CN114137472A - Intelligent electric energy meter state evaluation system with data sharing and service fusion functions - Google Patents
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Abstract
The invention discloses an intelligent electric energy meter state evaluation system with data sharing and service fusion functions, which belongs to the technical field of electric energy meter evaluation and comprises an external end, a platform end and a server, wherein the external end and the platform end are in communication connection with the server; the external end is arranged in the intelligent electric energy meter and used for collecting the operation data of the electric energy meter and sending the collected operation data of the electric energy meter to the server, and the platform end comprises a storage module and an evaluation module; the server sends the received electric energy meter operation data to the storage module for storage; the evaluation module is used for evaluating the running data of the electric energy meter, acquiring the running data of the electric energy meter, identifying the type of the running data of the electric energy meter, evaluating the state of the intelligent electric energy meter, knowing the running state of the current intelligent electric energy meter, avoiding dismantling and repairing the qualified intelligent electric energy meter, avoiding resource waste and solving the traditional management mode that the current intelligent electric energy meter is dismantled and dismantled within 8 years.
Description
Technical Field
The invention belongs to the technical field of electric energy meter evaluation, and particularly relates to an intelligent electric energy meter state evaluation system with data sharing and service fusion functions.
Background
The intelligent electric energy meter is one of the most basic devices of an intelligent power grid and undertakes tasks such as electric energy metering, data acquisition and transmission and the like. The quality of the running state of the intelligent electric energy meter also has great influence on the running quality of the intelligent power grid. The design life requirement of the current relevant regulations on the intelligent electric energy meter is not less than 10 years, the verification period of the electronic alternating current electric energy meter for the 1-level meter and the 2-level meter is not more than 8 years, the intelligent electric energy meter using 8 years needs to be disassembled and sent back to a laboratory for detection, the cost is huge, even if the verification is qualified, only 2 years of life remain, the current qualified intelligent electric energy meter is mostly scrapped, and serious waste is caused; therefore, a state evaluation system of an intelligent electric energy meter with data sharing and service fusion functions is needed to be provided at present, whether the electric energy meter has a fault or not is judged by evaluating the running state of the electric energy meter, and a traditional management mode that the electric energy meter is dismantled within 8 years is solved.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an intelligent electric energy meter state evaluation system with data sharing and service fusion functions.
The purpose of the invention can be realized by the following technical scheme:
the intelligent electric energy meter state evaluation system with the functions of data sharing and service integration comprises an external end, a platform end and a server, wherein the external end and the platform end are in communication connection with the server; the external end is arranged in the intelligent electric energy meter and used for collecting the operation data of the electric energy meter and sending the collected operation data of the electric energy meter to the server, and the platform end comprises a storage module and an evaluation module; the server sends the received electric energy meter operation data to the storage module for storage; the evaluation module is used for evaluating the operation data of the electric energy meter, and the specific method comprises the following steps:
the method comprises the steps of obtaining electric energy meter operation data, identifying types of the electric energy meter operation data, obtaining standard data of time corresponding to the types of the operation data, comparing the obtained standard data with corresponding data in the electric energy meter operation data, and calculating the accuracy rate ZL of the corresponding classified datatOn the basis of an analytic hierarchy process, a judgment matrix M (M) is established by comparing different factors pairwiseij)n×nFor the judgment matrix M ═ M (M)ij)n×nCarrying out consistency check, and after the consistency check is passed, utilizing a formula M omega*＝λmaxω*Calculating a weight vector ω*，λmaxFor the maximum feature root of the matrix, for ω*Normalizing to obtain weight distribution, and marking as weight index QZt(ii) a According to the formula
And obtaining the detection score of the current electric energy meter, and judging whether the current detection score reaches the standard.
Further, when the current detection score is judged not to meet the standard, a retest signal is generated and sent to the wireless transmission module, N times of detection evaluation is carried out again, and when the retest evaluation is still unqualified, the corresponding fault of the electric energy meter is judged.
Further, the pair structureThe decision matrix of (2) is checked for consistency, i.e.
The random consistency ratio RI is CI/CR; in the formula: n is the index number; when CR is reached<And when the consistency is met, judging that the matrix meets the consistency, otherwise, readjusting the judgment matrix until the consistency is met.
Further, the external end comprises an acquisition module and a wireless transmission module;
the acquisition module is used for acquiring the operating data of the intelligent electric energy meter in real time and sending the acquired operating data of the intelligent electric energy meter to the wireless transmission module, and the wireless transmission module sends the received operating data of the intelligent electric energy meter to the server.
Furthermore, the wireless transmission module comprises a 5G transmission unit, a plurality of 5G transmission channels are arranged in the 5G transmission unit, the 5G transmission channels are marked with priorities, and the electric energy meter operation data are transmitted through the 5G transmission channels.
Further, the method for transmitting the operation data of the electric energy meter through the 5G transmission channel comprises the following steps: the electric energy meter operation data are classified, the 5G transmission channel is matched with the electric energy meter operation classified data, and the electric energy meter operation classified data are transmitted through the corresponding 5G transmission channel.
Furthermore, the wireless transmission module further comprises a clustering unit, the clustering unit is used for clustering the received electric energy meter operation data to obtain operation clustering data, the operation clustering data are sent to the 5G transmission unit, and the 5G transmission unit transmits the obtained operation clustering data to the server by using the corresponding 5G transmission channel according to the priority.
Further, the method for clustering by the clustering unit comprises the following steps:
step SA 1: dividing the operating data into different sets according to the data types, marking the single data in the data types as a minimum unit, and marking the divided sets as a sample set D to be clustered, wherein the sample set D is { x }1,x2,...,xi,...,xn}; wherein x isiIs the ith minimum unitIs determined by the feature vector of (a),
i∈[1,n]n is the number of the smallest units in the data class; m is the characteristic number of the smallest unit,
the m characteristic value in the ith minimum unit;
step SA 2: all sample points in the sample set are taken as an independent cluster, and the distance d (C) between every two clusters is calculatedi,Cj)，
Step SA 3: setting the upper limit W of the number of minimum units in the cluster, and searching two cluster clusters C with the closest distancepAnd Cq，
Judging cluster CpAnd CqIf the sum of the minimum unit quantity of (C) is less than W, merging the cluster C if the sum of the minimum unit quantity of (C) is less than WpAnd CqAs a new cluster Cg(ii) a Otherwise, no combination is carried out;
step SA 4: recalculating new cluster CgDistance from all other classes;
step SA 5: and SA2 to SA4 are repeated to finish clustering of the running data of the electric energy meter, the obtained clustering data are marked with priorities, and the clustering data marked with the priorities are marked as running clustering data.
Compared with the prior art, the invention has the beneficial effects that: by evaluating the state of the intelligent electric energy meter, the running state of the current intelligent electric energy meter is known, the qualified intelligent electric energy meter is not dismantled and maintained any more, the resource waste is avoided, and the traditional management mode that the current intelligent electric energy meter is dismantled within 8 years is solved; the service life of the intelligent electric energy meter is prolonged. Different transmission channels are established by utilizing a 5G slicing technology, the transmission channels are reasonably planned according to the acquired data of different intelligent electric energy meters, and the transmission efficiency of the acquired data is improved; through the mutual cooperation of 5G transmission unit and clustering unit for gather data transmission's more high efficiency and intelligence.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an intelligent electric energy meter state evaluation system with data sharing and service fusion functions includes an external end, a platform end and a server, where the external end and the platform end are both in communication connection with the server;
the external end is arranged in the intelligent electric energy meter and comprises an acquisition module and a wireless transmission module;
the acquisition module is used for acquiring the operation data of the intelligent electric energy meter in real time, wherein the operation data comprises voltage, current and corresponding characteristic data, such as frequency, amplitude, phase and the like; and transmitting the collected electric energy meter operation data to a wireless transmission module, and transmitting the received electric energy meter operation data to a server by the wireless transmission module.
In one embodiment, in order to improve the transmission efficiency of data collected by an external terminal, a wireless transmission module performs data transmission by using a 5G transmission technology, so as to improve the transmission efficiency of the data, and the specific method includes:
the method comprises the steps of classifying the operation data of the electric energy meter, namely, classifying the priority of the data in the operation data of the electric energy meter, wherein in one embodiment, because the type of the operation data of the electric energy meter is basically fixed, a priority table is made in a manual mode, and then data matching is carried out to classify the priority of the operation data of the electric energy meter; in another embodiment, a mechanical learning model is established based on a CNN network or a DNN network, and the priority of the operation data of the electric energy meter is intelligently divided through the mechanical learning model; establishing 5G transmission channels with corresponding quantity according to the grading number,
the 5G transmission channel is established based on the 5G slicing technology, the slicing authority can be opened by renting with an operator, the priority of the 5G transmission channel is set, and the priority of the 5G transmission channel can be obtained in the dividing process according to the 5G slicing technology, for example: the network slice transmission method comprises a first transmission channel, a second transmission channel and a third transmission channel, wherein the first transmission channel, the second transmission channel and the third transmission channel are divided by applying the characteristics of uRLLC, eMB and mMTC respectively, resources distributed by different slices are mutually isolated and independent in a frequency domain and can be flexibly adjusted, and thus air interface congestion of one network slice does not influence other network slices; the uRLLC slice needs low delay and high reliability, needs to give priority to allocation resources, and reduces queuing delay; the eMB slice has a lot of service demands of high data volume, the throughput of the whole network can be improved by preferentially distributing resources, but the uRLLC slice does not have too high requirements on time delay; the scheduling priority of the mMTC slice is the lowest, and under most conditions of massive sensors, the uplink data volume is not large and the requirement on time delay is not high. Therefore, the urrllc slice is of a first priority (highest priority), the eMMB slice is of a second priority, and the mtc slice is of a third priority, and RB (resource block) resources are respectively configured according to the priorities.
And matching the 5G transmission channel with the operation grading data of the electric energy meter, wherein the operation grading data of the electric energy meter is the graded operation data of the electric energy meter, and transmitting the operation grading data of the electric energy meter through the corresponding 5G transmission channel. By utilizing the 5G slicing technology to establish different transmission channels, the efficiency of data transmission is improved.
In one embodiment, because the classification of the operation data of the electric energy meter has certain one-sidedness, the data transmission can be carried out in other modes; the wireless transmission module comprises a 5G transmission unit and a clustering unit;
the clustering unit is used for clustering the received electric energy meter operation data to obtain operation clustering data, and sending the operation clustering data to the 5G transmission unit, wherein a plurality of 5G transmission channels are arranged in the 5G transmission unit, the 5G transmission channels are marked with priorities and are directly rented with operators, a certain number of 5G transmission channels are opened by utilizing a 5G slicing technology, and the operation clustering unit can be directly used subsequently, so that the data processing amount is reduced, and the calculation power is saved; and transmitting the obtained operation clustering data to a server by using a corresponding 5G transmission channel according to the priority.
The method for clustering by the clustering unit comprises the following steps:
step SA 1: dividing the operation data into different sets according to the data types, namely dividing the operation data according to the different classifications of voltage, current and the like; marking single data in the data types as a minimum unit, namely marking the single data in the classified data such as voltage, current and the like as the minimum unit, and marking the divided set as a sample set D to be clustered as { x ═ x1,x2,...,xi,...,xn}; wherein x isiIs the feature vector of the ith minimum cell,
i∈[1,n]n is the number of the smallest units in the data class; m is the characteristic number of the smallest unit,
the data of the mth characteristic value in the ith minimum unit, such as a frequency value, an amplitude value and the like;
step SA 2: all sample points in the sample set are taken as an independent cluster, and the distance d (C) between every two clusters is calculatedi,Cj)，
Step SA 3: setting an upper limit W of the number of minimum units in the clustering cluster, wherein the upper limit W of the number of the minimum units in the clustering cluster is discussed and set by an expert group, and the data transmission capability of an external end is mainly considered; finding two nearest clusters CpAnd Cq，
Judging cluster CpAnd CqIf the sum of the minimum unit quantity of (C) is less than W, merging the cluster C if the sum of the minimum unit quantity of (C) is less than WpAnd CqAs a new cluster Cg(ii) a Otherwise, no combination is carried out;
step SA 4: recalculating new cluster CgDistance from all other classes;
step SA 5: and SA2 to SA4 are repeated to finish clustering of the running data of the electric energy meter, the obtained clustering data are marked with priorities, and the clustering data marked with the priorities are marked as running clustering data.
In one embodiment, the method of labeling the priority of the clustered data is: establishing a clustering characteristic priority table, wherein clustering data are clustered according to the minimum unit, and sorting can be performed according to the distance from a normal value interval, such as a voltage normal interval and a current normal interval, of the corresponding data of the electric energy meter by setting the normal value interval, wherein the farther the distance is, the earlier the sorting is, and abnormal data are transmitted preferentially; and matching the acquired clustering data with a clustering characteristic priority table to acquire corresponding priority.
In another embodiment, a normal value interval of corresponding data of the electric energy meter is set, a mechanical learning model is established based on a CNN network or a DNN network, a large amount of clustering data is obtained for training, and the clustering data priority is intelligently marked through the trained mechanical learning model.
The platform end is a data processing center and is used for processing data collected and sent by a plurality of external ends, and the data processing center comprises a storage module and an evaluation module;
the server sends the received electric energy meter operation data to the storage module for storage;
the evaluation module is used for evaluating the operation data of the electric energy meter, and the specific method comprises the following steps:
the method comprises the steps of obtaining electric energy meter operation data, identifying the type of the electric energy meter operation data, obtaining standard data of time corresponding to the type of the operation data, obtaining the standard data from a corresponding power grid platform, comparing the obtained standard data with corresponding data in the electric energy meter operation data, and calculating the accuracy rate ZL of the corresponding classified datatT represents classification data, t is 1, 2, 3 … …, o; o is a positive integer. Based on an analytic hierarchy process, different factors are compared pairwise (by adopting a judgment scale of 1-9), namely pairwise comparison of evaluation indexes, and a judgment matrix M ═ M (M ═ is establishedij)n×n，mijIs two factors miAnd mjThe ratio of the effect on the evaluation; for the judgment matrix M ═ Mij)n×nCarrying out consistency check, and after the consistency check is passed, utilizing a formula M omega*＝λmaxω*Calculating a weight vector ω*，λmaxTo determine the largest root of the features of the matrix,
for omega*Normalizing to obtain weight distribution, and marking as weight index QZt；
The constructed decision matrix is checked for consistency, i.e.
The random consistency ratio RI is CI/CR;
in the formula: n is the index number; for the 1-9 order judgment matrix, the RI values are respectively shown in the table;
when CR is less than 0.10, judging that the matrix meets the consistency, otherwise, readjusting the judgment matrix until the consistency is met;
according to the formula
Obtaining a detection score of the current electric energy meter, judging whether the current detection score reaches the standard, and not operating when the current detection score reaches the standard; when the current detection score is judged not to reach the standard, generating a retest signal, sending the retest signal to the wireless transmission module, and carrying out detection evaluation for N times again, wherein N is a positive integer and the general value range is [2, 5 ]]And when the retest evaluation is not qualified yet, judging the corresponding electric energy meter to be in fault.
Whether the current detection score reaches the standard or not is judged, a standard value can be set according to the service life of the corresponding electric energy meter, and corresponding comparison is carried out to judge whether the current detection score reaches the standard or not;
and the retest evaluation can be carried out by setting corresponding evaluation standards according to the service life of the corresponding electric energy meter, and limiting the unqualified quantity.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: the method comprises the steps that an external end is arranged in an intelligent electric energy meter, the operation data of the electric energy meter are collected and sent to a server, and the server sends the received operation data of the electric energy meter to a storage module for storage; the evaluation module evaluates the electric energy meter operation data, obtains the electric energy meter operation data, identifies the electric energy meter operation data types, obtains standard data of operation data types corresponding to time, compares the obtained standard data with corresponding data in the electric energy meter operation data, calculates the accuracy of corresponding classified data, establishes a judgment matrix by comparing different factors in pairs based on an analytic hierarchy process, carries out consistency check on the judgment matrix, calculates a weight vector by using a formula after passing verification, normalizes the weight vector to obtain weight distribution, and marks the weight distribution as a weight index; and obtaining the detection score of the current electric energy meter according to a formula, and judging whether the current detection score reaches the standard.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. The intelligent electric energy meter state evaluation system with the functions of data sharing and service integration is characterized by comprising an external end, a platform end and a server, wherein the external end and the platform end are in communication connection with the server; the external end is arranged in the intelligent electric energy meter and used for collecting the operation data of the electric energy meter and sending the collected operation data of the electric energy meter to the server, and the platform end comprises a storage module and an evaluation module; the server sends the received electric energy meter operation data to the storage module for storage; the evaluation module is used for evaluating the operation data of the electric energy meter, and the specific method comprises the following steps:
the method comprises the steps of obtaining electric energy meter operation data, identifying types of the electric energy meter operation data, obtaining standard data of time corresponding to the types of the operation data, comparing the obtained standard data with corresponding data in the electric energy meter operation data, and calculating the accuracy rate ZL of the corresponding classified datatOn the basis of an analytic hierarchy process, a judgment matrix M (M) is established by comparing different factors pairwiseij)n×nFor the judgment matrix M ═ M (M)ij)n×nCarrying out consistency check, and after the consistency check is passed, utilizing a formula M omega*＝λmaxω*Calculating a weight vector ω*To ω*Normalizing to obtain weight distribution, and marking as weight index QZt(ii) a According to the formula
And obtaining the detection score of the current electric energy meter, and judging whether the current detection score reaches the standard.
2. The system according to claim 1, wherein when it is determined that the current detection score does not meet the standard, a retest signal is generated and sent to the wireless transmission module, the detection and evaluation is performed N times again, and when the retest evaluation is still not qualified, a fault of the corresponding electric energy meter is determined.
3. The system for evaluating the status of an intelligent electric energy meter with data sharing and service integration functions as claimed in claim 1, wherein the constructed judgment matrix is checked for consistency, namely
The random consistency ratio RI is CI/CR; n is the index number; when CR is reached<And when the consistency is met, judging that the matrix meets the consistency, otherwise, readjusting the judgment matrix until the consistency is met.
4. The system for evaluating the state of the intelligent electric energy meter with the functions of data sharing and service integration according to claim 1, wherein the external end comprises an acquisition module and a wireless transmission module;
the acquisition module is used for acquiring the operating data of the intelligent electric energy meter in real time and sending the acquired operating data of the intelligent electric energy meter to the wireless transmission module, and the wireless transmission module sends the received operating data of the intelligent electric energy meter to the server.
5. The system of claim 4, wherein the wireless transmission module comprises a 5G transmission unit, the 5G transmission unit is provided with a plurality of 5G transmission channels, the 5G transmission channels are marked with priorities, and the transmission of the operation data of the electric energy meter is performed through the 5G transmission channels.
6. The system for evaluating the state of the intelligent electric energy meter with the functions of data sharing and service integration according to claim 5, wherein the method for transmitting the operation data of the electric energy meter through the 5G transmission channel comprises the following steps: the electric energy meter operation data are classified, the 5G transmission channel is matched with the electric energy meter operation classified data, and the electric energy meter operation classified data are transmitted through the corresponding 5G transmission channel.
7. The intelligent electric energy meter state evaluation system with the functions of data sharing and service integration according to claim 5, wherein the wireless transmission module further comprises a clustering unit, the clustering unit is used for clustering the received electric energy meter operation data to obtain operation clustering data, and sending the operation clustering data to the 5G transmission unit, and the 5G transmission unit transmits the obtained operation clustering data to the server by using a corresponding 5G transmission channel according to priority.
8. The system for evaluating the state of an intelligent electric energy meter with data sharing and service integration functions as claimed in claim 1, wherein λmaxJudging the maximum characteristic root of the matrix; t represents classification data, t is 1, 2, 3 … …, o; m isijIs two factors miAnd mjRatio of the effect on the evaluation.
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