RU2751991C1 - Method for predicting data on pressure changes in surrounding rocks of stope area - Google Patents

Abstract

FIELD: computer technology.

SUBSTANCE: invention relates to computer technology. The method for predicting the pressure change data of the surrounding rocks of the stope area includes creating a prediction model for the rate of change of the pressure change data of the surrounding rocks for any section of the stope area and integrating it to obtain a predictive variation model of the pressure change data of the surrounding rocks in a certain interval; multiple acquisition of a series of actual differences in ambient rock pressure data by collecting ambient rock pressure change data from different observation stations at different positions relative to the working face and using a predictive variation model of ambient rock pressure change data in a certain interval to perform a nonlinear regression on a series of actual differences in ambient rock pressure change data to determine the functional expression of the rate of change and the accumulated spread of the surrounding rock pressure change data.

EFFECT: providing a reliable prediction of the characteristics of the pressure change of the surrounding rocks of the stope area in a short time.

10 cl, 9 dwg, 7 tbl

Description

The present invention relates to a method for predicting pressure changes in the surrounding rock formation in a working and relates to the field of monitoring rocks surrounding a mine.

State of the art

Among the three types of coal mine workings, including development workings, preparatory workings and sewage workings, workings have a shorter service life, therefore, the design of their supports should only satisfy their intended use during the cleaning period. Consequently, the change in the pressure of the surrounding rocks of the production opening during excavation and cleaning is more intense than for the other two types of workings. Measurement of the deformation convergence of the surface of the working mine is one of the mandatory measuring operations during the excavation and cleaning of the working mine and is performed in the following main way: immediately after the excavation of the coal rock mass, some control points remain on two walls and the roof of the working as control measurement points; To measure small changes in the distance between any control points over a specific period of time, a test tool is used to calculate the convergence deformation and deformation rate of the roof and bottom, as well as the two walls of the working, which are used to assess engineering stability and guide the design of supports.

Observing the convergence of the surrounding rock of the working face, which includes subsidence of the roof of the working, bulging of the bottom, convergence of the walls of the working, convergence of the deep surrounding rock and the rest of the area of the mine, etc. is the most frequently used and most widely used method of monitoring the pressure of the surrounding rock of a mine working in mines at home and abroad. Among them, the most widely used and most basic method of locating measurement points for observing the convergence is the method of "cruciform" arrangement of measurement points, ie, the change and development of convergence of the roof, base and two walls over time is observed after the excavation of the working development. If the working section is large and a complex deformation process under load needs to be analyzed and studied, a deformation observation method with several peripheral measurement points is also used. The working development is located on the side of the working face front. For a significant period of time during the life of the working face, the working face is influenced by the production at the face of the working face and the stresses in it demonstrate an obvious asymmetry, therefore, the process of deformation development in it also has an obvious asymmetry. To study the deformation and its control characteristics, methods of "艹 -shaped" and "pit-like" arrangement of measurement points in the form are often used.

With regard to monitoring the pressure of the surrounding rocks of the working mine, in domestic and foreign mines, a method is mainly used that involves the use of stationary observation stations for long-term monitoring, which takes more time and is ineffective in predicting emergencies in the working workings associated with the pressure of the surrounding rocks.

Currently, there are mainly three types of widely used methods for studying the characteristics of pressure changes in the surrounding rocks of the working workings: in the first method, long-term field observation is carried out in the process of changing the pressure of the surrounding rocks of the working workings, for example, they observe the convergence of the roof and bottom, the load on the support and the settlement of the column (movable columns), which are generally referred to as "three quantities"; the second is a finite difference method (FDM), finite element method (FEM), boundary element method (BEM), discrete element method (DEM), Lagrange element method, digital differential analyzer interpolation method (DDA ), the method of polynomial elements (MEM), the method without elements and their mixed applications, as well as other methods of numerical modeling. ^{For example, the software FLAC 3D} , 3DEC or ANSYS is used to simulate the stress distribution law and the characteristics of the displacement distribution of the working; the third method is a method of research by experimental modeling of a similar material, which consists in creating in the laboratory in accordance with the principle of similarity of the model, similar to the prototype of a working mine, using instrumentation to observe mechanical parameters in the model and their distribution dependencies, using the results of model research for obtaining a conclusion about the mechanical phenomena that can occur in the prototype of a stope, and about the law of pressure distribution in the rock mass in order to solve actual engineering problems in the rock mass.

Although the dependence for the pressure of the surrounding rocks, obtained from long-term observation of the production working, is the most reliable and accurate, the biggest disadvantage is that this takes a long time.

When the method of numerical analysis is used to study the characteristics of the pressure change of the surrounding formation formation, the physical and mechanical parameters of the surrounding formation formation are used, while the actual conditions of the production formation are often very complex and the generated model is difficult to accurately reflect the actual state of the production formation, and the resulting the results are also very different from the actual condition and are usually used as a reference only.

A similar material modeling experiment is more suitable for studying the characteristics of pressure changes in the surrounding formation rocks in specific conditions. This method is rarely used to analyze the characteristics of the pressure changes of the surrounding rocks and the changes in the dependencies in a standard production opening.

Disclosure of the essence of the invention

The objective of the invention is to eliminate the disadvantages of the existing methods for studying the characteristics of pressure changes in the surrounding rocks of the working development of the prior art, such as high time consumption, low efficiency, low accuracy, poor predictability effect and limited scope. The present invention provides a method for predicting pressure change data from surrounding formation formation to reliably predict pressure change characteristics of surrounding formation formation over a short period of time.

Technical solution: a method for predicting pressure change data of surrounding rocks of a working development according to the present invention includes creating a model for predicting the rate of change of data of changes in pressure of surrounding rocks in any part of a working opening, and integrating it to obtain a predictive variational model of data changing pressure of surrounding rocks in a certain interval; multiple acquisition of a series of actual differences in pressure change data from surrounding rocks by collecting data on pressure changes from surrounding rocks from different observation stations in different positions relative to the face of the working face; and using a predictive variation model of the surrounding pressure change data over a defined interval to perform nonlinear regression on a series of actual differences in the surrounding rock pressure data to determine a functional expression for the rate of change and the cumulative scatter of the face pressure change data of the longwall.

The method for predicting pressure change data of the surrounding formation formation according to the present invention includes the following steps:

, и прогнозная вариационная модель данных изменения давления окружающих пород имеет вид

; время деформации, происходящей на любом участке выработки представляет собой

; данные изменения давления окружающих пород представляют собой данные по механике и смещению, относящиеся к изменению давления окружающих пород очистной выработки, включающие отделение кровли, проседание кровли, выпучивание подошвы, сближение двух стен, смещение глубокой формации породы, нагрузку на анкерные стержни и анкерные кабели очистной выработки;1) creation of a predictive variational model of the data on pressure changes of the surrounding rocks in the same section of the mine, and in the longwall, the model for predicting the rate of change in the pressure changes in the surrounding rocks at any section of the mine is v ( x ), the model for predicting the accumulated scatter of the pressure change data surrounding rocks looks like

, and the predictive variational model of the data on pressure changes in the surrounding rocks has the form

; the time of deformation occurring at any section of the mine is

; Surrounding pressure change data represent mechanical and displacement data related to the change in pressure of the surrounding rock formation, including roof separation, roof subsidence, bottom bulging, two-wall convergence, deep rock formation displacement, load on anchor rods and anchor cables of the longwall ;

where, v is the rate of change in the pressure change data of the surrounding rocks, u is the accumulated scatter of the data on the pressure change in the surrounding rocks; x is the distance from the working section to the working face front in the direction of the working face front advance, and when the working section is in front of the working face front, x < 0 , and when the working section is behind the working face front, x >0; the front of the working face at the stage of driving influence refers to the front of the working face during driving, and the front of the working face at the stage of influence of production refers to the front of the working face during cleaning; x ₀ - the distance from any section of the working to the front of the working face at the beginning of its deformation; x _m and x _n represent different distances from the mine section to the face of the working face, and x _m < x _n ; L is the daily drilling volume of the bottom hole,

2) obtaining a series of actual differences in the pressure change data of the surrounding rocks by observing the pressure of the surrounding rocks, and

, и после многократного сбора данных о изменении давления окружающих пород множеством станций наблюдения получают серию фактических отличий данных изменения давления окружающих пород

,

, …,

;to collect data on changes in the pressure of the surrounding rocks, a plurality of observation stations are placed in the same working development; As the face of the working face advances, the actual difference in the data on changes in the pressure of the surrounding rocks collected by one observation station from different positions relative to the face of the working face in two times is

, and after multiple collection of data on changes in the pressure of the surrounding rocks by many observation stations, a series of actual differences in the data of changes in the pressure of the surrounding rocks is obtained

,

, ...,

;

;where, U _xi - the value of the data on the change in the pressure of the surrounding rocks, collected by the observation station, when the distance to the front of the working face is x _i ; when the distance from the working section to the face front is x _i , the time of deformation impact on the working section is

;

3) predicting the data on pressure changes in the surrounding rocks of the working development by means of nonlinear regression, and

данных изменения давления окружающих пород используют для выполнения нелинейной регрессии на серии фактических отличий данных

,

, …,

изменения давления окружающих пород для получения параметров v(x) и u(x) прогнозных моделей, т.е. определения функционального выражения скорости изменения и накопленного разброса данных изменения давления окружающих пород очистной выработки, и в то же время получения скорости изменения и накопленного разброса данных изменения давления окружающих пород на участке выработки на любом расстоянии от фронта очистного забоя, а также диапазона влияния и продолжительности изменения давления окружающих пород.predictive variation model

pressure change data from surrounding rocks is used to perform nonlinear regression on a series of actual data differences

,

, ...,

changes in the pressure of the surrounding rocks to obtain the parameters v ( x ) and u ( x ) of the predictive models, i.e. determining the functional expression of the rate of change and the accumulated spread of the pressure change data of the surrounding rocks of the working face, and at the same time obtaining the rate of change and the accumulated spread of the data of the pressure change of the surrounding rocks in the working section at any distance from the face of the working face, as well as the range of influence and duration of the change the pressure of the surrounding rocks.

The advantage is that since the present invention uses a method of locating observing stations with a concentrated arrangement of observing stations at the stage of collecting data on pressure changes in the surrounding rocks, the collection of data on pressure changes in surrounding rocks can be completed in a short time (2 ~ 7 days). A variational model of the surrounding rock pressure data is used to perform non-linear regression on a series of actual differences in the surrounding rock pressure data obtained by observing the surrounding rock pressure, and the characteristics of the pressure change of the surrounding rock of a particular production opening can be predicted in a short time with high accuracy. Alternatively, the previous pressure change data from the surrounding rocks can be used to predict the final results and the overall pressure change in the surrounding rocks.

As a further clarification, all the curves in the family of curves contained in the forecasting model v ( x ) are concave curves during the production influence stage, and the corresponding functions are decreasing functions; all curves in the family of curves contained in the prediction model u ( x ) are convex curves, which are steep at first and then flat at the stage of driving influence, and all the corresponding functions are increasing functions.

As a further clarification, all the curves in the family of curves contained in the prediction model v ( x ) are bell-shaped curves that are convex in the middle and concave on both sides during the production influence stage, and all the corresponding functions first increase and then decrease; all curves in the family of curves contained in the prediction model u ( x ) are S-shaped increasing curves during the production impact stage.

The advantage is that the basic characteristics of the v ( x ) and u ( x ) prediction models are obtained by summing the general characteristics of the change in the pressure variation data of the surrounding rocks at the penetration stage and the production impact stage to provide recommendations for creating specific mathematical models.

There are many mathematical models that satisfy the above requirements and may not be exhaustive, but the following three mathematical models are preferred for the present invention:

As an additional clarification, the forecasting model v ( x ) at the stage of penetration influence is expressed in the following form

where a is the maximum rate of change in the data of changes in the pressure of the surrounding rocks; a and b are parameters to be defined; e is the base of the natural logarithm; and a > 0, 0 < b <1.

As an additional clarification, the forecasting model v (x) at the stage of penetration influence is expressed in the following form

where a is the maximum rate of change in the data of changes in the pressure of the surrounding rocks; a and c are parameters to be defined and a > 0, 0 < c <1.

As an additional clarification, the forecasting model v (x) at the stage of penetration impact and the stage of production impact is expressed in the following form

where k , d and μ are parameters to be determined, k > 0, 0 < d <1, -1000 <μ <1000.

As an additional improvement, observation stations are located within 100 m of the face of the working face; at the stage of the influence of production, observation stations are also placed within 50 m in front of the front of the working face.

The advantage is that for different workings, the ranges of changes in the pressure characteristics of the surrounding rocks in the workings are different, therefore, the placement of observation stations in accordance with the range of characteristics of changes in the pressure of the surrounding rocks of the workings will increase the efficiency of collecting data on changes in the pressure of the surrounding rocks, and determining the range of the location of observation stations can serve as reference information for monitoring the pressure of the surrounding rocks.

As a further clarification, by collecting more data on the pressure variation of the surrounding formation, the pressure variation characteristics of the surrounding formation formation can be predicted more accurately; with a denser arrangement of observation stations, the time required to collect data on changes in the pressure of the surrounding rocks will be reduced.

The advantage is that the large distance between adjacent observation stations does not facilitate the rapid collection of data on pressure changes in the surrounding rocks, and the tighter location of the observation stations helps to improve the accuracy of the forecast.

Nonlinear regression is performed using nonlinear regression numerical analysis software including ORIGIN, MATLAB, EXCEL, and SPSS.

There are many types of nonlinear regression numerical analysis software that are not comprehensive. Above are just some of the popular types.

As a further clarification, the production face has the same properties as the surrounding formation, a proper way to control the pressure of the surrounding rocks, and a constant daily drilling volume of the face front.

The advantage lies in the fact that the factors affecting the characteristics of the pressure change of the surrounding rocks may differ in different zones of the same production working, and if one type of characteristic of the pressure change of the surrounding rocks is obtained for the rocks surrounding the working under different conditions, then this characteristic is only within the limits of their actual conditions, and differ from the expression of the characteristic of the change in the pressure of the surrounding rocks of the working under the same conditions, therefore, the characteristic of the change in the pressure of the surrounding rocks of the working under these conditions can be obtained only if none of the observation stations is located in the range , in which the factors influencing the change in the pressure of the surrounding rocks of the working development, essentially remain unchanged.

The characteristic of the pressure variation of the surrounding rocks is the result of the analysis to which those skilled in the art pay key attention when studying the characteristic of the pressure variation of the surrounding formation of the production opening.

Advantageous Effect of the Present Invention: The method for predicting the pressure response of the surrounding formation formation in the present invention can be used to quickly and efficiently obtain the pressure variation characteristic of the surrounding formation formation, and has a wide range of applications. The specific benefits are as follows:

(1) It usually takes two to three days to complete the stage of observing the pressure of the surrounding rocks in predicting the characteristics of pressure changes in the surrounding rocks of a production working. Compared to long-term observation, the accuracy is somewhat low, but the efficiency is significantly improved;

(2) The method for predicting the pressure variation characteristics of the surrounding formation formation uses numerical analysis software such as SPSS, MATLAB and ORINGIN, but the data used is actually obtained, therefore the pressure variation characteristics of the surrounding formation formation displayed by it are closer to the actual state than in numerical modeling;

(3) The use of partial pressure change data from surrounding rocks to predict all the characteristics of pressure changes from the surrounding rock face of a face is an early warning of emergencies caused by the pressure of the surrounding rock and contributes to safe and efficient coal mining.

Brief Description of Drawings

FIG. 1 is a schematic view of the location of observation stations in the excavation road 110102 of the Jianda mine;

FIG. 2 is a schematic view of a "cross-shaped arrangement of measurement points" for measuring the approach of the working surface;

FIG. 3 shows a graph of the deformation of the excavation heading 110102 of the Jyanda mine every 2 days;

FIG. 4 shows the speed of convergence of the two walls of the Gyanda shaft 110102;

FIG. 5 shows the magnitude of the convergence of the two walls of the Gyanda shaft 110102;

FIG. 6 is a diagram of the location of the surface displacement monitoring stations in the excavation road 30102 of the Yuzhong mine;

FIG. 7 shows the results of predicting the deformation of the two walls of the excavation road 30102 of the Yuzhong mine.

FIG. 8 is a graph of the velocity v curve and the cumulative spread u of the predicted pressure variation data of the surrounding rocks as it changes with time t during the penetration influence stage;

FIG. 9 is a graph of the velocity curve v and the cumulative spread u of the predicted pressure variation data of the surrounding rocks as it changes along the x coordinate during the production influence stage.

Implementation of the invention

The technical solution of the present invention will be further described in detail with specific examples with reference to the accompanying drawings. Two examples are used to describe the present invention. First, as an example, we take as an example the forecast of the characteristics of changes in the pressure of the surrounding rocks of the excavation of the extraction heading 110102 of the Jiangda mine in Shouyang, Shanxi, at the stage of penetration influence; Then, as an example, a prediction of the characteristics of pressure changes in the surrounding rocks of the development of excavation road 30102 of the Yuzhong mine of the Shanxi Group, Duanwang, at the stage of mining influence is taken.

Example 1:

1) Location of observation stations

In this example, the entire process of changing the pressure of the surrounding rocks of the two walls of the working is predicted by collecting data on the displacement of the two walls of the working. A laser rangefinder, steel measuring tape, and other rangefinder instruments can be used as measuring instruments.

While observing the pressure of the surrounding rock face 110102 of the Jianda mine in Shouyang, Shanxi, the face advanced 450 meters. Due to the presence of tunneling machines, lining materials and uncleaned coal near the sinking of the face of the face, the observation stations could not be located on the mine section where the new excavation was made for observations. For the above reason, the first observation station was located closest to the face. As a rule, the pressure of the surrounding workings varies most intensively within 50 m behind the face front, and changes relatively weakly after 50 m.Thus, six observation stations (0 ~ 5) were placed within 50 m from the face front, and beyond this four observation stations were deployed at 25 m intervals, and then one observation station was placed at a distance of 50 m. Within 200 m behind the face, 11 observation stations were located, as shown in FIG. one.

Location of measurement points and observation stations: monitoring the approaching of the working surface includes monitoring the subsidence of the roof, buckling of the base and the convergence of two walls. Measuring tools: The tools were selected according to the size of the roadway and the requirements for the accuracy of the displacement test results. The measurement points were located "by the method of crosswise arrangement of measurement points", and the amount of convergence of the roof and base, as well as two walls of the working, was observed every day. The arrangement of the measurement points is shown in FIG. 2.

2) Registration of the results of the displacement of two walls of the working

On the first day, observation stations were installed and the dimensions of the working section were taken, and on the third day the dimensions of this working section were taken. The log of the opening width measurements and the amount of deformation are shown in Table 1. A histogram showing the variation in the opening width of the Jyanda shaft 110102 at different time periods is shown in FIG. 3.

Table 1. Protocol for registering the width of the excavation of the excavation road 110102 of the Jianda mine P / p No. 1 day. Distance from the face, m Time of the first equivalent deformation / day Working width AB / mm 3 days. Distance from the face, m Time of the second equivalent deformation / day Working width AB, mm Deformation value u _Δ for 2 days, mm 0 0 0 4638 twenty 2 4534 104 one 10 one 4557 thirty 3 4492 65 2 twenty 2 4500 40 4 4440 60 3 thirty 3 4380 fifty five 4323 57 4 40 4 4573 60 6 4539 34 five fifty five 4520 70 7 4500 twenty 6 75 7.5 4680 95 9.5 4661 nineteen 7 100 10 4627 120 12 4610 17 eight 125 12.5 4435 145 14.5 4420 fifteen nine 150 fifteen 4500 170 17 4488 12 10 200 twenty 4523 220 22 4520 3

3) Creation of mathematical models and forecasting using various mathematical models

Based on the deformation value u _{Δ of the} working in any period of time expressed by the four established mathematical models, and the two groups of the above data, including the first equivalent deformation time and the deformation value of the working width in 2 days, the SPSS software was used to conduct a nonlinear regression analysis. The analysis results are shown below:

(1) Types of exponential function

Its model for the strain rate is:

The expression of the input model in SPSS is: a / b * (exp (- b * t ) -exp (- b * (t + 2))). The initial value is set to a (1), b (0.999), and the range of values is a > = l, a <= 104, b > = 0.001, b <= 0.999. Parameter estimate: a = 59.193, b = 0.227. The analysis of the results is as follows: the standard error of a is 5.441, which is a very high value, suggesting that the confidence level of this estimate is low; the standard error of b is 0.031, which is a very low value, suggesting that the confidence level of this estimate is very high; the correlation between a and b is 0.806, which is a relatively high value; confidence factor R ² = 1 - (residual sum of squares) / (corrected sum of squares) = 0.934, indicating a very high degree of agreement.

The expression for the strain rate is:

The expression for the magnitude of the deformation is:

In this model, at t = 0, the maximum speed of convergence of the two walls of the working is 59.193 mm / day; let the speed of convergence of the two walls of the working is equal to v = 1 mm / day, then t = 17.977, in other words, starting from the 18th day, the working begins to pass into the stage of stable deformation, and by this moment the value of convergence of the two walls of the working has reached 256.357 mm; on the 22nd day, the value of the convergence of the two walls of the mine reaches 258.994; let t → ∞, the maximum value of the convergence of the two walls of the excavation under the influence of penetration is 260.762 mm.

(2) Composite function type

Its strain rate model is:

The amount of deformation of the working in 2 days is:

Regression is performed by curve estimation in SPSS. To obtain a / ln b * ( b ² -1) = 75.159, a composite function is selected, its standard error is 10.897, which is a very high value, suggesting that the confidence level of this estimate is low; b = 0.861, its standard error is 0.13, which is a very low value, suggesting that the confidence level of this estimate is very high; a = 43.484 is determined by calculations, the confidence factor R ² = l- (residual sum of squares) / (corrected sum of squares) = 0.914, which indicates a very high degree of agreement.

The expression for the strain rate is:

The strain magnitude formula is obtained by integrating the above formula and using the data "at t = 0, u = 0":

In this model, at t = 0, the maximum speed of convergence of two walls of the working is 43.484 mm / day; let the speed of convergence of two walls of the working v = 1 mm / day, then t = 25.206, in other words, starting from the 25th day, the working begins to pass into the stage of stable deformation, by this moment the value of convergence of the two walls of the working has reached 312.500 mm; let t → ∞, the maximum value of the convergence of the two walls of the working under the influence of penetration is 319.391 mm.

(3) Logistic function type

Its strain rate model is:

, начальное значение устанавливают равным a(1), k(1), μ(20). Диапазон значений: a > = 0,0001, a < = l, k > = l, k < = 1000000, μ > = -20, μ < = 20, Оценка параметра: a = 0,233, k = 9887,972, μ = -15,519. Анализ результатов выглядит следующим образом: стандартная ошибка a составляет 0,115, что является очень низким значением, позволяя предположить, что уровень достоверности этой оценки является очень высоким; стандартная ошибка k составляет 192 001,355, что является очень высоким значением, позволяя предположить, что уровень достоверности этой оценки является очень низким; стандартная ошибка μ равна 92,620, что является очень высоким значением, позволяя предположить, что уровень достоверности этой оценки является невысоким; коэффициент достоверности R² = l-(остаточная сумма квадратов)/(скорректированная сумма квадратов) = 0,932, что указывает на очень высокую степень соответствия.The input model expression in SPSS is:

, the initial value is set equal to a (1), k (1), μ (20). Range of values: a > = 0.0001, a <= l, k > = l, k <= 1000000, μ> = -20, μ <= 20, Parameter estimate: a = 0.233, k = 9887.972, μ = -15.519. The analysis of the results is as follows: the standard error of a is 0.115, which is a very low value, suggesting that the confidence level of this estimate is very high; the standard error of k is 192,001.355, which is a very high value, suggesting that the confidence level of this estimate is very low; the standard error of μ is 92.620, which is a very high value, suggesting that the level of confidence in this estimate is low; confidence factor R ² = l- (residual sum of squares) / (corrected sum of squares) = 0.932, indicating a very high degree of agreement.

The expression for the strain rate is:

The expression for the magnitude of the deformation is:

In this model, at t = 0, the maximum speed of convergence of the two walls of the working is 58.754 mm / day; let the speed of convergence of two walls of the working v = 1 mm / day, then t = 17.706, in other words, starting from the 18th day, the working begins to pass into the stage of stable deformation, by this moment the value of convergence of the two walls of the working reached 255.110 mm; let t → ∞, the maximum value of the convergence of the two walls of the excavation under the influence of penetration is 259.956 mm.

(4) Type of normal distribution function

Its strain rate model is:

From the results of the approximation of the above-mentioned exponential function model and the logistic function model, it can be learned that their degrees of confidence R ^{2 are} very high, and their analysis on important issues such as the rate of deformation of the working and the amount of deformation is essentially the same. Since all the values of the parameters of the normal distribution function model have a specific meaning, they are fundamentally important for analyzing the dependence of the pressure change in the surrounding rock formation. Since the results of the approximation of the exponential function model and the logistic function model are reliable, here the values of the parameters of the normal distribution model are solved using the data on the deformation rate of the development, expressed by the logistic function, as the data necessary for the regression of the normal distribution function model.

, B =

.SPSS is used for non-linear regression, let A =

, B =

...

The input model expression in SPSS is: A * 2.7183 ** (- (t-μ) ** 2 / B), the initial value is set to A (50), B (100), μ (0). Range of values: A> = 1, A <= 10,000, B > = 1, k <= 1,000,000, μ> = -500, μ <= 0, Parameter score: A = 766.365, B = 265.203, μ = -26.214 ... The analysis of the results is as follows: the standard error of a is 539.806, which is a very high value, suggesting that the confidence level of this estimate is very low; the B standard error is 56.015, which is a relatively high value, suggesting that the confidence level of this estimate is low; the standard error μ is 6.299, which is a relatively high value, suggesting that the confidence level of this estimate is low; confidence factor R ² = 1 - (residual sum of squares) / (corrected sum of squares) = 0.931, indicating a very high degree of agreement. We get σ = 11.515, k = 221202249, μ = -26.214

The expression for the strain rate is:

We transform the above formula for the standard normal distribution:

Using the table of integrals of the standard normal distribution, it is possible to calculate the amount of deformation of the roadway at various points in time during the stage of penetration influence.

According to this formula, at t = 0, the speed of convergence of the two walls of the working is 57.428 mm / day. According to the 3σ - principle, μ + 3σ = 8.31, in other words, starting from the 13th day, the deformation of the working at the stage of penetration influence essentially ends; let the speed of convergence of two walls of the working v = 1 mm / day, then t = 15.755, in other words, starting from the 16th day, the working begins to pass into the stage of stable deformation, and at this moment the value of deformation is 245.53 mm, and the maximum value the convergence of the two walls of the working under the influence of penetration is 249.96 mm. The above formula is converted to the standard normal distribution formula. The magnitude of the convergence of the development at different times can be obtained using the table of integrals of the normal distribution.

4) Analysis of forecast results

Table 2 shows the approach speed of the two walls of the Gyanda shaft 110102, FIG. 4 shows the rate of convergence of the two walls of the Gyanda shaft 110102, Table 3 shows the amount of convergence of the two walls of the Gyanda shaft 110102, and FIG. 5 shows the magnitude of the convergence of the two walls of the Gyanda shaft 110102.

Table 2. The speed of convergence of two walls of the excavation drift 110102 of the Jyanda mine Exponential function model, mm⋅day ^-1 Composite model, mm⋅day ^-1 Logistic function model, mm⋅day ^-1 Normal distribution function model, mm⋅day ^-1 59.19 43.48 58.75 57.43 47.17 37.44 47.05 46.95 37.59 32.24 37.60 38.09 29.96 27.75 29.99 30.68 23.87 23.90 23.89 24.52 19.03 20.58 19.01 19.45 15.16 17.72 15.11 15.31 12.08 15.25 12.00 11.96 9.63 13.13 9.53 9.28 7.67 11.31 7.56 7.14 6.12 9.74 6.00 5.46 4.87 8.38 4.76 4.14 3.88 7.22 3.77 3.11 3.10 6.21 2.99 2.32 2.47 5.35 2.37 1.72 1.97 4.61 1.88 1.27 1.57 3.97 1.49 0.93 1.25 3.41 1.18 0.67 0.99 2.94 0.93 0.48 0.79 2.53 0.74 0.34 0.63 2.18 0.59 0.24 0.50 1.88 0.46 0.17 0.40 1.62 0.37 0.12 0.32 1.39 0.29 0.08

Table 3. The magnitude of the convergence of the two walls of the excavation drift 110102 of the Jyanda mine Working deformation time, days Exponential function model Composite Model Logistic function model Normal distribution function model 0 0.00 0.00 0.00 0.00 one 52.96 40.39 52.68 48.66 2 95.16 75.16 94.84 92.90 3 128.79 105.10 128.49 128.30 4 155.59 130.88 155.32 152.63 five 176.95 153.07 176.67 174.75 6 193.97 172.18 193.66 192.45 7 207.53 188.63 207.15 205.72 eight 218.34 202.80 217.87 216.78 nine 226.96 215,00 226.37 225.63 10 233.82 225.50 233.12 232.26 eleven 239.29 234.54 238.47 236.69 12 243.65 242.33 242.72 238.90 13 247.13 249.03 246.08 243.32 fourteen 249.90 254.80 248.75 245.53 fifteen 252.10 259.77 250.86 245.53 sixteen 253.86 264.05 252.54 17 255.26 267.73 253.86 18 256.38 270.90 254.92 nineteen 257.27 273.63 255.75 twenty 257.98 275.99 256.41 21 258.54 278.01 256.93 22 258.99 279.75 257.35 23 259.35 281.25 257.67

An analysis of the dependences of the deformation of the roadway along the width of the excavation road 110102 of the Jianda mine over time additionally describes the effectiveness of mathematical models in observing the pressure of the surrounding rocks, and a comparison of different models shows that the exponential function model and the logistic function model have higher confidence levels, and their confidence coefficients reach 0.934 and 0.932, respectively, while the composite function has the lowest confidence level, which is only 0.914. The normal distribution function does not have an obvious effect when observing the pressure of the surrounding rock formation. Based on the analysis of observational data on the pressure of the surrounding rocks using a mathematical model, in a short time, the dependence of the change in the strain rate and the magnitude of the working strain in time is obtained. This not only demonstrates a powerful effect in modifying the way of observing the pressure of the surrounding rock formation, but also provides a more scientific method for studying the pressure dependences of the pressure of the surrounding rock formation. The result of the deformation analysis of the two walls of the roadway in the 110102 excavation road of the Jyanda mine is shown in Table 4.

From the results of observations, it can be learned that from the beginning of deformation to the final stabilization of the 110102 mining drift of the Jianda mine at the stage of penetration influence, 18 days passed, while in actual observation, observation was carried out only for 3 days in a row and the efficiency of obtaining the results of observation of the pressure of the surrounding rocks was increased by 83.3% compared to conventional observation, which significantly reduced the time required to obtain the dependence of the pressure change of the surrounding rocks.

Table 4. The result of the analysis of the deformation of two walls of the excavation of the excavation drift 110102 of the Jyanda mine Model type
Pos. Exponential function Composite function Logistic function Normal distribution function Confidence factor R ² 0.934 0.914 0.932 0.931 Maximum speed of convergence of two walls n _max , mm⋅day ^-1 59,193 43,484 58,754 57,428 Time of entering the stage of stable deformation / day 18 25 18 sixteen The magnitude of the convergence of two walls when entering the stage of stable deformation, mm 256.357 312,500 255,110 245.53 The final value of the convergence of two walls at the stage of penetration influence, mm 260,762 319,391 259,956 249.96

Example 2:

1) Selection of measuring instruments

In this example, the entire process of changing the pressure of the surrounding rocks of the two walls of the working is predicted by collecting data on the displacement of the two walls of the working. A laser rangefinder, steel measuring tape, and other rangefinder instruments can be used as measuring instruments.

2) Location of observation stations

During monitoring of the pressure of the surrounding rock formation, the daily drilling volume of the face 30104 front of the Yuzhong mine was 5 m / day. The reference length was 120 m along the movement of the face of the working face in the direction from open cut 30102, as the starting point of the rest of the working, and amounted to a total of 7 groups. The specific location of the surface displacement monitoring stations is shown in FIG. 6; Table 5 shows the positions of observation stations 1 ~ 7 in the pit 30102 of the Yuzhong mine;

Table 5. Positions of observation stations 1 ~ 7 in the excavation road 30102 of the Yuzhong mine No. of observation stations Distance from open cut 30102, m Initial distance from the face of the working face 30104, m one# 10 25 2 # thirty five 3 # fifty -fifteen 4# 70 -35 five# 90 -55 6 # 110 -75 7 # 130 -95

3) Registration of the results of the displacement of two walls of the working

The observation was recorded 5 times in 7 days. The results of recording the observation of the pressure of the surrounding rocks are shown in Table 6.

Table 6. The results of registration of observation of the pressure of the surrounding rocks of the extraction drift 30102 of the Yuzhong mine observation station 1 observation station 2 Observation time, days Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm one 25 4155 five 4350 2 thirty 4055 100 10 4300 fifty 3 34 3865 190 fourteen 4200 100 five 44.6 3718 147 24.6 4000 200 7 55.30 3620 98 35.3 3820 180 observation station 3 observation station 4 Observation time, days Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm one -fifteen 4625 -35 4485 2 -10 4615 10 -thirty 4485 0 3 -6 4583 32 -26 4485 0 five 4.6 4515 68 -15.4 4480 five 7 15.3 4410 105 -4.7 4460 twenty observation station 5 observation station 6 Observation time, days Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm one -55 4520 -75 4420 2 -fifty 4510 10 -70 4420 0 3 -46 4500 10 -66 4420 0 five -35.4 4500 0 -55.4 4420 0 7 -24.7 4500 0 -44.7 4420 0 observation station 7 Observation time, days Distance from the front of the working face 30104, m Overall width AB, mm Deformation value, mm one -95 4420 2 -90 4420 0 3 -86 4420 0 five -75.4 4420 0 7 -64.7 4420 0

4) Creation of mathematical models and forecasting using various mathematical models

Based on the deformation value u _{Δ of the} production between any distances from the face of the working face, expressed in the two generated models of the production influence stage, the SPSS software is used to perform nonlinear regression analysis.

(1) Logistic function type

Its strain rate model is:

It is known that in the process of moving the front of the working face, the amount of deformation between any sections of the working from the distance X ₁ to the distance X ₂ from the front of the face is:

The expression for the input model in SPSS is: k * (1 / (1 + exp (- a * (X ₂ -μ))) - 1 / (1 + exp (- a * (X _{1 -} μ)))), the initial value is set equal to k (100), a (0.1), μ (0), range of values: k > = 0.0001, k <= 4.000, a > = 0.0001, a <= 0.9999, μ> = -200, μ <= 200; parameter estimate: k = 739.304, a = 0.087, μ = 27.503. The analysis results are as follows: the standard error k is 65.829, which is a very high value, suggesting that the level of confidence in this estimate is low; the standard error of a is 0.010, which is a very low value, suggesting that the confidence level of this estimate is very high; the standard error μ is 1.086, which is a relatively low value, suggesting that the confidence level of this estimate is relatively high; confidence factor R ² = 1 - (residual sum of squares) / (corrected sum of squares) = 0.718, indicating a relatively high degree of agreement.

It is assumed that the functional relationship between the magnitude of the working deformation and the distance from the face in the initial production period is as follows:

The functional relationship between the rate of deformation of the working and the distance from the face in the initial period of production is as follows:

In this model, at x = 27.503, i.e., at a position at a distance of 27.503 m behind the front of the working face, the maximum speed of approach of the two walls of the working is 16.080 mm / day; working deformation is mainly manifested in the area (-20, 75), i.e., from 20 m ahead of the face front to 75 m behind the face front; at 100 m behind the face of the working face, the deformation of the working is essentially completed, the final value of the convergence of the two walls of the working can be up to 739 mm.

(2) Type of normal distribution function

Since the normal distribution function itself is a transcendental function and cannot be directly integrated to obtain a specific expression, it cannot be adjusted by the amount of deformation of the production over a specific period of time, as other models do. Here, the values of the parameters of the normal distribution model are obtained using the data of the deformation rate of the production, expressed by the logistic function, as the data necessary to obtain the regression in the model of the normal distribution function.

SPSS is used to perform non-linear regression to obtain k / (2π σ) = 15.611, its standard error is 0.149, which is a very low value, suggesting that the confidence level of this estimate is very high; 2σ ² = 670.618, its standard error is 14.743, which is a relatively low value, suggesting that the confidence level of this estimate is relatively high; μ = 27.503, its standard error is 0.201, which is a very low value, suggesting that the confidence level of this estimate is very high; σ = 18.311; k = 716.527. Confidence factor R ² = 1 - (residual sum of squares) / (corrected sum of squares) = 0.716, indicating a relatively high degree of agreement.

The expression for the strain rate is:

Using the table of integrals of the standard normal distribution, it is possible to calculate the amount of deformation of the roadway at different times during the stage of the penetration influence.

The range of influence on production at the stage of production influence is (μ-3σ, μ + 3σ), i.e. (-27.43, 82.436). The production starts to deform at 27.43 m ahead of the face of the working face, and the production under the influence of production begins to stabilize at 82.436 m behind the face of the face. The two walls of the mine reach a maximum convergence of 716.527 mm. At x = 0, i.e., near the front of the working face, the speed of convergence of the two walls of the working face is about 4.93 mm / day; with x = μ = 27.503, the maximum speed of convergence of two walls of the working mine is 15.611 mm.

5) Analysis of forecast results

The results of the prediction of the deformation of the two walls of the excavation road 30102 of the Yuzhong Mine are shown in Table 7 and in FIG. 7.

Table 7. The results of the prediction of the deformation of two walls of the excavation drift 30102 of the Yuzhong mine: Location relative to the front of the working face, m Logistic model Normal distribution model The speed of convergence of two walls, mm · day ^-1 The magnitude of the convergence of two walls, mm The speed of convergence of two walls, mm · day ^-1 The magnitude of the convergence of two walls, mm -35 0.28 3.20 0.05 0.00 -thirty 0.43 4.93 0.11 0.00 -25 0.65 7.60 0.26 1.50 -twenty 1.00 11.67 0.54 3.44 -fifteen 1.52 17.88 1.06 7.31 -10 2.28 27.26 1.92 14.47 -five 3.39 41.28 3.23 26.87 0 4.93 61.90 5.05 47.86 five 6.97 91.46 7.34 78.32 10 9.45 132.38 9.89 117.87 fifteen 12.13 186.33 12.36 178.06 twenty 14.48 253.11 14.35 244.26 25 15.89 329.56 15.47 318.35 thirty 15.89 409.65 15.47 398.17 35 14.48 486.10 14.36 472.26 40 12.13 552.90 12.37 538.61 45 9.46 606.87 9.89 598.66 fifty 6.98 647.80 7.34 638.21 55 4.94 677.38 5.06 668.66 60 3.39 698,00 3.23 689.01 65 2.29 712.03 1.92 702.05 70 1.52 721.42 1.06 709.22 75 1.00 727.63 0.54 713.09 80 0.65 731.70 0.26 715.02 85 0.43 734.37 0.11 716.53 90 0.28 736.10 0.05 716.53

Based on the observation results, it can be calculated that the impact of production of the 30102 mining road at Yuzhong mine lasts for 22 days, but when the observational data of the surrounding rock pressure is used for forecasting, the observation takes only 7 days, and the efficiency is significantly increased.

Although the present invention considers the displacement of two walls of a mine as examples, the technical solution of the present invention is also applicable to other types of observational data of pressure changes in the surrounding formation formation, such as roof separation, roof subsidence, deep formation displacement, bottom buckling, load on anchor rods and anchor cables. This is due to the fact that all the features of mathematical models, generalized in the technical solution according to the present invention, are applicable to them. In actual observation, the observed data do not fully match the mathematical models defined by this technical solution and associated images, but the actual data elements of the pressure change of the surrounding rocks fluctuate around the regression images; in other words, at least one of the function formulas encompassed by the mathematical models defined by the present invention can accurately represent the actual pressure variation characteristics of the surrounding rocks.

As shown in FIG. 8, in the driving influence stage, all the curves in the family of curves contained in the prediction model v (x) are concave curves, and the corresponding functions are decreasing functions; all curves in the family of curves contained in the prediction model u ( x ) are convex curves, which are steep at first and then flat at the stage of driving influence, and all the corresponding functions are increasing functions.

As shown in FIG. 9, at the production influence stage, all curves in the family of curves contained in the forecasting model v (x), are bell-shaped curves that are convex in the middle and concave on both sides, and all the corresponding functions first increase and then decrease; all curves in a family of curves contained in the modelu(x) forecasting, at the stage of production influence are S-shaped increasing curves.

The two examples in the present invention are only a simple application of the technical solution of the present invention. The purpose of the observation station placement method and the observation frequency is not the optimal plan provided according to the technical solution of the present invention. Subject to an increase in the number of observation stations and the frequency of observations and the use of more accurate measuring instruments, the technical solution of the present invention can be used to more quickly and accurately predict the characteristics of changes in the pressure of the surrounding rocks of the working excavation.

Claims (32)

1. A method for predicting data on changes in the pressure of the surrounding rocks of a working development, characterized in that said method includes: creation of a forecasting model for the rate of change in the pressure change data of the surrounding rocks for any section of the longwall and its integration to obtain a predictive variational model of the data on the change in the pressure of the surrounding rocks in a certain interval; multiple acquisition of a series of actual differences in pressure change data from surrounding rocks by collecting data on pressure changes from surrounding rocks from different observation stations in different positions relative to the face of the working face and the use of a predictive variational model of the surrounding rock pressure change data in a certain interval to perform nonlinear regression on a series of actual differences in the surrounding rock pressure change data to determine the functional expression of the rate of change and the accumulated scatter of the pressure change data of the surrounding rock face. 2. A method for predicting pressure change data of surrounding rocks of a working development according to claim 1, the forecasting method comprising the following steps: 1) the creation of a predictive variational model of data on changes in the pressure of the surrounding rocks in the same section of the mine, and in a longwall, the model for predicting the rate of change in the pressure change data of the surrounding rocks at any part of the working is v ( x ), the model for predicting the accumulated scatter of the pressure change data of the surrounding rocks has the form

, and the predictive variational model of the data on changes in the pressure of the surrounding rocks has the form

; the time of deformation occurring at any section of the mine is

; Surrounding pressure change data represent mechanical and displacement data related to the change in pressure of the surrounding rock formation, including roof separation, roof subsidence, bottom bulging, two-wall convergence, deep rock formation displacement, load on anchor rods and anchor cables of the longwall ; where v is the rate of change in the pressure change data of the surrounding rocks, u is the accumulated spread of the data on the pressure change in the surrounding rocks; x is the distance from the working section to the working face front in the direction of the working face front advance, moreover, when the working section is in front of the working face front, x <0, and when the working section is behind the working face front, x > 0, the working face is at the stage the influence of penetration refers to the front of the working face during excavation, and the front of the working face at the stage of influence of production refers to the front of the working face during cleaning; x ₀ - the distance from any section of the working to the front of the working face at the beginning of its deformation; x _m and x _n represent different distances from the mine section to the face of the working face, and

; L is the daily drilling volume of the bottom hole; 2) obtaining a series of actual differences in the pressure change data of the surrounding rocks by observing the pressure of the surrounding rocks, and to collect data on changes in the pressure of the surrounding rocks, a plurality of observation stations are simultaneously placed in one and the same longwall; As the face of the working face advances, the actual difference in the data on changes in the pressure of the surrounding rocks, collected by one observation station from different positions relative to the face of the working face in two times, is

, and after multiple collection of data on the pressure changes of the surrounding rocks by multiple observation stations, a series of actual differences in the data on the pressure changes of the surrounding rocks is obtained

,

, ...,

; where

- the distance from the observation stations to the face front during the j- th observation of the pressure of the surrounding rocks at the i- th observation station;

- the value of the data on the change in the pressure of the surrounding rocks, collected when the distance from the i- th observation station to the front of the face is

; 3) predicting the data on pressure changes in the surrounding rocks of the working development by means of nonlinear regression, and predictive variation model

pressure change data from surrounding rocks is used to perform nonlinear regression on a series of actual data differences

,

,….,

, changes in the pressure of the surrounding rocks to obtain the parameters v ( x ) and u ( x ) of the predictive models, i.e. determining the functional expression of the rate of change and the accumulated spread of the pressure change data of the surrounding rocks of the working face, and at the same time obtaining the rate of change and the accumulated spread of the data of the pressure change of the surrounding rocks in the working section at any distance from the face of the working face, as well as the range of influence and duration of the change the pressure of the surrounding rocks. 3. The method for predicting the pressure change data of the surrounding rocks of the working development according to claim 2, in which all the curves in the family of curves contained in the forecasting model v (x), at the influence stage, the penetrations are concave curves, and the corresponding functions are decreasing functions; all curves in a family of curves contained in the modelu(x) forecasting, at the stage of penetration influence, they are convex curves, which are initially steep and then flat, and all the corresponding functions are increasing functions; all curves in the family of curves contained in the forecasting model v (x) are bell-shaped curves in the production influence stage, which are convex in the middle and concave on both sides, and all the corresponding functions first increase and then decrease; all curves in the family of curves contained in the prediction model u ( x ) are S-shaped increasing curves during the production impact stage. 4. The method for predicting the pressure change data of the surrounding rocks of the production working according to claim 2, in which the forecasting model v (x) at the stage of penetration influence is expressed in the following form

where a is the maximum rate of change in the data of changes in the pressure of the surrounding rocks; a and b are parameters to be defined; e is the base of the natural logarithm; and a > 0, 0 < b <1. 5. The method for predicting the pressure change data of the surrounding rocks of the production working according to claim 2, in which the forecasting model v (x) at the stage of penetration impact expressed as follows

where a is the maximum rate of change in the data of changes in the pressure of the surrounding rocks; a and c are parameters to be defined and a > 0, 0 < c <1. 6. The method for predicting the pressure change data of the surrounding rocks of the production working according to claim 2, in which the forecasting model v (x) at the stage of penetration influence and the stage of production influence is expressed in the following form

where k , d and μ are parameters to be determined, k > 0, 0 < d <1, -1000 <μ <1000. 7. A method for predicting the data of changes in the pressure of the surrounding rocks of the working development according to claim 1, in which the observation stations are located within 100 m behind the face; at the stage of penetration influence, observation stations are also located within 50 m ahead of the face of the working face. 8. The method for predicting the data of changes in the pressure of the surrounding rocks of the production workings according to claim 1, in which when collecting a larger amount of data on changes in the pressure of the surrounding rocks, it is possible to more accurately predict the characteristics of changes in the pressure of the surrounding rocks of the production workings; with a denser arrangement of observation stations, the time required to collect data on changes in the pressure of the surrounding rocks will be reduced. 9. The method for predicting pressure change data of the surrounding rock formation of a longwall according to claim 1, wherein the nonlinear regression is completed using a numerical analysis software with a nonlinear regression function, and the numerical analysis software includes ORIGIN, MATLAB, EXCEL, and SPSS. 10. The method for predicting the pressure change data of the surrounding rocks of the working development according to claim 1, in which the working working has properties similar to the surrounding rock, the corresponding method for controlling the pressure of the surrounding rocks and the constant daily volume of drilling of the face front.

Images