Generative artificial intelligence indoor air cleaning system
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Solution Overview
Problem
Existing indoor air cleaning systems struggle to efficiently detect, locate, and circulate air pollution in indoor environments, particularly in residential settings, while achieving clean room requirements and optimizing system performance, cost, and noise reduction.
Innovation Solution
A generative artificial intelligence (AI) indoor air cleaning system that integrates a generative AI model with an indoor air cleaning system, utilizing air quality detectors, gas purification device hardware, and a calculation center to optimize the number, performance, and noise reduction of gas purification devices, minimizing installation costs and achieving clean room standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional air cleaning systems are deployed in indoor environments, then air pollution can be detected and filtered, but system complexity and installation costs increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimized deployment plans using generative AI models before actual installation. The calculation center generates multiple candidate schemes considering various constraints (noise, cost, performance) and selects the optimal configuration in advance, simplifying the actual deployment process while ensuring reliable air quality control.
Solution Approach 2:
The generative AI model acts as an intermediary between air quality requirements and system configuration. It translates complex requirements into optimized deployment plans, mediating between the need for reliable air cleaning and the desire for simple installation by generating pre-optimized configurations that balance both concerns.
2Productivity
If more gas purification devices are installed to improve air cleaning performance, then air pollution removal effectiveness increases, but installation costs and system complexity increase
Solution Approach 1:
The system changes parameters by optimizing the number, position, and configuration of purification devices based on generative AI calculations. Instead of simply increasing device quantity, the system adjusts multiple parameters (device count, placement locations, power settings) to achieve optimal air cleaning performance with minimized device quantity and cost.
Solution Approach 2:
The system applies partial action by deploying purification devices only where needed based on calculated air pollution patterns and room characteristics. The generative AI model identifies critical areas requiring purification and allocates devices strategically, avoiding unnecessary deployment in areas with good air quality, thus reducing total device count while maintaining effective air cleaning.
3Reliability
If high-performance gas purification devices are used to achieve clean room requirements, then air cleaning effectiveness improves, but noise levels and installation costs increase
Solution Approach 1:
The system implements dynamics by enabling real-time adjustment of device power levels and operational parameters based on actual air quality conditions and noise constraints. The generative AI model continuously monitors performance and dynamically optimizes device settings to maintain clean room requirements while minimizing noise and energy consumption, rather than operating at fixed high-performance settings.
Solution Approach 2:
The system changes operational parameters (power levels, flow rates, activation times) to balance performance with noise and cost constraints. By adjusting these parameters dynamically, the system achieves clean room requirements without consistently operating at maximum performance levels, thereby reducing noise and energy costs while maintaining necessary air quality standards.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects and circulates air pollution, achieving clean room requirements by optimizing system performance, reducing noise, and minimizing installation costs, thereby enhancing indoor air quality in residential environments.
Implementation Method 1
the air pollution in the indoor field is guided to pass through the filtration element for circulation and filtration, thereby gas state in the indoor field is cleaned to reach a clean room requirement
Data Source
AI summary
A generative artificial intelligence indoor air cleaning system is disclosed and includes a storage center, an air quality detector, an application software, a calculation center and an gas purification device hardware. The storage center collects, stores and forms a big data database including professional generated data and user generated data. The application software inputs the professional generated data and the user generated data to be stored in the storage center. The calculation center includes a generative artificial intelligence model. The professional generated data and the user generated data stored are captured by the calculation center through Internet of Things and processed through deep learning to form automatically-generated data. The gas purification device hardware receives a control command according to the automatically-generated data generated by the calculation center to regulate an activation operation for circulation and filtration, thereby gas state in the indoor field reaches a clean room requirement.