Abstract
In view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. Through the analysis of the main geological and mineral characteristics of mining subsidence, the geological and mineral characteristics are simplified according to the third similar theorem. The feature equation is obtained by using the equation analysis method and dimension analysis method. The original fuzzy clustering method is improved, and the IWFCM_CCS algorithm based on competitive merger strategy is obtained. The data of rock movement observation are analyzed by fuzzy clustering. The membership matrix and clustering center of observation station data are obtained, and the regression model based on the weight of membership degree is established. The accuracy and feasibility of the parameter prediction model are verified by analyzing and comparing the actual measurement data and the predicted results of the model. The method reduces the error of the predicted parameters caused by the observation data and provides a method for the future calculation of the predicted parameters.
Highlights
Mining subsidence is expected to be developing in the direction of intelligence and visualization
Coal mining affects the production and life safety of people around, so the prediction of mining area deformation becomes very important. e accuracy of rock movement parameters affects the accuracy of the predicted results
Aiming at the problem of inaccurate calculation of rock movement parameters due to the large amount of data generated by rock movement observation, a mining subsidence parameter prediction model based on big data has been established [7–12]. rough the analysis and processing of rock movement observation data, the mining subsidence parameter prediction model is derived, which improves the accuracy and calculation efficiency of rock movement parameters, thereby improving the accuracy of mining subsidence prediction and the safety of the coal mining process
Summary
Mining subsidence is expected to be developing in the direction of intelligence and visualization. E advancement of modern surveying and mapping science and technology has resulted in more and more data generated from the observation of surface subsidence. If these data are analyzed and processed quickly, abnormal data can be eliminated, and useful data can be extracted; this will greatly improve the accuracy and efficiency of settlement predictions [1–6]. Rough the analysis and processing of rock movement observation data, the mining subsidence parameter prediction model is derived, which improves the accuracy and calculation efficiency of rock movement parameters, thereby improving the accuracy of mining subsidence prediction and the safety of the coal mining process.
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