Object state prediction method, system, computer equipment and storage medium

Abstract

The invention discloses an object state prediction method, system, computer equipment and storage medium, wherein the object state prediction method includes: grouping monitoring data indicators of monitoring objects according to knowledge base and associated data mining, and constructing a state analysis model. The state analysis model described includes: a single index value prediction model, a main index distribution prediction model, and an object state classification prediction model; use sample data to train the state analysis model to obtain a calculation model system; input the current data flow of the state prediction object into the calculation Model system to predict the state of the object. The invention can effectively improve the intelligent monitoring of equipment operation and maintenance.

Description

technical field [0001] The present invention relates to the technical fields of big data and artificial intelligence, in particular to an object state prediction method, system, computer equipment and storage medium. Background technique [0002] With the continuous development of the communication industry, a comprehensive and high-quality communication infrastructure and communication network have gradually formed to realize wireless communication for various end users. In this basic communication network, a large number of base stations constitute the nodes. Based on 4G\5G technology, the base station provides services such as network access, data exchange, and communication for peripheral terminal equipment. The coverage of a base station includes multiple serving cells, and each serving cell provides communication guarantee for terminal users within a specific spatial range around the base station. [0003] In the process of providing wireless communication services, ...

AI Technical Summary

Problems solved by technology

In the actual operation process, due to the different operating conditions of different communities and the dynamic changes in the operating environment, the above static threshold mode cannot meet the dynamic state monitoring in actual situations, and these problems lead to low work efficiency in actual operation and maintenance work. In monitoring, a large number of false alarms appear due to the problem of threshold setting. Due to the particularity of communication operation and maintenance work, these false alarms need to be dealt with later, which leads to continuous increase in the cost of the entire operation and maintenance work and affects the performance of the communication infrastructure.

At the same time, due to the huge scale of the communication infrastructure, it is impossible to establish a personalized threshold monitoring system for the community. Therefore, these problems have a great impact on the operation and maintenance management of the communication infrastructure and need to be resolved urgently

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