CN110031790A - Electric energy meter Mission Capability detection method and device - Google Patents

Abstract

The present invention provides a method and device for detecting the task execution capability of an electric energy meter, wherein the method includes: expanding the task execution data of each electric energy meter to be detected in the time domain to obtain the time domain expansion of each electric energy meter to be detected Sampling data; according to the time domain expansion sampling data of all the electric energy meters to be detected, construct the original data matrix, and determine the covariance matrix corresponding to the original data matrix; determine the eigenvalues of the covariance matrix and the corresponding eigenvector matrix; according to the number of principal components , select a preset eigenvector from the eigenvector matrix to form an intercepted eigenvector matrix; determine the principal component matrix according to the intercepted eigenvector matrix and the original data matrix; and detect the task execution capability of the electric energy meter according to the principal component matrix. The above technical solution realizes the task data composed of Boolean quantity considering the timing and whether the task execution is successful or not, so as to detect the task execution capability of the electric energy meter.

Description

Method and device for detecting task execution capability of electric energy meter

technical field

The invention relates to the technical field of electric energy meters, in particular to a method and device for detecting the task execution capability of an electric energy meter.

Background technique

In order to further leverage the asset benefits of smart energy meters, reduce grid operating costs, and improve power supply reliability and user satisfaction, the company has done a lot of research and application on the non-metering functions of smart energy meters. The advanced application of smart energy meters has become an important means of distribution network operation and management. The company expands the development and application of non-metering functions of the metering and acquisition system, establishes a complete data sharing mechanism, and formulates work rules for smart energy meter data to support various professional applications, which are comprehensive and effective. Support professional data requirements such as transportation inspection, development, and safety and quality. In order to support related work, it is necessary to test the acquisition task execution capability and local network communication capability of the electric energy meter under the existing communication modes such as full carrier, half carrier, and broadband carrier in the station area. At present, there is no quantitative detection method for the collection capability of the electric energy meter in the collection station area and the local network communication. Therefore, it is necessary to integrate the tasks of the main station multi-data acquisition, cost control, time adjustment, price adjustment, etc., combined with the communication monitoring of the local communication channel, to realize the electric energy meter. A quantifiable and actionable measurement of task performance capability. The disadvantages of the existing detection scheme for the task execution capability of the electric energy meter are as follows.

First of all, in the prior art, the performance of the electric energy meter is often measured by various experimental means under laboratory conditions. These experimental values are all specific numbers. A typical sample data analysis method is principal component analysis and k-means clustering method. However, the object of this method is numerical sample data, and the data of the power meter execution task is usually whether the task execution is successful, that is, the task execution data is only success or failure rather than a specific quantified number. Therefore, the existing analysis methods based on numerical sample data cannot be used to detect the task performance capability of the electric energy meter.

Secondly, the method mainly used to analyze the performance of the electric energy meter is mainly the traditional principal component analysis method. The object of the traditional principal component analysis method is the data without time series. The sample data obtained through many experiments in the prior art is The data is not time series, so the traditional principal component analysis method can be used for this analysis. However, in the actual application environment, the power meter performs tasks in a sequential manner, the task sending is prior to the task receipt, and whether the task receipt is timely also reflects the ability of the power meter to perform tasks. Therefore, the traditional principal component analysis method for analyzing the performance of the electric energy meter cannot be used to detect the task execution capability of the electric energy meter.

SUMMARY OF THE INVENTION

An embodiment of the present invention provides a method for detecting the task execution capability of an electric energy meter, which is used to detect the task execution capability of an electric energy meter, and the method includes:

According to the task execution type and time of the electric energy meter, the task execution data of each electric energy meter to be detected is expanded in the time domain, and the time domain expanded sampling data of each electric energy meter to be detected is obtained; the time domain expanded sampling data is: Data on the success or failure of time-series tasks;

According to the time-domain extended sampling data of all the electric energy meters to be detected, the original data matrix is constructed, and the covariance matrix corresponding to the original data matrix is determined according to the original data matrix;

Determine the eigenvalues of the covariance matrix, and determine the eigenvector matrix corresponding to the covariance matrix according to the eigenvalues of the covariance matrix;

According to the number of principal components, a preset eigenvector is selected from the eigenvector matrix to form an intercepted eigenvector matrix; the principal component matrix is determined according to the intercepted eigenvector matrix and the original data matrix; the principal component matrix represents all Common features of the task execution capability of the electric energy meter to be tested;

According to the principal component matrix, the task execution capability of the electric energy meter is detected.

The embodiment of the present invention also provides a device for detecting the task execution capability of an electric energy meter, which is used to detect the task execution capability of an electric energy meter, and the device includes:

The time-domain extended sampling data determination unit is used to expand the task execution data of each electric energy meter to be detected in the time domain according to the task execution type and time of the electric energy meter to obtain the time domain expanded sampling of each electric energy meter to be detected. data; the time-domain extended sampling data is the data of whether the task with time sequence is executed successfully or not;

The original data matrix and covariance matrix determination unit is used for constructing the original data matrix according to the time domain expansion sampling data of all the electric energy meters to be detected, and determining the covariance matrix corresponding to the original data matrix according to the original data matrix;

an eigenvector matrix determining unit, used for determining the eigenvalues of the covariance matrix, and determining the eigenvector matrix corresponding to the covariance matrix according to the eigenvalues of the covariance matrix;

The intercepted eigenvector matrix and the principal component matrix determination unit are used to select a preset eigenvector from the eigenvector matrix according to the number of principal components to form an intercepted eigenvector matrix; according to the intercepted eigenvector matrix and the original data matrix , determine the principal component matrix; the principal component matrix represents the common feature of the task execution capability of all the electric energy meters to be detected;

The detection unit is configured to detect the task execution capability of the electric energy meter according to the principal component matrix.

An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the method for detecting the task execution capability of an electric energy meter.

Embodiments of the present invention further provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program for executing the method for detecting the task execution capability of an electric energy meter.

The technical solutions provided by the embodiments of the present invention have the following beneficial effects:

First of all, compared with the conventional principal component analysis method in the prior art, the object is numerical value, and it is impossible to analyze whether the task is successful or not, and thus cannot detect the ability of the electric energy meter to perform the task. The technical solution provided by the embodiment of the present invention The object to be processed is the data of the task executed by the electric energy meter, that is, the task execution data is only the data of success or failure, rather than a specific quantified number, so the method of the present invention is a Boolean quantity that can process the success or failure of the task execution. The method of sample data analysis can then solve the problem of detecting the task execution capability of the electric energy meter.

Secondly, compared with the traditional principal component analysis method in the prior art, which processes data without time sequence and cannot detect the ability of the electric energy meter to perform tasks, the technical solution provided by the embodiment of the present invention is to expand in the time domain. , which solves the problem that the electric energy meter performs tasks in a time sequence in the actual application environment. Because the task sending of the electric energy meter is prior to the task receipt, and whether the task receipt is timely also reflects the ability of the electric energy meter to perform the task. Therefore, the method of the present invention is an analysis method that can consider the time series of the sample data, so as to realize the detection of the ability of the electric energy meter to perform tasks.

In conclusion, the technical solutions provided by the embodiments of the present invention realize the detection of the ability of the electric energy meter to perform tasks.

Description of drawings

The accompanying drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present application, and do not constitute a limitation to the present invention. In the attached image:

1 is a schematic flowchart of a method for detecting a task execution capability of an electric energy meter according to an embodiment of the present invention;

2 is a schematic flowchart of a method for detecting a task execution capability of an electric energy meter in another embodiment of the present invention;

FIG. 3 is a schematic structural diagram of an apparatus for detecting a task execution capability of an electric energy meter according to an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but not to limit the present invention.

Since the inventor found that the existing detection methods for electric energy meters cannot detect the task execution capability of electric energy meters, the existing technical problem is: the object of the traditional principal component analysis method is numerical value instead of Boolean quantity, and it cannot analyze and detect whether the task execution is successful or not. At the same time, the sample data processed by the traditional principal component analysis method is data with no time sequence, and the task execution capability detection needs to consider the time sequence of the task data, so the traditional principal component analysis method is not suitable for the detection of the task execution capability of the electric energy meter.

Due to the above technical problems discovered by the inventor, a scheme for detecting (assessing) the task execution capability of an electric energy meter based on time-domain extended principal component analysis is proposed. The object of the principal component analysis method on this basis is the data of whether the task execution is successful or not with time sequence, and then the task execution capability of the electric energy meter is detected according to the results of the time domain extended principal component analysis method. The scheme for detecting the task execution capability of the electric energy meter based on the time domain extension analysis is described in detail as follows.

FIG. 1 is a schematic flowchart of a method for detecting a task execution capability of an electric energy meter according to an embodiment of the present invention. As shown in FIG. 1 , the method includes:

Step 101: According to the task execution type and time of the electric energy meter, the task execution data of each electric energy meter to be detected is expanded in the time domain, and the time domain expanded sampling data of each electric energy meter to be detected is obtained; the time domain expansion The sampled data is the data of whether the time-series tasks are executed successfully or not;

Step 102: construct an original data matrix according to the time-domain extended sampling data of all electric energy meters to be detected, and determine a covariance matrix corresponding to the original data matrix according to the original data matrix;

Step 103: determine the eigenvalues of the covariance matrix, and determine the eigenvector matrix corresponding to the covariance matrix according to the eigenvalues of the covariance matrix;

Step 104: according to the number of principal components, select a preset eigenvector from the eigenvector matrix to form an intercepted eigenvector matrix; determine a principal component matrix according to the intercepted eigenvector matrix and the original data matrix; the principal component The matrix represents the common characteristics of the task execution capability of all the electric energy meters to be tested;

Step 105: Detect the task execution capability of the electric energy meter according to the principal component matrix.

The technical solutions provided by the embodiments of the present invention have the following beneficial effects:

First of all, compared with the conventional principal component analysis method in the prior art, the object is numerical value, and it is impossible to analyze whether the task is successful or not, and thus cannot detect the ability of the electric energy meter to perform the task. The technical solution provided by the embodiment of the present invention The object to be processed is the data of the task executed by the electric energy meter, that is, the task execution data is only the data of success or failure, rather than a specific quantified number, so the method of the present invention is a Boolean quantity that can process the success or failure of the task execution. The method of sample data analysis can then solve the problem of detecting the task execution capability of the electric energy meter.

Secondly, compared with the traditional principal component analysis method in the prior art, which processes data without time sequence and cannot detect the ability of the electric energy meter to perform tasks, the technical solution provided by the embodiment of the present invention is to expand in the time domain. , which solves the problem that the electric energy meter performs tasks in a time sequence in the actual application environment. Because the task sending of the electric energy meter is prior to the task receipt, and whether the task receipt is timely also reflects the ability of the electric energy meter to perform the task. Therefore, the method of the present invention is an analysis method that can consider the time series of the sample data, so as to realize the detection of the ability of the electric energy meter to perform tasks.

In conclusion, the technical solutions provided by the embodiments of the present invention realize the detection of the ability of the electric energy meter to perform tasks.

The steps involved in the embodiments of the present invention are described in detail as follows.

1. First, the above step 101 is introduced.

In the above step 101 (see S1 in FIG. 2 ), it is assumed that the number of electric energy meters (electric energy meters to be detected) participating in the detection of the task execution capability of electric energy meters is N, the starting time of the original data is t _start , and the ending time is t _end , the test time length Δt is Δt=t _end −t _start , and the task execution data of the j-th electric energy meter is assumed to have M groups in total, and the time corresponding to the i-th group of tasks is t _ij . If the i-th task of the j-th electric energy meter is to send an instruction, the time-domain extended sampling data after the corresponding task execution data is extended in the time domain is V _ij ∠α _ij , and its amplitude is V _ij =1, which The phase angle is α _ij , α _ij =(t _ij -t _start )π/Δt; if the i-th task of the j-th electric energy meter is the task execution success receipt, the corresponding task execution data is expanded in the time domain. The time domain extended sampling data is V _ij ∠α _ij , its amplitude is V _ij =1, its phase angle is α _ij , α _ij =(t _ij -t _start )π/Δt+π; if the jth electric energy meter The i-th group of tasks is the task execution failure receipt, then the time domain extended sampling data after the corresponding task execution data is expanded in the time domain is V _ij ∠α _ij , its amplitude is V _ij =0.5, and its phase angle is α _ij , α _ij =(t _ij -t _start )π/Δt+π.

During specific implementation, the types of task execution of the electric energy meter include: the type of successful task execution and the type of task execution failure.

During specific implementation, the tasks of the electric energy meter may include tasks such as collection, cost control, time adjustment, and price adjustment.

2. Secondly, after the above-mentioned step 101 is introduced, the steps of normalization processing are performed (see S2 in FIG. 2 ).

In one embodiment, the method for detecting the task execution capability of an electric energy meter based on time-domain extended analysis may further include: normalizing the time-domain extended sampling data of each electric energy meter to be detected, to obtain the The time-domain extended sample data after normalization processing.

In specific implementation, normalizing the time-domain extended sampling data of each electric energy meter to be detected improves the efficiency and accuracy of data processing, and the specific normalization processing method may include: performing task execution data on the jth electric energy meter The time-domain extended sampling data V _j ∠α _j =[V _1j ∠α _1j ,V _2j ∠α _2j ,…,V _Mj ∠α _Mj ] ^T is normalized, j=1,2,…,N, normalized The normalized time domain extended sampling data of the jth electric energy meter task execution data obtained after normalization is V _j ∠α _j , the mean value of V _j ∠α _j is 0, and the standard deviation is 1.

3. Next, the above step 102 is introduced.

In one embodiment, an original data matrix is constructed according to the time-domain extended sampling data of all electric energy meters to be detected, and a covariance matrix corresponding to the original data matrix is determined according to the original data matrix, which may include:

According to the normalized time domain extended sampling data of all the electric energy meters to be detected, the original data matrix is constructed, and the covariance matrix corresponding to the original data matrix is determined according to the original data matrix.

During specific implementation, in the above step 102 (see S3 in FIG. 2 ), an original data matrix X with M rows and N columns is constructed, where M is the number of tasks performed at different times, N is the number of electric energy meters, and X=[ V ₁ ∠α ₁ ,V ₂ ∠α ₂ ,…,V _N ∠α _N ], X is a complex number matrix of M×N, calculate the covariance matrix C of X according to the original data matrix X, C=X ^H X, C is an N×N complex matrix.

4. Next, the above step 103 is introduced.

During specific implementation, in the above step 103 (see S4 in FIG. 2 ), all eigenvalues λ ₁ , λ ₂ ,...λ _N of the matrix C are calculated, and λ ₁ ≥λ ₂ ≥...≥λ _N ≥0, all the eigenvalues The values are all real numbers; for the eigenvalues λ _j , j=1,2,...,N, find the basic solution system of the homogeneous linear equation system (λ _j IC)=0, and obtain a set of eigenvectors of C for λ _j u _j , then the eigenvector matrix U=[u ₁ , u ₂ ,…u _N ], U is an N×N complex matrix, and satisfies U ^H CU=Λ, where Λ=diag(λ ₁ ,λ ₂ ,… , λ _N ).

5. Next, the above step 104 is introduced.

1. First, the steps of determining the number (quantity) of principal components after the above-mentioned step 103 are introduced (see S5 in FIG. 2 ).

During specific implementation, the number k of principal components is selected according to the Guttman criterion, that is, k=max{k|λ _k ≥1}.

2. Secondly, it is introduced that after the number of principal components is determined, a preset eigenvector (the first k eigenvectors) is selected from the eigenvector matrix according to the number of principal components to form a matrix of intercepted eigenvectors, and then the principal eigenvectors are determined. For the steps of the composition matrix, see S6 in FIG. 2 .

During specific implementation, the first to k eigenvectors are selected from the eigenvector matrix U according to the number k of principal components, that is, the intercepted eigenvector matrix U _k =[u ₁ , u ₂ ,…u _k ], U _k is an N×k complex number matrix, and the original data matrix X is transformed to obtain the principal component matrix P, then P represents the common characteristics of the execution of all N electric energy meter tasks (Therefore, the principal component matrix can also be referred to as the P common feature matrix P), and the time domain extended principal component analysis process is completed.

During specific implementation, transforming the original data matrix X to obtain the principal component matrix P, which may include: calculating the principal component matrix P by P=XU _k , where P is a complex matrix of M×k.

6. Next, the above-mentioned step 105 is introduced, referring to “S7-S10” in FIG. 2 .

In one embodiment, detecting the task execution capability of the electric energy meter according to the principal component matrix may include detecting the task execution capability of the electric energy meter according to one or any combination of the following methods:

According to the number of principal component components, detect the difference of the task execution ability of all the electric energy meters to be tested;

According to the mean value of all elements in the first column of the principal component matrix, detect the difference of the task execution capability of all the electric energy meters to be detected;

According to the variance of all elements in the first column of the principal component matrix, detect the difference in task execution capability of the common features of all electric energy meters to be detected;

According to the modulus of the first row element of the conjugate transposed matrix of the intercepted eigenvector matrix, the task execution capability of all the electric energy meters to be detected is detected.

During specific implementation, the task execution capability of the electric energy meter is detected according to one or any combination of the above methods, which improves the flexibility and accuracy of detecting the task execution capability of the electric energy meter.

1. First, introduce the steps of detecting the difference of the task execution capability of all the electric energy meters to be detected according to the number of principal component components, see S7 in FIG. 2 .

In one embodiment, according to the number of principal component components, detecting the differences in the task execution capabilities of all the electric energy meters to be detected may include: the smaller the number of principal component components, the smaller the difference in the task execution capabilities of all the electric energy meters to be detected, the main The larger the number of component components, the greater the difference in the task execution capabilities of all the electric energy meters to be detected.

In specific implementation, the number k of the principal components represents the difference in the task execution capabilities of all electric energy meters. The smaller the k is, the smaller the difference in the task execution capabilities of the electric energy meters involved in the evaluation. On the contrary, the larger the k is, the larger the electric energy meters involved in the evaluation are. The task execution capability varies greatly, and the k value is used to detect the difference in the task execution capability of all N electric energy meters.

During specific implementation, the principal component is the result of principal component analysis and calculation, and represents the result in the embodiment of the present invention.

2. Secondly, introduce the steps of detecting the difference of the task execution capability of all the electric energy meters to be detected according to the mean value of all elements in the first column of the principal component matrix, see S8 in FIG. 2 .

In one embodiment, according to the mean value of all elements in the first column of the principal component matrix, detecting the difference in the task execution capability of all the electric energy meters to be detected may include: the mean value of all the elements in the first column of the principal component matrix is close to the origin of the complex plane , which means that the task execution ability of all the electric energy meters to be detected is good, the proportion of the successful execution receipt of the task sent data is large, and the time required to receive the receipt is short; the mean value of all elements in the first column of the principal component matrix is far from the origin of the complex plane, It means that the task execution capability of all the electric energy meters to be detected is poor, the proportion of the task sending data to get a successful execution receipt is small, and the time required to receive the receipt is long.

In specific implementation, the first column p ₁ of the common feature matrix P is the dominant feature of the task execution capability of all electric energy meters, and the mean of all elements of p ₁ is closer to the origin of the complex plane (the origin of the complex plane is the origin of the complex number domain, and the abscissa represents the real part, the ordinate represents the imaginary part, and the origin is a complex number whose real part and imaginary part are both 0) means that all electric energy meters have good task execution ability, the proportion of task sending data to get a successful execution receipt is large, and the time required to receive the receipt is short, On the contrary, the mean value of all elements of p ₁ is far from the origin of the complex plane, which means that the task execution capability of all electric energy meters is poor, the proportion (proportion) of the successful execution receipt of the data sent by the task is small, and the time required to receive the receipt is long.

3. Next, introduce the steps of detecting the difference in task execution capability of all the common features of the electric energy meters to be detected according to the variance of all elements in the first column of the principal component matrix, see S9 in FIG. 2 .

In one embodiment, according to the variance of all elements in the first column of the principal component matrix, detecting the difference in task execution capability of all the common features of the electric energy meters to be detected may include: the variance of all elements in the first column of the principal component matrix is small, The difference in task execution capability representing the common features of all electric energy meters to be detected is small; the variance of all elements in the first column of the principal component matrix is large, and the difference in task execution capability representing the common characteristics of all electric energy meters to be detected is large.

In specific implementation, the variance _of all elements of p ₁ represents the difference in the task execution capability _of all electric energy meters. If the variance of the elements is large, the task execution capability of all the common features of the electric energy meters will vary greatly.

4. Finally, introduce the steps of detecting the task execution capability of all the electric energy meters to be detected according to the modulus of the first row element of the conjugate transposed matrix of the intercepted eigenvector matrix, see S10 in FIG. 2 .

In one embodiment, detecting the task execution capability of all electric energy meters to be detected according to the modulus of the first row element of the conjugate transpose matrix of the intercepted eigenvector matrix may include: in the first row element of the conjugate transpose matrix The modulus of any element is large, which means that the task execution capability of the electric energy meter to be detected corresponding to any element is good; the modulus of any element in the first row of the conjugate transpose matrix is large, which means that any element corresponds to The task execution capability of the electric energy meter to be detected is poor.

In the specific implementation, let U _H be the conjugate transpose matrix of the intercepted eigenvector matrix U _k , U _H =U _k ^H , then the jth column of the matrix U _H is the task performance of the jth electric energy meter and all electric energy meters. The relationship between the common features P of the matrix U _H , the first row of the matrix U H is the corresponding relationship between the N electric energy meters and the dominant feature p ₁ , wherein the element u _1j of the first row and the jth column of the matrix U _H is the jth electric energy. The corresponding relationship between the table and the dominant feature p ₁ , the larger the modulus of u _1j , the closer the task execution capability of the j-th electric energy meter is to the dominant feature, and the better the task execution capability of the electric energy meter. On the contrary, the higher the modulus of u _1j is. Small, it means that the task execution capability of the j-th electric energy meter deviates from the dominant characteristic, and the task execution capability of the electric energy meter is worse.

Based on the same inventive concept, an embodiment of the present invention also provides a device for detecting a task execution capability of an electric energy meter, such as the following embodiments. Because the principle of solving the problem of the electric energy meter task execution capability detection device is similar to the above-mentioned electric energy meter task execution capability detection method, the implementation of the electric energy meter task execution capability detection device can refer to the implementation of the electric energy meter task execution capability detection method. Repeat. As used below, the term "module" or "unit" may be a combination of software and/or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.

FIG. 3 is a schematic structural diagram of a device for detecting the task execution capability of an electric energy meter according to an embodiment of the present invention. As shown in FIG. 3 , the device includes:

The time domain expansion sampling data determination unit 01 is used for expanding the task execution data of each electric energy meter to be detected in the time domain according to the task execution type and time of the electric energy meter to obtain the time domain expansion of each electric energy meter to be detected. Sampling data; the time-domain extended sampling data is the data of whether the task with time sequence is executed successfully or not;

The original data matrix and covariance matrix determining unit 02 is used for constructing the original data matrix according to the time domain extended sampling data of all the electric energy meters to be detected, and determining the covariance matrix corresponding to the original data matrix according to the original data matrix;

The eigenvector matrix determining unit 03 is used to determine the eigenvalues of the covariance matrix, and according to the eigenvalues of the covariance matrix, determine the eigenvector matrix corresponding to the covariance matrix;

The intercepting eigenvector matrix and the principal component matrix determining unit 04 is used to select a preset eigenvector from the eigenvector matrix according to the number of principal components to form an intercepting eigenvector matrix; according to the intercepting eigenvector matrix and the original data matrix, determine the principal component matrix; the principal component matrix represents the common feature of the task execution capability of all the electric energy meters to be tested;

The detection unit 05 is configured to detect the task execution capability of the electric energy meter according to the principal component matrix.

In one embodiment, the above detection unit may be specifically configured to detect the task execution capability of the electric energy meter according to one or any combination of the following methods:

According to the number of principal component components, detect the difference of the task execution ability of all the electric energy meters to be tested;

According to the mean value of all elements in the first column of the principal component matrix, detect the difference of the task execution capability of all the electric energy meters to be detected;

According to the variance of all elements in the first column of the principal component matrix, detect the difference in task execution capability of the common features of all electric energy meters to be detected;

According to the modulus of the first row element of the conjugate transposed matrix of the intercepted eigenvector matrix, the task execution capability of all the electric energy meters to be detected is detected.

In one embodiment, the detection unit can be specifically used for:

The smaller the number of principal component components, the smaller the difference in the task execution capabilities of all the electric energy meters to be detected, the larger the number of principal component components, the larger the difference in the task execution capabilities of all the electric energy meters to be detected;

The mean value of all elements in the first column of the principal component matrix is close to the origin of the complex plane, which means that the task execution capability of all the electric energy meters to be detected is good, the proportion of the successful execution receipt of the data sent by the task is large, and the time required to receive the receipt is short; the principal component The mean value of all elements in the first column of the matrix is far away from the origin of the complex plane, which means that the task execution capability of all the electric energy meters to be detected is poor, the proportion of the task sending data receiving a successful execution receipt is small, and the time required to receive the receipt is long;

The variance of all elements in the first column of the principal component matrix is small, which represents a small difference in the task execution capability of all the electric energy meters to be detected; The ability to perform tasks varies greatly;

The modulus of any element in the first row of the conjugate transpose matrix is large, which means that the task execution capability of the electric energy meter to be detected corresponding to any element is good; any one of the elements in the first row of the conjugate transpose matrix If the modulus of an element is large, it means that the task execution capability of the electric energy meter to be detected corresponding to any element is poor.

In one embodiment, the apparatus for detecting the task execution capability of an electric energy meter may further include: a normalization processing unit, configured to perform normalization processing on the time-domain extended sampling data of each electric energy meter to be detected, to obtain all electric energy to be detected The time-domain extended sampling data after normalization of the table;

The original data matrix and the covariance matrix determining unit can be specifically used for: constructing the original data matrix according to the normalized time domain extended sampling data of all the electric energy meters to be detected, and determining the corresponding original data matrix according to the original data matrix. The covariance matrix of .

To sum up, the core innovation of the method of the present invention is that the object of the traditional principal component analysis method is a numerical value rather than a Boolean quantity, and it cannot analyze and detect whether the task execution is successful or not. Meanwhile, the sample data processed by the traditional principal component analysis method is no data. However, the task execution capability detection needs to consider the time sequence of the task data, so the traditional principal component analysis method is not suitable for the detection of the task execution capability of the electric energy meter. The method of the present invention expands the task execution data in the time domain according to the time of task execution, and the object of the principal component analysis method on this basis is the data of whether the task execution is successful or not with time sequence, which solves the problem of traditional principal component analysis. The method cannot detect the task execution capability of the electric energy meter.

An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the method for detecting the task execution capability of an electric energy meter.

Embodiments of the present invention further provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program for executing the method for detecting the task execution capability of an electric energy meter.

The beneficial technical effects of the technical solutions provided by the implementation of the present invention are as follows: it can be seen from the above technical solutions that the method for detecting the task execution capability of an electric energy meter based on time-domain extended principal component analysis in this embodiment, the object is the data of the electric energy meter execution task, That is, the task execution data is only success or failure rather than a specific quantified number. Therefore, the method of the present invention is a method that can process Boolean sample data analysis composed of task execution success or failure, so as to solve the problem of detecting the task execution of the electric energy meter. At the same time, this method extends the principal component analysis in the time domain, which solves the problem that the task execution of the electric energy meter in the actual application environment is time-sequential, because the task sending of the electric energy meter is prior to the task receipt, and Whether the task receipt is timely also reflects the power meter's ability to perform tasks. Therefore, the method of the present invention can consider the analysis method of the time series of the sample data to realize the detection of the ability of the electric energy meter to perform the task.

Obviously, those skilled in the art should understand that each module or each step of the above-mentioned embodiments of the present invention may be implemented by a general-purpose computing device, and they may be centralized on a single computing device, or distributed in multiple computing devices. network, they can optionally be implemented with program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, can be different from the The illustrated or described steps are performed in sequence, either by fabricating them separately into individual integrated circuit modules, or by fabricating multiple modules or steps of them into a single integrated circuit module. As such, embodiments of the present invention are not limited to any particular combination of hardware and software.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, various modifications and changes may be made to the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. A method for detecting the task execution capability of an electric energy meter, comprising: According to the task execution type and time of the electric energy meter, the task execution data of each electric energy meter to be detected is expanded in the time domain, and the time domain expanded sampling data of each electric energy meter to be detected is obtained; the time domain expanded sampling data is: Data on the success or failure of time-series tasks; According to the time-domain extended sampling data of all the electric energy meters to be detected, the original data matrix is constructed, and the covariance matrix corresponding to the original data matrix is determined according to the original data matrix; Determine the eigenvalues of the covariance matrix, and determine the eigenvector matrix corresponding to the covariance matrix according to the eigenvalues of the covariance matrix; According to the number of principal components, a preset eigenvector is selected from the eigenvector matrix to form an intercepted eigenvector matrix; the principal component matrix is determined according to the intercepted eigenvector matrix and the original data matrix; the principal component matrix represents all Common features of the task execution capability of the electric energy meter to be tested; According to the principal component matrix, the task execution capability of the electric energy meter is detected. 2 . The method for detecting the task execution capability of an electric energy meter according to claim 1 , wherein detecting the task execution capability of an electric energy meter according to the principal component matrix comprises detecting an electric energy meter according to one or any combination of the following methods. 3 . Ability to perform tasks: According to the number of principal component components, detect the difference of the task execution ability of all the electric energy meters to be tested; According to the mean value of all elements in the first column of the principal component matrix, detect the difference of the task execution capability of all the electric energy meters to be detected; According to the variance of all elements in the first column of the principal component matrix, detect the difference in task execution capability of the common features of all electric energy meters to be detected; According to the modulus of the first row element of the conjugate transposed matrix of the intercepted eigenvector matrix, the task execution capability of all the electric energy meters to be detected is detected. 3. The method for detecting the task execution capability of an electric energy meter according to claim 2, wherein, according to the number of principal component components, detecting the difference of the task execution capability of all electric energy meters to be detected, comprising: the smaller the number of principal component components , which means that the difference in the task execution capability of all the electric energy meters to be detected is small, and the larger the number of principal component components, the larger the difference in the task execution capability of all the electric energy meters to be detected; According to the mean value of all elements in the first column of the principal component matrix, the differences in the task execution capabilities of all the electric energy meters to be detected are detected, including: the mean value of all the elements in the first column of the principal component matrix is close to the origin of the complex plane, representing all electric energy meters to be detected. The task execution ability is good, the proportion of the successful execution receipt of the data sent by the task is large, and the time required to receive the receipt is short; the mean value of all elements in the first column of the principal component matrix is far away from the origin of the complex plane, representing all the energy meter tasks to be detected. The execution ability is poor, the proportion of the task sending data getting a successful execution receipt is small, and it takes a long time to receive the receipt; According to the variance of all elements in the first column of the principal component matrix, the difference in task execution capability of all the common features of the electric energy meters to be detected is detected, including: the variance of all the elements in the first column of the principal component matrix is small, which represents the commonality of all electric energy meters to be detected. The difference of the task execution ability of the feature is small; the variance of all elements in the first column of the principal component matrix is large, and the task execution ability of the common characteristics of all the electric energy meters to be detected is greatly different; According to the modulus of the first row element of the conjugate transpose matrix of the intercepted eigenvector matrix, the task execution capability of all the electric energy meters to be detected is detected, including: the modulus of any element in the first row element of the conjugate transpose matrix is large , which means that the task execution ability of the electric energy meter to be detected corresponding to any element is good; the modulus of any element in the first row of the conjugate transpose matrix is large, which represents the task of the electric energy meter to be detected corresponding to any element. Poor execution. 4. The method for detecting the task execution capability of an electric energy meter according to claim 1, further comprising: performing normalization processing on the time-domain extended sampling data of each electric energy meter to be detected, to obtain the data of all electric energy meters to be detected. Normalized time domain extended sampling data; According to the time-domain extended sampling data of all the electric energy meters to be detected, the original data matrix is constructed, and the covariance matrix corresponding to the original data matrix is determined according to the original data matrix, including: According to the normalized time domain extended sampling data of all the electric energy meters to be detected, the original data matrix is constructed, and the covariance matrix corresponding to the original data matrix is determined according to the original data matrix. 5. A device for detecting the task execution capability of an electric energy meter, comprising: The time-domain extended sampling data determination unit is used to expand the task execution data of each electric energy meter to be detected in the time domain according to the task execution type and time of the electric energy meter to obtain the time domain expanded sampling of each electric energy meter to be detected. data; the time-domain extended sampling data is the data of whether the task with time sequence is executed successfully or not; The original data matrix and covariance matrix determination unit is used for constructing the original data matrix according to the time domain expansion sampling data of all the electric energy meters to be detected, and determining the covariance matrix corresponding to the original data matrix according to the original data matrix; an eigenvector matrix determining unit, used for determining the eigenvalues of the covariance matrix, and determining the eigenvector matrix corresponding to the covariance matrix according to the eigenvalues of the covariance matrix; The intercepted eigenvector matrix and the principal component matrix determination unit are used to select a preset eigenvector from the eigenvector matrix according to the number of principal components to form an intercepted eigenvector matrix; according to the intercepted eigenvector matrix and the original data matrix , determine the principal component matrix; the principal component matrix represents the common feature of the task execution capability of all the electric energy meters to be detected; The detection unit is configured to detect the task execution capability of the electric energy meter according to the principal component matrix. 6. The device for detecting the task execution capability of an electric energy meter according to claim 5, wherein the detection unit is specifically configured to detect the task execution capability of the electric energy meter according to one or any combination of the following methods: According to the number of principal component components, detect the difference of the task execution ability of all the electric energy meters to be tested; According to the mean value of all elements in the first column of the principal component matrix, the difference of the task execution capability of all the electric energy meters to be detected is detected; According to the variance of all elements in the first column of the principal component matrix, detect the difference in task execution capability of the common features of all electric energy meters to be detected; According to the modulus of the first row element of the conjugate transposed matrix of the intercepted eigenvector matrix, the task execution capability of all the electric energy meters to be detected is detected. 7. The device for detecting the task execution capability of an electric energy meter according to claim 6, wherein the detecting unit is specifically used for: The smaller the number of principal component components, the smaller the difference in the task execution capabilities of all the electric energy meters to be detected, the larger the number of principal component components, the larger the difference in the task execution capabilities of all the electric energy meters to be detected; The mean value of all elements in the first column of the principal component matrix is close to the origin of the complex plane, which means that the task execution capability of all the electric energy meters to be detected is good, the proportion of the successful execution receipt of the data sent by the task is large, and the time required to receive the receipt is short; the principal component The mean value of all elements in the first column of the matrix is far away from the origin of the complex plane, which means that the task execution capability of all the electric energy meters to be detected is poor, the proportion of the task sending data receiving a successful execution receipt is small, and the time required to receive the receipt is long; The variance of all elements in the first column of the principal component matrix is small, which represents a small difference in the task execution capability of all the electric energy meters to be detected; The ability to perform tasks varies greatly; The modulus of any element in the first row of the conjugate transpose matrix is large, which means that the task execution capability of the electric energy meter to be detected corresponding to any element is good; any one of the elements in the first row of the conjugate transpose matrix If the modulus of an element is large, it means that the task execution capability of the electric energy meter to be detected corresponding to any element is poor. 8 . The device for detecting the task execution capability of an electric energy meter according to claim 5 , further comprising: a normalization processing unit configured to perform normalization processing on the time-domain extended sampling data of each electric energy meter to be detected. 9 . , to obtain the normalized time-domain extended sampling data of all the electric energy meters to be detected; The original data matrix and covariance matrix determining unit is specifically used for: constructing the original data matrix according to the normalized time domain extended sampling data of all the electric energy meters to be detected, and determining the corresponding value of the original data matrix according to the original data matrix. covariance matrix. 9. A computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements any of claims 1 to 4 when the processor executes the computer program the method. 10 . A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing any one of the methods of claims 1 to 4 . 11 .