Granary dangerous source safety monitoring method and system based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence, in particular to a granary dangerous source safety detection method and system based on artificial intelligence. The method comprises the following steps: acquiring an initial image of a granary area, and identifying a plurality of hazard sources and confidence coefficients of corresponding hazard source types; obtaining a risk monitoring index of each hazard source, and further obtaining a radiation feature map; constructing a data distribution diagram of the plurality of category channels according to the danger level labels corresponding to the danger sources; and acquiring a union set graph between any two data distribution graphs and a data distribution graph with a larger area under the same category of channels, thereby acquiring a difference image, determining hazard sources belonging to the same classification standard according to the difference image, inputting hazard source types, wind monitoring indexes and radiation characteristic graphs into the same target neural network, and outputting hazard levels. According to the invention, the target neural network has higher adaptability to the hazard sources of the same classification standard, and the potential safety hazard level of each hazard source can be obtained more accurately.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based granary hazard source safety monitoring method and system. Background technique [0002] In the daily production of grain storage and processing enterprises, fires or explosions often occur. Once danger occurs, it will cause great losses to the personnel and property of the entire enterprise, and the consequences will be disastrous. [0003] In order to ensure safe storage and production in grain processing enterprises, real-time monitoring of hazards in the production area of ​​grain processing enterprises is required. Due to the existence of various hazards in the factory, different hazards have different operating requirements and different normal operating ranges, making dangerous There are different methods for judging the hidden danger level of source safety, and the adaptability of judging the degree of danger based on the s...

AI Technical Summary

Problems solved by technology

[0002] In the production of daily grain storage and processing enterprises, fires or explosions often occur. Once a danger occurs, it will cause great losses to the personnel and property of the entire enterprise, and the consequences will be unimaginable.

[0003] In order to ensure safe storage and production in grain processing enterprises, real-time monitoring of hazards in the production area of ​​grain processing enterprises is required. Due to the existence of various hazards in the factory, different hazards have different operating requirements and different normal operating ranges, making dangerous There are different methods for judging the level of source safety hazards, and the adaptability of judging the degree of danger based on the same machine learning network is poor

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