CN117452315A - State evaluation method, device and equipment of intelligent ammeter and storage medium - Google Patents
State evaluation method, device and equipment of intelligent ammeter and storage medium Download PDFInfo
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Abstract
The application discloses a state evaluation method, device, equipment and storage medium of a smart meter, wherein the method comprises the following steps: acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes; respectively calculating the reference weight of each type of index according to the historical data matrix, and then constructing a reference cloud model of the intelligent ammeter based on the reference weight; comparing the reference cloud model with a cloud model to be detected corresponding to the data to be detected to obtain a cloud center of gravity offset, and constructing the cloud model to be detected based on the data to be detected; and evaluating the intelligent ammeter to be tested according to the cloud center offset to obtain an evaluation result, wherein the evaluation result comprises comments. The method and the device can solve the technical problems that the prior art depends on a fixed threshold value and a large amount of data for training, the complexity is high, and the actual application requirements are difficult to meet.
Description
Technical Field
The application relates to the technical field of equipment evaluation, in particular to a state evaluation method, device and equipment of an intelligent ammeter and a storage medium.
Background
Smart meters have been widely used in domestic, commercial and industrial environments as a key component in power systems. Unlike traditional ammeter, which is only used for measuring and recording electric energy, intelligent ammeter has functions of data storage, remote communication, real-time monitoring and the like besides electric energy measurement. This allows the grid operator to more accurately understand and manage the status and operation of the grid, further improving the stability and efficiency of the power supply. As smart meters are applied in more scenes, higher demands are increasingly placed on their state evaluation, as this is not only related to the normal operation of the meter itself, but also to the reliability of the overall power system and the user's power consumption experience.
There are various methods for evaluating the operation state of a smart meter. Based on, for example, fixed thresholds and empirical determinations, the system may trigger an alarm once a certain indicator exceeds a predetermined limit. However, this approach tends to be inadequate in dealing with complex, dynamic grid environments. For example, the operation state of the intelligent ammeter is predicted by combining with fuzzy logic or a neural network, and although the intelligent ammeter is effective in certain occasions, a large amount of training data is needed, the model is troublesome to construct, the calculated amount is large, and the application requirements of an actual power grid environment are difficult to meet.
Disclosure of Invention
The application provides a state evaluation method, device, equipment and storage medium of an intelligent ammeter, which are used for solving the technical problems that the prior art depends on a fixed threshold value and a large amount of data is trained, the complexity is high, and the actual application requirements are difficult to meet.
In view of this, a first aspect of the present application provides a method for evaluating a state of a smart meter, including:
acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes;
respectively calculating the reference weight of each type of index according to the historical data matrix, and then constructing a reference cloud model of the intelligent ammeter based on the reference weight;
comparing the reference cloud model with a cloud model to be detected corresponding to data to be detected to obtain a cloud center of gravity offset, wherein the cloud model to be detected is constructed based on the data to be detected;
and evaluating the intelligent ammeter to be tested according to the cloud center of gravity offset to obtain an evaluation result, wherein the evaluation result comprises comments.
Preferably, the obtaining the history data matrix from the preset history data according to the preset state evaluation index includes:
acquiring preset state evaluation indexes in a plurality of continuous preset time periods from preset historical data;
and carrying out average value normalization processing on the same type of preset state evaluation indexes in each preset time period to obtain a historical data matrix.
Preferably, the acquiring the history data matrix from the preset history data according to the preset state evaluation index further includes:
and performing index evaluation analysis according to the metering abnormal parameters, the overload parameters and the clock battery parameters of the intelligent electric meter to obtain a plurality of preset state evaluation indexes.
Preferably, after the reference weights of each class of indexes are calculated according to the historical data matrix, a reference cloud model of the smart meter is constructed based on the reference weights, and the method comprises the following steps:
respectively calculating the standard information entropy of each type of index according to the historical data matrix;
calculating the reference weight of each type of index according to the reference information entropy;
and calculating historical comprehensive expected parameters based on the reference weight and the historical samples in the historical data matrix to obtain a reference cloud model of the intelligent ammeter.
The second aspect of the present application provides a state evaluation device of a smart meter, including:
the matrix construction unit is used for acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes;
the model construction unit is used for respectively calculating the reference weight of each type of index according to the historical data matrix and then constructing a reference cloud model of the intelligent ammeter based on the reference weight;
the model comparison unit is used for comparing the reference cloud model with a cloud model to be detected corresponding to data to be detected to obtain a cloud center of gravity offset, and the cloud model to be detected is constructed based on the data to be detected;
and the state evaluation unit is used for evaluating the intelligent ammeter to be tested according to the cloud center of gravity offset to obtain an evaluation result, wherein the evaluation result comprises comments.
Preferably, the matrix construction unit is specifically configured to:
acquiring preset state evaluation indexes in a plurality of continuous preset time periods from preset historical data;
and carrying out average value normalization processing on the same type of preset state evaluation indexes in each preset time period to obtain a historical data matrix.
Preferably, the method further comprises:
and the index construction unit is used for carrying out index evaluation analysis according to the metering abnormal parameters, the overload parameters and the clock battery parameters of the intelligent electric meter to obtain a plurality of preset state evaluation indexes.
Preferably, the model building unit is specifically configured to:
respectively calculating the standard information entropy of each type of index according to the historical data matrix;
calculating the reference weight of each type of index according to the reference information entropy;
and calculating historical comprehensive expected parameters based on the reference weight and the historical samples in the historical data matrix to obtain a reference cloud model of the intelligent ammeter.
A third aspect of the present application provides a state evaluation device of a smart meter, the device including a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the state evaluation method of the smart meter according to the first aspect according to the instruction in the program code.
A fourth aspect of the present application provides a computer-readable storage medium for storing program code for executing the state evaluation method of the smart meter of the first aspect.
From the above technical solutions, the embodiments of the present application have the following advantages:
in the present application, a method for evaluating a state of a smart meter is provided, including: acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes; respectively calculating the reference weight of each type of index according to the historical data matrix, and then constructing a reference cloud model of the intelligent ammeter based on the reference weight; comparing the reference cloud model with a cloud model to be detected corresponding to the data to be detected to obtain a cloud center of gravity offset, and constructing the cloud model to be detected based on the data to be detected; and evaluating the intelligent ammeter to be tested according to the cloud center offset to obtain an evaluation result, wherein the evaluation result comprises comments.
According to the state evaluation method of the intelligent electric meter, the state of the intelligent electric meter is evaluated according to various preset state evaluation indexes constructed by historical data, and the specific parameter calculation is carried out by adopting the entropy weight method, so that the actual operation characteristics of the intelligent electric meter can be reflected and evaluated more accurately; in addition, a cloud model is dynamically generated by adopting historical data and data to be tested, and an evaluation result is obtained based on comparison of the cloud model instead of relying on a fixed threshold, so that the evaluation process is more flexible and reliable, the process does not need to rely on a large amount of data training, is simple and easy to execute, has less related calculation amount and small model, and can meet the actual application requirements. Therefore, the method and the device can solve the technical problems that the prior art depends on a fixed threshold value and a large amount of data for training, the complexity is high, and the actual application requirements are difficult to meet.
Drawings
Fig. 1 is a flow chart of a state evaluation method of a smart meter according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a state evaluation device of a smart meter according to an embodiment of the present application;
fig. 3 is a schematic diagram of cloud center of gravity offset distribution of a smart meter according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
For easy understanding, referring to fig. 1, an embodiment of a method for evaluating a state of a smart meter provided in the present application includes:
step 101, acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes.
Further, step 101 includes:
acquiring preset state evaluation indexes in a plurality of continuous preset time periods from preset historical data;
and carrying out average value normalization processing on the similar preset state evaluation indexes in each preset time period to obtain a historical data matrix.
Further, step 101, before further includes:
and performing index evaluation analysis according to the metering abnormal parameters, the overload parameters and the clock battery parameters of the intelligent electric meter to obtain a plurality of preset state evaluation indexes.
It should be noted that, according to the technical scheme of the embodiment, the historical data and the data to be tested need to be expressed according to the preset state evaluation index, so that the state evaluation of the intelligent ammeter is facilitated, so that the construction process of the preset state evaluation index of the embodiment does not take the historical data or the data to be tested as an object description, but independently describes the construction process of the preset state evaluation index, and the historical data and the data to be tested only need to be subjected to index calculation according to the construction process.
For the metering abnormal index, the electricity consumption data acquisition system can track, observe and analyze a large amount of user electricity consumption data in real time, so that various unusual data fluctuation can be rapidly identified. By deep analysis of these data, the master station is able to actively determine various metering unusual events; these metering anomalies are often closely related to the operating state of the smart meter. The occurrence of certain abnormal events may mean that the smart meter is currently having a possibility of certain faults; thus, potential fault risks of the smart meter caused by metering abnormal events can be evaluated through metering abnormal indexes.
For metering abnormal events such as uneven meter value, meter flying, meter backing, meter stopping, reverse electric quantity abnormality and the like, the Bayesian formula is adopted to calculate the posterior probability:
wherein P (A|B) i ) Representing the probability of failure of a smart meter in the event of a metering anomaly of that meter, P (B) i I A) represents the probability of metering abnormality occurring under the condition that the intelligent ammeter detects abnormality, P (A) represents the probability that event A is abnormal due to the fact that the intelligent ammeter is disassembled and detected, and P (B) i ) Representing event B i And (5) the probability of the ith abnormality of the intelligent ammeter.
For abnormal phenomena such as voltage out-of-limit, voltage loss, current flowing, current short phase and reverse equal trend, the correlation probability of the abnormal phenomena is measured by establishing the following evaluation index function because the abnormal phenomena frequently occur and the influence on the service life of the intelligent ammeter is larger:
wherein q i To count the occurrence times of the ith exception in the period, D i And (5) the probability of association of the occurrence of the ith abnormal condition and the abnormal condition of the intelligent ammeter is obtained.
The measurement abnormality index S can be integrated MA The expression is as follows:
the overload evaluation indexes of the intelligent ammeter, namely the voltage overload index and the current overload index, are considered in the working of the intelligent ammeter, and the tolerable voltage and current ranges are clearly marked on the nameplate of the intelligent ammeter. As a consumer device, the life of a smart meter may be compromised once its measured voltage, current or power exceeds a certain threshold.
If it is U N 、I N The rated voltage and the rated current of the intelligent ammeter are respectively,for voltage and current at time t, U cal 、I cal For the voltage and current measured during the statistical period, then:
then the voltage overload indicator S U And current overload index S I Can be expressed as:
wherein P is the number of days when the voltage exceeds the rated voltage in the statistical period; q is the number of days when the current exceeds the rated current in the counting period; t is a statistical period; u (U) p Is the average voltage of the day when the voltage exceeds the rated voltage; i q Is the average current on the current day when the current exceeds the rated current.
For the clock battery index, when the clock battery state of the intelligent ammeter is evaluated, battery under-voltage records in a statistical period are used as input data, and the occurrence frequency of the records is used as an evaluation standard. If the intelligent ammeter has a record of the battery under-voltage, the intelligent ammeter indicates that the clock battery may have defects and may need to be replaced. When the intelligent ammeter has records of multiple battery undervoltage, the battery is proved to be in an abnormal state for a long time. The present embodiment quantitatively evaluates the state of the clock battery according to these undervoltage records, and uses the following formula for detailed index evaluation:
and a represents the number of times of under-voltage recording of the clock battery, which is called by the master station 6 months before the intelligent ammeter is dismantled. It will be appreciated that 6 months is only an example, and the specific time may be selected according to the actual situation, which is not limited herein.
Assuming that the preset time period of the embodiment is one month, index data of a plurality of continuous months can be obtained in preset historical data, for example, 4 preset state evaluation indexes of a certain batch of intelligent electric meters running in the same batch of electric energy meters are obtained according to the preset historical data of m months, namely, a metering abnormality index, a voltage overload index, a current overload index and a clock battery index, and after data standardization is performed, a historical data matrix A can be obtained by taking the average value of each index in each month:
wherein,the results of data normalization at month m for each index are shown.
And 102, respectively calculating the reference weight of each type of index according to the historical data matrix, and then constructing a reference cloud model of the intelligent ammeter based on the reference weight.
Further, step 102 includes:
respectively calculating the reference information entropy of each type of index according to the historical data matrix;
calculating the reference weight of each type of index according to the reference information entropy;
and calculating historical comprehensive expected parameters based on the reference weight and the historical samples in the historical data matrix to obtain a reference cloud model of the intelligent ammeter.
It should be noted that, based on the history data matrix a, the reference information entropy of the j-th preset state evaluation index may be calculated:
wherein,the j index indicating the i month. According to the reference information entropy e j Then the reference weight of the j-th preset state evaluation index can be calculated:
the standard Zhengtai cloud model can be constructed according to preset historical data:
where n is the total number of samples for the j-th index,an ith normalized historical data sample representing an jth indicator, < >>Normalizing the maximum and minimum values of the historical data samples for the jth index, respectively, < + >>Is the expected value of the j-th index, +.>Is standard Zhengtai Yun MoEntropy of j-th index, +.>The super entropy of the j-th index of the standard Zhengtai cloud model is obtained. For the standard Zhengtai cloud model, the reference weight omega obtained by the calculation can be adopted j And respectively calculating historical comprehensive expected parameters of the integral model, such as expected values of the integral model, and the like, so as to obtain the reference cloud model of the intelligent ammeter.
And 103, comparing the reference cloud model with a cloud model to be detected corresponding to the data to be detected to obtain the cloud center of gravity offset, and constructing the cloud model to be detected based on the data to be detected.
If the ith sample of the data to be tested of the jth index is recorded asThe calculated corresponding index weight is marked as omega' j The specific calculation process of the index weight is the same as the calculation process of the reference weight of the history data, and will not be described herein. The initial cloud model constructed based on the data to be measured is expressed as follows:
wherein,respectively normalizing the maximum value and the minimum value of the data sample to be measured for the j index, and performing +.>Is the expected value of the j-th index, +.>Entropy of the j-th index, he j The super entropy of the j index.
Based on index weight omega' j The cloud model to be measured can be obtained:
wherein E is x E is the expected value of the cloud model to be tested n The He is the entropy of the cloud model to be measured, and the He is the super entropy of the cloud model to be measured. Comparing the cloud model to be detected with the reference cloud model to obtain the cloud center offset; it can be appreciated that the comparison process is a one-by-one comparison of core parameters, and the cloud center of gravity offset can be calculated based on the results.
And 104, evaluating the intelligent electric meter to be tested according to the cloud center of gravity offset to obtain an evaluation result, wherein the evaluation result comprises comments.
It should be noted that, the cloud center of gravity offset may reflect the deviation degree of the operation state of the smart meter, refer to fig. 3 specifically. And different membership degrees, namely the degree or probability of membership in a certain range of the preset deviation degree interval, can be reflected based on the preset deviation degree interval.
The preset deviation interval in this embodiment is a preset reference interval, and the belonging evaluation class, that is, the evaluation grade, can be determined according to the membership degree of the cloud center offset in the preset deviation interval. Referring to table 1 specifically, examples of comment levels corresponding to different preset deviation intervals are given.
Table 1 preset deviation interval evaluation level example
For ease of understanding, example cases are provided, see tables 2 and 3.
Table 2 example of cloud membership analysis for different metrics
Table 3 different index weight examples
The evaluation result of the smart meter is shown in table 4.
Table 4 evaluation results of smart meter
According to the state evaluation method of the intelligent electric meter, the state of the intelligent electric meter is evaluated according to various preset state evaluation indexes constructed by historical data, and the specific parameter calculation is carried out by adopting an entropy weight method, so that the actual operation characteristics of the intelligent electric meter can be reflected and evaluated more accurately; in addition, a cloud model is dynamically generated by adopting historical data and data to be tested, and an evaluation result is obtained based on comparison of the cloud model instead of relying on a fixed threshold, so that the evaluation process is more flexible and reliable, the process does not need to rely on a large amount of data training, is simple and easy to execute, has less related calculation amount and small model, and can meet the actual application requirements. Therefore, the embodiment of the application can solve the technical problems that the prior art depends on a fixed threshold value and a large amount of data for training, the complexity is high, and the actual application requirements are difficult to meet.
For easy understanding, please refer to fig. 2, the present application provides an embodiment of a status evaluation device of a smart meter, including:
a matrix construction unit 201, configured to obtain a historical data matrix from preset historical data according to preset state evaluation indexes, where the preset state evaluation indexes include a measurement anomaly index, a voltage overload index, a current overload index, and a clock battery index;
the model building unit 202 is configured to build a reference cloud model of the smart meter based on the reference weights after calculating the reference weights of each class of indexes according to the historical data matrix;
the model comparison unit 203 is configured to compare the reference cloud model with a cloud model to be tested corresponding to the data to be tested, to obtain a cloud center of gravity offset, and construct the cloud model to be tested based on the data to be tested;
and the state evaluation unit 204 is configured to evaluate the smart meter to be tested according to the cloud center of gravity offset, so as to obtain an evaluation result, where the evaluation result includes comments.
Further, the matrix construction unit 201 is specifically configured to:
acquiring preset state evaluation indexes in a plurality of continuous preset time periods from preset historical data;
and carrying out average value normalization processing on the similar preset state evaluation indexes in each preset time period to obtain a historical data matrix.
Further, the method further comprises the following steps:
the index construction unit 205 is configured to perform index evaluation analysis according to the metering abnormal parameter, the overload parameter and the clock battery parameter of the smart meter, so as to obtain a plurality of preset state evaluation indexes.
Further, the model building unit 202 is specifically configured to:
respectively calculating the reference information entropy of each type of index according to the historical data matrix;
calculating the reference weight of each type of index according to the reference information entropy;
and calculating historical comprehensive expected parameters based on the reference weight and the historical samples in the historical data matrix to obtain a reference cloud model of the intelligent ammeter.
The application also provides state evaluation equipment of the intelligent ammeter, which comprises a processor and a memory;
the memory is used for storing the program codes and transmitting the program codes to the processor;
the processor is configured to execute the state evaluation method of the smart meter in the method embodiment according to the instruction in the program code.
The application also provides a computer readable storage medium, wherein the computer readable storage medium is used for storing program codes, and the program codes are used for executing the state evaluation method of the intelligent ammeter in the embodiment of the method.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to execute all or part of the steps of the methods described in the embodiments of the present application by a computer device (which may be a personal computer, a server, or a network device, etc.). And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A state evaluation method of a smart meter, comprising:
acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes;
respectively calculating the reference weight of each type of index according to the historical data matrix, and then constructing a reference cloud model of the intelligent ammeter based on the reference weight;
comparing the reference cloud model with a cloud model to be detected corresponding to data to be detected to obtain a cloud center of gravity offset, wherein the cloud model to be detected is constructed based on the data to be detected;
and evaluating the intelligent ammeter to be tested according to the cloud center of gravity offset to obtain an evaluation result, wherein the evaluation result comprises comments.
2. The method for evaluating the state of a smart meter according to claim 1, wherein the obtaining the history data matrix from the preset history data according to the preset state evaluation index comprises:
acquiring preset state evaluation indexes in a plurality of continuous preset time periods from preset historical data;
and carrying out average value normalization processing on the same type of preset state evaluation indexes in each preset time period to obtain a historical data matrix.
3. The method for evaluating the state of a smart meter according to claim 1, wherein the acquiring the history data matrix from the preset history data according to the preset state evaluation index further comprises:
and performing index evaluation analysis according to the metering abnormal parameters, the overload parameters and the clock battery parameters of the intelligent electric meter to obtain a plurality of preset state evaluation indexes.
4. The method for evaluating the state of a smart meter according to claim 1, wherein after calculating the reference weights of each class of indexes according to the historical data matrix, constructing a reference cloud model of the smart meter based on the reference weights, comprises:
respectively calculating the standard information entropy of each type of index according to the historical data matrix;
calculating the reference weight of each type of index according to the reference information entropy;
and calculating historical comprehensive expected parameters based on the reference weight and the historical samples in the historical data matrix to obtain a reference cloud model of the intelligent ammeter.
5. A state evaluation device of a smart meter, comprising:
the matrix construction unit is used for acquiring a historical data matrix from preset historical data according to preset state evaluation indexes, wherein the preset state evaluation indexes comprise measurement abnormality indexes, voltage overload indexes, current overload indexes and clock battery indexes;
the model construction unit is used for respectively calculating the reference weight of each type of index according to the historical data matrix and then constructing a reference cloud model of the intelligent ammeter based on the reference weight;
the model comparison unit is used for comparing the reference cloud model with a cloud model to be detected corresponding to data to be detected to obtain a cloud center of gravity offset, and the cloud model to be detected is constructed based on the data to be detected;
and the state evaluation unit is used for evaluating the intelligent ammeter to be tested according to the cloud center of gravity offset to obtain an evaluation result, wherein the evaluation result comprises comments.
6. The state evaluation device of a smart meter according to claim 5, wherein the matrix construction unit is specifically configured to:
acquiring preset state evaluation indexes in a plurality of continuous preset time periods from preset historical data;
and carrying out average value normalization processing on the same type of preset state evaluation indexes in each preset time period to obtain a historical data matrix.
7. The state evaluation device of a smart meter according to claim 5, further comprising:
and the index construction unit is used for carrying out index evaluation analysis according to the metering abnormal parameters, the overload parameters and the clock battery parameters of the intelligent electric meter to obtain a plurality of preset state evaluation indexes.
8. The state evaluation device of a smart meter according to claim 5, wherein the model building unit is specifically configured to:
respectively calculating the standard information entropy of each type of index according to the historical data matrix;
calculating the reference weight of each type of index according to the reference information entropy;
and calculating historical comprehensive expected parameters based on the reference weight and the historical samples in the historical data matrix to obtain a reference cloud model of the intelligent ammeter.
9. A state evaluation device of a smart meter, the device comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the state evaluation method of the smart meter according to any one of claims 1 to 4 according to instructions in the program code.
10. A computer-readable storage medium storing a program code for executing the state evaluation method of the smart meter according to any one of claims 1 to 4.
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| CN202311407897.5A CN117452315A (en) | 2023-10-27 | 2023-10-27 | State evaluation method, device and equipment of intelligent ammeter and storage medium |
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