Equipment operation state edge cloud fusion diagnosis method and system

Abstract

The embodiment of the invention discloses an equipment operation state edge cloud fusion diagnosis method and system. The method comprises the steps that a cloud end carries out the adjustment of a cloud end initial diagnosis model constructed by historical data of equipment based on similar operation features through the historical data of target equipment and a fusion migration method, and a cloud diagnosis model of the target equipment is obtained. An edge end sends a cloud diagnosis model acquisition request to the cloud end, and the cloud end performs model migration based on the acquisition request; and the edge end uses the operation state data of the target equipment to adjust the cloud diagnosis model migrated to the edge end to construct an edge end diagnosis model more suitablefor the target equipment so as to perform real-time diagnosis on the target equipment and realize efficient and accurate reuse of the cloud model. Meanwhile, the diagnosis result and the real-time data can be uploaded to the cloud, a cloud sample set is updated, then the cloud diagnosis model is updated, continuous updating optimization of the edge diagnosis model is achieved, and the diagnosis accuracy and rapidity are improved.

Description

technical field [0001] This specification relates to the technical field of equipment operation status monitoring, and in particular, relates to a method and system for edge-cloud fusion diagnosis of equipment operation status. Background technique [0002] In the process of industrial production, large-scale equipment has the characteristics of complex structure, harsh working environment, and frequent adjustment operations, resulting in a high rate of sudden failure. security incident. In response to the above situation, it is necessary to change the maintenance mode of production equipment at present, from the traditional periodic planned maintenance mode and after-event maintenance mode to the predictive maintenance mode. Among them, the equipment monitoring and diagnosis system is an important means of predictive maintenance. However, in the traditional equipment monitoring system, most of them use a centralized monitoring system to monitor the equipment. Slower, high...

AI Technical Summary

Problems solved by technology

However, for most of the newly deployed equipment, the corresponding historical operating status data is less, which greatly limits the accuracy of the constructed diagnostic model

Therefore, in order to solve the problem of poor performance and difficult construction of the traditional fault diagnosis model in the environment of variable operating conditions or missing fault data, save resource computing costs for edge end users, and improve the accuracy of diagnosis results, there is an urgent need for a A more accurate and real-time equipment operation status monitoring method in the edge cloud detection method

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