How can an intelligent control system achieve accurate identification and response to cooking fumes, odors, and particulate matter through multi-sensor fusion?
Publish Time: 2026-02-18
With increasingly prominent urban air quality issues, home and commercial environments are placing higher demands on air purification systems. Traditional single-sensor purification devices struggle to cope with complex and ever-changing pollution sources, such as cooking fumes and odors from pets or garbage. Intelligent control systems widely employ multi-sensor fusion technology, integrating data from multiple sensors to achieve accurate identification, classification, and dynamic response to different pollutants.1. Multi-sensor Collaborative Perception: Building a Comprehensive Pollution MapThe core of multi-sensor fusion lies in "heterogeneous information complementarity." A typical intelligent purification system usually integrates the following types of sensors:Particulate matter sensor: Used for real-time monitoring of the concentration of suspended particles in the air;Metal oxide semiconductor gas sensor: Highly sensitive to volatile organic compounds, formaldehyde, smoke, etc.;Electrochemical sensor: Specifically detects harmful gases such as carbon monoxide and nitrogen dioxide;Temperature and humidity sensor: Assists in calibrating other sensor readings, improving environmental adaptability.When dealing with kitchen fumes, the system not only detects a sudden increase in PM2.5 concentration but also simultaneously captures significant changes in VOC levels. This multi-dimensional data cross-validation effectively avoids malfunctions caused by misjudgments from a single sensor.2. Data Fusion Algorithm: From Perception to Intelligent Decision-MakingAfter acquiring raw sensor data, the system performs feature extraction and pattern recognition using a fusion algorithm. Common methods include weighted averaging, Kalman filtering, fuzzy logic control, and the increasingly popular lightweight machine learning models. For example, when the system simultaneously detects high PM2.5, high VOCs, and sudden changes in temperature and humidity, it can be identified as a "cooking fume event"; if only VOCs increase while particulate matter remains stable, it is more likely to be "food spoilage or pet odor."By establishing a feature database of typical pollution scenarios, the system can classify the current environmental state and match the optimal purification strategy. For example, it can activate a high-volume airflow + electrostatic dust collection mode for cooking fumes and activate an activated carbon layer and photocatalytic module for odors, thereby achieving efficient resource utilization and energy consumption optimization.3. Closed-Loop Feedback and Adaptive Adjustment: Enhancing User ExperienceAn intelligent control system not only needs to be accurate in its detection but also responsive. Based on fusion recognition results, the system can dynamically adjust fan speed, filter operating mode, and runtime, and provide pollution type prompts and treatment suggestions through the user interface. Furthermore, some high-end systems possess learning capabilities—by recording user habits and environmental changes over a long period, they continuously optimize recognition thresholds and response logic to create personalized purification solutions. More importantly, multi-sensor fusion also supports self-diagnosis of faults. For example, when the particulate matter sensor continuously outputs abnormally high values while other sensors do not respond, the system can determine that the sensor is contaminated or malfunctioning and remind the user to clean or replace it, ensuring long-term operational reliability.Multi-sensor fusion technology endows air purification intelligent control systems with "human-like perception" capabilities, enabling them to accurately distinguish different pollution sources such as cooking fumes, odors, and particulate matter, and respond accordingly. This not only improves purification efficiency and energy efficiency ratio but also significantly improves indoor air quality and user health experience. In the future, with the development of edge computing and AI chips, such systems will become more miniaturized and intelligent, becoming an indispensable part of smart homes.
