Research On Intelligent Anti-interference Of Coal Mine Methane Monitoring Signal Based On Immune Algorithm

Gas disaster is a major danger in the safety production process of coal mines.On-line accurate monitoring of gas concentration has important significance for coal mine safety.However,due to the complex electromagnetic interference in mines,gas sensors are often subjected to disturbances in the data acquisition process,resulting in "large numbers",which can lead to false alarms.To solve this problem,this paper analyzes the characteristics of the interference signal and builds a gas “large number” interference recognition model based on the artificial immune algorithm.Then the model is used to learn the characteristics of the interference signal and the normal alarm signal.The immunization vaccine is extracted during the learning process to improve the accuracy of the model’s identification of interfering signals.Then using the optimization ability of adaptive immune genetic algorithm to find the optimal convergence step of the LMS algorithm,an adaptive filter is designed.The filter is used to filter out the interference data identified by the interference model.In order to verify whether the interference identification model designed is effective,the data collected first processed by an adaptive filter,and the data obtained after processing is compared with the data processed by the method designed in this paper;secondly,in order to verify the adaptive filtering The filter’s effect on the interference data is processed by Gaussian filtering,Butterworth filtering and wavelet analysis on the data after the interference identification model is judged,and then the processed results are compared with the results after the adaptive filter processing.Through comparative analysis of simulation data,it is proved that the method designed in this paper has relatively good effect on the filtering of gas interference data.