CN113902062A - Transformer area line loss abnormal reason analysis method and device based on big data - Google Patents
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Abstract
The invention discloses a method and a device for analyzing causes of abnormal line loss of a transformer area based on big data, wherein the method comprises the following steps: acquiring the full data of low-voltage users in a sample platform area; taking the type of the abnormal user in the low-voltage user total data as an independent variable, taking a data field in the low-voltage user total data as a dependent variable, and calculating a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method to obtain a line loss influence factor association relation; and inputting the acquired real-time low-voltage user total data of the transformer area into a preset high-loss reason classification model, outputting the abnormal user type, and identifying the line loss abnormal reason of the transformer area according to the abnormal user type. The transformer area high loss reason is analyzed by constructing a transformer area high loss reason analysis model, suspected abnormal users and abnormal user types under the high loss transformer area are identified, operation and maintenance personnel are assisted to solve the problem of low voltage transformer area high loss, and the management efficiency and the benefit of a company are improved.
Description
Technical Field
The invention belongs to the technical field of analysis of line loss abnormity of a transformer area, and particularly relates to a method and a device for analyzing causes of line loss abnormity of the transformer area based on big data.
Background
The line loss of a low-voltage distribution area in the power system has important influence on the whole power system, the high line loss rate enables the economic efficiency of power supply enterprises to be reduced and serious waste is caused to energy, the line loss of the low-voltage distribution area also becomes an important index for examining the power supply enterprises, and operation and maintenance units put a large amount of people and properties into the management of the line loss every year, so that the line loss of the distribution area is well treated, the method has very important significance for realizing more supply and less loss of the power supply enterprises and improving the operating income of the power supply enterprises.
In recent years, collected data is increasingly refined, and related data of a low-voltage transformer area is gradually improved, but the problem of the low-voltage high-loss transformer area still cannot be solved for a long time due to various reasons. Firstly, the number of high-loss transformer areas is large, the number of low-voltage users is huge, and operation and maintenance personnel and technical level cannot meet the requirement of the huge number of investigation and management; secondly, the reasons causing high loss of the low-voltage transformer area are numerous, such as file problems, acquisition problems, metering device faults, electricity stealing and the like, so that the identification of the reasons of abnormal operation of users is difficult; in addition, after the current analysis and treatment method aiming at a single high-loss transformer area falls down, the realization of accurate positioning of the abnormal users is difficult. Therefore, a method for efficiently solving the problem of the low-voltage high-loss transformer area is urgently needed.
Disclosure of Invention
The invention provides a method and a device for analyzing causes of line loss abnormality of a transformer area based on big data, which are used for solving at least one of the technical problems.
In a first aspect, the present invention provides a method for analyzing a cause of a line loss anomaly in a distribution room based on big data, including: acquiring low-voltage user full data of a sample station area from a power system data center station; taking the type of the abnormal user in the low-voltage user total data as an independent variable, taking a data field in the low-voltage user total data as a dependent variable, and calculating a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method to enable the correlation relationship of line loss influence factors to be achieved, wherein the type of the abnormal user comprises a file abnormal user, a collection abnormal user, a measurement abnormal user and a power stealing abnormal user, and the data field comprises station area daily loss electric quantity, a collection success rate, voltage and current; calculating the expression of the combined correlation coefficient of the dependent variable and the single independent variable as follows:
in the formula (I), wherein,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable; inputting the acquired real-time low-voltage user total data of the distribution room into a preset high-loss reason classification model, outputting the type of the abnormal user, and performing abnormal operation according to the abnormal operationAnd identifying the abnormal line loss reasons of the transformer area by the user type, wherein the preset high-loss reason classification model comprises the line loss influence factor incidence relation.
In a second aspect, the present invention provides an apparatus for analyzing cause of line loss abnormality in a distribution room based on big data, including: the acquisition module is configured to acquire low-voltage user full data of a sample station area from a power system data center station; the calculation module is configured to take the type of the abnormal user in the low-voltage user total data as an independent variable, take a data field in the low-voltage user total data as a dependent variable, and calculate a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method so as to enable a line loss influence factor correlation relationship to be achieved, wherein the type of the abnormal user comprises a file abnormal user, a collected abnormal user, a measured abnormal user and a power stealing abnormal user, and the data field comprises station area daily loss electric quantity, a collected success rate, voltage and current; calculating the expression of the combined correlation coefficient of the dependent variable and the single independent variable as follows:
in the formula (I), wherein,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable; and the output module is configured to input the acquired real-time low-voltage user full data of the transformer area into a preset high-loss reason classification model, so as to output the abnormal user type, and identify the line loss abnormal reason of the transformer area according to the abnormal user type, wherein the preset high-loss reason classification model contains the line loss influence factor association relation.
In a third aspect, an electronic device is provided, comprising: the analysis method comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the steps of the analysis method for the line loss anomaly cause of the transformer area based on the big data according to any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, the computer program including program instructions, which, when executed by a computer, cause the computer to execute the steps of the method for analyzing cause of line loss abnormality of a transformer area based on big data according to any one of the embodiments of the present invention.
According to the method and the device for analyzing the abnormal reason of the line loss of the transformer area based on the big data, the reason of the high loss of the transformer area is analyzed by constructing a transformer area high loss reason analysis model, suspected abnormal users and abnormal user types under the transformer area with high loss are identified, operation and maintenance personnel are assisted to solve the problem of the high loss of the low-voltage transformer area, and the management efficiency and the benefit of a company are improved. And single user, multi-user, many district users can be analyzed, the security is high, the operability is high, it is convenient to use.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a method for analyzing causes of line loss abnormality of a distribution room based on big data according to an embodiment of the present invention;
fig. 2 is a flowchart of another method for analyzing causes of line loss abnormality of a distribution room based on big data according to an embodiment of the present invention;
fig. 3 is a block diagram of a device for analyzing a cause of line loss abnormality of a distribution room based on big data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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.
Please refer to fig. 1, which shows a flowchart of a method for analyzing a cause of line loss anomaly in a distribution room based on big data according to the present application.
As shown in fig. 1, in step S101, low-voltage user full data of a sample station area is acquired in a power system data center station;
in step S102, a transaction user type in the low-voltage user total data is used as an independent variable, a data field in the low-voltage user total data is used as a dependent variable, and a combined correlation coefficient of a dependent variable and a single independent variable is calculated based on a complex correlation-pearson coefficient analysis method, so as to obtain a line loss influence factor association relationship, wherein the transaction user type includes a file transaction user, a collection transaction user, a measurement transaction user, and a power stealing transaction user, and the data field includes a station area daily power loss, a collection success rate, a voltage, and a current;
calculating the expression of the combined correlation coefficient of the dependent variable and the single independent variable as follows:
in the formula (I), the compound is shown in the specification,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable;
in step S103, inputting the acquired real-time low-voltage user full data of the distribution room into a preset high-loss reason classification model, so as to output a transaction user type, and identifying a distribution room line loss abnormal reason according to the transaction user type, wherein the preset high-loss reason classification model includes the line loss influence factor association relationship.
In summary, the method includes the steps of obtaining source end system data from a data center station, wherein the source end system data comprises system data such as an SG186 system, a power utilization information acquisition system and an inspection system, extracting features, carrying out line loss influence factor relevance analysis on the type of the abnormal motion user and the data field to obtain a relevance result, inputting the relevance result into a preset station area high loss reason classification model to judge the type of the abnormal motion and identify the abnormal motion user, and finally giving a conclusion according to a model judgment result to enable operation and maintenance personnel to carry out targeted management work on the high loss station area, so that the operation and maintenance personnel are assisted to solve the problem of high loss of the low voltage station area, and the management efficiency and the benefit of a company are improved. And single user, multi-user, many district users can be analyzed, the security is high, the operability is high, it is convenient to use.
Please refer to fig. 2, which shows a flowchart of another big data based analysis method for cause of line loss anomaly in a distribution room.
As shown in fig. 2, the method for analyzing the cause of line loss abnormality of the transformer area based on big data specifically includes the following steps:
step 1, data acquisition of data center
The method is characterized in that the sample platform area and the low-voltage user full data information under the sample platform area are obtained through the power system data center platform, and the method is different from the existing analysis method, all data are obtained from the data center platform, so that the barrier between systems and between professions is broken, the data real-time performance is higher, and the accuracy is higher. The standing book and archive data are mainly obtained from a relational database of a data center station, and the measurement data are mainly obtained from an HDFS distributed file system and comprise 51 data fields.
Analyzing the type, dimensionality, mean value, variance and the like of the data fields, quantizing the data of the fields, and extracting frequency characteristics, logic characteristics, statistical characteristics and the like of each data field according to the processed abnormal users and normal users. And (4) combining the conditions of the abnormal users and the normal users, and eliminating fields which have no influence on the loss through data dimension reduction.
Step 2, analyzing relevance of influence factors
A complex correlation-Pearson coefficient analysis method is provided for analyzing the correlation of the line loss influence factors, and specifically comprises the following steps:
firstly, classifying users into abnormal users and normal users according to the obtained data samples, wherein the abnormal user types are independent variables, the independent variable characteristics comprise file problems, acquisition problems, metering problems, electricity stealing problems and the like, and 31 data fields of daily power consumption, acquisition success rate, voltage, current and the like of a transformer area are dependent variables;
and secondly, calculating the Pearson coefficients of the respective variables and each dependent variable, and obtaining a Pearson coefficient between each independent variable and each dependent variable, wherein the Pearson coefficient of a certain dependent variable and a corresponding single independent variable is calculated by the following expression:
in the formula (I), the compound is shown in the specification,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
as to the number of times the argument characteristic occurs,
the number of times the dependent variable characteristic occurs,
is composed of
The average value of the samples of (a),
is composed of
The average value of the samples of (a),
is composed of
The standard deviation of (a) is determined,
is composed of
Standard deviation of (d);
thirdly, the change of one dependent variable is often influenced by the synthesis of a plurality of independent variables, so all independent variables are sequentially grouped with a single dependent variable, complex correlation coefficients of each group of independent variables and dependent variables are calculated, and each group obtains one complex correlation coefficient, wherein the expression for calculating the complex correlation coefficient of a certain dependent variable and a corresponding dependent variable group is as follows:
in the formula (I), the compound is shown in the specification,
is the complex correlation coefficient of a dependent variable and the corresponding dependent variable group,
the number of times the dependent variable characteristic occurs,
to obtain a regression value by regression on the independent variables,
is the mean value of the independent variables,
is as follows
The parameters of the linear regression equation for each independent variable feature,
is as follows
The number of occurrences of the individual independent variable features;
fourthly, weighting and combining the Pearson coefficient and the complex correlation coefficient, and calculating the combined correlation coefficient of a single independent variable and a dependent variable, wherein the weighting and combining formula is as follows:
in the formula (I), the compound is shown in the specification,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable;
and fifthly, obtaining the incidence relation of the line loss influence factors by combining the correlation coefficients.
Step 3, constructing a high-loss reason classification model
A high-loss reason classification model is constructed by utilizing iterative training of a machine learning weak classifier (decision tree) and is used for classifying types of abnormal users in a data set. After the model construction is completed, the correlation analysis conclusion of the line loss influence factors in the previous step is input into a classification model, and the following results can be obtained through model calculation: firstly, judging the type of the transaction (metering problem, acquisition problem, file problem and electricity stealing problem), and secondly, identifying the transaction user according to the type of the transaction user.
And selecting 30% of data in the region as a test set to test the model, wherein the test set comprises a transaction user and a normal user. And extracting field data of the test user to perform relevance analysis, inputting an analysis result into a high-loss reason classification model to perform result prediction to obtain abnormal suspected users, and calculating the recall ratio and precision ratio of the model.
Wherein: recall = suspected user number/actual transaction user, precision = actual transaction user/suspected user.
Step 4, auxiliary treatment of the system
According to the model calculation result, the system identifies the cause of the suspected abnormal user, the electricity stealing problem, the metering device problem, the collection problem, the file problem and the like are found out, the operation and maintenance personnel are sent to the site for verification and treatment according to the system troubleshooting reason, the operation and maintenance personnel do not perform blind carpet type troubleshooting on the high loss reason any more, meanwhile, the order dispatching accuracy is improved, and the work efficiency of basic teams and groups is improved.
In summary, the method of the present application can achieve the following technical effects:
1) the method and the system are high in data accuracy and strong in practicability, can effectively obtain the high loss reason, can quickly position problem users, and reduce time and manpower and material resources wasted in on-site carpet type troubleshooting.
2) The analysis recall ratio and precision ratio can be continuously improved through self-learning along with the continuous increase of the number of samples in the using process.
Fig. 3 is a block diagram illustrating a structure of an apparatus for analyzing cause of line loss abnormality of a distribution room based on big data according to the present application.
As shown in fig. 3, the apparatus 200 for analyzing the cause of the line loss abnormality in the transformer substation includes an obtaining module 210, a calculating module 220, and an output module 230.
The acquisition module 210 is configured to acquire low-voltage user full data of a sample station area in a power system data center station;
a calculating module 220, configured to use a type of a different user in the total data of the low-voltage users as an independent variable, use a data field in the total data of the low-voltage users as a dependent variable, and calculate a combined correlation coefficient of a dependent variable and a single independent variable based on a complex correlation-pearson coefficient analysis method, so as to obtain a line loss influence factor association relationship, where the type of the different user includes a profile different user, a collection different user, a measurement different user, and a power stealing different user, and the data field includes a station area daily power loss, a collection success rate, a voltage, and a current;
calculating the expression of the combined correlation coefficient of the dependent variable and the single independent variable as follows:
in the formula (I), the compound is shown in the specification,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable;
the output module 230 is configured to input the acquired real-time low-voltage user full data of the transformer area into a preset high-loss reason classification model, so as to output the abnormal user type, and identify the line loss abnormal reason of the transformer area according to the abnormal user type, wherein the preset high-loss reason classification model includes the line loss influence factor association relation.
It should be understood that the modules depicted in fig. 3 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 3, and are not described again here.
In other embodiments, an embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored, and the computer-executable instructions may execute the method for analyzing the cause of the line loss abnormality of the distribution room based on the big data in any of the above method embodiments;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
acquiring low-voltage user full data of a sample station area from a power system data center station;
taking the type of the abnormal user in the low-voltage user total data as an independent variable, taking a data field in the low-voltage user total data as a dependent variable, and calculating a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method to obtain a line loss influence factor association relation;
and inputting the acquired real-time low-voltage user total data of the transformer area into a preset high-loss reason classification model, outputting the abnormal user type, and identifying the line loss abnormal reason of the transformer area according to the abnormal user type.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the station area line loss abnormality cause analysis device based on large data, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely disposed with respect to the processor, and the remote memory may be connected to the big data based station area line loss anomaly cause analyzing apparatus through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 4. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running the nonvolatile software program, instructions and modules stored in the memory 320, that is, the method for analyzing the cause of the line loss abnormality of the transformer area based on the big data in the above method embodiment is realized. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the station area line loss abnormality cause analysis device based on big data. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to a station area line loss anomaly cause analysis device based on big data, and is used for a client, and the electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
acquiring low-voltage user full data of a sample station area from a power system data center station;
taking the type of the abnormal user in the low-voltage user total data as an independent variable, taking a data field in the low-voltage user total data as a dependent variable, and calculating a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method to obtain a line loss influence factor association relation;
and inputting the acquired real-time low-voltage user total data of the transformer area into a preset high-loss reason classification model, outputting the abnormal user type, and identifying the line loss abnormal reason of the transformer area according to the abnormal user type.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A big data-based analysis method for line loss anomaly reasons of a transformer area is characterized by comprising the following steps:
acquiring low-voltage user full data of a sample station area from a power system data center station;
taking the type of the abnormal user in the low-voltage user total data as an independent variable, taking a data field in the low-voltage user total data as a dependent variable, and calculating a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method to enable the correlation relationship of line loss influence factors to be achieved, wherein the type of the abnormal user comprises a file abnormal user, a collection abnormal user, a measurement abnormal user and a power stealing abnormal user, and the data field comprises station area daily loss electric quantity, a collection success rate, voltage and current;
calculating the expression of the combined correlation coefficient of the dependent variable and the single independent variable as follows:
in the formula (I), the compound is shown in the specification,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable;
inputting the acquired real-time low-voltage user full data of the transformer area into a preset high-loss reason classification model, outputting a transaction user type, and identifying the line loss abnormal reason of the transformer area according to the transaction user type, wherein the preset high-loss reason classification model comprises the line loss influence factor association relation.
2. The big-data-based analysis method for the line loss anomaly cause of the distribution room according to claim 1, wherein after the full amount of data of the low-voltage users of the sample distribution room is acquired, the method comprises the following steps:
and performing data dimension reduction processing on the low-voltage user full data to remove fields which have no influence on the line loss in the low-voltage user full data.
3. The method for analyzing the cause of the line loss abnormality of the transformer area based on the big data as claimed in claim 1, wherein the expression for calculating the complex correlation coefficient between a dependent variable and a corresponding dependent variable group is as follows:
in the formula (I), the compound is shown in the specification,
is the complex correlation coefficient of a dependent variable and the corresponding dependent variable group,
the number of times the dependent variable characteristic occurs,
to obtain a regression value by regression on the independent variables,
are all independent variablesThe value of the one or more of the one,
is as follows
The parameters of the linear regression equation for each independent variable feature,
is as follows
The number of occurrences of each independent variable feature.
4. The big-data-based analysis method for the cause of line loss abnormality of the transformer area according to claim 1, wherein the expression for calculating the pearson coefficient of a dependent variable and a corresponding single independent variable is as follows:
in the formula (I), the compound is shown in the specification,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
as to the number of times the argument characteristic occurs,
the number of times the dependent variable characteristic occurs,
is composed of
The average value of the samples of (a),
is composed of
The average value of the samples of (a),
is composed of
The standard deviation of (a) is determined,
is composed of
Standard deviation of (2).
5. The big-data-based distribution room line loss anomaly cause analysis method according to claim 1, wherein the preset high-loss cause classification model is a model constructed based on machine learning weak classifier iterative training.
6. The utility model provides a platform district line loss anomaly reason analytical equipment based on big data which characterized in that includes:
the acquisition module is configured to acquire low-voltage user full data of a sample station area from a power system data center station;
the calculation module is configured to take the type of the abnormal user in the low-voltage user total data as an independent variable, take a data field in the low-voltage user total data as a dependent variable, and calculate a combined correlation coefficient of a certain dependent variable and a single independent variable based on a complex correlation-Pearson coefficient analysis method so as to enable a line loss influence factor correlation relationship to be achieved, wherein the type of the abnormal user comprises a file abnormal user, a collected abnormal user, a measured abnormal user and a power stealing abnormal user, and the data field comprises station area daily loss electric quantity, a collected success rate, voltage and current;
calculating the expression of the combined correlation coefficient of the dependent variable and the single independent variable as follows:
in the formula (I), the compound is shown in the specification,
is the combined correlation coefficient of a dependent variable and a single independent variable,
is the complex correlation coefficient of a dependent variable and a corresponding independent variable set,
is the pearson coefficient of a dependent variable and the corresponding single independent variable,
is the weight of a dependent variable and the corresponding independent variable set,
the weight of a dependent variable and a corresponding single independent variable;
and the output module is configured to input the acquired real-time low-voltage user full data of the transformer area into a preset high-loss reason classification model, so as to output the abnormal user type, and identify the line loss abnormal reason of the transformer area according to the abnormal user type, wherein the preset high-loss reason classification model contains the line loss influence factor association relation.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 5.
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