CN114264788B - A method for determining the correlation between different areas of the working face and coal spontaneous combustion - Google Patents

Abstract

The invention discloses a method for determining the degree of correlation between different areas of a working face and coal spontaneous combustion, and relates to the field of coal spontaneous combustion prevention and control in goaf areas. This method continuously collects CO and O ₂ concentrations in different areas of the working surface, and then uses wavelet transform to conduct multi-scale evolution characteristic analysis of CO and O ₂ concentrations, and obtains the main periods and wavelet coefficients of CO and O 2 concentrations at different characteristic time scales; Secondly, the sine wave is selected to fit the wavelet coefficients of CO and O ₂ in the first main period, and the wave equation of the wavelet coefficients of CO and O ₂ is obtained; finally, the phase difference and amplitude of CO and O ₂ are obtained according to the wave equation, and by comparing the phases The difference and initial amplitude can be used to obtain the correlation between different regions and coal spontaneous combustion. The invention is easy to operate. By determining the correlation between different areas of the working surface and coal spontaneous combustion, it can be used to guide the focus of gas monitoring at different stages of the working surface, avoid the occurrence of redundant information, improve the efficiency of coal spontaneous combustion prevention and control, and is more conducive to fire prevention. occur.

Description

A method for determining the correlation between different areas of the working face and coal spontaneous combustion

Technical field

The invention relates to a method for determining the correlation between a region and coal spontaneous combustion. It is particularly suitable for a method for determining the correlation between different regions of a working face and coal spontaneous combustion. It belongs to the technical field of working face gas concentration monitoring.

Background technique

In coal mines, spontaneous coal combustion is one of the disasters that affects the safe mining of the working face. Different areas of the working face contain residual coal. Due to the effects of ventilation, mining, pressure, etc., the oxidation degree of the residual coal in different areas is different. If no addition is made, Taking different measures to prevent and extinguish fires will lead to a waste of resources and low efficiency in the prevention and control of coal spontaneous combustion.

Remaining coal consumes oxygen to generate CO, etc. Therefore, the _O2 and CO concentrations can reflect the degree of spontaneous combustion of coal. At present, industrial sites mainly determine whether coal spontaneous combustion occurs in the monitoring area by analyzing the absolute concentrations of O ₂ and CO and their change rates. However, the reliability of the monitoring area or the correlation with coal spontaneous combustion is not judged. Therefore, traditional methods It is blind and limited. For example, in order to control gas, some working faces are equipped with high-extraction tunnels. At the same time, the CO and O ₂ concentrations in the high-extraction tunnels are analyzed to analyze the degree of coal spontaneous combustion. In fact, it has not been verified whether the gas in the high-extraction tunnel is closely related to coal spontaneous combustion, and whether the correlation is strong or weak. Secondly, the existing method of dividing the three zones of coal spontaneous combustion in goafs by relying on _O2 concentration can only obtain a vague range of dangerous areas, which are distributed along the inclination direction of the working face. If the dangerous areas along the direction are obtained, the two are superimposed. , you can get a more accurate dangerous area. Therefore, by analyzing the gas concentrations behind different supports, the correlation ranking with coal spontaneous combustion can be obtained. Combined with the three-zone division, the final coal spontaneous combustion danger zone can be obtained.

Therefore, in view of the above problems, it is necessary to propose a more reasonable and effective method to quickly and accurately determine the correlation between different areas of the working face and spontaneous coal combustion, provide a basis for the division of spontaneous coal combustion areas on the working face and disaster management, and ensure that coal mines safe production.

Contents of the invention

In view of the shortcomings of the existing technology, a more reasonable and effective method is provided to quickly and accurately determine the correlation between different areas of the working face and coal spontaneous combustion, providing a basis for the division of coal spontaneous combustion areas and disaster management on the working face, ensuring A method for determining the correlation between different areas of the working face and coal spontaneous combustion in coal mine production safety.

In order to achieve the above purpose, the present invention is used to determine the correlation between different areas of the working surface and coal spontaneous combustion. It is characterized in that: by continuously collecting the concentration information of CO and _O2 in different areas of the working surface, and then using wavelet transform to transform the collected Conduct multi-scale evolution characteristic analysis of CO and O ₂ concentration information to obtain the main periods and wavelet coefficients of CO and O ₂ concentrations in each area under different characteristic time scales; then select sine waves to fit CO and O ₂ under the first main period The wavelet coefficients are used to obtain the wave equation of CO and O ₂ wavelet coefficients; finally, the phase difference and amplitude of CO and O ₂ are deduced according to the wave equation. By comparing the phase difference and the initial amplitude, the correlation between different regions and coal spontaneous combustion can be obtained.

Furthermore, different areas for continuous collection of CO and _O2 concentration information in the working face include: inside the upper corner bag wall, outside the upper corner bag wall, high extraction tunnels and goaf areas, focusing on monitoring the goaf area and upper corner bag Gas concentrations of CO and _O2 inside the wall.

Specifically, it includes the following steps:

Step 1. Collect O ₂ and CO in different areas of the underground mining working surface, and analyze the concentration information of O ₂ and CO in the different areas collected. The different areas include the upper corner bag wall, the upper corner bag wall, and the high corner bag wall. Tunnels and gobs;

Step 2: Process the O ₂ and CO concentrations based on wavelet transform to obtain the wavelet variance curves and wavelet coefficient cloud diagrams of the O ₂ and CO concentrations in different areas of the mining face;

Step 3: According to the wavelet variance curves of O ₂ and CO concentrations in different areas of the mining working face, obtain the characteristic time scale corresponding to the main period of O ₂ and CO in each area;

Step 4: According to the wavelet coefficient cloud diagram of O ₂ and CO concentration in different areas of the mining working surface, which contains multiple main periods, the characteristic scale corresponding to the largest wavelet variance is extracted as the first main period. The wavelet coefficient under the first main period is obtained as O2 and wavelet coefficients of CO and corresponding sampling time;

Step 5: Use the sine function to fit the wavelet coefficients under the first main period of O ₂ and CO to obtain the wave equations of the two;

Step 6. According to the wave equation of O ₂ and CO wavelet coefficients, obtain the corresponding initial amplitude and initial phase difference;

Step 7: Determine the correlation between different areas and coal spontaneous combustion based on the changes in initial amplitude and initial phase difference. If the difference between the initial amplitudes of CO and O ₂ is larger, it indicates that the changes in the two gases are more significant, and it is judged that the area is related to coal spontaneous combustion. The higher the correlation with spontaneous combustion.

Furthermore, the gas sampling period of O ₂ and CO in step 1 should be at least 1 month.

Furthermore, in step two, the basis function of the wavelet transform is the Morlet wavelet function.

Further, in step five, the equation of the sine function is y=y ₀ +Asin((xx _c )π/ω), where y ₀ is the initial vibration of the wave, A is the amplitude, ω is the angular velocity, and x _c is a known quantity .

Furthermore, the goaf area behind the hydraulic support is divided according to the position of each hydraulic support. By analyzing the correlation between the goaf area corresponding to different hydraulic supports and coal spontaneous combustion, and then combining the three coal spontaneous combustion zones, the dangerous area of coal spontaneous combustion can be accurately divided ,Specific steps are as follows:

S1 arranges bundle pipes in the goaf area behind the hydraulic support of the working face;

S2 uses the existing bundled tube automatic sampling device and monitoring system to collect goaf gas and conduct O ₂ and CO concentration analysis;

S3 uses oxygen concentration to divide coal spontaneous combustion into three zones. The division indicators are 15% and 5%. Among them, greater than 15% is the dissipation zone, less than 5% is the suffocation zone, and between the two is the oxidation zone;

S4 obtains the initial amplitude parameters and initial phase difference parameters in the wave equation through fitting to determine the correlation between the area behind each support and coal spontaneous combustion, and performs sorting; specifically, the hydraulic supports set within the oxidation zone are screened out. The ranking of the correlation between each bracket area and coal spontaneous combustion is used to represent the probability ranking of coal spontaneous combustion.

Suppose there are three groups of hydraulic supports A, B and C, and the intersection areas with the oxidation zone are area 1, area 2 and area 3 respectively. If the correlation between the areas corresponding to the three groups of hydraulic supports and coal spontaneous combustion is ranked as R _A > R _C >R _B , it means that area 1 has the highest probability of coal spontaneous combustion, followed by area 3, and area 2 has the smallest probability.

Beneficial effects: This invention proposes for the first time the concept of correlation between different areas of the working surface and coal spontaneous combustion. The invention is easy to operate. It can not only detect the gas concentration and its changes in different areas of the working face, but also sort the correlation between different areas of the working face and coal spontaneous combustion according to the multi-scale evolution characteristics of the gas concentration. First, it is used to guide the working face. Precise prevention and control of spontaneous coal combustion, secondly, can accurately divide coal spontaneous combustion dangerous areas based on correlation and the distribution of the three coal spontaneous combustion zones, which is conducive to preventing the occurrence of coal spontaneous combustion disasters and taking targeted control measures after the disaster occurs.

Description of the drawings

Figure 1 is a cloud diagram of CO wavelet coefficients in the bag wall in a specific embodiment of the present invention.

Figure 2 is a cloud diagram of O ₂ wavelet coefficients in the bag wall in a specific embodiment of the present invention.

Figure 3 is a CO wavelet variance diagram within the bag wall in a specific embodiment of the present invention.

Figure 4 is a wavelet variance diagram of _O2 in the bag wall in a specific embodiment of the present invention.

Figure 5 is a schematic diagram of the wavelet coefficient fluctuation curves of CO and _O2 in the bag wall under the first main period in a specific embodiment of the present invention.

Figure 6 is a cloud diagram of CO wavelet coefficients outside the bag wall in a specific embodiment of the present invention.

Figure 7 is a cloud diagram of O ₂ wavelet coefficients outside the bag wall in a specific embodiment of the present invention.

Figure 8 is a CO wavelet variance diagram outside the bag wall in a specific embodiment of the present invention.

Figure 9 is a wavelet variance diagram of O ₂ outside the bag wall in a specific embodiment of the present invention.

Figure 10 is the wavelet coefficient fluctuation curve of CO and _O2 outside the bag wall under the first main period in a specific embodiment of the present invention.

Figure 11 is a cloud diagram of high-lane CO wavelet coefficients in a specific embodiment of the present invention.

Figure 12 is a cloud diagram of high-lane _O2 wavelet coefficients in a specific embodiment of the present invention.

Figure 13 is a high-lane CO wavelet variance diagram in a specific embodiment of the present invention.

Figure 14 is a high-lane _O2 wavelet variance diagram in a specific embodiment of the present invention.

Figure 15 is a schematic diagram of the wavelet coefficient fluctuation curve under the first main period of high extraction CO in a specific embodiment of the present invention.

Figure 16 is a schematic diagram of the wavelet coefficient fluctuation curve under the first main period of high extraction lane _O2 in a specific embodiment of the present invention.

Figure 17 is a cloud diagram of CO wavelet coefficients in the goaf area in a specific embodiment of the present invention.

Figure 18 is a schematic diagram of the wavelet coefficient cloud diagram of _O2 in the goaf area in a specific embodiment of the present invention.

Figure 19 is a CO wavelet variance diagram of the goaf area in a specific embodiment of the present invention.

Figure 20 is a wavelet variance diagram of _O2 in the goaf area in a specific embodiment of the present invention.

Figure 21 is a schematic diagram of the wavelet coefficient fluctuation curve under the first main period of CO in the goaf area in a specific embodiment of the present invention.

Figure 22 is a schematic diagram of the wavelet coefficient fluctuation curve under the first main period of the goaf _O2 in the specific embodiment of the present invention.

Figure 23 is a schematic diagram of the absolute value of the initial phase difference between CO and O ₂ wavelet coefficients in a specific embodiment of the present invention.

Figure 24 is a schematic diagram of the initial amplitude of the CO and O ₂ concentration wavelet coefficient fitting equation in a specific embodiment of the present invention.

Figure 25 is a schematic diagram of accurately dividing coal spontaneous combustion dangerous areas according to the correlation between CO and O ₂ behind the rack and three zones in a specific embodiment of the present invention.

Figure 26 is a schematic diagram of the three zones of coal spontaneous combustion in a specific embodiment of the present invention.

Figure 27 is a schematic diagram of the present invention's precise division of coal spontaneous combustion dangerous areas based on the correlation between different bracket areas and coal spontaneous combustion in combination with three zones.

Detailed ways

The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

As shown in Figure 1, the present invention's method for determining the correlation between different areas of the working surface and coal spontaneous combustion is characterized by: continuously collecting the concentration information of CO and _O2 in different areas of the working surface, and then using wavelet transform to The collected CO and O ₂ concentration information was analyzed for multi-scale evolution characteristics, and the main periods and wavelet coefficients of CO and O ₂ concentrations in each area under different characteristic time scales were obtained; then sine waves were selected to fit CO and O ₂ in the first main Wavelet coefficients under the period, the wave equation of CO and O ₂ wavelet coefficients is obtained; finally, the phase difference and amplitude of CO and O ₂ are deduced according to the wave equation. By comparing the phase difference and the initial amplitude, the correlation between different regions and coal spontaneous combustion can be obtained relation. Different areas for continuous collection of CO and O ₂ concentration information in the working face include: inside the upper corner bag wall, outside the upper corner bag wall, high extraction tunnel and goaf area, focusing on monitoring the goaf area and inside the upper corner bag wall Gas concentrations of CO and _O2 .

Specifically, it includes the following steps:

Step 1. Collect O ₂ and CO in different areas of the underground mining working surface, and analyze the concentration information of O ₂ and CO in the different areas collected. The different areas include the upper corner bag wall, the upper corner bag wall, and the high corner bag wall. Drainage tunnels and goaf areas; the gas sampling period for O ₂ and CO should be at least 1 month;

Step 2: Process the O ₂ and CO concentrations based on wavelet transform to obtain the wavelet variance curves and wavelet coefficient cloud diagrams of the O ₂ and CO concentrations in different areas of the mining face. The basis function of the wavelet transform is the Morlet wavelet function;

Step 3: According to the wavelet variance curves of O ₂ and CO concentrations in different areas of the mining working face, obtain the characteristic time scale corresponding to the main period of O ₂ and CO in each area;

Step 4: According to the wavelet coefficient cloud diagram of O ₂ and CO concentration in different areas of the mining working surface, which contains multiple main periods, the characteristic scale corresponding to the largest wavelet variance is extracted as the first main period. The wavelet coefficient under the first main period is obtained as O2 and wavelet coefficients of CO and corresponding sampling time;

Step 5: Use the sine function to fit the wavelet coefficients under the first main period of O ₂ and CO to obtain the wave equations of the two. The equation of the sine function is y=y ₀ +Asin((xx _c )π/ω ), where y ₀ is the initial vibration of the wave, A is the amplitude, ω is the angular velocity, and x _c is a known quantity.

Step 6. According to the wave equation of O ₂ and CO wavelet coefficients, obtain the corresponding initial amplitude and initial phase difference;

Step 7: Determine the correlation between different areas and coal spontaneous combustion based on the changes in initial amplitude and initial phase difference. If the difference between the initial amplitudes of CO and O ₂ is larger, it indicates that the changes in the two gases are more significant, and it is judged that the area is related to coal spontaneous combustion. The higher the correlation with spontaneous combustion.

The goaf area behind the hydraulic support is divided according to the position of each hydraulic support. By analyzing the correlation between the goaf area corresponding to different hydraulic supports and coal spontaneous combustion, and then combining the three coal spontaneous combustion zones, the dangerous area of coal spontaneous combustion can be accurately divided. Specifically Proceed as follows:

S1 arranges bundle pipes in the goaf area behind the hydraulic support of the working face;

S2 uses the existing bundled tube automatic sampling device and monitoring system to collect goaf gas and conduct O ₂ and CO concentration analysis;

S3 uses oxygen concentration to divide coal spontaneous combustion into three zones. The division indicators are 15% and 5%. Among them, greater than 15% is the dissipation zone, less than 5% is the suffocation zone, and between the two is the oxidation zone;

S4 obtains the initial amplitude parameters and initial phase difference parameters in the wave equation through fitting to determine the correlation between the area behind each support and coal spontaneous combustion, and performs sorting; specifically, the hydraulic supports set within the oxidation zone are screened out. The ranking of the correlation between each bracket area and coal spontaneous combustion is used to represent the probability ranking of coal spontaneous combustion.

Assume that there are three groups of hydraulic supports arranged on the working surface: A, B and C, and the intersection areas with the oxidation zone are area 1, area 2 and area 3 respectively. If the correlation between the areas corresponding to the three groups of hydraulic supports and coal spontaneous combustion is ranked R _A >R _C >R _B means that area 1 has the highest probability of coal spontaneous combustion, followed by area 3, and area 2 has the smallest probability.

Example 1:

The present invention's method for determining the correlation between different areas of a working face and coal spontaneous combustion is described in detail by taking the 401103 working face of Hujiahe Coal Mine in Binchang, Shaanxi Province as an example:

Taking the O ₂ and CO concentrations in the upper corner bag wall, outside the upper corner bag wall, high extraction tunnel and goaf area as examples, we will analyze them in detail.

Step 1: Collect O ₂ and CO from different areas of the underground mining working surface and analyze their concentrations.

Step 2. Based on the wavelet transform, use Matlab and Surfer software to process the O ₂ and CO concentrations, and obtain the wavelet coefficient cloud diagram and wavelet variance curve of the O ₂ and CO concentrations, as shown in Figure 1-Figure 4 (inside the bag wall in the upper corner) , Figure 6-Figure 9 (in the upper corner bag wall), Figure 11-Figure 14 (high extraction lane), Figure 17-Figure 20 (gob area).

Step 3: According to the wavelet variance curve, obtain the characteristic time scale corresponding to the main period of O ₂ and CO;

Step 4: Extract the wavelet coefficients under the first main period according to the wavelet coefficient cloud image, and obtain the wavelet coefficients of O ₂ and CO and the corresponding sampling time.

Step 5: Use the sine function to fit the wavelet coefficients under the first main period of O ₂ and CO to obtain the wave equations of the two, as shown in Figure 5 (inside the upper corner bag wall), Figure 10 (in the upper corner bag wall) Outside), Figure 15 and Figure 16 (high extraction tunnel), Figure 21 and Figure 22 (goaf area).

Step 6. According to the wave equation of O ₂ and CO wavelet coefficients, obtain the corresponding initial amplitude and initial phase difference, as shown in Figure 23 and Figure 24.

Step 7: Determine the degree of correlation between different areas and coal spontaneous combustion based on the changes in initial amplitude and initial phase difference. As shown in Figure 24, the greater the difference in the initial amplitudes of CO and O ₂ is, it means that the changes of the two gases are the most significant. Further explanation The higher the correlation between this area and coal spontaneous combustion.

According to this case, it can be seen that the correlation degree with coal spontaneous combustion from large to small is: goaf area, inside the upper corner bag wall, outside the upper corner bag wall, and high extraction tunnel. Therefore, we should focus on monitoring the gas concentration in the goaf area and the bag wall in the upper corner. High extraction tunnels can be omitted from monitoring or the frequency of monitoring can be reduced to improve work efficiency.

The research area is subdivided into goaf areas corresponding to each support on the working face. By analyzing the correlation between the goaf areas corresponding to different supports and coal spontaneous combustion, and then combining the three coal spontaneous combustion zones, the dangerous area of coal spontaneous combustion can be accurately divided. The specific steps are as follows :

Expansion step 1. Arrange the beam tube behind the working surface frame. The arrangement is as shown in Figure 25.

Expansion step 2: Use bundled tube automatic sampling devices and monitoring systems to collect goaf gas, and conduct O ₂ and CO concentration analysis.

Expansion step three: Use oxygen concentration to divide coal spontaneous combustion into three zones. The division indicators are 15% and 5%. Among them, greater than 15% is the dissipation zone, less than 5% is the suffocation zone, and the area between the two is the oxidation zone, as shown in Figure 26.

The fourth step of expansion is to determine the correlation between the areas behind different supports and coal spontaneous combustion, and sort them. Assume that there are three groups of hydraulic supports A, B and C, and the intersection areas with the oxidation zone are area 1, area 2 and area 3 respectively. If the correlation between the areas corresponding to the three groups of hydraulic supports and coal spontaneous combustion is ranked as R _A > R _C >R _B , it means that area 1 has the highest probability of coal spontaneous combustion, followed by area 3, and area 2 has the smallest probability, as shown in Figure 27.

Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. In this way, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and equivalent technologies, the present invention is also intended to include these modifications and variations.

Use Matlab to calculate wavelet coefficients:

Select the Morlet complex wavelet function in the Matlab wavelet toolbox to perform wavelet transformation on the data sequence: click the submenu "Complex Continuous Wavelet 1-D" under "Wavelet1-D" to open the one-dimensional complex continuous wavelet interface, click "File "Load Signal" button under the menu to load the time series runoff.mat. In the present invention, cmor (1-1.5) is selected, the sampling period is 1, the maximum scale is 64, and the "Analyze" run button is clicked to calculate the wavelet coefficients. Save the wavelet coefficients as crunoff.mat file.

Use Matlab to calculate complex wavelet coefficients and variances

Import the crunoff.mat file into the Workspace under the Matlab interface. Next, start to calculate the real part and variance of the Morlet complex wavelet coefficients. The specific operation is: directly enter the function "shibu=real(coefs);" in "Command Windows", click the "Enter" key to calculate the real part; enter the function "fangcha=sum(abs(coefs).^2,2);", click the "Enter" key to calculate the variance.

How to draw the contour map of the real part of the wavelet coefficients:

First, copy the real part data of the wavelet coefficients to Excel, where column A is the time, column B is the scale, and column C is the corresponding real part values of the wavelet coefficients at different times and scales. Secondly, convert the data into the data format recognized by Suffer 12.0, and finally, draw the real part contour map of the wavelet coefficients.

The role of the real part contour map of wavelet coefficients in multi-time scale analysis:

The isoline diagram of the real part of the wavelet coefficient can reflect the periodic changes of the runoff sequence at different time scales and its distribution in the time domain, and can then determine the future change trend of runoff at different time scales. In order to clearly explain the role of the real part contour map of the wavelet coefficient in the multi-time scale analysis of runoff, when the real part value of the wavelet coefficient is positive, it represents the runoff wet season, and "H" represents the positive value center; When , it represents the runoff dry period, which is drawn with a dotted line, and "L" represents the negative value center.

In general, there are three types of periodic change patterns in the evolution process of watershed runoff: 18 to 32 years, 8 to 17 years, and 3 to 7 years. Among them, there are quasi-two oscillations of dry and abundant periods on the 18-32-year time scale; quasi-five oscillations on the 8-17-year time scale. At the same time, it can also be seen that the periodic changes at the above two scales are very stable and global during the entire analysis period; while the periodic changes at the 3 to 10-year scale are relatively stable after the 1980s.

Use Surfer to draw wavelet coefficient contour plots and variance plots:

Export the data in step 3, and then use Surfer software to directly draw the wavelet coefficient contour map. Use Origin software to draw variance plots directly. No additional calculations are required.

Claims (7)

1. A judging method for the degree of correlation between different areas of a working surface and spontaneous combustion of coal is characterized by comprising the following steps: by continuously capturing CO and O in different areas of the working surface ₂ Concentration information of (2) and then using wavelet transform to collect CO and O ₂ Performing multiscale evolution feature analysis on the concentration information to obtain CO and O in each region under different feature time scales ₂ A main period of the concentration and a wavelet coefficient; according to different areas O of the stope face ₂ The wavelet coefficient cloud image corresponding to the concentration of CO comprises a plurality of main periods, the characteristic scale corresponding to the maximum wavelet variance is extracted as the first main period, and then sine waves are selected to fit CO and O ₂ Wavelet coefficients at the first main period to obtain CO and O ₂ Wave equation of wavelet coefficients; finally deriving CO and O from wave equation ₂ The phase difference and the initial amplitude can be compared to obtain the relation between different areas and the coal spontaneous combustion relativityCO and O in ₂ The larger the area is, the larger the difference between CO and O2 is; if CO and O ₂ The larger the primary amplitude difference, the more significant the change in the two gases, and the higher the correlation between the region and spontaneous combustion of the coal is judged.

2. The method for determining the degree of correlation between different areas of a working surface and spontaneous combustion of coal according to claim 1, wherein the CO and O in the working surface are continuously collected ₂ Different areas of concentration information include: inside the upper corner pocket wall, outside the upper corner pocket wall, high-level drainage lane and goaf, and CO and O inside the goaf and the upper corner pocket wall are monitored with emphasis ₂ Is a gas concentration of (a).

3. The method for determining the degree of correlation between different areas of a working surface and spontaneous combustion of coal according to claim 1, comprising the following steps:

step one, collecting O in different areas of underground stope face ₂ And CO, and analyzing the collected different areas O ₂ The different areas comprise an upper corner bag sub-wall, a high pumping lane and a goaf;

step two, based on wavelet transformation, pair O ₂ And CO concentration to obtain O in different areas of the stope ₂ Wavelet variance curve and wavelet coefficient cloud graph of CO concentration;

step three, according to different areas O of the stope face ₂ Wavelet variance curve of CO concentration to obtain each region O ₂ A characteristic time scale corresponding to a main period of CO;

step four, according to different areas O of the stope face ₂ The wavelet coefficient cloud graph of the CO concentration comprises a plurality of main periods, a characteristic scale corresponding to the maximum wavelet variance is extracted as a first main period, and the wavelet coefficients of O2 and CO and corresponding sampling time are obtained under the first main period;

step five, utilizing sine function to make O ₂ Fitting with wavelet coefficient of first main period of CO to obtain twoIs a wave equation of (2);

step six, according to O ₂ And the wave equation of the CO wavelet coefficient to obtain corresponding primary amplitude and primary phase difference;

step seven, judging the degree of correlation between different areas and the spontaneous combustion of the coal according to the change conditions of the initial amplitude and the initial phase difference, if CO and O ₂ The larger the primary amplitude difference, the more significant the change in the two gases, and the higher the correlation between the region and spontaneous combustion of the coal is judged.

4. The method for determining the degree of correlation between different areas of a working surface and spontaneous combustion of coal according to claim 2, wherein the method comprises the following steps of: o in step one ₂ And a gas sampling period of CO of at least 1 month.

5. The method for determining the degree of correlation between different areas of a working surface and spontaneous combustion of coal according to claim 1, wherein the method comprises the following steps of: in the second step, the basis function of the wavelet transformation is Morlet wavelet function.

6. The method for determining the degree of correlation between different areas of a working surface and spontaneous combustion of coal according to claim 1, wherein in the fifth step, the equation of the sine function isWherein y is ₀ The primary vibration of the wave, A is amplitude, omega is angular velocity, x _c Is a known quantity.

7. The method for determining the degree of correlation between different areas of a working surface and spontaneous combustion of coal according to claim 1, wherein the method comprises the following steps of: dividing goafs behind the hydraulic supports according to the positions of the hydraulic supports, analyzing the correlation degree between goafs corresponding to different hydraulic supports and spontaneous combustion of coal, and then combining three spontaneous combustion zones of the coal to accurately divide a spontaneous combustion danger area of the coal, wherein the method comprises the following specific steps:

s1, arranging a beam tube in a goaf behind a working face hydraulic support;

s2, collecting goaf gas by using the existing beam tube automatic sampling device and monitoring system, and carrying out O ₂ And CO concentration analysis;

s3, dividing three spontaneous combustion zones of coal by using oxygen concentration, wherein the dividing indexes are 15% and 5%, more than 15% of the zones are heat dissipation zones, less than 5% of the zones are choking zones, and an oxidation zone is arranged between the zones;

s4, determining the correlation degree between the area behind each bracket and the spontaneous combustion of coal by fitting to obtain initial amplitude parameters and initial phase difference parameters in the wave equation, and sequencing; specifically, hydraulic supports arranged in the oxidation zone range are screened out, and the probability ranking of coal spontaneous combustion is represented by ranking of the correlation degree of each support area and coal spontaneous combustion;

the hydraulic supports arranged on the working surface are provided with three groups A, B and C, the crossing areas with the oxidation zone are respectively provided with an area 1, an area 2 and an area 3, if the correlation degree between the areas corresponding to the three groups of hydraulic supports and the spontaneous combustion of coal is ranked as R _A >R _C >R _B It is explained that the probability of spontaneous combustion of coal is greatest in zone 1, and secondly, zone 3 is smallest in zone 2.