Intelligent preventive test operation management and control system

Abstract

The invention discloses an intelligent preventive test operation management and control system, which comprises a standard test database module, an annual production plan intelligent supervision module, a test report intelligent diagnosis and analysis module and a test data intelligent analysis module. The standard test database module is used to establish a standard data structure model, form a data standard system, and construct a new standard test database; the annual production plan intelligent supervision module is used for carrying out intelligent analysis and accurate matching on arrangement data of the production plan; the test report intelligent diagnosis and analysis module is used for examining the normalization of the text content of the test report; and the test data intelligent analysis module is used for carrying out mutual comparison and clustering analysis on test report data of the test equipment, globally analyzing problems of plans and test reports, and researching test data generality laws according to cities, units, equipment and test types. According to the test data intelligent operation management and control system integrating planning, management, supervision and analysis, a traditional working mode is changed, existing scientific and technological means are fully used, the management efficiency and quality of test operation are greatly improved, essential changes are brought to overhaul test work, and the informatization degree of the overhaul test work is comprehensively improved.

Description

technical field [0001] The invention relates to the technical field of equipment risk assessment, in particular to an intelligent preventive test operation management and control system. Background technique [0002] Equipment defect diagnosis: In recent years, there have been many researches on the diagnosis of power grid equipment defects at home and abroad. Some scholars in my country mainly conduct intelligent diagnosis of equipment defects based on structured data such as equipment test data and operation data. In 2019, a research on GIS switch defect diagnosis method based on radiation electric field characteristic parameter SVM was carried out. This research is a GIS switch defect diagnosis method based on radiation electric field characteristic parameter SVM, including: 1. Experimental data preprocessing; 2. Build signal case knowledge base; 3. Obtain SVM defect diagnosis model; 4. Support vector machine defect diagnosis process. In this study, the operating transient...

AI Technical Summary

Problems solved by technology

[0008] 1) Most of the existing risk assessment methods based on equipment test data are single or limited, and fail to comprehensively consider the comprehensive impact of equipment internal factors on equipment risk, and the accuracy and pertinence of the assessment results need to be improved

[0009] 2) Since defects or failures are low-probability events, the existing defect or failure sample data cannot meet the requirements of intelligent evaluation methods for modeling samples, and it is difficult to obtain the correlation and evolution rules between state parameters and equipment risks, resulting in the evaluation model The key parameters are mainly selected based on experience, which seriously restricts the accuracy of the evaluation results and the practicability of the evaluation method.

[0010] 3) The existing equipment risk assessment methods rely on manual judgment, and the accuracy and efficiency need to be improved urgently, which seriously restricts the accuracy of equipment risk assessment

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