Robot inspection method, device and system for polycrystalline silicon production park

By constructing a dynamic risk map of multi-dimensional environmental data and combining it with a graph neural network model for global planning, and combining it with a proximal strategy optimization model for local adjustment, the problem that the existing inspection system cannot dynamically adjust the path is solved, and the safety and efficiency of the polysilicon production park are improved.

CN120686841APending Publication Date: 2025-09-23XINTE ENERGY CO LTD +1
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Patent Information

Application Number
CN202510852231.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-23
Publication Date
2025-09-23

AI Technical Summary

Technical Problem

The existing industrial automation inspection system in polysilicon production parks lacks effective integration of overall planning and local adjustments, and is unable to dynamically adjust inspection routes based on real-time risk conditions, resulting in an inability to respond to emergencies in a timely manner and posing safety risks.

Method used

By constructing a dynamic risk map of multi-dimensional environmental data, combining it with a graph neural network model for global planning, and using a proximal strategy optimization model for local adjustments, the inspection path is optimized and real-time dynamic optimization of the robot's inspection path is achieved.

Benefits of technology

It realizes real-time dynamic optimization of inspection routes, can quickly respond to emergencies, improves the flexibility and adaptability of inspections, enhances the safety and reliability of inspections, and ensures timely inspections of key equipment and areas.

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Abstract

The invention discloses a robot inspection method, device and system for a polycrystalline silicon production park. The method comprises the steps that firstly, a robot inspection path is planned according to a dynamic risk map, a global planning path is obtained, and the dynamic risk map is constructed based on multi-dimensional environment data; according to the global planning path, controlling the robot to inspect in the polycrystalline silicon production park; performing local adjustment on the global planning path based on the regional risk in the robot inspection process to obtain an adjusted global planning path; and finally, according to the adjusted global planning path, controlling the robot of the polycrystalline silicon production park to continue to inspect. According to the routing inspection method, the dynamic risk map is combined, and real-time dynamic optimization of the routing inspection path can be realized through cooperation of global planning and local adjustment, so that emergencies can be quickly responded.
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