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This software system is tailor-made for anti-collision escape fire doors. It integrates high-efficiency data processing and real-time monitoring technologies to fully optimize the emergency opening process of fire doors. The system can quickly process key data such as opening torque, time, and personnel entry and exit, establishing a scientific and rational fire door management model. Through accurate data recording and management, it significantly enhances the level of informatization and management efficiency.
Leveraging Internet of Things (IoT) technology, the system conducts full-process real-time monitoring of fire doors and their surrounding environments. Through high-frequency data collection鈥攁t an adjustable rate of over 100 times per minute鈥攊t promptly detects issues such as abnormal switch status, abnormal personnel activities (e.g., unauthorized intrusion), and door malfunctions. This ensures fire doors can open quickly and safely in emergency situations, providing reliable protection for personnel safety.
As a cloud computing software, the system employs Struts (Model-View-Controller pattern), Spring (Inversion of Control pattern), and Hibernate frameworks to achieve efficient MVC invocation and decoupling, significantly enhancing the system's scalability and maintainability. It also integrates binocular vision monitoring, door-side industrial control computers, and sensor networks to realize comprehensive collection and tracking of full-lifecycle data for fire doors, providing complete data support for subsequent management and optimization.
Leveraging minute-level high-frequency data monitoring and edge computing technology, the system can conduct real-time analysis of fire doors' operating status, providing dynamic early warnings for abnormal conditions such as jamming and torque overrun to ensure safe operation.
In addition, the system supports multi-role data collaborative interaction, enabling real-time data sharing and business collaboration through the Spring Cloud microservice architecture. It incorporates a big data analytics engine (e.g., Spark) and machine learning models (such as decision trees and random forests), which can accurately predict fire door failure types and occurrence times, automatically generate maintenance plans, effectively reduce failure rates and maintenance costs, and comprehensively improve the safety management level of fire doors.