IoT Large Model Emergency Supervision for Priority Data Retrieval
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional emergency management systems face inefficiencies in data collection, slow response times, and irrational resource allocation, making it difficult to manage emergency events effectively in smart cities.
Innovation Solution
A system utilizing an IoT large model for smart city emergency supervision, integrating multi-source data, employing intelligent algorithms for dynamic scheduling and decision-making, and optimizing emergency resource allocation through an emergency supervision management platform, emergency supervision sensing network platform, and emergency supervision object platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional emergency management systems are used, then system simplicity is maintained, but data collection efficiency is low and response time is slow
Solution Approach 1:
The system is divided into multiple functional modules including data collection module, data processing module, emergency level determination module, and resource scheduling module. Each module handles specific tasks independently, improving data collection efficiency while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
An emergency supervision management platform is introduced as an intermediary layer between various data sources and emergency response systems. This platform integrates multi-source data, processes information centrally, and coordinates resource allocation, thereby enhancing overall system productivity without requiring complete system redesign.
2Loss of time
If data retrieval prioritization based on emergency level is implemented, then response timeliness is improved, but data processing complexity increases
Solution Approach 1:
The system changes the parameter of data retrieval by implementing prioritization based on emergency levels. Different emergency levels (e.g., level 1, level 2, level 3) correspond to different retrieval priorities and processing intensities, allowing the system to respond quickly to critical events while reducing unnecessary processing for minor incidents.
Solution Approach 2:
Different data processing strategies are applied to different types of emergency data based on their emergency levels. High-emergency-level data receives immediate attention with simplified processing paths, while lower-level data undergoes more comprehensive analysis, optimizing response time for critical situations without uniformly increasing processing complexity across all data.
3Reliability
If multi-source data integration is performed, then comprehensive emergency supervision is achieved, but system complexity and data processing burden increase
Solution Approach 1:
The emergency supervision management platform is designed with universal functionality to handle multiple data sources including IoT sensors, cameras, emergency reports, and historical data. This single platform performs diverse functions such as data collection, processing, analysis, and resource coordination, achieving comprehensive supervision without proportionally increasing system complexity.
Solution Approach 2:
Multiple data sources and processing functions are merged into a unified emergency supervision management platform. Instead of having separate systems for each data source, the platform integrates them all, sharing common infrastructure and processing logic, thereby achieving comprehensive supervision while controlling overall system complexity through consolidation.
4Productivity
If dynamic resource scheduling is implemented, then resource allocation efficiency is improved, but control system complexity increases
Solution Approach 1:
The resource scheduling system transitions from static pre-allocated resources to dynamic scheduling based on real-time emergency levels and resource availability. The system automatically adjusts resource allocation (e.g., emergency vehicles, medical teams, equipment) according to changing conditions, improving allocation efficiency while using automated algorithms to manage control complexity.
Solution Approach 2:
The system implements feedback mechanisms where resource allocation decisions are continuously monitored and adjusted based on emergency development and resource status. This feedback loop enables dynamic optimization of resource scheduling efficiency while using systematic feedback control to manage the complexity of coordinating multiple resources across different locations and functions.
Data Source
AI summary
The present disclosure relates to a method, a system, and a storage medium for smart city emergency supervision based on an IoT large model, the method including: in response to receiving an emergency management request from a sub-platform, determining a data retrieval prioritization for the emergency management request based on a first emergency level of the emergency management request; retrieving emergency management data corresponding to the emergency management request from a database based on the data retrieval prioritization.