CN108022179B - Suspected electricity larceny subject factor determination method based on chi-square test - Google Patents
Suspected electricity larceny subject factor determination method based on chi-square test Download PDFInfo
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- CN108022179B CN108022179B CN201711200339.6A CN201711200339A CN108022179B CN 108022179 B CN108022179 B CN 108022179B CN 201711200339 A CN201711200339 A CN 201711200339A CN 108022179 B CN108022179 B CN 108022179B
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- 230000005611 electricity Effects 0.000 title claims abstract description 43
- 238000000546 chi-square test Methods 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 7
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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 invention discloses a method for determining suspected electricity larceny subject factors based on chi-square test, which takes correlation factors and classification variables to be determined as classification variables, verifies the correlation degree of factors by using the chi-square test mode, eliminates the factors with too low correlation degree with results, reserves the strongest correlation with results in a plurality of high correlation factors, comprehensively considers the correlation condition of combined factors, establishes a suspected electricity larceny analysis model based on the high correlation factor, and reduces the workload of electricity larceny inspectors for checking the electricity larceny factors.
Description
Technical Field
The invention relates to a method for constructing a suspected electricity larceny theme model, in particular to a method for determining suspected electricity larceny theme factors based on chi-square test.
Background
In recent years, the characteristics of high technology of electricity stealing means, concealed electricity stealing process, frequent electricity stealing behavior and the like, which are presented by the electricity stealing problem, cause difficulty for the power grid enterprises to identify electricity stealing users, so that effective means and methods for helping the power grid enterprises accurately identify the electricity stealing users are needed, and the traditional method for determining the suspected electricity stealing users is poor in timeliness and low in accuracy and is not matched with the rapid development of the current power grid.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides the system for detecting the characteristic factors of the electricity larceny user by analyzing the user behavior data, so that the accuracy of electricity inspection is improved, and the working pressure of the electricity inspection personnel for inspecting a large number of suspected electricity larceny related factors is reduced.
A method for determining suspected electricity larceny subject factor based on chi-square test includes (1) calculating chi-square value of each initial factor and result by chi-square test, wherein the calculation formula is as follows:wherein A is four values in an actual value four-grid table, T is four values in a theoretical value four-grid table, then a critical probability table is checked by inquiring a chi-square to obtain a relevant probability, and a factor larger than a threshold value is taken as a relevant factor;
(2) Removing the correlation factors with large correlation, and calculating the correlation among the correlation factors by adopting the Pelson coefficient, wherein the calculation formula is as follows:if the correlation of the two factors is larger than a set threshold, removing one of the two factors with small correlation probability, wherein the correlation factor can be generally measured between 0.8 and 0.9 according to the actual factor number;
(3) Determining a combination association factor, combining according to a maximum combination threshold from irrelevant factors, checking and verifying the association degree of each pair of combination factors and results by using a chi-square test, and adding the combination factor into a relevant factor queue if the association probability is greater than a given threshold;
(4) The related factors obtained in the steps (2) and (3) are suspected electricity larceny subject factors.
The method applies chi-square test to the selection of factors influencing the electricity stealing behavior of the user, has good adaptability, and has high superiority when the chi-square test is used for comparing the relevance of two or more samples and classification variables. Meanwhile, in order to keep the independence among the factors, the relevance among the factors is calculated by adopting the Pelson coefficients, and the factors with large relevance are removed. Considering that the combination factors can be related to the results, the correlation between the combination factors and the results is calculated by using chi-square test, and the combination factors with high correlation probability are also considered as the correlation factors.
The threshold value can be preferably selected to have a correlation probability of 0.8, and when the number of factors exceeding the correlation probability of 0.8 is more than 90% or less than 30% of the total number of factors, the correlation probability threshold value should be gradually increased or decreased by taking 0.01 as a step length until the number of factors exceeding the correlation probability of 0.8 is less than 90% or more than 30% of the total number of factors.
In summary, compared with the prior art, the invention has the following advantages:
according to the invention, the association factors and the classification variables to be determined are regarded as the classification variables, the association degree of the factors is verified by using the chi-square test mode, the factors with too low association degree with the results are removed, the strongest association with the results in a plurality of high-correlation factors is reserved, the association condition of the combination factors is comprehensively considered, a suspected electricity larceny analysis model based on the high-association factors is established, and the workload of electricity larceny inspectors for inspecting the electricity larceny factors is reduced.
Drawings
FIG. 1 is a flow chart of a suspected electricity theft subject factor determination method based on chi-square test of the present invention.
Detailed Description
The present invention will be described in more detail with reference to examples.
Example 1
A method for determining suspected fraudulent use of electricity based on chi-square test (1) using chi-square test,and calculating chi-square values of all initial factors and results, wherein a calculation formula is as follows:wherein A is four values in an actual value four-grid table, T is four values in a theoretical value four-grid table, then a critical probability table is checked by inquiring a chi-square to obtain a relevant probability, and a factor larger than a threshold value is taken as a relevant factor;
(2) Removing the correlation factors with large correlation, and calculating the correlation among the correlation factors by adopting the Pelson coefficient, wherein the calculation formula is as follows:if the correlation of the two factors is greater than a set threshold, removing one of the two factors with a small correlation probability;
(3) Determining a combination association factor, combining according to a maximum combination threshold from irrelevant factors, checking and verifying the association degree of each pair of combination factors and results by using a chi-square test, and adding the combination factor into a relevant factor queue if the association probability is greater than a given threshold;
(4) The related factors obtained in the steps (2) and (3) are suspected electricity larceny subject factors.
The threshold value can be preferably selected to have a correlation probability of 0.8, and when the number of factors exceeding the correlation probability of 0.8 is more than 90% or less than 30% of the total number of factors, the correlation probability threshold value should be gradually increased or decreased by taking 0.01 as a step length until the number of factors exceeding the correlation probability of 0.8 is less than 90% or more than 30% of the total number of factors.
Wherein the relevance factor can be generally measured between 0.8 and 0.9 according to the actual factor number.
As in fig. 1: firstly, selecting initial correlation factors, then checking the correlation between the calculation factors and the results by using a chi-square, adding the correlation into a correlation factor queue, then calculating the correlation between the factors by using Pelson coefficients, and removing the factors with the high correlation; and then calculating the correlation between the combined factors and the results, adding the combined factors with large correlation into a related factor queue, and finally obtaining all the high-correlation factors.
The beneficial effects of the invention are illustrated by the following specific experiments: the experimental data are formed into a sample by 2000 electricity larceny users selected by a certain electric company of the national network and related electricity utilization data thereof during electricity larceny, 2000 non-electricity larceny users and related electricity utilization data thereof for a period of time. 55 pieces of data are selected as initial relevant factors, and the correlation degree between each suspected factor and the result of whether electricity is stolen is calculated by using chi-square test, so that 22 relevant factors are obtained; then, calculating the correlation between factors by adopting the Pelson coefficient, and if the correlation is larger than a threshold value, removing the factor with small correlation probability in the two factors; and calculating the association probability between the combination factors and the results in the removed 33 pieces of data to obtain 1 pair of combination related factors. Greatly reduces the difficulty of investigation. Thus, the electricity stealing theme model is constructed.
The undescribed portion of this embodiment is identical to the prior art.
Claims (1)
1. The suspected electricity larceny subject factor determination method based on chi-square test is characterized by comprising the following steps:
(1) The selected 2000 electricity stealing users and the related electricity consumption data thereof during electricity stealing period, the 2000 non-electricity stealing users and the related electricity consumption data thereof for a period of time form a sample together; 55 pieces of data are selected from the sample as initial related factors, wherein the initial related factors are suspected factors;
and (3) adopting chi-square test to calculate the association degree of each suspected factor and the result of whether electricity is stolen, namely calculating the chi-square value of each initial related factor and the result, wherein the calculation formula is as follows:wherein A is four values in an actual value four-grid table, T is four values in a theoretical value four-grid table, then a critical probability table is checked by inquiring a chi-square to obtain a relevant probability, and a factor larger than a threshold value is taken as a relevant factor;
(2) Removing the correlation factors with large correlation, and calculating the correlation among the correlation factors by adopting the Pelson coefficient, wherein the calculation formula is as follows:if the correlation of the two factors is greater than a set threshold, removing one of the two factors with a small correlation probability;
(3) Determining a combination association factor, combining according to a maximum combination threshold from irrelevant factors, checking and verifying the association degree of each pair of combination factors and results by using a chi-square test, and adding the combination factor into a relevant factor queue if the association probability is greater than a given threshold;
(4) The related factors obtained in the steps (2) and (3) are suspected electricity larceny subject factors.
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