Valve remote monitoring and analysis system and method based on cloud platform

Through the remote monitoring and analysis system of valves based on cloud platform, the deep neural network model is used to predict the degree of corrosion and failure probability of valves, which solves the problems of insufficient real-time and inaccurate evaluation in traditional methods, and realizes accurate monitoring and life prediction of valve operating status, improving the safety and efficiency of the system.

CN119379270BActive Publication Date: 2025-04-04SHENZHEN HUANSHUI PIPE NETWORK TECH SERVICE CO LTD +1
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Patent Information

Application Number
CN202411965503.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-30
Publication Date
2025-04-04
Estimated Expiration
2044-12-30

AI Technical Summary

Technical Problem

Traditional valve monitoring and maintenance methods have problems such as insufficient real-time performance, inaccurate evaluation and difficulty in predicting failure risks, and it is impossible to effectively and dynamically evaluate the degree of corrosion and failure probability of valves.

Method used

A remote valve monitoring and analysis system based on the cloud platform is adopted to collect the corrosion degree characteristic data of the valve and upload it to the cloud platform. The deep neural network model is called to predict the corrosion degree coefficient and failure probability, and the operating status of the valve is dynamically evaluated.

Benefits of technology

Real-time monitoring of valve operating status and accurate life prediction, reduce maintenance costs, improve system safety and operating efficiency, and provide technical support for efficient operation and maintenance of industrial pipeline systems and intelligent upgrades.

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Abstract

The present invention relates to the field of data processing technology. The present invention discloses a valve remote monitoring and analysis system and method based on a cloud platform, including: collecting corrosion degree characteristic data of each valve; calling a first deep neural network model and a second deep neural network model; inputting the corrosion degree characteristic data into the first deep neural network model to obtain the corrosion degree coefficient of each valve; obtaining the failure reflection characteristic data of each valve, and inputting the failure reflection characteristic data into the second deep neural network model to obtain the failure probability of each valve at a future moment; determining the failure time of all target valves and each target valve according to the failure probability, calculating the remaining service life of all target valves according to the failure time, and grading the operating status according to the remaining service life; the present invention can dynamically evaluate the corrosion degree, failure probability and remaining service life of the valve, thereby realizing hierarchical management and differentiated maintenance.
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