Central Alert System for Crowd Threat Detection and Targeted Notification
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Solution Overview
Problem
Current solutions do not effectively analyze crowd behavior, classify crowd gatherings, or provide alert messages to users in proximity of potentially volatile gatherings, leading to delayed assistance during unruly events, especially in densely populated areas where crowd gatherings can pose threats to life and property.
Innovation Solution
A method and system that includes a Central Alert System with modules for user registration, crowd location determination, behavior analysis, threat classification, and alert message generation and transmission to registered user devices, using data from various sources and sensors to assess crowd behavior and notify users of potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If crowd monitoring is done using existing camera-based systems, then crowd density and location data can be collected, but crowd behavior analysis and threat classification cannot be performed
Solution Approach 1:
The patent combines multiple monitoring approaches (camera-based density monitoring, wireless device monitoring for behavior analysis, and social media data integration) into a unified crowd monitoring system. This merging allows the system to obtain both crowd density data and behavior analysis capabilities without requiring a single overly complex system, thereby resolving the contradiction between information completeness and system complexity.
Solution Approach 2:
The monitoring system is designed to perform multiple functions: counting crowd density, analyzing crowd behavior patterns, classifying threat levels, and generating alerts. By making the system multi-functional, it avoids the need for separate specialized systems for each function, thus reducing overall complexity while preventing information loss.
2Reliability
If alert notifications are transmitted to all users in a crowd gathering area, then more users can be warned of potential threats, but false alarms increase and cause user desensitization
Solution Approach 1:
The system applies different alert notification strategies to different users based on their specific location, behavior patterns, and the localized nature of the threat. Instead of uniform alerts to all users in an area, alerts are targeted to specific users who are actually at risk, thereby maintaining high reliability while reducing the total number of notifications and preventing false alarms.
Solution Approach 2:
The system dynamically changes alert notification parameters (such as alert threshold, notification method, and target user selection) based on the analyzed crowd behavior and threat classification. This allows the system to adjust the quantity and quality of alerts to match the actual threat level, ensuring reliability without excessive notifications.
3Loss of time
If real-time crowd behavior analysis is implemented, then timely threat detection is possible, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing crowd data from multiple sources, establishing baseline behavior patterns, and pre-classifying potential threats before critical incidents occur. This preliminary analysis reduces the computational burden during critical moments and enables faster response times without excessive energy consumption during threat events.
Solution Approach 2:
The crowd behavior analysis is conducted periodically at optimized intervals rather than continuously, with the analysis frequency adjusted based on crowd density, location sensitivity, and detected anomaly levels. This periodic approach balances timely threat detection with reduced computational energy consumption.
4Loss of information
If multiple data sources are integrated for crowd monitoring, then comprehensive crowd information is obtained, but system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces intermediary components such as centralized servers and standardized data protocols that mediate between multiple data sources (cameras, wireless devices, social media) and the analysis system. These intermediaries normalize and integrate data from diverse sources, obtaining comprehensive crowd information while managing system complexity through modular architecture and standardized interfaces.
Data Source
AI summary
A method for transmitting alert messages relating to a crowd gathering to one or more user devices in a communication network comprising the steps of receiving data from one or more data sources at a central alert system, wherein said data comprises the geo-location of the crowd gathering; classifying the crowd gathering based on processing the received data on one or more pre-determined criteria stored in the central alert system; identifying the geo-location of one or more user devices in proximity to the identified geo-location of the crowd gathering; generating an alert message based on the classification of the crowd gathering; and transmitting the generated alert messages to the one or more user devices.