Industrial data sample screening method based on complex network community discovery

Abstract

An industrial data sample screening method based on complex network community discovery comprises the steps of firstly using target data structure initial samples as complicated network nodes, calculating distances among the nodes, comparing the distances with interceptive threshold values to obtain an adjacent matrix representing node connecting relation, then using modularity maximization as an optimization objective, performing community discovery in the complicated network represented by the adjacent matrix, obtaining sample community division of corresponding problems under different situations, finally network node 'combination degree' evaluation indexes are provided, descending sorting is conducted on nodes in the network according to a combination degree value, a sample re-structure sample set is selected from each community according to a combination degree value average, and accordingly reduction of data sample sets is achieved under the situation that useful information in an original sample set is retained. Screened data samples are adopted to perform soft measurement, prediction and case-based reasoning, the accuracy of an established model can be further improved, and guarantee is provided for implementation of data-based optimized dispatching in the industrial process.

Description

technical field [0001] The invention belongs to the field of information technology, relates to theories such as data complex network construction, community discovery, hierarchical clustering, and community fusion, and is an industrial data sample screening method based on complex network community discovery. The present invention utilizes a large amount of historical data existing on the industrial site, first constructs initial samples of the target data to be screened as complex network nodes, calculates the distance between complex network nodes, and compares them with the truncation threshold to obtain an adjacency matrix representing the node connection relationship, and then uses The maximization of modularity is the optimization goal. Community discovery is carried out in the complex network represented by the adjacency matrix, and the sample community division in different situations corresponding to the problem is obtained. Finally, an evaluation index of "combinatio...

AI Technical Summary

Problems solved by technology

In order to solve the above problem, analyze the data provided by an industrial site, first construct the initial samples of the target data to be screened as complex network nodes, calculate the distance between samples, compare with the truncation threshold to obtain the adjacency matrix representing the connection relationship of nodes, Then, by taking the maximization of modularity as the optimization goal, community discovery is carried out in the complex network represented by the adjacency matrix, and the sample community division in different situations corresponding to the problem is obtained. Finally, an evaluation index of the "cohesion degree" of the network nodes is proposed, and the evaluation index of the community is obtained. The nodes are sorted in descending order according to the combination value, and the samples with higher combination degree are selected from each community to reconstruct the sample set

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