IoT System for Predicting Nucleic Acid Detection Demand
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for deploying nucleic acid detection points in smart cities are inefficient, lacking a scientific and rational approach to allocate limited resources effectively, which hampers the provision of convenient and timely nucleic acid testing services.
Innovation Solution
An Internet of Things (IoT) system that predicts nucleic acid detection person-time in various areas based on epidemic and environmental information, using a management platform to determine a deployment plan for nucleic acid detection points, including the placement of fixed and mobile points to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If nucleic acid detection points are deployed without prediction-based planning, then deployment is simple and quick, but resource allocation is inefficient and service coverage is uneven
Solution Approach 1:
The system performs preliminary prediction of nucleic acid detection demand using historical data and machine learning models before actual deployment. This allows the management platform to pre-calculate optimal detection point locations and allocate resources in advance, transforming reactive deployment into proactive planning that improves efficiency without requiring complex real-time adjustments
Solution Approach 2:
The system establishes a feedback loop where actual detection data is continuously fed back into the prediction model. This feedback mechanism allows the system to learn from past performance and improve its predictions over time, enabling more accurate resource allocation while maintaining a relatively simple deployment structure through iterative optimization
2Reliability
If detection resources are allocated uniformly across all areas, then implementation is straightforward, but areas with high infection risk receive insufficient attention
Solution Approach 1:
The system applies local quality by differentiating detection point deployment based on area-specific characteristics such as infection risk, population density, and historical data. Each area receives tailored detection resource allocation proportional to its actual needs, ensuring high-risk areas receive enhanced attention while low-risk areas receive appropriate services, all managed through an automated platform that maintains operational simplicity
Solution Approach 2:
The system dynamically adjusts deployment parameters such as detection point location, operating hours, and resource allocation based on changing epidemic conditions and predicted demand. This parameter optimization allows the system to adapt to different scenarios without requiring manual reconfiguration, maintaining ease of operation while improving epidemic control effectiveness through data-driven decision making
3Adaptability or versatility
If mobile detection points are added to fixed points, then service coverage and convenience are improved, but system complexity and management difficulty increase
Solution Approach 1:
The system implements dynamics by enabling detection points to transition between fixed and mobile states based on demand. Mobile detection points can be dynamically deployed to areas with sudden spikes in infection risk or high demand, while fixed points maintain stable operations. This dynamic capability provides service flexibility without requiring a completely complex system, as the mobile units operate as independent modules that integrate with the existing fixed point network
Solution Approach 2:
The system segments detection services into separate fixed and mobile components, allowing mobile detection points to be independently managed and deployed as needed. This segmentation reduces overall system complexity by enabling modular addition of mobile units without requiring complete redesign of the fixed infrastructure, while still providing enhanced adaptability and service coverage through the coordinated operation of both segments
Data Source
AI summary
Embodiments of the present disclosure provide a method and Internet of Things system for deploying nucleic acid detection points in a smart city. This method is executed based on a management platform of the Internet of Things system for deploying nucleic acid detection points in a smart city, comprising: predicting nucleic acid detection person-time in a preset future period in at least one area in multiple areas based on epidemic information and environmental information in multiple areas; determining a deployment plan of the nucleic acid detection points based on the predicted nucleic acid detection person-time.