AI Security System with Adaptive Patrol Routing
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Solution Overview
Problem
Conventional security systems are reactive and prone to human error, lacking real-time proactive capabilities and efficient data storage for incident evaluation, making them inefficient and not fool-proof.
Innovation Solution
An integrated security management system utilizing AI-assisted sensors and a classification model that detects security alerts in real-time, determines patrol routes, and learns from feedback to improve threat detection and response, enabling proactive security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual oversight by security personnel is used, then security monitoring can be performed, but human error causes reactive response instead of proactive prevention
Solution Approach 1:
The patent replaces manual security monitoring with an AI-based automated system that uses machine learning models to detect security incidents. The system processes data from multiple sensors and cameras automatically, eliminating human error and enabling proactive detection before incidents occur, thus resolving the contradiction between automation extent and response reliability.
Solution Approach 2:
The security system performs self-monitoring and self-response through automated AI algorithms that continuously learn from data and trigger appropriate actions without human intervention. The system autonomously analyzes sensor data, detects anomalies, and initiates security protocols, achieving both high automation and reliable response.
2Quantity of substance
If camera recordings are stored for short time-interval to manage data storage capacity, then storage requirements are reduced, but ability to evaluate past events is compromised
Solution Approach 1:
The system extracts and stores only critical security-relevant information and metadata rather than entire video recordings. The AI model identifies and saves only pertinent frames and event data, reducing storage requirements while maintaining the ability to evaluate past security events when needed.
Solution Approach 2:
The patent implements differential data retention strategies where different types of data are stored with different durations based on their importance. Critical security event data is retained longer while routine footage is discarded after short periods, optimizing storage capacity while preserving necessary historical information for security analysis.
3Device complexity
If reactive security approach is used, then system complexity is reduced, but real-time prevention capability is lost
Solution Approach 1:
The AI-based system performs preliminary analysis of sensor data continuously, detecting potential security incidents before they fully occur. The machine learning models predict and alert on emerging threats, enabling preventive action rather than reactive response, thus achieving fast real-time prevention capability.
Solution Approach 2:
The system maintains continuous monitoring and analysis of security parameters through automated AI processes that operate without interruption. This continuous action enables real-time detection and response to evolving security situations, achieving high response speed while managing system complexity through efficient automated processing.
Data Source
AI summary
An integrated security management system is provided. The system includes an application server and a plurality of sensors deployed in a geographical area. The application server receives first sensor data from the plurality of sensors and provide to a trained classification model as input and detects a security alert based on output thereof. The application server determines a patrol route that encompasses a location of security alert and transmits a surveillance request to electronic device of a security operator to patrol the patrol route and identifies one or more sensors that covers the location of the security alert and receives second sensor data therefrom based on location of the electronic device being same as location of the security alert. The application server further re-trains the classification model based on the second sensor data when feedback received from the electronic device indicates the security alert to be a false positive.