CN115308674A - Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter - Google Patents

Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter Download PDF

Info

Publication number
CN115308674A
CN115308674A CN202210911537.8A CN202210911537A CN115308674A CN 115308674 A CN115308674 A CN 115308674A CN 202210911537 A CN202210911537 A CN 202210911537A CN 115308674 A CN115308674 A CN 115308674A
Authority
CN
China
Prior art keywords
epitope
state
electric energy
state evaluation
energy meter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210911537.8A
Other languages
Chinese (zh)
Inventor
邢宇
孙艳玲
董贤光
翟晓卉
孙凯
刘蜜
赵吉福
杨剑
杜艳
邹喜林
于鲁
秦娇
张松梅
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
Marketing Service Center of State Grid Shandong Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
Marketing Service Center of State Grid Shandong Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, Marketing Service Center of State Grid Shandong Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202210911537.8A priority Critical patent/CN115308674A/en
Publication of CN115308674A publication Critical patent/CN115308674A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/04Testing or calibrating of apparatus covered by the other groups of this subclass of instruments for measuring time integral of power or current

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a method and a system for evaluating the epitope running state of an electric energy meter automatic verification assembly line, which comprises the following steps: acquiring historical test data of the epitopes on the verification production line under different running states, removing batch effects of the historical test data on the basis of normal epitope data, and constructing feature vectors of the epitopes under different running states; constructing a twin neural network, updating network parameters by setting boundary values when the twin neural network is trained based on the feature vectors of all epitopes, and constructing a state evaluation model by introducing a state judgment threshold; performing parameter optimization on the state evaluation model by adopting an Ulva gull optimization algorithm and taking the model evaluation reliability as a fitness function to obtain an optimized state evaluation model; and performing state evaluation on the epitope to be tested by adopting the optimized state evaluation model. The method effectively identifies the abnormal state of the epitope caused by performance degradation, and solves the problems of insufficient reliability and high labor cost in the traditional manual investigation.

Description

电能表自动化检定流水线表位运行状态评估方法及系统Method and system for evaluating the operating status of electric energy meter automatic verification assembly line meter position

技术领域technical field

本发明涉及电力计量在线监测技术领域,特别是涉及一种电能表自动化检定流水线表位运行状态评估方法及系统。The invention relates to the technical field of on-line monitoring of electric power metering, in particular to a method and system for evaluating the running state of a meter position of an automatic verification assembly line of an electric energy meter.

背景技术Background technique

本部分的陈述仅仅是提供了与本发明相关的背景技术信息,不必然构成在先技术。The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

电能表自动化检定流水线为智能电能表的正常运行提供保障,然而流水线的长期运行过程中,智能电能表频繁接入表位,会导致表位机械压接端子出现形变;同时长期带电运行会加速机械压接端子表面材料的氧化速度,导致端子锈蚀。表位机械压接环节的形变与锈蚀将直接影响误差试验结果的可靠性,进而影响智能电能表的检定质量。而目前人工定期检测方法无法及时响应流水线运维间隔中出现的异常工况。The automatic verification assembly line of the electric energy meter provides guarantee for the normal operation of the smart electric energy meter. However, during the long-term operation of the assembly line, the frequent access of the intelligent electric energy meter to the meter position will cause deformation of the mechanical crimping terminal of the meter position; at the same time, long-term electrified operation will accelerate the mechanical The rate of oxidation of the material on the surface of the crimped terminal, causing the terminal to corrode. The deformation and corrosion of the mechanical crimping link of the meter will directly affect the reliability of the error test results, and then affect the verification quality of the smart energy meter. However, the current manual periodic detection method cannot respond in time to the abnormal working conditions that occur during the operation and maintenance interval of the pipeline.

发明内容Contents of the invention

为了解决上述问题,本发明提出了一种电能表自动化检定流水线表位运行状态评估方法及系统,有效识别表位由于性能退化引起的异常状态,克服传统人工排查存在可靠性不足、人力成本高的问题,实现检定流水线表位状态的在线判别。In order to solve the above problems, the present invention proposes a method and system for evaluating the operation state of epitopes in the automatic verification pipeline of electric energy meters, which can effectively identify the abnormal state of epitopes caused by performance degradation, and overcome the problems of insufficient reliability and high labor costs in traditional manual inspections. To solve the problem, realize the online discrimination of the epitope status of the verification pipeline.

为了实现上述目的,本发明采用如下技术方案:In order to achieve the above object, the present invention adopts the following technical solutions:

第一方面,本发明提供一种电能表自动化检定流水线表位运行状态评估方法,包括:In the first aspect, the present invention provides a method for evaluating the operating state of the electric energy meter automatic verification assembly line, including:

获取检定流水线上表位在不同运行状态下的历史试验数据,以正常表位数据为基础,对历史试验数据进行批次效应去除,并构建不同运行状态下各表位的特征向量;Obtain the historical test data of the epitope on the verification pipeline under different operating states, remove the batch effect on the historical test data based on the normal epitope data, and construct the feature vector of each epitope under different operating states;

构建孪生神经网络,基于各表位的特征向量对孪生神经网络进行训练时通过设定边界值更新网络参数,且通过引入状态判断阈值,构建状态评估模型;Construct a twin neural network, update the network parameters by setting boundary values when training the twin neural network based on the eigenvectors of each epitope, and build a state evaluation model by introducing a state judgment threshold;

采用乌燕鸥优化算法,以模型评估可靠度为适应度函数,对状态评估模型进行参数寻优,得到优化后的状态评估模型;Using the black tern optimization algorithm, taking the model evaluation reliability as the fitness function, the parameters of the state evaluation model are optimized, and the optimized state evaluation model is obtained;

采用优化后的状态评估模型,对待测表位进行状态评估。The optimized state evaluation model is used to evaluate the state of the epitope to be tested.

作为可选择的实施方式,以正常表位数据为基础,采用平均中心法进行批次效应去除;As an optional implementation, based on the normal epitope data, the average center method is used to remove the batch effect;

Figure BDA0003774179560000021
Figure BDA0003774179560000021

其中,εik为第i个表位第k项试验的试验结果,

Figure BDA0003774179560000022
为第i个表位第k项试验批次效应去除后的试验结果;
Figure BDA0003774179560000023
为第k项试验批次效应的误差。Among them, εik is the test result of the i-th epitope k-th test,
Figure BDA0003774179560000022
is the test result after removing the test batch effect of the i-th epitope and the k-th test batch effect;
Figure BDA0003774179560000023
is the error of the batch effect of the kth test.

作为可选择的实施方式,所述孪生神经网络采用对比损失函数Lloss进行训练:As an optional implementation, the Siamese neural network is trained using a contrastive loss function L loss :

Figure BDA0003774179560000024
Figure BDA0003774179560000024

其中,margin为边界值,D(x1,x2)为样本x1、x2的相似性度量,y为支撑集对应标签,Q为支撑集数量。Among them, margin is the boundary value, D(x 1 , x 2 ) is the similarity measure of samples x 1 and x 2 , y is the label corresponding to the support set, and Q is the number of support sets.

作为可选择的实施方式,所述适应度函数K为:As an optional implementation manner, the fitness function K is:

Figure BDA0003774179560000025
Figure BDA0003774179560000025

Figure BDA0003774179560000031
Figure BDA0003774179560000031

Figure BDA0003774179560000032
Figure BDA0003774179560000032

其中,H0为观察符合率,a1、a2、a3分别代表实际为正常、告警、异常的样本经预测为正常、告警、异常的样本个数,S为总样本个数,He表示机遇符合率,b1、b2、b3分别为正常、告警、异常样本的真实个数,c1、c2、c3分别为预测为正常、告警、异常的样本个数。Among them, H 0 is the observed coincidence rate, a 1 , a 2 , and a 3 respectively represent the number of samples that are actually normal, warning, and abnormal samples that are predicted to be normal, warning, and abnormal samples, S is the total number of samples, and He Indicates the chance coincidence rate, b 1 , b 2 , b 3 are the real numbers of normal, warning, and abnormal samples respectively, and c 1 , c 2 , c 3 are the numbers of predicted normal, warning, and abnormal samples, respectively.

作为可选择的实施方式,所述参数寻优过程是优化边界值和状态判断阈值。As an optional implementation manner, the parameter optimization process is to optimize boundary values and state judgment thresholds.

作为可选择的实施方式,所述乌燕鸥优化算法中提出一种非线性控制参数A:As an optional implementation, a nonlinear control parameter A is proposed in the black tern optimization algorithm:

Figure BDA0003774179560000033
Figure BDA0003774179560000033

其中,fc为控制A频率的参数,r为迭代次数,R为最大迭代次数。Among them, f c is the parameter to control the frequency of A, r is the number of iterations, and R is the maximum number of iterations.

作为可选择的实施方式,将待测表位的待测样本与已知状态样本生成待测样本对,计算待测样本与已知状态样本的相似度,取各运行状态下的相似度均值作为最终相似度,根据最终相似度与状态判断阈值进行待测样本的表位状态判断。As an optional implementation, the sample to be tested and the known state sample of the epitope to be tested are used to generate a sample pair to be tested, the similarity between the sample to be tested and the known state sample is calculated, and the average value of the similarity under each operating state is taken as For the final similarity, the epitope status of the sample to be tested is judged according to the final similarity and the status judgment threshold.

第二方面,本发明提供一种电能表自动化检定流水线表位运行状态评估系统,包括:In the second aspect, the present invention provides a system for evaluating the operating state of the electric energy meter automatic verification assembly line, including:

训练集获取模块,被配置为获取检定流水线上表位在不同运行状态下的历史试验数据,以正常表位数据为基础,对历史试验数据进行批次效应去除,并构建不同运行状态下各表位的特征向量;The training set acquisition module is configured to obtain the historical test data of epitopes in different operating states on the verification pipeline, remove the batch effect on the historical test data based on the normal epitope data, and construct each table under different operating states bit eigenvector;

模型构建模块,被配置为构建孪生神经网络,基于各表位的特征向量对孪生神经网络进行训练时通过设定边界值更新网络参数,且通过引入状态判断阈值,构建状态评估模型;The model building module is configured to construct a twin neural network, update the network parameters by setting boundary values when training the twin neural network based on the feature vectors of each epitope, and construct a state evaluation model by introducing a state judgment threshold;

参数寻优模块,被配置为采用乌燕鸥优化算法,以模型评估可靠度为适应度函数,对状态评估模型进行参数寻优,得到优化后的状态评估模型;The parameter optimization module is configured to use the black tern optimization algorithm and use the model evaluation reliability as the fitness function to optimize the parameters of the state evaluation model to obtain the optimized state evaluation model;

状态评估模块,被配置为采用优化后的状态评估模型,对待测表位进行状态评估。The status evaluation module is configured to use the optimized status evaluation model to evaluate the status of the epitope to be tested.

第三方面,本发明提供一种电子设备,包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令,所述计算机指令被处理器运行时,完成第一方面所述的方法。In a third aspect, the present invention provides an electronic device, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are executed by the processor, the method described in the first aspect is completed. .

第四方面,本发明提供一种计算机可读存储介质,用于存储计算机指令,所述计算机指令被处理器执行时,完成第一方面所述的方法。In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the method described in the first aspect is completed.

与现有技术相比,本发明的有益效果为:Compared with prior art, the beneficial effect of the present invention is:

本发明提出的一种电能表自动化检定流水线表位运行状态评估方法及系统,采用孪生神经网络对电能表自动化检定流水线表位的运行状态进行判别,解决了小样本学习问题。The invention proposes a method and system for evaluating the running state of the automatic verification assembly line of electric energy meters, which uses a twin neural network to distinguish the operating state of the electric energy meter automatic verification assembly line, and solves the problem of small sample learning.

本发明提出的一种电能表自动化检定流水线表位运行状态评估方法及系统,采用乌燕鸥优化算法对孪生深度神经网络与状态判断机制引入的超参数边界值和阈值进行迭代寻优,提高模型评估的准确性与可靠性。The present invention proposes a method and system for evaluating the operating state of an electric energy meter automatic verification assembly line. The black tern optimization algorithm is used to iteratively optimize the hyperparameter boundary value and threshold introduced by the twin deep neural network and the state judgment mechanism, and the model is improved. Accuracy and reliability of assessments.

本发明提出的一种电能表自动化检定流水线表位运行状态评估方法及系统,采用批次效应消除,消除不同批次的电能表可能存在试验数据的分布差异,构建了以误差期望为时序特征的特征向量。The present invention proposes a method and system for evaluating the running state of the electric energy meter automatic verification assembly line, adopts batch effect elimination, eliminates the distribution difference of test data that may exist in different batches of electric energy meters, and constructs a time-series feature with error expectation Feature vector.

本发明附加方面的优点将在下面的描述中部分给出,部分将从下面的描述中变得明显,或通过本发明的实践了解到。Advantages of additional aspects of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

附图说明Description of drawings

构成本发明的一部分的说明书附图用来提供对本发明的进一步理解,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations to the present invention.

图1为本发明实施例1提供的电能表自动化检定流水线表位运行状态评估方法流程图;Fig. 1 is the flow chart of the method for evaluating the running state of the electric energy meter automatic verification assembly line meter position provided by Embodiment 1 of the present invention;

图2为本发明实施例1提供的状态评估模型图。FIG. 2 is a state assessment model diagram provided by Embodiment 1 of the present invention.

具体实施方式Detailed ways

下面结合附图与实施例对本发明做进一步说明。The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

应该指出,以下详细说明都是示例性的,旨在对本发明提供进一步的说明。除非另有指明,本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本发明的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,术语“包括”和“具有”以及他们的任何变形,意图在于覆盖不排他的包含,例如,包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元,而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。It should be noted that the terminology used here is only for describing specific embodiments, and is not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that the terms "comprising" and "having" and any variations thereof are intended to cover a non-exclusive Comprising, for example, a process, method, system, product, or device comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include steps or units not explicitly listed or for these processes, methods, Other steps or units inherent in a product or equipment.

在不冲突的情况下,本发明中的实施例及实施例中的特征可以相互组合。In the case of no conflict, the embodiments and the features in the embodiments of the present invention can be combined with each other.

实施例1Example 1

本实施例提出一种电能表自动化检定流水线表位运行状态评估方法,如图1所示,包括:This embodiment proposes a method for evaluating the running status of the electric energy meter automatic verification assembly line, as shown in Figure 1, including:

获取检定流水线上表位在不同运行状态下的历史试验数据,以正常表位数据为基础,对历史试验数据进行批次效应去除,并构建不同运行状态下各表位的特征向量;Obtain the historical test data of the epitope on the verification pipeline under different operating states, remove the batch effect on the historical test data based on the normal epitope data, and construct the feature vector of each epitope under different operating states;

构建孪生神经网络,基于各表位的特征向量对孪生神经网络进行训练时通过设定边界值更新网络参数,且通过引入状态判断阈值,构建状态评估模型;Construct a twin neural network, update the network parameters by setting boundary values when training the twin neural network based on the eigenvectors of each epitope, and build a state evaluation model by introducing a state judgment threshold;

采用乌燕鸥优化算法,以模型评估可靠度为适应度函数,对状态评估模型进行参数寻优,得到优化后的状态评估模型;Using the black tern optimization algorithm, taking the model evaluation reliability as the fitness function, the parameters of the state evaluation model are optimized, and the optimized state evaluation model is obtained;

采用优化后的状态评估模型,对待测表位进行状态评估。The optimized state evaluation model is used to evaluate the state of the epitope to be tested.

在本实施例中,所述运行状态包括正常、告警、异常。考虑到不同批次的电能表可能存在试验数据的分布差异,为消除由批次差异带来的影响,对历史试验数据进行批次效应去除处理,具体为:In this embodiment, the running status includes normal, warning and abnormal. Considering that different batches of electric energy meters may have differences in the distribution of test data, in order to eliminate the impact of batch differences, batch effect removal processing is performed on historical test data, specifically:

以正常表位数据为基础,采用“平均中心方法”进行批次效应去除:Based on the normal epitope data, the "average center method" was used to remove the batch effect:

Figure BDA0003774179560000061
Figure BDA0003774179560000061

其中,εik为第i个表位第k项误差试验测得的误差试验结果,

Figure BDA0003774179560000062
为第i个表位第k项误差试验批次效应去除后的误差试验结果;Among them, εik is the error test result measured by the i-th epitope k-th item error test,
Figure BDA0003774179560000062
is the error test result after removing the batch effect of the i-th epitope k-th error test;

Figure BDA0003774179560000063
Figure BDA0003774179560000063

其中,

Figure BDA0003774179560000064
为误差试验批次效应的误差,n为同一检定单元中正常表位的数量,εjk为第j个表位第k项误差试验测得的误差结果,其中每个表位进行k项误差试验,k=1,2,...,10。in,
Figure BDA0003774179560000064
is the error of the batch effect of the error test, n is the number of normal epitopes in the same test unit, εjk is the error result measured by the j-th epitope k-item error test, and each epitope is subjected to k-item error tests , k=1,2,...,10.

在本实施例中,基于批次效应去除后的试验结果,以T天的数据为时间窗口,构建正常、告警、异常等运行状态下各表位的时序特征向量x;In this embodiment, based on the test results after the batch effect is removed, the time-series feature vector x of each epitope under normal, alarm, abnormal and other operating states is constructed with the data of T days as the time window;

Figure BDA0003774179560000071
Figure BDA0003774179560000071

Figure BDA0003774179560000072
Figure BDA0003774179560000072

Figure BDA0003774179560000073
Figure BDA0003774179560000073

其中,m为表位i检测的电能表数量,

Figure BDA0003774179560000074
表示表位i对第v个电能表进行第k项误差试验得到的批次效应去除后的误差结果,t为当前时刻,T天的数据为t与其之前的T-1天共同构成;Among them, m is the number of electric energy meters detected by episite i,
Figure BDA0003774179560000074
Represents the error result after the batch effect is removed from the kth error test of the vth electric energy meter by episite i, t is the current moment, and the data of T days is composed of t and the previous T-1 day;

Figure BDA0003774179560000075
为第t天表位i对m个电能表进行第k项误差试验而计算得到的误差期望,
Figure BDA0003774179560000076
为T-u天前当天表位i对m个电能表进行第k项误差试验而计算得到的误差期望。
Figure BDA0003774179560000075
is the error expectation calculated by performing the k-th error test on m electric energy meters for the meter position i on the t-th day,
Figure BDA0003774179560000076
It is the error expectation calculated by performing the k-th error test on m electric energy meters for meter position i on the day before Tu.

在本实施例中,以各表位的特征向量为训练集构建孪生神经网络,状态评估模型包括孪生神经网络和阈值判断模块;如图2所示。In this embodiment, the Siamese neural network is constructed with the feature vectors of each epitope as the training set, and the state evaluation model includes the Siamese neural network and a threshold judgment module; as shown in FIG. 2 .

孪生神经网络包括两个相同模型参数的子神经网络,其目的是衡量输入样本经子神经网络所提取特征向量后的相似性,使子神经网络提取的特征更具备判别性;具体地:The twin neural network includes two sub-neural networks with the same model parameters. Its purpose is to measure the similarity of the input samples after the feature vectors extracted by the sub-neural network, so that the features extracted by the sub-neural network are more discriminative; specifically:

(1)基于批次效应去除后的流水线各表位的特征向量,构建输入样本对(x1,x2);(1) Construct an input sample pair (x 1 , x 2 ) based on the feature vector of each epitope in the pipeline after the batch effect is removed;

基于批次效应去除后的流水线各表位的特征向量构建训练集和测试集,从训练集中选取部分样本构建样本对形成支持集,对于支持集中每个样本,又分别在同类数据中和异类数据中随机选择一个数据组成数据对,进而构成支撑集;Based on the eigenvectors of each epitope in the pipeline after the batch effect is removed, the training set and the test set are constructed, and some samples are selected from the training set to construct sample pairs to form a support set. Randomly select a data to form a data pair, and then form a support set;

支撑集的数据组成形式有:正常和正常、正常和告警、正常和异常、告警和告警、告警和异常、异常和异常6种,以此得到输入样本对(x1,x2)。The data composition forms of the support set are: normal and normal, normal and warning, normal and abnormal, warning and warning, warning and abnormal, abnormal and abnormal, so as to obtain the input sample pair (x 1 , x 2 ).

(2)孪生神经网络中的Network模块采用1-D CNN-LSTM子网络模型,基于1-D CNN-LSTM子网络模型,得到输入样本对(x1,x2)的特征向量G(x1)和G(x2):(2) The Network module in the twin neural network adopts the 1-D CNN-LSTM sub-network model, based on the 1-D CNN-LSTM sub-network model, the feature vector G(x 1 of the input sample pair (x 1 , x 2 ) is obtained ) and G(x 2 ):

将样本x1、x2作为输入数据,分别输入特征提取模型中的两个相同神经网络模块Network中,这两个神经网络模块有共享参数权值W和偏置b,特征提取模块将两个输入样本x1、x2分别映射到子网络相同特征空间中,得到两个输入样本x1、x2的特征向量G(x1)、G(x2)。Take samples x 1 and x 2 as input data, and input them into two identical neural network modules Network in the feature extraction model respectively. These two neural network modules have shared parameter weight W and bias b, and the feature extraction module takes the two The input samples x 1 and x 2 are respectively mapped to the same feature space of the sub-network, and the feature vectors G(x 1 ) and G(x 2 ) of the two input samples x 1 and x 2 are obtained.

(3)相似度计算;(3) Similarity calculation;

相似度度量是用于计算样本对中两个样本特征向量之间的距离,以评定两个样本之间相似程度的一种度量,本实施例采用欧式距离,输入样本对(x1,x2)相似性度量为:The similarity measure is used to calculate the distance between two sample feature vectors in a sample pair to evaluate the similarity between two samples. In this embodiment, the Euclidean distance is used, and the input sample pair (x 1 , x 2 ) similarity measure is:

D(x1,x2)=||G(x1)-G(x2)|| (6)D(x 1 ,x 2 )=||G(x 1 )-G(x 2 )|| (6)

(4)孪生神经网络使用对比损失函数Lloss进行模型训练:(4) The twin neural network uses the contrastive loss function L loss for model training:

Figure BDA0003774179560000081
Figure BDA0003774179560000081

其中,margin为设定的边界值,当D(x1,x2)值小于边界值margin时,需要对孪生神经网络的权值W进行调整;y为支撑集对应标签,Q为支撑集数量。Among them, margin is the set boundary value. When the value of D(x 1 , x 2 ) is less than the boundary value margin, the weight W of the twin neural network needs to be adjusted; y is the label corresponding to the support set, and Q is the number of support sets .

当输入样本是两个子网络输入类别相同的样本时,对应标签为1,使最后一层特征映射向量的G(x1)和G(x2)距离函数尽可能相近;当输入样本为不同类别样本时,对应标签为0,使得特征向量距离函数尽可能远离。When the input samples are samples of the same input category of the two sub-networks, the corresponding label is 1, so that the G(x 1 ) and G(x 2 ) distance functions of the feature map vectors of the last layer are as close as possible; when the input samples are of different categories When sample, the corresponding label is 0, so that the feature vector distance function is as far away as possible.

在本实施例中,为实现表位状态的判断,在孪生神经网络后接入阈值判断模块,通过状态判断阈值β,完成待测样本的表位状态判断;In this embodiment, in order to realize the judgment of the epitope state, the threshold judgment module is connected after the twin neural network, and the judgment of the epitope state of the sample to be tested is completed through the state judgment threshold β;

将待测样本与已知状态样本生成待测样本对,将其输入到孪生神经网络中分别计算待测样本与已知状态样本的相似度,取各状态下的样本相似度均值作为最终的样本相似度DW;若DW<β,则为同类;反之,则为异类。The sample to be tested and the known state sample are used to generate a sample pair to be tested, which is input into the twin neural network to calculate the similarity between the sample to be tested and the known state sample, and the average value of the sample similarity in each state is taken as the final sample Similarity D W ; if D W <β, they are of the same type; otherwise, they are of the same type.

在本实施例中,基于孪生神经网络与状态判断机制构建的状态评估模型,引入了两个新的超参数margin、β,两者的变化对状态评估模型的评估性能具有较大的影响,因此本实施例采用乌燕鸥优化算法,以模型评估的可靠度为适应度函数,对margin、β进行迭代寻优,以提高模型状态评估的可靠性。In this embodiment, two new hyperparameters, margin and β, are introduced into the state evaluation model based on the twin neural network and the state judgment mechanism. The changes of the two have a greater impact on the evaluation performance of the state evaluation model, so In this embodiment, the sooty tern optimization algorithm is adopted, and the reliability of the model evaluation is used as the fitness function to iteratively optimize the margin and β, so as to improve the reliability of the model state evaluation.

将乌燕鸥的初始位置映射为边界值margin和状态判断阈值β,步骤如下:To map the initial position of the sooty tern to the boundary value margin and the state judgment threshold β, the steps are as follows:

(1)以模型评估的可靠度K作为适应度函数:(1) Take the reliability K of model evaluation as the fitness function:

Figure BDA0003774179560000091
Figure BDA0003774179560000091

其中,H0为观察符合率,

Figure BDA0003774179560000092
a1、a2、a3分别代表实际为正常、告警、异常的样本经过模型预测为正常、告警、异常的样本的个数,S为总样本个数;He表示机遇符合率,
Figure BDA0003774179560000093
其中b1、b2、b3分别为正常、告警、异常样本的真实个数,c1、c2、c3分别为模型预测为正常、告警、异常的样本个数。Among them, H0 is the observed coincidence rate,
Figure BDA0003774179560000092
a 1 , a 2 , and a 3 respectively represent the number of samples that are actually normal, warning, and abnormal after being predicted by the model to be normal, warning, and abnormal. S is the total number of samples; He represents the chance coincidence rate,
Figure BDA0003774179560000093
Among them, b 1 , b 2 , and b 3 are the real numbers of normal, warning, and abnormal samples, respectively, and c 1 , c 2 , and c 3 are the numbers of normal, warning, and abnormal samples predicted by the model.

(2)乌燕鸥优化算法(STO),迁徙和攻击猎物是乌燕鸥的独特行为,在迁徙过程中,乌燕鸥向群体中最强的乌燕鸥移动,然后,其他乌燕鸥开始更新它们的初始位置,需要避免乌燕鸥间的碰撞;具体的,乌燕鸥优化算法(STO)参数的更新方法如下:(2) Sooty Tern Optimization Algorithm (STO), migration and attacking prey are unique behaviors of sooty terns, during migration, sooty terns move to the strongest sooty tern in the group, then, other sooty terns start To update their initial positions, it is necessary to avoid collisions between sooty terns; specifically, the update method of sooty tern optimization algorithm (STO) parameters is as follows:

①初始化种群规模num,最大迭代次数R,随机初始化乌燕鸥的初始位置;① Initialize the population size num, the maximum number of iterations R, and randomly initialize the initial position of the sooty tern;

②评价适应度函数:根据适应度函数计算每只乌燕鸥位置的适应度,通过比较找出最优适应度值,确定种群最佳位置Pbest(r);②Evaluate the fitness function: Calculate the fitness of each sooty tern's position according to the fitness function, find out the optimal fitness value by comparison, and determine the best position P best (r) of the population;

③乌燕鸥进行迁移操作:③The sooty tern performs the migration operation:

Figure BDA0003774179560000101
Figure BDA0003774179560000101

其中,Cs表示不会和其他燕鸥碰撞的新位置;Ps(r)表示乌燕鸥当前所在位置,A为乌燕鸥在给定空间的运动方式;r表示迭代的次数;R表示最大迭代次数;fc用来控制A的频率,这里fc取值为2,控制A从2下降到0;B是一个随机变量;Ms是当前位置向最优位置移动的过程;Pbest(r)是乌燕鸥的全局最优位置;randvalue是一个介于0和1之间的随机数;Ls是当前位置向最优位置更新的轨迹。Among them, C s represents the new position that will not collide with other terns; P s (r) represents the current position of the sooty tern, A is the movement mode of the sooty tern in a given space; r represents the number of iterations; R represents The maximum number of iterations; f c is used to control the frequency of A, where f c takes a value of 2 to control A from 2 to 0; B is a random variable; M s is the process of moving the current position to the optimal position; P best (r) is the global optimal position of the sooty tern; randvalue is a random number between 0 and 1; L s is the trajectory of updating the current position to the optimal position.

然而,线性控制参数不能表征实际收敛过程,实际收敛过程是非线性的,由此,本实施例提出一种非线性控制参数A:However, the linear control parameters cannot characterize the actual convergence process, which is nonlinear. Therefore, this embodiment proposes a nonlinear control parameter A:

Figure BDA0003774179560000102
Figure BDA0003774179560000102

该方法中A的值在递减的过程中呈现一个非线性的变化趋势,可以更好的改善全局寻优能力,每次迭代既能避免乌燕鸥之间的位置冲突,也可以更好的平衡探索与开发。In this method, the value of A presents a non-linear change trend in the process of decreasing, which can better improve the global optimization ability. Each iteration can not only avoid the position conflict between black terns, but also better balance Explore and develop.

④乌燕鸥进行攻击操作:④ Black tern attack operation:

乌燕鸥在迁徙过程中可以通过翅膀增加飞行高度,也可以调整速度和攻击角度,攻击猎物时,乌燕鸥在空中的盘旋行为可被定义:Sooty terns can increase flight height through wings during migration, and can also adjust speed and attack angle. When attacking prey, the sooty tern's hovering behavior in the air can be defined:

Figure BDA0003774179560000111
Figure BDA0003774179560000111

其中,x′,y′,z′为模拟燕鸥在三维空间中盘旋的位置,λ是盘旋螺旋的半径;θ是[0,2π]的随机角度;w0

Figure BDA0003774179560000112
ω是定义螺旋形状的常数,本实施例设置为1,e是自然对数的底数;Among them, x′, y′, z′ are the positions of simulated terns circling in three-dimensional space, λ is the radius of the spiral; θ is a random angle in [0,2π]; w 0 ,
Figure BDA0003774179560000112
ω is the constant that defines spiral shape, and the present embodiment is set to 1, and e is the base number of natural logarithm;

⑤乌燕鸥更新位置:⑤The update location of Black Tern:

Ps(r+1)=(Ls×(x′+y′+z′))×Pbest(r) (12)P s (r+1)=(L s ×(x′+y′+z′))×P best (r) (12)

其中,Ps(r+1)为乌燕鸥更新后的位置,Ls是当前位置向最优位置更新的轨迹,Pbest(r)是种群最佳位置;Among them, P s (r+1) is the updated position of the sooty tern, L s is the trajectory of updating the current position to the optimal position, and P best (r) is the best position of the population;

⑥计算各乌燕鸥个体位置的适应度值,并记录全局最优值:⑥ Calculate the fitness value of each sooty tern individual position, and record the global optimal value:

Figure BDA0003774179560000113
Figure BDA0003774179560000113

其中,

Figure BDA0003774179560000114
表示乌燕鸥位置更新后模型评估的可靠度,
Figure BDA0003774179560000115
为乌燕鸥位置更新后的观察符合率,
Figure BDA0003774179560000116
a1更、a2更、a3更分别为乌燕鸥位置更新后实际为正常、告警、异常的样本经过模型预测为正常、告警、异常的样本的个数,S为乌燕鸥位置更新后的总样本个数;
Figure BDA0003774179560000117
表示乌燕鸥位置更新后的机遇符合率,
Figure BDA0003774179560000121
其中b1更、b2更、b3更分别为正常、告警、异常样本的真实个数,c1更、c2更、c3更分别为模型预测为正常、告警、异常的样本个数。in,
Figure BDA0003774179560000114
Indicates the reliability of the model evaluation after the Sooty Tern's position is updated,
Figure BDA0003774179560000115
is the observed coincidence rate after the location update of the sooty tern,
Figure BDA0003774179560000116
a 1 update , a 2 update , and a 3 update are respectively the number of samples that are actually normal, warning, and abnormal after the position update of the sooty tern, and the number of samples that are predicted to be normal, warning, and abnormal by the model, and S is the position of the sooty tern The total number of samples after the update;
Figure BDA0003774179560000117
Indicates the chance coincidence rate after the Sooty Tern's position is updated,
Figure BDA0003774179560000121
Among them, b 1 update , b 2 update , and b 3 update are the real numbers of normal, warning, and abnormal samples respectively, and c 1 update , c 2 update , and c 3 update are the number of samples predicted to be normal, warning, and abnormal by the model respectively. .

式(13)为将乌燕鸥更新后(即r+1次迭代时)的个体位置,带入式(8),算出每个个体的适应度,选出最优适应度的具体计算过程。Equation (13) is the specific calculation process of taking the updated individual position of Sooty Tern (that is, at the time of r+1 iterations) into Equation (8) to calculate the fitness of each individual and select the optimal fitness.

求出乌燕鸥群体中适应度值的全局最优值,并记录位置:Find the global optimum of fitness values in a population of sooty terns, and record the location:

[Kbest Pbest(r+1)]=max(K) (14)[K best P best (r+1)]=max(K) (14)

⑦判断可靠度是否大于设定阈值:若是,则以当前乌燕鸥位置映射的边界值margin、阈值β作为模型的最佳超参数;若否,则更新乌燕鸥优化算法的参数及最佳位置,重复步骤③-⑦,直到满足最大迭代次数。⑦Judge whether the reliability is greater than the set threshold: if yes, use the boundary value margin and threshold β of the current sooty tern position map as the best hyperparameters of the model; if not, update the parameters of the sooty tern optimization algorithm and the best position, repeat steps ③-⑦ until the maximum number of iterations is met.

在本实施例中,基于训练后的状态评估模型,以待测表位的特征向量为输入,对流水线各表位进行状态判别。In this embodiment, based on the trained state evaluation model, the feature vector of the epitope to be tested is used as input, and the state of each epitope in the pipeline is discriminated.

实施例2Example 2

本实施例提供一种电能表自动化检定流水线表位运行状态评估系统,包括:This embodiment provides a system for evaluating the running state of the electric energy meter automatic verification assembly line, including:

训练集获取模块,被配置为获取检定流水线上表位在不同运行状态下的历史试验数据,以正常表位数据为基础,对历史试验数据进行批次效应去除,并构建不同运行状态下各表位的特征向量;The training set acquisition module is configured to obtain the historical test data of epitopes in different operating states on the verification pipeline, remove the batch effect on the historical test data based on the normal epitope data, and construct each table under different operating states bit eigenvector;

模型构建模块,被配置为构建孪生神经网络,基于各表位的特征向量对孪生神经网络进行训练时通过设定边界值更新网络参数,且通过引入状态判断阈值,构建状态评估模型;The model building module is configured to construct a twin neural network, update the network parameters by setting boundary values when training the twin neural network based on the feature vectors of each epitope, and construct a state evaluation model by introducing a state judgment threshold;

参数寻优模块,被配置为采用乌燕鸥优化算法,以模型评估可靠度为适应度函数,对状态评估模型进行参数寻优,得到优化后的状态评估模型;The parameter optimization module is configured to use the black tern optimization algorithm and use the model evaluation reliability as the fitness function to optimize the parameters of the state evaluation model to obtain the optimized state evaluation model;

状态评估模块,被配置为采用优化后的状态评估模型,对待测表位进行状态评估。The status evaluation module is configured to use the optimized status evaluation model to evaluate the status of the epitope to be tested.

此处需要说明的是,上述模块对应于实施例1中所述的步骤,上述模块与对应的步骤所实现的示例和应用场景相同,但不限于上述实施例1所公开的内容。需要说明的是,上述模块作为系统的一部分可以在诸如一组计算机可执行指令的计算机系统中执行。It should be noted here that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that, as a part of the system, the above-mentioned modules can be executed in a computer system such as a set of computer-executable instructions.

在更多实施例中,还提供:In further embodiments, there is also provided:

一种电子设备,包括存储器和处理器以及存储在存储器上并在处理器上运行的计算机指令,所述计算机指令被处理器运行时,完成实施例1中所述的方法。为了简洁,在此不再赘述。An electronic device includes a memory, a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the method described in Embodiment 1 is completed. For the sake of brevity, details are not repeated here.

应理解,本实施例中,处理器可以是中央处理单元CPU,处理器还可以是其他通用处理器、数字信号处理器DSP、专用集成电路ASIC,现成可编程门阵列FPGA或者其他可编程逻辑器件、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。It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

存储器可以包括只读存储器和随机存取存储器,并向处理器提供指令和数据、存储器的一部分还可以包括非易失性随机存储器。例如,存储器还可以存储设备类型的信息。The memory may include read-only memory and random access memory, and provide instructions and data to the processor, and a part of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

一种计算机可读存储介质,用于存储计算机指令,所述计算机指令被处理器执行时,完成实施例1中所述的方法。A computer-readable storage medium is used for storing computer instructions, and when the computer instructions are executed by a processor, the method described in Embodiment 1 is completed.

实施例1中的方法可以直接体现为硬件处理器执行完成,或者用处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器、闪存、只读存储器、可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器,处理器读取存储器中的信息,结合其硬件完成上述方法的步骤。为避免重复,这里不再详细描述。The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software module may be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware. To avoid repetition, no detailed description is given here.

本领域普通技术人员可以意识到,结合本实施例描述的各示例的单元即算法步骤,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申请的范围。Those skilled in the art can appreciate that the units of the examples described in this embodiment, that is, the algorithm steps, can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

上述虽然结合附图对本发明的具体实施方式进行了描述,但并非对本发明保护范围的限制,所属领域技术人员应该明白,在本发明的技术方案的基础上,本领域技术人员不需要付出创造性劳动即可做出的各种修改或变形仍在本发明的保护范围以内。Although the specific implementation of the present invention has been described above in conjunction with the accompanying drawings, it does not limit the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, those skilled in the art do not need to pay creative work Various modifications or variations that can be made are still within the protection scope of the present invention.

Claims (10)

1. The method for evaluating the epitope running state of the automatic verification assembly line of the electric energy meter is characterized by comprising the following steps of:
acquiring historical test data of the epitopes on the verification production line under different running states, removing batch effects of the historical test data on the basis of normal epitope data, and constructing feature vectors of the epitopes under different running states;
constructing a twin neural network, updating network parameters by setting a boundary value when training the twin neural network based on the characteristic vector of each epitope, and constructing a state evaluation model by introducing a state judgment threshold;
performing parameter optimization on the state evaluation model by adopting an Ulva gull optimization algorithm and taking the model evaluation reliability as a fitness function to obtain an optimized state evaluation model;
and performing state evaluation on the epitope to be tested by adopting the optimized state evaluation model.
2. The method for evaluating the epitope running state of the automatic verification assembly line of the electric energy meter according to claim 1, characterized in that the batch effect removal is performed by adopting an average center method based on normal epitope data;
Figure FDA0003774179550000011
wherein epsilon ik Test results for the kth test for the ith epitope,
Figure FDA0003774179550000012
The test result after the kth test batch effect of the ith epitope is removed;
Figure FDA0003774179550000013
error for the k-th trial batch effect.
3. The method for evaluating the epitope running state of the automatic verification assembly line of an electric energy meter according to claim 1, wherein the twin neural network adopts a contrast loss function L loss Training is carried out:
Figure FDA0003774179550000014
wherein margin is a boundary value, D (x) 1 ,x 2 ) Is a sample x 1 、x 2 Y is the label corresponding to the support set, and Q is the number of the support sets.
4. The method for evaluating the epitope running state of the automatic verification assembly line of the electric energy meter according to claim 1, wherein the fitness function K is as follows:
Figure FDA0003774179550000021
Figure FDA0003774179550000022
Figure FDA0003774179550000023
wherein H 0 To observe the coincidence rate, a 1 、a 2 、a 3 Respectively representing the number of samples which are actually normal, alarm and abnormal and are predicted to be normal, alarm and abnormal, S is the total number of samples, H e Representing chance coincidence rate, b 1 、b 2 、b 3 The actual number of normal, alarm and abnormal samples, c 1 、c 2 、c 3 The numbers of samples predicted to be normal, alarm and abnormal are respectively.
5. The method for evaluating the epitope running state of the automatic verification assembly line of an electric energy meter according to claim 1, wherein the parameter optimizing process is optimizing a boundary value and a state judgment threshold value.
6. The method for evaluating the epitope running state of the automatic verification assembly line of the electric energy meter according to claim 1, wherein a nonlinear control parameter A is provided in the Woofer optimization algorithm:
Figure FDA0003774179550000024
wherein, f c For the parameters controlling the frequency a, R is the number of iterations and R is the maximum number of iterations.
7. The method for evaluating the epitope running state of the electric energy meter automatic verification assembly line according to claim 1, characterized in that a pair of samples to be tested is generated by a sample to be tested of the epitope to be tested and a sample in a known state, the similarity between the sample to be tested and the sample in the known state is calculated, the mean value of the similarities in all running states is taken as the final similarity, and the epitope state judgment of the sample to be tested is carried out according to the final similarity and a state judgment threshold.
8. Electric energy meter automated verification assembly line epitope running state evaluation system which characterized in that includes:
the training set acquisition module is configured to acquire historical test data of the epitopes on the verification production line in different running states, remove batch effects on the historical test data on the basis of normal epitope data, and construct feature vectors of the epitopes in different running states;
the model building module is configured to build a twin neural network, update network parameters by setting boundary values when the twin neural network is trained on the basis of the feature vectors of all epitopes, and build a state evaluation model by introducing a state judgment threshold;
the parameter optimizing module is configured to adopt an gull-shaped optimization algorithm, take the model evaluation reliability as a fitness function, and conduct parameter optimizing on the state evaluation model to obtain an optimized state evaluation model;
and the state evaluation module is configured to carry out state evaluation on the epitope to be tested by adopting the optimized state evaluation model.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
CN202210911537.8A 2022-07-28 2022-07-28 Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter Pending CN115308674A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210911537.8A CN115308674A (en) 2022-07-28 2022-07-28 Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210911537.8A CN115308674A (en) 2022-07-28 2022-07-28 Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter

Publications (1)

Publication Number Publication Date
CN115308674A true CN115308674A (en) 2022-11-08

Family

ID=83858346

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210911537.8A Pending CN115308674A (en) 2022-07-28 2022-07-28 Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter

Country Status (1)

Country Link
CN (1) CN115308674A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116128095A (en) * 2022-11-18 2023-05-16 中国人民解放军陆军装甲兵学院 Method for evaluating combat effectiveness of ground-air unmanned platform
CN116125173A (en) * 2022-12-30 2023-05-16 北京邮电大学 A Neural Network-Based Method and Device for UHV Converter Valve State Evaluation

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105117602A (en) * 2015-08-28 2015-12-02 国家电网公司 Metering apparatus operation state early warning method
CN109800894A (en) * 2019-01-22 2019-05-24 广东电网有限责任公司 One kind is based on deep learning discovery metering automation pipeline stall diagnostic method and diagnostic system
CN109828230A (en) * 2019-04-02 2019-05-31 国网新疆电力有限公司电力科学研究院 The localization method of automatic calibration of electric energy meter assembly line epitope failure
CN111398886A (en) * 2020-04-09 2020-07-10 国网山东省电力公司电力科学研究院 Detection method and system for automatically detecting online abnormity of epitope of assembly line
CN113569475A (en) * 2021-07-21 2021-10-29 上海工程技术大学 A fault diagnosis system for subway axle box bearings based on digital twin technology
CN113762486A (en) * 2021-11-11 2021-12-07 中国南方电网有限责任公司超高压输电公司广州局 Method and device for constructing fault diagnosis model of converter valve and computer equipment
CN114091553A (en) * 2020-08-06 2022-02-25 长沙理工大学 Diagnosis method for rolling bearing fault

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105117602A (en) * 2015-08-28 2015-12-02 国家电网公司 Metering apparatus operation state early warning method
CN109800894A (en) * 2019-01-22 2019-05-24 广东电网有限责任公司 One kind is based on deep learning discovery metering automation pipeline stall diagnostic method and diagnostic system
CN109828230A (en) * 2019-04-02 2019-05-31 国网新疆电力有限公司电力科学研究院 The localization method of automatic calibration of electric energy meter assembly line epitope failure
CN111398886A (en) * 2020-04-09 2020-07-10 国网山东省电力公司电力科学研究院 Detection method and system for automatically detecting online abnormity of epitope of assembly line
CN114091553A (en) * 2020-08-06 2022-02-25 长沙理工大学 Diagnosis method for rolling bearing fault
CN113569475A (en) * 2021-07-21 2021-10-29 上海工程技术大学 A fault diagnosis system for subway axle box bearings based on digital twin technology
CN113762486A (en) * 2021-11-11 2021-12-07 中国南方电网有限责任公司超高压输电公司广州局 Method and device for constructing fault diagnosis model of converter valve and computer equipment

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116128095A (en) * 2022-11-18 2023-05-16 中国人民解放军陆军装甲兵学院 Method for evaluating combat effectiveness of ground-air unmanned platform
CN116128095B (en) * 2022-11-18 2024-05-07 中国人民解放军陆军装甲兵学院 Method for evaluating combat effectiveness of ground-air unmanned platform
CN116125173A (en) * 2022-12-30 2023-05-16 北京邮电大学 A Neural Network-Based Method and Device for UHV Converter Valve State Evaluation

Similar Documents

Publication Publication Date Title
CN115018021B (en) Machine room abnormity detection method and device based on graph structure and abnormity attention mechanism
CN110068774B (en) Method, device and storage medium for estimating state of health of lithium battery
CN112116058B (en) A Transformer Fault Diagnosis Method Based on Particle Swarm Optimization for Multi-granularity Cascade Forest Model
CN115308674A (en) Method and system for evaluating epitope running state of automatic verification assembly line of electric energy meter
CN109617888B (en) Abnormal flow detection method and system based on neural network
CN113159167B (en) Inland-based chlorophyll a inversion method for different types of water bodies
CN110598854A (en) GRU model-based transformer area line loss rate prediction method
CN116910493B (en) Construction method and device of equipment fault diagnosis model based on multi-source feature extraction
CN110147323A (en) A kind of change intelligence inspection method and device based on generation confrontation network
CN111310722A (en) Power equipment image fault identification method based on improved neural network
Oliinyk et al. The decision tree construction based on a stochastic search for the neuro-fuzzy network synthesis
CN110210973A (en) Insider trading recognition methods based on random forest and model-naive Bayesian
CN116736133A (en) Early prediction method for capacity degradation track of lithium ion battery in full life cycle
CN117276600B (en) Fault diagnosis method of proton exchange membrane fuel cell system based on PSO-GWO-DELM
CN112257348A (en) Method for predicting long-term degradation trend of lithium battery
CN116774086A (en) Lithium battery health state estimation method based on multi-sensor data fusion
CN116794547A (en) A method for predicting the remaining service life of lithium-ion batteries based on AFSA-GRU
CN117318025A (en) Short-term load prediction method based on weighted gray correlation projection method
CN118962455A (en) A battery analysis method
CN112016774A (en) A method and system for identifying the operating state of distribution network based on data enhancement technology
CN116577667A (en) Lithium ion energy storage battery fault diagnosis method for deep online migration
CN117077052A (en) Dry-type transformer abnormality detection method based on working condition identification
CN117056678B (en) Machine pump equipment operation fault diagnosis method and device based on small sample
CN114021465A (en) Robust state estimation method and system for power system based on deep learning
CN117371608A (en) Pig house multi-point temperature and humidity prediction method and system based on deep learning

Legal Events

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