Method for efficient deployment of a cluster of air purification devices in large indoor and outdoor spaces
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Solution Overview
Problem
Existing air purification systems face challenges in efficiently deploying air purification devices in large indoor and outdoor spaces due to the complexity of pollutant distribution and spatial constraints, leading to suboptimal air quality improvement.
Innovation Solution
A method that utilizes a computer system to simulate pollutant distribution in a three-dimensional representation of the target space based on data from air sensors, boundary conditions, and device characteristics, calculating optimal positions for air purification devices to maximize their impact on air quality, considering spatial constraints such as power availability and safety zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If air purification devices are deployed in large indoor and outdoor spaces without simulation-based optimization, then deployment complexity is reduced, but air quality improvement effectiveness deteriorates
Solution Approach 1:
The system performs preliminary simulation and analysis of pollutant distribution patterns before actual device deployment. By pre-calculating optimal positions based on simulated pollutant transport, the system determines where devices should be placed to maximize effectiveness, thereby resolving the contradiction between simplified deployment and improved effectiveness.
Solution Approach 2:
The system creates a virtual three-dimensional representation (digital twin) of the target space that replicates the physical environment's geometry, boundary conditions, and pollutant distribution characteristics. This virtual model allows for simulation and optimization without requiring physical trial-and-error deployments, thus maintaining deployment simplicity while achieving optimal air quality improvement.
2Productivity
If air purification devices are deployed without considering spatial constraints and pollutant distribution, then deployment speed is increased, but air quality improvement effectiveness deteriorates
Solution Approach 1:
The system pre-calculates optimal device positions by simulating pollutant distribution and analyzing spatial constraints before deployment. This preliminary optimization step provides a ready-made deployment plan that can be executed quickly, maintaining high deployment speed while ensuring devices are positioned for maximum effectiveness.
Solution Approach 2:
The system identifies specific locations within the target space where pollutant concentrations are highest and where device placement will have the greatest impact. By positioning devices at these critical locations rather than uniformly distributing them, the system achieves better air quality improvement with fewer devices, thus maintaining productivity while improving reliability.
3Reliability
If the number of air purification devices is increased to improve air quality coverage, then air quality improvement effectiveness is improved, but investment cost increases
Solution Approach 1:
The system identifies specific high-pollutant areas and positions devices strategically at these locations rather than uniformly distributing them throughout the space. This targeted approach ensures that each device addresses the most critical pollution problems, achieving effective air quality improvement with a smaller number of devices and reduced investment cost.
Solution Approach 2:
The simulation system allows users to test different device quantities and configurations to find the minimum number of devices required to achieve desired air quality targets. By identifying the threshold where additional devices provide diminishing returns, the system optimizes the balance between effectiveness and cost, avoiding excessive device deployment.
4Ease of operation
If air purification devices are positioned without simulation optimization, then device placement simplicity is improved, but pollutant reduction effectiveness deteriorates
Solution Approach 1:
The system uses a virtual three-dimensional representation of the target space to simulate and optimize device positions before actual deployment. This digital model allows for complex simulation and optimization calculations without affecting physical deployment simplicity, as the optimized positions are predetermined and can be implemented straightforwardly in the real world.
Solution Approach 2:
The system performs all complex simulation, optimization, and position calculation work before actual device deployment. By pre-determining optimal positions based on simulated pollutant distribution and spatial constraints, the system eliminates the need for complex real-time decision-making during deployment, maintaining placement simplicity while ensuring optimal pollutant reduction effectiveness.
Data Source
AI summary
A method for distributing a set of air purification devices in a target space comprising: accessing a void volume representing the target space; accessing a set of observed parameter data streams recorded by a set of air sensors with the target space during an observation period, the set of observed parameter data streams comprising a set of pollutant concentration data streams of a pollutant, a set of air speed data streams, and a set of air direction data streams; simulating a distribution of the pollutant in the void volume reproducing the set of observed parameter data streams based on the set of observed parameter data streams; accessing a set of device characteristics for a set of air purification devices to be deployed within the target space; and calculating a set of device positions in the void volume based on the distribution of the pollutant and the set of device characteristics.