基于GRU神经网络的巷道平均风速获取研究

doi:10.11872/j.issn.1005-2518.2021.05.054

Abstract

Abstract:

With the advent of the intelligent era， computer simulation algorithms such as machine learning and deep learning have played a role in many fields such as aerospace， medical treatment， education and communication. For the traditional industry of mining， the concept of smart mine has become a research hotspot of relevant researchers in recent years. Intelligent technologies such as machine learning have been used in pedestrian detection， gas prediction， coal rock identification has been successfully applied to practical production， but the intelligent acquisition of parameters in intelligent ventilation system is still in a blank. Therefore， under the background of smart mine， aiming at the problem that the mine intelligent ventilation system can’t obtain the wind speed in time and then complete the subsequent ventilation system solution and optimization， the training set required by the neural network is obtained by using the simulation of tunnel wind speed distribution in ANSYS. Based on manual measurement and wind speed sensor monitoring data， the prediction model of roadway average wind speed based on gated recurrent unit neural network was constructed. Firstly， the neural network model was proposed， and then the Adam optimization algorithm was used to preprocess the data such as outlier processing and normalization. After the structural processing of the wind speed at the monitoring points of the roadway with different shapes， it was used to train the neural network to find out the strong nonlinear relationship between the wind speed at each point and the average wind speed， so that the predicted wind speed is close to the actual average wind speed of the roadway. Finally， the prediction model of roadway average wind speed based on GRU neural network was constructed. Taking the measured data of Wangjialing coal mine as the model test set， the results show that the GRU neural network model has high precision and strong generalization ability， and can obtain the average wind speed of roadway， which will provide a roadway average wind speed prediction model with advanced technology， scientific process and accurate results for the mine intelligent ventilation system. Moreover， the strong prediction ability of in-depth learning will provide intelligent data for the solution and optimization of ventilation network， it can be extended to the acquisition of ventilation parameters in other metal mines to popularize the intelligent acquisition of ventilation parameters.

Key words: smart mine, GRU neural network, monitoring and monitoring system, average wind speed, intelligent ventilation system

CLC Number:

TD76

Liangshan SHAO,Shuangshuang WEN. Research on Obtaining Average Wind Speed of Roadway Based on GRU Neural Network[J].Gold Science and Technology, 2021, 29(5): 709-718.

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算法遍历次数	训练集损失	均方误差	平均绝对误差	测试集损失	均方误差损失	平均绝对误差损失
1	3.66E-02	3.66E-02	1.31E-01	1.80E-02	1.80E-02	1.20E-01
2	2.50E-02	2.50E-02	1.22E-01	1.40E-02	1.40E-02	1.07E-01
3	1.91E-02	1.91E-02	1.07E-01	9.96E-03	9.96E-03	8.98E-02
4	1.41E-02	1.41E-02	9.02E-02	6.80E-03	6.80E-03	7.47E-02
5	1.01E-02	1.01E-02	7.42E-02	4.63E-03	4.63E-03	6.30E-02
6	6.99E-03	6.99E-03	6.10E-02	2.52E-03	2.52E-03	4.54E-02
7	4.78E-03	4.78E-03	4.80E-02	1.40E-03	1.40E-03	3.36E-02
8	3.27E-03	3.27E-03	3.78E-02	7.84E-04	7.84E-04	2.51E-02
9	2.30E-03	2.30E-03	3.06E-02	3.84E-04	3.84E-04	1.68E-02
10	1.70E-03	1.70E-03	2.49E-02	1.91E-04	1.91E-04	9.91E-03
11	1.33E-03	1.33E-03	2.05E-02	1.32E-04	1.32E-04	7.53E-03
12	1.12E-03	1.12E-03	1.81E-02	1.32E-04	1.32E-04	8.89E-03
13	9.92E-04	9.92E-04	1.73E-02	1.54E-04	1.54E-04	1.03E-02
14	9.19E-04	9.19E-04	1.70E-02	1.77E-04	1.77E-04	1.07E-02
15	8.71E-04	8.71E-04	1.68E-02	1.99E-04	1.99E-04	1.12E-02
16	8.42E-04	8.42E-04	1.69E-02	2.23E-04	2.23E-04	1.25E-02
17	8.21E-04	8.21E-04	1.67E-02	2.32E-04	2.32E-04	1.27E-02
18	8.03E-04	8.03E-04	1.68E-02	2.50E-04	2.50E-04	1.37E-02
19	7.90E-04	7.90E-04	1.67E-02	2.55E-04	2.55E-04	1.40E-02
20	7.78E-04	7.78E-04	1.66E-02	2.53E-04	2.53E-04	1.39E-02

断面	断面尺寸（高度×宽度）/m	V1	V2	V3	V4	V5	V6	V7	V8	V9	实测平均风速 /（m·s^-1）
断面1	3.84×3.84	0.7525	0.7772	0.8142	0.7402	0.7710	0.8142	0.7525	0.7648	0.7278	0.7683
断面2	4.55×3.87	1.3706	1.3770	1.4204	1.3706	1.5571	1.1282	1.3644	1.4701	1.1220	1.3534
断面3	4.63×3.71	1.8317	1.6652	1.5048	1.7885	1.7453	1.5295	1.8810	1.6652	1.3692	1.6645
断面4	2.54×3.83	0.5737	0.6107	0.4935	0.5737	0.6107	0.5243	0.5243	0.5860	0.5737	0.5634
断面5	5.52×4.04	0.4104	0.3398	0.3339	0.3810	0.4163	0.4222	0.4104	0.3457	0.3339	0.3771
断面6	4.51×5.47	4.0442	4.4602	4.3770	3.6116	4.4435	4.4269	3.2456	3.9943	3.9943	4.0664
断面7	3.02×3.81	10.6212	11.4010	9.2175	9.8413	11.0891	11.0891	8.5312	10.9331	8.7496	10.1637

断面	断面尺寸（高度×宽度）/m	V1	V2	V3	V4	V5	V6	V7	V8	V9	实测平均风速 /（m·s^-1）
断面1	4.98×3.47	2.1620	2.0180	2.1620	2.2772	2.1908	2.2628	2.2628	2.2484	2.2628	2.2052
断面2	4.79×4.14	0.4503	0.4010	0.3332	0.4318	0.3887	0.3332	0.4380	0.3640	0.3517	0.3880
断面3	3.41×4.90	0.4318	0.4010	0.3640	0.4503	0.4627	0.4010	0.3578	0.4812	0.4010	0.4168
断面4	3.17×4.43	0.7095	0.6472	0.6472	0.7718	0.7157	0.7282	0.7406	0.7406	0.7095	0.7123
断面5	2.54×3.33	1.3233	1.1557	1.1325	1.4126	1.4238	1.3345	1.3456	1.4238	1.4462	1.3331
断面6	2.74×4.58	6.0981	5.7862	5.9110	6.5972	6.5660	6.5348	6.4413	6.5348	6.2541	6.3026

断面类型	断面	实测风速/（m·s^-1）	预测风速/（m·s^-1）	准确率/%	断面类型	断面	实测风速/（m·s^-1）	预测风速/（m·s^-1）	准确率/%
拱形锚喷	断面1	0.76	0.59	0.78	矩形锚喷	断面1	2.20	2.20	1
	断面2	1.35	1.23	0.91		断面2	0.38	0.34	0.9
	断面3	1.66	1.62	0.98		断面3	0.41	0.41	1
	断面4	0.59	0.33	0.56		断面4	0.71	0.49	0.69
	断面5	0.37	0.27	0.73		断面5	1.33	1.28	0.96
	断面6	4.06	4.42	1.08		断面6	6.30	6.68	1.06
	断面7	10.16	10.29	1.01

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Research on Obtaining Average Wind Speed of Roadway Based on GRU Neural Network

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