiment of the present application, the execution subject may be the performance supervision device of the AI network model. In the embodiment of the present application, the performance supervision device of the AI network model performs the performance supervision method of the AI network model as an example to illustrate the performance supervision device of the AI network model provided by the embodiment of the present application.
请参阅图4，本申请实施例提供的一种AI网络模型的性能监督装置，可以是终端内的装置，如图4所示，该AI网络模型的性能监督装置400可以包括以下模块：Please refer to Figure 4. An embodiment of the present application provides a performance monitoring device for an AI network model, which can be a device in a terminal. As shown in Figure 4, the performance monitoring device 400 for the AI network model can include the following modules:
获取模块401，用于获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；The acquisition module 401 is used to acquire first information, the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal;
第一发送模块402或者第一确定模块403，第一发送模块402用于向网络侧设备发送所述第一信息，第一确定模块403用于根据所述第一信息确定所述目标AI网络模型的性能。The first sending module 402 or the first determining module 403. The first sending module 402 is used to send the first information to the network side device. The first determining module 403 is used to determine the target AI network model according to the first information. performance.
需要说明的是，如图4所示实施例中，AI网络模型的性能监督装置400同时包括第一发送模块402和第一确定模块403为例进行举例说明，在实施中，AI网络模型的性能监督装置400可以仅包括第一发送模块402和第一确定模块403中的一个，在此不作具体限定。It should be noted that, in the embodiment shown in Figure 4, the performance monitoring device 400 of the AI network model includes both the first sending module 402 and the first determining module 403. For example, in implementation, the performance of the AI network model The supervision device 400 may include only one of the first sending module 402 and the first determining module 403, which is not specifically limited here.
可选地，所述第一信息包括以下至少一项：Optionally, the first information includes at least one of the following:
在M个连续时间单位内，基于所述目标AI网络模型确定的所述终端的位置信息和运动状态信息，M为正整数；Within M continuous time units, the location information and motion status information of the terminal determined based on the target AI network model, M is a positive integer;
采用第一方式确定的所述终端的位置信息，所述第一方式不包括所述目标AI网络模型对应的定位方式；The location information of the terminal is determined using a first method that does not include the positioning method corresponding to the target AI network model;
基于所述目标AI网络模型确定的所述终端的位置信息的误差或者置信度；The error or confidence of the location information of the terminal determined based on the target AI network model;
所述终端的信道测量信息的变化幅度或变化率；The change amplitude or rate of change of the terminal’s channel measurement information;
采用第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离信息，所述第二方式不包括所述目标AI网络模型对应的方式；The distance information between the terminal and the positioning reference unit PRU or the transmission and reception node TRP is determined in a second way, and the second way does not include a way corresponding to the target AI network model;
采用所述第二方式确定的所述终端与其他终端的距离信息；The distance information between the terminal and other terminals determined using the second method;
基于所述目标AI网络模型确定的所述其他终端的位置信息；The location information of the other terminals determined based on the target AI network model;
视距传播LOS或非视距传播NLOS指示信息；Line-of-sight propagation LOS or non-line-of-sight propagation NLOS indication information;
所述其他终端的标识信息；The identification information of the other terminals;
所述PRU或TRP的标识信息。The identification information of the PRU or TRP.
可选地，第一确定模块403，用于执行以下至少一项：Optionally, the first determination module 403 is used to perform at least one of the following:
在第一距离与第二距离之间的差异大于或者等于第一阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一距离为基于所述运动状态信息确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，所述第二距离为基于所述目标AI网络模型确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，M大于1；When the difference between the first distance and the second distance is greater than or equal to a first threshold, it is determined that the target AI network model is invalid, wherein the first distance is the terminal determined based on the motion state information. The distance between the position of the terminal in the first time unit and the position of the terminal in the Mth time unit. The second distance is the position of the terminal in the first time unit and the position determined based on the target AI network model. The distance between the positions of the terminals in the Mth time unit, M is greater than 1;
在采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第二阈值的情况下，确定所述目标AI网络模型失效；When the distance between the location of the terminal determined using the first method and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold, determine the target AI network model Failure;
在第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第三阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一位置为采用所述第一方式确定的所述终端的N个位置的均值或加权均值，N为大于1的整数；When the distance between the first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, it is determined that the target AI network model is invalid, wherein the first The position is the average or weighted average of the N positions of the terminal determined using the first method, and N is an integer greater than 1;
在所述终端测量的信道状态信息的变化幅度或变化率大于或者等于第四阈值的情况下，确定所述目标AI网络模型失效；When the change amplitude or change rate of the channel state information measured by the terminal is greater than or equal to the fourth threshold, it is determined that the target AI network model is invalid;
在第三距离与第四距离之间的差异大于或者等于第五阈值的情况下，确定所述目标AI网络模型失效，其中，所述第三距离为采用所述第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离，所述第四距离为基于所述目标AI网络模型确定的所述终端的位置与所述PRU或TRP的位置之间的距离；When the difference between the third distance and the fourth distance is greater than or equal to the fifth threshold, it is determined that the target AI network model is invalid, wherein the third distance is the terminal determined by the second method. The distance from the positioning reference unit PRU or the transmission reception node TRP, the fourth distance is the distance between the position of the terminal determined based on the target AI network model and the position of the PRU or TRP;
在第五距离与第六距离之间的差异大于或者等于第六阈值的情况下，确定所述目标AI网络模型失效，其中，所述第五距离为采用所述第二方式确定的所述终端与其他终端的距离，所述第六距离为基于所述目标AI网络模型确定的所述终端的位置与基于所述目标AI网络模型确定的所述其他终端的位置之间的距离；When the difference between the fifth distance and the sixth distance is greater than or equal to the sixth threshold, it is determined that the target AI network model is invalid, wherein the fifth distance is the terminal determined by the second method. The distance from other terminals, the sixth distance is the distance between the location of the terminal determined based on the target AI network model and the location of the other terminal determined based on the target AI network model;
在所述目标AI网络模型为LOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的LOS占比小于或者等于第七阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in a LOS scenario, and the LOS or NLOS indication information indicates that the LOS proportion of the terminal is less than or equal to the seventh threshold, it is determined that the target AI network model is invalid. ;
在所述目标AI网络模型为NLOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的NLOS占比小于或者等于第八阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in an NLOS scenario, and the LOS or NLOS indication information indicates that the NLOS proportion of the terminal is less than or equal to the eighth threshold, it is determined that the target AI network model is invalid. ;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最小误差大于或者等于第九阈值的情况下，确定所述目标AI网络模型失效；In the case where the minimum error of the location information of the terminal determined based on the target AI network model is greater than or equal to the ninth threshold, it is determined that the target AI network model is invalid;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最大置信度小于或者等于第十阈值的情况下，确定所述目标AI网络模型失效。When the maximum confidence of the location information of the terminal determined based on the target AI network model is less than or equal to the tenth threshold, it is determined that the target AI network model is invalid.
可选地，所述终端的信道测量信息包括以下至少一项：Optionally, the channel measurement information of the terminal includes at least one of the following:
信道状态信息，所述信道状态信息包括：时域信道状态信息、频域信道状态信息、空域信道状态信息、时延多普勒域信道状态信息以及功率时延谱中的至少一项；Channel state information, the channel state information includes: at least one of time domain channel state information, frequency domain channel state information, air domain channel state information, delay Doppler domain channel state information, and power delay spectrum;
信道特征信息，所述信道特征信息包括：多普勒频域、首径时延、首径功率、首径相位、首径角度、最大H径的时延、最大H径的功率、最大H径的相位以及最大H径的角度中的至少一项，H为大于或者等于1的整数；Channel characteristic information, which includes: Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path At least one of the phase and the angle of the maximum H diameter, H is an integer greater than or equal to 1;
信道质量信息，所述信道质量信息包括：参考信号接收功率RSRP、参考信号接收质量RSRQ、信噪比SNR以及信号与干扰加噪声比SINR中的至少一项。Channel quality information, the channel quality information includes: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference plus noise ratio SINR.
可选地，AI网络模型的性能监督装置400还包括：Optionally, the AI network model performance monitoring device 400 also includes:
第一接收模块，用于接收来自所述网络侧设备的第二指示信息或第三指示信息，其中，所述第二指示信息用于指示所述终端测量和/或上报所述第一信息，所述第三指示信息用于指示所述终端对所述目标AI网络模型进行性能监督。A first receiving module configured to receive second indication information or third indication information from the network side device, where the second indication information is used to instruct the terminal to measure and/or report the first information, The third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model.
可选地，所述第三指示信息包括第一性能监督方法的标识信息，所述第三指示信息用于指示所述终端按照所述第一性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the third instruction information includes identification information of the first performance supervision method, and the third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model according to the first performance supervision method. .
可选地，所述第三指示信息还包括第二信息，所述第二信息用于辅助所述目标AI网络模型的性能监督。Optionally, the third indication information also includes second information, and the second information is used to assist performance supervision of the target AI network model.
可选地，AI网络模型的性能监督装置400还包括：Optionally, the AI network model performance monitoring device 400 also includes:
第二发送模块，用于向所述网络侧设备发送第一请求信息，所述第一请求信息用于请求对所述目标AI网络模型进行性能监督。The second sending module is configured to send first request information to the network side device, where the first request information is used to request performance supervision of the target AI network model.
可选地，所述第一请求信息包括第二性能监督方法的标识信息，所述第一请求信息用于请求按照所述第二性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the first request information includes identification information of the second performance supervision method, and the first request information is used to request performance supervision of the target AI network model according to the second performance supervision method.
可选地，AI网络模型的性能监督装置400还包括：Optionally, the AI network model performance monitoring device 400 also includes:
第二接收模块，用于接收来自所述网络侧设备的第四指示信息，所述第四指示信息用于指示所述目标AI网络模型的性能。The second receiving module is configured to receive fourth indication information from the network side device, where the fourth indication information is used to indicate the performance of the target AI network model.
可选地，在所述第四指示信息指示所述目标AI网络模型失效的情况下，所述第四指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fourth indication information indicates that the target AI network model is invalid, the fourth indication information also indicates the reason why the target AI network model is invalid.
可选地，AI网络模型的性能监督装置400还包括：Optionally, the AI network model performance monitoring device 400 also includes:
第三发送模块，用于向所述网络侧设备发送第五指示信息，所述第五指示信息用于指示所述目标AI网络模型的性能。The third sending module is configured to send fifth indication information to the network side device, where the fifth indication information is used to indicate the performance of the target AI network model.
可选地，在所述第五指示信息指示所述目标AI网络模型失效的情况下，所述第五指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fifth indication information indicates that the target AI network model is invalid, the fifth indication information also indicates the reason why the target AI network model is invalid.
本申请实施例提供的AI网络模型的性能监督装置400，能够实现如图2所示方法实施例中终端实现的各个过程，且能够取得相同的有益效果，为避免重复，在此不再赘述。The performance monitoring device 400 of the AI network model provided by the embodiment of the present application can implement various processes implemented by the terminal in the method embodiment shown in Figure 2, and can achieve the same beneficial effects. To avoid duplication, they will not be described again here.
请参阅图5或图6，本申请实施例提供的另一种AI网络模型的性能监督装置，可以是网络侧设备内的装置。Please refer to Figure 5 or Figure 6. Another performance monitoring device for an AI network model provided by an embodiment of the present application may be a device in a network-side device.
如图5所示，一种AI网络模型的性能监督装置500可以包括以下模块：As shown in Figure 5, a performance monitoring device 500 for an AI network model may include the following modules:
第三接收模块501，用于接收来自终端的第一信息，其中，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；The third receiving module 501 is used to receive first information from the terminal, where the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal;
第二确定模块502，用于根据所述第一信息确定目标AI网络模型的性能。The second determination module 502 is used to determine the performance of the target AI network model according to the first information.
如图6所示，另一种AI网络模型的性能监督装置600可以包括以下模块：As shown in Figure 6, another performance monitoring device 600 of an AI network model may include the following modules:
第四接收模块601，用于接收来自终端的第五指示信息，其中，所述第五指示信息用于指示所述目标AI网络模型的性能。The fourth receiving module 601 is configured to receive fifth indication information from the terminal, where the fifth indication information is used to indicate the performance of the target AI network model.
需要说明的是，在实施中，网路侧设备还可以同时包括上述第三接收模块501、第二确定模块502和第四接收模块601，如图5或图6仅作为两种可能的AI网络模型的性能监督装置的举例。It should be noted that in implementation, the network side device may also include the above-mentioned third receiving module 501, the second determining module 502 and the fourth receiving module 601 at the same time. As shown in Figure 5 or Figure 6, they are only two possible AI networks. Examples of model performance monitoring devices.
可选地，所述第一信息包括以下至少一项：Optionally, the first information includes at least one of the following:
在M个连续时间单位内，基于所述目标AI网络模型确定的所述终端的位置信息和运动状态信息，M为正整数；Within M continuous time units, the location information and motion status information of the terminal determined based on the target AI network model, M is a positive integer;
采用第一方式确定的所述终端的位置信息，所述第一方式不包括所述目标AI网络模型对应的定位方式；The location information of the terminal is determined using a first method that does not include the positioning method corresponding to the target AI network model;
基于所述目标AI网络模型确定的所述终端的位置信息的误差或者置信度；The error or confidence of the location information of the terminal determined based on the target AI network model;
所述终端的信道测量信息的变化幅度或变化率；The change amplitude or rate of change of the terminal’s channel measurement information;
采用第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离信息，所述第二方式不包括所述目标AI网络模型对应的方式；The distance information between the terminal and the positioning reference unit PRU or the transmission and reception node TRP is determined in a second way, and the second way does not include a way corresponding to the target AI network model;
采用所述第二方式确定的所述终端与其他终端的距离信息；The distance information between the terminal and other terminals determined using the second method;
基于所述目标AI网络模型确定的所述其他终端的位置信息；The location information of the other terminals determined based on the target AI network model;
视距传播LOS或非视距传播NLOS指示信息；Line-of-sight propagation LOS or non-line-of-sight propagation NLOS indication information;
所述其他终端的标识信息；The identification information of the other terminals;
所述PRU或TRP的标识信息。The identification information of the PRU or TRP.
可选地，第二确定模块502，用于执行以下至少一项：Optionally, the second determination module 502 is used to perform at least one of the following:
在第一距离与第二距离之间的差异大于或者等于第一阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一距离为基于所述运动状态信息确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，所述第二距离为基于所述目标AI网络模型确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，M大于1；When the difference between the first distance and the second distance is greater than or equal to a first threshold, it is determined that the target AI network model is invalid, wherein the first distance is the terminal determined based on the motion state information. The distance between the position of the terminal in the first time unit and the position of the terminal in the Mth time unit. The second distance is the position of the terminal in the first time unit and the position determined based on the target AI network model. The distance between the positions of the terminals in the Mth time unit, M is greater than 1;
在采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第二阈值的情况下，确定所述目标AI网络模型失效；When the distance between the location of the terminal determined using the first method and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold, determine the target AI network model Failure;
在第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第三阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一位置为采用所述第一方式确定的所述终端的N个位置的均值或加权均值，N为大于1的整数；When the distance between the first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, it is determined that the target AI network model is invalid, wherein the first The position is the average or weighted average of the N positions of the terminal determined using the first method, and N is an integer greater than 1;
在所述终端测量的信道状态信息的变化幅度或变化率大于或者等于第四阈值的情况下，确定所述目标AI网络模型失效；When the change amplitude or change rate of the channel state information measured by the terminal is greater than or equal to the fourth threshold Next, it is determined that the target AI network model is invalid;
在第三距离与第四距离之间的差异大于或者等于第五阈值的情况下，确定所述目标AI网络模型失效，其中，所述第三距离为采用所述第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离，所述第四距离为基于所述目标AI网络模型确定的所述终端的位置与所述PRU或TRP的位置之间的距离；When the difference between the third distance and the fourth distance is greater than or equal to the fifth threshold, it is determined that the target AI network model is invalid, wherein the third distance is the terminal determined by the second method. The distance from the positioning reference unit PRU or the transmission reception node TRP, the fourth distance is the distance between the position of the terminal determined based on the target AI network model and the position of the PRU or TRP;
在第五距离与第六距离之间的差异大于或者等于第六阈值的情况下，确定所述目标AI网络模型失效，其中，所述第五距离为采用所述第二方式确定的所述终端与其他终端的距离，所述第六距离为基于所述目标AI网络模型确定的所述终端的位置与基于所述目标AI网络模型确定的所述其他终端的位置之间的距离；When the difference between the fifth distance and the sixth distance is greater than or equal to the sixth threshold, it is determined that the target AI network model is invalid, wherein the fifth distance is the terminal determined by the second method. The distance from other terminals, the sixth distance is the distance between the location of the terminal determined based on the target AI network model and the location of the other terminal determined based on the target AI network model;
在所述目标AI网络模型为LOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的LOS占比小于或者等于第七阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in a LOS scenario, and the LOS or NLOS indication information indicates that the LOS proportion of the terminal is less than or equal to the seventh threshold, it is determined that the target AI network model is invalid. ;
在所述目标AI网络模型为NLOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的NLOS占比小于或者等于第八阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in an NLOS scenario, and the LOS or NLOS indication information indicates that the NLOS proportion of the terminal is less than or equal to the eighth threshold, it is determined that the target AI network model is invalid. ;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最小误差大于或者等于第九阈值的情况下，确定所述目标AI网络模型失效；In the case where the minimum error of the location information of the terminal determined based on the target AI network model is greater than or equal to the ninth threshold, it is determined that the target AI network model is invalid;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最大置信度小于或者等于第十阈值的情况下，确定所述目标AI网络模型失效。When the maximum confidence of the location information of the terminal determined based on the target AI network model is less than or equal to the tenth threshold, it is determined that the target AI network model is invalid.
可选地，所述终端的信道测量信息包括以下至少一项：Optionally, the channel measurement information of the terminal includes at least one of the following:
信道状态信息，所述信道状态信息包括：时域信道状态信息、频域信道状态信息、空域信道状态信息、时延多普勒域信道状态信息以及功率时延谱中的至少一项；Channel state information, the channel state information includes: at least one of time domain channel state information, frequency domain channel state information, air domain channel state information, delay Doppler domain channel state information, and power delay spectrum;
信道特征信息，所述信道特征信息包括：多普勒频域、首径时延、首径功率、首径相位、首径角度、最大H径的时延、最大H径的功率、最大H径的相位以及最大H径的角度中的至少一项，H为大于或者等于1的整数；Channel characteristic information, which includes: Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path At least one of the phase and the angle of the maximum H diameter, H is an integer greater than or equal to 1;
信道质量信息，所述信道质量信息包括：参考信号接收功率RSRP、参考信号接收质量RSRQ、信噪比SNR以及信号与干扰加噪声比SINR中的至少一项。Channel quality information, the channel quality information includes: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference plus noise ratio SINR.
可选地，AI网络模型的性能监督装置500或AI网络模型的性能监督装置600还包括：Optionally, the performance monitoring device 500 of the AI network model or the performance monitoring device 600 of the AI network model also includes:
第四发送模块，用于向所述终端发送第二指示信息或第三指示信息，其中，所述第二指示信息用于指示所述终端测量和/或上报所述第一信息，所述第三指示信息用于指示所述终端对所述目标AI网络模型进行性能监督。The fourth sending module is configured to send second indication information or third indication information to the terminal, wherein the second indication information is used to instruct the terminal to measure and/or report the first information, and the third indication information is used to instruct the terminal to measure and/or report the first information. The third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model.
可选地，所述第三指示信息包括第一性能监督方法的标识信息，所述第三指示信息用于指示所述终端按照所述第一性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the third instruction information includes identification information of the first performance supervision method, and the third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model according to the first performance supervision method. .
可选地，所述第三指示信息还包括第二信息，所述第二信息用于辅助所述目标AI网络模型的性能监督。Optionally, the third indication information also includes second information, and the second information is used to assist performance supervision of the target AI network model.
可选地，AI网络模型的性能监督装置500或AI网络模型的性能监督装置600还包括：Optionally, the performance monitoring device 500 of the AI network model or the performance monitoring device 600 of the AI network model also includes:
第五接收模块，用于接收来自所述终端的第一请求信息，所述第一请求信息用于请求对所述目标AI网络模型进行性能监督。The fifth receiving module is configured to receive first request information from the terminal, where the first request information is used to request performance supervision of the target AI network model.
可选地，所述第一请求信息包括第二性能监督方法的标识信息，所述第一请求信息用于请求按照所述第二性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the first request information includes identification information of the second performance supervision method, and the first request information is used to request performance supervision of the target AI network model according to the second performance supervision method.
可选地，AI网络模型的性能监督装置500或AI网络模型的性能监督装置600还包括：Optionally, the performance monitoring device 500 of the AI network model or the performance monitoring device 600 of the AI network model also includes:
第五发送模块，用于向所述终端发送第四指示信息，所述第四指示信息用于指示所述目标AI网络模型的性能。The fifth sending module is configured to send fourth indication information to the terminal, where the fourth indication information is used to indicate the performance of the target AI network model.
可选地，在所述第四指示信息指示所述目标AI网络模型失效的情况下，所述第四指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fourth indication information indicates that the target AI network model is invalid, the fourth indication information also indicates the reason why the target AI network model is invalid.
可选地，在所述第五指示信息指示所述目标AI网络模型失效的情况下，所述第五指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fifth indication information indicates that the target AI network model is invalid, the fifth indication information also indicates the reason why the target AI network model is invalid.
本申请实施例提供的AI网络模型的性能监督装置500或AI网络模型的性能监督装置600，能够实现如图3所示方法实施例中网络侧设备实现的各个过程，且能够取得相同的有益效果，为避免重复，在此不再赘述。The performance supervision device 500 or the performance supervision device 600 of the AI network model provided by the embodiment of the present application can implement each process implemented by the network side device in the method embodiment as shown in Figure 3, and can achieve the same beneficial effects. , to avoid repetition, will not be repeated here.
本申请实施例中的AI网络模型的性能监督装置可以是电子设备，例如具有操作系统的电子设备，也可以是电子设备中的部件，例如集成电路或芯片。该电子设备可以是终端，也可以为除终端之外的其他设备。示例性的，终端可以包括但不限于上述所列举的终端11的类型，其他设备可以为服务器、网络附属存储器(Network Attached Storage，NAS)等，本申请实施例不作具体限定。The performance monitoring device of the AI network model in the embodiment of the present application may be an electronic device, such as an electronic device with an operating system, or may be a component in the electronic device, such as an integrated circuit or chip. The electronic device may be a terminal or other devices other than the terminal. For example, terminals may include but are not limited to the types of terminals 11 listed above, and other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., which are not specifically limited in the embodiment of this application.
本申请实施例提供的AI网络模型的性能监督装置能够实现图2或图3所示方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。The performance supervision device of the AI network model provided by the embodiment of the present application can implement each process implemented by the method embodiment shown in Figure 2 or Figure 3, and achieve the same technical effect. To avoid duplication, it will not be described again here.
可选的，如图7所示，本申请实施例还提供一种通信设备700，包括处理器701和存储器702，存储器702上存储有可在所述处理器701上运行的程序或指令，例如，该通信设备700为终端时，该程序或指令被处理器701执行时实现如图2所示方法实施例的各个步骤，且能达到相同的技术效果。该通信设备700为网络侧设备时，该程序或指令被处理器701执行时实现如图3所示方法实施例的各个步骤，且能达到相同的技术效果，为避免重复，这里不再赘述。Optionally, as shown in Figure 7, this embodiment of the present application also provides a communication device 700, which includes a processor 701 and a memory 702. The memory 702 stores programs or instructions that can be run on the processor 701, for example. , when the communication device 700 is a terminal, when the program or instruction is executed by the processor 701, each step of the method embodiment shown in Figure 2 is implemented, and the same technical effect can be achieved. When the communication device 700 is a network-side device, when the program or instruction is executed by the processor 701, each step of the method embodiment shown in Figure 3 is implemented, and the same technical effect can be achieved. To avoid duplication, the details will not be described here.
本申请实施例还提供一种终端，包括处理器和通信接口，所述通信接口用于获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；所述通信接口还用于向网络侧设备发送所述第一信息或者所述处理器用于根据所述第一信息确定所述目标AI网络模型的性能。An embodiment of the present application also provides a terminal, including a processor and a communication interface. The communication interface is used to obtain first information. The first information is used to determine the performance of a target AI network model. The target AI network model is For locating the terminal; the communication interface is also used to send the first information to the network side device or the processor is used to determine the performance of the target AI network model based on the first information.
该终端实施例与上述终端侧方法实施例对应，上述方法实施例的各个实施过程和实现方式均可适用于该终端实施例中，且能达到相同的技术效果。具体地，图8为实现本申请实施例的一种终端的硬件结构示意图。This terminal embodiment corresponds to the above-mentioned terminal-side method embodiment. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this terminal embodiment, and can achieve the same technical effect. Specifically, FIG. 8 is a schematic diagram of the hardware structure of a terminal that implements an embodiment of the present application.
该终端800包括但不限于：射频单元801、网络模块802、音频输出单元803、输入单元804、传感器805、显示单元806、用户输入单元807、接口单元808、存储器809以及处理器810等中的至少部分部件。The terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809, a processor 810, etc. At least some parts.
本领域技术人员可以理解，终端800还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器810逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图8中示出的终端结构并不构成对终端的限定，终端可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。Those skilled in the art can understand that the terminal 800 may also include a power supply (such as a battery) that supplies power to various components. The power supply may be logically connected to the processor 810 through a power management system, thereby managing charging, discharging, and power consumption through the power management system. Management and other functions. The terminal structure shown in FIG. 8 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or some components may be combined or arranged differently, which will not be described again here.
应理解的是，本申请实施例中，输入单元804可以包括图形处理单元(Graphics Processing Unit，GPU)8041和麦克风8042，图形处理器8041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元806可包括显示面板8061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板8061。用户输入单元807包括触控面板8071以及其他输入设备8072中的至少一种。触控面板8071，也称为触摸屏。触控面板8071可包括触摸检测装置和触摸控制器两个部分。其他输入设备8072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。It should be understood that in the embodiment of the present application, the input unit 804 may include a graphics processing unit (Graphics Processing Unit, GPU) 8041 and a microphone 8042. The graphics processor 8041 is responsible for the image capture device (GPU) in the video capture mode or the image capture mode. Process the image data of still pictures or videos obtained by cameras (such as cameras). The display unit 806 may include a display panel 8061, which may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072 . Touch panel 8071, also known as touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include but are not limited to physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be described again here.
本申请实施例中，射频单元801接收来自网络侧设备的下行数据后，可以传输给处理器810进行处理；另外，射频单元801可以向网络侧设备发送上行数据。通常，射频单元801包括但不限于天线、放大器、收发信机、耦合器、低噪声放大器、双工器等。In this embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 801 can transmit it to the processor 810 for processing; in addition, the radio frequency unit 801 can send uplink data to the network side device. Generally, the radio frequency unit 801 includes, but is not limited to, an antenna, amplifier, transceiver, coupler, low noise amplifier, duplexer, etc.
存储器809可用于存储软件程序或指令以及各种数据。存储器809可主要包括存储程序或指令的第一存储区和存储数据的第二存储区，其中，第一存储区可存储操作系统、至少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器809可以包括易失性存储器或非易失性存储器，或者，存储器809可以包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(Random Access Memory，RAM)，静态随机存取存储器(Static RAM，SRAM)、动态随机存取存储器(Dynamic RAM，DRAM)、同步动态随机存取存储器(Synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(Double Data Rate SDRAM，DDRSDRAM)、增强型同步动态随机存取存储器(Enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(Synch link DRAM，SLDRAM)和直接内存总线随机存取存储器(Direct Rambus RAM，DRRAM)。本申请实施例中的存储器809包括但不限于这些和任意其它适合类型的存储器。Memory 809 may be used to store software programs or instructions as well as various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, Image playback function, etc.) etc. Additionally, memory 809 may include volatile memory or non-volatile memory, or memory 809 may include both volatile and non-volatile memory. Among them, non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically removable memory. Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM) , SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). Memory 809 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.
处理器810可包括一个或多个处理单元；可选的，处理器810集成应用处理器和调制解调处理器，其中，应用处理器主要处理涉及操作系统、用户界面和应用程序等的操作，调制解调处理器主要处理无线通信信号，如基带处理器。可以理解的是，上述调制解调处理器也可以不集成到处理器810中。The processor 810 may include one or more processing units; optionally, the processor 810 integrates an application processor and a modem processor, where the application processor mainly handles operations related to the operating system, user interface, application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the above modem The processor may not be integrated into the processor 810.
其中，射频单元801，用于获取第一信息，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；Among them, the radio frequency unit 801 is used to obtain first information, the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal;
射频单元801，还用于向网络侧设备发送所述第一信息，或者，处理器810，用于根据所述第一信息确定所述目标AI网络模型的性能。The radio frequency unit 801 is also configured to send the first information to the network side device, or the processor 810 is configured to determine the performance of the target AI network model based on the first information.
可选地，所述第一信息包括以下至少一项：Optionally, the first information includes at least one of the following:
在M个连续时间单位内，基于所述目标AI网络模型确定的所述终端的位置信息和运动状态信息，M为正整数；Within M continuous time units, the location information and motion status information of the terminal determined based on the target AI network model, M is a positive integer;
采用第一方式确定的所述终端的位置信息，所述第一方式不包括所述目标AI网络模型对应的定位方式；The location information of the terminal is determined using a first method that does not include the positioning method corresponding to the target AI network model;
基于所述目标AI网络模型确定的所述终端的位置信息的误差或者置信度；The error or confidence of the location information of the terminal determined based on the target AI network model;
所述终端的信道测量信息的变化幅度或变化率；The change amplitude or rate of change of the terminal’s channel measurement information;
采用第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离信息，所述第二方式不包括所述目标AI网络模型对应的方式；The distance information between the terminal and the positioning reference unit PRU or the transmission and reception node TRP is determined in a second way, and the second way does not include a way corresponding to the target AI network model;
采用所述第二方式确定的所述终端与其他终端的距离信息；The distance information between the terminal and other terminals determined using the second method;
基于所述目标AI网络模型确定的所述其他终端的位置信息；The location information of the other terminals determined based on the target AI network model;
视距传播LOS或非视距传播NLOS指示信息；Line-of-sight propagation LOS or non-line-of-sight propagation NLOS indication information;
所述其他终端的标识信息；The identification information of the other terminals;
所述PRU或TRP的标识信息。The identification information of the PRU or TRP.
可选地，处理器810执行的所述根据所述第一信息确定所述目标AI网络模型的性能，包括以下至少一项：Optionally, the determination of the performance of the target AI network model based on the first information performed by the processor 810 includes at least one of the following:
在第一距离与第二距离之间的差异大于或者等于第一阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一距离为基于所述运动状态信息确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，所述第二距离为基于所述目标AI网络模型确定的所述终端在第1时间单位的位置与所述终端在第M时间单位的位置之间的距离，M大于1；When the difference between the first distance and the second distance is greater than or equal to a first threshold, it is determined that the target AI network model is invalid, wherein the first distance is the terminal determined based on the motion state information. The distance between the position of the terminal in the first time unit and the position of the terminal in the Mth time unit. The second distance is the position of the terminal in the first time unit and the position determined based on the target AI network model. The distance between the positions of the terminals in the Mth time unit, M is greater than 1;
在采用所述第一方式确定的所述终端的位置与基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第二阈值的情况下，确定所述目标AI网络模型失效；When the distance between the location of the terminal determined using the first method and the location of the terminal determined based on the target AI network model is greater than or equal to a second threshold, determine the target AI network model Failure;
在第一位置与所述基于所述目标AI网络模型确定的所述终端的位置之间的距离大于或者等于第三阈值的情况下，确定所述目标AI网络模型失效，其中，所述第一位置为采用所述第一方式确定的所述终端的N个位置的均值或加权均值，N为大于1的整数；When the distance between the first position and the position of the terminal determined based on the target AI network model is greater than or equal to a third threshold, it is determined that the target AI network model is invalid, wherein the first The position is the average or weighted average of the N positions of the terminal determined using the first method, and N is an integer greater than 1;
在所述终端测量的信道状态信息的变化幅度或变化率大于或者等于第四阈值的情况下，确定所述目标AI网络模型失效；When the change amplitude or change rate of the channel state information measured by the terminal is greater than or equal to the fourth threshold, it is determined that the target AI network model is invalid;
在第三距离与第四距离之间的差异大于或者等于第五阈值的情况下，确定所述目标AI网络模型失效，其中，所述第三距离为采用所述第二方式确定的所述终端与定位参考单元PRU或传输接收节点TRP的距离，所述第四距离为基于所述目标AI网络模型确定的所述终端的位置与所述PRU或TRP的位置之间的距离；When the difference between the third distance and the fourth distance is greater than or equal to the fifth threshold, it is determined that the target AI network model is invalid, wherein the third distance is the terminal determined by the second method. with positioning reference The distance from the unit PRU or the transmission receiving node TRP, the fourth distance is the distance between the location of the terminal determined based on the target AI network model and the location of the PRU or TRP;
在第五距离与第六距离之间的差异大于或者等于第六阈值的情况下，确定所述目标AI网络模型失效，其中，所述第五距离为采用所述第二方式确定的所述终端与其他终端的距离，所述第六距离为基于所述目标AI网络模型确定的所述终端的位置与基于所述目标AI网络模型确定的所述其他终端的位置之间的距离；When the difference between the fifth distance and the sixth distance is greater than or equal to the sixth threshold, it is determined that the target AI network model is invalid, wherein the fifth distance is the terminal determined by the second method. The distance from other terminals, the sixth distance is the distance between the location of the terminal determined based on the target AI network model and the location of the other terminal determined based on the target AI network model;
在所述目标AI网络模型为LOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的LOS占比小于或者等于第七阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in a LOS scenario, and the LOS or NLOS indication information indicates that the LOS proportion of the terminal is less than or equal to the seventh threshold, it is determined that the target AI network model is invalid. ;
在所述目标AI网络模型为NLOS场景下使用的网络模型，且所述LOS或NLOS指示信息指示所述终端的NLOS占比小于或者等于第八阈值的情况下，确定所述目标AI网络模型失效；When the target AI network model is a network model used in an NLOS scenario, and the LOS or NLOS indication information indicates that the NLOS proportion of the terminal is less than or equal to the eighth threshold, it is determined that the target AI network model is invalid. ;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最小误差大于或者等于第九阈值的情况下，确定所述目标AI网络模型失效；In the case where the minimum error of the location information of the terminal determined based on the target AI network model is greater than or equal to the ninth threshold, it is determined that the target AI network model is invalid;
在所述基于所述目标AI网络模型确定的所述终端的位置信息的最大置信度小于或者等于第十阈值的情况下，确定所述目标AI网络模型失效。When the maximum confidence of the location information of the terminal determined based on the target AI network model is less than or equal to the tenth threshold, it is determined that the target AI network model is invalid.
可选地，所述终端的信道测量信息包括以下至少一项：Optionally, the channel measurement information of the terminal includes at least one of the following:
信道状态信息，所述信道状态信息包括：时域信道状态信息、频域信道状态信息、空域信道状态信息、时延多普勒域信道状态信息以及功率时延谱中的至少一项；Channel state information, the channel state information includes: at least one of time domain channel state information, frequency domain channel state information, air domain channel state information, delay Doppler domain channel state information, and power delay spectrum;
信道特征信息，所述信道特征信息包括：多普勒频域、首径时延、首径功率、首径相位、首径角度、最大H径的时延、最大H径的功率、最大H径的相位以及最大H径的角度中的至少一项，H为大于或者等于1的整数；Channel characteristic information, which includes: Doppler frequency domain, first path delay, first path power, first path phase, first path angle, maximum H path delay, maximum H path power, maximum H path At least one of the phase and the angle of the maximum H diameter, H is an integer greater than or equal to 1;
信道质量信息，所述信道质量信息包括：参考信号接收功率RSRP、参考信号接收质量RSRQ、信噪比SNR以及信号与干扰加噪声比SINR中的至少一项。Channel quality information, the channel quality information includes: at least one of reference signal received power RSRP, reference signal received quality RSRQ, signal-to-noise ratio SNR, and signal-to-interference plus noise ratio SINR.
可选地，射频单元801在执行所述获取第一信息之前，还用于接收来自所述网络侧设备的第二指示信息或第三指示信息，其中，所述第二指示信息用于指示所述终端测量和/或上报所述第一信息，所述第三指示信息用于指示所述终端对所述目标AI网络模型进行性能监督。Optionally, before performing the acquisition of the first information, the radio frequency unit 801 is also configured to receive second indication information or third indication information from the network side device, wherein the second indication information is used to indicate the The terminal measures and/or reports the first information, and the third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model.
可选地，所述第三指示信息包括第一性能监督方法的标识信息，所述第三指示信息用于指示所述终端按照所述第一性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the third instruction information includes identification information of the first performance supervision method, and the third instruction information is used to instruct the terminal to perform performance supervision on the target AI network model according to the first performance supervision method. .
可选地，所述第三指示信息还包括第二信息，所述第二信息用于辅助所述目标AI网络模型的性能监督。Optionally, the third indication information also includes second information, and the second information is used to assist performance supervision of the target AI network model.
可选地，射频单元801在执行所述接收来自所述网络侧设备的第二指示信息或第三指示信息之前，还用于向所述网络侧设备发送第一请求信息，所述第一请求信息用于请求对所述目标AI网络模型进行性能监督。Optionally, before performing the reception of the second indication information or the third indication information from the network side device, the radio frequency unit 801 is also configured to send first request information to the network side device. The first request The information is used to request performance supervision of the target AI network model.
可选地，所述第一请求信息包括第二性能监督方法的标识信息，所述第一请求信息用于请求按照所述第二性能监督方法对所述目标AI网络模型进行性能监督。Optionally, the first request information includes identification information of the second performance supervision method, and the first request information is used to request performance supervision of the target AI network model according to the second performance supervision method.
可选地，射频单元801在执行所述向网络侧设备发送所述第一信息之后，还用于接收来自所述网络侧设备的第四指示信息，所述第四指示信息用于指示所述目标AI网络模型的性能。Optionally, after performing the sending of the first information to the network side device, the radio frequency unit 801 is further configured to receive fourth indication information from the network side device, where the fourth indication information is used to indicate the The performance of the target AI network model.
可选地，在所述第四指示信息指示所述目标AI网络模型失效的情况下，所述第四指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fourth indication information indicates that the target AI network model is invalid, the fourth indication information also indicates the reason why the target AI network model is invalid.
可选地，在处理器810执行所述根据所述第一信息确定所述目标AI网络模型的性能之后，射频单元801还用于向所述网络侧设备发送第五指示信息，所述第五指示信息用于指示所述目标AI网络模型的性能。Optionally, after the processor 810 determines the performance of the target AI network model according to the first information, the radio frequency unit 801 is further configured to send fifth indication information to the network side device, and the fifth The indication information is used to indicate the performance of the target AI network model.
可选地，在所述第五指示信息指示所述目标AI网络模型失效的情况下，所述第五指示信息还指示所述目标AI网络模型失效的原因。Optionally, in the case where the fifth indication information indicates that the target AI network model is invalid, the fifth indication information also indicates the reason why the target AI network model is invalid.
本申请实施例提供的终端800，能够执行如图4所示AI网络模型的性能监督装置400中的各模块执行的各个过程，且能够取得相同的有益效果，为避免重复，在此不再赘述。The terminal 800 provided by the embodiment of the present application can execute each process performed by each module in the performance monitoring device 400 of the AI network model as shown in Figure 4, and can achieve the same beneficial effects. To avoid duplication, they will not be described again here. .
本申请实施例还提供一种网络侧设备，包括处理器和通信接口，所述通信接口用于接收来自终端的第一信息，所述处理器用于根据所述第一信息确定目标AI网络模型的性能，其中，所述第一信息用于确定目标AI网络模型的性能，所述目标AI网络模型用于对所述终端进行定位；或者，所述通信接口用于接收来自终端的第五指示信息，其中，所述第五指示信息用于指示所述目标AI网络模型的性能。Embodiments of the present application also provide a network side device, including a processor and a communication interface. The communication interface is used to receive first information from a terminal. The processor is used to determine the target AI network model based on the first information. Performance, wherein the first information is used to determine the performance of the target AI network model, and the target AI network model is used to locate the terminal; or the communication interface is used to receive fifth indication information from the terminal , wherein the fifth indication information is used to indicate the performance of the target AI network model.
该网络侧设备实施例与上述网络侧设备方法实施例对应，上述方法实施例的各个实施过程和实现方式均可适用于该网络侧设备实施例中，且能达到相同的技术效果。This network-side device embodiment corresponds to the above-mentioned network-side device method embodiment. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this network-side device embodiment, and can achieve the same technical effect.
具体地，本申请实施例还提供了一种网络侧设备。如图9所示，该网络侧设备900包括：天线901、射频装置902、基带装置903、处理器904和存储器905。天线901与射频装置902连接。在上行方向上，射频装置902通过天线901接收信息，将接收的信息发送给基带装置903进行处理。在下行方向上，基带装置903对要发送的信息进行处理，并发送给射频装置902，射频装置902对收到的信息进行处理后经过天线901发送出去。Specifically, the embodiment of the present application also provides a network side device. As shown in Figure 9, the network side device 900 includes: an antenna 901, a radio frequency device 902, a baseband device 903, a processor 904 and a memory 905. Antenna 901 is connected to radio frequency device 902. In the uplink direction, the radio frequency device 902 receives information through the antenna 901 and sends the received information to the baseband device 903 for processing. In the downlink direction, the baseband device 903 processes the information to be sent and sends it to the radio frequency device 902. The radio frequency device 902 processes the received information and then sends it out through the antenna 901.
以上实施例中网络侧设备执行的方法可以在基带装置903中实现，该基带装置903包括基带处理器。The method performed by the network side device in the above embodiment can be implemented in the baseband device 903, which includes a baseband processor.
基带装置903例如可以包括至少一个基带板，该基带板上设置有多个芯片，如图9所示，其中一个芯片例如为基带处理器，通过总线接口与存储器905连接，以调用存储器905中的程序，执行以上方法实施例中所示的网络设备操作。The baseband device 903 may include, for example, at least one baseband board on which multiple chips are disposed, as shown in FIG. 9 . One of the chips is, for example, a baseband processor, which is connected to the memory 905 through a bus interface to call the Program to perform the network device operations shown in the above method embodiments.
该网络侧设备还可以包括网络接口906，该接口例如为通用公共无线接口(Common Public Radio Interface，CPRI)。The network side device may also include a network interface 906, which is, for example, a Common Public Radio Interface (CPRI).
具体地，本发明实施例的网络侧设备900还包括：存储在存储器905上并可在处理器904上运行的指令或程序，处理器904调用存储器905中的指令或程序执行图5或图6所示各模块执行的方法，并达到相同的技术效果，为避免重复，故不在此赘述。Specifically, the network side device 900 in this embodiment of the present invention also includes: instructions or programs stored in the memory 905 and executable on the processor 904. The processor 904 calls the instructions or programs in the memory 905 to execute Figure 5 or Figure 6 Place It shows the execution method of each module and achieves the same technical effect. To avoid duplication, it will not be repeated here.
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现如图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。Embodiments of the present application also provide a readable storage medium. Programs or instructions are stored on the readable storage medium. When the program or instructions are executed by a processor, each process of the method embodiment shown in Figure 2 or Figure 3 is implemented. , and can achieve the same technical effect, so to avoid repetition, they will not be described again here.
其中，所述处理器为上述实施例中所述的终端中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器ROM、随机存取存储器RAM、磁碟或者光盘等。Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disk or optical disk, etc.
本申请实施例另提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。An embodiment of the present application further provides a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions. The implementation is as shown in Figure 2 or Figure 3. Each process of the method embodiment is shown, and the same technical effect can be achieved. To avoid repetition, the details will not be described here.
应理解，本申请实施例提到的芯片还可以称为系统级芯片，系统芯片，芯片系统或片上系统芯片等。It should be understood that the chips mentioned in the embodiments of this application may also be called system-on-chip, system-on-a-chip, system-on-chip or system-on-chip, etc.
本申请实施例另提供了一种计算机程序/程序产品，所述计算机程序/程序产品被存储在存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。The embodiment of the present application further provides a computer program/program product, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement Figure 2 or Figure 3 Each process of the method embodiment shown can achieve the same technical effect. To avoid repetition, it will not be described again here.
本申请实施例还提供了一种通信系统，包括：终端和网络侧设备，所述终端可用于执行如图2所示的AI网络模型的性能监督方法的步骤，所述网络侧设备可用于执行如图3所示的AI网络模型的性能监督方法的步骤。Embodiments of the present application also provide a communication system, including: a terminal and a network side device. The terminal can be used to perform the steps of the performance supervision method of the AI network model as shown in Figure 2. The network side device can be used to perform The steps of the performance supervision method of the AI network model are shown in Figure 3.
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。It should be noted that, in this document, the terms "comprising", "comprises" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article or apparatus that includes that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions may be performed, for example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a computer software product that is essentially or contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk , CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.
上面结合附图对本申请的实施例进行了描述，但是本申请并不局限于上述的具体实施方式，上述的具体实施方式仅仅是示意性的，而不是限制性的，本领域的普通技术人员在本申请的启示下，在不脱离本申请宗旨和权利要求所保护的范围情况下，还可做出很多形式，均属于本申请的保护之内。The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Inspired by this application, many forms can be made without departing from the purpose of this application and the scope protected by the claims, all of which fall within the protection of this application.
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