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A correlation mining method and device for a large user abnormal power consumption analysis data set
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A technology for abnormal power consumption and data analysis, which is applied in the fields of electrical digital data processing, data processing application, digital data information retrieval, etc.
Pending Publication Date: 2021-02-05
STATE GRID JIANGSU ELECTRIC POWER CO ELECTRIC POWER RES INST +2
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[0003] In order to overcome the above deficiencies, the present invention discloses a correlation mining method and device for a large user abnormal power consumption analysis data set, which solves the problem that the existing large user abnormal power consumption analysis data set has a large amount of data, resulting in a large amount of follow-up calculations , by mining the correlation between multi-source data, the amount of computing data for the abnormal power consumption analysis algorithm is reduced
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Embodiment 1
[0096]A correlation mining method for large user abnormal power consumption analysis data set, which performs correlation analysis and pruningprocessing on the large user abnormal power consumption analysis data set, including steps:
[0097] Step 1: Scan the abnormal power consumption analysis data set S of large users. S is composed of n user information:
[0098] S={S 1 ,S 2 ,...,S x ,...,S n} (57)
[0099] Among them, x=1,2,...,n, the xth user information S x Include multiple module information:
[0100] S x ={L id , L cl , L cal ,C tv (t),T ag} (58)
[0101] Among them, L id is the positioning information module, L cl is the category information module, L cal For computing information module, C tv (t) is a time-varying information module, t is time, T ag is the calibration information module.
[0102] Location information module L id , consisting of multiple encrypted and desensitized information elements, which are used to mark various location inform...
Embodiment 2
[0155] A correlation mining device for large user abnormal power consumption analysis data sets, comprising:
[0156] The frequent itemset screening module is used to discriminate the support degree of the large user abnormal power consumption analysis data set, and obtain multiple frequent itemsets after screening;
[0157] The correlation mining module is used to judge the confidence degree of the abnormal power consumption analysis data set through the screened multiple frequent item sets, and obtain the relevant large user abnormal power consumption analysis data set.
[0158] Further, the data set S for abnormal power consumption analysis of large users consists of n user information:
[0159] S={S 1 ,S 2 ,...,S x ,...,S n} (76)
[0160] Among them, x=1,2,...,n, the xth user information S x for:
[0161] S x ={L id , L cl , L cal ,C tv (t),T ag} (77)
[0162] Among them, L id is the positioning information module, L cl is the category information module, L...
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Abstract
The invention discloses a correlation mining method and device for a large user abnormal power consumptionanalysis data set, and the method comprises the steps of carrying out the discrimination of the support degree of the large user abnormal power consumptionanalysis data set, and obtaining a plurality of screened frequent item sets; and carrying out confidence judgment on the abnormal power utilization analysis data set through the plurality of screened frequent item sets to obtain a large user abnormal power consumption analysis data set with correlation. According to the invention, thecorrelation between the item sets of the abnormal power consumption analysis data set of the large user is mined through judgment of the support degree and the confidence degree, the operation data volume of the abnormal power utilization analysis algorithm is reduced, the system analysis efficiency is effectively improved, and the invention has a good application prospect.
Description
technical field [0001] The invention relates to a correlation mining method and device for a large user's abnormal power consumption analysis data set, and belongs to the technical field of power systems. Background technique [0002] With the gradual increase in the investigation and punishment of abnormal electricity consumption behaviors, electricity stealing behaviors are becoming more and more hidden, and the technical means are getting higher and higher. Abnormal electricity consumption behaviors are professional, intelligent, and concealed. With the help of conventional statistical analysis, it is no longer competent, and the application of a new generation of artificial intelligence technology has gradually become the mainstream. Standardized data sets are the basis of big datamachine learning analysis. However, due to the variety of data items and large amount of data contained in the abnormal power consumption analysis data set of large users, the calculation of d...
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