Research On Monitoring Algorithm Of Air Pollutant Emission

In the rapid economic development today,the serious air pollution caused by industrial modernization has already affected human's production and life.Therefore,the environmental monitoring becomes the hot spot,while the application of wireless sensor network has provided the great development space for the monitoring of emission sources of pollutant gases,thus people's requirements on the real-time monitoring technology of pollutant gases emission are getting higher and higher.On this basis,this paper gives the research on monitoring algorithm of pollution sources emission.In the wireless sensor network,there are many sensor nodes.When the sensor nodes perceive the information of pollution source,they will produce the concentration monitoring value of the same pollution source and different pollution sources at the nodes.In order to improve the accuracy of the source intensity calculation,this paper proposed the improved BP neural network to classify these two kinds of monitoring data,and based on the same pollution source monitoring data,this paper used adaptive simulated annealing algorithm to optimize the source intensity calculation,and achieved the real-time monitoring of pollution sources' intensity.In short,this paper mainly introduced the data classification model based on improved BP neural network and adaptive simulated annealing(ASA)algorithm and improved algorithm about ASA,and applied these to the process of back-calculation of pollution sources' intensity.Based on the application of BP neural network in data classification,this paper proposed the data classification model of BP neural network based on the selectively update cuckoo search(SUCS)algorithm,which was compared with BP algorithm with local minimum detection algorithm(LMDBP)and the improved BP neural network model based on the cuckoo search algorithm(BPCS).Based on the Iris dataset,this paper has obtained the simulation results based on MATLAB platform by respectively training and testing these three models,and this paper analyzed the performance and data classification ability of these three algorithms according to this results and summarized the advantages and disadvantages of BP neural network based on SUCS algorithm.In addition,the paper also compared the BP network model based on SUCS algorithm with support vector machine(SVM)in data classification,and obtained the conclusion that the classification ability of this model needs to be improved.After the sensor monitoring data were classified by the BP neural network based on SUCS algorithm,in this paper,the classification results were combined with the Gaussian diffusion model to carry out the back calculation of sources' intensity to achieve real-time monitoring.In the process of sources intensity calculation,this paper got the conclusion that the adaptive simulated annealing algorithm can compute the instantaneous emission rate of the pollution sources.And at the same time,in order to improve the convergence speed of the ASA algorithm,this paper proposed the improved adaptive simulated annealing algorithm and proved that the improved ASA algorithm was superior to the ASA algorithm in convergence speed.However,in terms of computing the continuous emission rate of pollution sources,the improved ASA algorithm can not make accurate operation and can not even converge due to the influence of noise of data collection and the transmission process and the output error of the classification model.