Method and system for preventing and controlling natural ignition disasters in large-area goaf of deep high-ground-temperature thick-coal-seam mine

Abstract

The invention belongs to the technical field of mine safety prevention and control, and particularly relates to a method and system for preventing and controlling natural ignition disasters in a large-area goaf of a deep high-ground-temperature thick-coal-seam mine. According to the method and the system, gas data reserved in each goaf and data of an air leakage channel reserved between the goaf and a ventilation space are collected respectively to judge the fire occurrence probability, corresponding pressure regulating measures can be taken according to the judgment result, and pressure energy in the air leakage channel of the goaf is reduced; the design of the method and the system is based on the overall situation of the mine, the technical theories of ventilation management, coal seam spontaneous ignition and the like of the mine are comprehensively considered, the control effect can be ensured, and the recovery level can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of mine safety prevention and control, and specifically relates to a method and system for preventing and controlling the spontaneous combustion disaster of large-area gob areas in deep high-geotemperature thick coal seams mines. Background Technique

[0002] Coal is an important basic energy source and raw material in China and has an important strategic position in the national economy. After years of production equipment upgrading and mine reconstruction and expansion, the number of extra-large mines has increased significantly, and the production capacity has been significantly improved. However, a large number of large-area gob areas have been generated. For deep high-geotemperature thick coal seam mines, there is residual coal in the gob area, and the high geotemperature provides good heat storage conditions. When there is air leakage, it will cause great difficulties in preventing and controlling internal fires. If not properly disposed of, it will seriously threaten the safety of the mine. Therefore, it is necessary to deeply carry out research on the technology for preventing and controlling the spontaneous combustion disaster of large-area gob areas in deep high-geotemperature thick coal seam mines to ensure the safe production of the mine. Summary of the Invention

[0003] Aiming at the above problems existing in the prior art, the purpose of the present invention is to provide a method and system for preventing and controlling the spontaneous combustion disaster of large-area gob areas in deep high-geotemperature thick coal seam mines. Starting from the overall perspective of the mine, it comprehensively considers technical theories such as mine ventilation management and coal seam spontaneous combustion, and effectively solves the prevention and control evaluation of the existing spontaneous combustion disaster of large-area gob areas in high-geotemperature thick coal seam mines.

[0004] In order to solve the above problems, the technical solutions adopted by the present invention are as follows: A method for preventing and controlling the spontaneous combustion disaster of large-area gob areas in deep high-geotemperature thick coal seam mines, the method comprising the following steps: (1) Collect the types of gases remaining in each gob area and the concentrations of each gas, analyze and process the collected gas data, and judge the probability of fire occurrence; (2) Collect the positions and length information of the air leakage channels remaining between the gob area and the ventilation space, analyze and process the data of the collected air leakage channels, and judge the probability of fire occurrence; (3) According to the probability of fire occurrence judged in steps (1) and (2), take corresponding pressure regulating measures to reduce the pressure energy of the air leakage channels in the gob area.

[0005] Further, in step (1), the process of judging the probability of fire occurrence is as follows: (1.1) Collect the types of gases remaining in multiple areas without the possibility of spontaneous combustion outside the gob area in the mine and the concentrations of each gas to obtain multiple initial safe state operation data; (1.2) Screen the multiple initial safe state operation data to obtain multiple cleaned safe state operation data; (1.3) Based on the multiple cleaning safety status operation data, represent the corresponding concentrations of the remaining gas types in the form of a range to form a historical data set, evaluate the probability of each gas causing a fire, define the evaluated data as the fire triggering factor, and use the finally obtained fire triggering factor as the label; (1.4) Define the gas type and its corresponding concentration as environmental parameters, perform data fusion on the normalized environmental parameters in the historical data set in two dimensions of type and content, and form a set of the fused data in each dimension to obtain a fused feature data set in two dimensions; (1.5) Calculate the mutual information and correlation coefficient between the fused feature data set and the corresponding fire triggering factor respectively, select the fused feature data set with the greatest influence, establish a prediction model based on the BP neural network, and train the model with the selected fused feature data set with the greatest influence and the corresponding fire triggering factor; (1.6) Import the collected gas data into the model, output the prediction result to obtain the predicted fire triggering factor, and compare the predicted fire triggering factor with the fire triggering factor obtained in step (1.3) to judge the probability of a fire occurring.

[0006] Further, in step (2), the process of judging the probability of a fire occurring is as follows: (2.1) Establish a fire triggering judgment strategy, which includes at least one judgment rule in the fire triggering judgment strategy, and any one of the judgment rules corresponds to two judgment results; (2.2) The fire triggering judgment strategy includes a preliminary judgment strategy and a detailed judgment strategy; The preliminary judgment strategy includes the following rules: Judge whether the air leakage channel can be normally ventilated; The detailed judgment strategy includes the following rules: Judge whether the length of the air leakage channel meets the ventilation volume requirement; Judge whether the position of the air leakage channel meets the ventilation volume requirement; Make a separate judgment on each air leakage channel. If the result of the preliminary judgment is that it can be normally ventilated, then make a detailed judgment on this air leakage channel; if the result of the preliminary judgment is that it cannot be normally ventilated, then record it as a fire probability increase factor Enter the data set; If the result of the detailed judgment is that the length does not meet the ventilation volume requirement, then record it as a fire probability increase factor Enter the data set; otherwise, record it as a fire probability decrease factor Enter the data set; If the result of the detailed judgment is that the position does not meet the ventilation volume requirement, then the fire rising probability factor P3 is recorded and enters the dataset; otherwise, it is recorded as the fire decreasing probability factor enters the dataset; Among them, , ; (2.3) For multiple air leakage channels, the total fire occurrence probability is calculated as:

[0007] Among them, represents the event of fire occurrence, is the conditional probability of the ventilation volume of the air leakage channel during the fire occurrence, is the prior probability of fire occurrence, is the marginal probability of fire occurrence, is the fire occurrence probability.

[0008] A prevention and control system for the natural fire disaster in large - area gob areas of deep high - geotemperature thick - coal - seam mines includes: A gas collection module, which collects the types of gases remaining in each gob area and the concentration of each gas; An environmental monitoring module, which collects the position and length information of the air leakage channels remaining between the gob area and the ventilation space; A pressure energy adjustment module, which can adjust the pressure energy in the air leakage channels of the gob area according to instructions; An upper computer processing module, which is communicatively connected to the gas collection module, the environmental monitoring module and the pressure energy adjustment module at the same time, and can analyze and process the collected gas data and air leakage channel data, judge the fire occurrence probability, and send corresponding instructions to the pressure energy adjustment module according to the judged fire occurrence probability.

[0009] Furthermore, the gas collection module includes gas collection devices arranged inside the gob area and at the corners, inlets and outlets of each air leakage channel.

[0010] Furthermore, the gas collection module collects the types of gases remaining in multiple areas without the possibility of natural fire outside the gob area in the mine and the concentration of each gas, and obtains multiple initial safe - state operation data; The host computer processing module filters multiple pieces of the initial safety status operation data to obtain multiple pieces of cleaned safety status operation data, and represents the corresponding concentrations of the remaining gas types in the form of a range based on the multiple pieces of cleaned safety status operation data to form a historical data set. Then, it evaluates the probability of each gas causing a fire, defines the evaluated data as a fire triggering factor, and uses the finally obtained fire triggering factor as a label. The host computer processing module can define the gas type and its corresponding concentration as environmental parameters, perform data fusion on the normalized environmental parameters in the historical data set in two dimensions of type and content, form a set of the fused data on each dimension to obtain a fused feature data set in two dimensions, calculate the mutual information and correlation coefficient between the fused feature data set and the corresponding fire triggering factor respectively, select the fused feature data set with the greatest influence degree, establish a prediction model based on the BP neural network, train the model with the selected fused feature data set with the greatest influence and the corresponding fire triggering factor. Finally, the host computer processing module imports the collected gas data into the model, outputs a prediction result to obtain a predicted fire triggering factor, and judges the fire occurrence probability by comparing the predicted fire triggering factor with the fire triggering factor recorded as a label.

[0011] Furthermore, the host computer processing module can establish a fire triggering judgment strategy. In the fire triggering judgment strategy, there are at least one judgment rule, and any one of the judgment rules corresponds to two judgment results. The fire triggering judgment strategy includes a preliminary judgment strategy and a detailed judgment strategy. The preliminary judgment strategy includes the following rules: Judge whether the air leakage passage can be normally ventilated. The detailed judgment strategy includes the following rules: Judge whether the length of the air leakage passage meets the requirement of the ventilation volume; judge whether the position of the air leakage passage meets the requirement of the ventilation volume. Make a separate judgment on each air leakage passage. If the result of the preliminary judgment is that it can be normally ventilated, then make a detailed judgment on this air leakage passage; if the result of the preliminary judgment is that it cannot be normally ventilated, then record it as a fire rising probability factor Enter the data set. If the result of the detailed judgment is that the length does not meet the requirement of the ventilation volume, then record it as a fire rising probability factor Enter the data set; otherwise, record it as a fire decreasing probability factor Enter the data set. If the result of the detailed judgment is that the position does not meet the requirement of the ventilation volume, then record it as a fire rising probability factor P3 and enter the data set; otherwise, record it as a fire decreasing probability factor Enter the dataset; Among them, , ; For multiple air leakage channels, the total probability of fire occurrence is calculated as:

[0012] Among them, represents the event of fire occurrence, is the conditional probability of the ventilation volume of the air leakage channel during the fire occurrence, is the prior probability of fire occurrence, is the marginal probability of fire occurrence, is the probability of fire occurrence.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: The prevention and control method and system designed by the present invention can ensure the "no dead angle" investigation of the hidden danger of spontaneous combustion in large-area goafs. And according to the investigation results, it can effectively carry out restoration and adjustment for the problem areas, which is conducive to scientifically scheduling the construction period and orderly implementing construction management. The design of this method and system is based on the overall situation of the mine, comprehensively considering technical theories such as mine ventilation management and coal seam spontaneous combustion. It can not only ensure the prevention and control effect but also improve the restoration level. Specific implementation manners

[0014] The present invention will be further described below in conjunction with specific embodiments.

[0015] A certain mine in Shandong mainly mines the No. 3 (3 上 ) coal seam, with an average coal thickness of 7.03 m. The coal types are mainly fat coal and 1 / 3 coking coal, which is a Class II spontaneous combustion coal seam, and the shortest spontaneous combustion period is 64 - 68 days. The well depth exceeds 800 m, the depth of the constant temperature zone of the mine is 50 m, the temperature is 18.9 °C, the average geothermal gradient is 2.88 °C / 100 m, and the average temperature of the floor of the main mined coal seam is 44.38 °C, all in the first-level or second-level high-temperature areas, and most of them are in the second-level high-temperature areas. After years of mining activities, three large-area goafs, namely the first mining area, the second mining area, and the 36 mining area, have been formed underground. It is difficult to control the internal fire disaster, and if not properly handled, it will seriously threaten the safety of the mine.

[0016] In order to effectively prevent and control the natural fire disaster in the large-area goafs of the mine, this method adopts the following steps: (1) Collect the types of gases remaining in each goaf and the concentrations of each gas, analyze and process the collected gas data, and judge the probability of fire occurrence; (2) Collect the positions and length information of the air leakage channels remaining between the goaf and the ventilation space, analyze and process the collected data of the air leakage channels, and judge the probability of fire occurrence; (3) According to the fire occurrence probability obtained in steps (1) and (2), corresponding pressure regulation measures are taken to reduce the pressure energy of the air leakage channels in the goaf.

[0017] Among them, the process of judging the fire occurrence probability based on gas data is as follows: (1.1) Collect the types of gases remaining in multiple areas without the possibility of spontaneous combustion outside the goaf in the mine and the concentrations of each gas to obtain multiple initial safe state operation data; (1.2) Screen the multiple initial safe state operation data to obtain multiple cleaned safe state operation data; (1.3) According to the multiple cleaned safe state operation data, represent the corresponding concentrations of the remaining gas types in the form of a range to form a historical data set, and evaluate the probability of each gas causing a fire. The data obtained from the evaluation is defined as the fire triggering factor, and the finally obtained fire triggering factor is used as the label; (1.4) Define the gas type and its corresponding concentration as environmental parameters, perform data fusion on the normalized environmental parameters in the historical data set in two dimensions of type and content, and form a set of the fused data in each dimension to obtain a fused feature data set in two dimensions; (1.5) Calculate the mutual information and correlation coefficient between the fused feature data set and the corresponding fire triggering factor respectively, select the fused feature data set with the greatest influence, establish a prediction model based on the BP neural network, and train the model with the selected fused feature data set with the greatest influence and the corresponding fire triggering factor; (1.6) Import the collected gas data into the model, output the prediction result to obtain the predicted fire triggering factor, and compare the predicted fire triggering factor with the fire triggering factor obtained in step (1.3) to judge the fire occurrence probability.

[0018] The process of judging the fire occurrence probability based on the air leakage channel data is as follows: (2.1) Establish a fire triggering judgment strategy, which includes at least one judgment rule in the fire triggering judgment strategy, and any one of the judgment rules corresponds to two judgment results; (2.2) The fire triggering judgment strategy includes a preliminary judgment strategy and a detailed judgment strategy; The preliminary judgment strategy includes the following rules: Judge whether the air leakage channel can be normally ventilated; The detailed judgment strategy includes the following rules: Judge whether the length of the air leakage channel meets the ventilation volume requirement; Judge whether the position of the air leakage channel meets the ventilation volume requirement; For each air leakage passage, make a separate judgment. If the preliminary judgment result is that normal ventilation is possible, then conduct a detailed judgment on this air leakage passage; if the preliminary judgment result is that normal ventilation is not possible, then record it as a fire rise probability factor Enter the dataset; If the detailed judgment result is that the length does not meet the ventilation volume requirement, then record it as a fire rise probability factor Enter the dataset; otherwise, record it as a fire decline probability factor Enter the dataset; If the detailed judgment result is that the position does not meet the ventilation volume requirement, then record it as a fire rise probability factor P3 and enter the dataset; otherwise, record it as a fire decline probability factor Enter the dataset; Among them, , ; (2.3) For multiple air leakage passages, calculate the total fire occurrence probability as:

[0019] Among them, represents the event of fire occurrence, is the conditional probability of the ventilation volume of the air leakage passage at the time of fire occurrence, is the prior probability of fire occurrence, is the marginal probability of fire occurrence, is the fire occurrence probability.

[0020] Thus, by adopting the above technical solution, it is possible to comprehensively judge the overall fire occurrence probability from two aspects: the gas data in the goaf and the air leakage passage data, and finally take corresponding pressure regulation measures according to the judged fire occurrence probability to reduce the pressure energy of the air leakage passage in the goaf.

[0021] In this embodiment, the corresponding prevention and control system adopted includes a gas collection module, and the gas collection module collects the types of gases remaining in each goaf and the concentrations of each gas; An environmental monitoring module, and the environmental monitoring module collects the position and length information of the air leakage passages remaining between the goaf and the ventilation space; A pressure energy adjustment module, and the pressure energy adjustment module can adjust the pressure energy in the air leakage passage of the goaf according to instructions; An upper computer processing module, and the upper computer processing module is communicatively connected to the gas collection module, the environmental monitoring module and the pressure energy adjustment module at the same time, and can analyze and process the collected gas data and air leakage passage data, judge the fire occurrence probability, and send corresponding instructions to the pressure energy adjustment module according to the judged fire occurrence probability.

[0022] Among them, the gas collection module includes gas collection devices arranged inside the gob area and at the corners and inlets / outlets of each air leakage passage.

[0023] The gas collection module collects the types of gases remaining in multiple areas outside the gob area in the mine where there is no possibility of spontaneous combustion and the concentrations of various gases, and obtains multiple initial safe state operation data. The host computer processing module screens the multiple initial safe state operation data to obtain multiple cleaned safe state operation data, and based on the multiple cleaned safe state operation data, represents the corresponding concentrations of the remaining gas types in the form of a range to form a historical data set, and evaluates the probability of each gas causing a fire, defines the evaluated data as a fire triggering factor, and uses the finally obtained fire triggering factor as a label. The host computer processing module can define the gas type and its corresponding concentration as environmental parameters, perform data fusion on the normalized environmental parameters in the historical data set in two dimensions of type and content, form a set of the fused data in each dimension to obtain a fused feature data set in two dimensions, calculate the mutual information and correlation coefficient between the fused feature data set and the corresponding fire triggering factor respectively, select the fused feature data set with the greatest influence degree, establish a prediction model based on the BP neural network, train the model through the selected fused feature data set with the greatest influence and the corresponding fire triggering factor, and finally the host computer processing module imports the collected gas data into the model, outputs the prediction result, obtains the predicted fire triggering factor, and judges the fire occurrence probability by comparing the predicted fire triggering factor with the fire triggering factor recorded as a label.

[0024] The host computer processing module can establish a fire triggering judgment strategy. In the fire triggering judgment strategy, there are at least one judgment rule, and any judgment rule corresponds to two judgment results. The fire triggering judgment strategy includes a preliminary judgment strategy and a detailed judgment strategy. The preliminary judgment strategy includes the following rules: Judge whether the air leakage passage can be ventilated normally. The detailed judgment strategy includes the following rules: Judge whether the length of the air leakage passage meets the requirements of the ventilation volume; judge whether the position of the air leakage passage meets the requirements of the ventilation volume. Make a separate judgment on each air leakage passage. If the result of the preliminary judgment is that it can be ventilated normally, then make a detailed judgment on this air leakage passage; if the result of the preliminary judgment is that it cannot be ventilated normally, then record it as a fire rising probability factor. Enter the data set. If the result of the detailed judgment is that the length does not meet the requirements of the ventilation volume, then record it as a fire rising probability factor. Enter the dataset; otherwise, it is recorded as the fire decline probability factor Enter the dataset; If the result of the detailed judgment is that the position does not meet the ventilation volume requirement, it is recorded as the fire increase probability factor P3 and enters the dataset; otherwise, it is recorded as the fire decline probability factor Enter the dataset; Among them, , ; For multiple air leakage channels, the total fire occurrence probability is calculated as:

[0025] Among them, represents the event of fire occurrence, is the conditional probability of the ventilation volume of the air leakage channel during the fire occurrence, is the prior probability of fire occurrence, is the marginal probability of fire occurrence, is the fire occurrence probability.

[0026] In this embodiment, it is found that among the sealed gob areas of roadway 47 in the mine, a total of 38 need to be treated. In addition, according to on-site mediation, the ventilation system was adjusted, and some negative pressure areas were restored to positive pressure areas to achieve pressure equalization in the entire gob area.

[0027] In other embodiments, nitrogen injection for fire prevention can also be used for placement. Nitrogen is continuously injected into the gob area to be inerted, and at the same time, underground mobile nitrogen production, grouting, etc. are used to improve the operation efficiency. After actual operation, it is found that the oxygen concentration in 3 large-scale gob areas in the mine always remains within 10%, and no natural fire index gases such as CO, CH4, and C2H2 appear again.

[0028] In other embodiments, a more perfect natural fire prediction and warning system can also be established.

[0029] For example, an automatic beam tube monitoring system is added. In 1 year, the KJ1234 coal spontaneous combustion online monitoring system was fully built. The system mainly consists of multi-parameter sensors, wireless repeaters, data bases, switches, and ground central stations. The basic operation principle of the system is: the multi-parameter sensors regularly extract gas samples from the gob area through beam tubes and automatically analyze them. The data is transmitted wirelessly and transmitted to the data base through the wireless repeater, then transmitted to the switch through optical fiber, enters the industrial ring network, and finally is summarized to the ground central station.

[0030] At present, the sampling interval of the multi-parameter sensor is set to take an air sample once at 9:00 in the morning shift (maintenance shift) every day, and it can automatically analyze gases such as O2, CO, CO2, CH4, C2H2, and C2H4 in the extracted air sample. As of October 2024, this system has been fully equipped in all key control areas such as 44 goaf seals in the mine, fire prevention observation points in the roadway along the goaf, and fire prevention observation stations at the return air corner of the coal mining face.

[0031] It is also possible to combine the method of periodic manual sampling and analysis. For key areas such as goaf seals and fire prevention observation points in the roadway along the goaf, the fire prevention observer takes an air sample manually and conducts chemical analysis in the ground analysis room using a gas chromatograph analyzer (model: KSS-5690B gas chromatograph analyzer), achieving full coverage of the mine once every 3 days.

[0032] At the same time, continue to improve the safety monitoring system. Set CO sensors at locations such as coal mining faces, return airflows of drivage coal roadways using the roadway along the goaf construction technology, district return airways, main return airways, and total return airways. The alarm value is 24 ppm, realizing all-weather online monitoring of CO in the above areas.

Claims

1. A method for preventing and controlling spontaneous combustion disasters in large-scale goaf areas of deep, high-temperature and thick coal seam mines, characterized in that: The method comprises the following steps: (1) Collect the gas types and gas concentrations remaining in each goaf, analyze and process the collected gas data, and determine the probability of fire occurrence; (2) Collect the location and length information of the air leakage channel between the goaf and the ventilation space, analyze and process the collected data of the air leakage channel, and determine the probability of fire occurrence; (3) Based on the fire occurrence probability determined in step (1) and step (2), corresponding pressure regulating measures are taken to reduce the pressure energy of the air leakage channel in the goaf.

2. The method for preventing and controlling natural fire disasters in large-scale goaf areas of deep, high-temperature and thick coal seam mines according to claim 1 is characterized in that: In step (1), the process of determining the probability of fire occurrence is: (1.1) Collect the types and concentrations of gases remaining in multiple areas outside the goaf of the mine without the possibility of spontaneous combustion, and obtain multiple initial safe state operation data; (1.2) screening the plurality of initial safety state operation data to obtain a plurality of cleaning safety state operation data; (1.3) Based on the plurality of cleaning safety state operation data, the corresponding concentrations of the remaining gas types are expressed in the form of a range to form a historical data set, and the probability that each gas may cause a fire is evaluated, and the evaluated data is defined as a fire initiation factor, and the fire initiation factor finally obtained is used as a label; (1.4) Define the gas type and its corresponding concentration as environmental parameters, fuse the data of the normalized environmental parameters in the historical data set in terms of type and content, and combine the fused data in each dimension into a set to obtain a fused feature data set in two dimensions; (1.5) Calculate the mutual information and correlation coefficient between the fused feature data set and the corresponding fire initiation factors respectively, select the fused feature data set with the greatest influence, establish a prediction model based on BP neural network, and train the model through the selected fused feature data set with the greatest influence and the corresponding fire initiation factors; (1.6) The collected gas data is imported into the model, and the prediction result is output to obtain the predicted fire initiation factor. The predicted fire initiation factor is compared with the fire initiation factor obtained in step (1.3) to determine the probability of fire occurrence.

3. The method for preventing and controlling natural fire disasters in large-area goaf areas of deep, high-temperature and thick coal seam mines according to claim 1 is characterized in that: In step (2), the process of determining the probability of fire occurrence is: (2.1) establishing a fire initiation judgment strategy, wherein the fire initiation judgment strategy includes at least one judgment rule, and any one of the judgment rules corresponds to two judgment results; (2.2) The fire initiation judgment strategy includes a preliminary judgment strategy and a detailed judgment strategy; The preliminary judgment strategy includes the following rules: Determining whether the air leakage channel can be ventilated normally; The detailed judgment strategy includes the following rules: Determine whether the length of the air leakage channel meets the ventilation volume requirement; Determine whether the position of the air leakage channel meets the ventilation volume requirement; Each air leakage channel is judged separately. If the preliminary judgment result is that normal ventilation is possible, a detailed judgment is made on the air leakage channel; if the preliminary judgment result is that normal ventilation is not possible, it is recorded as the fire rising probability factor. Enter the data set; If the result of the detailed judgment is that the length does not meet the ventilation volume requirements, it will be recorded as the fire rise probability factor Enter into the data set; otherwise, it is recorded as the fire reduction probability factor Enter the data set; If the result of the detailed judgment is that the location does not meet the ventilation requirements, it is recorded as the fire rising probability factor P3 and entered into the data set; otherwise, it is recorded as the fire falling probability factor Enter the data set; in, , ; (2.3) For multiple air leakage channels, the total probability of fire occurrence is calculated as: in, Indicates the occurrence of a fire. is the conditional probability of ventilation volume in the air leakage channel when a fire occurs, is the prior probability of fire occurrence, is the marginal probability of fire occurrence, is the probability of fire occurrence.

4. A prevention and control system for a method for preventing and controlling spontaneous combustion disasters in large-area goaf areas of deep, high-temperature and thick coal seam mines, characterized in that: include: A gas collection module, which collects the types of gases and concentrations of gases remaining in each goaf; An environmental monitoring module, which collects information on the location and length of the air leakage channel between the goaf and the ventilation space; A pressure energy regulating module, which can regulate the pressure energy in the air leakage channel of the goaf according to instructions; The host computer processing module is communicatively connected with the gas collection module, the environmental monitoring module and the pressure energy regulation module at the same time, and can analyze and process the collected gas data and the data of the air leakage channel, judge the probability of fire occurrence, and send corresponding instructions to the pressure energy regulation module according to the judged probability of fire occurrence.

5. The system for preventing and controlling natural fire disasters in large-scale goaf areas of deep, high-temperature and thick coal seam mines according to claim 4 is characterized in that: The gas collection module comprises gas collection equipment arranged inside the goaf and at the corners, inlet and outlet of each air leakage channel.

6. The system for preventing and controlling natural fire disasters in large-scale goaf areas of deep, high-temperature and thick coal seam mines according to claim 4 is characterized in that: The gas collection module collects gas types and gas concentrations remaining in multiple areas without spontaneous combustion possibility outside the goaf in the mine, and obtains multiple initial safety state operation data; The host computer processing module screens the plurality of initial safety state operation data to obtain a plurality of cleaning safety state operation data, and represents the corresponding concentration of the remaining gas types in the form of a range according to the plurality of cleaning safety state operation data to form a historical data set, and evaluates the probability that each gas may cause a fire, defines the evaluated data as a fire initiation factor, and uses the fire initiation factor finally obtained as a label; The host computer processing module can define the gas type and its corresponding concentration as environmental parameters, perform data fusion on the two dimensions of type and content for the normalized environmental parameters in the historical data set, form the fused data on each dimension into a set, obtain a fused feature data set on the two dimensions, respectively calculate the mutual information and correlation coefficient between the fused feature data set and the corresponding fire initiation factor, select the fused feature data set with the greatest influence, establish a prediction model based on the BP neural network, train the model by selecting the fused feature data set with the greatest influence and the corresponding fire initiation factor, and finally import the collected gas data into the model, output the prediction result, obtain the predicted fire initiation factor, compare the predicted fire initiation factor with the fire initiation factor recorded as a label, and judge the probability of fire occurrence.

7. The system for preventing and controlling natural fire disasters in large-scale goaf areas of deep, high-temperature and thick coal seam mines according to claim 4 is characterized in that: The host computer processing module can establish a fire initiation judgment strategy, in which at least one judgment rule is included, and any one of the judgment rules corresponds to two judgment results; The fire initiation judgment strategy includes a preliminary judgment strategy and a detailed judgment strategy; The preliminary judgment strategy includes the following rules: Determining whether the air leakage channel can be ventilated normally; The detailed judgment strategy includes the following rules: Determine whether the length of the air leakage channel meets the ventilation volume requirement; Determine whether the position of the air leakage channel meets the ventilation volume requirement; Each air leakage channel is judged separately. If the preliminary judgment result is that normal ventilation is possible, a detailed judgment is made on the air leakage channel; if the preliminary judgment result is that normal ventilation is not possible, it is recorded as the fire rising probability factor. Enter the data set; If the result of the detailed judgment is that the length does not meet the ventilation volume requirements, it will be recorded as the fire rise probability factor Enter into the data set; otherwise, it is recorded as the fire reduction probability factor Enter the data set; If the result of the detailed judgment is that the location does not meet the ventilation requirements, it is recorded as the fire rising probability factor P3 and entered into the data set; otherwise, it is recorded as the fire falling probability factor Enter the data set; in, , ; For multiple air leakage channels, the total probability of fire occurrence is calculated as: in, Indicates the occurrence of a fire. is the conditional probability of ventilation volume in the air leakage channel when a fire occurs, is the prior probability of fire occurrence, is the marginal probability of fire occurrence, is the probability of fire occurrence.

Images