技术领域Technical Field
本申请涉及非侵入式负荷分解技术领域，尤其涉及非侵入式电动自行车电池识别技术。The present application relates to the technical field of non-invasive load decomposition, and in particular to a non-invasive electric bicycle battery identification technology.
背景技术Background Art
经研究表明，电气火灾尤其是重特大电气火灾的高发不止与电气线路故障等因素有关，还有部分人员违规将电动自行车或其电池私自带入室内充电而诱发的热失控所致。为了保障用户的生命财产的安全及电力系统的安全运行，科学界及工业界开发了大量基于图像识别技术的电动自行车入户识别装置，并且随着人工智能技术的不断发展，各类图像识别算法被移植其中，其对电动自行车的识别准确度也日益提高。Studies have shown that the high incidence of electrical fires, especially major electrical fires, is not only related to factors such as electrical line failures, but also caused by thermal runaway caused by some people illegally bringing electric bicycles or their batteries indoors to charge. In order to protect the safety of users' lives and property and the safe operation of power systems, the scientific and industrial communities have developed a large number of electric bicycle home recognition devices based on image recognition technology. With the continuous development of artificial intelligence technology, various image recognition algorithms have been transplanted into it, and the accuracy of electric bicycle recognition has also been increasing.
此类电动自行车入户识别装置的大量应用极大地降低了部分人员私自将电动自行车带入室内充电的风险，但由于便携式、小型化的锂电池的大面积推广应用使得用户可直接将电动自行车电池进行拆卸后再带入室内或住宅楼道间充电，而基于图像识别技术的电动自行车入户识别装置多安装于电梯或住宅楼入口处且只能识别电动自行车而非其内部的电池，致使其对电动自行车违禁入户充电的识别存在较大的防控漏洞。The large-scale application of such electric bicycle home entry identification devices has greatly reduced the risk of some people bringing electric bicycles indoors for charging without permission. However, the widespread promotion and application of portable and miniaturized lithium batteries allows users to directly disassemble the electric bicycle batteries and then bring them indoors or into residential corridors for charging. Electric bicycle home entry identification devices based on image recognition technology are mostly installed at the entrances of elevators or residential buildings and can only identify electric bicycles rather than the batteries inside them, resulting in a large prevention and control loophole in the identification of electric bicycles illegally entering homes for charging.
上述内容仅用于辅助理解本申请的技术方案，并不代表承认上述内容是现有技术。The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art.
发明内容Summary of the invention
本申请的主要目的在于提供一种非侵入式电动自行车电池识别技术，旨在解决现有基于图像识别技术的电动自行车入户识别装置很难甚至无法识别用户私自将电动自行车电池拆卸带入室内或楼道间充电的技术问题。The main purpose of this application is to provide a non-invasive electric bicycle battery identification technology, which aims to solve the technical problem that the existing electric bicycle home identification devices based on image recognition technology are difficult or even unable to identify users who privately disassemble electric bicycle batteries and bring them indoors or into the corridor for charging.
为实现上述目的，本申请提出一种非侵入式电动自行车电池识别技术，所述非侵入式电动自行车电池识别技术包括：To achieve the above objectives, the present application proposes a non-invasive electric bicycle battery identification technology, which includes:
获取用户用电网络的干路电流信号；Obtain the main current signal of the user's power network;
基于所述干路电流信号的周期特征对所述干路电流信号序列进行等间隔抽取，获得实时样本电流信号；Based on the periodic characteristics of the main current signal, the main current signal sequence is extracted at equal intervals to obtain a real-time sample current signal;
对所述实时样本电流信号进行小波变换，获得所述实时样本电流信号各层的小波细节系数；Performing wavelet transform on the real-time sample current signal to obtain wavelet detail coefficients of each layer of the real-time sample current signal;
基于所述小波细节系数确定电动自行车电池的充电指示特征值；Determine a charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient;
根据所述充电指示特征值进行电动自行车电池识别。The electric bicycle battery is identified according to the charging indication characteristic value.
在一实施例中，所述基于所述小波细节系数确定电动自行车电池的充电指示特征值的步骤，包括：In one embodiment, the step of determining the charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient includes:
基于所述小波细节系数确定所述实时样本电流信号各层之间的相关系数；Determine the correlation coefficient between each layer of the real-time sample current signal based on the wavelet detail coefficient;
基于所述相关系数确定电动自行车电池的充电指示特征值。A charging indication characteristic value of the electric bicycle battery is determined based on the correlation coefficient.
在一实施例中，所述基于所述相关系数确定电动自行车电池的充电指示特征值的步骤，包括：In one embodiment, the step of determining the charging indication characteristic value of the electric bicycle battery based on the correlation coefficient includes:
确定所述相关系数的五次方根，将所述五次方根作为电动自行车电池的充电指示特征值；Determine the fifth root of the correlation coefficient, and use the fifth root as a charging indication characteristic value of the electric bicycle battery;
和/或，确定所述相关系数与含电动自行车充电电流相关系数的协方差，并将所述协方差作为电动自行车电池的充电指示特征。And/or, determining the covariance between the correlation coefficient and the correlation coefficient of the electric bicycle charging current, and using the covariance as a charging indication feature of the electric bicycle battery.
在一实施例中，所述根据所述充电指示特征值进行电动自行车电池识别的步骤，包括：In one embodiment, the step of identifying the battery of the electric bicycle according to the charging indication characteristic value includes:
获取指示特征阈值；Obtaining an indicator feature threshold;
基于所述充电指示特征值和所述指示特征阈值进行判断；Making a judgment based on the charging indication characteristic value and the indication characteristic threshold;
在所述充电指示特征值均大于所述指示特征阈值时，确定存在正在充电的电动自行车或电动自行车电池；When the charging indication characteristic values are all greater than the indication characteristic threshold, it is determined that there is an electric bicycle or an electric bicycle battery being charged;
在存在不大于所述指示特征阈值的充电指示特征值时，确定不存在正在充电的电动自行车或电动自行车电池。When there is a charging indication characteristic value that is not greater than the indication characteristic threshold, it is determined that there is no electric bicycle or electric bicycle battery being charged.
在一实施例中，所述基于所述充电指示特征值和所述指示特征阈值进行判断的步骤之后，还包括：In one embodiment, after the step of judging based on the charging indication characteristic value and the indication characteristic threshold, the method further includes:
确定大于所述指示特征阈值的充电指示特征值占比；Determining a proportion of charging indication characteristic values greater than the indication characteristic threshold;
若所述充电指示特征值占比超出预设占比阈值，则确定所述用户用电网络中存在潜在的电动自行车电池充电行为。If the proportion of the charging indication characteristic value exceeds a preset proportion threshold, it is determined that there is potential electric bicycle battery charging behavior in the user's power network.
本申请提出的一个或多个技术方案，至少具有以下技术效果：One or more technical solutions proposed in this application have at least the following technical effects:
本申请通过获取用户用电网络的干路电流信号；基于干路电流信号的周期特征对干路电流信号序列进行等间隔抽取，获得实时样本电流信号；对实时样本电流信号进行小波变换，获得实时样本电流信号各层的小波细节系数；基于小波细节系数确定电动自行车电池的充电指示特征值；根据充电指示特征值进行电动自行车电池识别。由于是通过对用户用电网络干路电流信号进行抽样计算，并对抽样获得的实时样本电流信号进行小波变换处理以完成对用户用电网络的干路电流信号不同频段信号的分离，进而实现对不同频段信号进行特征提取并与特定段纹波分布比对，以完成用户用电网络潜在电动自行车充电行为的评估，相较于传统的基于图像识别技术进行电动自行车电池识别的方案，本申请的方案可有效地避免基于图像识别技术的电动自行车入户识别装置的盲区，且更具有实用性。The present application obtains the main current signal of the user's power network; extracts the main current signal sequence at equal intervals based on the periodic characteristics of the main current signal to obtain the real-time sample current signal; performs wavelet transform on the real-time sample current signal to obtain the wavelet detail coefficients of each layer of the real-time sample current signal; determines the charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient; and identifies the electric bicycle battery according to the charging indication characteristic value. Since the main current signal of the user's power network is sampled and calculated, and the real-time sample current signal obtained by sampling is processed by wavelet transform to complete the separation of the main current signal of the user's power network with different frequency bands, and then the feature extraction of the signal of different frequency bands is realized and compared with the ripple distribution of a specific segment to complete the evaluation of the potential charging behavior of the electric bicycle in the user's power network, compared with the traditional solution of electric bicycle battery identification based on image recognition technology, the solution of the present application can effectively avoid the blind spot of the electric bicycle home identification device based on image recognition technology, and is more practical.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本申请的实施例，并与说明书一起用于解释本申请的原理。The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
为了更清楚地说明本申请实施例或现有技术中的技术方案，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的介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
图1为本申请非侵入式电动自行车电池识别技术实施例一提供的流程示意图；FIG1 is a schematic diagram of a process flow provided in Example 1 of the non-invasive electric bicycle battery identification technology of the present application;
图2为本申请实施例电动自行车电池识别装置的模块结构示意图；FIG2 is a schematic diagram of the module structure of an electric bicycle battery identification device according to an embodiment of the present application;
图3为本申请实施例中非侵入式电动自行车电池识别技术涉及的硬件运行环境的设备结构示意图。FIG3 is a schematic diagram of the device structure of the hardware operating environment involved in the non-invasive electric bicycle battery identification technology in the embodiment of the present application.
本申请目的实现、功能特点及优点将结合实施例，参照附图做进一步说明。The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.
具体实施方式DETAILED DESCRIPTION
应当理解，此处所描述的具体实施例仅仅用以解释本申请的技术方案，并不用于限定本申请。It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.
为了更好地理解本申请的技术方案，下面将结合说明书附图以及具体的实施方式进行详细的说明。In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.
本申请实施例的主要解决方案是：获取用户用电网络的干路电流信号；基于所述干路电流信号的周期特征对所述干路电流信号序列进行等间隔抽取，获得实时样本电流信号；对实时样本电流信号进行小波变换，获得实时样本电流信号各层的小波细节系数；基于小波细节系数确定电动自行车电池的充电指示特征值；根据充电指示特征值进行电动自行车电池识别。The main solution of the embodiment of the present application is: obtaining the main current signal of the user's power network; extracting the main current signal sequence at equal intervals based on the periodic characteristics of the main current signal to obtain a real-time sample current signal; performing wavelet transform on the real-time sample current signal to obtain the wavelet detail coefficients of each layer of the real-time sample current signal; determining the charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient; and identifying the electric bicycle battery according to the charging indication characteristic value.
目前现有技术是通过图像识别技术来进行电动自行车入户识别，对于电动车电池这种便携式部件无法进行有效防控。The current existing technology uses image recognition technology to identify electric bicycles entering homes, but it is unable to effectively prevent and control portable components such as electric vehicle batteries.
本申请提供一种解决方案，采用了非侵入式负荷分解技术，利用用户用电网络的电流、电压等信息分析用户用电网络中的负载情况，可以实现对用户用电网络中潜在的电动自行车或电动自行车电池的充电行为进行预警，从而有效地避免基于图像识别技术的电动自行车入户识别装置的盲区。The present application provides a solution that adopts non-invasive load decomposition technology and uses the current, voltage and other information of the user's power network to analyze the load conditions in the user's power network. It can provide early warning of potential charging behavior of electric bicycles or electric bicycle batteries in the user's power network, thereby effectively avoiding the blind spots of electric bicycle entry recognition devices based on image recognition technology.
需要说明的是，本实施例的执行主体可以是一种具有数据处理、网络通信以及程序运行功能的计算服务设备，例如服务器、计算机等，或者是一种能够实现上述功能的电子设备、电路保护装置、虚拟装置等。以下以电动自行车电池识别设备（简称识别设备）为例，对本实施例及下述各实施例进行说明。It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a server, a computer, etc., or an electronic device, a circuit protection device, a virtual device, etc. that can realize the above functions. The following takes an electric bicycle battery identification device (referred to as the identification device) as an example to illustrate this embodiment and the following embodiments.
基于此，本申请实施例提供了一种非侵入式电动自行车电池识别技术，参照图1，图1为本申请非侵入式电动自行车电池识别技术第一实施例的流程示意图。Based on this, an embodiment of the present application provides a non-invasive electric bicycle battery identification technology. Referring to FIG. 1 , FIG. 1 is a flow chart of a first embodiment of the non-invasive electric bicycle battery identification technology of the present application.
本实施例中，所述非侵入式电动自行车电池识别技术包括步骤S10~S40：In this embodiment, the non-invasive electric bicycle battery identification technology includes steps S10 to S40:
步骤S10，获取用户用电网络的干路电流信号；Step S10, obtaining a main circuit current signal of the user's power network;
步骤S20，基于所述干路电流信号的周期特征对所述干路电流信号序列进行等间隔抽取，获得实时样本电流信号；Step S20, extracting the main current signal sequence at equal intervals based on the periodic characteristics of the main current signal to obtain a real-time sample current signal;
需要说明的是，为了避免用户携带便携式、小型化的电池进入室内或住宅楼道间等待监测区域进行充电，设备识别可以对待监测区域对应的用户用电网络中的电流信号进行实时监测，以获得用户用电网络的干路电流信号，通过对该干路电流信号进行采样，即可获得实时样本电流信号。It should be noted that in order to avoid users carrying portable, miniaturized batteries into indoors or in residential corridors to wait for the monitored area to charge, the device identification can monitor the current signal in the user power network corresponding to the monitored area in real time to obtain the main current signal of the user power network. By sampling the main current signal, the real-time sample current signal can be obtained.
应当理解的是，对于用户用电网络中的用电行为，可以通过对实时样本电流信号进行解析确定。具体地，对于电动自行车电池充电的行为，识别设备可以通过检测实时样本电流信号中是否含有充电相关的指示特征进行判断。It should be understood that the user's electricity consumption behavior in the electricity network can be determined by analyzing the real-time sample current signal. Specifically, for the behavior of charging the battery of an electric bicycle, the identification device can determine whether the real-time sample current signal contains charging-related indication features.
在本申请实施例中，可以通过高精度电流互感器或其他装置，并于用户配电进线处实时采集用户用电网络中的干路电流信号，获得干路电流信号序列。In the embodiment of the present application, a high-precision current transformer or other device can be used to collect the main current signal in the user's power network in real time at the user's power distribution line to obtain a main current signal sequence.
需要说明的是，对于非侵入式的电荷识别技术，一般可以以秒为单位进行负荷分解。因此，在本申请实施例中上述干路电流信号序列可以是指某一秒对应的电流信号序列。此外，对于不同的电动自行车充电器，以及电动自行车电池对于电压的响应特性，可以确定进行电流信号采样的采样频率。如，电流信号序列采样频率为40k时，某一秒干路电流信号序列对应的长度为40k。It should be noted that for non-invasive charge identification technology, load decomposition can generally be performed in seconds. Therefore, in the embodiment of the present application, the above-mentioned main current signal sequence may refer to the current signal sequence corresponding to a certain second. In addition, for different electric bicycle chargers, and the response characteristics of the electric bicycle battery to the voltage, the sampling frequency for current signal sampling can be determined. For example, when the current signal sequence sampling frequency is 40k, the length corresponding to the main current signal sequence of a certain second is 40k.
另外，还需要说明的是，对于电动自行车电池进行充电的充电电流，虽然其由于受整流桥等元器件的影响，不再是标准的正弦波，但其交流侧的周期性仍然存在。因此，为了确定电动自行车电池的充电特性，本申请实施例中需至少观察一个完整周期的电流信号。In addition, it should be noted that, although the charging current of the electric bicycle battery is no longer a standard sine wave due to the influence of components such as the rectifier bridge, the periodicity of the AC side still exists. Therefore, in order to determine the charging characteristics of the electric bicycle battery, at least one complete cycle of the current signal needs to be observed in the embodiment of the present application.
需要解释的是，本申请实施例中不对干路电流信号的长度进行限制，可以根据具体应用中的情况去确定，如一个电流周波或多个电流周波等。It should be explained that the length of the trunk current signal is not limited in the embodiment of the present application and can be determined according to the situation in the specific application, such as one current cycle or multiple current cycles.
需要说明的是，对于一秒内的抽取次数，本申请实施例不加以限制，可以是4次、5次或者其他次数。为了便于描述，本申请实施例中以5次为例对本申请的方案加以说明。It should be noted that the number of extractions within one second is not limited in the present embodiment, and can be 4, 5 or other times. For ease of description, the present embodiment takes 5 times as an example to illustrate the solution of the present application.
在本申请实施例的一种实现方式中，基于本申请的电流信号采样频率以及周期，可以确定抽样电流信号的长度。如，采样频率为40k、一秒内的周期数量为50个时，对应的抽样电流信号长度为800。为了满足等间隔的抽样原则，对于第一组抽样信号，也即将在前10个周期内随机抽取，获得长度为800的子序列，以此类推，则是与间隔9个电流周波后的第10个电流周波。In one implementation of the present application, based on the current signal sampling frequency and cycle of the present application, the length of the sampled current signal can be determined. For example, when the sampling frequency is 40k and the number of cycles in one second is 50, the corresponding sampled current signal length is 800. In order to meet the sampling principle of equal intervals, for the first group of sampled signals, that is, We will randomly select in the first 10 cycles to obtain a subsequence of length 800, and so on. is with The 10th current cycle after an interval of 9 current cycles.
在具体实现中，识别设备可以获取用户用电网络的干路电流信号，并基于干路电流信号的周期特征对干路电流信号序列进行等间隔抽取，获得实时样本电流信号。由于是通过抽样计算的方式获取实时样本电流信号，既可以满足电动自行车充电识别算法的抗干扰能力，同时又能极大地降低需要处理的数据运算量，减少了对识别设备的算力需求。In the specific implementation, the identification device can obtain the main current signal of the user's power network, and extract the main current signal sequence at equal intervals based on the periodic characteristics of the main current signal to obtain a real-time sample current signal. Since the real-time sample current signal is obtained by sampling calculation, it can not only meet the anti-interference ability of the electric bicycle charging identification algorithm, but also greatly reduce the amount of data calculation that needs to be processed, reducing the computing power requirements of the identification device.
步骤S30，对所述实时样本电流信号进行小波变换，获得所述实时样本电流信号各层的小波细节系数；Step S30, performing wavelet transform on the real-time sample current signal to obtain wavelet detail coefficients of each layer of the real-time sample current signal;
应当理解的是，通过对实时样本电流信号进行小波变换，可以获得若干个小波变换层。对于小波变换层的数量，可以根据实际应用中的需求进行选择，本申请实施例对此不加以限制。为了便于描述，本申请实施例中以4层为例，对本申请的方案加以说明。It should be understood that by performing wavelet transform on the real-time sample current signal, several wavelet transform layers can be obtained. The number of wavelet transform layers can be selected according to the needs of the actual application, and the embodiment of the present application does not limit this. For the convenience of description, the embodiment of the present application takes 4 layers as an example to illustrate the scheme of the present application.
在本申请实施例的一种实现方式中，可以采用小波基为多贝西小波系列中的db4小波函数进行小波变换，获得各层的小波细节系数，其中为小波变换层数，为抽样的组数，为小波细节系数中的第个值。In one implementation of the embodiment of the present application, the db4 wavelet function in the Dobesi wavelet series can be used as the wavelet basis to perform wavelet transform to obtain the wavelet detail coefficients of each layer. ,in is the number of wavelet transform layers, is the number of sampling groups, is the first wavelet detail coefficient value.
步骤S40，基于所述小波细节系数确定电动自行车电池的充电指示特征值；Step S40, determining a charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient;
步骤S50，根据所述充电指示特征值进行电动自行车电池识别。Step S50: identifying the battery of the electric bicycle according to the charging indication characteristic value.
需要说明的是，在对电动自行车电池进行充电时，其会产生一些特定的电流信号。该电流信号通常包含了电池充电过程中的各种细微变化，如纹波特征、电压变化、电流波动等，通过小波细节系数，可以实现对电池中细微信息的捕捉，从而判断用户用电网络中是否存在电动自行车电池的充电指示特征。It should be noted that when charging an electric bicycle battery, it will generate some specific current signals. This current signal usually contains various subtle changes in the battery charging process, such as ripple characteristics, voltage changes, current fluctuations, etc. Through the wavelet detail coefficient, it is possible to capture subtle information in the battery, thereby determining whether there are charging indication characteristics of the electric bicycle battery in the user's power network.
可以理解的是，上述充电指示特征值也即用于判断用户用电网络中是否存在正在充电的电动自行车电池的特征值。在用户用电网络中的充电指示特征值高出预设阈值时，可以说明用户用电网络中可能潜在有正在充电的电动自行车电池；若充电指示特征值低于预设阈值，则不存在正在充电的电动自行车电池。It can be understood that the above-mentioned charging indication characteristic value is also a characteristic value used to determine whether there is an electric bicycle battery being charged in the user's power network. When the charging indication characteristic value in the user's power network is higher than the preset threshold, it can be said that there may be an electric bicycle battery being charged in the user's power network; if the charging indication characteristic value is lower than the preset threshold, there is no electric bicycle battery being charged.
在具体实现中，识别设备通过对实时样本电流信号进行小波变换，获得实时样本电流信号各层的小波细节系数；基于小波细节系数确定电动自行车电池的充电指示特征值；根据充电指示特征值进行电动自行车电池识别。由于是通过对用户用电网络中的实时样本电流信号进行小波变换，进而实现对电流信号的分析，相较于使用图像识别技术进行电动自行车电池识别的方案，本申请的方案具备更广的识别范围和适用性。In a specific implementation, the recognition device performs a wavelet transform on the real-time sample current signal to obtain the wavelet detail coefficients of each layer of the real-time sample current signal; determines the charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient; and identifies the electric bicycle battery according to the charging indication characteristic value. Since the current signal is analyzed by performing a wavelet transform on the real-time sample current signal in the user's power network, the solution of this application has a wider recognition range and applicability compared to the solution of using image recognition technology to identify the battery of an electric bicycle.
在本申请实施例的一种实现方式中，所述基于所述小波细节系数确定电动自行车电池的充电指示特征值的步骤，包括：In an implementation of the embodiment of the present application, the step of determining the charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient includes:
步骤S31，基于所述小波细节系数确定所述实时样本电流信号各层之间的相关系数；Step S31, determining the correlation coefficient between each layer of the real-time sample current signal based on the wavelet detail coefficient;
步骤S32，基于所述相关系数确定电动自行车电池的充电指示特征值。Step S32: determining a charging indication characteristic value of the electric bicycle battery based on the correlation coefficient.
需要说明的是，为了更好地滤除外部谐波的干扰，本申请实施例中增加了实时样本电流信号在不同尺度下小波细节系数相关性的计算，进而提高了识别的准确性。It should be noted that, in order to better filter out the interference of external harmonics, the embodiment of the present application adds the calculation of the correlation of the wavelet detail coefficients of the real-time sample current signal at different scales, thereby improving the accuracy of recognition.
具体地，小波细节系数的相关系数的计算方式如下所示：Specifically, the correlation coefficient of the wavelet detail coefficient The calculation method of is as follows:
； ;
可以理解的是，通过计算可得出第一层小波细节系数与第二层小波细节系数的相关系数为，第二层小波细节系数与第三层小波细节系数的相关系数为，第三层小波细节系数与第四层小波细节系数的相关系数为。It can be understood that the correlation coefficient between the first layer wavelet detail coefficient and the second layer wavelet detail coefficient can be obtained by calculation: , the correlation coefficient between the second layer wavelet detail coefficient and the third layer wavelet detail coefficient is , the correlation coefficient between the third layer wavelet detail coefficient and the fourth layer wavelet detail coefficient is .
本申请实施例中，以求取第一层小波细节系数与第二层小波细节系数的相关系数以及第三层小波细节系数与第四层小波细节系数的相关系数为例进行说明。In the embodiment of the present application, the correlation coefficient between the first layer wavelet detail coefficient and the second layer wavelet detail coefficient and the correlation coefficient between the third layer wavelet detail coefficient and the fourth layer wavelet detail coefficient are taken as examples for explanation.
需要说明的是，为了更为贴近电动自行车电池充电电流的纹波特征，本申请实施例基于小波细节系数的相关性系数，进行五次方根和/或协方差计算，从而获得充电指示特征值。也即，所述基于所述相关系数确定电动自行车电池的充电指示特征值的步骤，包括：It should be noted that, in order to be closer to the ripple characteristics of the charging current of the electric bicycle battery, the embodiment of the present application performs fifth root and/or covariance calculation based on the correlation coefficient of the wavelet detail coefficient to obtain the charging indication characteristic value. That is, the step of determining the charging indication characteristic value of the electric bicycle battery based on the correlation coefficient includes:
确定所述相关系数对应的五次方根，将所述五次方根作为电动自行车电池的充电指示特征值。The fifth root corresponding to the correlation coefficient is determined, and the fifth root is used as a charging indication characteristic value of the electric bicycle battery.
和/或，确定所述相关系数与含电动车充电电流相关系数的协方差，并将所述协方差作为电动自行车电池的充电指示特征值。And/or, determining the covariance between the correlation coefficient and the correlation coefficient of the electric vehicle charging current, and using the covariance as a charging indication characteristic value of the electric bicycle battery.
在本申请实施例的一种实现方式中，可以计算与的五次方根和。具体地，计算方法可以如下所示：In one implementation of the embodiment of the present application, it is possible to calculate and The fifth root of and Specifically, the calculation method can be as follows:
； ;
； ;
在本申请实施例的一种实现方式中，可以计算与与包含电动自行车充电电流与的协方差和。具体地，计算方法可以如下所示：In one implementation of the embodiment of the present application, it is possible to calculate and With the included electric bicycle charging current and The covariance of and Specifically, the calculation method can be as follows:
； ;
； ;
可以理解的是，在确定各相关系数对应的五次方根时，即可将该五次方根作为电动自行车的充电指示特征。在确定各相关系数对应的协方差时，即可将该协方差作为电动自行车的充电指示特征。在实际应用中，充电指示特征可以是五次方根和/或协方差，本申请实施例对此不加以限制。It is understandable that when the fifth root corresponding to each correlation coefficient is determined, the fifth root can be used as the charging indication feature of the electric bicycle. When the covariance corresponding to each correlation coefficient is determined, the covariance can be used as the charging indication feature of the electric bicycle. In practical applications, the charging indication feature can be the fifth root and/or the covariance, and the embodiments of the present application are not limited to this.
应当理解的是，该充电指示特征具有不受其他用电负荷电流干扰，且与电动自行车电池的电流信号强相关的特点。It should be understood that the charging indication feature is not affected by the current of other electrical loads and is strongly correlated with the current signal of the electric bicycle battery.
本申请实施例通过基于小波细节系数确定实时样本电流信号各层之间的相关系数；确定相关系数对应的五次方根及协方差，将五次方根及协方差作为电动自行车电池的充电指示特征值。由于是通过确定不同小波变换层之间小波细节系数的相关系数，能够更好地滤除外部谐波的干扰；同时通过小波细节系数的相关系数的五次方根及协方差作为电动自行车电池充电的充电指示特征，能够更为贴近电动自行车充电电流的纹波特征，且不受其他用电负荷的影响，能够确保本申请提出的非侵入式非侵入式电动自行车电池识别技术对违禁电动自行车充电识别的可靠性及抗干扰能力，提高了本申请方案的实用性。The embodiment of the present application determines the correlation coefficient between each layer of the real-time sample current signal based on the wavelet detail coefficient; determines the fifth root and covariance corresponding to the correlation coefficient, and uses the fifth root and covariance as the charging indication characteristic value of the electric bicycle battery. Since the correlation coefficient of the wavelet detail coefficient between different wavelet transform layers is determined, the interference of external harmonics can be better filtered out; at the same time, the fifth root and covariance of the correlation coefficient of the wavelet detail coefficient are used as the charging indication characteristics of the electric bicycle battery charging, which can be closer to the ripple characteristics of the electric bicycle charging current and is not affected by other power loads. It can ensure the reliability and anti-interference ability of the non-intrusive electric bicycle battery identification technology proposed in this application for the identification of prohibited electric bicycle charging, and improve the practicality of the present application scheme.
在本申请实施例的一种实现方式中，所述根据所述充电指示特征值进行电动自行车电池识别的步骤，包括：In an implementation of the embodiment of the present application, the step of identifying the battery of the electric bicycle according to the charging indication characteristic value includes:
步骤S41，获取指示特征阈值；Step S41, obtaining an indication feature threshold;
步骤S21，基于所述充电指示特征值和所述指示特征阈值进行判断；Step S21, making a judgment based on the charging indication characteristic value and the indication characteristic threshold;
步骤S43，在所述充电指示特征值均大于所述指示特征阈值时，确定存在正在充电的电动自行车电池；Step S43, when the charging indication characteristic values are all greater than the indication characteristic threshold, determining that there is an electric bicycle battery being charged;
步骤S44，在存在不大于所述指示特征阈值的充电指示特征值时，确定不存在正在充电的电动自行车电池。Step S44: when there is a charging indication characteristic value that is not greater than the indication characteristic threshold, it is determined that there is no electric bicycle battery being charged.
可以理解的是，上述指示特征阈值也即预先设置的特征阈值，通过该阈值可以判断用户用电网络中是否存在正在充电的电动自行车电池。该指示特征阈值的获取方式可以是基于先验知识进行设定，如基于特定频段的纹波分布获得，也可以是基于深度学习等方法生成，本申请实施例对此不加以限制。It is understandable that the above-mentioned indication characteristic threshold is also a pre-set characteristic threshold, by which it can be determined whether there is an electric bicycle battery being charged in the user's power network. The acquisition method of the indication characteristic threshold can be set based on prior knowledge, such as obtaining it based on the ripple distribution of a specific frequency band, or it can be generated based on methods such as deep learning, and the embodiments of the present application are not limited to this.
需要说明的是，对于各层对应的指示特征阈值，其值可以相同也可以不同，本申请实施例对此不加以限制。It should be noted that the values of the indication feature thresholds corresponding to each layer may be the same or different, and the embodiments of the present application do not impose any limitation on this.
应当理解的是，通过将各小波变换层的充电指示特征值与指示特征阈值进行比对，若充电指示特征值超过指示特征阈值，则可以视为用户用电网络中出现疑似电动自行车电池类的负荷。It should be understood that by comparing the charging indication characteristic value of each wavelet transform layer with the indication characteristic threshold, if the charging indication characteristic value exceeds the indication characteristic threshold, it can be regarded as a load suspected of being an electric bicycle battery in the user's power network.
在本申请实施例的一种实现方式中，可以将和与其对应的指示特征阈值进行比较，若和均超过预设阈值，则可以视为用户用电网络中出现疑似电动自行车电池类的负荷。示例性的，在本申请实施例中，电动自行车的上述指示特征阈值的取值可以为5，也可以是其他数值，本申请实施例对此不加以限制。In one implementation of the embodiment of the present application, and Compared with its corresponding indicator feature threshold, if and If both exceed the preset threshold, it can be regarded as a load suspected of being an electric bicycle battery in the user's power network. For example, in the embodiment of the present application, the value of the above-mentioned indication characteristic threshold of the electric bicycle can be 5 or other values, which is not limited in the embodiment of the present application.
可以理解的是，基于上述只是特征阈值进行判断，在满足判断条件时，可以认为用户用电网络中存在潜在的电动自行车电池充电行为。It can be understood that, based on the above-mentioned characteristic threshold, when the judgment conditions are met, it can be considered that there is potential electric bicycle battery charging behavior in the user's power network.
在本申请实施例的一种实现方式中，所述基于所述充电指示特征值和所述指示特征阈值进行判断的步骤之后，还包括：In an implementation of the embodiment of the present application, after the step of judging based on the charging indication characteristic value and the indication characteristic threshold, the method further includes:
确定大于所述指示特征阈值的充电指示特征值占比；Determining a proportion of charging indication characteristic values greater than the indication characteristic threshold;
若所述充电指示特征值占比超出预设占比阈值，则确定所述用户用电网络中存在潜在的电动自行车电池充电行为。If the proportion of the charging indication characteristic value exceeds a preset proportion threshold, it is determined that there is potential electric bicycle battery charging behavior in the user's power network.
需要说明的是，为了降低误判断的可能，本申请实施例可以设置有预设占比阈值。在大于指示特征阈值的充电指示特征值占总充电指示特征的比例超出预设占比阈值时，可以认为用户用电网络中存在潜在的电动自行车电池充电行为。It should be noted that in order to reduce the possibility of misjudgment, the embodiment of the present application may be provided with a preset proportion threshold. When the proportion of the charging indication characteristic value greater than the indication characteristic threshold to the total charging indication characteristic exceeds the preset proportion threshold, it can be considered that there is potential electric bicycle battery charging behavior in the user's power network.
本申请实施例通过获取指示特征阈值；基于充电指示特征值和指示特征阈值进行判断；在充电指示特征值均大于指示特征阈值时，确定存在正在充电的电动自行车电池；在存在不大于指示特征阈值的充电指示特征值时，确定不存在正在充电的电动自行车电池。由于是基于充电指示特征值和指示特征阈值进行判断，从而对用户用电网络中潜在电动自行车电池的充电行为进行评估，与传统基于图像识别的电动自行车入户识别方法相比，本方案技术实现简单，仅需通过对用户配电进线处的干路电流的抽样计算即可完成所述电动自行车充电指示特征的计算，计算复杂度较低，计算量也更少，同时可以有效避免基于图像识别技术的电动自行车入户识别装置的盲区。The embodiment of the present application obtains the indication characteristic threshold; makes a judgment based on the charging indication characteristic value and the indication characteristic threshold; determines that there is an electric bicycle battery being charged when the charging indication characteristic values are all greater than the indication characteristic threshold; determines that there is no electric bicycle battery being charged when there is a charging indication characteristic value that is not greater than the indication characteristic threshold. Since the judgment is based on the charging indication characteristic value and the indication characteristic threshold, the charging behavior of potential electric bicycle batteries in the user's power network is evaluated. Compared with the traditional electric bicycle home identification method based on image recognition, the technical implementation of this solution is simple. The calculation of the electric bicycle charging indication characteristic can be completed by sampling and calculating the main current at the user's power distribution line. The calculation complexity is low and the amount of calculation is also less. At the same time, it can effectively avoid the blind spot of the electric bicycle home identification device based on image recognition technology.
本申请实施例实现简单，仅需对用户干路电流信号进行少量的抽样计算即可实现对用户用电网络内潜在的电动自行车充电行为进行监测，可有效解决现有基于图像识别技术的电动自行车入户识别装置很难甚至无法识别用户私自将电动自行车电池拆卸带入室内或楼道间充电的行业难题，且整体方案的实施对硬件电路要求较低，便于推广应用。The embodiments of the present application are simple to implement, and only a small amount of sampling calculations of the user's main current signal are required to monitor the potential charging behavior of electric bicycles in the user's power network. This can effectively solve the industry problem that existing electric bicycle home entry identification devices based on image recognition technology are difficult or even unable to identify users who privately remove electric bicycle batteries and bring them indoors or into the corridor for charging. The implementation of the overall solution has low requirements for hardware circuits, which is easy to promote and apply.
本申请实施例通过获取用户用电网络的实时样本电流信号；对实时样本电流信号进行小波变换，获得实时样本电流信号各层的小波细节系数；基于小波细节系数确定电动自行车电池的充电指示特征值；根据充电指示特征值进行电动自行车电池识别。由于是通过对用户用电网络干路电流信号进行抽样计算，并对抽样获得的实时样本电流信号进行小波变换处理以完成对用户用电网络的干路电流信号不同频段信号的分离，进而实现对不同频段信号进行特征提取并与特定段纹波分布比对，以完成用户用电网络潜在电动自行车充电行为的评估，相较于传统的基于图像识别技术进行电动自行车电池识别的方案，本申请的方案可有效地避免基于图像识别技术的电动自行车入户识别装置的盲区，且更具有实用性。The embodiment of the present application obtains the real-time sample current signal of the user's power network; performs wavelet transform on the real-time sample current signal to obtain the wavelet detail coefficients of each layer of the real-time sample current signal; determines the charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient; and identifies the electric bicycle battery according to the charging indication characteristic value. Since the main current signal of the user's power network is sampled and calculated, and the real-time sample current signal obtained by sampling is processed by wavelet transform to complete the separation of the main current signal of the user's power network with different frequency bands, and then the feature extraction of the signals of different frequency bands is realized and compared with the ripple distribution of a specific segment to complete the evaluation of the potential charging behavior of the electric bicycle in the user's power network, compared with the traditional solution of electric bicycle battery identification based on image recognition technology, the solution of the present application can effectively avoid the blind spot of the electric bicycle home identification device based on image recognition technology, and is more practical.
本申请还提供一种电动自行车电池识别装置，请参照图2，所述电动自行车电池识别装置包括：The present application also provides an electric bicycle battery identification device, referring to FIG. 2 , the electric bicycle battery identification device comprises:
信号获取模块10，用于获取用户用电网络的实时样本电流信号；The signal acquisition module 10 is used to acquire the real-time sample current signal of the user's power network;
信号分解模块20，用于对所述实时样本电流信号进行小波变换，获得所述实时样本电流信号各层的小波细节系数；A signal decomposition module 20, configured to perform wavelet transformation on the real-time sample current signal to obtain wavelet detail coefficients of each layer of the real-time sample current signal;
特征确定模块30，用于基于所述小波细节系数确定电动自行车电池的充电指示特征值；A feature determination module 30, used to determine a charging indication feature value of an electric bicycle battery based on the wavelet detail coefficient;
充电识别模块40，用于根据所述充电指示特征值进行电动自行车电池识别。The charging identification module 40 is used to identify the battery of the electric bicycle according to the charging indication characteristic value.
本申请提供的电动自行车电池识别装置，采用上述实施例中的非侵入式电动自行车电池识别技术，能够解决所述的技术问题。与现有技术相比，本申请提供的电动自行车电池识别装置的有益效果与上述实施例提供的非侵入式电动自行车电池识别技术的有益效果相同，且所述电动自行车电池识别装置中的其他技术特征与上述实施例方法公开的特征相同，在此不做赘述。The electric bicycle battery identification device provided by the present application adopts the non-invasive electric bicycle battery identification technology in the above-mentioned embodiment, which can solve the technical problems. Compared with the prior art, the beneficial effects of the electric bicycle battery identification device provided by the present application are the same as the beneficial effects of the non-invasive electric bicycle battery identification technology provided by the above-mentioned embodiment, and the other technical features in the electric bicycle battery identification device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.
本申请提供一种电动自行车电池识别设备，电动自行车电池识别设备包括：至少一个处理器；以及，与至少一个处理器通信连接的存储器；其中，存储器存储有可被至少一个处理器执行的指令，指令被至少一个处理器执行，以使至少一个处理器能够执行上述实施例一中的非侵入式电动自行车电池识别技术。The present application provides an electric bicycle battery identification device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the non-invasive electric bicycle battery identification technology in the above-mentioned embodiment one.
下面参考图3，其示出了适于用来实现本申请实施例的电动自行车电池识别设备的结构示意图。本申请实施例中的电动自行车电池识别设备可以包括但不限于诸如移动电话、笔记本电脑、数字广播接收器、PDA（Personal Digital Assistant：个人数字助理）、PAD（Portable Application Description：平板电脑）、PMP（Portable Media Player：便携式多媒体播放器）、车载终端（例如车载导航终端）等等的移动终端以及诸如数字TV、台式计算机等等的固定终端。图3示出的电动自行车电池识别设备仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。Referring to FIG3 below, it shows a schematic diagram of the structure of an electric bicycle battery identification device suitable for implementing the embodiment of the present application. The electric bicycle battery identification device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electric bicycle battery identification device shown in FIG3 is only an example and should not bring any limitations to the functions and scope of use of the embodiments of the present application.
如图3所示，电动自行车电池识别设备可以包括处理装置1001（例如中央处理器、图形处理器等），其可以根据存储在只读存储器（ROM：Read Only Memory）1002中的程序或者从存储装置1003加载到随机访问存储器（RAM：Random Access Memory）1004中的程序而执行各种适当的动作和处理。在RAM1004中，还存储有电动自行车电池识别设备操作所需的各种程序和数据。处理装置1001、ROM1002以及RAM1004通过总线1005彼此相连。输入/输出（I/O）接口1006也连接至总线。通常，以下系统可以连接至I/O接口1006：包括例如触摸屏、触摸板、键盘、鼠标、图像传感器、麦克风、加速度计、陀螺仪等的输入装置1007；包括例如液晶显示器（LCD：Liquid Crystal Display）、扬声器、振动器等的输出装置1008；包括例如磁带、硬盘等的存储装置1003；以及通信装置1009。通信装置1009可以允许电动自行车电池识别设备与其他设备进行无线或有线通信以交换数据。虽然图中示出了具有各种系统的电动自行车电池识别设备，但是应理解的是，并不要求实施或具备所有示出的系统。可以替代地实施或具备更多或更少的系统。As shown in FIG3 , the electric bicycle battery identification device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the electric bicycle battery identification device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I/O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the electric bicycle battery identification device to communicate with other devices wirelessly or wired to exchange data. Although the electric bicycle battery identification device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.
特别地，根据本申请公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本申请公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置从网络上被下载和安装，或者从存储装置1003被安装，或者从ROM1002被安装。在该计算机程序被处理装置1001执行时，执行本申请公开实施例的方法中限定的上述功能。In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.
本申请提供的电动自行车电池识别设备，采用上述实施例中的非侵入式电动自行车电池识别技术，能解决所述的技术问题。与现有技术相比，本申请提供的电动自行车电池识别设备的有益效果与上述实施例提供的非侵入式电动自行车电池识别技术的有益效果相同，且该电动自行车电池识别设备中的其他技术特征与上一实施例方法公开的特征相同，在此不做赘述。The electric bicycle battery identification device provided by the present application adopts the non-invasive electric bicycle battery identification technology in the above embodiment to solve the technical problems. Compared with the prior art, the beneficial effects of the electric bicycle battery identification device provided by the present application are the same as the beneficial effects of the non-invasive electric bicycle battery identification technology provided by the above embodiment, and the other technical features of the electric bicycle battery identification device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.
应当理解，本申请公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式的描述中，具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
本申请提供一种计算机可读存储介质，具有存储在其上的计算机可读程序指令（即计算机程序），计算机可读程序指令用于执行上述实施例中的非侵入式电动自行车电池识别技术。The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the non-invasive electric bicycle battery identification technology in the above-mentioned embodiment.
本申请提供的计算机可读存储介质例如可以是U盘，但不限于电、磁、光、电磁、红外线、或半导体的系统、系统或器件，或者任意以上的组合。计算机可读存储介质的更具体地例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器（RAM：Random Access Memory）、只读存储器（ROM：Read Only Memory）、可擦式可编程只读存储器（EPROM：Erasable Programmable Read Only Memory或闪存）、光纤、便携式紧凑磁盘只读存储器（CD-ROM：CD-Read Only Memory）、光存储器件、磁存储器件、或者上述的任意合适的组合。在本实施例中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、系统或者器件使用或者与其结合使用。计算机可读存储介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF（Radio Frequency：射频）等等，或者上述的任意合适的组合。The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.
上述计算机可读存储介质可以是电动自行车电池识别设备中所包含的；也可以是单独存在，而未装配入电动自行车电池识别设备中。The computer-readable storage medium may be included in the electric bicycle battery identification device; or may exist independently without being assembled into the electric bicycle battery identification device.
上述计算机可读存储介质承载有一个或者多个程序，当上述一个或者多个程序被电动自行车电池识别设备执行时，使得电动自行车电池识别设备：The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electric bicycle battery identification device, the electric bicycle battery identification device:
获取用户用电网络的实时样本电流信号；Obtain real-time sample current signals from the user's power network;
对所述实时样本电流信号进行小波变换，获得所述实时样本电流信号各层的小波细节系数；Performing wavelet transform on the real-time sample current signal to obtain wavelet detail coefficients of each layer of the real-time sample current signal;
基于所述小波细节系数确定电动自行车电池的充电指示特征值；Determine a charging indication characteristic value of the electric bicycle battery based on the wavelet detail coefficient;
根据所述充电指示特征值进行电动自行车电池识别。The electric bicycle battery is identified according to the charging indication characteristic value.
可以以一种或多种程序设计语言或其组合来编写用于执行本申请的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网（LAN：Local Area Network）或广域网（WAN：Wide Area Network）—连接到用户计算机，或者，可以连接到外部计算机（例如利用因特网服务提供商来通过因特网连接）。Computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).
附图中的流程图和框图，图示了按照本申请各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and/or flow chart, and the combination of the boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
描述于本申请实施例中所涉及到的模块可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，模块的名称在某种情况下并不构成对该单元本身的限定。The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not, in some cases, constitute a limitation on the unit itself.
本申请提供的可读存储介质为计算机可读存储介质，所述计算机可读存储介质存储有用于执行上述非侵入式电动自行车电池识别技术的计算机可读程序指令（即计算机程序），能够解决所述的技术问题。与现有技术相比，本申请提供的计算机可读存储介质的有益效果与上述实施例提供的非侵入式电动自行车电池识别技术的有益效果相同，在此不做赘述。The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned non-invasive electric bicycle battery identification technology, and can solve the above-mentioned technical problems. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the non-invasive electric bicycle battery identification technology provided in the above-mentioned embodiment, and will not be repeated here.
本申请还提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述的非侵入式电动自行车电池识别技术的步骤。The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned non-invasive electric bicycle battery identification technology when executed by a processor.
本申请提供的计算机程序产品能够解决所述的技术问题。与现有技术相比，本申请提供的计算机程序产品的有益效果与上述实施例提供的非侵入式电动自行车电池识别技术的有益效果相同，在此不做赘述。The computer program product provided in this application can solve the technical problem. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the non-invasive electric bicycle battery identification technology provided in the above embodiment, which will not be repeated here.
以上所述仅为本申请的部分实施例，并非因此限制本申请的专利范围，凡是在本申请的技术构思下，利用本申请说明书及附图内容所作的等效结构变换，或直接/间接运用在其他相关的技术领域均包括在本申请的专利保护范围内。The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct/indirect applications in other related technical fields are included in the patent protection scope of the present application.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410979060.6ACN118534240B (en) | 2024-07-22 | 2024-07-22 | A non-intrusive electric bicycle battery identification technology |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410979060.6ACN118534240B (en) | 2024-07-22 | 2024-07-22 | A non-intrusive electric bicycle battery identification technology |
| Publication Number | Publication Date |
|---|---|
| CN118534240Atrue CN118534240A (en) | 2024-08-23 |
| CN118534240B CN118534240B (en) | 2024-11-22 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410979060.6AActiveCN118534240B (en) | 2024-07-22 | 2024-07-22 | A non-intrusive electric bicycle battery identification technology |
| Country | Link |
|---|---|
| CN (1) | CN118534240B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170351288A1 (en)* | 2016-06-07 | 2017-12-07 | Shenzhen Zhidian Energy Technology LTD | Non-invasive online real-time electric load identification method and identification system |
| CN109508908A (en)* | 2018-12-25 | 2019-03-22 | 深圳市城市公共安全技术研究院有限公司 | Non-invasive load recognition model training method, load monitoring method and device |
| CN111707908A (en)* | 2020-07-29 | 2020-09-25 | 中国科学技术大学先进技术研究院 | Method, device and storage medium for detecting arc fault in series with multiple load circuits |
| CN115754565A (en)* | 2022-11-30 | 2023-03-07 | 广西电网有限责任公司电力科学研究院 | Method and system for detecting potential safety hazard of electric bicycle during charging |
| WO2023030838A1 (en)* | 2021-08-31 | 2023-03-09 | Robert Bosch Gmbh | A non-intrusive load monitoring device for a site and method therefore |
| CN116359666A (en)* | 2023-03-02 | 2023-06-30 | 中国科学技术大学先进技术研究院 | Arc fault detection and identification method, system and arc fault detector |
| CN116578898A (en)* | 2023-04-19 | 2023-08-11 | 贵州电网有限责任公司 | A battery recognition method for electric bicycles based on fryze theory and 1DCNN |
| CN117471157A (en)* | 2023-11-02 | 2024-01-30 | 华北电力大学 | Non-invasive electric bicycle load charging event detection and extraction method |
| CN118311459A (en)* | 2023-02-28 | 2024-07-09 | 比亚迪股份有限公司 | Battery life prediction method, device, storage medium and equipment |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170351288A1 (en)* | 2016-06-07 | 2017-12-07 | Shenzhen Zhidian Energy Technology LTD | Non-invasive online real-time electric load identification method and identification system |
| CN109508908A (en)* | 2018-12-25 | 2019-03-22 | 深圳市城市公共安全技术研究院有限公司 | Non-invasive load recognition model training method, load monitoring method and device |
| CN111707908A (en)* | 2020-07-29 | 2020-09-25 | 中国科学技术大学先进技术研究院 | Method, device and storage medium for detecting arc fault in series with multiple load circuits |
| WO2023030838A1 (en)* | 2021-08-31 | 2023-03-09 | Robert Bosch Gmbh | A non-intrusive load monitoring device for a site and method therefore |
| CN115754565A (en)* | 2022-11-30 | 2023-03-07 | 广西电网有限责任公司电力科学研究院 | Method and system for detecting potential safety hazard of electric bicycle during charging |
| CN118311459A (en)* | 2023-02-28 | 2024-07-09 | 比亚迪股份有限公司 | Battery life prediction method, device, storage medium and equipment |
| CN116359666A (en)* | 2023-03-02 | 2023-06-30 | 中国科学技术大学先进技术研究院 | Arc fault detection and identification method, system and arc fault detector |
| CN116578898A (en)* | 2023-04-19 | 2023-08-11 | 贵州电网有限责任公司 | A battery recognition method for electric bicycles based on fryze theory and 1DCNN |
| CN117471157A (en)* | 2023-11-02 | 2024-01-30 | 华北电力大学 | Non-invasive electric bicycle load charging event detection and extraction method |
| Publication number | Publication date |
|---|---|
| CN118534240B (en) | 2024-11-22 |
| Publication | Publication Date | Title |
|---|---|---|
| CN112420055A (en) | Method and device for substation status recognition based on voiceprint feature | |
| CN118656709B (en) | Multi-space and multi-parameter fusion electric vehicle charging identification method | |
| CN110441594A (en) | Electric vehicle charging behavior monitoring method, device and computer readable storage medium | |
| CN114721835A (en) | Edge data center server energy consumption prediction method, system, device and medium | |
| WO2025113445A1 (en) | Low-voltage fault arc identification method and apparatus, medium and device | |
| CN112598027B (en) | Abnormality identification method and device for equipment, terminal equipment and storage medium | |
| CN118445734A (en) | Tokamak experimental data intelligent processing method and system | |
| CN108009068B (en) | Information recording method, information recording device and intelligent terminal | |
| CN118449279B (en) | Intelligent safety electricity management system based on cloud service | |
| CN118534240A (en) | A non-intrusive electric bicycle battery identification technology | |
| CN113869599A (en) | Fish disease development prediction method, system, equipment and medium | |
| CN118300280B (en) | Magnetic levitation type safety electric device and state detection method | |
| CN119535131A (en) | Fault arc detection method, device, equipment and storage medium | |
| CN118858723A (en) | Electricity theft detection method, electronic device and storage medium | |
| CN117518004A (en) | Method, device, equipment and storage medium for testing lithium precipitation probability of battery | |
| CN116160878B (en) | Battery replacement control system and method, computer readable medium and electronic equipment | |
| CN112560977A (en) | Non-invasive load decomposition method based on sparse classifier hierarchical algorithm | |
| CN117579461A (en) | Fault prediction method, device, equipment and storage medium | |
| CN116338492A (en) | Power battery state of charge determining method and related equipment | |
| CN116578898A (en) | A battery recognition method for electric bicycles based on fryze theory and 1DCNN | |
| CN114545067A (en) | Method and device for analyzing line loss of power consumption line, electronic equipment and storage medium | |
| CN110458743B (en) | Community management method, device, equipment and storage medium based on big data analysis | |
| CN112488535B (en) | Energy network security measurement method, system and data platform | |
| CN118865079A (en) | Battery vehicle detection method, device, equipment, storage medium and product based on camera | |
| CN118584270A (en) | Insulation failure warning method, device, equipment, storage medium and product |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |