Statistics-based goaf oxygen concentration reasonable distribution interval prediction method

Abstract

The invention discloses a statistics-based goaf oxygen concentration reasonable distribution interval prediction method, which comprises the following steps: S1, setting a detection point behind a bracket, and continuously collecting gas at the detection point and detecting the oxygen concentration in the pushing and mining process of a working face to obtain multiple groups of detection data; s2, drawing a 2D nuclear density distribution cloud chart of the oxygen concentration according to the obtained multiple groups of detection data; s3, according to the nuclear density distribution cloud picture, performing linear fitting on the relationship between the oxygen concentration y at the detection point position and the length x of the goaf to obtain a linear function y = F (x) and a prediction area; s4, the vertical distance delta C between the upper boundary line and the lower boundary line is obtained through measurement, when the length x of the goaf is equal to x0, the reasonable distribution interval of the oxygen concentration at the position of the detection point is (C < lower >, C < upper >), and C < upper > is equal to C0 + delta C / 2; c < lower > = C < 0 >-delta C / 2; c0 = F (x0). According to the method, the coal oxidation process is predicted by determining the reasonable distribution interval of the O2 concentration of the goaf, and the influence of mutation data on the division result is weakened.

Description

Technical Field

[0001] The invention relates to the technical field of goaf coal spontaneous combustion prevention and early warning, and in particular to a method for predicting a reasonable distribution interval of oxygen concentration in a goaf based on statistics. Background Art

[0002] Coal spontaneous combustion is one of the disasters that affect the safe recovery of coal working faces. Goafs are areas where coal spontaneous combustion frequently occurs, which may lead to major safety accidents, casualties and interruption of mine production. Proper early warning of coal spontaneous combustion in goafs is a key issue in preventing and controlling coal spontaneous combustion disasters.

[0003] Gas is the main indicator for warning the degree of danger of coal spontaneous combustion. The commonly used gases on site are O2 and CO. By using bundle tubes to collect gas data from different areas of the goaf, the degree of coal spontaneous combustion in the goaf and the distribution of dangerous areas are judged according to the gas concentration and its changing trend. The traditional method of predicting coal spontaneous combustion is to use O2 concentration to divide the goaf into three areas with 18% and 5% as critical points, namely the "three zones" of coal spontaneous combustion: O2 concentration greater than 18% is the heat dissipation zone, between 5% and 18% is the oxidation zone, and less than 5% is the asphyxiation zone.

[0004] In actual production, gas concentration is affected by many factors, such as mining, roof collapse, structural belts, inert gas injection, etc. Therefore, the gas concentration is not stable, but fluctuates within a certain range. If the fluctuation range of the gas is not mastered, it is easy to cause misjudgment and false alarm of coal spontaneous combustion. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, one purpose of the present invention is to propose a method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics. This method determines the reasonable distribution interval of O2 concentration in goaf for different mines and working faces by summarizing the distribution characteristics of a large amount of data, and further predicts the coal oxidation process. This method weakens the influence of mutation data on the division results and improves the accuracy of data analysis.

[0006] According to an embodiment of the present invention, a method for predicting a reasonable distribution interval of oxygen concentration in a goaf based on statistics comprises the following steps: S1: at least one detection point is set behind a support of a coal mining face, and during the pushing and mining process of the coal mining face, the gas at the detection point is continuously collected and the oxygen concentration in the gas is detected to obtain a plurality of groups of detection data corresponding to the detection point position, each group of detection data comprises the length of the goaf, and the oxygen concentration value at the detection point position at the goaf length; S2: based on the obtained plurality of groups of detection data, a 2D kernel density diagram of oxygen concentration is drawn with the goaf length as the horizontal coordinate and the oxygen concentration value as the vertical coordinate. S3: According to the 2D kernel density distribution cloud map of oxygen concentration, a linear fit is performed on the relationship between the oxygen concentration y at the detection point and the goaf length x, and a straight line function y=F(x) of the change of oxygen concentration y with the goaf length x is obtained, as well as a prediction area of ​​oxygen concentration under a set prediction interval, the straight line obtained by fitting is located within the prediction area, and the straight line is parallel to the upper boundary line and the lower boundary line of the prediction area; S4: The vertical distance ΔC between the upper boundary line and the lower boundary line is measured, and when the goaf length x=x0, the reasonable distribution interval of the oxygen concentration at the detection point is (C 下 , C 上 ), where: C 上 =C0+ΔC / 2; C 下 =C0-ΔC / 2; C0=F(x0).

[0007] According to the statistically-based prediction method for the reasonable distribution interval of oxygen concentration in goafs of the embodiment of the present invention, the reasonable distribution interval of oxygen concentration in goafs is determined by summarizing the distribution characteristics of a large amount of data. When collecting data on site, when the data point falls outside the reasonable distribution interval, it indicates that there is abnormal coal oxidation in the current goaf, and when the data falls within the reasonable distribution interval, it indicates that the goaf belongs to conventional oxidation. Thus, the influence of data mutation on the division result is weakened, and the accuracy of data processing is improved. By summarizing the distribution characteristics of a large amount of data, the reasonable distribution interval of O2 concentration is directly given, which can be further applied to the early warning of spontaneous combustion of coal in goafs, which meets the requirements for the formulation of on-site early warning rules in the Interpretation of the Detailed Rules for Fire Prevention and Extinguishing in Coal Mines, and has great application value. In addition, the method can determine the degree of coal spontaneous combustion danger by determining the distribution characteristics of O2, omitting the three-band division, and no longer needing to draw the three-band distribution map. The gas distribution interval in the kernel density distribution cloud map is clear, and no further data processing is required. The reasonable distribution interval of oxygen concentration is directly obtained from the kernel density cloud map. The method is more adaptable and easy to operate, and the efficiency of division is greatly improved.

[0008] In addition, the method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to an embodiment of the present invention may also have the following additional technical features: According to one embodiment of the present invention, the set prediction interval value is 85%~95%.

[0009] According to one embodiment of the present invention, the set prediction interval value is 95%.

[0010] According to one embodiment of the present invention, in S3: before fitting the functional relationship between x and y, first determine at least one inflection point of oxygen concentration change according to the 2D kernel density distribution cloud map of oxygen concentration, divide the goaf length range into multiple segmented intervals with the goaf length value corresponding to the inflection point of oxygen concentration change as the demarcation point, respectively fit the linear relationship between the oxygen concentration y and the goaf length x corresponding to each segmented interval, and obtain the linear function y=F(x) of the oxygen concentration y corresponding to each segmented interval changing with the goaf length x and the predicted area; in S4, measure the ΔC corresponding to each segmented interval, and determine in which segmented interval x0 is located, and then bring it into the linear function corresponding to the segmented interval to calculate C0, so as to obtain the reasonable distribution interval (C) of the oxygen concentration at the detection point when the goaf length x=x0. 下 , C 上 ).

[0011] According to one embodiment of the present invention, in S3, there is one inflection point of the oxygen concentration change, and the goaf length value corresponding to the inflection point of the oxygen concentration change is x1. The goaf length range is divided into two segmented intervals (x≤x1) and (x>x1) with the goaf length value x1 as the dividing point, and then the following are fitted respectively: when the segmented interval is (x≤x1), the corresponding linear function y=F1(x) of the oxygen concentration y changing with the goaf length x and the corresponding predicted area; when the segmented interval is (x>x1), the corresponding linear function y=F2(x) of the oxygen concentration y changing with the goaf length x and The corresponding prediction area; in S4: when the segmentation interval is measured to be (x≤x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC1; when the segmentation interval is measured to be (x>x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC2; determine in which segmentation interval range x0 is located, when x0 is located in the segmentation interval (x≤x1), C0=F1(x0), ΔC=ΔC1; when x0 is located in the segmentation interval (x>x1), C0=F2(x0), ΔC=ΔC2.

[0012] According to one embodiment of the present invention, in S3, there are two inflection points of oxygen concentration change, which are respectively a first inflection point of oxygen concentration change and a second inflection point of oxygen concentration change. The goaf length value corresponding to the first inflection point of oxygen concentration change is x1, and the goaf length value corresponding to the second inflection point of oxygen concentration change is x2, and x1<x2. The goaf length value x1 is used as the and x2 as the dividing points to divide the goaf length range into three segmented intervals of (x<x1), (x1≤x≤x2) and (x>x2), and then fit them respectively: when the segmented interval is (x<x1), the corresponding linear function y=F1(x) of the oxygen concentration y varying with the goaf length x and the corresponding predicted area; when the segmented interval is (x1≤x≤x2), the corresponding linear function y=F2(x) of the oxygen concentration y varying with the goaf length x and the corresponding predicted area; when the segmented interval is (x>x2), the corresponding linear function y=F3(x) of the oxygen concentration y varying with the goaf length x and the corresponding predicted area; in S4: when the segmented interval is (x<x1), the corresponding predicted area is obtained by measurement. The vertical distance between the upper boundary line and the lower boundary line is ΔC1; when the segmented interval is measured to be (x1≤x≤x2), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC2; when the segmented interval is measured to be (x>x2), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC3; determine in which segmented interval range x0 is located, when x0 is located in the segmented interval (x<x1), C0=F1(x0), ΔC=ΔC1; when x0 is located in the segmented interval (x1≤x≤x2), C0=F2(x0), ΔC=ΔC2; when x0 is located in the segmented interval (x>x2), C0=F3(x0), ΔC=ΔC3.

[0013] According to one embodiment of the present invention, a plurality of the detection points are arranged behind the support of the coal mining face, and the plurality of the detection points are arranged at intervals from each other in a direction parallel to the arrangement direction of the support.

[0014] According to an embodiment of the present invention, in S1, before the detection point enters the goaf, a gas sampling bundle tube is pre-laid behind the support to collect gas at the detection point.

[0015] According to one embodiment of the present invention, in S1, after the detection point enters the goaf, a gas collection channel is drilled from a tunnel adjacent to the detection point toward the goaf, and one end of the gas collection channel away from the tunnel extends to the detection point to collect the gas at the detection point through the gas collection channel.

[0016] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 Schematic diagram of the arrangement of bundle pipes in the goaf of a working face according to an embodiment of the present invention; Figure 2 It is a kernel density distribution cloud diagram of O2 concentration of the first specific embodiment of the present invention; Figure 3 The first embodiment of the present invention is to determine the position of the inflection point of the oxygen concentration change in the kernel density distribution cloud diagram of the O2 concentration; Figure 4 In the first specific embodiment of the present invention, the O2 concentration is segmentedly fitted with the inflection point of oxygen concentration change as the dividing point; Figure 5 is a kernel density distribution cloud diagram of O2 concentration of the second specific embodiment of the present invention; Figure 6 The second embodiment of the present invention is to determine the position of the inflection point of the oxygen concentration change in the kernel density distribution cloud diagram of the O2 concentration; Figure 7 In the second specific embodiment of the present invention, the O2 concentration is segmentedly fitted with the inflection point of the oxygen concentration change as the dividing point.

[0018] Reference numerals: Working face 10; support 20; goaf 30; detection point 40; bundle pipe 50. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0020] Reference below Figure 1-Figure 7 A method for predicting a reasonable distribution interval of oxygen concentration in a goaf 30 based on statistics according to an embodiment of the present invention is described.

[0021] refer to Figure 1-Figure 7 As shown, according to an embodiment of the present invention, the method for predicting the reasonable distribution interval of oxygen concentration in the goaf 30 based on statistics includes the following steps: S1: Figure 1As shown in the figure, at least one detection point 40 is set behind the support 20 of the coal mining face 10, and during the pushing and mining process of the coal mining face 10, the gas at the detection point 40 is continuously collected and the oxygen concentration in the gas is detected to obtain multiple groups of detection data corresponding to the position of the detection point 40, each group of detection data includes the length of the goaf 30 and the oxygen concentration value at the detection point 40 at the length of the goaf.

[0022] It should be noted that the coal mining face 10 includes a plurality of supports 20, and the plurality of supports 20 are located in the cut eye, and the plurality of supports 20 are sequentially arranged along the length direction of the cut eye. According to the arrangement order of the supports 20, the plurality of supports 20 can be sequentially numbered as 01#, 02#, 03#, etc. The detection point 40 can be located directly behind a certain number of supports 20, so that it is convenient to identify the detection point 40. Of course, the present application is not limited to this, and the detection point 40 may not be directly opposite to the support 20.

[0023] It should be noted that, in the present application, “rear” refers to the direction opposite to the pushing and mining direction of the working surface 10 .

[0024] In some embodiments, only one detection point 40 is set behind the support 20 of the coal mining face 10, that is, the detection point 40 includes only one. In some embodiments, multiple detection points 40 are set behind the support 20 of the coal mining face 10, that is, the detection point 40 may include multiple, such as Figure 1 As shown in FIG. 1 , a plurality of detection points 40 may be spaced apart from each other in a direction parallel to the arrangement direction of the brackets 20 .

[0025] When there are multiple detection points 40, some of the multiple detection points 40 may be directly opposite to the bracket 20, and another part of the multiple detection points 40 may not be directly opposite to the bracket 20. Figure 1 In the example shown, there are six detection points 40, which are numbered 01#, 02#, 03#, 04#, 05#, and 06# in the arrangement direction. Among them, the 05# detection point 40 is directly opposite to the (n+1)# bracket 20, that is, the 05# detection point 40 is located directly behind the (n+1)# bracket 20, and the 06# detection point 40 is directly opposite to the (n+4)# bracket 20, that is, the 06# detection point 40 is located directly behind the (n+4)# bracket 20. The 01# detection point 40, the 02# detection point 40, the 03# detection point 40, and the 04# detection point 40 are not directly opposite to the bracket 20.

[0026] Of course, the present application is not limited thereto, and in some other embodiments, the plurality of detection points 40 may all be directly opposite to the bracket 20 ; or in some other embodiments, the plurality of detection points 40 may not be directly opposite to the bracket 20 .

[0027] S2: Based on the multiple sets of detection data obtained, a 2D kernel density distribution cloud map of oxygen concentration is drawn with the goaf length as the horizontal coordinate and the oxygen concentration value as the vertical coordinate.

[0028] S3: According to the 2D kernel density distribution cloud map of oxygen concentration, a linear fit is performed on the relationship between the oxygen concentration y at the detection point 40 and the goaf length x to obtain the straight line function y=F(x) of the change of oxygen concentration y with goaf length x, as well as the predicted area of ​​oxygen concentration within the set prediction interval. The fitted straight line is within the predicted area, and the straight line is parallel to the upper and lower boundary lines of the predicted area.

[0029] S4: Measure the vertical distance (parallel to the y-axis) ΔC between the upper boundary line and the lower boundary line of the prediction area. When the goaf length x=x0, the reasonable distribution range of the oxygen concentration at the detection point 40 is (C 下 , C 上 ), where: C 上 =C0+ΔC / 2; C 下 =C0-ΔC / 2; C0=F(x0).

[0030] It should be noted that in this application, "prediction interval" refers to the ratio of the number of test data in the prediction area to the total number of test data. "Set prediction interval" refers to the specific value of the prediction interval selected manually according to the needs when determining the reasonable distribution range of oxygen concentration.

[0031] Optionally, the prediction interval value is set to 85% to 95%. That is, the "set prediction interval value" can be greater than or equal to 85% and less than or equal to 95%. Exemplarily, the prediction interval value can be set to 85%, 86%, 87%, 89%, 92%, 92.5%, 94%, 94.5% or 95%. By setting the prediction interval value to 85% to 95%, the selection of a reasonable distribution interval of oxygen concentration can be made more reasonable, while weakening the impact of data mutations on the division results and improving the accuracy of data processing, it can avoid data processing being too conservative and excluding some data belonging to conventional oxidation phenomena.

[0032] According to the statistically-based prediction method for the reasonable distribution interval of oxygen concentration in goaf 30 of the embodiment of the present invention, the reasonable distribution interval of oxygen concentration in goaf 30 is determined by summarizing the distribution characteristics of a large amount of data. When collecting data on site, when the data point falls outside the reasonable distribution interval, it indicates that there is abnormal coal oxidation in the current goaf 30, and when the data falls within the reasonable distribution interval, it indicates that the goaf 30 belongs to conventional oxidation. Thus, the influence of data mutation on the division result is weakened, and the accuracy of data processing is improved. By summarizing the distribution characteristics of a large amount of data, the reasonable distribution interval of O2 concentration is directly given, which can be further applied to the early warning of coal spontaneous combustion in goaf 30, which meets the requirements for the formulation of on-site early warning rules in the Interpretation of the Detailed Rules for Fire Prevention and Extinguishing in Coal Mines, and has great application value. In addition, the method can determine the degree of coal spontaneous combustion danger by determining the distribution characteristics of O2, omitting the three-band division, and no longer needing to draw the three-band distribution map. The gas distribution interval in the kernel density distribution cloud map is clear, and no further data processing is required. The reasonable distribution interval of oxygen concentration is directly obtained from the kernel density cloud map. The method is more adaptable and easy to operate, and the division efficiency is greatly improved.

[0033] In some embodiments of the present invention, in step S3: before fitting the functional relationship between x and y, first determine at least one inflection point of oxygen concentration change according to the 2D kernel density distribution cloud map of oxygen concentration, divide the goaf length range into multiple segmented intervals with the goaf length value corresponding to the inflection point of oxygen concentration change as the dividing point, fit the linear relationship between the oxygen concentration y and the goaf length x corresponding to each segmented interval respectively, and obtain the linear function y=F(x) and the prediction area of ​​the oxygen concentration y corresponding to each segmented interval changing with the goaf length x; in S4, measure the ΔC corresponding to each segmented interval respectively, and determine in which segmented interval range x0 is located, and then bring it into the linear function corresponding to the segmented interval to calculate C0, so as to obtain the reasonable distribution interval (C) of the oxygen concentration at the detection point 40 when the goaf length x=x0. 下 , C 上 ).

[0034] By determining the inflection point of the oxygen concentration change, the goaf length value corresponding to the inflection point of the oxygen concentration change is used as the dividing point, the goaf length is segmented, and on the basis of the segmentation of the goaf length, the oxygen concentration y and the goaf length x at the detection point 40 are segmentedly fitted, and the reasonable distribution prediction area of ​​the oxygen concentration is segmentedly determined, thereby improving the accuracy of the reasonable distribution range of the oxygen concentration.

[0035] It should be noted that, in the present application, the number of inflection points of oxygen concentration change may be one or more, such as two, three or four.

[0036] In some embodiments of the present invention, see Figure 2-Figure 7 In S3, there is one inflection point of oxygen concentration change, and the goaf length value corresponding to the inflection point of oxygen concentration change is x1. The goaf length range is divided into two segmented intervals (x≤x1) and (x>x1) with the goaf length value x1 as the dividing point. Then, the following are fitted respectively: when the segmented interval is (x≤x1), the corresponding linear function y=F1(x) of oxygen concentration y changing with goaf length x and the corresponding prediction area; when the segmented interval is (x>x1), the corresponding linear function y=F2(x) of oxygen concentration y changing with goaf length x and the corresponding prediction area; In S4: when the segmentation interval is measured to be (x≤x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC1; when the segmentation interval is measured to be (x>x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC2. Determine in which segmentation interval x0 is located, when x0 is in the segmentation interval (x≤x1), C0=F1(x0), ΔC=ΔC1; when x0 is in the segmentation interval (x>x1), C0=F2(x0), ΔC=ΔC2.

[0037] The following is a detailed description using two specific embodiments as examples: Embodiment 1: Take the gas monitoring data of goaf 30 behind 30# support 20 of 401103 working face of Mengcun Coal Mine as an example for specific description.

[0038] S1. During the mining process of the 401103 working face, the gas at the detection point 40 behind the 30# support 20 is continuously collected and the O2 concentration in the gas is detected to obtain multiple sets of detection data corresponding to the detection point 40; S2. Based on the multiple sets of test data obtained, the goaf length is used as the horizontal axis and the oxygen concentration is used as the vertical axis. Figure 2 2D kernel density distribution cloud diagram of oxygen concentration shown in; S3. According to Figure 2 The 2D kernel density distribution cloud diagram of oxygen concentration shown in , determines an inflection point of oxygen concentration change, such as Figure 3 As shown in the figure, the coordinates of the inflection point of the oxygen concentration change are (30m, 15%). The goaf length value x1=30m corresponding to the inflection point of the oxygen concentration change is used as the dividing point to divide the goaf length range into two segmented intervals: (x≤30m) and (x>30m).

[0039] Linear fitting is performed on the oxygen concentration in the two segmented intervals (x≤30m) and (x>30m), respectively, so as to obtain the fitting straight line and prediction area corresponding to each segmented interval (such as Figure 4 ), as follows: y= F1(x)=18.96-0.1115x(x≤30); y= F2(x)= 23.65-0.2451x(x>30m); S4: In the segmented interval (x≤30), the vertical distance between the upper boundary line and the lower boundary line of the predicted area is ΔC1=3.1%; in the segmented interval (x>30m), the vertical distance between the upper boundary line and the lower boundary line of the predicted area is ΔC2=5.6%. Therefore, the reasonable distribution range (C) of the oxygen concentration at the detection point 40 when the specific goaf length x=x0 can be calculated. 下 , C 上 ).

[0040] Assuming that the length of the goaf is x=x0=20m, and x0 is within the segmented interval (x≤30), substitute into the equation y= F1(x0)=18.96-0.1115 x0= C0=16.73%; C 上 = C0+ΔC1 / 2=16.73%+3.1% / 2=18.28%; C 下 =C0-ΔC1 / 2=16.73%-3.1% / 2=15.18%; That is, the reasonable distribution range of O2 concentration corresponding to 20m behind the 30# support 20 of the 401103 mining face is (15.18%, 18.28%).

[0041] Assuming that the length of the goaf is x=x0=40m, and x0 is located in the segmented interval (x>30m), substitute into the equation y= F2(x0)=23.65-0.2451x0=C0=13.846%; C 上 = C0+ΔC2 / 2=13.846%+5.6% / 2=16.646%; C 下 = C0-ΔC2 / 2=13.846%-5.6% / 2=11.046%; That is, the reasonable distribution range of O2 concentration corresponding to 40m behind the 30# support 20 of the 401103 mining face is (11.046%, 16.646%). Example 2: Take the gas monitoring data of the goaf 30 behind the 60# support 20 of a working face in Hujiahe Mine as an example for specific description: S1. During the mining process of the working face 10, the gas at the detection point 40 behind the 60# support 20 is continuously collected and the O2 concentration in the gas is detected to obtain multiple groups of detection data corresponding to the detection point 40; S2. Based on the multiple sets of test data obtained, the goaf length is used as the horizontal axis and the oxygen concentration is used as the vertical axis. Figure 5 2D kernel density distribution cloud diagram of oxygen concentration shown in; S3. According to Figure 5 The 2D kernel density distribution cloud diagram of oxygen concentration shown in , determines an inflection point of oxygen concentration change, such as Figure 6 As shown in , the coordinates of the inflection point of the oxygen concentration change are also (30m, 15%). The goaf length value x1=30m corresponding to the inflection point of the oxygen concentration change is used as the dividing point to divide the goaf length range into two segmented intervals: (x≤30m) and (x>30m).

[0042] Linear fitting is performed on the oxygen concentration in the two segmented intervals (x≤30m) and (x>30m), respectively, so as to obtain the fitting straight line and prediction area corresponding to each segmented interval (such as Figure 7 ), as follows: y= F1(x)= 18.91-0.1165x (x≤30); y= F2(x)= 20.27-0.1788x (x>30m); S4: In the segmented interval (x≤30), the vertical distance between the upper boundary line and the lower boundary line of the predicted area is ΔC1=3.5%; in the segmented interval (x>30m), the vertical distance between the upper boundary line and the lower boundary line of the predicted area is ΔC2=6.9%. Therefore, the reasonable distribution range (C 下 , C 上 ).

[0043] Assuming that the length of the goaf is x=x0=20m, and x0 is within the segmented interval (x≤30), substitute into the equation y= F1(x0)=18.91-0.1165 x0= C0=16.58%; C 上 = C0+ΔC1 / 2=16.58%+3.5% / 2=18.33%; C 下 =C0-ΔC1 / 2=16.58%-3.5% / 2=14.83%; That is, the reasonable distribution range of O2 concentration corresponding to 20m behind the 60# support 20 of the mining face is (14.83%, 18.33%).

[0044] Assuming that the length of the goaf is x=x0=40m, and x0 is located in the segmented interval (x>30m), substitute into the equation y=F2(x0)=20.27-0.1788 x0=C0=13.118%; C 上 = C0+ΔC2 / 2=13.118%+6.9% / 2=16.568%; C 下 = C0-ΔC2 / 2=13.118%-6.9% / 2=9.668%; That is, the reasonable distribution range of O2 concentration corresponding to 40m behind the 60# support 20 of the mining face is (9.668%, 16.568%).

[0045] In some embodiments of the present invention, in S3, there are two inflection points of oxygen concentration change, which are the first inflection point of oxygen concentration change and the second inflection point of oxygen concentration change. The goaf length value corresponding to the first inflection point of oxygen concentration change is x1, and the goaf length value corresponding to the second inflection point of oxygen concentration change is x2, and x1<x2. The goaf length range is divided into three segmented intervals of (x<x1), (x1≤x≤x2) and (x>x2) with the goaf length values ​​x1 and x2 as dividing points, and then fitted respectively: when the segmented interval is (x<x1), the corresponding linear function y=F1(x) of the oxygen concentration y changing with the goaf length x and the corresponding prediction area; when the segmented interval is (x1≤x≤x2), the corresponding linear function y=F2(x) of the oxygen concentration y changing with the goaf length x and the corresponding prediction area; when the segmented interval is (x>x2), the corresponding linear function y=F3(x) of the oxygen concentration y changing with the goaf length x and the corresponding prediction area; In S4: when the segmentation interval is measured to be (x<x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC1; when the segmentation interval is measured to be (x1≤x≤x2), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC2; when the segmentation interval is measured to be (x>x2), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC3.

[0046] Determine in which segmented interval x0 is located. When x0 is in the segmented interval (x<x1), C0=F1(x0), ΔC=ΔC1; when x0 is in the segmented interval (x1≤x≤x2), C0=F2(x0), ΔC=ΔC2; when x0 is in the segmented interval (x>x2), C0=F3(x0), ΔC=ΔC3.

[0047] In this embodiment, there are two inflection points of oxygen concentration change, and the prediction method for the reasonable distribution range of oxygen concentration is similar to the above-mentioned prediction method with only one inflection point of oxygen concentration change, with the only difference being that a segmented interval is added, which will not be described in detail here.

[0048] In an optional embodiment of the present invention, Figure 1 As shown in FIG. 1 , in S1 , before the detection point 40 enters the goaf 30 , a gas sampling bundle tube 50 is pre-laid behind the support 20 to collect gas at the detection point 40 . This can reduce the construction difficulty and facilitate the operation.

[0049] In an optional embodiment of the present invention, in S1, after the detection point 40 enters the goaf 30, a gas collection channel is drilled from the tunnel adjacent to the detection point 40 toward the goaf 30, and one end of the gas collection channel away from the tunnel extends to the detection point 40, so as to collect gas at the detection point 40 through the gas collection channel. In this way, there is no need to lay the gas sampling bundle tube 50 in advance, thereby preventing the bundle tube 50 from being damaged by falling waste rock.

[0050] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0051] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A statistical method for predicting the reasonable distribution interval of oxygen concentration in goaf, characterized in that: The following steps are involved: S1: at least one detection point is set behind the support of the coal mining face, and during the pushing and mining process of the coal mining face, the gas at the detection point is continuously collected and the oxygen concentration in the gas is detected to obtain multiple groups of detection data corresponding to the detection point position, each group of detection data includes the length of the goaf and the oxygen concentration value at the detection point position at the goaf length; S2: based on the obtained multiple groups of detection data, a 2D kernel density distribution cloud map of oxygen concentration is drawn with the goaf length as the horizontal coordinate and the oxygen concentration value as the vertical coordinate; S3: According to the 2D kernel density distribution cloud map of the oxygen concentration, a linear fit is performed on the relationship between the oxygen concentration y at the detection point and the goaf length x, to obtain a straight line function y=F(x) of the change of the oxygen concentration y with the goaf length x, and a prediction area of ​​the oxygen concentration within a set prediction interval, wherein the straight line obtained by fitting is within the prediction area, and the straight line is parallel to the upper boundary line and the lower boundary line of the prediction area; S4: Measure the vertical distance ΔC between the upper boundary line and the lower boundary line. When the length of the goaf x=x0, the reasonable distribution range of the oxygen concentration at the detection point is (C 下 , C 上 ),in: C 上 =C0+ΔC / 2;C 下 =C0-ΔC / 2;C0=F(x0)。 2. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to claim 1 is characterized in that: The set prediction interval value is 85%~95%.

3. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to claim 2 is characterized in that: The set prediction interval value is 95%.

4. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to claim 1 is characterized in that: In the S3: Before fitting the functional relationship between x and y, first determine at least one inflection point of oxygen concentration change according to the 2D kernel density distribution cloud map of the oxygen concentration, divide the goaf length range into multiple segmented intervals with the goaf length value corresponding to the inflection point of the oxygen concentration change as the dividing point, respectively fit the linear relationship between the oxygen concentration y and the goaf length x corresponding to each segmented interval, and obtain the linear function y=F(x) of the oxygen concentration y corresponding to each segmented interval changing with the goaf length x and the predicted area; In S4, the ΔC corresponding to each segmented interval is measured respectively, and it is determined in which segmented interval x0 is located, and then C0 is calculated by substituting it into the linear function corresponding to the segmented interval, so as to obtain the reasonable distribution range (C 下 , C 上 ).

5. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to claim 4 is characterized in that: In S3, there is one inflection point of oxygen concentration change, and the goaf length value corresponding to the inflection point of oxygen concentration change is x1. The goaf length value x1 is used as the dividing point to divide the goaf length range into two segmented intervals of (x≤x1) and (x>x1), and then fit them respectively: When the segment interval is (x≤x1), the corresponding linear function y=F1(x) of the oxygen concentration y changing with the goaf length x and the corresponding predicted area; When the segment interval is (x>x1), the corresponding linear function y=F2(x) of the oxygen concentration y changing with the goaf length x and the corresponding predicted area; In said S4: When the segment interval is measured to be (x≤x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC1; When the segment interval is measured to be (x>x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC2; Determine which segmentation interval x0 is in. When x0 is in the segmentation interval (x≤x1), C0=F1(x0), ΔC=ΔC1; When x0 is within the segmented interval (x>x1), C0=F2(x0), ΔC=ΔC2.

6. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to claim 4 is characterized in that: In S3, there are two inflection points of oxygen concentration change, which are the first inflection point of oxygen concentration change and the second inflection point of oxygen concentration change. The goaf length value corresponding to the first inflection point of oxygen concentration change is x1, and the goaf length value corresponding to the second inflection point of oxygen concentration change is x2, and x1<x2. The goaf length range is divided into three segmented intervals of (x<x1), (x1≤x≤x2) and (x>x2) with the goaf length values ​​x1 and x2 as dividing points, and then fitted respectively: When the segment interval is (x<x1), the corresponding linear function y=F1(x) of the oxygen concentration y changing with the goaf length x and the corresponding predicted area; When the segment interval is (x1≤x≤x2), the corresponding linear function y=F2(x) of the oxygen concentration y changing with the goaf length x and the corresponding predicted area; When the segment interval is (x>x2), the corresponding linear function y=F3(x) of the oxygen concentration y changing with the goaf length x and the corresponding predicted area; In said S4: When the segment interval is measured to be (x<x1), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC1; When the segment interval is measured to be (x1≤x≤x2), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC2; When the segment interval is measured to be (x>x2), the vertical distance between the upper boundary line and the lower boundary line of the corresponding prediction area is ΔC3; Determine which segmentation interval x0 is within. When x0 is within the segmentation interval (x<x1), C0=F1(x0), ΔC=ΔC1; When x0 is within the segmented interval (x1≤x≤x2), C0=F2(x0), ΔC=ΔC2; When x0 is within the segmented interval (x>x2), C0=F3(x0), ΔC=ΔC3.

7. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to claim 1 is characterized in that: A plurality of the detection points are arranged behind the support of the coal mining working face, and the plurality of the detection points are arranged at intervals from each other in a direction parallel to the arrangement direction of the support.

8. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to any one of claims 1 to 7, characterized in that: In S1, before the detection point enters the goaf, a gas sampling bundle tube is pre-laid behind the support to collect gas at the detection point.

9. The method for predicting the reasonable distribution interval of oxygen concentration in goaf based on statistics according to any one of claims 1 to 7, characterized in that: In S1, after the detection point enters the goaf, a gas collection channel is drilled from a tunnel adjacent to the detection point toward the goaf, and one end of the gas collection channel away from the tunnel extends to the detection point to collect the gas at the detection point through the gas collection channel.

Images