CN114692427A - Equipment calibration method and device - Google Patents
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Abstract
Description
Translated fromChinese技术领域technical field
本公开涉及设备校准技术领域，尤其涉及一种设备校准方法及装置。The present disclosure relates to the technical field of device calibration, and in particular, to a device calibration method and device.
背景技术Background technique
在使用设备之前，需要校准设备、调试设备。其中，校准设备是为了调整设备的参数，使得设备可以正常工作。校准设备在使用设备的整个过程中非常重要，校准设备的好坏，直接关系到设备的工作成果。现有技术往往是利用信号发生器或频谱仪等专用的硬件校准仪器来校准设备的参数，因为信号发生器和频谱仪价格昂贵，造成校准设备的成本很高，同时，使用现有技术校准设备的精确度也有待进一步提高。Before using the equipment, it is necessary to calibrate and debug the equipment. The purpose of calibrating the device is to adjust the parameters of the device so that the device can work normally. The calibration equipment is very important in the whole process of using the equipment. The quality of the calibration equipment is directly related to the work results of the equipment. In the prior art, special hardware calibration instruments such as signal generators or spectrum analyzers are often used to calibrate the parameters of the equipment. Because the signal generators and spectrum analyzers are expensive, the cost of the calibration equipment is very high. At the same time, the existing technology is used to calibrate the equipment. The accuracy also needs to be further improved.
在实现本公开构思的过程中，发明人发现相关技术中至少存在如下技术问题：对设备进行校准成本高和精确度低的问题。During the process of realizing the concept of the present disclosure, the inventor found that there are at least the following technical problems in the related art: the problems of high cost and low accuracy for calibrating the device.
发明内容SUMMARY OF THE INVENTION
有鉴于此，本公开实施例提供了一种设备校准方法、装置、电子设备及计算机可读存储介质，以解决现有技术中，对设备进行校准成本高和精确度低的问题。In view of this, the embodiments of the present disclosure provide a device calibration method, device, electronic device, and computer-readable storage medium, so as to solve the problems of high cost and low accuracy of device calibration in the prior art.
本公开实施例的第一方面，提供了一种设备校准方法，包括：获取训练参数，以及获取目标设备的第一射频参数和环境参数；利用校准设备对训练参数进行校准，得到校准结果；对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；根据优化后的第一逻辑图对仿真模型进行训练；将环境参数输入仿真模型，输出第二射频参数；当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。A first aspect of the embodiments of the present disclosure provides a device calibration method, including: acquiring training parameters, and acquiring first radio frequency parameters and environmental parameters of a target device; using a calibration device to calibrate the training parameters to obtain a calibration result; The training parameters and calibration results are subjected to linear regression processing to obtain regression results, and a simulation model is constructed according to the regression results; the first logic diagram is extracted from the simulation model, and the first logic diagram is optimized according to the calibration results; A logic diagram is used to train the simulation model; the environmental parameters are input into the simulation model, and the second radio frequency parameter is output; when the difference between the second radio frequency parameter and the first radio frequency parameter is greater than the preset threshold, the second radio frequency parameter and the first radio frequency parameters to calibrate the target device.
本公开实施例的第二方面，提供了一种设备校准装置，包括：获取模块，被配置为获取训练参数，以及获取目标设备的第一射频参数和环境参数；校准模块，被配置为利用校准设备对训练参数进行校准，得到校准结果；构建模块，被配置为对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；提取模块，被配置为从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；训练模块，被配置为根据优化后的第一逻辑图对仿真模型进行训练；模型模块，被配置为将环境参数输入仿真模型，输出第二射频参数；校准模块，被配置为当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。A second aspect of the embodiments of the present disclosure provides an apparatus for calibrating equipment, including: an acquisition module configured to acquire training parameters, and to acquire first radio frequency parameters and environmental parameters of a target device; and a calibration module configured to use calibration The equipment calibrates the training parameters to obtain the calibration results; the building module is configured to perform linear regression processing on the training parameters and the calibration results to obtain the regression results, and builds a simulation model according to the regression results; the extraction module is configured to extract the results from the simulation model. The first logic diagram is extracted, and the first logic diagram is optimized according to the calibration result; the training module is configured to train the simulation model according to the optimized first logic diagram; the model module is configured to input environmental parameters into the simulation The model outputs the second radio frequency parameter; the calibration module is configured to calibrate the target device based on the second radio frequency parameter and the first radio frequency parameter when the difference between the second radio frequency parameter and the first radio frequency parameter is greater than a preset threshold.
本公开实施例的第三方面，提供了一种电子设备，包括存储器、处理器以及存储在存储器中并且可在处理器上运行的计算机程序，该处理器执行计算机程序时实现上述方法的步骤。In a third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when the processor executes the computer program.
本公开实施例的第四方面，提供了一种计算机可读存储介质，该计算机可读存储介质存储有计算机程序，该计算机程序被处理器执行时实现上述方法的步骤。In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the foregoing method are implemented.
本公开实施例与现有技术相比存在的有益效果是：获取训练参数，以及获取目标设备的第一射频参数和环境参数；利用校准设备对训练参数进行校准，得到校准结果；对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；根据优化后的第一逻辑图对仿真模型进行训练；将环境参数输入仿真模型，输出第二射频参数；当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。采用上述技术手段，解决现有技术中对设备进行校准成本高和精确度低的问题，进而降低校准设备的成本，提高校准设备的精确度。Compared with the prior art, the embodiments of the present disclosure have the following beneficial effects: acquiring training parameters, as well as acquiring the first radio frequency parameters and environmental parameters of the target device; calibrating the training parameters by using a calibration device to obtain a calibration result; The calibration result is subjected to linear regression processing to obtain the regression result, and a simulation model is constructed according to the regression result; the first logic diagram is extracted from the simulation model, and the first logic diagram is optimized according to the calibration result; according to the optimized first logic diagram The simulation model is trained; the environmental parameters are input into the simulation model, and the second radio frequency parameter is output; when the difference between the second radio frequency parameter and the first radio frequency parameter is greater than the preset threshold, based on the second radio frequency parameter and the first radio frequency parameter, the The target device is calibrated. By adopting the above technical means, the problems of high cost and low accuracy of calibrating equipment in the prior art are solved, thereby reducing the cost of calibrating equipment and improving the accuracy of the calibrating equipment.
附图说明Description of drawings
为了更清楚地说明本公开实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它的附图。In order to illustrate the technical solutions in the embodiments of the present disclosure more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only for the present disclosure. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.
图1是本公开实施例的应用场景的场景示意图；FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure;
图2是本公开实施例提供的一种设备校准方法的流程示意图；2 is a schematic flowchart of a device calibration method provided by an embodiment of the present disclosure;
图3是本公开实施例提供的一种设备校准装置的结构示意图；3 is a schematic structural diagram of a device calibration device provided by an embodiment of the present disclosure;
图4是本公开实施例提供的一种电子设备的结构示意图。FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.
具体实施方式Detailed ways
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本公开实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本公开。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本公开的描述。In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and techniques are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
下面将结合附图详细说明根据本公开实施例的一种设备校准方法和装置。A device calibration method and apparatus according to embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
图1是本公开实施例的应用场景的场景示意图。该应用场景可以包括终端设备1、2和3、服务器4以及网络5。FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include
终端设备1、2和3可以是硬件，也可以是软件。当终端设备1、2和3为硬件时，其可以是具有显示屏且支持与服务器4通信的各种电子设备，包括但不限于智能手机、平板电脑、膝上型便携计算机和台式计算机等；当终端设备1、2和3为软件时，其可以安装在如上的电子设备中。终端设备1、2和3可以实现为多个软件或软件模块，也可以实现为单个软件或软件模块，本公开实施例对此不作限制。进一步地，终端设备1、2和3上可以安装有各种应用，例如数据处理应用、即时通信工具、社交平台软件、搜索类应用、购物类应用等。
服务器4可以是提供各种服务的服务器，例如，对与其建立通信连接的终端设备发送的请求进行接收的后台服务器，该后台服务器可以对终端设备发送的请求进行接收和分析等处理，并生成处理结果。服务器4可以是一台服务器，也可以是由若干台服务器组成的服务器集群，或者还可以是一个云计算服务中心，本公开实施例对此不作限制。The server 4 can be a server that provides various services, for example, a background server that receives requests sent by the terminal device that establishes a communication connection with it, and the background server can receive and analyze the requests sent by the terminal device. result. The server 4 may be one server, or a server cluster composed of several servers, or may also be a cloud computing service center, which is not limited in this embodiment of the present disclosure.
需要说明的是，服务器4可以是硬件，也可以是软件。当服务器4为硬件时，其可以是为终端设备1、2和3提供各种服务的各种电子设备。当服务器4为软件时，其可以是为终端设备1、2和3提供各种服务的多个软件或软件模块，也可以是为终端设备1、2和3提供各种服务的单个软件或软件模块，本公开实施例对此不作限制。It should be noted that the server 4 may be hardware or software. When the server 4 is hardware, it may be various electronic devices that provide various services to the
网络5可以是采用同轴电缆、双绞线和光纤连接的有线网络，也可以是无需布线就能实现各种通信设备互联的无线网络，例如，蓝牙(Bluetooth)、近场通信(Near FieldCommunication，NFC)、红外(Infrared)等，本公开实施例对此不作限制。The network 5 can be a wired network connected by coaxial cables, twisted pairs and optical fibers, or a wireless network that can realize interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication, NFC), infrared (Infrared), etc., which are not limited in this embodiment of the present disclosure.
用户可以通过终端设备1、2和3经由网络5与服务器4建立通信连接，以接收或发送信息等。需要说明的是，终端设备1、2和3、服务器4以及网络5的具体类型、数量和组合可以根据应用场景的实际需求进行调整，本公开实施例对此不作限制。The user can establish a communication connection with the server 4 through the
图2是本公开实施例提供的一种设备校准方法的流程示意图。图2的设备校准方法可以由图1的终端设备或服务器执行。如图2所示，该设备校准方法包括：FIG. 2 is a schematic flowchart of a device calibration method provided by an embodiment of the present disclosure. The device calibration method of FIG. 2 may be performed by the terminal device or the server of FIG. 1 . As shown in Figure 2, the device calibration method includes:
S201，获取训练参数，以及获取目标设备的第一射频参数和环境参数；S201, acquiring training parameters, and acquiring first radio frequency parameters and environmental parameters of the target device;
S202，利用校准设备对训练参数进行校准，得到校准结果；S202, use the calibration equipment to calibrate the training parameters to obtain a calibration result;
S203，对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；S203, performing linear regression processing on the training parameters and calibration results to obtain regression results, and constructing a simulation model according to the regression results;
S204，从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；S204, extracting the first logic diagram from the simulation model, and optimizing the first logic diagram according to the calibration result;
S205，根据优化后的第一逻辑图对仿真模型进行训练；S205, the simulation model is trained according to the optimized first logic diagram;
S206，将环境参数输入仿真模型，输出第二射频参数；S206, input the environmental parameters into the simulation model, and output the second radio frequency parameters;
S207，当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。S207 , when the difference between the second radio frequency parameter and the first radio frequency parameter is greater than the preset threshold, calibrate the target device based on the second radio frequency parameter and the first radio frequency parameter.
需要说明的是，目标设备的环境参数，包括：目标设备所处实地环境的参数和网络环境的参数等。环境参数用于描述目标设备所属环境的环境状况。第一射频参数是目标设备当前的射频参数。不同的环境状况对应的目标设备的第二射频参数不同，第二射频参数是目标设备在该环境状况下，理论上最优的射频参数。目标设备可以是任何一种领域中的设备，比如目标设备为测控领域，可以是移动通信测试仪表、综测仪和程控电源等。对目标设备进行校准，就是将目标设备现有的第一射频参数更新为理论上最优的第二射频参数。It should be noted that the environmental parameters of the target device include: parameters of the on-site environment where the target device is located, parameters of the network environment, and the like. The environmental parameters are used to describe the environmental conditions of the environment to which the target device belongs. The first radio frequency parameter is the current radio frequency parameter of the target device. The second radio frequency parameters of the target device corresponding to different environmental conditions are different, and the second radio frequency parameters are theoretically optimal radio frequency parameters of the target device under the environmental conditions. The target device can be a device in any field. For example, the target device is in the field of measurement and control, and can be a mobile communication test instrument, a comprehensive test instrument, and a program-controlled power supply. The calibration of the target device is to update the existing first radio frequency parameters of the target device to theoretically optimal second radio frequency parameters.
训练参数包括多种环境参数，每种环境参数已经被标注了其对应的第二射频参数。利用校准设备对训练参数进行校准，可以是删除或者修正训练参数中部分不合预设规则的数据，比如某个环境参数对应的第二射频参数不对，那么需要标注某个环境参数对应的第二射频参数。线性回归处理可以是常用的任何一种拟合方法，比如最小二乘法的拟合方法，当然线性回归处理也可以是借助一些软件实现，比如excel、matlab。将环境参数作为自变量，第二射频参数作为因变量，将环境参数和第二射频参数输入上述软件中，就可以输出回归结果。比如回归结果是一则函数，那么根据回归结果构建仿真模型，就以该则函数为主体，可以添加其他的要求构建仿真模型。其他的要求，比如需要将数据用图像展示，那么仿真模型就需要在该则函数的基础上增加数据到图像的转换关系。第一逻辑图是仿真模型所表达的逻辑，可以理解为一种映射关系，或者和仿真模型等价的函数表达。仿真模型是根据回归结果构建的，第一逻辑图是从仿真模型中提取到的。第一逻辑图可以是在回归结果的基础上，增加了上文“其他的要求”类的东西。根据校准结果对第一逻辑图进行优化，可以是根据校准结果对第一逻辑图进行修正。根据优化后的第一逻辑图对仿真模型进行训练，可以理解为，根据优化后的第一逻辑图，通过反向传播的方法更新仿真模型的模型参数。The training parameters include a variety of environmental parameters, and each environmental parameter has been marked with its corresponding second radio frequency parameter. Using calibration equipment to calibrate the training parameters may be to delete or correct some data in the training parameters that do not conform to the preset rules. For example, if the second radio frequency parameter corresponding to an environmental parameter is incorrect, then the second radio frequency corresponding to an environmental parameter needs to be marked. parameter. The linear regression processing can be any of the commonly used fitting methods, such as the fitting method of the least squares method. Of course, the linear regression processing can also be implemented with the help of some software, such as excel and matlab. Taking the environmental parameter as the independent variable and the second radio frequency parameter as the dependent variable, and inputting the environmental parameter and the second radio frequency parameter into the above software, the regression result can be output. For example, if the regression result is a function, then the simulation model is constructed according to the regression result, and the function is used as the main body, and other requirements can be added to construct the simulation model. For other requirements, such as the need to display the data with an image, the simulation model needs to add the conversion relationship between the data and the image on the basis of the function. The first logic diagram is the logic expressed by the simulation model, which can be understood as a mapping relationship, or a function expression equivalent to the simulation model. The simulation model is constructed according to the regression results, and the first logic diagram is extracted from the simulation model. The first logic diagram can be based on the regression result, adding something like the above "other requirements". The optimization of the first logic diagram according to the calibration result may be to correct the first logic diagram according to the calibration result. Training the simulation model according to the optimized first logic diagram can be understood as updating the model parameters of the simulation model by means of back propagation according to the optimized first logic diagram.
本公开实施例因为可以借助模型实现对目标设备的校准，不需要再使用校准仪器，所以降低校准设备的成本，同时通过提高模型的精度，可以提高校准设备的精确度。Since the embodiment of the present disclosure can realize the calibration of the target device with the help of the model, it is unnecessary to use the calibration instrument, so the cost of the calibration device is reduced, and the accuracy of the calibration device can be improved by improving the accuracy of the model.
根据本公开实施例提供的技术方案，获取训练参数，以及获取目标设备的第一射频参数和环境参数；利用校准设备对训练参数进行校准，得到校准结果；对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；根据优化后的第一逻辑图对仿真模型进行训练；将环境参数输入仿真模型，输出第二射频参数；当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。采用上述技术手段，解决现有技术中对设备进行校准成本高和精确度低的问题，进而降低校准设备的成本，提高校准设备的精确度。According to the technical solutions provided by the embodiments of the present disclosure, the training parameters are obtained, as well as the first radio frequency parameters and the environmental parameters of the target device; the training parameters are calibrated by the calibration device to obtain the calibration results; the linear regression processing is performed on the training parameters and the calibration results , obtain the regression result, and construct the simulation model according to the regression result; extract the first logic diagram from the simulation model, and optimize the first logic diagram according to the calibration result; train the simulation model according to the optimized first logic diagram; The environmental parameters are input into the simulation model, and the second radio frequency parameters are output; when the difference between the second radio frequency parameters and the first radio frequency parameters is greater than a preset threshold, the target device is calibrated based on the second radio frequency parameters and the first radio frequency parameters. By adopting the above technical means, the problems of high cost and low accuracy of calibrating equipment in the prior art are solved, thereby reducing the cost of calibrating equipment and improving the accuracy of the calibrating equipment.
对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型之后，方法还包括：使用仿真模型和神经网络模型构建仿真网络模型；利用训练参数对仿真网络模型进行训练；从训练后的仿真网络模型中提取出第二逻辑图，并根据校准结果对第二逻辑图进行优化；根据优化后的第二逻辑图对仿真网络模型进行再训练；将环境参数输入仿真网络模型，输出第二射频参数。After linear regression processing is performed on the training parameters and the calibration results, the regression results are obtained, and after the simulation model is constructed according to the regression results, the method further includes: using the simulation model and the neural network model to construct the simulation network model; using the training parameters to train the simulation network model; The second logic diagram is extracted from the trained simulation network model, and the second logic diagram is optimized according to the calibration result; the simulation network model is retrained according to the optimized second logic diagram; the environmental parameters are input into the simulation network model , and output the second radio frequency parameter.
使用仿真模型和神经网络模型构建仿真网络模型，可以是仿真模型后接神经网络模型。本公开实施例是对仿真网络模型进行了两次训练，进而提高训练后模型的精度。第二逻辑图类似于第一逻辑图，对第二逻辑图进行优化类似于对第一逻辑图进行优化。因为使用数学方法构建的仿真模型，其的精度在到达一定程度后，是很难再提升的，但是神经网络模型就不存在这种问题。神经网络模型可以通过大量的机器学习训练，尽可能的提高可以模型的精度。所以本公开实施例利用神经网络模型提高仿真模型的精度。具体地，在仿真网络模型中，仿真模型的输出是神经网络模型的输入，神经网络模型是第二射频参数(虽然仿真模型是基于环境参数到第二射频参数的映射关系建立的，但是实际应用中仿真模型的输出可能并不是第二射频参数)。Use the simulation model and the neural network model to build a simulation network model, which can be a simulation model followed by a neural network model. In the embodiment of the present disclosure, the simulation network model is trained twice, thereby improving the accuracy of the trained model. The second logic diagram is similar to the first logic diagram, and optimizing the second logic diagram is similar to optimizing the first logic diagram. Because the accuracy of the simulation model constructed by mathematical methods is difficult to improve after reaching a certain level, but the neural network model does not have this problem. The neural network model can be trained by a large amount of machine learning to improve the accuracy of the model as much as possible. Therefore, the embodiments of the present disclosure utilize the neural network model to improve the accuracy of the simulation model. Specifically, in the simulation network model, the output of the simulation model is the input of the neural network model, and the neural network model is the second radio frequency parameter (although the simulation model is established based on the mapping relationship between the environmental parameters and the second radio frequency parameters, the actual application The output of the simulation model in , may not be the second RF parameter).
需要说明的是，神经网络模型可以是任何一种常用的神经网络模型，比如Faster-RCNN。本公开中的训练方法都是类似于深度学习训练的方法。It should be noted that the neural network model can be any commonly used neural network model, such as Faster-RCNN. The training methods in this disclosure are all similar to deep learning training methods.
根据优化后的第二逻辑图对仿真网络模型进行再训练，包括：根据优化后的第二逻辑图，确定出训练参数对应的训练数据集；在整个训练过程中，第一轮训练：在冻结神经网络模型的情况下，使用训练数据集训练仿真网络模型，以更新仿真网络模型中的仿真模型的参数；第二轮训练：在冻结仿真模型的情况下，使用训练数据集训练仿真网络模型，以更新仿真网络模型中的神经网络模型的参数；第三轮训练：使用训练数据集训练仿真网络模型，以更新仿真网络模型中的仿真模型和神经网络模型的参数。Retraining the simulation network model according to the optimized second logic diagram includes: determining the training data set corresponding to the training parameters according to the optimized second logic diagram; in the whole training process, the first round of training: after freezing In the case of a neural network model, use the training data set to train the simulation network model to update the parameters of the simulation model in the simulation network model; the second round of training: in the case of freezing the simulation model, use the training data set to train the simulation network model, to update the parameters of the neural network model in the simulation network model; the third round of training: use the training data set to train the simulation network model to update the parameters of the simulation model and the neural network model in the simulation network model.
根据优化后的第二逻辑图，确定出训练参数对应的训练数据集，比如，某个环境参数对应的第二射频参数不对，那么可以使用优化后的第二逻辑图标注出某个环境参数对应的正确的第二射频参数。所以可以将训练数据集看做更新后的训练参数。该步骤类似于使用校准设备对训练参数进行校准中的更正某个环境参数对应不对的第二射频参数。但是因为第二逻辑图是从训练后的仿真网络模型中提取出的，所以第二逻辑图是比校准设备更优的。According to the optimized second logic diagram, the training data set corresponding to the training parameters is determined. For example, if the second radio frequency parameter corresponding to a certain environmental parameter is incorrect, the optimized second logic diagram can be used to mark the corresponding environmental parameter the correct second RF parameters. Therefore, the training data set can be regarded as the updated training parameters. This step is similar to correcting a second radio frequency parameter that does not correspond to a certain environmental parameter in calibrating the training parameter by using the calibration device. But because the second logic graph is extracted from the trained simulated network model, the second logic graph is better than the calibration device.
根据优化后的第二逻辑图对仿真网络模型进行再训练，包括三轮训练。第一轮训练，在冻结神经网络模型的情况下，使用训练数据集训练仿真网络模型，可以理解为只训练仿真模型，该步用于调整仿真模型的参数；第二轮训练：在冻结仿真模型的情况下，使用训练数据集训练仿真网络模型，可以理解为只训练神经网络模型，该步是最为常见的神经网络模型的训练；第三轮训练：使用训练数据集训练仿真网络模型，是同时训练仿真模型和神经网络模型，该步用于微调仿真网络模型的参数。The simulation network model is retrained according to the optimized second logic diagram, including three rounds of training. The first round of training, in the case of freezing the neural network model, uses the training data set to train the simulation network model, which can be understood as only training the simulation model, and this step is used to adjust the parameters of the simulation model; the second round of training: freeze the simulation model In the case of using the training data set to train the simulation network model, it can be understood that only the neural network model is trained. This step is the most common neural network model training; the third round of training: using the training data set to train the simulation network model is a simultaneous Train the simulation model and the neural network model. This step is used to fine-tune the parameters of the simulation network model.
根据优化后的第二逻辑图对仿真网络模型进行再训练之后，方法还包括：将环境参数输入仿真网络模型中的神经网络模型，输出第二射频参数。After the simulation network model is retrained according to the optimized second logic diagram, the method further includes: inputting the environmental parameters into the neural network model in the simulation network model, and outputting the second radio frequency parameters.
本公开实施例中仿真模型只用于再训练中，辅助调整神经网络模型的参数(本公开实施例不同于上面的实施例，是将神经网络模型作为主体，将仿真模型作为辅助，上文中的实施例是将神经网络模型作为辅助，将仿真模型作为主体，这种方法的好处，可以帮助神经网络模型尽快收敛。本公开实施例的网络结构可以是仿真模型和神经网络模型并行构建仿真网络模型，这样仿真模型就可以用于约束神经网络模型的输入和输出)，在完成对仿真网络模型进行再训练之后，从仿真网络模型中提取神经网络模型，或者在仿真网络模型中去掉仿真模型的部分，只使用神经网络模型对环境参数进行映射。In the embodiment of the present disclosure, the simulation model is only used for retraining to assist in adjusting the parameters of the neural network model (the embodiment of the present disclosure is different from the above embodiments in that the neural network model is used as the main body and the simulation model is used as an auxiliary, the above The embodiment is that the neural network model is used as an auxiliary, and the simulation model is used as the main body, and the benefits of this method can help the neural network model to converge as soon as possible. The network structure of the embodiment of the present disclosure can be that the simulation model and the neural network model are constructed in parallel with the simulation network model. , so that the simulation model can be used to constrain the input and output of the neural network model), after completing the retraining of the simulation network model, extract the neural network model from the simulation network model, or remove the part of the simulation model from the simulation network model , using only the neural network model to map the environmental parameters.
也就是获取训练参数，以及获取目标设备的第一射频参数和环境参数之后，方法还包括：利用训练参数对神经网络模型进行训练，使得神经网络模型学习并保存有环境参数和第二射频参数的对应关系；利用校准设备，对训练参数进行校准，得到校准结果；从神经网络模型中提取出第三逻辑图，并根据校准结果对第三逻辑图进行优化；根据优化后的第三逻辑图对神经网络模型进行再训练；将环境参数输入神经网络模型，输出第二射频参数；当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。That is, after acquiring the training parameters, and acquiring the first radio frequency parameters and the environmental parameters of the target device, the method further includes: using the training parameters to train the neural network model, so that the neural network model learns and saves the environment parameters and the second radio frequency parameters. Correspondence; use the calibration equipment to calibrate the training parameters to obtain the calibration result; extract the third logic diagram from the neural network model, and optimize the third logic diagram according to the calibration results; The neural network model is retrained; the environmental parameters are input into the neural network model, and the second radio frequency parameter is output; when the difference between the second radio frequency parameter and the first radio frequency parameter is greater than the preset threshold, based on the second radio frequency parameter and the first radio frequency parameter , to calibrate the target device.
第三逻辑图类似于第一逻辑图，不再赘述。现有技术是通过校准设备对目标设备的射频参数进行校准的，本公开首次引入神经网络模型，通过训练后的神经网络模型确定环境参数对应的第二射频参数，进而校准目标设备的射频参数。The third logic diagram is similar to the first logic diagram, and details are not repeated here. In the prior art, the radio frequency parameters of the target device are calibrated through calibration equipment. The present disclosure introduces a neural network model for the first time, and determines the second radio frequency parameters corresponding to the environmental parameters through the trained neural network model, thereby calibrating the radio frequency parameters of the target device.
利用训练参数对神经网络模型进行训练，使得神经网络模型学习并保存有环境参数和第二射频参数的对应关系之前，方法还包括：获取与目标设备同一应用领域下的同一类型的多个设备的设备信息；根据多个设备的设备信息更新神经网络模型的模型参数。Before the neural network model is trained by using the training parameters, so that the neural network model learns and saves the corresponding relationship between the environmental parameters and the second radio frequency parameter, the method further includes: acquiring the data of multiple devices of the same type in the same application field as the target device. Device information; update the model parameters of the neural network model according to the device information of multiple devices.
如果直接利用训练参数对神经网络模型进行训练，训练时间会很长，为了提高训练的效率，本公开实施例对神经网络模型进行训练之前，先根据多个设备的设备信息更新神经网络模型的模型参数。根据多个设备的设备信息更新神经网络模型的模型参数，可以理解为将神经网络模型的模型参数的值更新到目标设备对应的区间中。设备信息，包括：设备的应用领域的信息、设备的类型和设备的工作参数等，比如一个设备的工作频率的理论区间。通过上述技术手段，可以提高训练中神经网络模型的收敛速度。If the training parameters are directly used to train the neural network model, the training time will be very long. In order to improve the training efficiency, before training the neural network model in this embodiment of the present disclosure, the model of the neural network model is updated according to the device information of multiple devices. parameter. Updating the model parameters of the neural network model according to the device information of the multiple devices can be understood as updating the values of the model parameters of the neural network model to the interval corresponding to the target device. Device information, including: information on the application field of the device, the type of the device, and the operating parameters of the device, such as the theoretical range of the operating frequency of a device. Through the above technical means, the convergence speed of the neural network model during training can be improved.
为了提高训练后神经网络模型的精度，本公开实施例对神经网络模型进行再训练。校准结果是校准设备推荐的第二的参数值。In order to improve the accuracy of the neural network model after training, the embodiments of the present disclosure retrain the neural network model. The calibration result is the second parameter value recommended by the calibration equipment.
在一个可选实施例中，第一射频参数，包括：第一工作频率、第一信噪比、第一信号增益、第一发射信号功率、第一发射信号频率和第一s参数；第二射频参数，包括：第二工作频率、第二信噪比、第二信号增益、第二发射信号功率、第二发射信号频率和和第二s参数。In an optional embodiment, the first radio frequency parameter includes: a first operating frequency, a first signal-to-noise ratio, a first signal gain, a first transmit signal power, a first transmit signal frequency, and a first s parameter; the second The radio frequency parameters include: the second operating frequency, the second signal-to-noise ratio, the second signal gain, the second transmit signal power, the second transmit signal frequency and the second s parameter.
s参数，包括：特征阻抗、串扰、插入损耗和回波损耗等。举例说明，本公开实施例可以通过调整目标设备的工作电路，改变第一s参数，进而基于第二射频参数校准第一射频参数。目标设备可以在工作时，选用不同的电路，进而选用s参数第二的电路。不同的电路对应的信号增益不同，可以根据第二信号增益选用最合适的电路。还可以根据第二发射信号功率调整第一发射信号功率等。s-parameters, including: characteristic impedance, crosstalk, insertion loss and return loss, etc. For example, in the embodiment of the present disclosure, the first s parameter can be changed by adjusting the working circuit of the target device, and then the first radio frequency parameter can be calibrated based on the second radio frequency parameter. The target device can select different circuits during operation, and then select the circuit with the second s parameter. Different circuits have different signal gains, and the most suitable circuit can be selected according to the second signal gain. The power of the first transmission signal and the like may also be adjusted according to the power of the second transmission signal.
需要说明的是，本公开实施例中的第一射频参数和第二射频参数中的任何一个参数都可以对应一个区间的值。比如第二发射信号功率可以是一个区间的发射信号功率。It should be noted that, any one of the first radio frequency parameter and the second radio frequency parameter in the embodiment of the present disclosure may correspond to a value in an interval. For example, the second transmit signal power may be a range of transmit signal power.
在一个可选实施例中，获取目标设备的环境参数；分别基于光波反射原理、光波散射原理、光波折射原理和光波绕射原理，建立光波反射约束条件、光波散射约束条件、光波折射约束条件和光波绕射约束条件；以光波传播能量损耗最小为准则，建立光波传播能量函数；基于光波反射约束条件、光波散射约束条件、光波折射约束条件、光波绕射约束条件和光波传播能量函数，构建数学模型；将环境参数输入数学模型，输出目标设备的第二工作波长；根据工作波长确定目标设备的第二工作频率；当第二工作频率和第一工作频率的差值大于预设阈值时，基于第二工作频率和第一工作频率，对目标设备进行校准。In an optional embodiment, environmental parameters of the target device are acquired; based on the principle of light wave reflection, light wave scattering principle, light wave refraction principle and light wave diffraction principle, respectively, establish light wave reflection constraints, light wave scattering constraints, light wave refraction constraints and Constraints of light wave diffraction; the light wave propagation energy function is established with the minimum light wave propagation energy loss as the criterion; based on the light wave reflection constraints, light wave scattering constraints, light wave refraction constraints, light wave diffraction constraints and light wave propagation energy functions, construct mathematics model; input the environmental parameters into the mathematical model, and output the second working wavelength of the target device; determine the second working frequency of the target device according to the working wavelength; when the difference between the second working frequency and the first working frequency is greater than the preset threshold, based on The second operating frequency and the first operating frequency are used to calibrate the target device.
其中，第二射频参数包括第二工作频率，第一射频参数包括第一工作频率。Wherein, the second radio frequency parameter includes the second working frequency, and the first radio frequency parameter includes the first working frequency.
本公开实施例属于数学建模的思想。不同的波长在一个环境中的传播能力存在差异，这种传播差异，包括：反射差异、散射差异、折射差异和绕射差异等。为了增强目标设备的工作波在环境中的传播能力，降低在传播过程中的损耗，本公开实施例基于光波反射原理建立光波反射约束条件，基于光波散射原理建立光波散射约束条件，基于光波折射原理建立光波折射约束条件，基于光波绕射原理建立光波绕射约束条件。通过上述约束条件可以将目标设备的工作波的波长控制在一定区间内，然后根据光波传播能量函数，从该区间中求解出目标设备的第二工作波长。因为波长和频率满足一定的条件，所以可以根据工作波长确定目标设备的第二工作频率。环境参数包括了目标设备所处环境的所有信息，比如目标设备所处环境有哪些影响光波传播的遮挡物，遮挡物的位置和大小等信息。The embodiments of the present disclosure belong to the idea of mathematical modeling. Different wavelengths have differences in their ability to propagate in an environment. Such propagation differences include: reflection differences, scattering differences, refraction differences, and diffraction differences. In order to enhance the propagation capability of the working wave of the target device in the environment and reduce the loss in the propagation process, the embodiments of the present disclosure establish light wave reflection constraints based on the light wave reflection principle, establish light wave scattering constraints based on the light wave scattering principle, and establish light wave scattering constraints based on the light wave refraction principle. The constraints of light wave refraction are established, and the constraints of light wave diffraction are established based on the principle of light wave diffraction. Through the above constraints, the wavelength of the working wave of the target device can be controlled within a certain interval, and then the second working wavelength of the target device can be obtained from the interval according to the light wave propagation energy function. Because the wavelength and frequency satisfy certain conditions, the second working frequency of the target device can be determined according to the working wavelength. The environmental parameters include all the information about the environment where the target device is located, such as the obstructions in the environment where the target device is located, which affects the propagation of light waves, the location and size of the obstructions, and other information.
在一个可选实施例中，获取目标设备的环境参数；根据环境参数，确定目标设备的以下至少之一的参数：最优电压、最优电流和抗干扰信息；基于以下至少之一的参数：最优电压、最优电流和抗干扰信息，确定目标设备的最优发射信号功率，其中，第二射频参数包括最优发射信号功率；使用最优发射信号功率除以当前发射信号功率，得到相除值；根据相除值确定最优信号增益，其中，第二射频参数包括最优信号增益，第一射频参数包括当前发射信号功率；根据最优信号增益确定目标设备的工作电路。In an optional embodiment, the environmental parameters of the target device are obtained; according to the environmental parameters, at least one of the following parameters of the target device is determined: optimal voltage, optimal current and anti-interference information; based on at least one of the following parameters: The optimal voltage, optimal current and anti-interference information are used to determine the optimal transmission signal power of the target device, wherein the second radio frequency parameter includes the optimal transmission signal power; the optimal transmission signal power is divided by the current transmission signal power to obtain the phase signal. division value; determine the optimal signal gain according to the division value, wherein the second radio frequency parameter includes the optimal signal gain, and the first radio frequency parameter includes the current transmit signal power; determine the working circuit of the target device according to the optimal signal gain.
目标设备在不同的环境下，或者目标设备的工作存在差异，那么目标设备所被允许的发射信号功率会存在不同，或者在考虑到设备功耗的情况下，目标设备的最佳的发射信号功率会存在不同。为了解决上述技术问题，本公开实施例通过环境参数，确定目标设备的最优电压、或最优电流、或抗干扰信息。因为目标设备的内阻是可以知道的，所以可以根据电压、电流、电阻和功率的关系，确定目标设备的最优发射信号功率。抗干扰信息用于表明目标设备的发射信号功率应具有的抗干扰的能力。在某些情况下，如果目标设备需要较强的抗干扰的能力，那么应该增强目标设备的发射信号功率，如果目标设备不需要较强的抗干扰的能力，那么应该根据具体情境适当降低目标设备的发射信号功率。所以根据抗干扰信息，也确定目标设备的最优发射信号功率。The target device is in different environments, or the work of the target device is different, then the allowable transmit signal power of the target device will be different, or considering the power consumption of the device, the optimal transmit signal power of the target device will be different. In order to solve the above technical problem, the embodiment of the present disclosure determines the optimal voltage, or the optimal current, or the anti-interference information of the target device through environmental parameters. Because the internal resistance of the target device can be known, the optimal transmit signal power of the target device can be determined according to the relationship between voltage, current, resistance and power. The anti-jamming information is used to indicate the anti-jamming capability of the transmitted signal power of the target device. In some cases, if the target device needs strong anti-interference ability, then the transmit signal power of the target device should be enhanced. If the target device does not need strong anti-interference ability, then the target device should be appropriately reduced according to the specific situation. transmit signal power. Therefore, according to the anti-interference information, the optimal transmit signal power of the target device is also determined.
本公开实施例可以为目标设备提供多个增益电路，进而为目标设备提供多种增益。可以将最优发射信号功率除以当前发射信号功率得到的相除值，理解为目标设备需要的增益，也就是最优信号增益。当前信号增益是当前时刻目标设备正在使用的增益。在最优信号增益之后，可以根据最优信号增益确定目标设备的工作电路。确定目标设备的工作电路也是对目标设备进行校准的一种方法。The embodiments of the present disclosure can provide multiple gain circuits for the target device, thereby providing multiple gains for the target device. The division value obtained by dividing the optimal transmit signal power by the current transmit signal power can be understood as the gain required by the target device, that is, the optimal signal gain. The current signal gain is the gain being used by the target device at the current moment. After the optimal signal gain, the working circuit of the target device can be determined according to the optimal signal gain. Determining the operating circuit of the target device is also a method of calibrating the target device.
上述所有可选技术方案，可以采用任意结合形成本申请的可选实施例，在此不再一一赘述。All the above-mentioned optional technical solutions can be combined arbitrarily to form optional embodiments of the present application, which will not be repeated here.
下述为本公开装置实施例，可以用于执行本公开方法实施例。对于本公开装置实施例中未披露的细节，请参照本公开方法实施例。The following are the apparatus embodiments of the present disclosure, which can be used to execute the method embodiments of the present disclosure. For details not disclosed in the apparatus embodiments of the present disclosure, please refer to the method embodiments of the present disclosure.
图3是本公开实施例提供的一种设备校准装置的示意图。如图3所示，该设备校准装置包括：FIG. 3 is a schematic diagram of a device calibration apparatus provided by an embodiment of the present disclosure. As shown in Figure 3, the equipment calibration device includes:
获取模块301，被配置为获取训练参数，以及获取目标设备的第一射频参数和环境参数；The acquiring
校准模块302，被配置为利用校准设备对训练参数进行校准，得到校准结果；The
构建模块303，被配置为对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；The
提取模块304，被配置为从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；The
训练模块305，被配置为根据优化后的第一逻辑图对仿真模型进行训练；The
模型模块306，被配置为将环境参数输入仿真模型，输出第二射频参数；The
校准模块307，被配置为当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。The
需要说明的是，目标设备的环境参数，包括：目标设备所处实地环境的参数和网络环境的参数等。环境参数用于描述目标设备所属环境的环境状况。第一射频参数是目标设备当前的射频参数。不同的环境状况对应的目标设备的第二射频参数不同，第二射频参数是目标设备在该环境状况下，理论上最优的射频参数。目标设备可以是任何一种领域中的设备，比如目标设备为测控领域，可以是移动通信测试仪表、综测仪和程控电源等。对目标设备进行校准，就是将目标设备现有的第一射频参数更新为理论上最优的第二射频参数。It should be noted that the environmental parameters of the target device include: parameters of the on-site environment where the target device is located, parameters of the network environment, and the like. The environmental parameters are used to describe the environmental conditions of the environment to which the target device belongs. The first radio frequency parameter is the current radio frequency parameter of the target device. The second radio frequency parameters of the target device corresponding to different environmental conditions are different, and the second radio frequency parameters are theoretically optimal radio frequency parameters of the target device under the environmental conditions. The target device can be a device in any field. For example, the target device is in the field of measurement and control, and can be a mobile communication test instrument, a comprehensive test instrument, and a program-controlled power supply. The calibration of the target device is to update the existing first radio frequency parameters of the target device to theoretically optimal second radio frequency parameters.
训练参数包括多种环境参数，每种环境参数已经被标注了其对应的第二射频参数。利用校准设备对训练参数进行校准，可以是删除或者修正训练参数中部分不合预设规则的数据，比如某个环境参数对应的第二射频参数不对，那么需要标注某个环境参数对应的第二射频参数。线性回归处理可以是常用的任何一种拟合方法，比如最小二乘法的拟合方法，当然线性回归处理也可以是借助一些软件实现，比如excel、matlab。将环境参数作为自变量，第二射频参数作为因变量，将环境参数和第二射频参数输入上述软件中，就可以输出回归结果。比如回归结果是一则函数，那么根据回归结果构建仿真模型，就以该则函数为主体，可以添加其他的要求构建仿真模型。其他的要求，比如需要将数据用图像展示，那么仿真模型就需要在该则函数的基础上增加数据到图像的转换关系。第一逻辑图是仿真模型所表达的逻辑，可以理解为一种映射关系，或者和仿真模型等价的函数表达。仿真模型是根据回归结果构建的，第一逻辑图是从仿真模型中提取到的。第一逻辑图可以是在回归结果的基础上，增加了上文“其他的要求”类的东西。根据校准结果对第一逻辑图进行优化，可以是根据校准结果对第一逻辑图进行修正。根据优化后的第一逻辑图对仿真模型进行训练，可以理解为，根据优化后的第一逻辑图，通过反向传播的方法更新仿真模型的模型参数。The training parameters include a variety of environmental parameters, and each environmental parameter has been marked with its corresponding second radio frequency parameter. Using calibration equipment to calibrate the training parameters may be to delete or correct some data in the training parameters that do not conform to the preset rules. For example, if the second radio frequency parameter corresponding to an environmental parameter is incorrect, then the second radio frequency corresponding to an environmental parameter needs to be marked. parameter. The linear regression processing can be any of the commonly used fitting methods, such as the fitting method of the least squares method. Of course, the linear regression processing can also be implemented with the help of some software, such as excel and matlab. Taking the environmental parameter as the independent variable and the second radio frequency parameter as the dependent variable, and inputting the environmental parameter and the second radio frequency parameter into the above software, the regression result can be output. For example, if the regression result is a function, then the simulation model is constructed according to the regression result, and the function is used as the main body, and other requirements can be added to construct the simulation model. For other requirements, such as the need to display the data with an image, the simulation model needs to add the conversion relationship between the data and the image on the basis of the function. The first logic diagram is the logic expressed by the simulation model, which can be understood as a mapping relationship, or a function expression equivalent to the simulation model. The simulation model is constructed according to the regression results, and the first logic diagram is extracted from the simulation model. The first logic diagram can be based on the regression result, adding something like the above "other requirements". The optimization of the first logic diagram according to the calibration result may be to correct the first logic diagram according to the calibration result. Training the simulation model according to the optimized first logic diagram can be understood as updating the model parameters of the simulation model by means of back propagation according to the optimized first logic diagram.
本公开实施例因为可以借助模型实现对目标设备的校准，不需要再使用校准仪器，所以降低校准设备的成本，同时通过提高模型的精度，可以提高校准设备的精确度。Since the embodiment of the present disclosure can realize the calibration of the target device with the help of the model, it is unnecessary to use the calibration instrument, so the cost of the calibration device is reduced, and the accuracy of the calibration device can be improved by improving the accuracy of the model.
根据本公开实施例提供的技术方案，获取训练参数，以及获取目标设备的第一射频参数和环境参数；利用校准设备对训练参数进行校准，得到校准结果；对训练参数和校准结果进行线性回归处理，得到回归结果，并根据回归结果构建仿真模型；从仿真模型中提取出第一逻辑图，并根据校准结果对第一逻辑图进行优化；根据优化后的第一逻辑图对仿真模型进行训练；将环境参数输入仿真模型，输出第二射频参数；当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。采用上述技术手段，解决现有技术中对设备进行校准成本高和精确度低的问题，进而降低校准设备的成本，提高校准设备的精确度。According to the technical solutions provided by the embodiments of the present disclosure, the training parameters are obtained, as well as the first radio frequency parameters and the environmental parameters of the target device; the training parameters are calibrated by the calibration device to obtain the calibration results; the linear regression processing is performed on the training parameters and the calibration results , obtain the regression result, and construct the simulation model according to the regression result; extract the first logic diagram from the simulation model, and optimize the first logic diagram according to the calibration result; train the simulation model according to the optimized first logic diagram; The environmental parameters are input into the simulation model, and the second radio frequency parameters are output; when the difference between the second radio frequency parameters and the first radio frequency parameters is greater than a preset threshold, the target device is calibrated based on the second radio frequency parameters and the first radio frequency parameters. By adopting the above technical means, the problems of high cost and low accuracy of calibrating equipment in the prior art are solved, thereby reducing the cost of calibrating equipment and improving the accuracy of the calibrating equipment.
可选地，构建模块303还被配置为使用仿真模型和神经网络模型构建仿真网络模型；利用训练参数对仿真网络模型进行训练；从训练后的仿真网络模型中提取出第二逻辑图，并根据校准结果对第二逻辑图进行优化；根据优化后的第二逻辑图对仿真网络模型进行再训练；将环境参数输入仿真网络模型，输出第二射频参数。Optionally, the
使用仿真模型和神经网络模型构建仿真网络模型，可以是仿真模型后接神经网络模型。本公开实施例是对仿真网络模型进行了两次训练，进而提高训练后模型的精度。第二逻辑图类似于第一逻辑图，对第二逻辑图进行优化类似于对第一逻辑图进行优化。因为使用数学方法构建的仿真模型，其的精度在到达一定程度后，是很难再提升的，但是神经网络模型就不存在这种问题。神经网络模型可以通过大量的机器学习训练，尽可能的提高可以模型的精度。所以本公开实施例利用神经网络模型提高仿真模型的精度。具体地，在仿真网络模型中，仿真模型的输出是神经网络模型的输入，神经网络模型是第二射频参数(虽然仿真模型是基于环境参数到第二射频参数的映射关系建立的，但是实际应用中仿真模型的输出可能并不是第二射频参数)。Use the simulation model and the neural network model to build a simulation network model, which can be a simulation model followed by a neural network model. In the embodiment of the present disclosure, the simulation network model is trained twice, thereby improving the accuracy of the trained model. The second logic diagram is similar to the first logic diagram, and optimizing the second logic diagram is similar to optimizing the first logic diagram. Because the accuracy of the simulation model constructed by mathematical methods is difficult to improve after reaching a certain level, but the neural network model does not have this problem. The neural network model can be trained by a large amount of machine learning to improve the accuracy of the model as much as possible. Therefore, the embodiments of the present disclosure utilize the neural network model to improve the accuracy of the simulation model. Specifically, in the simulation network model, the output of the simulation model is the input of the neural network model, and the neural network model is the second radio frequency parameter (although the simulation model is established based on the mapping relationship between the environmental parameters and the second radio frequency parameters, the actual application The output of the simulation model in , may not be the second RF parameter).
需要说明的是，神经网络模型可以是任何一种常用的神经网络模型，比如Faster-RCNN。本公开中的训练方法都是类似于深度学习训练的方法。It should be noted that the neural network model can be any commonly used neural network model, such as Faster-RCNN. The training methods in this disclosure are all similar to deep learning training methods.
可选地，构建模块303还被配置为根据优化后的第二逻辑图，确定出训练参数对应的训练数据集；在整个训练过程中，第一轮训练：在冻结神经网络模型的情况下，使用训练数据集训练仿真网络模型，以更新仿真网络模型中的仿真模型的参数；第二轮训练：在冻结仿真模型的情况下，使用训练数据集训练仿真网络模型，以更新仿真网络模型中的神经网络模型的参数；第三轮训练：使用训练数据集训练仿真网络模型，以更新仿真网络模型中的仿真模型和神经网络模型的参数。Optionally, the
根据优化后的第二逻辑图，确定出训练参数对应的训练数据集，比如，某个环境参数对应的第二射频参数不对，那么可以使用优化后的第二逻辑图标注出某个环境参数对应的正确的第二射频参数。所以可以将训练数据集看做更新后的训练参数。该步骤类似于使用校准设备对训练参数进行校准中的更正某个环境参数对应不对的第二射频参数。但是因为第二逻辑图是从训练后的仿真网络模型中提取出的，所以第二逻辑图是比校准设备更优的。According to the optimized second logic diagram, the training data set corresponding to the training parameters is determined. For example, if the second radio frequency parameter corresponding to a certain environmental parameter is incorrect, the optimized second logic diagram can be used to mark the corresponding environmental parameter the correct second RF parameters. Therefore, the training data set can be regarded as the updated training parameters. This step is similar to correcting a second radio frequency parameter that does not correspond to a certain environmental parameter in calibrating the training parameter by using the calibration device. But because the second logic graph is extracted from the trained simulated network model, the second logic graph is better than the calibration device.
根据优化后的第二逻辑图对仿真网络模型进行再训练，包括三轮训练。第一轮训练，在冻结神经网络模型的情况下，使用训练数据集训练仿真网络模型，可以理解为只训练仿真模型，该步用于调整仿真模型的参数；第二轮训练：在冻结仿真模型的情况下，使用训练数据集训练仿真网络模型，可以理解为只训练神经网络模型，该步是最为常见的神经网络模型的训练；第三轮训练：使用训练数据集训练仿真网络模型，是同时训练仿真模型和神经网络模型，该步用于微调仿真网络模型的参数。The simulation network model is retrained according to the optimized second logic diagram, including three rounds of training. The first round of training, in the case of freezing the neural network model, uses the training data set to train the simulation network model, which can be understood as only training the simulation model, and this step is used to adjust the parameters of the simulation model; the second round of training: freeze the simulation model In the case of using the training data set to train the simulation network model, it can be understood that only the neural network model is trained. This step is the most common neural network model training; the third round of training: using the training data set to train the simulation network model is a simultaneous Train the simulation model and the neural network model. This step is used to fine-tune the parameters of the simulation network model.
可选地，构建模块303还被配置为将环境参数输入仿真网络模型中的神经网络模型，输出第二射频参数。Optionally, the
本公开实施例中仿真模型只用于再训练中，辅助调整神经网络模型的参数(本公开实施例不同于上面的实施例，是将神经网络模型作为主体，将仿真模型作为辅助，上文中的实施例是将神经网络模型作为辅助，将仿真模型作为主体，这种方法的好处，可以帮助神经网络模型尽快收敛。本公开实施例的网络结构可以是仿真模型和神经网络模型并行构建仿真网络模型，这样仿真模型就可以用于约束神经网络模型的输入和输出)，在完成对仿真网络模型进行再训练之后，从仿真网络模型中提取神经网络模型，或者在仿真网络模型中去掉仿真模型的部分，只使用神经网络模型对环境参数进行映射。In the embodiment of the present disclosure, the simulation model is only used for retraining to assist in adjusting the parameters of the neural network model (the embodiment of the present disclosure is different from the above embodiments in that the neural network model is used as the main body and the simulation model is used as an auxiliary, the above The embodiment is that the neural network model is used as an auxiliary, and the simulation model is used as the main body, and the benefits of this method can help the neural network model to converge as soon as possible. The network structure of the embodiment of the present disclosure can be that the simulation model and the neural network model are constructed in parallel with the simulation network model. , so that the simulation model can be used to constrain the input and output of the neural network model), after completing the retraining of the simulation network model, extract the neural network model from the simulation network model, or remove the part of the simulation model from the simulation network model , using only the neural network model to map the environmental parameters.
可选地，第一校准模块302还被配置为利用训练参数对神经网络模型进行训练，使得神经网络模型学习并保存有环境参数和第二射频参数的对应关系；利用校准设备，对训练参数进行校准，得到校准结果；从神经网络模型中提取出第三逻辑图，并根据校准结果对第三逻辑图进行优化；根据优化后的第三逻辑图对神经网络模型进行再训练；将环境参数输入神经网络模型，输出第二射频参数；当第二射频参数和第一射频参数的差值大于预设阈值时，基于第二射频参数和第一射频参数，对目标设备进行校准。Optionally, the
第三逻辑图类似于第一逻辑图，不再赘述。现有技术是通过校准设备对目标设备的射频参数进行校准的，本公开首次引入神经网络模型，通过训练后的神经网络模型确定环境参数对应的第二射频参数，进而校准目标设备的射频参数。The third logic diagram is similar to the first logic diagram, and details are not repeated here. In the prior art, the radio frequency parameters of the target device are calibrated through calibration equipment. The present disclosure introduces a neural network model for the first time, and determines the second radio frequency parameters corresponding to the environmental parameters through the trained neural network model, thereby calibrating the radio frequency parameters of the target device.
可选地，第一校准模块302还被配置为获取与目标设备同一应用领域下的同一类型的多个设备的设备信息；根据多个设备的设备信息更新神经网络模型的模型参数。Optionally, the
如果直接利用训练参数对神经网络模型进行训练，训练时间会很长，为了提高训练的效率，本公开实施例对神经网络模型进行训练之前，先根据多个设备的设备信息更新神经网络模型的模型参数。根据多个设备的设备信息更新神经网络模型的模型参数，可以理解为将神经网络模型的模型参数的值更新到目标设备对应的区间中。设备信息，包括：设备的应用领域的信息、设备的类型和设备的工作参数等，比如一个设备的工作频率的理论区间。通过上述技术手段，可以提高训练中神经网络模型的收敛速度。If the training parameters are directly used to train the neural network model, the training time will be very long. In order to improve the training efficiency, before training the neural network model in this embodiment of the present disclosure, the model of the neural network model is updated according to the device information of multiple devices. parameter. Updating the model parameters of the neural network model according to the device information of the multiple devices can be understood as updating the values of the model parameters of the neural network model to the interval corresponding to the target device. Device information, including: information on the application field of the device, the type of the device, and the operating parameters of the device, such as the theoretical range of the operating frequency of a device. Through the above technical means, the convergence speed of the neural network model during training can be improved.
为了提高训练后神经网络模型的精度，本公开实施例对神经网络模型进行再训练。校准结果是校准设备推荐的第二的参数值。In order to improve the accuracy of the neural network model after training, the embodiments of the present disclosure retrain the neural network model. The calibration result is the second parameter value recommended by the calibration equipment.
在一个可选实施例中，第一射频参数，包括：第一工作频率、第一信噪比、第一信号增益、第一发射信号功率、第一发射信号频率和第一s参数；第二射频参数，包括：第二工作频率、第二信噪比、第二信号增益、第二发射信号功率、第二发射信号频率和和第二s参数。In an optional embodiment, the first radio frequency parameter includes: a first operating frequency, a first signal-to-noise ratio, a first signal gain, a first transmit signal power, a first transmit signal frequency, and a first s parameter; the second The radio frequency parameters include: the second operating frequency, the second signal-to-noise ratio, the second signal gain, the second transmit signal power, the second transmit signal frequency and the second s parameter.
s参数，包括：特征阻抗、串扰、插入损耗和回波损耗等。举例说明，本公开实施例可以通过调整目标设备的工作电路，改变第一s参数，进而基于第二射频参数校准第一射频参数。目标设备可以在工作时，选用不同的电路，进而选用s参数第二的电路。不同的电路对应的信号增益不同，可以根据第二信号增益选用最合适的电路。还可以根据第二发射信号功率调整第一发射信号功率等。s-parameters, including: characteristic impedance, crosstalk, insertion loss and return loss, etc. For example, in the embodiment of the present disclosure, the first s parameter can be changed by adjusting the working circuit of the target device, and then the first radio frequency parameter can be calibrated based on the second radio frequency parameter. The target device can select different circuits during operation, and then select the circuit with the second s parameter. Different circuits have different signal gains, and the most suitable circuit can be selected according to the second signal gain. The power of the first transmission signal and the like may also be adjusted according to the power of the second transmission signal.
需要说明的是，本公开实施例中的第一射频参数和第二射频参数中的任何一个参数都可以对应一个区间的值。比如第二发射信号功率可以是一个区间的发射信号功率。It should be noted that, any one of the first radio frequency parameter and the second radio frequency parameter in the embodiment of the present disclosure may correspond to a value in an interval. For example, the second transmit signal power may be a range of transmit signal power.
可选地，获取模块301还被配置为获取目标设备的环境参数；分别基于光波反射原理、光波散射原理、光波折射原理和光波绕射原理，建立光波反射约束条件、光波散射约束条件、光波折射约束条件和光波绕射约束条件；以光波传播能量损耗最小为准则，建立光波传播能量函数；基于光波反射约束条件、光波散射约束条件、光波折射约束条件、光波绕射约束条件和光波传播能量函数，构建数学模型；将环境参数输入数学模型，输出目标设备的第二工作波长；根据工作波长确定目标设备的第二工作频率；当第二工作频率和第一工作频率的差值大于预设阈值时，基于第二工作频率和第一工作频率，对目标设备进行校准。Optionally, the
其中，第二射频参数包括第二工作频率，第一射频参数包括第一工作频率。Wherein, the second radio frequency parameter includes the second working frequency, and the first radio frequency parameter includes the first working frequency.
本公开实施例属于数学建模的思想。不同的波长在一个环境中的传播能力存在差异，这种传播差异，包括：反射差异、散射差异、折射差异和绕射差异等。为了增强目标设备的工作波在环境中的传播能力，降低在传播过程中的损耗，本公开实施例基于光波反射原理建立光波反射约束条件，基于光波散射原理建立光波散射约束条件，基于光波折射原理建立光波折射约束条件，基于光波绕射原理建立光波绕射约束条件。通过上述约束条件可以将目标设备的工作波的波长控制在一定区间内，然后根据光波传播能量函数，从该区间中求解出目标设备的第二工作波长。因为波长和频率满足一定的条件，所以可以根据工作波长确定目标设备的第二工作频率。环境参数包括了目标设备所处环境的所有信息，比如目标设备所处环境有哪些影响光波传播的遮挡物，遮挡物的位置和大小等信息。The embodiments of the present disclosure belong to the idea of mathematical modeling. Different wavelengths have differences in their ability to propagate in an environment. Such propagation differences include: reflection differences, scattering differences, refraction differences, and diffraction differences. In order to enhance the propagation capability of the working wave of the target device in the environment and reduce the loss in the propagation process, the embodiments of the present disclosure establish light wave reflection constraints based on the light wave reflection principle, establish light wave scattering constraints based on the light wave scattering principle, and establish light wave scattering constraints based on the light wave refraction principle. The constraints of light wave refraction are established, and the constraints of light wave diffraction are established based on the principle of light wave diffraction. Through the above constraints, the wavelength of the working wave of the target device can be controlled within a certain interval, and then the second working wavelength of the target device can be obtained from the interval according to the light wave propagation energy function. Because the wavelength and frequency satisfy certain conditions, the second working frequency of the target device can be determined according to the working wavelength. The environmental parameters include all the information about the environment where the target device is located, such as the obstructions in the environment where the target device is located, which affects the propagation of light waves, the location and size of the obstructions, and other information.
可选地，获取模块301还被配置为获取目标设备的环境参数；根据环境参数，确定目标设备的以下至少之一的参数：最优电压、最优电流和抗干扰信息；基于以下至少之一的参数：最优电压、最优电流和抗干扰信息，确定目标设备的最优发射信号功率，其中，第二射频参数包括最优发射信号功率；使用最优发射信号功率除以当前发射信号功率，得到相除值；根据相除值确定最优信号增益，其中，第二射频参数包括最优信号增益，第一射频参数包括当前发射信号功率；根据最优信号增益确定目标设备的工作电路。Optionally, the acquiring
目标设备在不同的环境下，或者目标设备的工作存在差异，那么目标设备所被允许的发射信号功率会存在不同，或者在考虑到设备功耗的情况下，目标设备的最佳的发射信号功率会存在不同。为了解决上述技术问题，本公开实施例通过环境参数，确定目标设备的最优电压、或最优电流、或抗干扰信息。因为目标设备的内阻是可以知道的，所以可以根据电压、电流、电阻和功率的关系，确定目标设备的最优发射信号功率。抗干扰信息用于表明目标设备的发射信号功率应具有的抗干扰的能力。在某些情况下，如果目标设备需要较强的抗干扰的能力，那么应该增强目标设备的发射信号功率，如果目标设备不需要较强的抗干扰的能力，那么应该根据具体情境适当降低目标设备的发射信号功率。所以根据抗干扰信息，也确定目标设备的最优发射信号功率。The target device is in different environments, or the work of the target device is different, then the allowable transmit signal power of the target device will be different, or considering the power consumption of the device, the optimal transmit signal power of the target device will be different. In order to solve the above technical problem, the embodiment of the present disclosure determines the optimal voltage, or the optimal current, or the anti-interference information of the target device through environmental parameters. Because the internal resistance of the target device can be known, the optimal transmit signal power of the target device can be determined according to the relationship between voltage, current, resistance and power. The anti-jamming information is used to indicate the anti-jamming capability of the transmitted signal power of the target device. In some cases, if the target device needs strong anti-interference ability, then the transmit signal power of the target device should be enhanced. If the target device does not need strong anti-interference ability, then the target device should be appropriately reduced according to the specific situation. transmit signal power. Therefore, according to the anti-interference information, the optimal transmit signal power of the target device is also determined.
本公开实施例可以为目标设备提供多个增益电路，进而为目标设备提供多种增益。可以将最优发射信号功率除以当前发射信号功率得到的相除值，理解为目标设备需要的增益，也就是最优信号增益。当前信号增益是当前时刻目标设备正在使用的增益。在最优信号增益之后，可以根据最优信号增益确定目标设备的工作电路。确定目标设备的工作电路也是对目标设备进行校准的一种方法。The embodiments of the present disclosure can provide multiple gain circuits for the target device, thereby providing multiple gains for the target device. The division value obtained by dividing the optimal transmit signal power by the current transmit signal power can be understood as the gain required by the target device, that is, the optimal signal gain. The current signal gain is the gain being used by the target device at the current moment. After the optimal signal gain, the working circuit of the target device can be determined according to the optimal signal gain. Determining the operating circuit of the target device is also a method of calibrating the target device.
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本公开实施例的实施过程构成任何限定。It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present disclosure.
图4是本公开实施例提供的电子设备4的示意图。如图4所示，该实施例的电子设备4包括：处理器401、存储器402以及存储在该存储器402中并且可在处理器401上运行的计算机程序403。处理器401执行计算机程序403时实现上述各个方法实施例中的步骤。或者，处理器401执行计算机程序403时实现上述各装置实施例中各模块/单元的功能。FIG. 4 is a schematic diagram of an electronic device 4 provided by an embodiment of the present disclosure. As shown in FIG. 4 , the electronic device 4 of this embodiment includes: a
示例性地，计算机程序403可以被分割成一个或多个模块/单元，一个或多个模块/单元被存储在存储器402中，并由处理器401执行，以完成本公开。一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述计算机程序403在电子设备4中的执行过程。Illustratively, the
电子设备4可以是桌上型计算机、笔记本、掌上电脑及云端服务器等电子设备。电子设备4可以包括但不仅限于处理器401和存储器402。本领域技术人员可以理解，图4仅仅是电子设备4的示例，并不构成对电子设备4的限定，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，例如，电子设备还可以包括输入输出设备、网络接入设备、总线等。The electronic device 4 may be an electronic device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The electronic device 4 may include, but is not limited to, the
处理器401可以是中央处理单元(Central Processing Unit，CPU)，也可以是其它通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其它可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。The
存储器402可以是电子设备4的内部存储单元，例如，电子设备4的硬盘或内存。存储器402也可以是电子设备4的外部存储设备，例如，电子设备4上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步地，存储器402还可以既包括电子设备4的内部存储单元也包括外部存储设备。存储器402用于存储计算机程序以及电子设备所需的其它程序和数据。存储器402还可以用于暂时地存储已经输出或者将要输出的数据。The
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。实施例中的各功能单元、模块可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中，上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。另外，各功能单元、模块的具体名称也只是为了便于相互区分，并不用于限制本申请的保护范围。上述系统中单元、模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the above-mentioned system, reference may be made to the corresponding process in the foregoing method embodiments, which will not be repeated here.
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本公开的范围。Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this disclosure.
在本公开所提供的实施例中，应该理解到，所揭露的装置/电子设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/电子设备实施例仅仅是示意性的，例如，模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通讯连接可以是通过一些接口，装置或单元的间接耦合或通讯连接，可以是电性，机械或其它的形式。In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/electronic device and method may be implemented in other manners. For example, the apparatus/electronic device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. Multiple units or components may be Incorporation may either be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
另外，在本公开各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读存储介质中。基于这样的理解，本公开实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，计算机程序可以存储在计算机可读存储介质中，该计算机程序在被处理器执行时，可以实现上述各个方法实施例的步骤。计算机程序可以包括计算机程序代码，计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。计算机可读介质可以包括：能够携带计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、电载波信号、电信信号以及软件分发介质等。需要说明的是，计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如，在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on this understanding, the present disclosure realizes all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program is processed when the When the device is executed, the steps of the foregoing method embodiments may be implemented. A computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, Read-Only Memory (ROM), random access memory Memory (Random Access Memory, RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in computer-readable media may be modified as appropriate in accordance with the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not be Including electrical carrier signals and telecommunication signals.
以上实施例仅用以说明本公开的技术方案，而非对其限制；尽管参照前述实施例对本公开进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本公开各实施例技术方案的精神和范围，均应包含在本公开的保护范围之内。The above embodiments are only used to illustrate the technical solutions of the present disclosure, but not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The recorded technical solutions are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the present disclosure. within the scope of protection.
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