WO2020019998A1 - 一种回采巷道矿压显现数据的预测方法 - Google Patents

一种回采巷道矿压显现数据的预测方法 Download PDF

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WO2020019998A1
WO2020019998A1 PCT/CN2019/095719 CN2019095719W WO2020019998A1 WO 2020019998 A1 WO2020019998 A1 WO 2020019998A1 CN 2019095719 W CN2019095719 W CN 2019095719W WO 2020019998 A1 WO2020019998 A1 WO 2020019998A1
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pressure
mining
roadway
data
appearance
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郑西贵
王文凯
艾德春
刘灿灿
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中国矿业大学
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Priority to RU2020140889A priority patent/RU2751991C1/ru
Priority to CA3118506A priority patent/CA3118506C/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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  • the invention relates to a method for predicting the appearance pressure data of mining roadways, and belongs to the technical field of surrounding rock control of mining roadways.
  • the mining roadway has a short service life, its support design is only required to meet the normal use during mining. Therefore, the mining roadway during mining and mining Compared with the other two types of roadways, the appearance of underground pressure is also more intense.
  • the measurement of the convergence of the surface deformation of the mining roadway is one of the must-measure items during the excavation and mining of the mining roadway.
  • the basic method is as follows: Immediately after the excavation of the coal and rock mass, some anchor points are buried in the two sides of the roadway and the roof as the measurement base Using monitoring instruments to measure small changes in the distance between any base points within a certain period of time, to calculate the convergence deformation and deformation rate of the roof and floor and the two sides of the roadway, in order to assess the stability of the project and guide the support design purpose.
  • Observation of the approaching rock mass of mining roadway is the most commonly used and most commonly used method of mine pressure observation in domestic and foreign mines. Its contents include the roof sinking amount of the roadway, the amount of floor swell, the approaching amount of the roadway, and the deep surrounding rock. Approach amount and the remaining area of the roadway.
  • the most widely used and most basic approach point observation method is the "cross-shaped" layout method, that is, after the excavation of the mining roadway, the observation of the top and bottom approaches and the changes of the two approaches in time and development
  • the deformation observation method of surrounding multiple measuring points is also used.
  • the mining roadway is on the side of the working face.
  • the first type is to perform long-term field observations during the change of mining pressure in mining roadways, such as observing the top and bottom plate approach, support load and pillars.
  • the stress distribution law and displacement distribution characteristics of the roadway is the research method of similar material simulation experiments. It is based on similar principles in the laboratory to make models similar to the prototype of the mining roadway. Its distribution law uses the results of research on the model to infer the possible mechanical phenomena and the law of rock mass pressure distribution in the prototype of mining roadway, so as to solve the practical problems in rock mass engineering production.
  • the numerical analysis method is used to study the characteristics of the mining pressure in the mining roadway, the physical and mechanical parameters of the surrounding rock of the roadway are used. Because the conditions of the mining roadway are often very complicated in actual conditions, the model established is difficult to accurately reflect the mining roadway. The actual situation, the results obtained are also significantly different from the actual situation, and generally only used as a reference.
  • the present invention provides a mining roadway. Prediction method of underground pressure appearance data to reliably predict the appearance characteristics of underground pressure in mining roadway in a short time.
  • a method for predicting the appearance pressure of mining pressure in the mining roadway of the present invention is to first establish a prediction model for the change rate of the appearance pressure data of the section of any mining roadway, and integrate it to obtain the predicted change of the appearance pressure data in a certain interval.
  • Model by collecting multiple times the pressure data of different pressure stations at different positions relative to the working face, a series of actual pressure values of the pressure data are obtained; using the predicted change model of the pressure pressure data in a certain interval
  • a series of actual differences in the appearance pressure of the underground pressure are used for non-linear regression to determine the function expression of the change speed and cumulative change amount of the appearance pressure of the mining roadway.
  • a method for predicting the appearance pressure data of a mining roadway in the present invention includes the following steps:
  • the prediction model of the change rate of the pressure appearance data of any roadway section is v (x), and the prediction model of the cumulative change amount of the pressure appearance data is The model for predicting the change in the pressure of the ground pressure is:
  • the deformation time experienced by any section of the roadway is The mining pressure appearance data is mechanical and displacement data related to the mining pressure appearance of the mining roadway, including the roof delamination amount of the mining roadway, the roof sinking amount, the bottom drum amount, the approaching amount of the two sides, the displacement amount of the deep rock layer, Bolt load and anchor cable load;
  • v is the change speed of the mine pressure manifestation data
  • u is the cumulative change amount of the mine pressure manifestation data
  • x is the distance of the roadway section relative to the work surface in the advancing direction of the work surface, when the roadway section is in front of the work surface x ⁇ 0, when the cross section of the roadway is behind the working face, x>0
  • the working face is the driving face during the driving influence stage, and the mining face during the mining influence stage
  • x 0 is when any roadway section begins to deform Distance to the working surface
  • x m and x n represent different distances of the roadway section relative to the working surface
  • L is the daily footage of the working surface
  • Predicted change model using mining pressure visualization data The actual difference ⁇ u (x 1, j , x 1, j + 1 ), ⁇ u (x 2, j , x 2, j + 1 ), ..., ⁇ u (x i, j , x i, j + 1 ) to perform non-linear regression to obtain the parameters of the prediction models v (x) and u (x), that is, to determine the function expression of the change rate and cumulative change amount of the mining pressure appearance data in the mining roadway, At the same time, the change speed and cumulative change amount of the underground pressure manifestation data of the roadway section at an arbitrary distance from the working face, and the extent and duration of the influence of the underground pressure manifestation are obtained.
  • the advantage is that the present invention adopts a densely-arranged station layout method in the acquisition stage of mining pressure data acquisition, which can complete the collection of mining pressure visualization data in a short time (2-7 days).
  • the actual difference of a series of underground pressure manifestation data is used for non-linear regression by using the predicted change model of the established underground pressure manifestation data, and it is possible to predict the characteristics of the underground pressure variation of a specific mining roadway in a short time, and its accuracy is high. .
  • You can also predict the final result and the entire process of the appearance of the mineral pressure through the previous data of the appearance of the mineral pressure.
  • the curves in the curve family included in the prediction model v (x) are all concave curves in the influence stage of the tunneling, and the corresponding functions are subtractive functions; the curves included in the prediction model u (x) The curves in the family are all convex curves that are steeper and then slower during the influence stage of the tunneling, and the corresponding functions are increasing functions.
  • the curves in the curve family included in the prediction model v (x) are all bell-shaped curves with convex middle and concave sides in the mining influence stage, and the corresponding functions are first increased and then decreased;
  • the curves in the curve family included in the prediction model u (x) are all S-shaped growth curves during the mining influence stage.
  • the advantage is that the basic characteristics of the prediction models v (x) and u (x) are derived through the induction of the general characteristics of the changes in the pressure data of the mining impact stage and the mining impact stage, which are specific mathematical models. Got established to provide guidance.
  • the prediction model v (x) is expressed as
  • a is the maximum change rate of the pressure data; a and b are parameters to be determined; e is a natural constant; and a> 0,0 ⁇ b ⁇ 1.
  • the prediction model v (x) is expressed as
  • a is the maximum change rate of the pressure data; a and c are parameters to be determined, where a> 0,0 ⁇ c ⁇ 1.
  • the prediction model v (x) is expressed as
  • k, d, and ⁇ are all parameters to be determined, k> 0,0 ⁇ d ⁇ 1, -1000 ⁇ ⁇ 1000.
  • the measuring station is arranged within a range of 100m behind the working face; during the mining influence stage, the measuring station is also arranged within 50m in front of the mining face.
  • the advantage is that for different mining roadways, the range of the mine pressure manifestation in the mine roadway is different. Placing the measurement station in the range of the mine pressure manifestation roadway will improve the efficiency of the mine pressure visualization data collection and the range of the survey station layout.
  • the limitation is to provide a reference for the mine pressure observation.
  • the more the mining pressure manifestation data is collected the more accurately the mining pressure manifestation characteristics of the mining roadway can be accurately predicted; the denser the stations are arranged, the shorter the time for collecting the mining pressure manifestation data.
  • the advantage is that if the distance between adjacent stations is too large, it is not conducive to the rapid collection of pressure data, and the denser the arrangement of the stations, the greater the accuracy of the prediction.
  • the non-linear regression is performed by a numerical analysis software having a non-linear regression function, and the numerical analysis software includes ORIGIN, MATLAB, EXCEL, and SPSS.
  • the surrounding rock characteristics of the mining roadway are the same, the pressure control method is the same, and the daily footage of the working face does not change.
  • the advantage is that because the same mining roadway is in different sections, the factors that affect the characteristics of the appearance of the pressure may be different. If a type of appearance of the pressure is obtained for the surrounding rock of the roadway under different conditions, this feature can only be introduced. Because of their actual conditions, rather than the representation of the characteristics of the mining pressure of the mining roadway under the same condition, so the conditions can not be obtained without arranging stations within the range that affects the factors of the mining pressure of the mining roadway to remain basically the same. Underground tunnel pressure characteristics.
  • the beneficial effect of the present invention is that the method for predicting the appearance characteristics of mining pressure in a mining roadway can quickly and efficiently obtain the appearance characteristics of mining pressure in a mining roadway, and has a wide application range.
  • the specific advantages are as follows:
  • Figure 1 is a schematic layout of the 110102 transport station in the Kianda Mine
  • Figure 2 is a schematic diagram of the "cross point" of the road surface convergence measurement
  • Figure 3 is a map of the amount of deformation of the roadway in any two days of the 110102 transportation along the Jianda mine;
  • Figure 4 shows the approach speed of the two gangs along the trough of 110102 in Kianda Mine
  • Figure 5 shows the approach of the two gangs along the channel of the Kianda Mine 110102
  • Figure 6 is the layout of the surface displacement measuring station of the 30102 transport channel in Youzhong Mine
  • Figure 7 shows the prediction results of the deformation of the two gangs along the trough of Youzhong Mine 30102.
  • FIG. 8 is a graph showing the change rate v and the cumulative change amount u of the predicted pressure data in the influence stage of the excavation with time t;
  • FIG. 9 is a graph showing the change rate v and cumulative change amount u of the predicted pressure data during the mining influence stage with the coordinate x;
  • the first one is to take the prediction of the characteristics of the roadway pressure in the influence stage of the tunneling and digging of the 110102 transport tunnel in Shouyang, Ji'an, Shanxi, as an example;
  • the prediction of the appearance characteristics of the roadway pressure at the impact stage of the 30102 transportation along the mine in Youzhong Mine is taken as an example.
  • the entire process of the appearance of the mine pressure in the two lanes of the roadway is collected by collecting displacement data of the two lanes of the roadway.
  • the measuring instrument used may be a distance measuring instrument such as a laser rangefinder or a steel tape measure.
  • Measurement points and station layout Monitoring of the convergence of the roadway surface includes the amount of sinking of the roof, the amount of bottom drum, and the approach of the two sides.
  • Measuring instrument Select the instrument according to the size of the roadway section and the accuracy requirements of the displacement test results. The "cross-point method” is used to arrange the measuring points, and the top and bottom of the roadway and the approach of the two sides are observed daily. The arrangement of measuring points is shown in Figure 2.
  • the station was arranged on the first day and the size of the roadway section was recorded. On the third day, the size of the roadway section was recorded.
  • the size record and deformation of the roadway width are shown in Table 1.
  • the histogram of the variation of the width of the tunnel along the channel in the Kianda Mine 110102 over time is shown in Figure 3.
  • the deformation speed expression is:
  • the deformation expression is:
  • the deformation speed expression is:
  • the expression of the input model in SPSS is: k * (1 / (1 + exp (-a * (t + 2- ⁇ )))-1 / (1 + exp (-a * (t- ⁇ )))) ,
  • the initial value is set to a (1), k (1), ⁇ (20).
  • the standard error of a is 0.115, which is very low, and the confidence of this estimate is high;
  • the standard error of k is 192001.355, which is very high, and the confidence of this estimate is low;
  • the standard error of ⁇ is 92.620, very High, the confidence of this estimate is not high;
  • the deformation speed expression is:
  • the deformation expression is:
  • the standard error of a is 539.806, which is very high, and the confidence of this estimate is low
  • the standard error of B is 56.015, which is high, and the confidence of this estimate is low
  • the standard error of ⁇ is 6.299, which is High, the confidence level of this estimate is low
  • ⁇ 11.515
  • k 22120.2249
  • ⁇ -26.214
  • the deformation speed expression is:
  • Table 2 is the approach speed of the two gangs of the Jianda Mine 110102 transport trough.
  • Figure 4 is the speed chart of the two gangs of the Jianda Mine 110102 transport along the trough.
  • Fig. 5 is a close-up diagram of the two gangs along the transport channel of the Kianda Mine 110102.
  • the entire process of the appearance of the mine pressure in the two lanes of the roadway is collected by collecting displacement data of the two lanes of the roadway.
  • the measuring instrument used may be a distance measuring instrument such as a laser rangefinder or a steel tape measure.
  • the daily footage of the 30104 mining face of Youzhong Mine was 5m / d.
  • the monitoring length is 7 groups from the starting point of the retention lane to the cutting point of 30102 along the working surface to 120m.
  • the specific surface displacement station arrangement is shown in Figure 6; Table 5 is the position table of the station 1 ⁇ 7 # for the transport trough 30102 of Youzhong Mine;
  • the deformation amount is:
  • the expression of the input model in SPSS is: k * (1 / (1 + exp (-a * (X 2 - ⁇ )))-1 / (1 + exp (-a * (X 1 - ⁇ )))))
  • the results are analyzed as follows:
  • the two sides of the roadway have a maximum approach speed of 16.080mm / d; the deformation of the roadway mainly occurs in the interval (-20,75), that is, the work Between 20m in front of the face and 75m behind the working face; at 100m behind the working face, the deformation of the roadway is basically completed, and the final approach of the two sides of the roadway can reach 739mm.
  • the normal distribution function itself is a transcendental function, it cannot be directly integrated to obtain a certain formula, so he cannot fit the roadway deformation amount within a certain time like other models.
  • the parameter value of the normal distribution model is solved by using the data of the laneway deformation speed represented by the Logistic function as the data required for the regression of the normal distribution function model.
  • the deformation speed expression is:
  • the affected range of the roadway during the mining impact stage is ( ⁇ -3 ⁇ , ⁇ + 3 ⁇ ), that is (-27.43, 82.436).
  • the roadway begins to deform at 27.43m in front of the mining face, and the mining affected 82.436m behind the working face.
  • the roadway started to stabilize, and the two sides of the roadway reached the maximum approach amount of 716.527mm.
  • Table 7 and Figure 7 show the prediction results of the deformation of the two gangs along the trough of Youzhong Mine 30102.
  • the embodiment of the present invention takes the displacement of the two sides of the roadway as an example
  • the technical solution of the present invention is used for other kinds of observation data of the mining roadway pressure, such as the amount of roof delamination, roof subsidence, and the depth of the rock layer.
  • the amount of displacement, the volume of the roadway floor, the load of the anchor rod, the load of the anchor cable, etc. are also applicable, because the characteristics of the mathematical model summarized in the technical solution of the present invention are applicable to them.
  • the curves in the family of curves included in the prediction model v (x) are all concave curves during the influence phase of the tunneling, and the corresponding function is a subtraction function;
  • the prediction model u (x The curves in the curve family included in) are all convex curves that are steeper and then slower during the tunneling impact stage, and the corresponding functions are all increasing functions.
  • the curves in the curve family included in the prediction model v (x) are both convex and concave bell-shaped curves in the mining influence stage, and the corresponding functions are first After increasing, it decreases; the curves in the curve family included in the prediction model u (x) are all S-shaped growth curves during the mining influence stage.
  • the two embodiments of the present invention are only a simple application of the technical solution of the present invention.
  • the arrangement of the station and the setting of the observation frequency are not the optimal designs made according to the technical solution of the present invention.
  • the technical solution of the present invention can be used to achieve a faster and more accurate prediction of the appearance characteristics of the mining pressure in the mining roadway.

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Abstract

一种回采巷道矿压显现数据的预测方法，属于矿山巷道围岩控制技术领域。所述方法利用矿压显现数据的预测变化量模型对一系列的矿压显现数据的实际差值进行非线性回归，得到确定的回采巷道矿压显现数据的变化速度与累积变化量的函数表达式，进而得到回采巷道的矿压显现特征。利用所述回采巷道矿压显现数据的预测方法可以快速、高效的预测回采巷道的矿压显现特征，适用范围较广。

Description

一种回采巷道矿压显现数据的预测方法 技术领域
本发明涉及一种回采巷道矿压显现数据的预测方法，属于矿山巷道围岩控制技术领域。
背景技术
在煤矿的开拓巷道、准备巷道和回采巷道这三类巷道中，回采巷道因其服务年限较短，故其支护设计也只是满足回采期间正常使用即可，因而回采巷道在掘进和回采期间的矿压显现相对于另外两类巷道也是比较强烈的。测量回采巷道表面变形收敛量是巷道是回采巷道掘进和回采期间的必测项目之一，其基本方法为：在开挖煤岩体后立即在巷道的两帮和顶板埋设一些锚固点作为测量基点，用监测仪器在一定时段内通过量测任意基点间距离上的微小变化，从而计算出巷道的顶底板、两帮的收敛变形量和变形速率，用以达到评定工程稳定性，指导支护设计目的。
回采巷道围岩移近量的观测是国内外矿山最常用、也是最普遍应用的巷道矿压观测方法，其内容包括巷道顶板下沉量、底板鼓起量、巷帮移近量、深部围岩移近量以及巷道剩余断面积等。其中应用最广泛、也是最基本的移近量观测的测点布置方法是“十字形”布点法，即回采巷道开挖后观测顶底移近量和两帮移近量随时间的变化与发展，当巷道断面较大，需要分析研究变形的复杂受力过程时，也有采用周边多测点变形观测方法，回采巷道处于工作面一侧，在其服务期间的相当一段时间，要受工作面采动的影响，其受力有明显的不对称性，因而其变形发展过程也有明显的不对称性，为了研究变形及其控制特点，往往采用“艹字形”和“井字形”的测点布置方法。
在回采巷道的矿压观测中，国内外矿山主要采用固定测站，长期监测的的方法，所需花费的时间较长，而且预测回采巷矿压灾害效果也较差。
目前，研究回采巷道的矿压显现特征的常用的方法主要有三类：第1类是在回采巷道的矿压变化过程中进行长期的现场观测，如观测顶底板移近量、支架载荷量和支柱(活柱)下缩量，一般称为“三量”；第2类是基于有限差分法(FDM)、有限元法(FEM)、边界元法(BEM)、离散元法(DEM)、拉格朗日元法、不连续变形分析法(DDA)、流行元法(MEM)、无单元法及其混合应用等各种数值模拟技术的数值分析方法，如使用FLAC 3D、3DEC、ANSYS软件模拟巷道的应力分布规律、和位移分布特征等；第3类是相似材料模拟实验研究方法，它是在实验室内按相似原理制作与回采巷道原型相似的模型，借助测试仪表观测模型内力学参数及其分布规律，利用在模型上研究的结果，借以推断回采巷道原型中可能发生的力学现象以及岩体压力分布的规律，从而解决岩体工程生产中的实际问题。
虽然对回采巷道进行长期观测得到的矿压规律是最真实、最准确的，但是它最大的缺点是需要的时间较长。
由于数值分析方法研究回采巷道的矿压显现特征时，利用的是巷道围岩的物理、力学参数，而由于回采巷道在实际情况下条件往往非常复杂，所建立的模型很难准确反映回采巷道的真实情况，得到的结果也与实际情况差别较大，一般仅作为参考。
而相似材料模拟实验更适合对特殊条件下的巷道矿压显现特征进行研究，对一般回采巷道的矿压显现特征和变化规律，很少使用这种方法。
发明内容
发明目的：针对现有技术中存在的研究回采巷道矿压显现特征的方法所耗费的时间长、效率低、准确度低、预测效果差、适用范围有限等不足，本发明提供了一种回采巷道矿压显现数据的预测方法，以在短时间内可靠地预测回采巷道的矿压显现特征。
技术方案：本发明的一种回采巷道矿压显现数据的预测方法为先建立任意回采巷道断面矿压显现数据的变化速度的预测模型，将其积分后得到矿压显现数据在一定区间的预测变化量模型；通过多次采集不同测站在相对于工作面不同位置时的矿压显现数据，得到一系列的矿压显现数据的实际差值；利用矿压显现数据一定区间的预测变化量模型对一系列的矿压显现数据的实际差值进行非线性回归，即可确定回采巷道矿压显现数据的变化速度和累积变化量的函数表达式。
本发明的一种回采巷道矿压显现数据的预测方法包括以下步骤：
1)建立同一巷道断面矿压显现数据的预测变化量模型
在回采巷道中，任意巷道断面的矿压显现数据的变化速度的预测模型为v(x)，矿压显现数据的累积变化量的预测模型为
Figure PCTCN2019095719-appb-000001
矿压显现数据的预测变化量模型为
Figure PCTCN2019095719-appb-000002
任意巷道断面经历的变形时间为
Figure PCTCN2019095719-appb-000003
所述矿压显现数据为与回采巷道矿压显现相关的力学、位移数据，包括回采巷道的顶板离层量、顶板下沉量、底鼓量、两帮移近量、深部岩层的位移量、锚杆载荷量和锚索载荷量；
其中，v为矿压显现数据的变化速度，u为矿压显现数据的累积变化量；x为在工作面的推进方向上，巷道断面相对于工作面的距离，当巷道断面在工作面前方时x<0，当巷道断面在工作面后方时x＞0；所述工作面在掘进影响阶段是指掘进工作面，在采动影响阶段是指回采工作面；x 0为任意巷道断面开始变形时相对于工作面的距离；x m与x n表示巷道断面相对于 工作面的不同距离，且x m<x n；L为工作面的日进尺；
2)通过矿压观测得到一系列的矿压显现数据的实际差值
在同一回采巷道内同时布置多个测站来采集矿压显现数据；随着工作面的推进，同一测站在相对于工作面不同位置时采集到的两次矿压显现数据的实际差值为Δu(x i,j,x i,j+1)＝|U i,j+1-U i,j|，对多个测站多次采集矿压显现数据后，得到一系列的矿压显现数据的实际差值Δu(x 1,j,x 1,j+1)、Δu(x 2,j,x 2,j+1)、……、Δu(x i,j,x i,j+1)；
其中，
Figure PCTCN2019095719-appb-000004
为测站相对于工作面的距离为x i时采集的矿压显现数据值；巷道断面相对于工作面的距离为x i时，该巷道断面经历的变形时间为
Figure PCTCN2019095719-appb-000005
3)通过非线性回归预测回采巷道的矿压显现数据
利用矿压显现数据的预测变化量模型
Figure PCTCN2019095719-appb-000006
对一系列的矿压显现数据的实际差值Δu(x 1,j,x 1,j+1)、Δu(x 2,j,x 2,j+1)、……、Δu(x i,j,x i,j+1)进行非线性回归，得到预测模型v(x)和u(x)的参数，即确定出回采巷道矿压显现数据的变化速度和累积变化量的函数表达式，同时得到相对于工作面任意距离的巷道断面的矿压显现数据的变化速度和累积变化量，及矿压显现影响的范围和持续的时间。
其优点为，本发明在矿压显现数据采集阶段采取密集布置测站的测站布置方式，可在较短的时间(2～7天)完成矿压显现数据的采集，通过对矿压观测得到一系列的矿压显现数据的实际差值利用建立的矿压显现数据的预测变化量模型进行非线性回归，可在短时间内预测得到特定回采巷道的矿压变化特征，且其准确度较高。也可以通过前期的矿压显现数据来预测矿压显现的最终结果和全过程。
作为进一步的限定，所述预测模型v(x)所包含的曲线族中的曲线在掘进影响阶段均为凹曲线，所对应的函数为减函数；所述预测模型u(x)所包含的曲线族中的曲线在掘进影响阶段均为先陡后缓的凸曲线，所对应的函数均为增函数。
作为进一步的限定，所述预测模型v(x)所包含的曲线族中的曲线在采动影响阶段均为中间凸两边凹的钟形曲线，所对应的函数均先增大后减小；所述预测模型u(x)所包含的曲线族中的曲线在采动影响阶段均为S型增长曲线。
其优点在于，通过对掘进影响阶段和采动影响阶段的矿压显现数据的变化的一般特征的 归纳，得出了预测模型v(x)和u(x)的基本特征，为具体的数学模型得建立提供了指导。
满足上述要求的数学模型很多，不能穷举，但是本发明优选的数学模型有以下三种:
作为进一步的限定，所述预测模型v(x)在掘进影响阶段表示为
v(x)＝ae -bx      (1)
式中a为矿压显现数据的最大变化速度；a、b为待定参数；e为自然常数；并且，a>0,0<b<1。
作为进一步的限定，所述预测模型v(x)在掘进影响阶段表示为
v(x)＝ac x      (2)
式中a为矿压显现数据的最大变化速度；a、c为待定参数，其中a>0,0<c<1。
作为进一步的限定，所述预测模型v(x)在掘进影响阶段和采动影响阶段都表示为
Figure PCTCN2019095719-appb-000007
式中k、d、μ均为待定参数，k>0,0<d<1,-1000<μ<1000。
作为进一步的改进，所述测站布置在工作面后方100m范围内；在采动影响阶段，所述测站同时布置在回采工作面前方50m内。
其优点在于，对于不同的回采巷道，回采巷道中矿压显现的范围是有差异的，将测站布置在矿压显现的巷道范围内会提高矿压显现数据采集的效率，对测站布置范围的限定是为进行矿压观测时提供一种参考。
作为进一步的限定，所述矿压显现数据采集的越多，则越能准确的预测回采巷道的矿压显现特征；所述测站布置的越密集，矿压显现数据采集的时间越短。
其优点在于，如果相邻测站距离太大，不利于快速采集矿压显现数据，测站布置越密集，其对预测的准确度也有很大提高。
所述非线性回归利用具有非线性回归功能的数值分析软件完成，所述数值分析软件包括ORIGIN、MATLAB、EXCEL和SPSS。
所述的具有非线性回归功能的数值分析软件的种类很多，不能穷举，上述列出的仅仅是常用的几种。
作为进一步的限定，所述回采巷道的围岩性质相同、矿压控制方式一致、工作面的日进尺不变。
其优点在于，由于同一条回采巷道在不同区段，其影响矿压显现特征的因素可能会有差 异，如果对不同条件的巷道围岩得到一种的矿压显现特征，这个特征只能是介于他们的实际情况之间，而不是对同一条件的回采巷道的矿压显现特征的表示，所以在影响回采巷道矿压显现的因素保持基本不变的范围内不布置测站，才能得到该条件下的巷道矿压显现特征。
所谓的矿压显现特征是本领域所属技术人员研究回采巷道矿压显现特征时重点关注的分析结果。
本发明的有益效果：利用本发明的一种回采巷道矿压显现特征的预测方法可以快速、高效的得到回采巷道的矿压显现特征，适用范围较广。具体优点如下：
(1)回采巷道矿压显现特征的预测中的矿压观测阶段一般只需要两三天的时间即可完成，与长期观测相比，虽然准确度会有一定的降低，但是效率却得到了很大的提高；
(2)回采巷道矿压显现特征的预测方法中虽然使用了如SPSS、MATLAB、ORINGIN等数值分析软件，但是由于其利用的数据是实际观测的，所以其反映的回采巷道矿压显现特征比数值模拟更接近真实情况；
(3)利用部分的矿压显现数据预测完整的回采巷道矿压的显现特征，对矿压灾害发生有预警功能，有利于煤矿的安全高效生产。
附图说明
图1为基安达矿110102运输顺槽测站布置示意图；
图2为巷道表面收敛测“十字布点”示意；
图3为基安达矿110102运输顺槽任意2天内巷道变形量图；
图4为基安达矿110102运输顺槽两帮移近速度；
图5为基安达矿110102运输顺槽两帮移近量；
图6为友众矿30102运输顺槽表面位移测站布置图；
图7为友众矿30102运输顺槽两帮变形的预测结果。
图8为预测矿压显现数据的变化速度v和累积变化量u在掘进影响阶段随时间t变化的曲线图；
图9为预测矿压显现数据的变化速度v和累积变化量u在采动影响阶段随坐标x变化的曲线图；
具体实施方式
下面结合说明书附图，通过具体的实施例对本发明的的技术方案作进一步详细的说明。本发明中主要以2个实施例为例来说明，第一个以山西寿阳基安达矿110102运输顺槽掘进影响阶段的巷道矿压显现特征的预测为例；第二个以山西段王集团友众矿30102运输顺槽采动影响阶段的巷道矿压显现特征的预测为例。
第一个实施例：
1)测站的布置
本实施例通过采集巷道两帮的位移数据来预测其巷道两帮矿压显现的全过程，采用的测量仪器可以为激光测距仪、钢卷尺等距离测量仪器。
在对山西寿阳基安达矿110102工作面运输顺槽进行矿压观测时，工作面已经推进了450m。由于掘进工作面附近有掘进机械、支护材料和未清理的煤，使得新掘巷道断面处无法布置测站进行观测，故首个测站设置在距离工作面最近处，由于工作面后方50m的范围内往往巷道矿压变化比较剧烈，50m之后矿压变化相对变缓和。故在距离工作面50m范围内布置6个测站(0～5)，之后每隔25m布置一个测站，连续布置4个，之后再隔50m布置一个测站，在工作面后方200m内共布置11个测站，如图1所示。
测点与测站布置：巷道表面收敛量监测包括顶板下沉量、底鼓量、两帮移近量。测量仪器：根据巷道断面大小和对位移测试结果的精度要求，选择仪器。采用“十字布点法”布置测点，每天观测巷道的顶底板和两帮移近量。测点布置方式如图2所示。
2)巷道两帮位移的记录结果
第1天布置好测站并记录巷道断面的尺寸，第3天又去记录巷道断面的尺寸，其巷道宽度的尺寸记录及变形量如表1所示。基安达矿110102运输顺槽巷道宽度不时间段的变化量的柱状图如图3所示。
表1基安达矿110102运输顺槽巷道宽度记录表
Figure PCTCN2019095719-appb-000008
3)建立数学模型，并用不同数学模型进行预测
根据建立的四种数学模型表示的任意时间段内巷道变形量u Δ，利用上述第1次测量时的等效变形时间和2天内的巷道宽度的变形量2组数据，利用SPSS软件进行非线性回归分析。其分析结果如下：
(1)指数函数类型
其变形速度模型为：
v＝ae -bt
SPSS中输入模型的表达式为：a/b*(exp(-b*t)-exp(-b*(t+2)))，初始值设为a(1),b(0.999)，取值范围：a>＝1,a<＝104，b>＝0.001,b<＝0.999。参数估值：a＝59.193,b＝0.227。结果分析如下：a的标准误为5.441，很大，此估值的置信度不高；b的标准误为0.031，很低，说明此估值的置信度很高；a和b的相关性为0.806，相关性较高；确定性系数R 2＝1-(残差平方和)/(校正平方和)＝0.934，拟合度很高。
变形速度表达式为：
v＝59.193e -0.227t
变形量表达式为：
u＝260.762(1-e -0.227t)
在此模型下，当t＝0时，巷道两帮有最大移近速度59.193mm/d；令巷道两帮移近速度v＝1mm/d，得到t＝17.977，即从第18天开始，巷道开始进入稳定变形阶段，此时巷道两帮的移近量已经达到了256.357mm；第22天时巷道两帮的移近量达到了258.994；令t→∞，得到由掘进影响的巷道两帮的最大移近量为260.762mm。
(2)复合函数类型
其变形速度模型为：
v＝ab t
巷道两天内的变形量为：
Figure PCTCN2019095719-appb-000009
通过SPSS中的曲线估计来进行回归，选择复合函数，得到a/lnb*(b 2-1)＝75.159，其标准误差为10.897，很大，此估值的置信度不高；b＝0.861,其标准误差为0.13，很小，此估值的置信度很高；通过计算得到a＝43.484，确定性系数R 2＝1-(残差平方和)/(校正平方和)＝0.914，拟合度很高。
变形速度表达式为：
n＝43.484×0.861 t
对上式积分，并且由t＝0时，u＝0得到变形量表达式为：
u＝290.550×(1-0.861 t)
在此模型下，当t＝0时，巷道两帮有最大移近速度43.484mm/d；令巷道两帮的移近速度v＝1mm/d，得到t＝25.206，即从第25天开始，巷道开始进入稳定变形阶段，此时巷道两帮的移近量已经达到了312.500mm，令t→∞，得到由掘进影响的巷道两帮的最大移近量为 319.391mm。
(3)Logsitic函数类型
其变形速度模型为：
Figure PCTCN2019095719-appb-000010
SPSS中输入模型的表达式为：k*(1/(1+exp(-a*(t+2-μ)))-1/(1+exp(-a*(t-μ))))，初始值设为a(1),k(1),μ(20)。取值范围：a>＝0.0001,a<＝1,k>＝1,k<＝1000000,μ>＝-20,μ<＝20。参数估值：a＝0.233,k＝9887.972,μ＝-15.519。结果分析如下：a的标准误为0.115，很低，此估值的置信度很高；k的标准误为192001.355，很高，此估值的置信度很低；μ的标准误为92.620，很高，此估值的置信度不高；确定性系数R 2＝1-(残差平方和)/(校正平方和)＝0.932，拟合度很高。
变形速度表达式为：
Figure PCTCN2019095719-appb-000011
变形量表达式为：
Figure PCTCN2019095719-appb-000012
在此模型下，当t＝0时，巷道两帮有最大移近速度58.754mm/d；令巷道两帮的移近速度v＝1mm/d，得到t＝17.706，即从第18天开始，巷道开始进入稳定变形阶段，此时巷道两帮的移近量已经达到了255.110mm，令t→∞，得到由掘进影响的巷道两帮的最大移近量为259.956mm。
(4)正态分布函数类型
其变形速度模型为：
Figure PCTCN2019095719-appb-000013
由前面的指数函数模型和Logistic函数模型的拟合结果可知，他们的确定度R 2都很高，并且二者对巷道变形速度和变形量等重要问题的分析基本相同，由于正态分布函数模型的参数值都有特殊的含义，对于分析巷道矿压变化规律至关重要。由于指数函数模型和Logistic函数模型的拟合结果较为可信，所以此处通过利用Logistic函数表示的巷道变形速度的数据作为正态分布函数模型回归所需的数据，来求解正态分布模型的参数值。
用SPSS进行非线性回归，另
Figure PCTCN2019095719-appb-000014
SPSS中输入模型的表达式为：A*2.7183**(-(t-μ)**2/B)，初始值设为A(50),B(100),μ(0)。取值范围：A>＝1,A<＝10000,B>＝1,k<＝1000000,μ>＝-500,μ<＝0。参数估值：A＝766.365,B＝265.203, μ＝-26.214。结果分析如下：a的标准误为539.806，很高，此估值的置信度很低；B的标准误为56.015，较高，此估值的置信度较低；μ的标准误为6.299，较高，此估值的置信度较低；确定性系数R 2＝1-(残差平方和)/(校正平方和)＝0.931，拟合度很高。得到σ＝11.515，k＝22120.2249，μ＝-26.214
变形速度表达式为：
Figure PCTCN2019095719-appb-000015
将上式作标准正态分布的变换：
Figure PCTCN2019095719-appb-000016
借助标准正态分布积分表可求得掘进影响阶段不同时间的巷道变形量，。
Figure PCTCN2019095719-appb-000017
在此表达式下，当t＝0时，巷道两帮的移近速度为57.428mm/d。根据3σ原则，μ+3σ＝8.31，即在第13天开始，掘进影响阶段的巷道变形基本完成；令巷道两帮的移近速度v＝1mm/d，得到t＝15.755，即从第16天开始，巷道开始进入稳定变形阶段，此时的变形量为245.53mm,掘进影响下的巷道两帮的最大移近量为249.96mm。将上述公式化为标准正态分布公式，可以利用正态分布积分表得到巷道各时间的移近量。
4)预测结果分析
表2为基安达矿110102运输顺槽两帮移近速度表，图4为基安达矿110102运输顺槽两帮移近速度图，表3为基安达矿110102运输顺槽两帮移近量，图5为基安达矿110102运输顺槽两帮移近量图。
表2基安达矿110102运输顺槽两帮移近速度表
Figure PCTCN2019095719-appb-000018
Figure PCTCN2019095719-appb-000019
表3基安达矿110102运输顺槽两帮移近量
Figure PCTCN2019095719-appb-000020
通过对基安达矿110102运输顺槽巷道宽度随时间的变形规律的分析，进一步说明了数学模型在巷道矿压观测中的有效性，并且通过各种模型的比较，可知指数函数模型和Logistic函数模型这两种模型的可信度较高，其确定性系数分别达到了0.934和0.932，而复合函数的可信度最低，仅为0.914。而正态分布函数在掘进巷道的矿压观测中的作用并不明显。通过数学模型对短期内巷道矿压观测数据的分析得到了巷道的变形速度和变形量随时间的变化规律，这不仅对巷道矿压观测方法本身有积极地改进作用，而且对于研究巷道矿压的变化规律也有 了更加科学的方法。基安达矿110102运输顺槽巷道两帮变形分析结果如表4所示。
在观测结果中可知，基安达矿110102运输顺槽在掘进影响阶段从开始变形到最终稳定需要18天的时间，而在实际观测中只是进行了连续3天的观测，其得到矿压观测结果的效率比进行常规观测提高了83.3％，极大地缩短了得到矿压变规律所需的时间。
表4基安达矿110102运输顺槽巷道两帮变形分析结果
Figure PCTCN2019095719-appb-000021
第二个实施例：
1)测量仪器的选择
本实施例通过采集巷道两帮的位移数据来预测其巷道两帮矿压显现的全过程，采用的测量仪器可以为激光测距仪、钢卷尺等距离测量仪器。
2)测站的布置
进行巷道矿压观测期间，友众矿30104回采工作面的日进尺为5m/d。监测长度从留巷起始点为30102切眼处沿工作面推进方向至120m，共7组。具体表面位移测站布置如图6所示；表5为友众矿30102运输顺槽1～7#测站位置表；
表5友众矿30102运输顺槽1～7#测站位置表
Figure PCTCN2019095719-appb-000022
3)巷道两帮位移的记录结果
用7天的时间共进行5次观测记录，矿压观测记录结果如表6所示。
表6友众矿30102运输顺槽矿压观测记录表
Figure PCTCN2019095719-appb-000023
4)建立数学模型，并用不同数学模型进行预测
根据建立的采动影响阶段的两种模型表示的距离回采工作面任意距离间的巷道变形量u Δ的模型，利用SPSS软件进行非线性回归分析。
(1)Logistic函数类型
其变形速度模型为：
Figure PCTCN2019095719-appb-000024
可知，在回采工作面推进过程中，任意巷道断面从距离工作面X 1处到X 2处，其变形量为：
Figure PCTCN2019095719-appb-000025
SPSS中输入模型的表达式为：k*(1/(1+exp(-a*(X 2-μ)))-1/(1+exp(-a*(X 1-μ))))，初始值设为k(100),a(0.1),μ(0)，取值范围：k>＝0.0001,k<＝4000，a>＝0.0001,a<＝0.9999，μ>＝-200,μ<＝200；参数估值：k＝739.304,a＝0.087,μ＝27.503。结果分析如下：k的标准误为65.829，很大，此估值的置信度不高；a的标准误为0.010，很低，说明此估值的置信度很高；μ的标准误为1.086，较低，说明此估值的置信度较高；确定性系数R 2＝1-(残差平方和)/(校正平方和)＝0.718，拟合度较高。
初次采动期间巷道的变形量与工作面距离之间的函数关系假设为：
Figure PCTCN2019095719-appb-000026
初次采动期间巷道的变形速度与工作面距离之间的函数关系
Figure PCTCN2019095719-appb-000027
在此模型下，当x＝27.503时，即回采工作面后方27.503m附近处，巷道两帮有最大移近速度16.080mm/d；巷道的变形主要发生在区间(-20,75)，即工作面前方20m至工作面后方75m之间；在回采工作面后方100m处，巷道变形基本完成，巷道两帮的最终移近量可以达到739mm。
(2)正态分布函数类型
因为正态分布函数本身属于超越函数，不能直接积分来得到确定的式子，所以他不能像其他模型那样通过一定时间内的巷道变形量来进行拟合。此处通过利用Logistic函数表示的巷道变形速度的数据作为正态分布函数模型回归所需的数据，来求解正态分布模型的参数值。
用SPSS进行非线性回归，得到
Figure PCTCN2019095719-appb-000028
其标准误差为0.149，很低，此估值的置信度很高；2σ 2＝670.618,其标准误差为14.743，较低，此估值的置信度较高；μ＝27.503，其标准误差为0.201，很低，此估值的置信度很高；σ＝18.311；k＝716.527。确定性系数R 2＝1-(残差平方和)/(校正平方和)＝0.716，拟合度较高。
变形速度表达式为：
Figure PCTCN2019095719-appb-000029
借助标准正态分布积分表可求得掘进影响阶段不同时间的巷道变形量。
Figure PCTCN2019095719-appb-000030
采动影响阶段巷道受影响的范围为(μ-3σ，μ+3σ)，即(-27.43，82.436)，回采工作面前方27.43m处巷道开始变形，工作面后方82.436m处受采动影响的巷道开始稳定，巷道两帮达 到最大移近量716.527mm。在x＝0，即回采工作面附近，回采巷道的两帮的移近速度约为4.93mm/d；在x＝μ＝27.503处，回采巷道两帮有最大移近速度15.611mm。
5)预测结果分析
友众矿30102运输顺槽两帮变形的预测结果如表7和图7所示。
表7友众矿30102运输顺槽两帮变形的预测结果:
Figure PCTCN2019095719-appb-000031
根据观测结果可以计算得到，友众矿30102运输顺槽采动影响的持续时间为22天，但是通过利用矿压观测数据预测，只进行了7天的观测，效率得到了很大的提高。
虽然本发明的实施例中是以巷道两帮的位移为例，但是本发明的技术方案对于回采巷道矿压显现的其他种类的观测数据，如顶板离层量、顶板下沉量、岩层深部的位移量、巷道底鼓量、锚杆载荷、锚索载荷等都同样适用，这是因为本发明的技术方案中概括的数学模型的特点对他们都是适用的。虽然在实际观测中，观测到的数据并不是完全符合本技术方案限定 的数学模型及对应的图像，但是其实际矿压显现数据点是围绕其回归图像上下波动的；换言之，在本发明限定的数学模型所涵盖的函数式中总会至少有一个可以比较准确的反映实际的矿压显现特征。
如图8所示，掘进影响阶段，所述预测模型v(x)所包含的曲线族中的曲线在掘进影响阶段均为凹曲线，所对应的函数为减函数；所述预测模型u(x)所包含的曲线族中的曲线在掘进影响阶段均为先陡后缓的凸曲线，所对应的函数均为增函数。
如图9所示，在采动影响阶段，所述预测模型v(x)所包含的曲线族中的曲线在采动影响阶段均为中间凸两边凹的钟形曲线，所对应的函数均先增大后减小；所述预测模型u(x)所包含的曲线族中的曲线在采动影响阶段均为S型增长曲线。
本发明中的两个实施例只是对本发明技术方案的简单的运用，其测站的布置方式和观测频率的设定并不是根据本发明的技术方案作出的最佳设计，在测站数量增加、观测频率增加、利用精度更高的测量仪器的情况下，则利用本发明的技术方案可以达到更快、更准确的预测回采巷道矿压的显现特征。

Claims (10)

  1. 一种回采巷道矿压显现数据的预测方法，其特征在于：所述预测方法为先建立任意回采巷道断面矿压显现数据的变化速度的预测模型，将其积分后得到矿压显现数据在一定区间的预测变化量模型；通过多次采集不同测站在相对于工作面不同位置时的矿压显现数据，得到一系列的矿压显现数据的实际差值；利用矿压显现数据一定区间的预测变化量模型对一系列的矿压显现数据的实际差值进行非线性回归，即可确定回采巷道矿压显现数据的变化速度和累积变化量的函数表达式。
  2. 根据权利要求1所述的回采巷道矿压显现数据的预测方法，其特征在于：所述预测方法包括以下步骤：
    1)建立同一巷道断面矿压显现数据的预测变化量模型
    在回采巷道中，任意巷道断面的矿压显现数据的变化速度的预测模型为v(x)，矿压显现数据的累积变化量的预测模型为
    Figure PCTCN2019095719-appb-100001
    矿压显现数据的预测变化量模型为
    Figure PCTCN2019095719-appb-100002
    任意巷道断面经历的变形时间为
    Figure PCTCN2019095719-appb-100003
    所述矿压显现数据为与回采巷道矿压显现相关的力学、位移数据，包括回采巷道的顶板离层量、顶板下沉量、底鼓量、两帮移近量、深部岩层的位移量、锚杆载荷量和锚索载荷量；
    其中，v为矿压显现数据的变化速度，u为矿压显现数据的累积变化量；x为在工作面的推进方向上，巷道断面相对于工作面的距离，当巷道断面在工作面前方时x<0，当巷道断面在工作面后方时x＞0；所述工作面在掘进影响阶段是指掘进工作面，在采动影响阶段是指回采工作面；x 0为任意巷道断面开始变形时相对于工作面的距离；x m与x n表示巷道断面相对于工作面的不同距离，且x m<x n；L为工作面的日进尺；
    2)通过矿压观测得到一系列的矿压显现数据的实际差值
    在同一回采巷道内同时布置多个测站来采集矿压显现数据；随着工作面的推进，同一测站在相对于工作面不同位置时采集到的两次矿压显现数据的实际差值为Δu(x i,j,x i,j+1)＝|U i,j-U i,j+1|，对多个测站多次采集矿压显现数据后，得到一系列的矿压显现数据的实际差值Δu(x 1,j,x 1,j+1)、Δu(x 2,j,x 2,j+1)、……、Δu(x i,j,x i,j+1)；
    其中，x i,j为对第i个测站进行第j次矿压观测时，该测站相对于工作面的距离；U i,j为当第i个测站相对于工作面的距离为x i,j时采集到的矿压显现数据值；
    3)通过非线性回归预测回采巷道的矿压显现数据
    利用矿压显现数据的预测变化量模型
    Figure PCTCN2019095719-appb-100004
    对一系列的矿压显现数据的实际差值Δu(x 1,j,x 1,j+1)、Δu(x 2,j,x 2,j+1)、……、Δu(x i,j,x i,j+1)进行非线性回归，得到预测模型v(x)和u(x)的参数，即确定出回采巷道矿压显现数据的变化速度和累积变化量的函数表达式，同时得到相对于工作面任意距离的巷道断面的矿压显现数据的变化速度和累积变化量，及矿压显现影响的范围和持续的时间。
  3. 根据权利要求2所述的回采巷道矿压显现数据的预测方法，其特征在于：所述预测模型v(x)所包含的曲线族中的曲线在掘进影响阶段均为凹曲线，所对应的函数为减函数；所述预测模型u(x)所包含的曲线族中的曲线在掘进影响阶段均为先陡后缓的凸曲线，所对应的函数均为增函数；
    所述预测模型v(x)所包含的曲线族中的曲线在采动影响阶段均为中间凸两边凹的钟形曲线，所对应的函数均先增大后减小；所述预测模型u(x)所包含的曲线族中的曲线在采动影响阶段均为S型增长曲线。
  4. 根据权利要求2所述的回采巷道矿压显现数据的预测方法，其特征在于：所述预测模型v(x)在掘进影响阶段表示为
    v(x)＝ae -bx  (1)
    式中a为矿压显现数据的最大变化速度；a、b为待定参数；e为自然常数；并且，a>0,0<b<1。
  5. 根据权利要求2所述的回采巷道矿压显现数据的预测方法，其特征在于：所述预测模型v(x)在掘进影响阶段表示为
    v(x)＝ac x  (2)
    式中a为矿压显现数据的最大变化速度；a、c为待定参数，其中a>0,0<c<1。
  6. 根据权利要求2所述的回采巷道矿压显现数据的预测方法，其特征在于：所述预测模型v(x)在掘进影响阶段和采动影响阶段都表示为
    Figure PCTCN2019095719-appb-100005
    式中k、d、μ均为待定参数，k>0,0<d<1,-1000<μ<1000。
  7. 根据权利要求1所述的回采巷道矿压显现数据的预测方法，其特征在于：所述测站 布置在工作面后方100m范围内；在采动影响阶段，所述测站同时布置在回采工作面前方50m范围内。
  8. 根据权利要求1所述的回采巷道矿压显现数据的预测方法，其特征在于：所述矿压显现数据采集的越多，则越能准确的预测回采巷道的矿压显现特征；所述测站布置的越密集，矿压显现数据采集的时间越短。
  9. 根据权利要求1所述的回采巷道矿压显现数据的预测方法，其特征在于：所述非线性回归利用具有非线性回归功能的数值分析软件完成，所述数值分析软件包括ORIGIN、MATLAB、EXCEL和SPSS。
  10. 根据权利要求1所述的回采巷道矿压显现数据的预测方法，其特征在于：所述回采巷道的围岩性质相同、矿压控制方式一致、工作面的日进尺不变。
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