CN114019205A

CN114019205A - Electricity stealing identification method and system

Info

Publication number: CN114019205A
Application number: CN202110806759.9A
Authority: CN
Inventors: 李宏伟; 潘志远; 刘海客; 刘书阁; 刘朝阳; 宋新新; 张正茂; 郑鑫; 邢凤民; 宋哲; 张心一
Original assignee: State Grid Corp of China SGCC; State Grid of China Technology College; Shandong Electric Power College
Current assignee: State Grid Corp of China SGCC; State Grid of China Technology College; Shandong Electric Power College
Priority date: 2021-07-16
Filing date: 2021-07-16
Publication date: 2022-02-08

Abstract

The invention provides an electricity stealing identification method and system, belonging to the technical field of electricity stealing prevention in power engineering, and the method comprises the steps of obtaining user information of a transformer area, total electric quantity of a transformer area gateway and power consumption information of a user; calculating the line loss rate of each station area based on the station area user information, the station area gateway total electric quantity and the user power consumption information, and taking the station area with the large line loss rate change as a suspicious station area for electricity stealing; calculating the power consumption change rate of a certain accumulated duration of the users in the suspicious station area; and sequencing according to the change rate of the electricity consumption to determine the suspicious electricity stealing users. The invention can obtain more accurate suspicious electricity stealing user groups, can comprehensively analyze the use condition of the electricity consuming users in the region for the electricity marketing inspectors, displays the long-time historical record information of each electricity consuming user, and finds out the suspected electricity stealing user groups from the historical record information, thereby facilitating the inspectors to take further action.

Description

Electricity stealing identification method and system

Technical Field

The invention relates to the technical field of electricity stealing prevention of a power system, in particular to an electricity stealing identification method and system for performing parallel statistics by using a distributed mass database.

Background

In power system engineering, electricity stealing occurs frequently. The power marketing inspection force is enhanced in order to avoid the behavior of electricity stealing and manage the aspect; technical level, power supply station etc. can adopt anti-theft ammeter case, seal to whole metering device with measuring box or measuring cabinet, install the pickproof lock additional, implement means such as on-line monitoring to the electricity distribution room, perhaps also can adopt technical means such as smart electric meter's monitoring.

If suspected electricity stealing users can be found out quickly among a large number of electricity consuming users, the workload of electricity marketing inspection personnel can be greatly reduced, and most of the traditional electricity stealing identification methods find the suspected electricity stealing users from two aspects of line loss and electricity consumption. On one hand, the economic operation condition of the power grid of the power supply department can be investigated and analyzed, and the electricity stealing is detected from the line loss rate index, namely, the change condition of the line loss rate is longitudinally compared from time. For example, the line loss rate of a line or a distribution transformer suddenly increases or suddenly decreases in a certain time period, especially in a sudden increase situation. On the other hand, the capacity can be checked against the capacity, and the capacity of the electric equipment of the customer refers to the actual using capacity and is related to the constitution condition of the electric equipment.

The electric equipment composition condition mainly refers to the continuous load and the interrupted load respectively account for what percentage, but not the power load and the lighting load respectively account for what percentage. For example, household electricity, lighting, fans, televisions, washing machines, and the like belong to interruptive loads; to treat factory electricity, lighting and power are often used simultaneously, essentially continuously loaded if produced in three shifts. The electricity consumption can be checked before and after, and the electricity consumption of the client in the current month is checked with the electricity consumption in the previous month or the electricity consumption in the previous month. If the reason should be found out when the electric quantity is suddenly increased or decreased, the previous month should be searched; if the power is suddenly reduced, the month should be looked up.

The electricity consumption and energy consumption information acquisition system is an important component of smart power grid construction, and the analysis of transformer area line loss and the monthly synchronization ring ratio analysis of electricity consumption of a certain user are mostly included in an application layer of the system, so that certain effect on electricity larceny prevention is achieved. However, the electricity consumption energy information collection system uses a relational commercial library as a storage platform, and in view of the fact that the relational commercial library adopts a centralized storage and access mechanism, the amount of stored information is much less than that of a distributed mass database, and when a large amount of data is accessed, the access performance is far inferior to that of the distributed mass database adopting a parallel access algorithm, and the information collected by electricity consumption energy is mass data of several years or even ten years.

Therefore, when the electricity consumption energy information acquisition system analyzes the line loss of the transformer area and the monthly cycle ratio of the electricity consumption of a certain user, the related data can only be partial stored data, deeper statistical analysis cannot be performed by using all data, and the electricity stealing information identification is not accurate enough.

Disclosure of Invention

The invention aims to provide a method and a system for identifying electricity stealing by utilizing a distributed mass database to carry out parallel statistics and carrying out deeper statistical analysis on the line loss and the electricity consumption, so as to solve at least one technical problem in the background technology.

In order to achieve the purpose, the invention adopts the following technical scheme:

in one aspect, the present invention provides a method for identifying electricity stealing, including:

acquiring station area user information, station area gateway total electric quantity and user power consumption information;

calculating the line loss rate of each station area based on the station area user information, the station area gateway total electric quantity and the user power consumption information, and taking the station area with the large line loss rate change as a suspicious station area for electricity stealing;

calculating the power consumption change rate of a certain accumulated duration of the users in the suspicious station area;

and sequencing according to the change rate of the electricity consumption to determine the suspicious electricity stealing users.

Preferably, the data in the relational database is imported into a station area user information table and a user electricity consumption information table in the HBase, the station area user information table and the user electricity consumption information table are established, and the station area user information and the user electricity consumption information are obtained based on the established station area user information table and the user electricity consumption information table.

Preferably, the establishing of the station area user information table and the user electricity consumption information table includes:

obtaining the identification of the transformer area from a transformer area information table;

and associating all user identifications and measuring point identifications in the local station area in the measuring point information table according to the station area identification, and importing the station area identification and the user identification into the station area user information table in the HBase.

Preferably, the establishing of the table area user electricity consumption information table includes:

aiming at the user identification of each user, acquiring a collection point number and a collection user relation identification through the user identification by an acquisition object information table;

associating the measuring point identification by the corresponding relation table of the acquisition object information table and the electric energy meter and the measuring point through the measuring point identification;

acquiring an acquisition object identifier from an acquisition object information table through the metering point identifier;

and acquiring the power consumption data of the corresponding user in a given time period according to the acquired object identifier, and importing the user identifier and the power consumption data into a user power consumption information table in HBase.

Preferably, the calculating the line loss rate of the cell area includes:

generating corresponding relations between all the transformer areas and users thereof based on the transformer area user information;

acquiring the power consumption of the user in a statistical time period based on the corresponding relation between the distribution area and the user;

and accumulating the electric quantity of the user in the statistical time period to be divided by the total electric quantity of the gateway of the station area to obtain the actual electric utilization ratio, and subtracting the actual electric utilization ratio from 1 to obtain the line loss rate of the station area.

Preferably, the calculating the power consumption change rate of the user in the suspicious station area for a certain accumulated time period includes:

generating user identifications in all the transformer areas based on the transformer area user information table;

acquiring all past electricity consumption record data of the user, and acquiring a plurality of electricity node values by taking the accumulated time length as the accumulated frequency;

and calculating the difference value between the maximum electric quantity node value and other electric quantity node values, and calculating the ratio of the difference value to the maximum electric quantity node value, namely the power consumption change rate of the user in the corresponding accumulated time length.

Preferably, the power consumption change rates of all the users are sorted from large to small, the user with the highest rank is the most suspicious electricity stealing user, and the like.

In a second aspect, the present invention provides a system for identifying electricity stealing, comprising:

the acquisition module is used for acquiring the information of the station area users, the total electric quantity of the station area gateway and the information of the electric quantity used by the users;

the first calculation module is used for calculating the line loss rate of each station area based on the station area user information, the station area gateway total electric quantity and the user power consumption information, and taking the station area with large line loss rate change as a suspicious station area for electricity stealing;

the second calculation module is used for calculating the power consumption change rate of a certain accumulated duration of the users in the suspicious station area;

and the determining module is used for determining the suspicious electricity stealing users according to the sorting of the change rate of the electricity consumption.

In a third aspect, the present invention provides a non-transitory computer readable storage medium for storing computer instructions which, when executed by a processor, implement a theft identification method as described above.

In a fourth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein a processor is connected with the memory, the computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory to make the electronic device execute the instructions of the electricity stealing identification method as described above.

The invention has the beneficial effects that: the method can obtain a more accurate suspicious electricity stealing user group, can comprehensively analyze the use condition of the electricity users in the region for the electricity marketing auditors, displays long-time historical record information of each electricity user, and finds out the suspected electricity stealing user group from the historical record information, thereby facilitating the auditors to take further action.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

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 description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a power stealing identification method according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below by way of the drawings are illustrative only and are not to be construed as limiting the invention.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

For the purpose of facilitating an understanding of the present invention, the present invention will be further explained by way of specific embodiments with reference to the accompanying drawings, which are not intended to limit the present invention.

It should be understood by those skilled in the art that the drawings are merely schematic representations of embodiments and that the elements shown in the drawings are not necessarily required to practice the invention.

Example 1

This embodiment 1 provides an electricity stealing identification system, which includes:

In this embodiment 1, the above-mentioned electricity stealing identification system is used to implement an electricity stealing identification method, which includes:

the method comprises the steps that an acquisition module is used for acquiring station area user information, station area gateway total electric quantity and user power consumption information;

calculating the line loss rate of each station area by adopting a first calculation module based on the station area user information, the station area gateway total electric quantity and the user power consumption information, and taking the station area with large line loss rate change as a suspicious station area for electricity stealing;

calculating the power consumption change rate of a certain accumulated duration of the users in the suspicious station area by adopting a second calculation module;

and (4) adopting a determining module to determine the suspicious electricity stealing users according to the sorting of the change rate of the electricity consumption.

In this embodiment 1, when the electricity stealing identification method is implemented, data in the relational database is imported into the station area user information table and the user electricity consumption information table in the HBase, the station area user information table and the user electricity consumption information table are established, and the station area user information and the user electricity consumption information are obtained based on the established station area user information table and the user electricity consumption information table.

Establishing a station area user information table and a user electricity consumption information table, comprising:

The method for establishing the power consumption information table of the station area users comprises the following steps:

The line loss rate of the station area is calculated by the following steps:

Calculating the power consumption change rate of the users in the suspicious station area within a certain accumulated time period comprises the following steps:

And sorting the change rates of the power consumption of all the users from large to small, wherein the user with the highest rank is the most suspected power stealing user, and the rest is the second suspected power stealing user.

Example 2

This embodiment 2 provides an electricity stealing identification system, which relies on an SG186 marketing service management system and an electric energy information acquisition system, and first introduces data required for electricity stealing prevention into a mass database of a Hadoop platform from a relational database of the two systems, and then analyzes various statistical indexes of line loss and user electricity consumption in a platform area in parallel by using MapReduce provided by the Hadoop platform, and ranks all users according to index values, so that electricity marketing inspectors can lock suspicious electricity stealing users according to the ranking. Compared with the traditional electricity larceny prevention algorithm based on the relational database, the method based on the mass and parallel computing can obtain a more accurate suspicious electricity larceny user group.

In this embodiment 2, the electricity stealing identification system includes:

and the acquisition module is used for acquiring the station area user information, the total electric quantity of the station area gateway and the user electricity consumption information.

And the first calculation module is used for calculating the line loss rate of each station area based on the station area user information, the station area gateway total electric quantity and the user power consumption information, and taking the station area with the large line loss rate change as a suspicious station area for electricity stealing. In the first calculation module, the line loss rate change situation of the distribution area is longitudinally compared in time, and the situation that the line loss rate of a certain distribution transformer is suddenly increased or suddenly reduced in a certain time period is found out, so that the suspicious area of electricity stealing is locked.

And the second calculation module is used for calculating the power consumption change rate of the users in the suspicious station area within a certain accumulated time.

The method comprises the steps of classifying users according to national grid electricity utilization standards, classifying according to contract capacity and operation modes (single shift, two shifts, three shifts or continuous) of the users, calculating electricity consumption change rate of the users in each classified class, finding out the maximum amount of the electricity consumption change rate of each week, and ranking in the user classes according to indexes.

The SG186 marketing business data is imported into a Hadoop-based massive database from a relational commercial database, so that historical data of electricity stealing analysis is richer, and an analysis result is more accurate. The parallel computing support module based on MapReduce adopts a parallel algorithm to analyze the line loss rate of a transformer area and the power consumption of a user, so that the ranking of the electricity stealing suspicion degree can be quickly given.

In embodiment 2, the above power stealing identification system is used to implement a power stealing identification method, which includes:

and the acquisition module is used for acquiring the information of the station area users, the total electric quantity of the station area gateway and the information of the electric quantity used by the users.

In this embodiment 2, when the electricity stealing identification method is implemented, data in the relational database is imported into the station area user information table and the user electricity consumption information table in the HBase, the station area user information table and the user electricity consumption information table are established, and the station area user information and the user electricity consumption information are obtained based on the established station area user information table and the user electricity consumption information table.

Specifically, in this embodiment 2, a station area user information table, a station area gateway electric quantity table, and a user power consumption information table need to be established in the distributed mass database HBase. These two tables are not directly presented in SG186 marketing services management system, and need to be associated by other tables to obtain the two tables needed by Hbase. In SG186, to find out the power information of all power users in a certain distribution area within a period of time, a plurality of relevant tables need to be queried, including: the system comprises a metering point information table C _ MP, an acquisition point information table R _ CP, an acquisition object information table R _ COLL _ OBJ, a corresponding relation table C _ METER _ MP _ RELA of the electric energy METER and the metering point, a station area information table G _ TG, an acquisition point and user relation information table R _ CP _ CONS _ RELA and the like.

Specifically, in the obtaining module, the data in the relational database may be imported into the station area user information table and the user electricity consumption information table in the HBase through the following steps:

obtaining the identification TG _ ID of the station area from a station area information table G _ TG;

associating all user identifications CONS _ ID and measuring point identifications MP _ ID of the cell in a 'measuring point information table C _ MP' according to the cell TG _ ID, and introducing the cell and the user identifications CONS _ ID into a cell user information table in HBase;

Specifically, for the identifier CONS _ ID of each user, the "acquisition object information table R _ CP _ CONS _ RELA" obtains an acquisition point number (CP _ NO) and an acquisition user relationship identifier (CP _ CONS _ ID) from the CONS _ ID; then, the identification METER _ ID of the metering point is correlated by the MP _ ID through the 'acquisition object information table C _ MP' and the 'corresponding relation table C _ METER _ MP _ RELA of the electric energy METER and the metering point'; acquiring an acquisition object identifier COLL _ OBJ _ ID from an acquisition object information table R _ COLL _ OBJ through the identifier METER _ ID of the metering point; and acquiring the power consumption data of the corresponding user in a given time period from the library according to the collection object identifier COLL _ OBJ _ ID, and importing the user identifier CONS _ ID and the power consumption data into a user power consumption information table in the HBase.

The line loss rate of the station area is calculated by the following steps:

Specifically, based on the HBase distributed mass database, the line loss rate of a distribution area can be found within ten minutes by the following steps:

reading a user information table of a transformer area of HBase, generating corresponding relations between all transformer areas and users of the transformer areas, and storing the corresponding relations as files;

firstly, starting a Map process to read the file, executing a task of < platform area ID and user ID >, namely obtaining the power consumption of the user in a statistical period from an HBase library;

a result of the Map process (a linked list with a node value of < district ID and user electric quantity >) Shuffle to a Reduce process, and the Reduce accumulates the corresponding electric quantity according to the district ID;

reading a gateway meter of the HBase, calculating the total electric quantity measured by the gateway of each station in the statistical time period, dividing the accumulated electric quantity of the station by the gateway electric quantity to obtain the actual electric utilization ratio, and subtracting the actual electric utilization ratio from 1 to obtain the line loss rate of the station.

Specifically, based on the Hbase distributed mass database, the percentage of the weekly power consumption mutation (i.e. the rate of change of the available power) of all the users is found in the Map process:

reading a station area user information table of HBase, generating user identifications in all station areas, and storing the user identifications as files;

starting a Map process to read the file, executing a task of < user ID, accumulated time (n days) >, namely obtaining all past electricity utilization record data of the user from an HBase library, wherein the electricity utilization record data in the HBase are continuously stored in one or more regions according to the time sequence, so that all the electricity utilization record data can be read out at one time and are stored in a memory;

the Map process accumulates the total power consumption amount before n days from the program execution time as a first node value according to the accumulation frequency of n days; accumulating the electricity consumption for n days later as the value of the second node, repeating the operation until the last n days of accumulated nodes, and if the last n days are not enough, not accumulating the plurality of days;

therefore, the historical electricity utilization condition of the user is reflected by the node values, the node of the maximum value is found out, the difference value between other node values and the maximum value is solved, the ratio of the difference value and the maximum value is solved, and the ratio is output, namely the change rate of the electricity utilization quantity.

Finally, the statistical indexes of all users are compared in the Reduce process, and a list ordered according to the index values is given: and (3) carrying out a Shuffle process on the result of the Map process (a linked list with a node value of < user ID, maximum ratio of sudden change of the electric quantity of the weekly user) to a Reduce process, sequencing the Reduce according to the maximum ratio of sudden change of the user, and outputting a plurality of ordered linked lists with values of < user ID, maximum ratio of sudden change of the electric quantity of the weekly user >, wherein each linked list indicates a class of electricity user groups with the same property.

Example 3

This embodiment 3 provides an electricity stealing identification system, which relies on an SG186 marketing service management system and an electric energy information acquisition system, and first introduces data required for electricity stealing prevention into a mass database of a Hadoop platform from a relational database of the two systems, and then analyzes various statistical indexes of line loss and user electricity consumption in a platform area in parallel by using MapReduce provided by the Hadoop platform, and ranks all users according to index values, so that electricity marketing inspectors can lock suspicious electricity stealing users according to the ranking. Compared with the traditional electricity larceny prevention algorithm based on the relational database, the method based on the mass and parallel computing can obtain a more accurate suspicious electricity larceny user group.

In this embodiment 3, the electricity stealing identification system includes:

step S1: and the acquisition module is used for acquiring the information of the station area users, the total electric quantity of the station area gateway and the information of the electric quantity used by the users.

Because the basic requirements of different periods and different electricity prices provided by a smart grid need to be supported, the data acquisition frequency of the smart meter needs to be accurate to an hour at least, 87960 database records are needed for recording the electricity utilization information of one user per year, and all electricity utilization users in one region are counted in units of one hundred thousand, so that at least 8 hundred million database records are used for storing the electricity utilization information of one year. If a relational database is used for storage, it takes 35 minutes to test to find information for a user from the 8 billion database records. If the strategy of dividing the table according to the user or the time is adopted, too many tables in the database are caused, great difficulty is brought to the management and maintenance of the database system, and obviously a faster non-relational storage mode is required to be adopted.

Therefore, in this embodiment 3, the mass data processing platform Hadoop provides a non-relational distributed mass database HBase, which is based on key value index, and can use the user name as a key value, and store the power consumption information of the same user in all time periods into the continuous area of the disk, so that it is only necessary to read the area to obtain the data of a certain user, and it is not necessary to continuously move the magnetic head of the disk like the relational database, and skip picking up the data of the user one by one in the disk. HBase takes a region as a unit to store data of a table, when the data volume of one region is increased to exceed the rated capacity of the HBase, the region can be divided into two regions with the rated capacity, and the flexibility of data storage is greatly increased by a mechanism; on the other hand, it supports distributed storage, i.e. several areas in a table can be stored in different machines respectively, which makes the storage capacity of the table reach to the theoretical limitless, so it is a true distributed mass database.

For the current power service, which is developed based on the conventional relational database, the strategy is to store short-term data and continuously transfer the outdated data to an external tape or disk, which brings great trouble to statistics of the timeout length. In the application of electricity larceny prevention, a transformer area user information table, a transformer area gateway electricity meter and a user electricity consumption information table are established in a distributed mass database HBase. These two tables are not directly presented in SG186 marketing services management system, and need to be associated by other tables to obtain the two tables needed by Hbase. In SG186, to find out the power information of all power users in a certain distribution area within a period of time, a plurality of relevant tables need to be queried, including: the system comprises a metering point information table C _ MP, an acquisition point information table R _ CP, an acquisition object information table R _ COLL _ OBJ, a corresponding relation table C _ METER _ MP _ RELA of the electric energy METER and the metering point, a station area information table G _ TG, an acquisition point and user relation information table R _ CP _ CONS _ RELA and the like.

The data in the relational database can be imported into a station area user information table and a user electricity consumption information table in the HBase through the following steps:

aiming at the identification CONS _ ID of each user, acquiring a point number (CP _ NO) and an acquisition user relation identification (CP _ CONS _ ID) through the CONS _ ID by using an acquisition object information table R _ CP _ CONS _ RELA; then, the identification METER _ ID of the metering point is correlated by the MP _ ID through the 'acquisition object information table C _ MP' and the 'corresponding relation table C _ METER _ MP _ RELA of the electric energy METER and the metering point';

acquiring an acquisition object identifier COLL _ OBJ _ ID from an acquisition object information table R _ COLL _ OBJ through the identifier METER _ ID of the metering point;

and acquiring the power consumption data of the corresponding user in a given time period from the library according to the collection object identifier COLL _ OBJ _ ID, and importing the user identifier CONS _ ID and the power consumption data into a user power consumption information table in the HBase.

Step S2: and calculating the line loss rate of each station area by adopting a first calculation module based on the station area user information, the station area gateway total electric quantity and the user power consumption information, and taking the station area with large line loss rate change as a suspicious station area for electricity stealing.

Specifically, the line loss of a certain distribution area within a certain time period is calculated, and statistics needs to be performed on all users in the distribution area. If a relational database is used, the speed of one user is counted in minutes, and the line loss calculation speed of the whole distribution area is expected to be counted in days. In this embodiment 3, based on the HBase distributed mass database, the line loss rate of one distribution area can be obtained within ten minutes through the following steps:

Step S3: and calculating the power consumption change rate of the users in the suspicious region within a certain accumulated time by adopting a second calculation module.

Specifically, based on the Hbase distributed mass database, the percentage of the week power consumption mutation of all the users is calculated in the Map process:

starting a Map process to read the file, executing a task of < user ID, accumulated time length of 7 days >, namely obtaining all past electricity utilization record data of the user from an HBase library, wherein the electricity utilization record data in the HBase are continuously stored in one or more regions according to the time sequence, so that all the electricity utilization record data can be read out at one time and put into a memory;

the Map process accumulates the total electricity consumption amount before 7 days from the program execution time as a first node value according to the accumulation frequency of 7 days; accumulating the electricity consumption for 7 days later as the value of the second node, repeating the operation until the last 7 days of accumulated nodes, and if the last 7 days are less than 7 days, not accumulating the days;

thus, the historical electricity utilization condition of the user is reflected by the node values, the node of the maximum value is found out, the difference value between other node values and the maximum value is solved, the ratio of the difference value and the maximum value is solved, and the ratio is output;

step S4: and (4) adopting a determining module to determine the suspicious electricity stealing users according to the sorting of the change rate of the electricity consumption.

Specifically, the statistical indexes of all users are compared in the Reduce process, and a list ordered according to the index values is given: and (3) carrying out a Shuffle process on the result of the Map process (a linked list with a node value of < user ID, maximum ratio of sudden change of the electric quantity of the weekly user) to a Reduce process, sequencing the Reduce according to the maximum ratio of sudden change of the user, and outputting a plurality of ordered linked lists with values of < user ID, maximum ratio of sudden change of the electric quantity of the weekly user >, wherein each linked list indicates a class of electricity user groups with the same property.

Example 4

In order to perform more deep statistical analysis on line loss and power consumption in a full-data and full-user mode, embodiment 4 of the present invention provides a power stealing prevention method based on a distributed mass database and parallel computation, which is particularly suitable for power marketing inspectors to lock suspected power stealing users.

In order to achieve the above purpose, in this embodiment 4, based on the SG186 marketing service management system and the electric energy information acquisition system, data required for preventing electricity theft is first imported from the relational database of the two systems into the mass database of the Hadoop platform. And then, analyzing various statistical indexes of the line loss of the transformer area and the power consumption of the users in parallel by using MapReduce provided by the Hadoop platform, and ranking all the users according to the index values, so that power marketing inspectors can lock suspected electricity stealing users according to the ranking. Compared with the traditional electricity larceny prevention algorithm based on the relational database, the method based on the mass and parallel computing can obtain a more accurate suspicious electricity larceny user group.

In this embodiment 4, the line loss rate change situation of the distribution area is longitudinally compared in time, and the situation that the line loss rate of a certain distribution transformer suddenly increases or suddenly decreases in a certain time period is found out, so as to lock the suspicious area of electricity stealing. Classifying the users according to the national grid electricity utilization standard, then classifying according to the contract capacity and the operation mode (single shift, two shifts, three shifts or continuous) of the users, finally calculating the users in each classified class, finding out the maximum amount of the weekly electricity utilization change rate of the users, and ranking in the user class according to the index. And the SG186 marketing service data is imported into a Hadoop-based massive database from a relational commercial database, so that the historical data of electricity stealing analysis is richer, and the analysis result is more accurate. And finally, analyzing the line loss rate of the transformer area and the power consumption of the user by adopting a parallel algorithm, thereby quickly giving the ranking of the electricity stealing suspicion degree.

The electricity larceny prevention method using the distributed mass database for parallel statistics provided in this embodiment 4 can comprehensively analyze the usage of the electricity consumers in this area for the electricity marketing auditor, show the long-time history information of each electricity consumer, and find out the suspected electricity larceny user group from the history information, thereby facilitating the auditor to take further action.

As shown in fig. 1, the electricity larceny prevention method using a distributed mass database for parallel statistics provided in this embodiment 4 includes: the method comprises the steps of calculating the longitudinal ring ratio of the line loss of a station area in parallel (longitudinally comparing the line loss rate change condition of the station area in time, finding out the condition that the line loss rate of a certain distribution transformer is suddenly increased or suddenly reduced in a certain time period so as to lock a suspicious area of electricity stealing), calculating the transverse ring ratio of the electricity consumption of a user in parallel (dividing the user into categories according to the national network electricity consumption standard, dividing according to the contract capacity and the operation mode (single shift, two shifts, three shifts or continuous) of the user, calculating the user in each divided category, finding out the maximum amount of the electricity consumption change rate per week, ranking in the user category according to indexes), introducing a distributed mass database (finishing SG186 business data and introducing the mass database based on Hadoop from a relational commercial database, leading the historical data of electricity stealing analysis to be richer, and the analysis result to be more accurate), and calculating in parallel based on MapReduce (adopting a parallel algorithm to analyze the line loss rate of the station area and the electricity consumption of the user, so that the electricity theft suspicion degree ranking can be given quickly).

In this embodiment 4, importing the distributed mass database includes:

because the basic requirements of different electricity prices in different time periods provided by a smart grid need to be supported, the data acquisition frequency of the smart meter needs to be accurate to an hour at least, 87960 database records are needed for recording the electricity utilization information of one user per year, and all electricity utilization users in one region are counted in units of one hundred thousand, so that at least 8 hundred million database records are needed for storing the electricity utilization information per year. If the relational database is used for storage, 35 minutes is needed to search the 8 hundred million database records for the information of a certain user through testing. If the strategy of dividing the table according to the user or the time is adopted, too many tables in the database are caused, great difficulty is brought to the management and maintenance of the database system, and obviously a faster non-relational storage mode is required to be adopted.

The mass data processing platform Hadoop provides a non-relational distributed mass database HBase, which is based on key value index, namely, a user name can be used as a key value, and the power consumption information of all time periods of the same user can be stored in a continuous area of a disk, so that the data of a certain user can be obtained only by reading the area, the situation that the magnetic head of the disk is not moved continuously like a relational database, and the data of the user is picked out in the disk one by one in a jumping manner. HBase takes a region as a unit to store data of a table, when the data volume of one region is increased to exceed the rated capacity of the HBase, the region can be divided into two regions with the rated capacity, and the flexibility of data storage is greatly increased by a mechanism; on the other hand, it supports distributed storage, i.e. several areas in a table can be stored in different machines respectively, which makes the storage capacity of the table reach to the theoretical limitless, so it is a true distributed mass database.

However, based on the traditional relational database, the strategy is to store short-term data and continuously transfer the expired data to an off-board tape or disk, which brings great trouble to statistics over a long time, so that the expired data must be transferred to the distributed mass database HBase.

In this embodiment 4, a station area user information table, a station area gateway electric quantity table, and a user power consumption information table need to be established in the distributed mass database HBase. These two tables are not directly presented in SG186 marketing services management system, and need to be associated by other tables to obtain the two tables needed by Hbase. In SG186, to find out the power information of all power users in a certain distribution area within a period of time, a plurality of relevant tables need to be queried, including: the system comprises a metering point information table C _ MP, an acquisition point information table R _ CP, an acquisition object information table R _ COLL _ OBJ, a corresponding relation table C _ METER _ MP _ RELA of the electric energy METER and the metering point, a station area information table G _ TG, an acquisition point and user relation information table R _ CP _ CONS _ RELA and the like.

In this embodiment 4, the data in the relational database can be imported into the station area user information table and the user power consumption information table in the HBase through the following steps:

In this embodiment 4, the MapReduce-based parallel computation includes:

MapReduce is a distributed computing framework for parallel computing of mass data. The core step of the MapReduce framework is mainly divided into two parts, namely Map and Reduce. When a computing job is submitted to a MapReduce framework, the computing job is firstly split into a plurality of Map tasks, then the Map tasks are distributed to different nodes to be executed, each Map task processes one part of input data, and after the Map tasks are completed, the Map tasks generate intermediate files which are used as the input data of Reduce tasks. The main goal of Reduce task is to put together and output the outputs of several maps.

MapReduce includes two core processes: the Shuffle process and the Sort process. The Shuffle process refers to a process that starts with the Map producing output, includes the system performing sorting and passing the Map output to Reducer as input. Starting from the Map end, when Map starts to generate output, it does not simply write data to disk, but writes data to buffer in memory first, and does some pre-ordering to improve efficiency. When the data in the buffer reaches a certain threshold, the system will start a background thread to split the content of the buffer to the disk. During Split, the output of Map will continue to write to the buffer, but if the buffer is full, Map will be blocked until Split is complete. When the data in the memory reaches the Split threshold, a new Split file is generated, so that when the Map task finishes writing the last output record, a plurality of Split files may exist. Before the Map task is completed, all split files are merged and sorted into an index file and a data file. After merging the split files, the Map deletes all the temporary split files and informs the TaskTracker that the task is completed.

In the Reduce part of Shuffle, the input data of the Reduce task is distributed among the outputs of multiple Map tasks within the cluster. Map tasks may be completed at different times, and as soon as one of the Map tasks completes, the Reduce task begins to copy its output, with the copied data being superimposed on disk. There is a background thread that merges them into a larger sorted file, which saves time for later merging. And when all Map outputs are copied, the Reduce task enters a merging stage, a Sort process is started to merge and Sort all Map outputs, and the work can be finished after being repeated for many times.

In this embodiment 4, the vertical loop ratio of the line loss of the parallel computing platform area includes:

calculating the line loss of a certain distribution area within a certain time length needs to make statistics on all users in the distribution area. If a relational database is used, the speed of one user is counted in minutes, and the line loss calculation speed of the whole distribution area is expected to be counted in days.

In this embodiment 4, based on the HBase distributed mass database, the line loss rate of one distribution area can be obtained within ten minutes through the following steps:

In this embodiment 4, the parallel computing of the horizontal ring ratio of the power consumption of the user includes:

the horizontal ring ratio is a comparison of statistical indexes of all users, and is sorted by index value, so that a user or a group of users with the largest index value is found. Considering the difference of user categories, firstly, the users are classified according to national grid electricity utilization standard, then are classified according to the contract capacity and the operation mode (single shift, two shifts, three shifts or continuous) of the users, and finally, the users in each classified category are calculated. In comparison, it is reasonable to select the sudden change degree of the total amount of the weekly power consumption as a unified evaluation index, because the sudden change error is larger if the accumulated time of the user is in days, and the possibility of electricity stealing in certain days in months may be reduced if the accumulated time of the user is in months, so in this embodiment 4, one week is used as the accumulated time. Based on an Hbase distributed mass database, firstly, the percentage of the sudden change of the peripheral electricity consumption of all users is calculated in the Map process, then, the statistical indexes of all users are compared in the Reduce process, and a list sorted according to the index values is given:

and (3) carrying out a Shuffle process on the result of the Map process (a linked list with a node value of < user ID, maximum ratio of sudden change of the electric quantity of the weekly user) to a Reduce process, sequencing the Reduce according to the maximum ratio of sudden change of the user, and outputting a plurality of ordered linked lists with values of < user ID, maximum ratio of sudden change of the electric quantity of the weekly user >, wherein each linked list indicates a class of electricity user groups with the same property.

In the linked list output after the MapReduce processing, the most front user is the most suspicious electricity stealing user, the user in the second node in front of the linked list is the second suspicious electricity stealing user, and so on.

Example 5

Embodiment 5 of the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which when executed by a processor, implement an instruction of a power stealing identification method, the method including:

Example 6

Embodiment 6 of the present invention provides a computer program (product) comprising a computer program for implementing a method of electricity stealing recognition as described above when run on one or more processors, the method comprising:

Example 7

Embodiment 7 of the present invention provides an electronic device, including: a processor, a memory, and a computer program; wherein a processor is connected with the memory, a computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory to make the electronic device execute the electricity stealing identification method as described above, the method comprising:

In summary, the electricity stealing identification method and system provided by the embodiment of the invention have more comprehensive statistical data, and the electricity stealing prevention method based on the distributed mass database and the parallel computation is realized by carrying out deeper statistical analysis on the line loss and the electricity consumption of the whole data and the whole users, so that the electricity stealing identification method and system are particularly suitable for electricity marketing inspectors to lock suspicious electricity stealing users.

By means of an SG186 marketing business management system and an electric energy information acquisition system, firstly, data required for electricity larceny prevention are imported into a mass database of a Hadoop platform from a relational database of the two systems. And then, analyzing various statistical indexes of the line loss of the transformer area and the power consumption of the users in parallel by using MapReduce provided by the Hadoop platform, and ranking all the users according to the index values, so that power marketing inspectors can lock suspected electricity stealing users according to the ranking. Compared with the traditional electricity larceny prevention algorithm based on the relational database, the method based on the mass and parallel computing can obtain a more accurate suspicious electricity larceny user group. The method can comprehensively analyze the service condition of the electricity users in the region for electricity marketing inspectors, show long-time historical record information of the electricity users, and find out a suspected electricity stealing user group, so that the inspectors can conveniently take further action.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts based on the technical solutions disclosed in the present invention.

Claims

1. A method for identifying electricity stealing behavior, comprising:

2. The electricity stealing identification method according to claim 1, wherein the data in the relational database is imported into a station area user information table and a user electricity consumption information table in the HBase, the station area user information table and the user electricity consumption information table are established, and the station area user information and the user electricity consumption information are obtained based on the established station area user information table and the user electricity consumption information table.

3. The electricity stealing identification method of claim 2, wherein establishing the distribution area user information table and the user power consumption information table comprises:

4. The electricity stealing identification method according to claim 3, wherein establishing an electricity consumption information table for the district users comprises:

5. The method of claim 1, wherein calculating the line loss rate of the distribution area comprises:

6. The electricity stealing identification method according to claim 1, wherein calculating the rate of change of power usage for a cumulative duration of users in the suspect region comprises:

7. The electricity stealing identification method according to claim 6, wherein the change rates of the electricity consumption of all the users are sorted from large to small, the user with the highest ranking is the most suspicious electricity stealing user, and so on.

8. An electricity stealing identification system, comprising:

9. A non-transitory computer-readable storage medium storing computer instructions which, when executed by a processor, implement the theft recognition method of any one of claims 1-7.

10. An electronic device, comprising: a processor, a memory, and a computer program; wherein a processor is connected with the memory, a computer program is stored in the memory, and the processor executes the computer program stored in the memory when the electronic device is running, so as to cause the electronic device to execute the instructions of the electricity stealing recognition method according to any one of claims 1 to 7.