Net cage netting damage detection method and device based on mathematical twinning and storage medium

Abstract

The invention discloses a net cage netting damage detection method and device based on a mathematical twinborn technology and a storage medium, and relates to the technical field of disaster prevention and reduction of aquaculture engineering and ocean engineering, and the method develops a digital twinborn body for netting damage detection by using a method of combining numerical simulation and an artificial neural network. The identification model can carry out rapid damage identification according to environmental parameters and tension sensing data. Firstly, an equivalent numerical model of the fishing net is used for simulating the completeness and damage conditions of the fishing net to obtain a large amount of simulation sensing data, then the data are used for training an artificial neural network, the artificial neural network can identify whether the netting is damaged or not according to different simulation sensing signals, and finally, according to the real sensing data, the artificial neural network can identify whether the netting is damaged or not. The identification model can rapidly identify whether the netting is damaged or not. According to the invention, real-time damage monitoring of the fishing net is realized, the labor cost is saved, the detection efficiency is improved, and the limitation of netting or environmental interference is overcome.

Description

technical field [0001] The invention relates to the technical field of disaster prevention and reduction in aquaculture engineering and marine engineering, and in particular to a method, device and storage medium for detecting damage to cage netting based on mathematical twin technology. Background technique [0002] Net clothing (or fishing net) is an important part of fish farming facilities. As a complex flexible structure, it is very easy to be damaged in the deep sea environment with harsh sea conditions. If it is not discovered in time, it will cause economic losses to the aquaculture industry. , so net damage detection is one of the key technologies for the development of deep sea fish farming. [0003] At present, the method of detecting the damage of fishing nets is mainly that the staff dive into the water for manual inspection. In addition, domestic and foreign scholars mainly put forward three relatively new methods: the first is the buried wire detection method....

AI Technical Summary

Problems solved by technology

[0004] Manual inspection is labor-intensive and inefficient; the wire-embedded detection method needs to install metal wires around the net, which will increase the load on the net and easily cause fatigue damage, thereby shortening the service life of the net; the sonar detection method cannot determine The location of the damage and the extent of the damage. At the same time, if there are fish swimming around the cage, the sonar will also detect the damage, resulting in inaccurate detection results; the image analysis method has a very high impact on the pixel quality of the camera and the turbidity of the water. requirements, it is not easy to judge whether the netting in turbid water is damaged

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