Power grid information system device state early warning method based on reverse fuzzy hierarchical analysis

Abstract

The present invention discloses a power grid information system device state early warning method based on reverse fuzzy hierarchical analysis, and belongs to the technical field of power grid information system hardware device state early warning. The method comprises the steps of: constructing a difference index system reflecting an operation state of a power grid information system device; fitting a certain item daily operation curve of the device; solving an accumulated deviation value, performing mapping of 1-9 degree of the accumulated deviation value obtained by each index and the corresponding index, and obtaining a fuzzy consistent judgment matrix; calculating each index weight by employing the fuzzy consistent judgment matrix, and performing summation of weights of indexes belonging to a single device and performing averaging of the number of the indexes included by the device, and performing reverse obtaining of the weight of the single device; and making grading early warning for the operation state of the device and the information system. The method provided by the invention can fully respond to problems such as device aging or updating, business volume fluctuation and increasing of devices, can reflect change of the device state in real time, and can achieve dynamic early warning.

Description

technical field [0001] The invention belongs to the technical field of early warning of the state of hardware equipment of a power grid information system, and in particular relates to a state early warning method of power grid information system equipment based on inverse fuzzy hierarchy analysis. Background technique [0002] The power grid information system is an important part of supporting the production and operation of power grid enterprises. It conducts real-time evaluation and early warning of the operation status of the hardware equipment of the power grid enterprise information system, which helps power grid enterprises to operate stably, reliably and stably, and continue to provide high-quality power services to the people. . The power grid information system covers all aspects of the production and operation of the enterprise, including I6000 system, ERP system, PMS system, etc. Each system is composed of different types and a large number of devices through a ...

AI Technical Summary

Problems solved by technology

[0003] Traditional early warning methods usually use data abnormal point mutation or abnormal trend as the basis for early warning, but the basis for judging data sudden change and abnormal trend is usually the detection information base and database formed by users' long-term monitoring and accumulation. These information come from the long-term operation of the system. Historical information, the update rate is slow, unable to cope with equipment aging or updating, business volume fluctuations, emergencies, etc.

At the same time, the traditional early warning method evaluates the status of a single device, and finally integrates it to describe the system status. This early warning method strips the connection between devices and cannot accurately and comprehensively reflect the status of the entire system.

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