Intelligent early warning system for monitoring device leakage

Abstract

The invention provides an intelligent early warning system for monitoring device leakage, capable of achieving rapid early warning processing of automatic identification and alarm information sendingof the system after liquid leakage of the device occurs at night and under severe weather conditions. The intelligent early warning system comprises a video acquisition module, a video image enhancement module, a data labeling module, a model construction module, a model learning module, a data verification module and a leakage early warning module. The video image enhancement module respectivelyprocesses video images at night, in rainy days and in haze weather conditions by adopting a self-adaptive enhancement algorithm based on closed operation, a fuzzy C-means algorithm and a Retinex algorithm. The data labeling module is used for classifying the liquid leakage states into three types, namely permeation, leakage and injection. The model construction module constructs a five-layer deepneural network model, acquires characteristic parameters through supervised training, and verifies and optimizes the deep neural network through the data verification module. The intelligent early warning system for monitoring device leakage also has the advantages of timely response, high intelligent degree and the like.

Description

technical field [0001] The invention relates to an intelligent video monitoring system, in particular to an intelligent early warning system for leakage of a monitoring device based on a deep learning neural network model. Background technique [0002] Petrochemical plants have a large number of pipelines, towers, tanks and other equipment for flammable, explosive or corrosive liquids in the process of production, storage and transportation. These equipment are prone to leakage during use, which may cause fire and explosion, which brings loss. According to relevant standards, each enterprise has set up a certain number of monitoring equipment on site, but in view of the massive amount of video data, it is difficult for the staff of the management and control platform to maintain a high degree of concentration for a long time, and the efficiency of manual search is low. The impact of weather and smog affects the judgment of the staff on the surveillance video, which greatly ...

AI Technical Summary

Problems solved by technology

[0002] Petrochemical plants have a large number of pipelines, towers, tanks and other equipment for flammable, explosive or corrosive liquids in the process of production, storage and transportation. These equipment are prone to leakage during use, which may lead to fire and explosion, which brings loss

According to relevant standards, each enterprise has set up a certain number of monitoring equipment on site, but in view of the massive amount of video data, it is difficult for the staff of the management and control platform to maintain a high degree of concentration for a long time, and the efficiency of manual search is low. The impact of weather and smog affects the judgment of the staff on the surveillance video, which greatly affects the accuracy of the early warning of the leakage of the video surveillance field device

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