A method and system for operation and maintenance data mine and compliance checking

Abstract

A method and system for operation and maintenance data mine and compliance checking are provided. The method includes obtaining operation and maintenance data information, calculating and obtaining unstructured data according to that operation and maintenance data information, Processing the unstructured data by word segmentation, and converting the obtained text model after word segmentation intoa digital vector model; Obtaining an operation dimension data processing model through machine learning algorithm training according to the digital vector model; Performing clustering processing on the digital vector model according to the operation dimension data processing module and/or obtaining key element points of the digital vector model; Clustering according to the structured data in theoperation dimension data information and the digital vector model and/or obtaining the key element points of the digital vector model to obtain the structured operation dimension data, and obtaining the operation dimension data checking result according to the structured operation dimension data.

Description

technical field [0001] The present invention relates to data management, in particular to an operation and maintenance data mining and compliance inspection method and system. Background technique [0002] In the current field of data management, with the continuous expansion of business scope and the successive production of new application systems, the maintenance department is facing the problem of shortage of human resources and increasing pressure of production compliance. Among them, in the field of data management, production operation and maintenance activities will generate a large number of operation and maintenance data such as alarms and events. It exists in structured or semi-structured form. Even similar alarms cannot be directly merged and counted due to the difference in content; at the same time, the event content described in natural language cannot be identified by simply extracting semantic rules. This has brought troubles to the analysis of operation a...

AI Technical Summary

Problems solved by technology

Even for similar alarms, they cannot be directly merged and counted due to the difference in content; at the same time, the event content described in natural language cannot be identified by simply extracting semantic rules

This has brought troubles to the analysis of operation and maintenance behavior; due to the existence of massive operation and maintenance behavior data, security compliance inspections are mostly conducted by spot checks, and it is difficult to achieve full coverage

Even if you choose spot checks, you will often be troubled by the same type of alarms or events in different forms, which consumes a lot of labor and time costs

At the same time, the existing traditional data maintenance methods also have the following problems: 1. The existing problems cannot be exposed in time due to the low efficiency of manual processing; 2. The original data is not intuitive enough, and front-line personnel and even management personnel cannot directly grasp key information from the original detailed records and trends, cannot be referenced and associated analysis, and the form is not dynamic enough, so the data availability rate is low; 3. The hidden dangers of the system are not known in advance; As a key focus item, monitoring and incidents are also included in the scope of production compliance inspection. Since each team generates a large number of monitoring alarms and events every day, compliance inspection is extremely labor-intensive, and resources are limited. The inspection work cannot cover 100% of monitoring alarms and event records, and there are inspection omissions; at the same time, simple manual statistics and walk-through inspections cannot form a comprehensive and systematic correlation analysis, and the accuracy rate cannot be fully guaranteed

Since the number of K cannot be known in advance, it takes a lot of work to adjust the K parameters

[0006] 3. Custom semantic rules cannot adapt to all scenarios

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