阅读说明：本技术 一种基于计量大数据筛查窃电用户的方法及装置 (Method and device for screening electricity stealing users based on big metering data ) 是由 李奋强 林国光 罗元发 曾晓丹 陈代威 郭春浩 汤浩 池春生 金星 凌福强 于 2020-04-28 设计创作，主要内容包括：本发明实施例公开了一种基于计量大数据筛查窃电用户的方法及装置,其中方法包括：获取计量大数据,其中,计量大数据包括用电量、台区损失电量和线损率；根据计量大数据,确定窃电行为特征；利用预设算法并根据窃电行为特征,计算窃电行为相关参数阈值；利用预设算法并根据目标台区的计量大数据,计算目标台区内用户的窃电行为相关参数；将窃电行为相关参数与窃电行为相关参数阈值进行比较；根据比较结果筛查目标台区内的窃电嫌疑用户。本发明实施例提供的技术方案实现了精准有效的定位窃电用户,提高了筛查窃电用户的工作效率。(The embodiment of the invention discloses a method and a device for screening electricity stealing users based on metering big data, wherein the method comprises the following steps: acquiring big metering data, wherein the big metering data comprises electricity consumption, station area loss electricity and line loss rate; determining the characteristics of electricity stealing behavior according to the big metering data; calculating a power stealing behavior related parameter threshold value by using a preset algorithm according to the power stealing behavior characteristics; calculating relevant parameters of electricity stealing behaviors of users in the target distribution area by using a preset algorithm according to the big metering data of the target distribution area; comparing the electricity stealing behavior related parameter with an electricity stealing behavior related parameter threshold; and screening the electricity stealing suspected users in the target transformer area according to the comparison result. The technical scheme provided by the embodiment of the invention realizes accurate and effective positioning of the electricity stealing users and improves the working efficiency of screening the electricity stealing users.)

1. A method for screening electricity stealing users based on metering big data is characterized by comprising the following steps:

acquiring big metering data, wherein the big metering data comprises electricity consumption, area loss electricity and line loss rate;

determining the electricity stealing behavior characteristics according to the big metering data;

calculating a power stealing behavior related parameter threshold value by using a preset algorithm according to the power stealing behavior characteristics;

calculating relevant parameters of electricity stealing behaviors of users in the target distribution area by using the preset algorithm and according to the big metering data of the target distribution area;

comparing the electricity stealing behavior related parameter to the electricity stealing behavior related parameter threshold;

and screening the electricity stealing suspected users in the target transformer area according to the comparison result.

2. The method for screening electricity stealing users based on big metering data as claimed in claim 1, wherein determining electricity stealing behavior characteristics according to the big metering data comprises:

extracting the power consumption of historical electricity stealing users, the power loss of the distribution room and the line loss rate within set time from the big metering data;

analyzing the daily power consumption of the historical electricity stealing users and the station area lost electricity and the line loss rate corresponding to the daily power consumption to obtain the common characteristics of the power consumption of the historical electricity stealing users and the common characteristics of the station area lost electricity and the line loss rate corresponding to the common characteristics;

and determining the electricity stealing behavior characteristics according to the common characteristics of the electricity consumption of the historical electricity stealing users, the common characteristics of the corresponding station area electricity loss and the common characteristics of the line loss rate.

3. The method for screening electricity stealing users based on big metering data according to claim 2, wherein the electricity stealing behavior characteristics comprise:

the electricity consumption at the beginning and the end of the month is more than zero, and the electricity consumption in the month for more than or equal to 10 days is zero;

or the line loss rate or the station area power loss at the beginning and the end of the month is lower than the line loss rate or the station area power loss in the month.

4. The method for screening electricity stealing users based on big metering data as claimed in claim 1, wherein the threshold value of the electricity stealing behavior related parameter is calculated by using a preset algorithm and according to the electricity stealing behavior characteristics, and the threshold value comprises at least one of the following:

calculating the variance threshold of the power consumption of the historical power stealing users according to the power consumption of the historical power stealing users;

calculating a Pearson correlation coefficient threshold value between the power consumption and the line loss rate of the historical power stealing users according to the power consumption of the historical power stealing users and the line loss rate in the corresponding time day;

and calculating a Pearson correlation coefficient threshold value of the power consumption of the historical electricity stealing users and the station area loss electricity in the corresponding time day according to the power consumption of the historical electricity stealing users and the station area loss electricity in the corresponding time day.

5. The method for screening electricity stealing users based on big metering data as claimed in claim 4, wherein the step of calculating the electricity stealing behavior related parameters of the users in the target area according to the big metering data of the target area by using the preset algorithm comprises at least one of the following steps:

calculating the variance of the power consumption of the users according to the power consumption of the users in the target station area;

calculating a Pearson correlation coefficient between the power consumption of the user and the line loss rate according to the power consumption of the user in the target station area and the line loss rate in the corresponding time day;

calculating a Pearson correlation coefficient of the power consumption of the user and the station area loss electric quantity in the corresponding time day according to the power consumption of the user in the target station area and the station area loss electric quantity in the corresponding time day;

screening suspected electricity stealing users in the target area according to the comparison result, wherein the suspected electricity stealing users comprise at least one of the following:

if the variance is larger than or equal to the variance threshold, judging that the current user is a suspected electricity stealing user;

and if the absolute value of the Pearson correlation coefficient is greater than or equal to the absolute value of the Pearson correlation coefficient threshold, judging that the current user is a suspected electricity stealing user.

6. The method for screening electricity stealing users based on big metering data as claimed in claim 1, further comprising:

calculating relevant parameters of electricity stealing behaviors of users in the target distribution area based on the preset algorithm and according to big metering data of the target distribution area, comparing the relevant parameters of the electricity stealing behaviors with relevant parameter thresholds of the electricity stealing behaviors, screening suspected users of the electricity stealing in the target distribution area according to comparison results, and generating a rule algorithm so as to screen the suspected users of the electricity stealing by the rule algorithm.

7. The method for screening electricity stealing users based on big metering data as claimed in claim 6, further comprising:

and integrating the rule algorithm into an Excel macro file by using Visual Basic script.

8. The method for screening electricity stealing users based on big metering data as claimed in claim 1, further comprising, after screening electricity stealing suspected users in the target area according to the comparison result:

and generating a data billboard and a user power curve graph of the target station area.

9. The method for screening users who steal electricity based on big metering data as claimed in claim 8, further comprising, after generating the data billboard and user electricity graph of the target station area:

and (4) carrying out exception marking on the electricity stealing suspected user.

10. A device for screening electricity stealing users based on metering big data, comprising:

the acquisition module is used for acquiring big metering data, wherein the big metering data comprises electricity consumption, district loss electricity and line loss rate;

the electricity stealing behavior characteristic determining module is used for determining electricity stealing behavior characteristics according to the metering big data;

the first calculation module is used for calculating a power stealing behavior related parameter threshold value by using a preset algorithm according to the power stealing behavior characteristics;

the second calculation module is used for calculating relevant parameters of electricity stealing behaviors of users in a target distribution area by using the preset algorithm according to the big metering data of the target distribution area;

a comparison module for comparing the electricity stealing behavior related parameter with the electricity stealing behavior related parameter threshold;

and the screening module is used for screening the electricity stealing suspected users in the target platform area according to the comparison result.

Technical Field

The embodiment of the invention relates to the field of screening electricity stealing, in particular to a method and a device for screening electricity stealing users based on metering big data.

Background

Along with the rapid increase of the number of electric power customers, the electricity stealing phenomenon is increasingly serious, the electricity stealing not only damages the economic benefits of power supply companies, but also brings hidden dangers to the electricity utilization safety. The economic loss caused by the loss of electric quantity every year is huge, but the cases which can be successfully investigated by the power supply department only account for a small part. When electricity stealing users are in the process of electricity stealing, the abnormal operation of a power grid can be caused, the safe electricity utilization is influenced, and even in some electricity stealing cases, fire, explosion and the like caused by short circuit occur.

The traditional electricity stealing prevention inspection mode mainly takes the field inspection of the total users in a platform area, has certain limitation and tardiness, and has low working efficiency for screening electricity stealing users.

Disclosure of Invention

The embodiment of the invention provides a method and a device for screening electricity stealing users based on big metering data, which are used for accurately and effectively positioning the electricity stealing users and improving the working efficiency of screening the electricity stealing users.

In a first aspect, an embodiment of the present invention provides a method for screening a power stealing user based on metering big data, including:

acquiring big metering data, wherein the big metering data comprises electricity consumption, area loss electricity and line loss rate;

determining the electricity stealing behavior characteristics according to the big metering data;

calculating a power stealing behavior related parameter threshold value by using a preset algorithm according to the power stealing behavior characteristics;

calculating relevant parameters of electricity stealing behaviors of users in the target distribution area by using the preset algorithm and according to the big metering data of the target distribution area;

comparing the electricity stealing behavior related parameter to the electricity stealing behavior related parameter threshold;

and screening the electricity stealing suspected users in the target transformer area according to the comparison result.

Optionally, determining the electricity stealing behavior characteristic according to the big metering data includes:

extracting the power consumption of historical electricity stealing users, the power loss of the distribution room and the line loss rate within set time from the big metering data;

analyzing the daily power consumption of the historical electricity stealing users and the station area lost electricity and the line loss rate corresponding to the daily power consumption to obtain the common characteristics of the power consumption of the historical electricity stealing users and the common characteristics of the station area lost electricity and the line loss rate corresponding to the common characteristics;

and determining the electricity stealing behavior characteristics according to the common characteristics of the electricity consumption of the historical electricity stealing users, the common characteristics of the corresponding station area electricity loss and the common characteristics of the line loss rate.

Optionally, the electricity stealing behavior features include:

the electricity consumption at the beginning and the end of the month is more than zero, and the electricity consumption in the month for more than or equal to 10 days is zero;

or the line loss rate or the station area power loss at the beginning and the end of the month is lower than the line loss rate or the station area power loss in the month.

Optionally, the threshold of the relevant parameter of the electricity stealing behavior is calculated by using a preset algorithm according to the electricity stealing behavior characteristics, and the threshold includes at least one of the following:

calculating the variance threshold of the power consumption of the historical power stealing users according to the power consumption of the historical power stealing users;

calculating a Pearson correlation coefficient threshold value between the power consumption and the line loss rate of the historical power stealing users according to the power consumption of the historical power stealing users and the line loss rate in the corresponding time day;

and calculating a Pearson correlation coefficient threshold value of the power consumption of the historical electricity stealing users and the station area loss electricity in the corresponding time day according to the power consumption of the historical electricity stealing users and the station area loss electricity in the corresponding time day.

Optionally, the calculating, by using the preset algorithm and according to the big measurement data of the target distribution area, the relevant parameters of the electricity stealing behavior of the user in the target distribution area includes at least one of the following:

calculating the variance of the power consumption of the users according to the power consumption of the users in the target station area;

calculating a Pearson correlation coefficient between the power consumption of the user and the line loss rate according to the power consumption of the user in the target station area and the line loss rate in the corresponding time day;

calculating a Pearson correlation coefficient of the power consumption of the user and the station area loss electric quantity in the corresponding time day according to the power consumption of the user in the target station area and the station area loss electric quantity in the corresponding time day;

screening suspected electricity stealing users in the target area according to the comparison result, wherein the suspected electricity stealing users comprise at least one of the following:

if the variance is larger than or equal to the variance threshold, judging that the current user is a suspected electricity stealing user;

and if the absolute value of the Pearson correlation coefficient is greater than or equal to the absolute value of the Pearson correlation coefficient threshold, judging that the current user is a suspected electricity stealing user.

Optionally, the method further includes:

calculating relevant parameters of electricity stealing behaviors of users in the target distribution area based on the preset algorithm and according to big metering data of the target distribution area, comparing the relevant parameters of the electricity stealing behaviors with relevant parameter thresholds of the electricity stealing behaviors, screening suspected users of the electricity stealing in the target distribution area according to comparison results, and generating a rule algorithm so as to screen the suspected users of the electricity stealing by the rule algorithm.

Optionally, the method further includes:

and integrating the rule algorithm into an Excel macro file by using Visual Basic script.

Optionally, after screening the suspected electricity stealing users in the target platform area according to the comparison result, the method further includes:

and generating a data billboard and a user power curve graph of the target station area.

Optionally, after generating the data billboard and the user power curve graph of the target station area, the method further includes:

and (4) carrying out exception marking on the electricity stealing suspected user.

In a second aspect, an embodiment of the present invention provides an apparatus for screening a power stealing user based on metering big data, including:

the acquisition module is used for acquiring big metering data, wherein the big metering data comprises electricity consumption, district loss electricity and line loss rate;

the electricity stealing behavior characteristic determining module is used for determining electricity stealing behavior characteristics according to the metering big data;

the first calculation module is used for calculating a power stealing behavior related parameter threshold value by using a preset algorithm according to the power stealing behavior characteristics;

the second calculation module is used for calculating relevant parameters of electricity stealing behaviors of users in a target distribution area by using the preset algorithm according to the big metering data of the target distribution area;

a comparison module for comparing the electricity stealing behavior related parameter with the electricity stealing behavior related parameter threshold;

and the screening module is used for screening the electricity stealing suspected users in the target platform area according to the comparison result.

According to the method and the device for screening the electricity stealing users based on the big metering data, the big metering data are obtained, wherein the big metering data comprise electricity consumption, station area electricity loss and line loss rate; determining the electricity stealing behavior characteristics according to the big metering data; calculating a power stealing behavior related parameter threshold value by using a preset algorithm according to the power stealing behavior characteristics; calculating relevant parameters of electricity stealing behaviors of users in the target distribution area by using the preset algorithm and according to the big metering data of the target distribution area; comparing the electricity stealing behavior related parameter to the electricity stealing behavior related parameter threshold; and screening the electricity stealing suspected users in the target transformer area according to the comparison result. The electricity stealing behavior related parameter threshold is calculated according to the electricity stealing behavior characteristics of the electricity stealing users, the electricity stealing suspected users are screened by comparing the electricity stealing behavior related parameters of the users in the target distribution room through the electricity stealing behavior related parameter threshold, the accurate and effective positioning of the electricity stealing users is realized, and the working efficiency of screening the electricity stealing users is improved.

Drawings

FIG. 1 is a flowchart of a method for screening users who steal electricity based on big metering data according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for screening electricity stealing users based on big metering data according to a second embodiment of the present invention;

FIG. 3 is a flowchart of a method for screening electricity stealing users based on metering big data according to a third embodiment of the present invention;

fig. 4 is a graph comparing power consumption of suspected electricity stealing users with power loss of distribution areas according to a third embodiment of the present invention;

fig. 5 is a block diagram of a device for screening electricity stealing users based on metering big data according to a fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.