CN117171144A - Electric power internet of things terminal data interaction method and storage device - Google Patents
Electric power internet of things terminal data interaction method and storage device Download PDFInfo
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Abstract
The application provides a data interaction method and a storage device for an electric power internet of things terminal, which belong to the technical field of the internet of things and specifically comprise the following steps: the reliability is determined based on the fluctuation amount and the occurrence number of the abnormal collected data in the first time threshold and the number of times of the abnormal state of the power equipment in the last year, when the reliability is larger than the first threshold, the suspected abnormal collected data serving as other abnormal collected data exist in the collected data of other internet of things terminals of the power equipment, the sum of the quantity of the other abnormal collected data, the ratio of the other abnormal data to the other collected data and the weight of the other abnormal data is based on, an evaluation model based on a machine learning algorithm is adopted to obtain the evaluation reliability of the abnormal collected data, and when the reliability is larger than the second threshold, the reliability evaluation value of the abnormal collected data is obtained based on the evaluation reliability and the reliability, and whether the abnormal collected data is in the abnormal state is determined based on the reliability evaluation reliability, so that the accuracy of data interaction is improved.
Description
Technical Field
The application belongs to the technical field of the Internet of things, and particularly relates to a data interaction method and a storage device of an electric power Internet of things terminal.
Background
In order to realize data interaction of the terminal of the Internet of things, the working state of the equipment of the Internet of things is obtained in an Internet of things data interaction method and system based on artificial intelligence of the application of patent publication No. CN115086343A, and the data acquisition task is segmented according to the working state; the segmented data acquisition task is issued to corresponding Internet of things equipment, and acquisition data fed back by the Internet of things equipment are received in real time; generating evaluation information of each Internet of things device according to the acquired data, and sending the evaluation information to each Internet of things device, but the following technical problems exist:
1. the dynamic evaluation of the credibility of the data of the internet of things equipment based on the evaluation information of the internet of things equipment is ignored, and the operation state of the internet of things equipment, particularly the failure state of the power equipment, can not be accurately and reliably known if the credibility of the acquired data of the internet of things equipment cannot be evaluated.
2. The reliability evaluation of the acquired data of the internet of things equipment is not considered by combining with other data of the electric equipment monitored by the internet of things equipment, if the temperature data of the transformer monitored by the internet of things equipment is suddenly changed, but the current, the voltage, the power factor, the running state of the cooling fan and the like of the transformer monitored by other internet of things equipment are not suddenly changed, the reliability of the temperature data which is monitored by the internet of things equipment and is required is obviously lower, so that if the operation state evaluation cannot be performed by combining with other data monitored by the internet of things equipment, the fault state of the electric equipment cannot be accurately and reliably known.
Aiming at the technical problems, the application provides a data interaction method and a storage device for an electric power Internet of things terminal.
Disclosure of Invention
The application realizes the aim, and adopts the following technical scheme:
according to one aspect of the application, a method for data interaction of an electric power internet of things terminal is provided.
The data interaction method for the electric power internet of things terminal is characterized by comprising the following steps of:
s11, acquiring acquisition data of an Internet of things terminal of the power equipment in real time, and taking the acquisition data as abnormal acquisition data when the acquisition data is in a suspected abnormal state, and entering step S12;
s12, determining the reliability of the abnormal acquisition data based on the fluctuation amount of the abnormal acquisition data in a first time threshold, the occurrence frequency of the abnormal acquisition data in the first time threshold and the frequency of the abnormal state of the power equipment in the last year, judging whether the reliability of the abnormal acquisition data is greater than the first threshold, if so, entering a step S13, otherwise, returning to the step S11;
s13, based on the acquired data of other Internet of things terminals of the power equipment, taking the acquired data of the other Internet of things terminals with suspected abnormalities as other abnormal acquired data, determining the weight of the other abnormal data based on the relevance of the other abnormal data and the abnormal acquired data, obtaining the evaluation reliability of the abnormal acquired data by adopting an evaluation model based on a machine learning algorithm based on the sum of the number of the other abnormal acquired data, the ratio of the other abnormal data to the other acquired data and the weight of the other abnormal data, and judging whether the evaluation reliability of the abnormal acquired data is greater than a second threshold value, if yes, determining that the abnormal acquired data has abnormalities, otherwise, entering step S14;
s14, based on the evaluation reliability and the credibility of the abnormal acquisition data, constructing a credibility evaluation value of the abnormal acquisition data, and determining whether the abnormal acquisition data is in an abnormal state or not based on the credibility evaluation value of the abnormal acquisition data.
The reliability of the abnormal collected data is determined based on the fluctuation amount of the abnormal collected data in the first time threshold, the occurrence times of the abnormal collected data in the first time threshold and the times of the abnormal state of the power equipment in the last year, so that the reliability of the abnormal collected data is evaluated from multiple angles, the accuracy and the comprehensiveness of judgment are further improved, and the accurate judgment of the abnormal state from the angle of data fluctuation is realized.
The reliability of the abnormal collected data is determined by further combining the collected data of other internet of things terminals of the power equipment, so that the reliability of the abnormal collected data is further judged from the angle of associated data, the technical problem of judgment errors caused by data abnormality is further prevented, and the reliability and accuracy of judgment are improved.
The reliability evaluation value is constructed by comprehensively considering the evaluation reliability and the reliability, so that the comprehensive judgment of the reliability evaluation value and the associated data is realized, the reliability and the accuracy of the reliability evaluation value are ensured, and a foundation is laid for accurately judging the state of the abnormally acquired data.
The further technical scheme is that when the fluctuation amount of the collected data in the second time threshold is larger than the first fluctuation amount threshold or when the running state of the power equipment reflected by the collected data is abnormal, the collected data is determined to be in a suspected abnormal state.
The further technical scheme is that the first time threshold is determined according to the type of the abnormal collected data and the importance degree of the power equipment, wherein the more important the type of the abnormal collected data is, the more important the importance degree of the power equipment is, the smaller the first time threshold is.
The further technical scheme is that the specific steps of the evaluation of the credibility of the abnormal acquisition data are as follows:
s21, acquiring the occurrence times of the abnormal acquisition data in a first time threshold, judging whether the occurrence times of the abnormal acquisition data in the first time threshold are larger than the first time threshold, if so, determining that the reliability of the abnormal acquisition data is 1, and if not, entering step S22;
s22, judging whether the occurrence times of the abnormal acquisition data in the first time threshold are larger than a second time threshold and the fluctuation amount of the abnormal acquisition data in the first time threshold is smaller than a second fluctuation amount threshold, wherein the second time threshold is smaller than the first time threshold, if yes, entering a step S23; if not, go to step S24;
s23, determining whether the number of times of abnormal states of the collected data of the same type of power equipment in the last year is smaller than a third time threshold value, if so, determining that the reliability of the abnormal collected data is 1, and if not, entering a step S24;
s24, based on the fluctuation amount of the abnormal collected data in the first time threshold, the occurrence frequency of the abnormal collected data in the first time threshold and the frequency of the abnormal state of the collected data in the last year of the power equipment, determining the credibility of the abnormal collected data by adopting an evaluation model based on a machine learning algorithm.
The further technical scheme is that the electric equipment of the same type is determined according to the voltage class, manufacturer and application place of the electric equipment.
The correlation between the other abnormal data and the abnormal acquisition data is determined by adopting a principal component analysis-based mode, and the correlation coefficient is normalized to obtain the weight of the other abnormal data.
The further technical scheme is that the specific steps of the construction of the evaluation reliability of the abnormal acquisition data are as follows:
s31, judging whether the number of other abnormal data is larger than a first number threshold, if so, entering a step S34, and if not, entering a step S32;
s32, judging whether the ratio of the other abnormal data to the other acquired data is larger than a first ratio threshold, if so, entering a step S34, and if not, entering a step S33;
s33, judging whether the sum of the weights of the other abnormal data is larger than a third threshold value, if so, entering a step S34, and if not, taking the ratio of the other abnormal data to the other acquired data as the evaluation credibility of the abnormal acquired data;
and S34, based on the sum of the number of the other abnormal acquired data, the ratio of the other abnormal data to the other acquired data and the weight of the other abnormal data, adopting an evaluation model based on a machine learning algorithm to obtain the evaluation reliability of the abnormal acquired data.
The further technical scheme is that the credibility evaluation value is determined by adopting a mathematical model based on an analytic hierarchy process based on evaluation credibility and credibility of the abnormal acquisition data.
The method comprises the steps that if and only if the credible evaluation value is larger than a set threshold value, abnormal data are determined to be in an abnormal state, when the abnormal data are in the abnormal state, data interaction is conducted between a server and the Internet of things terminal, and collected data of the Internet of things terminal are obtained again at least based on a third time threshold value.
In another aspect, an embodiment of the present application provides a computer apparatus, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and executing the data interaction method of the electric power Internet of things terminal when the processor runs the computer program.
In another aspect, the present application provides a computer storage device, on which a computer program is stored, where when the computer program is executed in a computer, the computer is caused to execute the above-mentioned method for data interaction between terminals of the electric power internet of things.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Detailed Description
Example embodiments will now be described more fully. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
Example 1
In order to solve the problems, according to one aspect of the application, the application provides a data interaction method for an electric power internet of things terminal.
The data interaction method for the electric power internet of things terminal is characterized by comprising the following steps of:
s11, acquiring acquisition data of an Internet of things terminal of the power equipment in real time, and taking the acquisition data as abnormal acquisition data when the acquisition data is in a suspected abnormal state, and entering step S12;
specifically, when the fluctuation amount of the collected data in the second time threshold is larger than the first fluctuation amount threshold or when the operation state of the power equipment reflected by the collected data is abnormal, determining that the collected data is in a suspected abnormal state.
Specifically, if the rated value of the running temperature of the transformer is 70 ℃, and when the collected data is 150 ℃, determining that the collected data is in a suspected abnormal state.
S12, determining the reliability of the abnormal acquisition data based on the fluctuation amount of the abnormal acquisition data in a first time threshold, the occurrence frequency of the abnormal acquisition data in the first time threshold and the frequency of the abnormal state of the power equipment in the last year, judging whether the reliability of the abnormal acquisition data is greater than the first threshold, if so, entering a step S13, otherwise, returning to the step S11;
specifically, the first time threshold is determined according to the type of the abnormal collected data and the importance degree of the power equipment, wherein the more important the type of the abnormal collected data is, the more important the importance degree of the power equipment is, the smaller the first time threshold is.
Specifically, the specific steps of evaluating the credibility of the abnormal acquired data are as follows:
s21, acquiring the occurrence times of the abnormal acquisition data in a first time threshold, judging whether the occurrence times of the abnormal acquisition data in the first time threshold are larger than the first time threshold, if so, determining that the reliability of the abnormal acquisition data is 1, and if not, entering step S22;
in another possible embodiment, when the threshold value is greater than the first time number, the process proceeds directly to step S24, where the reliability of the abnormally collected data is evaluated in combination with various factors.
S22, judging whether the occurrence times of the abnormal acquisition data in the first time threshold are larger than a second time threshold and the fluctuation amount of the abnormal acquisition data in the first time threshold is smaller than a second fluctuation amount threshold, wherein the second time threshold is smaller than the first time threshold, if yes, entering a step S23; if not, go to step S24;
s23, determining whether the number of times of abnormal states of the collected data of the same type of power equipment in the last year is smaller than a third time threshold value, if so, determining that the reliability of the abnormal collected data is 1, and if not, entering a step S24;
s24, based on the fluctuation amount of the abnormal collected data in the first time threshold, the occurrence frequency of the abnormal collected data in the first time threshold and the frequency of the abnormal state of the collected data in the last year of the power equipment, determining the credibility of the abnormal collected data by adopting an evaluation model based on a machine learning algorithm.
The evaluation model based on the machine learning algorithm adopts an evaluation model based on a GSO-BPNN algorithm, wherein the idea of optimizing the BP neural network by the improved firefly algorithm is to design the structure of the BP neural network according to required output and output parameters so as to determine the coding length of an individual in the firefly algorithm, each firefly individual comprises a weight and a threshold in the BP neural network, and then the position update, the decision radius update and the fluorescein update of the firefly population are carried out by the improved firefly algorithm, and meanwhile, the fitness value of the firefly individual is calculated according to the proposed fitness function, so that the aim of searching the optimal individual of the objective function value is fulfilled. Therefore, more excellent initial weight and threshold in the BP neural network are obtained, and then the optimization processing is continuously carried out according to the BP neural network, so that the BP neural network predicted value with the optimal solution is obtained.
In another possible embodiment, if the set of firefly individual neighbors is an empty set, then the location update formula is;
x i (t+1)＝x best (t+1)
wherein xbest (t+1) is a value obtained by selecting a value obtained by randomly searching M times in a neighborhood range with the current position as a starting point when the firefly field space is empty, wherein the evaluation formula of M is as follows:
wherein round () is a rounded function, σ is a constant, tmax is the maximum, the number of iterations, and t is the current number of iterations. Because the firefly algorithm is closer to the peak as the number of iterations of the algorithm increases, M is designed herein to decrease exponentially as the number of iterations increases, when the number of iterations is the algorithm set maximum or near maximum, the round () function calculation results are close to 1, and the M result is close to 2, thus ensuring that fireflies can also select a relatively better position in the later iteration process.
Specifically, the same type of power equipment is determined according to the voltage class, manufacturer and application place of the power equipment.
S13, based on the acquired data of other Internet of things terminals of the power equipment, taking the acquired data of the other Internet of things terminals with suspected abnormalities as other abnormal acquired data, determining the weight of the other abnormal data based on the relevance of the other abnormal data and the abnormal acquired data, obtaining the evaluation reliability of the abnormal acquired data by adopting an evaluation model based on a machine learning algorithm based on the sum of the number of the other abnormal acquired data, the ratio of the other abnormal data to the other acquired data and the weight of the other abnormal data, and judging whether the evaluation reliability of the abnormal acquired data is greater than a second threshold value, if yes, determining that the abnormal acquired data has abnormalities, otherwise, entering step S14;
specifically, the relevance of the other abnormal data and the abnormal acquisition data is obtained by determining the relevance coefficient of the other abnormal data and the abnormal acquisition data in a mode based on principal component analysis and carrying out normalization processing on the relevance coefficient.
Specifically, the construction of the evaluation reliability of the abnormal acquisition data comprises the following specific steps:
s31, judging whether the number of other abnormal data is larger than a first number threshold, if so, entering a step S34, and if not, entering a step S32;
s32, judging whether the ratio of the other abnormal data to the other acquired data is larger than a first ratio threshold, if so, entering a step S34, and if not, entering a step S33;
s33, judging whether the sum of the weights of the other abnormal data is larger than a third threshold value, if so, entering a step S34, and if not, taking the ratio of the other abnormal data to the other acquired data as the evaluation credibility of the abnormal acquired data;
and S34, based on the sum of the number of the other abnormal acquired data, the ratio of the other abnormal data to the other acquired data and the weight of the other abnormal data, adopting an evaluation model based on a machine learning algorithm to obtain the evaluation reliability of the abnormal acquired data.
S14, based on the evaluation reliability and the credibility of the abnormal acquisition data, constructing a credibility evaluation value of the abnormal acquisition data, and determining whether the abnormal acquisition data is in an abnormal state or not based on the credibility evaluation value of the abnormal acquisition data.
Specifically, the credibility evaluation value is determined by adopting a mathematical model based on an analytic hierarchy process based on the evaluation credibility and credibility of the abnormal acquisition data.
Specifically, if and only if the credible evaluation value is larger than a set threshold value, determining that the abnormal data is in an abnormal state, and when the abnormal data is in the abnormal state, performing data interaction with the internet of things terminal based on a server, and acquiring acquired data of the internet of things terminal again based on at least a third time threshold value.
The reliability of the abnormal collected data is determined based on the fluctuation amount of the abnormal collected data in the first time threshold, the occurrence times of the abnormal collected data in the first time threshold and the times of the abnormal state of the power equipment in the last year, so that the reliability of the abnormal collected data is evaluated from multiple angles, the accuracy and the comprehensiveness of judgment are further improved, and the accurate judgment of the abnormal state from the angle of data fluctuation is realized.
The reliability of the abnormal collected data is determined by further combining the collected data of other internet of things terminals of the power equipment, so that the reliability of the abnormal collected data is further judged from the angle of associated data, the technical problem of judgment errors caused by data abnormality is further prevented, and the reliability and accuracy of judgment are improved.
The reliability evaluation value is constructed by comprehensively considering the evaluation reliability and the reliability, so that the comprehensive judgment of the reliability evaluation value and the associated data is realized, the reliability and the accuracy of the reliability evaluation value are ensured, and a foundation is laid for accurately judging the state of the abnormally acquired data.
Example 2
An embodiment of the present application provides a computer apparatus, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: and executing the data interaction method of the electric power Internet of things terminal when the processor runs the computer program.
Example 3
The application provides a computer storage device, which stores a computer program, and when the computer program is executed in a computer, the computer is caused to execute the data interaction method of the electric power Internet of things terminal.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other manners as well. The system embodiments described above are merely illustrative.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage device. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage means, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. The aforementioned storage device includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present application as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present application. The technical scope of the present application is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The data interaction method for the electric power internet of things terminal is characterized by comprising the following steps of:
s11, acquiring acquisition data of an Internet of things terminal of the power equipment in real time, and taking the acquisition data as abnormal acquisition data when the acquisition data is in a suspected abnormal state, and entering step S12;
s12, determining the reliability of the abnormal acquisition data based on the fluctuation amount of the abnormal acquisition data in a first time threshold, the occurrence frequency of the abnormal acquisition data in the first time threshold and the frequency of the abnormal state of the power equipment in the last year, judging whether the reliability of the abnormal acquisition data is greater than the first threshold, if so, entering a step S13, otherwise, returning to the step S11;
s13, based on the acquired data of other Internet of things terminals of the power equipment, taking the acquired data of the other Internet of things terminals with suspected abnormalities as other abnormal acquired data, determining the weight of the other abnormal data based on the relevance of the other abnormal data and the abnormal acquired data, obtaining the evaluation reliability of the abnormal acquired data by adopting an evaluation model based on a machine learning algorithm based on the sum of the number of the other abnormal acquired data, the ratio of the other abnormal data to the other acquired data and the weight of the other abnormal data, and judging whether the evaluation reliability of the abnormal acquired data is greater than a second threshold value, if yes, determining that the abnormal acquired data has abnormalities, otherwise, entering step S14;
s14, based on the evaluation reliability and the credibility of the abnormal acquisition data, constructing a credibility evaluation value of the abnormal acquisition data, and determining whether the abnormal acquisition data is in an abnormal state or not based on the credibility evaluation value of the abnormal acquisition data.
2. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein when the fluctuation amount of the collected data in the second time threshold is larger than a first fluctuation amount threshold or when the operation state of the electric power equipment reflected by the collected data is abnormal, the collected data is determined to be in a suspected abnormal state.
3. The method for data interaction between the power internet of things terminals according to claim 1, wherein the first time threshold is determined according to the type of the abnormal collected data and the importance degree of the power equipment, and the more important the type of the abnormal collected data is, the less important the importance degree of the power equipment is, and the first time threshold is smaller.
4. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein the specific steps of evaluating the credibility of the abnormally collected data are as follows:
s21, acquiring the occurrence times of the abnormal acquisition data in a first time threshold, judging whether the occurrence times of the abnormal acquisition data in the first time threshold are larger than the first time threshold, if so, determining that the reliability of the abnormal acquisition data is 1, and if not, entering step S22;
s22, judging whether the occurrence times of the abnormal acquisition data in the first time threshold are larger than a second time threshold and the fluctuation amount of the abnormal acquisition data in the first time threshold is smaller than a second fluctuation amount threshold, wherein the second time threshold is smaller than the first time threshold, if yes, entering a step S23; if not, go to step S24;
s23, determining whether the number of times of abnormal states of the collected data of the same type of power equipment in the last year is smaller than a third time threshold value, if so, determining that the reliability of the abnormal collected data is 1, and if not, entering a step S24;
s24, based on the fluctuation amount of the abnormal collected data in the first time threshold, the occurrence frequency of the abnormal collected data in the first time threshold and the frequency of the abnormal state of the collected data in the last year of the power equipment, determining the credibility of the abnormal collected data by adopting an evaluation model based on a machine learning algorithm.
5. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein the electric power equipment of the same type is determined according to a voltage class, a manufacturer and an application place of the electric power equipment.
6. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein the relevance of the other abnormal data and the abnormal acquisition data is obtained by determining the relevance coefficient of the other abnormal data and the abnormal acquisition data in a mode based on principal component analysis and normalizing the relevance coefficient.
7. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein the specific steps of constructing the evaluation reliability of the abnormal acquisition data are as follows:
s31, judging whether the number of other abnormal data is larger than a first number threshold, if so, entering a step S34, and if not, entering a step S32;
s32, judging whether the ratio of the other abnormal data to the other acquired data is larger than a first ratio threshold, if so, entering a step S34, and if not, entering a step S33;
s33, judging whether the sum of the weights of the other abnormal data is larger than a third threshold value, if so, entering a step S34, and if not, taking the ratio of the other abnormal data to the other acquired data as the evaluation credibility of the abnormal acquired data;
and S34, based on the sum of the number of the other abnormal acquired data, the ratio of the other abnormal data to the other acquired data and the weight of the other abnormal data, adopting an evaluation model based on a machine learning algorithm to obtain the evaluation reliability of the abnormal acquired data.
8. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein the credibility evaluation value is determined by adopting a mathematical model based on a hierarchical analysis method based on evaluation credibility and credibility of the abnormally collected data.
9. The method for data interaction of the electric power internet of things terminal according to claim 1, wherein if and only if the credible evaluation value is larger than a set threshold value, the abnormal data is determined to be in an abnormal state, and when the abnormal data is in an abnormal state, data interaction is performed with the internet of things terminal based on a server, and acquired data of the internet of things terminal is obtained again based on at least a third time threshold value.
10. A computer storage device having stored thereon a computer program which, when executed in a computer, causes the computer to perform a method of data interaction of an electric internet of things terminal according to any of claims 1-9.
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| CN202310444861.8A CN117171144A (en) | 2023-04-24 | 2023-04-24 | Electric power internet of things terminal data interaction method and storage device |
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| CN202310444861.8A CN117171144A (en) | 2023-04-24 | 2023-04-24 | Electric power internet of things terminal data interaction method and storage device |
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