Design And Implementation Of Air Pollution Source Inversion System Based On M-ACO Algorithm

In recent years,the pace of my country’s economic construction and the speed of industrial construction have been accelerating.Sudden air pollution accidents caused by excessive emissions from large stationary pollution sources such as factories have brought huge harm to the lives and properties of the people.After the accident,how to quickly and accurately inverse the pollution source is the key to effectively prevent the further spread of air pollution.The traditional source list checking method can check all the suspected pollution sources in the pollution source list one by one to achieve the purpose of inversing the pollution sources.However,this method is blind,has low efficiency and success rate,and consumes a lot of manpower and material resources.Faced with this situation,this paper designs and implements an air pollution source inversion system based on the Modified-Ant Colony Optimization(M-ACO)algorithm to improve the shortcomings of the source list screening method.The main work is as follows:(1)The point source Gaussian diffusion model is used as the forward diffusion model of pollutants,and the square loss function is used as the prototype,then establish the pollution source inversion model,finally transform the problem of pollution source inversion into a global optimization problem.(2)In view of the shortcomings of traditional Ant Colony Optimization(ACO)algorithm,such as easy to fall into local extremum and slow convergence speed at the initial iteration,this paper adopts the ideas of selection,elimination and crossover to avoid the algorithm falling into local extremum,and proposes the mechanism of reward and punishment factors to accelerate the convergence speed at the initial iteration.Finally,the improved algorithm is called M-ACO algorithm.(3)A comparative experiment is designed,which uses the ACO algorithm and the M-ACO algorithm to solve the pollution source inversion model respectively,and obtains the results of the four parameters of the plane abscissa,ordinate,source intensity and height of the inversion pollution source.By comparing with the four parameter values of the real pollution source,the efficiency and accuracy of the MACO algorithm in the pollution source inversion compared with the ACO algorithm are verified.It is also verified that the use of the M-ACO algorithm greatly reduces the consumption of manpower and material resources compared to the source list checking method.(4)Combining M-ACO algorithm with Web development technologies such as Spring Boot,this paper designed and implemented an air pollution source inversion system integrating logging,monitoring,inversion,management and other functions,and comprehensively evaluated the system through system testing.The results show that the system can meet the needs of users and achieve the expected goals.