CN109063929A - It opposes electricity-stealing analysis and early warning method, apparatus and computer readable storage medium - Google Patents
It opposes electricity-stealing analysis and early warning method, apparatus and computer readable storage medium Download PDFInfo
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- CN109063929A CN109063929A CN201810995094.9A CN201810995094A CN109063929A CN 109063929 A CN109063929 A CN 109063929A CN 201810995094 A CN201810995094 A CN 201810995094A CN 109063929 A CN109063929 A CN 109063929A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The present invention provides one kind and opposes electricity-stealing analysis and early warning method, apparatus and computer readable storage medium.The analysis and early warning method of opposing electricity-stealing includes: to obtain learning sample data and the instant data of prediction；The prediction includes: class of subscriber, instantaneous flow, line loss, phase angle and electricity consumption with instant data；The learning sample data include: the historical data and instantaneous flow, line loss, the historical data at phase angle and electricity consumption of stealing case；Expert model of opposing electricity-stealing is determined according to the learning sample data；Using the input quantity for predicting that instant data is used to oppose electricity-stealing expert model described in, expert model of opposing electricity-stealing described in operation exports the stealing suspicion coefficient of each user according to preset Quantitative marking rule；According to the stealing suspicion coefficient prediction stealing suspicion family.This method can reduce cost of labor, improve the working efficiency opposed electricity-stealing.
Description
Technical field
The present invention relates to Prevention Stealing Electricity Technology field, more particularly, to opposing electricity-stealing, analysis and early warning method, apparatus and computer can
Read storage medium.
Background technique
For a long time, a few peoples are driven by interests takes various unlawful means to implement stealing, causes national electric energy to be lost, damage
The legitimate rights and interests of Hai Liao power supply enterprise have upset normal supply order, and have seriously endangered the safe operation of power grid.To surreptitiously
Electric behavior carries out effectively prevention and strong strike, and to maintenance social equity, it is important to ensure that ordered electric has for safeguard state interests
Meaning.Improved day by day with remote meter reading, traditional meter reader, which changes, monthly arrive live meter reading in the past and is accustomed to, and relieves power stealing
Person is found the burden at heart of cheating, leads to the trend further rampant there are stealing situation, thus, how in electrical energy metering meter
Charge system carries out stealing detection, and preventing electricity charge loss is extremely urgent problem.
Summary of the invention
It is an object of the invention in view of the above problems in the prior art, provide one kind oppose electricity-stealing analysis and early warning method,
Device and computer storage medium are for solving the deficiencies in the prior art.
Specifically, it opposes electricity-stealing analysis and early warning method the embodiment of the invention provides one kind, comprising:
Expert model of opposing electricity-stealing is determined according to learning sample data, and the learning sample data include: the use of stealing case
Family classification and instantaneous flow, line loss, the historical data at phase angle and electricity consumption；
It obtains and predicts instant data, the prediction includes: class of subscriber, instantaneous flow, line loss, phase angle with instant data
And electricity consumption；
Using the input quantity for predicting that instant data is used to oppose electricity-stealing expert model described in, oppose electricity-stealing expert described in operation
Model exports the stealing suspicion coefficient of each user according to preset Quantitative marking rule；
Stealing suspicion family is determined according to the stealing suspicion coefficient.
As a further improvement of the above technical scheme, the method also includes: by the sample of the stealing user investigated
Expert model is opposed electricity-stealing described in data inputting to optimize to the expert model of opposing electricity-stealing.
As a further improvement of the above technical scheme, the optimization process uses decision Tree algorithms.
As a further improvement of the above technical scheme, the class of subscriber includes: special change user and low-voltage customer；It is anti-to steal
Electric expert model includes: special to become that user opposes electricity-stealing expert model and low-voltage customer is opposed electricity-stealing expert model.
As a further improvement of the above technical scheme, the method also includes: obtain going through for the stealing suspicion user
History electricity consumption data；By current electricity consumption data curve compared with the history electricity consumption track of user, stolen so that further screening confirmation is described
Whether electric suspicion user has stealing suspicion.
It opposes electricity-stealing analysis and early warning device the embodiment of the invention also provides one kind, comprising:
It opposes electricity-stealing expert model determining module, for determining expert model of opposing electricity-stealing, according to learning sample data
Practise the class of subscriber and instantaneous flow, line loss, the historical data at phase angle and electricity consumption that sample data includes: stealing case；
Instant data acquisition module predicts instant data for obtaining, and the prediction includes: user class with instant data
Not, instantaneous flow, line loss, phase angle and electricity consumption；
Stealing suspicion coefficient obtains module, for using instant data to oppose electricity-stealing described in expert model the prediction
Input quantity, expert model of opposing electricity-stealing described in operation, the stealing suspicion system of each user is exported according to preset Quantitative marking rule
Number；
Stealing suspicion determining module, for determining stealing suspicion family according to the stealing suspicion coefficient.
As a further improvement of the above technical scheme, further includes: optimization module, stealing user's for will investigate
Expert model is opposed electricity-stealing described in sample data typing to optimize to the expert model of opposing electricity-stealing.
As a further improvement of the above technical scheme, the optimization module optimizes processing using decision Tree algorithms.
As a further improvement of the above technical scheme, further includes: screening module, for obtaining the stealing suspicion user
History electricity consumption data, by current electricity consumption data curve compared with the history electricity consumption track of user, with further screening confirm institute
State whether stealing suspicion user has stealing suspicion.
The embodiment of the invention also provides a kind of computer readable storage mediums, are stored with computer program, described
Computer program, which is performed, implements above-described analysis and early warning method of opposing electricity-stealing.
This is at least had the following beneficial effects: compared with existing well-known technique using technical solution provided by the invention
Analysis and early warning method and device of opposing electricity-stealing can reduce cost of labor, improve the working efficiency opposed electricity-stealing.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the analysis and early warning method flow schematic diagram of opposing electricity-stealing that one embodiment of the invention proposes；
Fig. 2 is the analysis and early warning apparatus structure schematic diagram of opposing electricity-stealing that one embodiment of the invention proposes.
Main element symbol description:
100- opposes electricity-stealing expert model determining module；The instant data acquisition module of 200-；300- stealing suspicion coefficient obtains
Module；400- stealing suspicion determining module.
Specific embodiment
Hereinafter, the various embodiments of the disclosure will be described more fully.The disclosure can have various embodiments, and
It can adjust and change wherein.It should be understood, however, that: there is no disclosure protection scope is limited to specific reality disclosed herein
The intention of example is applied, but the disclosure should be interpreted as to all in the spirit and scope for covering the various embodiments for falling into the disclosure
Adjustment, equivalent and/or optinal plan.
Hereinafter, can the term " includes " used in the various embodiments of the disclosure or " may include " instruction disclosed in
Function, operation or the presence of element, and do not limit the increase of one or more functions, operation or element.In addition, such as existing
Used in the various embodiments of the disclosure, term " includes ", " having " and its cognate are meant only to indicate special characteristic, number
Word, step, operation, the combination of element, component or aforementioned item, and be understood not to exclude first one or more other
Feature, number, step, operation, element, component or aforementioned item combined presence or increase one or more features, number,
Step, operation, element, component or aforementioned item combination a possibility that.
In the various embodiments of the disclosure, statement " at least one of A or/and B " includes the text listed file names with
Any combination or all combinations.For example, statement " A or B " or " at least one of A or/and B " may include A, may include B or can
Including A and B both.
The statement (" first ", " second " etc.) used in the various embodiments of the disclosure can be modified in various implementations
Various constituent element in example, but respective sets can not be limited into element.For example, the above statement is not intended to limit the suitable of the element
Sequence and/or importance.The above statement is only used for the purpose for differentiating an element and other elements.For example, the first user fills
It sets and indicates different user device with second user device, although the two is all user apparatus.For example, not departing from each of the disclosure
In the case where the range of kind embodiment, first element is referred to alternatively as second element, and similarly, second element is also referred to as first
Element.
It should also be noted that if a constituent element ' attach ' to another constituent element by description, it can be by the first composition member
Part is directly connected to the second constituent element, and " connection " third can form between the first constituent element and the second constituent element
Element.On the contrary, when a constituent element " being directly connected to " is arrived another constituent element, it will be appreciated that in the first constituent element
And second third constituent element is not present between constituent element.
The term used in the various embodiments of the disclosure " user " can be indicated using the people of electronic device or using electricity
The device (for example, artificial intelligence electronic device) of sub-device.
The term used in the various embodiments of the disclosure is used only for the purpose of describing specific embodiments and not anticipates
In the various embodiments of the limitation disclosure.Unless otherwise defined, otherwise all terms used herein (including technical term and
Scientific term) there is contain identical with the various normally understood meanings of embodiment one skilled in the art of the disclosure
Justice.The term (term such as limited in the dictionary generally used) be to be interpreted as have in the related technical field
The identical meaning of situational meaning and Utopian meaning or meaning too formal will be interpreted as having, unless this
It is clearly defined in disclosed various embodiments.
Embodiment 1
As shown in Figure 1, oppose electricity-stealing analysis and early warning method the embodiment of the invention provides one kind, this method comprises:
S101, expert model of opposing electricity-stealing is determined according to learning sample data, the learning sample data include: stealing case
Class of subscriber and instantaneous flow, line loss, the historical data at phase angle and electricity consumption.
Class of subscriber includes specially becoming user and low-voltage customer.Instantaneous flow includes: instantaneous voltage value and instantaneous current value.Line loss
It include: day line loss and moon line loss.Electricity consumption includes: month electricity consumption and daily power consumption.
Foundation is opposed electricity-stealing after expert model, the foundation for just having data reference to compare.
Expert model of opposing electricity-stealing sufficiently is established with mathematical algorithms such as decision trees whole on the basis of abbreviated analysis model
, relatively accurate mathematical analysis model, and constantly training is carried out to model using emulation technology and is optimized.Pass through mathematics point
When representational stealing user is provided in analysis model analysis, system can be by the electricity consumption behavior in stealing user's stealing period
Data are put into intelligence learning library, and system is by improved decision Tree algorithms automatically according to the stealing feature in intelligent characteristic library
Automatically electricity consumption behavior user similar therewith, summarizes and shows anomalous discrimination index existing for going to compare in discovery mass data
Optimisation strategy, rule-based algorithm optimisation strategy etc., closed loop, constantly improve electricity stealing analysis efficiency.
S102, obtain predict instant data, it is described predict with instant data include: class of subscriber, instantaneous flow, line loss,
Phase angle and electricity consumption.
Predict with instant data be user power utilization latest data information, be to judge whether user has electricity stealing recently
Important evidence.
S103, the prediction is used instant data oppose electricity-stealing described in asing the input quantity of expert model, run it is described it is anti-surreptitiously
Electric expert model exports the stealing suspicion coefficient of each user according to preset Quantitative marking rule.
Expert model oppose electricity-stealing according to the instant data of prediction of input, is exported according to preset Quantitative marking rule each
The stealing suspicion coefficient of user.Specifically, predict that each item data of instant data requires and oppose electricity-stealing in expert model
Preset value be compared.According to interval range locating for the item data, a scoring is determined to the item data.According to every item number
According to scoring be multiplied with weight ratio shared by contiguous items.The product addition of multiple data finally be can be obtained by into each use
The stealing suspicion coefficient at family.
S104, stealing suspicion family is determined according to the stealing suspicion coefficient.
When preset value of the stealing suspicion coefficient of user more than expert model setting of opposing electricity-stealing, mark the user as stealing
Electric suspicion user.
Follow-up work personnel can visit at random carries out power utility check to the user, to verify the user with the presence or absence of surreptitiously
Electric behavior.
Analysis and early warning method of opposing electricity-stealing may also include that opposes electricity-stealing described in the sample data typing for the stealing user that will have been investigated
Expert model is to optimize the expert model of opposing electricity-stealing.
Expert model of opposing electricity-stealing, which passes through, constantly receives the sample data of stealing user to constantly improve the assay of its own
Prediction rule, to keep prediction more accurate and reliable.
Expert model of opposing electricity-stealing, which optimizes, can be used decision Tree algorithms.
Decision Tree algorithms are a kind of methods for approaching discrete function value.It is a kind of typical classification method, first logarithm
According to being handled, readable rule and decision tree are generated using inductive algorithm, then new data is analyzed using decision.This
Decision tree is the process classified by series of rules to data in matter.
Class of subscriber includes: special change user and low-voltage customer；Expert model of opposing electricity-stealing includes: special to become user and oppose electricity-stealing expert
Model and low-voltage customer are opposed electricity-stealing expert model.Corresponding expert model of opposing electricity-stealing is used for different users.Specially become user
There are significant differences with some sample datas of low-voltage customer, by establishing specific anti-electricity-theft expert model, for different
Class of subscriber is evaluated, and the accuracy of evaluation is higher.
For specially becoming user, system combines abnormal determination index and power factor, phase angle, electric current electricity by monitoring automatically
Association analysis between the parameters such as pressure carries out comprehensive judgement, passes through the abnormal determination index system and each quasi-representative stealing of foundation
The abnormal expert analysis model of mode calculates suspicion family coefficient and grade and is added to monitoring pond progress manual confirmation.
For low-voltage customer, system is averaged by user's history moon electricity, platform area month line loss, industry, and moon electricity etc. is automatic to be supervised
Survey, carry out comprehensive judgement in conjunction with abnormal determination index and the expert analysis model of foundation, and be subject to electricity consumption track, line loss exception etc.
It is analyzed, to lock suspicion family.
It opposes electricity-stealing analysis and early warning method further include: obtain the history electricity consumption data of the stealing suspicion user；It will use at present
Electric data and curves confirm whether the stealing suspicion user has stealing compared with the history electricity consumption track of user, with further screening
Suspicion.
After determining stealing suspicion user, can by current electricity consumption data curve compared with the history electricity consumption track of user, when
Within a preset range, and the user does not have the record of any stealing to the difference compared, and can exclude the user is stealing suspicion
User.
Embodiment 2
As shown in Fig. 2, opposing electricity-stealing analysis and early warning device the embodiment of the invention provides one kind, which includes: to oppose electricity-stealing
Expert model determining module 100, instant data acquisition module 200, stealing suspicion coefficient obtain module 300 and stealing suspicion determines
Module 400.
It opposes electricity-stealing expert model determining module 100, it is described for determining expert model of opposing electricity-stealing according to learning sample data
Learning sample data include: the class of subscriber and instantaneous flow, line loss, the historical data at phase angle and electricity consumption of stealing case.
Class of subscriber includes specially becoming user and low-voltage customer.Instantaneous flow includes: instantaneous voltage value and instantaneous current value.Line loss
It include: day line loss and moon line loss.Electricity consumption includes: month electricity consumption and daily power consumption.
Foundation is opposed electricity-stealing after expert model, the foundation for just having data reference to compare.
Instant data acquisition module 200 obtains and predicts instant data, and the prediction includes: user class with instant data
Not, instantaneous flow, line loss, phase angle and electricity consumption.
Predict with instant data be user power utilization latest data information, be to judge whether user has electricity stealing recently
Important evidence.
Stealing suspicion coefficient obtains module 300, for using instant data to oppose electricity-stealing described in expert's mould the prediction
The input quantity of type, expert model of opposing electricity-stealing described in operation are disliked according to the stealing that preset Quantitative marking rule exports each user
Doubt coefficient.
Expert model oppose electricity-stealing according to the instant data of prediction of input, is exported according to preset Quantitative marking rule each
The stealing suspicion coefficient of user.Specifically, predict that each item data of instant data requires and oppose electricity-stealing in expert model
Preset value be compared.According to interval range locating for the item data, a scoring is determined to the item data.According to every item number
According to scoring be multiplied with weight ratio shared by contiguous items.The product addition of multiple data finally be can be obtained by into each use
The stealing suspicion coefficient at family.
Stealing suspicion determining module 400, for determining stealing suspicion family according to the stealing suspicion coefficient.
When preset value of the stealing suspicion coefficient of user more than expert model setting of opposing electricity-stealing, mark the user as stealing
Electric suspicion user.
Follow-up work personnel can visit at random carries out power utility check to the user, to verify the user with the presence or absence of surreptitiously
Electric behavior.
Analysis and early warning device of opposing electricity-stealing may also include that optimization module, the sample data of the stealing user for will investigate
Expert model is opposed electricity-stealing described in typing to optimize to the expert model of opposing electricity-stealing.
Expert model of opposing electricity-stealing, which passes through, constantly receives the sample data of stealing user to constantly improve the assay of its own
Prediction rule, to keep prediction more accurate and reliable.
Expert model of opposing electricity-stealing, which optimizes, can be used decision Tree algorithms.
Analysis and early warning device of opposing electricity-stealing may also include that screening module, and the history for obtaining the stealing suspicion user is used
Electric data；By current electricity consumption data curve compared with the history electricity consumption track of user, confirm that the stealing is disliked with further screening
Whether doubtful user has stealing suspicion.
After determining stealing suspicion user, can by current electricity consumption data curve compared with the history electricity consumption track of user, when
Within a preset range, and the user does not have the record of any stealing to the difference compared, and can exclude the user is stealing suspicion
User.
The present invention also provides a kind of computer readable storage mediums, are stored with computer program, in the computer
Program is performed the analysis and early warning method of opposing electricity-stealing implemented in 1.
It will be appreciated by those skilled in the art that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, module in attached drawing or
Process is not necessarily implemented necessary to the present invention.
It will be appreciated by those skilled in the art that the module in device in implement scene can be described according to implement scene into
Row is distributed in the device of implement scene, can also be carried out corresponding change and is located at the one or more dresses for being different from this implement scene
In setting.The module of above-mentioned implement scene can be merged into a module, can also be further split into multiple submodule.
Aforementioned present invention serial number is for illustration only, does not represent the superiority and inferiority of implement scene.Disclosed above is only the present invention
Several specific implementation scenes, still, the present invention is not limited to this, and the changes that any person skilled in the art can think of is all
Protection scope of the present invention should be fallen into.
Claims (10)
- The analysis and early warning method 1. one kind is opposed electricity-stealing characterized by comprisingExpert model of opposing electricity-stealing is determined according to learning sample data, and the learning sample data include: the user class of stealing case Not and instantaneous flow, line loss, the historical data at phase angle and electricity consumption；It obtains and predicts instant data, the prediction includes: class of subscriber, instantaneous flow, line loss, phase angle and use with instant data Electricity；Using the input quantity for predicting that instant data is used to oppose electricity-stealing expert model described in, expert's mould of opposing electricity-stealing described in operation Type exports the stealing suspicion coefficient of each user according to preset Quantitative marking rule；Stealing suspicion family is determined according to the stealing suspicion coefficient.
- 2. analysis and early warning method of opposing electricity-stealing as described in claim 1, which is characterized in that the method also includes: it will investigate Stealing user sample data typing described in oppose electricity-stealing expert model to optimize to the expert model of opposing electricity-stealing.
- 3. analysis and early warning method of opposing electricity-stealing as claimed in claim 2, which is characterized in that the optimization process is calculated using decision tree Method.
- 4. analysis and early warning method of opposing electricity-stealing as described in claim 1, which is characterized in that the class of subscriber includes: that special become is used Family and low-voltage customer；Expert model of opposing electricity-stealing includes: special to become that user opposes electricity-stealing expert model and low-voltage customer is opposed electricity-stealing expert's mould Type.
- 5. analysis and early warning method of opposing electricity-stealing as described in claim 1, which is characterized in that the method also includes: described in acquisition The history electricity consumption data of stealing suspicion user；By current electricity consumption data curve compared with the history electricity consumption track of user, with into one Step screening confirms whether the stealing suspicion user has stealing suspicion.
- The analysis and early warning device 6. one kind is opposed electricity-stealing characterized by comprisingIt opposes electricity-stealing expert model determining module, for determining expert model of opposing electricity-stealing, the study sample according to learning sample data Notebook data includes: the class of subscriber and instantaneous flow, line loss, the historical data at phase angle and electricity consumption of stealing case；Instant data acquisition module, for obtain predict instant data, it is described predict with instant data include: class of subscriber, Instantaneous flow, line loss, phase angle and electricity consumption；Stealing suspicion coefficient obtains module, for using instant data to oppose electricity-stealing described in the input of expert model the prediction It measures, expert model of opposing electricity-stealing described in operation exports the stealing suspicion coefficient of each user according to preset Quantitative marking rule；Stealing suspicion determining module, for determining stealing suspicion family according to the stealing suspicion coefficient.
- 7. analysis and early warning device of opposing electricity-stealing as claimed in claim 6, which is characterized in that further include: optimization module, being used for will It is excellent to carry out to the expert model of opposing electricity-stealing that expert model is opposed electricity-stealing described in the sample data typing of the stealing user of verification Change.
- 8. analysis and early warning device according to claim 7 of opposing electricity-stealing, which is characterized in that the optimization module uses decision tree Algorithm optimizes processing.
- 9. analysis and early warning device according to claim 6 of opposing electricity-stealing, which is characterized in that further include: screening module, for obtaining The history electricity consumption data for taking the stealing suspicion user, by current electricity consumption data curve compared with the history electricity consumption track of user, Confirm whether the stealing suspicion user has stealing suspicion with further screening.
- 10. a kind of computer readable storage medium, which is characterized in that it is stored with computer program, in the computer program It is performed and implements the analysis and early warning method of opposing electricity-stealing of any of claims 1-5.
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| CN109633517A (en) * | 2018-12-27 | 2019-04-16 | 国网湖南省电力有限公司 | The method of split-phase timesharing lookup stealing |
| CN109885559A (en) * | 2019-01-23 | 2019-06-14 | 长春工程学院 | A kind of stealing user discovery method based on the analysis of user's sample data difference characteristic |
| CN110082579A (en) * | 2019-05-21 | 2019-08-02 | 国网湖南省电力有限公司 | A kind of area's Intelligent power-stealing prevention monitoring method, system, equipment and medium |
| CN110491061A (en) * | 2019-08-26 | 2019-11-22 | 华北电力大学（保定） | A kind of oppose electricity-stealing evidence-obtaining system and the method for augmented reality |
| CN111539843A (en) * | 2020-04-17 | 2020-08-14 | 国网新疆电力有限公司电力科学研究院 | Data-driven intelligent early warning method for preventing electricity stealing |
| CN112348270A (en) * | 2020-11-12 | 2021-02-09 | 国网山东省电力公司聊城市茌平区供电公司 | Abnormal electricity consumption customer detection method and device |
| CN114154999A (en) * | 2021-10-27 | 2022-03-08 | 国网河北省电力有限公司营销服务中心 | Electricity stealing prevention method, device, terminal and storage medium |
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