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With the development of the urban industry in recent years, air pollution in areas such as factories and streets has become more and more serious. Air quality problems directly affect the normal lives of residents. Effectively predicting the future air condition in the area through relevant historical data has high application value for early warning of this area. Through the study of the previous monitoring data, it is found that the pollutant data of adjacent monitoring stations are correlated in more periods. Therefore, this paper proposes a hybrid model based on CNN and Bi-LSTM, using CNN to synthesize multiple adjacent stations with strong correlations to extract spatial features between data, and using Bi-LSTM to extract features in the time dimension to finally achieve pollutant concentration prediction. Using the historical data of 40 monitoring stations in different locations of Fushun city to conduct research. By comparing with the traditional prediction model, the results prove that the model proposed in this paper has higher accuracy and stronger robustness.

At present, the construction dust has caused great losses to people's healthy life and national economic development. In order to solve the shortcomings of the existing real-time sensor network detection of construction dust such as poor accuracy, this paper proposes an automatic identification of construction dust based on computer vision and improved K-Means clustering algorithm. We extract the saturation of HSV color model of each image to form a text data set. We determine the median value of the data set as the initial centroid of clustering, reduce the number of iterations and get the global optimal solution. Mahalanobis distance is used as similarity measure to cluster, which reduces the difference between different feature measures, improves the accuracy, and realizes the automatic recognition of construction dust based on computer vision. For the improved K-Means algorithm, the precision, recall rate and harmonic mean value are used to analyze the clustering results. The experimental results show that the improved K-Means algorithm has good robustness and high accuracy, and the automatic recognition rate can reach 89.33%.