An Intelligent Maintenance Plan Optimization System

Abstract

The invention provides a system and a method for intelligent repair schedule optimization compilation. The system comprises a model algorithm filtering module, an evaluation rating module, a model algorithm library module and a goal constraint library module. The model algorithm filtering module obtains rating information and algorithms and models corresponding to the rating information from the model algorithm library module, filters the rating information and chooses the algorithm which is highest in rating to carry out optimization compilation operation. A user rates the optimization compilation operation result through the evaluation rating module, and stores the rating result in the model algorithm library module or the goal constraint library module. An algorithm which can meet requirements of the specific user can be filtered from a store where a large number of algorithms are accumulated according to the method adjustment of the user and history rating. After use for a long time, algorithm choices in difference and parameter preset in difference, pointing to different conditions, can be also accumulated, and the intelligent repair optimization compilation system can also be more adaptive to various environments and requirements of dynamic change.

Description

technical field [0001] The invention belongs to the technical field of maintenance plans, and in particular relates to an intelligent maintenance plan optimization system and method. Background technique [0002] Intelligent equipment maintenance planning is the requirement of the future smart grid. Now there are a lot of researches on maintenance planning optimization at home and abroad, and different algorithms or modeling methods are proposed for different target constraints. However, there are huge difficulties in implementation, because the maintenance plan optimization in reality cannot be summarized by a simple one or two objectives or constraints. Often, although the plan formulated in a certain way achieves this goal, it does not It performed extremely poorly on another indicator, so it had to be edited manually, which took a lot of manpower and effort. [0003] Moreover, for our country, the rapid development of the power industry often makes the algorithm corresp...

AI Technical Summary

Problems solved by technology

However, there are huge difficulties in implementation, because the maintenance plan optimization in reality cannot be summarized by a simple one or two objectives or constraints. Often, although the plan formulated in a certain way achieves this goal, it does not It performed extremely poorly on another indicator, so it had to be manually edited and revised, which consumed a lot of manpower and energy

[0003] Moreover, for our country, the rapid development of the power industry often makes the algorithm corresponding to a region invalid quickly, and some algorithms lose their effectiveness due to the introduction of a large number of new equipment or new lines before they are even adjusted to become the best and most suitable algorithm.

At this time, it is often a waste of a lot of manpower to develop new algorithms according to the actual situation in real time, and it is imperative to introduce a scalable and learnable intelligent programming system

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