Smart City Emergency Data Scheduling for Predictive Resource Allocation
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Solution Overview
Problem
Existing multimodal emergency management systems in smart cities face challenges with unbalanced resource allocation, low timeliness of collaboration among different platforms, and the inability to obtain data volumes for future emergency management, leading to inefficiencies in data processing and response.
Innovation Solution
A system and method utilizing a large model of IoT that includes an emergency supervision management platform, sensor network platform, and object platform, which determines target datasets and processing orders based on computing resources, predicts pending data volumes and resource occupancy, and generates control instructions for rescue vehicles and other resources to optimize data transmission and emergency response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from multiple sub-data centers is processed simultaneously without priority scheduling, then all data can be processed, but resource allocation becomes unbalanced and processing efficiency decreases
Solution Approach 1:
The system performs preliminary actions by predicting pending data volumes and resource occupancy conditions before actual data processing occurs. The emergency supervision management platform forecasts future data volumes from multiple sub-data centers and pre-determines processing priorities, allowing resources to be allocated in advance rather than reactively, thus improving processing efficiency while maintaining manageable resource allocation complexity
Solution Approach 2:
The system segments the emergency management data processing into distinct priority levels and handles different sub-data center contributions separately. By dividing the overall processing task into prioritized segments based on predicted resource occupancy, the system can allocate computing resources more effectively across multiple sources without creating unmanageable allocation complexity
2Loss of time
If computing resources are allocated without predicting future data volumes, then resource allocation is simple, but timeliness of emergency response deteriorates
Solution Approach 1:
The system applies preliminary action by predicting pending data volumes and resource occupancy conditions before emergency data actually arrives. The emergency supervision management platform uses historical data and forecasting models to anticipate future data volumes, allowing timely allocation of computing resources in advance, thus reducing emergency response time while the predictive modeling manages the processing complexity
3Measurement precision
If all emergency management data is processed with equal priority, then processing is straightforward, but critical emergencies cannot be identified and responded to promptly
Solution Approach 1:
The system applies local quality by assigning different processing priorities to different data sources and data types based on their criticality. The emergency supervision management platform identifies which sub-data centers and which types of emergency data require higher priority processing, applying differentiated quality standards to different parts of the data stream, thus improving risk assessment accuracy while the prioritization framework manages processing complexity
Data Source
AI summary
A system and a method for multimodal emergency management of a smart city based on a large model of internet of things are provided. The method is executed by an emergency supervision management platform. The method includes: based on a preset cycle, for each of a plurality of sub-data centers, determining a second target dataset and a target processing order based on a remaining computing resource, a reference computing resource, and a first target dataset; predicting a pending data volume based on first historical data; based on the reference computing resource and the pending data volume, predicting a resource occupancy condition, and generating an overload condition; determining a data transmission order based on the target processing orders of the plurality of sub-data centers, and transmitting the second target dataset based on the data transmission order.