CN115049410A - Electricity stealing behavior identification method and device, electronic equipment and computer readable storage medium - Google Patents
Electricity stealing behavior identification method and device, electronic equipment and computer readable storage medium Download PDFInfo
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- CN115049410A CN115049410A CN202210745113.9A CN202210745113A CN115049410A CN 115049410 A CN115049410 A CN 115049410A CN 202210745113 A CN202210745113 A CN 202210745113A CN 115049410 A CN115049410 A CN 115049410A
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Abstract
The embodiment of the invention provides a method and a device for identifying electricity stealing behavior, electronic equipment and a computer readable storage medium, belonging to the field of data processing, wherein the method comprises the following steps: according to the user identification of the abnormal acquisition information, the user corresponding to the acquisition information and the power utilization scene to which the user belongs are determined, all the acquisition information of the user in the power utilization interval is determined from the cache and is used as a target information set, a power utilization analysis model corresponding to the power utilization scene is determined, so that according to the power utilization analysis model and the characteristic quantity of each piece of acquisition information in the target information set, the power utilization condition of the user in the power utilization interval is analyzed, the power utilization analysis value of the user is obtained, and whether the power utilization of the user belongs to the power utilization behavior is judged according to the power utilization analysis value, real-time monitoring and identification are achieved, power utilization analysis is carried out by using the exclusive power utilization analysis model of the power utilization scene, accurate analysis is achieved, the accuracy of power utilization identification can be improved, and the identification effect is improved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a device for identifying electricity stealing behavior, electronic equipment and a computer readable storage medium.
Background
The electricity stealing is the behavior of using illegal means to not measure or less measure the electricity consumption with the aim of illegally occupying the electric energy and not paying or less paying the electricity fee. Due to the influence of electricity stealing behaviors on economic benefits and return on investment of power enterprises, the problems that identification of the electricity stealing behaviors of users and implementation of electricity stealing prevention work are urgently needed to be solved.
The traditional method for identifying the electricity stealing behavior comprises manual regular inspection and system identification. The mode of artifical periodic inspection, it is with high costs and inefficiency. The system identification mode is that the system identifies data according to the collected data, the fraudulent event and the working condition information, so as to identify the electricity stealing behavior, but the problem of poor identification effect still exists.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus, an electronic device and a computer readable storage medium for identifying electricity stealing behavior, which can solve the problem of poor identification effect of the current electricity stealing behavior identification method.
In order to achieve the above object, the embodiments of the present invention adopt the following technical solutions.
In a first aspect, an embodiment of the present invention provides a method for identifying electricity stealing behavior, which is applied to an electronic device, where a relationship table is stored on the electronic device, and a corresponding relationship among a user identifier, a user, and a user scene is recorded on the relationship table, and the method includes:
when the current consumed acquisition information in the message queue is determined to be abnormal, determining a user corresponding to the acquisition information and a power utilization scene to which the user belongs according to a user identifier of the acquisition information, wherein the acquisition information is electricity stealing related behavior information of a certain user, which is monitored and acquired by monitoring equipment in real time;
determining all collected information of the user in the electricity stealing interval from the cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity utilization scene;
analyzing the power utilization condition of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain a power stealing analysis value of the user, wherein the characteristic quantity is used for representing the power stealing related behavior to which the collected information belongs;
and judging whether the electricity utilization of the user belongs to electricity stealing behaviors or not according to the electricity stealing analysis value.
Further, the electricity stealing analysis model comprises a selected combination of characteristic quantities;
the step of analyzing the power consumption situation of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain the power stealing analysis value of the user comprises the following steps:
analyzing each piece of collected information in the target information set to obtain a characteristic quantity corresponding to each piece of collected information;
extracting feature quantities which accord with the selected feature quantity combination from all the feature quantities to serve as selected feature quantities, and calculating the electricity stealing weight of each selected feature quantity;
and analyzing the electricity utilization condition of the user in the electricity stealing interval based on all the selected characteristic quantities and the electricity stealing weight to obtain an electricity stealing analysis value of the user.
Further, the electricity stealing analysis model further comprises a calculation weight of each selected characteristic quantity, and the step of calculating the electricity stealing weight of each selected characteristic quantity comprises the following steps:
inquiring the weighting factor of each selected characteristic quantity from a preset weighting factor table;
and for each selected characteristic quantity, taking the product of the calculated weight of the selected characteristic quantity and the weighting factor as the electricity stealing weight of the selected characteristic quantity.
Further, the step of analyzing the power consumption situation of the user in the power stealing interval based on all the selected feature quantities and the power stealing weights to obtain a power stealing analysis value of the user includes:
analyzing and obtaining a correlation coefficient between each selected characteristic quantity and each remaining selected characteristic quantity aiming at each selected characteristic quantity;
and calculating a weighted average value of all the selected characteristic quantities according to all the selected characteristic quantities and the correlation coefficients, and taking the weighted average value as a power stealing analysis value of the user.
Further, the electricity stealing analysis model further comprises an electricity stealing threshold;
the step of judging whether the electricity utilization of the user belongs to electricity stealing behavior or not according to the electricity stealing analysis value comprises the following steps:
judging whether the electricity stealing analysis value is larger than or equal to the electricity stealing threshold value or not, if so, confirming that the electricity utilization behavior of the user belongs to electricity stealing, and generating an electricity stealing work order of the electricity utilization;
wherein the electricity stealing work order is used for prompting operation and maintenance personnel to carry out electricity stealing prevention treatment on the user.
Further, the electricity stealing analysis model also comprises an observation threshold and a suspicion threshold;
the step of judging whether the electricity utilization of the user belongs to electricity stealing behavior according to the electricity stealing analysis value further comprises the following steps:
if the electricity stealing analysis value is smaller than the electricity stealing threshold, judging whether the electricity stealing analysis value is smaller than the suspected threshold, if not, confirming that the behavior of the user belongs to suspected electricity stealing, and generating an alarm notice;
if the electricity stealing analysis value is smaller than the suspicion threshold value, judging whether the electricity stealing analysis value is smaller than the observation threshold value, and if not, setting the user identifier as the observation electricity stealing.
Further, the method further comprises a step of obtaining the weighting factor, which comprises:
for each electricity stealing related behavior, configuring a unique corresponding characteristic quantity for the electricity stealing related behavior;
establishing a normal distribution curve according to historical data about electricity stealing;
for each of the feature quantities, a weighting factor for the feature quantity is arranged based on distribution data of the feature quantity in the normal distribution curve.
Further, the calculation formula of the correlation coefficient includes:
wherein R is jk Representing the correlation coefficient between the jth selected feature quantity and the kth selected feature quantity, Cov (j, k) representing the covariance of the jth selected feature quantity and the kth selected feature quantity, Var [ j]The variance, Var k, representing the jth selected feature]Representing the variance of the kth selected feature quantity.
In a second aspect, an embodiment of the present invention provides an electricity stealing behavior recognition apparatus, which is applied to an electronic device, where a relationship table is stored in the electronic device, and a corresponding relationship among a user identifier, a user, and a user scene is recorded on the relationship table, and the electricity stealing behavior recognition apparatus includes a preprocessing module, a calculation module, and a recognition module;
the preprocessing module is used for determining a user corresponding to the acquired information and an electricity utilization scene to which the user belongs according to the user identification of the acquired information when the acquired information consumed currently in the message queue is determined to be abnormal, determining all the acquired information of the user in an electricity stealing interval from a cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity utilization scene;
the collected information is the electricity stealing related behavior information of a certain user, which is monitored and collected by monitoring equipment in real time;
the calculation module is used for analyzing the power utilization condition of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain a power stealing analysis value of the user, wherein the characteristic quantity is used for representing the power stealing related behavior to which the collected information belongs;
and the identification module is used for judging whether the electricity utilization of the user belongs to electricity stealing behaviors or not according to the electricity stealing analysis value.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor and a memory, where the memory stores a computer program capable of being executed by the processor, and the processor can execute the computer program to implement the electricity stealing behavior identification method according to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the electricity stealing behavior identification method according to the first aspect.
According to the electricity stealing behavior identification method, the electricity stealing behavior identification device, the electronic equipment and the computer readable storage medium, the collected information of the electricity stealing related behaviors which are monitored and collected in real time is put into the message queue, and when the collected information consumed by the message queue at present is determined to be abnormal, the user corresponding to the collected information and the electricity utilization scene to which the user belongs are determined, so that the electricity stealing analysis model corresponding to the electricity utilization scene and the characteristic quantity of each piece of collected information of the user in the electricity stealing interval are used for analyzing the electricity stealing analysis value of the user, whether the electricity utilization of the user is the electricity stealing behavior is judged according to the electricity stealing analysis value, real-time monitoring identification is achieved, the electricity utilization scene is divided, electricity utilization analysis is conducted by using the electricity stealing analysis model exclusive to the electricity utilization scene, accurate analysis is achieved, and therefore the accuracy of electricity stealing identification can be improved, and the identification effect is improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a block diagram illustrating a system for identifying electricity stealing behavior according to an embodiment of the present invention.
Fig. 2 is a flow chart of a method for identifying electricity stealing behavior according to an embodiment of the present invention.
Fig. 3 shows a schematic flow diagram of part of the sub-step of step S15 in fig. 2.
Fig. 4 shows a schematic flow chart of a part of the sub-steps of step S152 in fig. 3.
Fig. 5 is a second flow chart of the method for identifying electricity stealing behavior according to the embodiment of the present invention.
Fig. 6 shows a schematic flow chart of a part of the sub-steps of step S153 in fig. 3.
Fig. 7 shows a schematic flow diagram of part of the sub-step of step S17 in fig. 2.
Fig. 8 is a block diagram illustrating an electricity stealing behavior recognition apparatus according to an embodiment of the present invention.
Fig. 9 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 100-a system for identifying electricity stealing behavior; 110-an electronic device; 120-a monitoring device; 130-electric meter; 140-a concentrator; 150-electricity stealing behavior recognition means; 160-a pre-processing module; 170-a calculation module; 180-identification module.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Due to the influence of electricity stealing behaviors on economic benefits and return on investment of power enterprises, the problems that identification of the electricity stealing behaviors of users and implementation of electricity stealing prevention work are urgently needed to be solved.
The traditional method for identifying the electricity stealing behavior comprises manual regular inspection and system identification.
In the manual regular inspection mode, a salesman analyzes suspicion of electricity stealing users by observing data such as electric quantity periodic consumption data, working condition data, ammeter fraud event data and the like collected by a system, adopts collected data to carry out manual screening, checks working condition information and fraud events and identifies the flow of electricity stealing behaviors. The manual identification has strong subjectivity and large data volume, so the error rate is high. Meanwhile, the method has long time consumption, high cost and low efficiency.
The system identification mode is that on the basis of manual identification, the system analyzes data such as collected data, fraud events, working condition information and the like in a period of time to identify electricity stealing behaviors. This method has a large data size, consumes a large amount of computing resources, and still has a problem of poor recognition effect.
Based on the above consideration, the embodiment of the invention provides an electricity stealing behavior identification scheme, which can solve the problems of large calculation amount, high error rate, poor identification effect and the like existing in the existing electricity stealing behavior identification method. Hereinafter, this scheme will be described in detail.
The electricity stealing behavior identification method provided by the invention can be applied to the electricity stealing behavior identification system 100 shown in fig. 1, and comprises an electronic device 110 and a plurality of monitoring devices 120, wherein the electronic device 110 is in communication connection with the plurality of monitoring devices 120 in a wired or wireless mode.
Each monitoring device 120 is configured to perform at least one monitoring and collecting task for monitoring the concentrator 140 or the electric meter 130 of a certain user.
It should be understood that the concentrator 140 is down-linked to a plurality of electricity meters 130. The Concentrator 140(Concentrator) is a central management device and a control device of the remote centralized meter reading system, and is responsible for functions of regularly reading data of a terminal (i.e., the electric meter 130), transmitting commands of the system, communicating data, managing a network, recording events, transversely transmitting data, and the like.
Each monitoring and collecting task comprises a target object, a sampling period, the number of the monitoring and collecting task, a monitoring event and data collected by the target.
The monitoring device 120 executes each monitoring and collecting task, performs monitoring and sampling on the target object, collects target data at intervals of a sampling period to obtain collected information, uses the serial number as a feature identifier of the collected information, and uploads the collected information added with the identifier to the electronic device 110.
It should be understood that the collected information also includes the user identification of the target object. The user may be an individual home user, an organization or group, a company, a school, etc. of any power consuming unit.
The electronic device 110 is configured to receive the collected information of any monitoring device 120, determine a characteristic quantity for characterizing the electricity stealing related behavior to which the collected information belongs according to the characteristic identifier of the collected information, and buffer the collected information in a message queue according to a receiving order.
Among these, electricity stealing related activities include, but are not limited to: the consumption electric quantity is 0, the wiring is abnormal, and the behavior of suspected electricity stealing such as opening the meter 130 cover is carried out. The characteristic quantity is a characteristic quantity which is preset and used for representing the electricity stealing related behavior to which the collected information belongs.
The electronic device 110 is further configured to consume the collected information in the message queue according to the arrangement order, so as to determine whether the collected information is abnormal.
In detail, a determination condition may be set for each feature amount, and the electronic device 110 determines the determination condition based on the feature identifier of the collected information and determines whether the collected information is abnormal based on the determination condition. For example, if the collected information is the power consumption amount of the electricity meter 130 and the determination condition is that the power consumption amount is 0, if the power consumption amount in the collected information is 0, it is determined that the collected information is abnormal.
Or when the acquired information is the prompt information triggered by the abnormal event, the acquired information is judged to be abnormal when the abnormal event of the electricity stealing related behavior corresponding to the acquired information is determined. For example, when it is determined that the collected information is the notification information for triggering the abnormal wiring of the electricity meter 130, it is determined that the collected information is abnormal.
It should be understood that the consumed collection information may be deleted from the message queue and stored in the cache, or the consumed collection information may be added with the consumed identifier but not deleted.
The electronic device 110 further stores a relationship table, where the relationship table records a corresponding relationship among the user identifier, the user, and the user scene, and the electronic device 110 is further configured to implement a method for identifying electricity stealing behavior.
Further, the electronic device 110 further develops a feature quantity library, and the feature quantity library records a corresponding relationship between the feature identifier and the feature quantity, that is, a corresponding relationship between the serial number and the feature quantity.
The electronic device 110 may be a server, a server cluster, or a terminal, among others.
To describe the power stealing phase identification scheme in more detail, in one embodiment, referring to fig. 2, a power stealing behavior identification method is provided, which includes the following steps. In the present embodiment, the electricity stealing behavior identification method is applied to the electronic device 110 shown in fig. 1.
And S11, when the current consumed acquisition information in the message queue is determined to be abnormal, determining the user corresponding to the acquisition information and the electricity utilization scene of the user according to the user identification of the acquisition information.
When the electronic device 110 determines that the currently consumed acquisition information in the message queue is abnormal, the relationship table is queried to find out the relationship among the user identifier, the user and the power utilization scene, so as to determine the user corresponding to the acquisition information and the power utilization scene to which the user belongs.
The collected information is information about behavior related to electricity stealing of a certain user, which is monitored and collected by the monitoring device 120 in real time. When the collected information is abnormal data or abnormal prompt information, the collected information can be judged to be abnormal.
In the invention, the users all refer to the electricity utilization units, if the electricity utilization units are companies, the users are companies, and if the electricity utilization units are home users, the users are home users. Each electricity utilization unit corresponds to at least one electricity meter 130, so that the user based on the collected information obtained by the electricity meters 130 is the electricity utilization unit.
And S13, determining all the collected information of the user in the electricity stealing interval from the cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity using scene.
The electricity stealing interval is a time interval determined by the analysis time length, for example, if the analysis time length is one day and the collection time of the collected information is 4 months, 20 days and 14 points, the electricity stealing interval is: 14 o 'clock at 19/4-20/4-15 o' clock. The analysis duration can be adjusted or modified according to requirements.
The collected information at a plurality of time points is cached in the electronic device 110, so that after the abnormal user of the collected information is determined, all the collected information of the user in the electricity stealing interval can be extracted from the cache of the electronic device 110.
The difference between the electricity consumption amount and the electricity utilization standard is considered, and the accuracy of electricity stealing behavior identification is improved. According to different electricity consumption and the like, the scene segmentation is carried out to obtain a plurality of electricity consumption scenes. For example, scenes such as industrial and commercial power utilization, large power utilization, household power utilization, high-voltage power utilization, low-voltage power utilization, and the like can be divided. And then according to the difference of the power utilization scene, a dedicated electricity stealing analysis model is formulated for each power utilization scene, different electricity stealing analysis models have different analysis emphasis points, and the analysis standards are that the electricity stealing analysis models correspond to the power utilization scenes one to one. The electronic device 110 is further configured with a model library, and the model library is used for storing electricity stealing analysis models of various electricity utilization scenes.
It should be understood that there may be different scene division standards according to actual applications, actual requirements, actual purposes, and the like.
The electricity utilization scene to which each user belongs can be preset, so that after the electricity utilization scene to which the abnormal information collection user belongs is determined, the electricity stealing analysis model can be determined by inquiring the model base.
And S15, analyzing the electricity utilization condition of the user in the electricity stealing interval according to the electricity stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain the electricity stealing analysis value of the user.
The characteristic quantity is used for representing electricity stealing related behaviors to which the collected information belongs. Also, the characteristic amount may be one numerical value.
For example, when the electricity stealing related behavior to which the collected information belongs is that the electricity consumption is 0, the feature quantity of the collected information may be 3, and at this time, the feature quantity 3 represents the behavior of "the electricity consumption is 0". Or, when the collected information is prompt information of abnormal wiring, the electricity stealing related behavior to which the collected information belongs is abnormal wiring, and at this time, the characteristic quantity of the collected information may be 2, and 2 represents that the electricity meter 130 is abnormal wiring.
Each characteristic quantity is a value exclusive to the corresponding electricity stealing related behavior, and specific numerical values of the characteristic quantities can be set according to historical experience or after historical data are analyzed.
The feature quantity library of the electronic device 110 stores and records the corresponding relationship between the feature identifier and the feature quantity, and the collected information has the feature identifier, and the feature quantity of the collected information can be queried according to the feature identifier.
And S17, judging whether the electricity utilization of the user belongs to the electricity stealing behavior according to the electricity stealing analysis value.
In the electricity stealing behavior identification method, the collected information of the electricity stealing related behaviors which is monitored and collected in real time is put into the message queue, and when the collected information consumed by the message queue at present is abnormal, the user corresponding to the collected information and the electricity utilization scene to which the user belongs are determined, so that the electricity stealing analysis model corresponding to the electricity utilization scene and the characteristic quantity of each piece of collected information of the user in the electricity stealing interval are used, the electricity stealing analysis value of the user is analyzed, whether the electricity utilization of the user is the electricity stealing behavior is judged according to the electricity stealing analysis value, the real-time monitoring identification is realized, the electricity utilization scene is divided, the electricity utilization analysis is carried out by using the electricity stealing analysis model exclusive to the electricity utilization scene, the accurate analysis is realized, the accuracy of the electricity stealing identification can be improved, and the identification effect is improved.
In order to improve the accuracy of the electricity stealing behavior identification, the electricity stealing analysis model may include selected feature quantity combinations, a calculation weight for each selected feature quantity, and a decision threshold. And selecting the characteristic quantity combination to represent the electricity stealing related behaviors focused by the electricity utilization scene corresponding to the electricity stealing analysis model. It should be noted that the selected characteristic quantity, the calculation weight and the decision threshold of the electricity stealing analysis model of different electricity utilization scenes are different.
In order to reduce the amount and complexity of calculations while improving the accuracy of the electricity stealing analysis, in one embodiment, referring to fig. 3, the above step S15 may be implemented by the following sub-steps.
And S151, analyzing each piece of collected information in the target information set to obtain a characteristic quantity corresponding to each piece of collected information.
Specifically, for each piece of collected information, a feature table (in which a correspondence between feature identifiers and feature quantities is recorded) pre-stored on the electronic device 110 is queried according to the feature identifier of the piece of collected information, so as to obtain the feature quantity of the piece of collected information.
And S152, extracting the characteristic quantity which is in accordance with the selected characteristic quantity combination from all the characteristic quantities to be used as the selected characteristic quantity, and calculating the electricity stealing weight of each selected characteristic quantity.
The electronic device 110 extracts the feature quantity belonging to the selected feature quantity combination as the selected feature quantity based on the selected feature quantity combination in the electricity stealing analysis model, and filters other feature quantities to calculate the electricity stealing weight of each selected feature quantity.
The magnitude of the electricity stealing weight of the selected feature quantity can be understood as: and in the electricity stealing interval, the selected characteristic quantity corresponds to the possible electricity stealing value of the electricity stealing related behavior.
S153, analyzing the electricity utilization condition of the user in the electricity stealing interval based on all the selected characteristic quantities and the electricity stealing weights to obtain an electricity stealing analysis value of the user.
Through the steps S151-S153, the electric larceny related behavior of the user is subjected to targeted and accurate analysis, so that a more accurate electric larceny analysis value is obtained.
The calculation mode of the electricity stealing weight of each selected characteristic quantity can be flexibly set, for example, the calculation can be carried out according to a preset rule, and the calculation mode can also be obtained by adopting machine algorithm fitting.
In one possible embodiment, referring to fig. 4, the calculation weight of each selected feature quantity may be calculated by the following sub-steps.
S1521, a weighting factor for each selected feature quantity is looked up from a preset weighting factor table.
The weighting factor of each feature quantity may be set according to historical experience, or may be calculated by using a preset rule, which is not specifically limited in this embodiment.
After the feature quantities are determined, a corresponding weighting factor is determined for each feature quantity, and the correspondence between the feature quantities and the weighting factors is recorded in a weighting factor table, and the weighting factor table is stored in the electronic device 110. Thus, after determining the selected feature quantity, the electronic device 110 can look up the weighting factor for each selected feature quantity from the weighting factor table.
The weighting factors can be used to characterize: in historical data or experience, the electricity stealing related behaviors corresponding to the characteristic quantities form the probability of the electricity stealing behaviors.
S1522, for each selected feature, the product of the calculated weight of the selected feature and the weighting factor is used as the electricity stealing weight of the selected feature.
The calculation weight is specified by a power stealing analysis model of a power utilization scene corresponding to a user, and the calculation weight of the selected characteristic quantity represents the probability that the power stealing related behavior corresponding to the selected characteristic quantity forms the power stealing behavior under the power utilization scene.
And W represents the calculated weight, and Wf represents the weighting factor, so that the electricity stealing weight P of the selected characteristic quantity is as follows: p ═ W × Wf.
Through the steps S1521-S1523, the electricity stealing weight which is closer to the actual situation and more accurate can be obtained.
In order to make the weighting factors of the characteristic quantities as close as possible to the situation in the actual electricity utilization scene, in one embodiment, the electricity stealing behavior identification method provided by the invention further comprises the step of obtaining the weighting factors. Referring to fig. 5, this step may be implemented by the following steps.
And S21, configuring a unique corresponding characteristic quantity for each electricity stealing related behavior.
The feature values in the feature value library of the electronic device 110 are also obtained in the manner of S21.
And S22, establishing a normal distribution curve according to historical data about electricity stealing.
The historical data is various data of the user electric meter 130 collected after the user steals electricity or prompt information of events. Namely, after the historical data is the electricity stealing of the user, the collected information corresponding to each characteristic quantity is collected.
S23, for each feature quantity, a weighting factor for the feature quantity is arranged based on the distribution data of the feature quantity in the normal distribution curve.
Specifically, the weight ratio of the distribution of the feature amount in the normal distribution curve may be used as the weighting factor of the feature amount.
In actual calculation, the areas can be distinguished, a normal distribution curve about electricity stealing is established, and then a weighting factor matched with the areas is determined in a manner of S23.
After the electricity stealing weights are obtained, the calculation mode of the electricity stealing analysis value can be flexibly set, for example, the sum of the weight characteristic values of all the selected characteristic quantities can be used as the electricity stealing analysis value, and machine learning can be adopted to fit the electricity stealing weights of all the selected characteristic quantities to obtain the electricity stealing analysis value. In the present embodiment, no particular limitation is imposed.
In one possible implementation, referring to fig. 6, the following steps may also be adopted to implement step S153.
S1531, for each selected feature quantity, a correlation coefficient between the selected feature quantity and each of the remaining selected feature quantities is analyzed and obtained.
The calculation method of the correlation coefficient may be flexibly set, for example, obtained by using machine learning or neural network fitting, or may be calculated according to a preset rule, which is not limited uniquely in this embodiment.
In a possible implementation, the correlation coefficient may be calculated by using a calculation formula, and the calculation formula of the correlation coefficient may include:
wherein R is jk Representing the correlation coefficient between the jth selected feature quantity and the kth selected feature quantity, Cov (j, k) representing the covariance of the jth selected feature quantity and the kth selected feature quantity, Var [ j]The variance, Var k, representing the jth selected feature]Representing the variance of the kth selected feature quantity.
The larger the correlation coefficient between the two characteristic quantities, the greater the likelihood that both constitute electricity stealing behavior.
S1532 calculates a weighted average value for all the selected feature quantities based on all the selected feature quantities and the correlation coefficients, and takes the weighted average value as a power stealing analysis value of the user.
The calculation formula of the weighted average may include:
wherein E represents an analysis value for electricity stealing, P i And the electricity stealing weight for representing the ith selected characteristic quantity, and n represents the number of the selected characteristic quantities.
Through the above S1531 to S1532, the correlation between the selected feature quantities is considered in the process of calculating the electricity stealing weight, so that the electricity stealing analysis value is more accurate.
The judgment threshold in the electricity stealing analysis model can comprise an observation threshold, a suspicion threshold and an electricity stealing threshold, and the observation threshold, the suspicion threshold and the electricity stealing threshold in the electricity stealing analysis model corresponding to different electricity utilization scenes are different.
On this basis, referring to fig. 7, the above step S17 can be realized by the following sub-steps.
And S171, judging whether the electricity stealing analysis value is larger than or equal to the electricity stealing threshold value. If not, step S172 is executed, and if yes, step S177 is executed.
And S172, judging whether the electricity stealing analysis value is smaller than a suspected threshold value. If yes, step S173 is executed, and if no, step S176 is executed.
And S173, judging whether the electricity stealing analysis value is smaller than the observation threshold value. If not, step S174 is executed. If yes, go to step S175.
And S174, setting the identification of the user as observation of electricity stealing. The identification of observing electricity stealing is used for prompting the operation and maintenance personnel to further observe the electricity utilization of the user. At this time, the user is an observation user.
And S175, judging that the electricity utilization of the user does not belong to electricity stealing behaviors.
And S176, confirming that the behavior of the user belongs to suspected electricity stealing, and generating an alarm notice. The alarm notification is used for warning the user and reminding the operation and maintenance personnel. At this time, the user is a suspected user.
And S177, confirming that the electricity utilization behavior of the user belongs to electricity stealing, and generating an electricity stealing work order of electricity utilization. Wherein, the electricity stealing work order is used for prompting the operation and maintenance personnel to carry out electricity stealing prevention treatment on the user. At this time, the user is a power stealing user.
Through the above steps S171 to S176, it can be determined whether the electricity usage of the user belongs to electricity stealing behavior.
According to the electricity stealing behavior identification method provided by the invention, exclusive characteristic quantities are formulated for electricity stealing related behaviors in advance, weighting factors are trained for the characteristic quantities, users are classified for scene segmentation, and respective electricity stealing analysis models are formulated for a plurality of electricity using scenes obtained by the scene segmentation. Therefore, when the current consumed acquisition information in the message queue is abnormal, electricity stealing analysis is triggered, electricity stealing analysis is carried out according to the electricity stealing analysis model to which the user acquiring the information belongs and all the acquisition information of the user in the electricity stealing interval, an electricity stealing analysis value is obtained, and then electricity stealing behaviors are identified according to the electricity stealing analysis value.
Through the above steps S11-S17 and their related sub-steps, it is achieved that the electricity stealing weight and the weighted average (i.e., electricity stealing analysis value) of each user are calculated in real time once abnormal collected information occurs. And calculating the electricity stealing analysis value according to the electricity utilization scene of the user and according to scenes based on the electricity stealing analysis model corresponding to the electricity utilization scene. Based on the electricity stealing analysis model, invalid data can be filtered, the calculation amount and the calculation complexity are reduced, the pressure is calculated and shared, the pressure peak value is reduced, and the performance bottleneck is relieved. And outputting the electricity stealing work orders of the electricity stealing behaviors in a hierarchy (an observation user, a suspected user and an electricity stealing user) according to the electricity stealing analysis value. The method and the device realize the segmentation of the type and the suspicion grade of the electricity stealing users, and finally lock the object for checking the electricity stealing, thereby effectively reducing the labor cost.
Based on the above concept of the electricity stealing behavior identification method, referring to fig. 8, in one embodiment, there is provided an electricity stealing behavior identification apparatus 150, and the electricity stealing behavior identification apparatus 150 can be applied to the electronic device 110 shown in fig. 1. The electricity stealing behavior recognition apparatus 150 includes a preprocessing module 160, a calculation module 170, and a recognition model.
The preprocessing module 160 is configured to, when it is determined that the currently consumed acquisition information in the message queue is abnormal, determine, according to the user identifier of the acquisition information, a user corresponding to the acquisition information and a power consumption scenario to which the user belongs, determine, from the cache, all the acquisition information of the user in the power stealing interval, serve as a target information set, and determine a power stealing analysis model corresponding to the power consumption scenario.
The collected information is information about behavior related to electricity stealing of a certain user, which is monitored and collected by the monitoring device 120 in real time.
And the calculating module 170 is configured to analyze the power consumption situation of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set, so as to obtain a power stealing analysis value of the user.
The characteristic quantity is used for representing electricity stealing related behaviors to which the collected information belongs.
And the identification module 180 is used for judging whether the electricity utilization of the user belongs to electricity stealing behavior according to the electricity stealing analysis value.
The working principle of the electricity stealing behavior recognition device 150 is as follows: when the preprocessing module 160 determines that the collected information currently consumed by the message queue is abnormal, it determines the user corresponding to the collected information and the power utilization scenario to which the user belongs, so that the calculation module 170 analyzes the power stealing analysis value of the user by using the power stealing analysis model corresponding to the power utilization scenario and the feature quantity of each piece of collected information of the user in the power stealing interval, and then the identification module 180 determines whether the power utilization of the user is a power stealing behavior according to the power stealing analysis value.
Steal electric behavior recognition device 150 and can realize real-time supervision discernment, and cut apart the power consumption scene, use the exclusive electric analysis model of stealing of power consumption scene to carry out the power consumption analysis, realize accurate analysis to can improve the degree of accuracy of stealing electric identification, in order to promote the recognition effect.
For specific definition of the electricity stealing behavior recognition device 150, the above definition of the electricity stealing behavior recognition method can be referred to, and will not be described herein again. The respective modules in the above-described electricity stealing behavior recognizing apparatus 150 can be wholly or partially implemented by software, hardware, and a combination thereof. The modules may be embedded in a hardware form or may be independent of a processor in the electronic device 110, or may be stored in a memory in the electronic device 110 in a software form, so that the processor calls to execute operations corresponding to the modules.
In one embodiment, an electronic device 110 is provided, and the electronic device 110 may be a server, and its internal structure diagram may be as shown in fig. 9. The electronic device 110 includes a processor, memory, and a network interface connected by a system bus. Wherein the processor of the electronic device 110 is configured to provide computing and control capabilities. The memory of the electronic device 110 includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data generated in the process of executing the electricity stealing behavior identification method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of theft behavior identification.
Those skilled in the art will appreciate that the configuration shown in fig. 9 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation on the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown in fig. 9, or may combine certain components, or have a different arrangement of components.
In one embodiment, the electricity stealing behavior recognition apparatus 150 provided by the present invention can be implemented in the form of a computer program, which can run on the electronic device 110 as shown in fig. 9. The memory of the electronic device 110 may store various program modules constituting the electricity stealing behavior recognition apparatus 150, such as the preprocessing module 160, the calculation module 170, and the recognition module 180 shown in fig. 8. The computer program constituted by the respective program modules causes the processor to execute the steps in the electricity stealing behavior identification method described in this specification.
For example, the electronic device 110 shown in fig. 9 may perform the steps S11-S13 through the preprocessing module 160 in the electricity stealing behavior recognition apparatus 150 shown in fig. 8. The electronic device 110 may perform step S15 through the calculation module 170. The electronic device 110 may perform step S17 through the identification module 180.
In one embodiment, an electronic device 110 is provided, comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program: when the current consumed acquisition information in the message queue is determined to be abnormal, determining a user corresponding to the acquisition information and an electricity utilization scene to which the user belongs according to the user identification of the acquisition information, wherein the acquisition information is electricity stealing related behavior information of a certain user, which is monitored and acquired in real time by the monitoring equipment 120; determining all collected information of the user in the electricity stealing interval from the cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity utilization scene; analyzing the power utilization condition of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain a power stealing analysis value of the user, wherein the characteristic quantity is used for representing the power stealing related behavior to which the collected information belongs; and judging whether the electricity utilization of the user belongs to electricity stealing behavior or not according to the electricity stealing analysis value.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: when the current consumed acquisition information in the message queue is determined to be abnormal, determining a user corresponding to the acquisition information and a power utilization scene to which the user belongs according to the user identification of the acquisition information, wherein the acquisition information is the electricity stealing related behavior information of a certain user, which is monitored and acquired by the monitoring equipment 120 in real time; determining all collected information of the user in the electricity stealing interval from the cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity utilization scene; analyzing the power utilization condition of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain a power stealing analysis value of the user, wherein the characteristic quantity is used for representing the power stealing related behavior to which the collected information belongs; and judging whether the electricity utilization of the user belongs to electricity stealing behavior or not according to the electricity stealing analysis value.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent 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 medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for identifying electricity stealing behavior is applied to electronic equipment, a relationship table is stored in the electronic equipment, and the relationship table records the corresponding relationship among user identification, users and user scenes, and the method comprises the following steps:
when the current consumed acquisition information in the message queue is determined to be abnormal, determining a user corresponding to the acquisition information and a power utilization scene to which the user belongs according to a user identifier of the acquisition information, wherein the acquisition information is electricity stealing related behavior information of a certain user, which is monitored and acquired by monitoring equipment in real time;
determining all collected information of the user in the electricity stealing interval from the cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity utilization scene;
analyzing the electricity utilization condition of the user in the electricity stealing interval according to the electricity stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain an electricity stealing analysis value of the user, wherein the characteristic quantity is used for representing electricity stealing related behaviors to the collected information;
and judging whether the electricity utilization of the user belongs to electricity stealing behaviors or not according to the electricity stealing analysis value.
2. The electricity stealing behavior identification method according to claim 1, wherein the electricity stealing analysis model comprises a selected combination of characteristic quantities;
the step of analyzing the power consumption situation of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain the power stealing analysis value of the user comprises the following steps:
analyzing each piece of collected information in the target information set to obtain a characteristic quantity corresponding to each piece of collected information;
extracting feature quantities which accord with the selected feature quantity combination from all the feature quantities to serve as selected feature quantities, and calculating the electricity stealing weight of each selected feature quantity;
and analyzing the electricity utilization condition of the user in the electricity stealing interval based on all the selected characteristic quantities and the electricity stealing weight to obtain an electricity stealing analysis value of the user.
3. The electricity stealing behavior identification method according to claim 2, wherein the electricity stealing analysis model further comprises a calculation weight for each selected feature quantity, and the step of calculating the electricity stealing weight for each selected feature quantity comprises:
inquiring the weighting factor of each selected characteristic quantity from a preset weighting factor table;
and for each selected characteristic quantity, taking the product of the calculated weight of the selected characteristic quantity and the weighting factor as the electricity stealing weight of the selected characteristic quantity.
4. The method for identifying electricity stealing behavior according to claim 2, wherein the step of analyzing the electricity usage of the user in the electricity stealing interval based on all the selected characteristic quantities and the electricity stealing weights to obtain an electricity stealing analysis value of the user comprises:
analyzing and obtaining a correlation coefficient between each selected characteristic quantity and each remaining selected characteristic quantity aiming at each selected characteristic quantity;
and calculating a weighted average value of all the selected characteristic quantities according to all the selected characteristic quantities and the correlation coefficient, and taking the weighted average value as a power stealing analysis value of the user.
5. The electricity stealing behavior identification method according to claim 1 or 2, wherein the electricity stealing analysis model further comprises an electricity stealing threshold;
the step of judging whether the electricity utilization of the user belongs to electricity stealing behavior or not according to the electricity stealing analysis value comprises the following steps:
judging whether the electricity stealing analysis value is larger than or equal to the electricity stealing threshold value or not, if so, confirming that the electricity utilization behavior of the user belongs to electricity stealing, and generating an electricity stealing work order of the electricity utilization;
wherein the electricity stealing work order is used for prompting operation and maintenance personnel to carry out electricity stealing prevention treatment on the user.
6. The electricity stealing behavior identification method according to claim 5, wherein the electricity stealing analysis model further comprises an observation threshold and a suspicion threshold;
the step of judging whether the electricity utilization of the user belongs to electricity stealing behavior according to the electricity stealing analysis value further comprises the following steps:
if the electricity stealing analysis value is smaller than the electricity stealing threshold, judging whether the electricity stealing analysis value is smaller than the suspected threshold, if not, confirming that the behavior of the user belongs to suspected electricity stealing, and generating an alarm notice;
if the electricity stealing analysis value is smaller than the suspicion threshold value, judging whether the electricity stealing analysis value is smaller than the observation threshold value, and if not, setting the user identifier as the observation electricity stealing.
7. A method for identifying electricity stealing behavior according to claim 3, further comprising the step of obtaining the weighting factor, which comprises:
for each electricity stealing related behavior, configuring a unique corresponding characteristic quantity for the electricity stealing related behavior;
establishing a normal distribution curve according to historical data about electricity stealing;
for each of the feature quantities, a weighting factor for the feature quantity is arranged based on distribution data of the feature quantity in the normal distribution curve.
8. The device for identifying the electricity stealing behavior is applied to electronic equipment, a relation table is stored in the electronic equipment, a corresponding relation among a user identifier, a user and a user scene is recorded in the relation table, and the device for identifying the electricity stealing behavior comprises a preprocessing module, a calculating module and an identifying module;
the preprocessing module is used for determining a user corresponding to the acquired information and an electricity utilization scene to which the user belongs according to the user identification of the acquired information when the acquired information consumed currently in the message queue is determined to be abnormal, determining all the acquired information of the user in an electricity stealing interval from a cache as a target information set, and determining an electricity stealing analysis model corresponding to the electricity utilization scene;
the collected information is the electricity stealing related behavior information of a certain user, which is monitored and collected by monitoring equipment in real time;
the calculation module is used for analyzing the power utilization condition of the user in the power stealing interval according to the power stealing analysis model and the characteristic quantity of each piece of collected information in the target information set to obtain a power stealing analysis value of the user, wherein the characteristic quantity is used for representing the power stealing related behavior to which the collected information belongs;
and the identification module is used for judging whether the electricity utilization of the user belongs to electricity stealing behaviors or not according to the electricity stealing analysis value.
9. An electronic device, comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being capable of executing the computer program to implement the electricity stealing behavior identification method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of electricity stealing behavior identification according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578789A (en) * | 2023-04-25 | 2023-08-11 | 联桥科技有限公司 | Method and device for screening electricity stealing users based on metering big data |
| CN116777121A (en) * | 2023-08-18 | 2023-09-19 | 武汉振铭科技发展有限公司 | Illegal electricity consumption checking method based on big data, storage medium and electronic equipment |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116578789A (en) * | 2023-04-25 | 2023-08-11 | 联桥科技有限公司 | Method and device for screening electricity stealing users based on metering big data |
| CN116777121A (en) * | 2023-08-18 | 2023-09-19 | 武汉振铭科技发展有限公司 | Illegal electricity consumption checking method based on big data, storage medium and electronic equipment |
| CN116777121B (en) * | 2023-08-18 | 2023-11-03 | 武汉振铭科技发展有限公司 | Illegal electricity consumption checking method based on big data, storage medium and electronic equipment |
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