Smart park safety management system

By introducing virtual protective walls and real-time data analysis into the smart park security management system, the problem of insufficient flexibility and intelligence of traditional security management systems is solved, and the virtual boundaries and risk predictions are dynamically adjusted, which significantly improves the safety prevention and control level of the park.

CN119722406AInactive Publication Date: 2025-03-28JIANGSU SENDI TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411749703.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-02
Publication Date
2025-03-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

The security management system of the existing smart park relies on physical fences and manual patrols, lacks flexibility and intelligence, and cannot effectively detect and warn of abnormal behaviors, resulting in safety hazards and lag in response.

Method used

A smart park security management system was designed, including a virtual protective wall definition module, a behavioral data acquisition and analysis module, anomaly behavior detection and warning module, a dynamic adjustment module and a risk prediction module. Through real-time data analysis and machine learning algorithms, virtual boundaries and risk prediction can be dynamically adjusted.

Benefits of technology

The system can dynamically adjust the virtual protection wall based on real-time data, improve the protection capabilities of key areas, and reduce safety risks; accurately detect abnormal behaviors, reduce false alarms and missed reports, and take protective measures in a timely manner to improve the overall safety prevention and control level.

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Abstract

The invention discloses a smart park safety management system, and relates to the technical field of smart parks, the system uses an environment sensor group and a positioning device to collect position, temperature, humidity, illumination intensity and other multi-dimensional data in real time through a behavior data collection and analysis module, and calculates the moving speed, path curvature and stay time; and constructing a behavior data set based on a geographic information system (GIS), and storing the behavior data set in a time sequence database. And the abnormal behavior detection and early warning module extracts a behavior data set, constructs a feature vector and calculates an abnormal behavior probability by using a machine learning algorithm. By setting an abnormal threshold value, the system can accurately detect behaviors such as border crossing, speed abnormity and path deviation, and triggers an alarm or prompt. According to the real-time analysis based on the behavior data set, the probability of false alarm and missing alarm in a traditional system is remarkably reduced, potential safety risks can be rapidly recognized, and protective measures can be taken in time.
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