Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high performance algorithms. The framework proposed in this paper provides a general process including five basic tasks, and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework, and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation.
Indoor sudden pollutant leakage brings environmental pollution and occupational exposure, so it is more and more important to obtain the location and identification of leakage sources. Through the forward method based on machine learning, this paper establishes a reverse traceability model for indoor multiple pollution sources. The POD method is used to obtain a large number of intermediate working condition data. The data pre-processing strategy of first normalization and then random forest feature screening can effectively improve the accuracy and generalization ability of the model. Based on a real environmental room case, model verification and sensor deployment optimization are carried out. The results show that the four sensors deployed in a specific location can achieve more than 95% positioning accuracy. In addition, the leakage possibility ranking component embedded in the model can effectively guide the staff to check the leakage points in turn, and the efficiency of three checks is as high as 99.91%.
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