Prediction method of filling slurry performance index based on big data
A filling slurry performance technology, applied in the field of filling slurry performance index prediction based on big data, can solve the problems of difficult selection of cement, large consumption of consumables, high cost, etc., to shorten the test cycle and reduce production costs , The effect of improving the prediction accuracy
Active Publication Date: 2021-08-31
CENT SOUTH UNIV +1
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[0007] The invention provides a method for predicting the performance index of filling slurry based on big data to solve the problems of long cycle, high cost, large consumption of consumables, and adaptability of cement and tailings in existing filling material tests. Difficult to choose the best cement technical problem
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[0056] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in various ways defined and covered below.
[0057] Such as figure 1 As shown, the preferred embodiment of the present invention provides a method for predicting the performance index of filling slurry based on big data, which is used to quickly and accurately predict the performance index of filling slurry used in the filling mining method. The slurry performance index prediction method includes the following steps:
[0058] Step S1: Construct the filling slurry database, which includes the physical and / or chemical characteristic parameters of each component of the filling slurry composition, and the performance index value of the filling slurry;
[0059] Step S2: Establish a prediction model for the performance index of the filling slurry;
[0060] Step S3: Learning and training the prediction model of t...
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The invention discloses a method for predicting the performance index of filling slurry based on big data. The performance index prediction method of filling slurry based on big data in the present invention, by constructing a filling slurry database, then establishing a prediction model and performing learning and training, to obtain an optimal prediction model that meets the prediction accuracy requirements, when it is necessary to predict mines When predicting and evaluating the performance index of the filling slurry, it is only necessary to input the physical and / or chemical characteristic parameters of each component of the filling slurry composition of the mine to be predicted, and then the performance index value of the filling slurry of the mine to be predicted can be obtained. The method for predicting the performance index of filling slurry based on big data of the present invention can quickly and accurately predict and evaluate the performance index of filling slurry in mines, which is conducive to improving the accuracy of investment decision-making and evaluation of filling projects, and controlling and optimizing filling production costs. Moreover, as the number of evaluations continues to increase and the amount of data continues to increase, the prediction accuracy of the prediction model will also continue to improve.
Description
technical field [0001] The invention relates to the technical field of mine filling in mining engineering, in particular to a method for predicting the performance index of filling slurry based on big data. Background technique [0002] With the development and utilization of resources, on the one hand, most mines in my country will gradually enter deep mining, develop and utilize difficult-to-mining ore bodies, and recover the residual ore of the mined deposits; on the other hand, the leading direction of national policies has changed, and rough mining methods have been gradually banned. In 2017, a large number of mines were shut down after environmental protection inspections, and a one-vote veto system for environmental protection was implemented. The filling mining method has been proved to have the best adaptability in protecting the ecological environment of the mining area, regional geological structure, protective mining, and mining safety. For this reason, some prov...
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IPC IPC(8): G06F30/20
CPCG06F30/20
Inventor 赖伟罗周全刘婉莹吴亚斌秦亚光文磊
Owner CENT SOUTH UNIV