An intelligent identification method for park risks based on location information

Through real-time multi-source data collection and grid-based spatiotemporal alignment, combined with the improved YOLOv8 model and entropy weight method, the problems of data fragmentation and rigid risk assessment in the park security monitoring system are solved, and efficient anomaly detection and accurate early warning are achieved.

CN120544205BActive Publication Date: 2025-09-23SHENYANG SURVEYING & MAPPING RES INST CO LTD
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Patent Information

Application Number
CN202511045578.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-29
Publication Date
2025-09-23
Estimated Expiration
2045-07-29

AI Technical Summary

Technical Problem

The existing campus security monitoring system suffers from the fragmentation of multi-source data, insufficient video detection capabilities, and rigid risk assessment mechanisms, leading to the fragmentation of spatiotemporal information, making it difficult to implement dynamic risk linkage analysis, resulting in a high false detection and missed detection rate, and delayed or false alarm warnings.

Method used

Through real-time multi-source data collection and gridded spatiotemporal alignment, combined with the improved YOLOv8 model and entropy weight method, abnormal behavior recognition and dynamic risk calculation of video streams are achieved, and a dual-threshold decision-making mechanism is used for accurate alarms.

Benefits of technology

It has improved the detection rate of abnormal targets, reduced the false detection rate, increased the early warning response speed and accident identification rate, reduced false alarms, and achieved the transition from passive monitoring to active prevention and control.

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Abstract

The present invention discloses a method for intelligently identifying park risks based on location information, which relates to the technical field of park safety management. The method collects personnel and vehicle positioning data, fixed risk source coordinates, equipment status, environmental parameters and video stream data in real time; adopts the Geohash algorithm to divide the park into grids, and realizes spatiotemporal alignment of multi-source data through a sliding time window; improves and trains the YOLOv8 model to output real-time park abnormal behavior detection results; quantifies the behavior risks of mobile targets based on the entropy weight method, and superimposes the risks of equipment and environmental exceeding the standard to generate grid-level superimposed risk values; combines the real-time park abnormal behavior detection results with the superimposed risk values ​​to obtain the final risk results. The present invention breaks through the data island limitations of traditional monitoring systems, improves the risk identification rate and early warning response speed, reduces the risk false detection rate, and realizes the transformation from passive response to active prevention and control.
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