IoT Fire Rescue Plan Generation from Predicted Fire and Flow
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Solution Overview
Problem
Current fire protection systems in smart cities lack the ability to automatically generate fire rescue plans based on real-time data and historical information, hindering efficient coordination among different units for effective fire rescue operations.
Innovation Solution
An IoT system comprising a user platform, service platform, management platform, sensing network platform, and object platform, which processes monitoring data to predict fire and flow information, and determines a rescue plan including the count of fire trucks and ambulances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual fire rescue plan generation is used, then system complexity is low, but rescue response time and efficiency are insufficient
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing fire scene data, historical fire rescue data, and other relevant data in databases before actual fire rescue operations. This preliminary data preparation enables rapid automated plan generation when fires occur, significantly reducing response time without requiring complex real-time decision-making algorithms during the emergency.
Solution Approach 2:
The fire rescue plan generation system operates autonomously by automatically retrieving relevant data from databases, processing the information through predefined algorithms, and generating rescue plans without requiring manual intervention. This self-service capability eliminates the bottleneck of manual plan creation while maintaining systematic and standardized rescue procedures.
2Productivity
If automated fire rescue plan generation is implemented, then rescue efficiency improves, but data processing complexity increases
Solution Approach 1:
The data processing system is segmented into distinct functional modules: data collection module for gathering fire scene data and historical data, data processing module for analyzing and filtering information, and plan generation module for creating rescue plans. This segmentation allows each module to handle specific data processing tasks independently, reducing overall system complexity while maintaining high automation efficiency.
Solution Approach 2:
The system manages data processing complexity by changing parameters such as data formats, storage structures, and retrieval queries based on the specific fire rescue scenarios. By adapting data processing parameters to match the requirements of different fire types and locations, the system maintains efficient automated plan generation without requiring overly complex universal processing algorithms.
3Reliability
If integration of multiple departments is achieved through IoT, then coordination effectiveness improves, but system integration complexity increases
Solution Approach 1:
The system employs a universal data platform and standardized communication protocols that can integrate multiple departments and systems (fire department, police, ambulance, traffic management) without requiring separate integration solutions for each department. This multi-functional platform approach improves coordination effectiveness while controlling integration complexity through standardization.
Solution Approach 2:
The system introduces an intermediary data processing layer that acts as a mediator between different departments and systems. This intermediary layer standardizes data formats, manages communication protocols, and coordinates information flow between fire department, police, ambulance services, and traffic management systems, thereby improving coordination effectiveness while isolating the complexity of multi-department integration from individual systems.
Data Source
AI summary
The present disclosure provides methods and Internet of Things (IoT) systems for determining a fire rescue plan in a smart city. The method includes obtaining monitoring data of a disaster area in a first period of time; predicting fire information in a second period of time based on the monitoring data; determining flow information of the disaster area based on the monitoring data, the flow information including at least one of traffic flow information or human flow information; and determining a rescue plan based on the fire information and the flow information, the rescue plan including at least one of a count of fire trucks or a count of ambulances.