Blasting Vibration Prediction Method Based on Spark Gene Expression Optimization

Abstract

The invention provides a blasting vibration prediction method based on Spark gene expression optimization, which relates to the field of machine learning. The invention uses data preprocessing technology to process blasting data to obtain a sample data set. By calculating the inconsistency rate of each condition attribute, in After deleting the condition attributes whose incongruity rate is lower than the threshold in the original data set, a new data set is generated as the input data set, and the improved gene expression method is used to optimize the function on each node, and the blasting vibration effect prediction function can be obtained , so as to obtain the predicted value of the blasting vibration peak velocity. The invention can better solve the problem of training efficiency under the condition of massive data. By adopting a new generation of parallel computing technology and improving the gene expression programming algorithm to optimize the global function of the blasting data, the convergence speed can be greatly improved without affecting the training accuracy. Under the condition of improving the training efficiency.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a blasting vibration prediction method. Background technique [0002] Blasting vibration usually refers to the strength of the vibration wave generated by blasting, which can be described by the speed, frequency and time of blasting vibration. In practice, the peak value of blasting vibration velocity is often used to describe blasting vibration. Blasting vibration prediction is to study the relationship between blasting vibration influencing factors and blasting vibration, among which blasting vibration influencing factors include total charge, segment charge, blast center distance, geological conditions and elevation difference and many other factors. [0003] Prediction of blasting vibration is an effective method to reduce the risk factor of blasting, optimize the blasting scheme and evaluate the safety level of blasting, and it is particularly important to study efficient and...

AI Technical Summary

Problems solved by technology

However, with the rapid expansion of the data volume of engineering blasting, the traditional stand-alone method of processing data sets will have serious problems, such as insufficient memory, long training time, etc.

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