Self-adaptive shielding method and system for goaf gas, terminal and storage medium

Abstract

The invention belongs to the technical field of mining area safety control, and particularly relates to a self-adaptive shielding method and system for goaf gas, a terminal and a storage medium, and the method comprises the steps: monitoring the gas concentration and distribution of a goaf and a working face in real time through a distributed gas sensor; according to the gas concentration and distribution data, a continuous water curtain barrier is dynamically formed at the boundary of the goaf, a wind-direction-adjustable wind curtain barrier is formed on the outer side of the water curtain barrier, and the water curtain barrier and the wind curtain barrier are monitored in real time; according to gas concentration and distribution data monitored in real time, a continuous water curtain barrier and a wind-direction-adjustable wind curtain barrier are dynamically formed, double barriers are formed, and diffusion of harmful gas in the goaf is effectively blocked. By calculating the barrier strength index and combining the fuzzy rule base and the MLP neural network, an accurate control parameter adjustment instruction is generated, real-time dynamic adjustment of the water curtain barrier and the air curtain barrier is achieved, and the barrier effect and the energy utilization efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of mine safety control, and in particular relates to an adaptive barrier method, system, terminal and storage medium for gas in a goaf. Background Art

[0002] In the process of coal mining, goaf is the space left after the ore body is mined. Due to the complexity of geological structure and the limitation of ventilation conditions, these areas often become places where harmful gases (such as methane, carbon monoxide, etc.) accumulate. The accumulation of these gases not only poses a serious threat to mine safety production, but also may cause major safety accidents such as fire and explosion. Therefore, how to effectively monitor and block the diffusion of goaf gas has become a key issue that needs to be solved in the coal industry.

[0003] Traditional methods for goaf gas control mainly include ventilation dilution, physical barrier isolation and extraction. However, these methods have certain limitations in practical applications. Although the ventilation dilution method can reduce the gas concentration in the goaf, it has limited effect on the control of high-concentration gases and is difficult to cope with the situation of rapid changes in gas concentration. The physical barrier isolation method mostly uses fixed barriers, such as firewalls and sealing walls. Although these methods can form effective barriers, they lack flexibility and are difficult to adjust according to real-time changes in gas concentration. Although the extraction method can effectively reduce the gas concentration, it has high energy consumption and puts great pressure on the mine ventilation system. Summary of the invention

[0004] In view of the defects that traditional goaf gas management methods in the prior art have certain limitations in practical applications, the present invention provides an adaptive barrier method, system, terminal and storage medium for goaf gas to solve the above technical problems.

[0005] In a first aspect, the present invention provides an adaptive barrier method for goaf gas, comprising: Step S1, real-time monitoring of gas concentration and distribution in goaf and working face by distributed gas sensors; Step S2: dynamically forming a continuous water curtain barrier at the boundary of the goaf according to the gas concentration and distribution data, forming a wind curtain barrier with adjustable wind direction outside the water curtain barrier, and performing real-time monitoring on the water curtain barrier and the wind curtain barrier; Step S3, calculating the barrier strength index based on the gas concentration data and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; Step S4, generating control parameter adjustment instructions corresponding to the water curtain barrier and the wind curtain barrier respectively based on the fuzzy rule base and the MLP neural network according to the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; Step S5: The water curtain barrier and the air curtain barrier respectively adjust their operating parameters according to the corresponding control parameter adjustment instructions, achieving a dual adaptive barrier for the gob gas.

[0006] A further improvement of this technical solution is that step S1 specifically includes: Deploy a distributed gas sensor network at the gob boundary and key positions of the working face, and the distributed gas sensor network is arranged in a honeycomb topology; Synchronously obtain the real-time gas concentration data of each sensor, perform Kalman filter denoising processing on the original gas concentration data, and generate a dynamic gas concentration sequence with timestamp marks; Fuse the denoised gas concentration data corresponding to multiple nodes, construct a three-dimensional gas concentration distribution model through a preset spatial interpolation algorithm, generate a gas concentration heat map covering the gob and the working face in real time, and update the data at a preset frequency.

[0007] A further improvement of this technical solution is that the control methods for the water curtain barrier and the air curtain barrier in step S2 include: Divide the generated gas concentration heat map into several grid cells, and determine whether the gas concentration corresponding to the grid cell exceeds the first preset concentration threshold; If the gas concentration of any grid cell exceeds the first preset concentration threshold, trigger the linkage start instruction for the water curtain barrier and the air curtain barrier; If the gas concentrations of several adjacent grid cells simultaneously exceed the first preset concentration threshold, determine the corresponding area as a diffusion risk area, and start the sprinkler head group corresponding to the coordinates in the water curtain barrier and the fan array corresponding to the coordinates in the air curtain barrier; Monitor the water curtain barrier and the air curtain barrier in real time.

[0008] A further improvement of this technical solution is that the formula for calculating the barrier strength index in step S3 is: ; Where is the barrier strength index, is the current gas concentration, is the target gas concentration, is the current water curtain flow rate, is the target water curtain flow rate, is the current air curtain pressure, is the target air curtain pressure, 、 、 are the weight coefficients.

[0009] A further improvement of this technical solution is that step S4 specifically includes: Convert the input parameters into corresponding fuzzy language variables. The input parameters include the barrier intensity index BI, as well as the monitored gas concentration, concentration change rate, ambient temperature, and ambient humidity; Match the fuzzy values of the input parameters according to the predefined fuzzy rules to generate fuzzy outputs; Input the input parameters into the trained MLP neural network to calculate the adjustment amounts corresponding to the water curtain barrier and the air curtain barrier; The calculation formula for the adjustment amount of the water curtain barrier in the MLP neural network is: ; Where, is the output of the MLP neural network of the water curtain barrier; is the mapping function of the water curtain barrier in the MLP neural network; is the input gas concentration data; is the input gas concentration change rate; is the ambient temperature, is the ambient humidity; The calculation formula for the adjustment amount of the air curtain barrier in the MLP neural network is: ; Where, is the output of the MLP neural network of the air curtain barrier; is the mapping function of the air curtain barrier in the MLP neural network; Combine the fuzzy output and the MLP neural network output to calculate the final adjustment amounts of the water curtain barrier and the air curtain barrier; The calculation formula for the final adjustment amount of the water curtain barrier is: ; Where, is the final adjustment amount of the water curtain barrier; is the fuzzy output of the water curtain barrier; is the weight of the fuzzy output; is the weight of the MLP neural network output; The calculation formula for the final adjustment amount of the air curtain barrier is: ; Where, is the final adjustment amount of the air curtain barrier; is the fuzzy output of the air curtain barrier; Generate the control parameter adjustment instructions corresponding to the water curtain barrier and the air curtain barrier according to the calculated final adjustment amounts.

[0010] A further improvement of this technical solution is that the predefined fuzzy rules are: When the gas concentration is higher than the second preset concentration threshold and the gas concentration change rate is positive, increase the water curtain intensity and enhance the air curtain intensity; When the gas concentration is lower than the second preset concentration threshold and the gas concentration change rate is negative, reduce the water curtain intensity and lower the air curtain intensity.

[0011] A further improvement of this technical solution is that step S5 specifically includes: The local controllers of the water curtain barrier and the air curtain barrier receive the corresponding control parameter adjustment instructions and parse the control parameters in the instructions; the control parameters of the water curtain barrier include the spraying angle, flow rate, and atomization degree of the nozzles; the control parameters of the air curtain barrier include the wind speed and wind direction of the fans; Perform CRC check on the received control parameter adjustment instructions. If the check fails, trigger the preset retransmission mechanism; After the check is successful, control the water curtain barrier and the air curtain barrier to operate according to the control parameters.

[0012] In a second aspect, the present invention provides an adaptive barrier system for gob gas, including: A data acquisition module for real-time monitoring of the gas concentration and distribution in the gob and the working face through distributed gas sensors; A barrier module for dynamically forming a continuous water curtain barrier at the gob boundary according to the gas concentration and distribution data, forming an adjustable air curtain barrier outside the water curtain barrier, and performing real-time monitoring on the water curtain barrier and the air curtain barrier; A barrier strength calculation module for calculating the barrier strength index based on the gas concentration data and the real-time monitoring data of the water curtain barrier and the air curtain barrier; An adjustment instruction generation module for generating control parameter adjustment instructions corresponding to the water curtain barrier and the air curtain barrier respectively based on the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the air curtain barrier, based on a fuzzy rule base and an MLP neural network; A parameter adjustment module for controlling the water curtain barrier and the air curtain barrier to adjust their operating parameters according to the corresponding control parameter adjustment instructions respectively, so as to achieve a dual adaptive barrier for gob gas.

[0013] In a third aspect, the present invention provides a terminal, including: A processor and a memory, where The memory is used to store a computer program, The processor is used to call and run the computer program from the memory, so that the terminal executes the method of the above terminal.

[0014] In a fourth aspect, the present invention provides a computer storage medium, in which instructions are stored, and when it runs on a computer, it causes the computer to execute the methods described in the above aspects.

[0015] The beneficial effects of the present invention are as follows. According to the real-time monitored gas concentration and distribution data, the present invention dynamically forms a continuous water curtain barrier and an adjustable wind curtain barrier with adjustable wind direction, forming a double barrier to effectively block the diffusion of harmful gases in the goaf. By calculating the barrier strength index, combining with the fuzzy rule base and the MLP neural network, precise control parameter adjustment instructions are generated to realize the real-time dynamic adjustment of the water curtain barrier and the wind curtain barrier, improving the blocking effect and energy utilization efficiency. The adaptive barrier method of the present invention can dynamically adjust the operating parameters of the water curtain barrier and the wind curtain barrier according to the real-time change of the gas concentration, forming an efficient gas blocking barrier. By effectively monitoring and blocking the diffusion of harmful gases in the goaf, the present invention can significantly reduce the occurrence probability of safety accidents such as mine fires and explosions, and ensure the life safety and physical health of miners.

[0016] In addition, the design principle of the present invention is reliable and the structure is simple, having a very wide application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0019] Figure 2 It is a schematic block diagram of the system according to an embodiment of the present invention.

[0020] Figure 3 It is a schematic structural diagram of a terminal provided by an embodiment of the present invention.

[0021] 210 is a data acquisition module, 220 is a barrier module, 230 is a barrier strength calculation module, 240 is an adjustment instruction generation module, and 250 is a parameter adjustment module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the purpose, features, and advantages of the present invention more obvious and understandable, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the specific embodiments of the present invention. Obviously, the following described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs. The terms used in the description of the present invention herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention.

[0024] Figure 1 is a schematic flowchart of a method according to an embodiment of the present invention. Among them, Figure 1 The execution subject can be an adaptive barrier system for gob gas. According to different requirements, the order of steps in this flowchart can be changed, and some can be omitted.

[0025] As Figure 1 shown, the method includes: Step S1: Real-time monitor the gas concentration and distribution in the gob and the working face through distributed gas sensors; Step S2: Dynamically form a continuous water curtain barrier at the gob boundary according to the gas concentration and distribution data, form an adjustable wind curtain barrier outside the water curtain barrier, and real-time monitor the water curtain barrier and the wind curtain barrier; Step S3: Calculate the barrier strength index based on the gas concentration data and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; Step S4: Generate control parameter adjustment instructions corresponding to the water curtain barrier and the wind curtain barrier respectively based on the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the wind curtain barrier, based on a fuzzy rule base and an MLP neural network; Step S5: The water curtain barrier and the wind curtain barrier respectively adjust their operating parameters according to the corresponding control parameter adjustment instructions to achieve a dual adaptive barrier for gob gas.

[0026] For the convenience of understanding the present invention, the principle of the adaptive barrier method for gob gas of the present invention is described below, and the process of adaptive barrier for gob gas in the embodiment is combined to further describe the adaptive barrier method for gob gas provided by the present invention.

[0027] Step S1: Real-time monitor the gas concentration and distribution in the gob and the working face through distributed gas sensors. The specific method includes: Step S11: Deploy a distributed gas sensor network at key positions on the gob boundary and the working face, and the distributed gas sensor network is arranged in a honeycomb topology; Step S12: Synchronously obtain the real-time gas concentration data of each sensor, perform Kalman filter denoising processing on the original gas concentration data, and generate a dynamic gas concentration sequence with time stamp marks; Step S13: Integrate the denoised gas concentration data corresponding to multiple nodes, construct a three-dimensional gas concentration distribution model through a preset spatial interpolation algorithm, generate a gas concentration heat map covering the goaf and the working face in real time, and update the data at a preset frequency.

[0028] Specifically, step S11: Deploy a distributed gas sensor network.

[0029] First, a distributed gas sensor (including a laser absorption spectroscopy sensor) network was deployed at key positions on the goaf boundary and the working face (such as the intersection of the goaf boundary and the roadway, the top and bottom of the goaf, areas with complex geological structures, the advancing front of the working face, abandoned roadways around the goaf, and areas with high gas emissions). These sensor nodes are arranged in a honeycomb topology to ensure comprehensive coverage monitoring of the gas concentration in the goaf and the working face. The choice of the honeycomb topology is based on its efficient data transmission characteristics and redundancy. Each sensor node can exchange data with adjacent nodes, forming a multi-path data transmission network, which improves the reliability and stability of the monitoring system.

[0030] Step S12: Real-time data acquisition and denoising processing.

[0031] Subsequently, the real-time gas concentration data of each sensor was acquired synchronously. To ensure the accuracy of the data, Kalman filter denoising processing was performed on the original gas concentration data. Kalman filter is an efficient data processing algorithm that can estimate the optimal state value through iterative calculation in the presence of noise interference. In this embodiment, Kalman filter was used to remove the noise components in the gas concentration data and generate a dynamic gas concentration sequence with timestamp marks. The timestamp marks enable the tracing of the acquisition time of each data point, providing convenience for subsequent data analysis and trend prediction.

[0032] Step S13: Construct a three-dimensional gas concentration distribution model.

[0033] Finally, integrate the denoised gas concentration data corresponding to multiple nodes and construct a three-dimensional gas concentration distribution model through a preset spatial interpolation algorithm. The spatial interpolation algorithm is a mathematical method for estimating the values of unknown data points based on known data points. In this embodiment, the Kriging interpolation algorithm was adopted, which takes into account the spatial correlation and variability of the data points and can generate a more accurate three-dimensional gas concentration distribution model. Through this model, a gas concentration heat map covering the goaf and the working face was generated in real time, and the data was updated at a preset frequency (such as every minute or every hour). The display of the gas concentration heat map enables the mine managers to intuitively understand the spatial distribution of the gas concentration in the goaf, providing strong data support for subsequent treatment measures.

[0034] Step S2: Based on the gas concentration and distribution data, a continuous water curtain barrier is dynamically formed at the goaf boundary, a wind curtain barrier with adjustable wind direction is formed outside the water curtain barrier, and the water curtain barrier and the wind curtain barrier are monitored in real time.

[0035] At the goaf boundary, based on the gas concentration and distribution data obtained in Step S1, the key areas where high-concentration gas is likely to accumulate and diffuse are determined through a three-dimensional gas concentration distribution model. For these areas, the layout of the water curtain barrier is planned to form a continuous coverage. Specifically, when laying out, based on the goaf boundary line, it is extended outward by a certain distance (such as 10 - 20 meters), and a nozzle group is arranged within this range. The nozzle groups are arranged at equal intervals along the boundary line to form a continuous water curtain barrier. Each nozzle group consists of multiple high-pressure fine water mist nozzles, and the nozzles are designed to be rotary and can achieve 360-degree all-round spraying. The spraying angle and flow rate of the nozzles are adjustable, and according to the gas concentration distribution, the spraying parameters of the nozzles are adjusted in real time through the central control system to achieve the best water curtain coverage effect. The nozzle groups are connected by flexible water pipes to ensure the continuity of the water curtain. The water source comes from the mine water supply system, and the water is pressurized to a set pressure (such as 5 - 8 MPa) by a high-pressure water pump and then transported to each nozzle group. A pressure sensor and a regulating valve are set at the outlet of the water pump to monitor and adjust the water supply pressure in real time to ensure the stability and spraying effect of the water curtain. According to the change of the gas concentration distribution data, the central control system adjusts the spraying parameters of the water curtain barrier in real time. When the gas concentration in a certain area exceeds the preset threshold, the spraying flow rate and angle of the nozzle group in this area are automatically increased to strengthen the blocking effect of the water curtain; when the gas concentration drops to the safe range, the spraying flow rate is reduced to save water resources.

[0036] Outside the water curtain barrier, a fan array is arranged along the direction of the water curtain barrier to form an adjustable wind direction wind curtain barrier. The layout of the fan array corresponds to the sprinkler group of the water curtain barrier to ensure that the wind curtain can cover the outer area of the water curtain barrier and form a double barrier. Axial flow fans with high efficiency and energy saving are used for the fans, which have characteristics such as large air volume, low noise, and adjustable wind direction. Each fan is equipped with an independent frequency converter and control system to achieve precise adjustment of the wind speed and wind direction. The fan array is fixed on the top or side wall of the roadway through brackets to ensure stable operation. According to the gas concentration distribution data and wind direction information, the central control system adjusts the wind speed and wind direction of the fan array in real time. When the gas concentration in a certain area increases, the wind speed of the fans in that area is increased to strengthen the blocking effect of the wind curtain; at the same time, according to the change of the wind direction, the wind direction of the fans is adjusted to ensure that the wind curtain is always opposite to the diffusion direction of harmful gases. When the gas concentration drops to the safe range, the wind speed of the fans is reduced to reduce energy consumption. During the control process of the water curtain barrier and the wind curtain barrier, coordinated control of the two is achieved. When the gas concentration in a certain area increases, the spray flow rate of the water curtain barrier and the wind speed of the wind curtain barrier in that area are increased simultaneously to form a double-strengthened blocking effect; when the gas concentration decreases, the operating parameters of both are reduced simultaneously to achieve energy-saving operation.

[0037] Monitoring sensors are deployed at key positions of the water curtain barrier and the wind curtain barrier, including gas concentration sensors, flow sensors, pressure sensors, wind speed sensors, and wind direction sensors, etc. These sensors monitor the operating parameters of the water curtain and the wind curtain and the gas concentration changes in the surrounding environment in real time. According to the monitoring data and analysis results, the central control system adjusts the operating parameters of the water curtain and the wind curtain in real time. When it is detected that the gas concentration in a certain area increases abnormally, the early warning mechanism is immediately triggered, and the parameters of the water curtain and the wind curtain in that area are automatically adjusted to strengthen the blocking effect. At the same time, the abnormal situation is notified to the mine management personnel so that further treatment measures can be taken in a timely manner.

[0038] Among them, the control methods of the water curtain barrier and the wind curtain barrier in step S2 include: Step S21: Divide the generated gas concentration heat map into several grid cells, and judge whether the gas concentration corresponding to the grid cell exceeds the first preset concentration threshold; Step S22: If the gas concentration of any grid cell exceeds the first preset concentration threshold, trigger the linkage start command of the water curtain barrier and the wind curtain barrier; Step S23: If the gas concentrations of several adjacent grid cells exceed the first preset concentration threshold at the same time, determine the corresponding area as the diffusion risk area, and start the sprinkler group with the corresponding coordinates in the water curtain barrier and the fan array with the corresponding coordinates in the wind curtain barrier; Step S24: Monitor the water curtain barrier and the wind curtain barrier in real time.

[0039] In addition, the formula for calculating the barrier strength index in step S3 is: ; wherein, is the barrier strength index, is the current gas concentration, is the target gas concentration, is the current water curtain flow rate, is the target water curtain flow rate, is the current air curtain pressure, is the target air curtain pressure, , , are the weight coefficients.

[0040] Among them, the water curtain plays a role in blocking, diluting, and settling gas pollutants in the barrier system. The ratio of the target water curtain flow rate to the current water curtain flow rate reflects the comparison between the amount of water required for the water curtain barrier to achieve the ideal efficiency and the actual amount of water. If , it indicates that the current water curtain flow rate is insufficient and may not be able to effectively block and dilute the gas pollutants at the target concentration; conversely, if , it means that the current water curtain flow rate is relatively sufficient, but there may be a problem of water resource waste. A stable water curtain flow rate is the key to ensuring the continuous and effective operation of the barrier system. By monitoring the change of , the fluctuation of the water curtain flow rate can be detected in a timely manner, and corresponding measures can be taken to adjust the water curtain flow rate to ensure the stability of the barrier system.

[0041] The air curtain plays a role in isolating and guiding the air flow in the barrier system. The ratio of the target air curtain pressure to the current air curtain pressure reflects the comparison between the air pressure required for the air curtain barrier to achieve the ideal efficiency and the actual air pressure. If , it indicates that the current air curtain pressure is insufficient and may not be able to effectively isolate the intrusion of external gases; conversely, if , there may be excessive air pressure, resulting in increased energy consumption and resource waste. The reasonable adjustment of the air curtain pressure can guide the air flow direction and reduce the diffusion of gas pollutants in the barrier area. By monitoring the change of , the air curtain pressure setting can be optimized to improve the barrier effect.

[0042] The water curtain and the air curtain have a synergistic effect in the barrier system. The water curtain can wash and dilute some gaseous pollutants, while the air curtain can further block the diffusion of the remaining gases. Incorporating the water curtain flow rate and the air curtain pressure into the barrier strength index evaluation system can more comprehensively reflect the overall effectiveness of the barrier system. For example, when the water curtain flow rate is insufficient, even if the air curtain pressure is large, it may not be able to completely block the gas diffusion; and when the air curtain pressure is insufficient, even if the water curtain flow rate is sufficient, there may still be a risk of gas leakage. Therefore, only when the water curtain flow rate and the air curtain pressure act synergistically can the best barrier effect be achieved.

[0043] The gas concentration data comes from the three-dimensional gas concentration model generated in step S1; the water curtain flow rate and the air curtain pressure data are collected by the real-time monitoring module in step S2; the target parameters ( , , ) are dynamically set according to the mine geological conditions and stored in the system database. (Gas concentration weight): The default value is 0.5, reflecting the direct impact of gas concentration exceeding the standard on safety; (Water curtain weight): The default value is 0.3, reflecting the dominant role of the water curtain as a physical barrier; (Air curtain weight): The default value is 0.2, characterizing the auxiliary blocking and air flow guiding functions of the air curtain. The dynamic adjustment mechanism of the parameters is as follows: Adaptive adjustment based on the gas diffusion rate: When the concentration change rate (ΔCg / Δt) exceeds the threshold (such as +0.5% / min), automatically increase to 0.6, and decrease , to 0.3 and 0.1, giving priority to suppressing gas diffusion; when the concentration is stable (ΔCg / Δt < ±0.1% / min), restore the default weights.

[0044] Environmental factor compensation adjustment: In a high-temperature (T > 35°C) environment, due to increased water curtain evaporation, decrease to 0.25, and increase to 0.25; in a high-humidity (H > 80%) environment, the air curtain resistance increases, decrease to 0.15, and increase to 0.55.

[0045] Recalculate the weights every 24 hours according to the historical control effect (such as the barrier efficiency η): ; where k is the learning rate (default 0.05); is the current barrier efficiency η, is the target barrier efficiency η, is the weight before recalculation; is the recalculated weight.

[0046] Step S4: Based on the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the air curtain barrier, generate control parameter adjustment instructions corresponding to the water curtain barrier and the air curtain barrier respectively based on the fuzzy rule base and the MLP neural network. The specific method includes: Step S41: Convert the input parameters into corresponding fuzzy language variables. The input parameters include the barrier strength index BI and the monitored gas concentration, concentration change rate, ambient temperature, and ambient humidity. Step S42: According to the predefined fuzzy rules, match the fuzzy values of the input parameters and generate fuzzy outputs. Step S43: Input the input parameters into the trained MLP neural network to calculate the adjustment amounts corresponding to the water curtain barrier and the air curtain barrier. The calculation formula for the adjustment amount of the water curtain barrier in the MLP neural network is: ; where is the output of the MLP neural network for the water curtain barrier; is the mapping function of the water curtain barrier in the MLP neural network; is the input gas concentration data; is the input gas concentration change rate; is the ambient temperature, is the ambient humidity; The calculation formula for the adjustment amount of the air curtain barrier in the MLP neural network is: ; where is the output of the MLP neural network for the air curtain barrier; is the mapping function of the air curtain barrier in the MLP neural network; Step S44: Combine the fuzzy output and the MLP neural network output to calculate the final adjustment amounts of the water curtain barrier and the air curtain barrier. The calculation formula for the final adjustment amount of the water curtain barrier is: ; where is the final adjustment amount of the water curtain barrier; is the fuzzy output of the water curtain barrier; is the weight of the fuzzy output; is the weight of the MLP neural network output; The calculation formula for the final adjustment amount of the air curtain barrier is: ; where is the final adjustment amount of the air curtain barrier; is the fuzzy output of the air curtain barrier; Step S45: Generate control parameter adjustment instructions for the water curtain barrier and the air curtain barrier according to the calculated final adjustment amount.

[0047] Among them, the predefined fuzzy rules are: When the gas concentration is higher than the second preset concentration threshold and the gas concentration change rate is positive, increase the water curtain intensity and enhance the air curtain intensity; When the gas concentration is lower than the second preset concentration threshold and the gas concentration change rate is negative, reduce the water curtain intensity and lower the air curtain intensity.

[0048] Specifically, according to the physical characteristics and control requirements of goaf gas control, the following key input parameters are selected and their fuzzy semantic levels are defined: Barrier Intensity Index (BI): Reflects the comprehensive efficiency of the current barrier system, divided into three levels: "weak" (BI ≤ 0.4), "medium" (0.4 < BI ≤ 0.7), "strong" (BI > 0.7); Gas Concentration (Cg): Based on the target concentration Ct, divided into "low risk" (Cg < 0.8Ct), "critical risk" (0.8Ct ≤ Cg ≤ 1.2Ct), "high risk" (Cg > 1.2Ct); Concentration Change Rate (ΔCg / Δt): Characterizes the gas diffusion trend, divided into "negative" (ΔCg / Δt < -1 ppm / s), "stable" (-1 ppm / s ≤ ΔCg / Δt ≤ +1 ppm / s), "positive" (ΔCg / Δt > +1 ppm / s); Ambient Temperature (T): According to the mine safety regulations, divided into "low temperature" (T < 5°C), "normal temperature" (5°C ≤ T ≤ 30°C), "high temperature" (T > 30°C); Ambient Humidity (H): According to the water curtain evaporation characteristics, divided into "dry" (H < 30%), "moderate" (30% ≤ H ≤ 70%), "humid" (H > 70%).

[0049] The Gaussian membership function is used to describe the "low risk" and "high risk" levels, with the center points set at 0.6Ct and 1.4Ct respectively, and the standard deviation of 0.2Ct, reflecting the sensitive response to extreme risks; the "critical risk" level uses a triangular function, with the vertex at Ct and the base span from 0.8Ct to 1.2Ct, ensuring a smooth transition to concentration fluctuations. The trapezoidal membership function is used to divide the "negative", "stable", and "positive" levels. For example, the "negative" interval covers ΔCg / Δt < -2 ppm / s to -0.5 ppm / s to avoid misjudgment caused by noise; the membership functions of ambient temperature and humidity use S-shaped and Z-shaped curves. For example, the S-shaped function of the "high temperature" level starts to rise at 25°C and reaches full membership at 35°C, adapting to the gradual change characteristics of mine temperature.

[0050] Among them, the fuzzification process is as follows: For each input parameter, calculate its membership degree value (0 - 1) in all relevant fuzzy sets; For example, when the gas concentration Cg = 1.1Ct, its membership degrees may be: "critical risk" 0.8, "high risk" 0.2, "low risk" 0; Through the combination of membership degree values, form a multi - dimensional fuzzy input vector to represent the semantic features of the current working condition.

[0051] The fuzzified semantic variables will drive the matching and reasoning of the fuzzy rule base: Rule triggering logic: According to the membership degree values of each parameter, select the semantic label with the highest membership degree as the main decision basis; for example, if the gas concentration belongs to both "critical risk" (0.6) and "high risk" (0.4) at the same time, then give priority to matching the rules related to "high risk".

[0052] Multi - parameter coupling processing: For parameters with coupling relationships (such as high temperature and dryness), design a composite fuzzy set (such as "high - temperature dryness"), and its membership degree is the product of the membership degrees of temperature and humidity; distinguish the importance of parameters through weight assignment. For example, the weight coefficient of gas concentration is 0.6, and that of environmental parameters is 0.4.

[0053] The present invention effectively filters sensor noise and short - term interference through fuzzification processing, reducing the false alarm rate; through semantic mapping, the system can process imprecise inputs (such as "relatively high concentration") and adapt to the complex and changeable underground environment; the unified semantic framework realizes the cross - dimensional correlation analysis of gas concentration, environmental parameters and equipment status.

[0054] Step S5: The water curtain barrier and the air curtain barrier respectively adjust their operating parameters according to the corresponding control parameter adjustment instructions to achieve a dual adaptive barrier for the gob gas. The specific method includes: Step S51: The local controllers of the water curtain barrier and the air curtain barrier receive the corresponding control parameter adjustment instructions and parse the control parameters in the instructions; the control parameters of the water curtain barrier include the spraying angle, flow rate and atomization degree of the nozzles; the control parameters of the air curtain barrier include the wind speed and wind direction of the fan; Step S52: Perform CRC check on the received control parameter adjustment instructions. If the check fails, trigger a preset re - transmission mechanism; Step S53: After the check is successful, control the water curtain barrier and the air curtain barrier to operate according to the control parameters.

[0055] Specifically, according to the final adjustment amount of the water curtain barrier , calculate the spraying angle of the nozzle : ; Among them, is the initial spraying angle of the nozzle; is the angle adjustment step size (preset constant); is the piecewise adjustment function of the spraying angle, and its calculation formula is: ; Among them, and are both the final preset adjustment amounts of the water curtain barrier, and .

[0056] According to the final adjustment amount of the water curtain barrier, calculate the flow rate Q of the nozzle, and its calculation formula is: ; Among them, is the initial flow rate of the nozzle; is the flow rate adjustment step size; is the adjustment function of the nozzle flow rate, and its calculation formula is: ; Among them, is the adjustment proportional coefficient.

[0057] According to the final adjustment amount of the water curtain barrier, calculate the atomization degree D of the nozzle, and its calculation formula is: ; Among them, is the initial atomization degree of the nozzle; is the atomization adjustment step size; is the adjustment function of the nozzle atomization degree, which is adjusted based on the humidity of the goaf (a humidity sensor is installed in the goaf), and its calculation formula is: ; Among them, and are both the preset humidity thresholds of the goaf, and .

[0058] According to the final adjustment amount of the air curtain barrier, calculate the wind speed V of the fan, and its calculation formula is: ; Among them, is the initial wind speed obtained by the fan; is the wind speed adjustment step size of the fan; is the adjustment function of the fan wind speed, and its calculation formula is: ; Among them, and is the cross-sectional area threshold of the gob roadway, and .

[0059] According to the final adjustment amount of the air curtain barrier , calculate the wind direction deviation angle of the fan , and its calculation formula is:[[]] ; Among them, is the initial wind direction of the fan; is the adjustment step of the fan wind direction; is the adjustment function of the fan wind direction deviation angle, and its calculation formula is:[[]] ; Among them, the deviation of the air flow field is determined by the data of the air flow field sensor installed in the gob.[[]]

[0060] In addition, the adaptive barrier method of the present invention optimizes energy consumption while ensuring the gas barrier effect by introducing an energy efficiency index (Energy Efficiency Index, EEI). The calculation formula of EEI is:[[]] EEI=(QgΔC) / (Ew+Ef); Among them, EEI is the energy efficiency index, Qg is the gas treatment volume, ΔC is the gas concentration reduction, Ew is the water curtain energy consumption, and Ef is the air curtain energy consumption. This index is used to quantify the gas treatment efficiency of the system under unit energy consumption and guide the energy-saving optimization of the control strategy.[[]]

[0061] Specifically, the gas treatment volume Qg is calculated according to the gob volume and the gas diffusion rate:[[]] , where A is the cross-sectional area of the gob (m²), v is the average gas flow velocity (m / s, obtained through the wind speed sensor), and t is the system operation time (h). Dynamic calibration: When the three-dimensional gas concentration model detects a local concentration gradient, calculate the gas treatment volume Qg by region segmentation.[[]]

[0062] Concentration reduction amount ΔC: defined as the difference between the initial concentration Cinitial and the current concentration Ccurrent:[[]] , where the data source: the time-stamped concentration sequence generated in step S1, take the moving average value in the recent 1 hour.[[]]

[0063] The water curtain energy consumption Ew is calculated by the pump power and the operation time:[[]] , where Pw is the rated power of the pump (kW), and tw is the effective operation time of the pump (h, excluding the intermittent shutdown period).[[]]

[0064] The air curtain energy consumption Ef is fitted based on the output frequency and power curve of the fan inverter:[[]] , where \(f_i\) is the real-time frequency (Hz) of the \(i\)-th fan, and \(k_f\) is the fan power coefficient.

[0065] By integrating the EEI energy efficiency index, the present invention realizes the dual optimization of the performance and energy consumption of the gob gas barrier system. The EEI not only drives the adjustment of the control strategy as the core parameter for real-time monitoring, but also ensures the efficient and reliable operation of the system in a complex underground environment through the dynamic weight correction and anomaly handling mechanism.

[0066] In some embodiments, the adaptive barrier system 200 for gob gas may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the adaptive barrier system 200 for gob gas can be stored in the memory of the computer device and executed by at least one processor to execute (see the details in Figure 1 the description) the adaptive barrier function of gob gas.

[0067] In this embodiment, the adaptive barrier system 200 for gob gas can be divided into multiple functional modules according to the functions it performs, as Figure 2 shown. The functional modules may include: a data acquisition module 210, a barrier module 220, a barrier strength calculation module 230, an adjustment instruction generation module 240, and a parameter adjustment module 250. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in the subsequent embodiments.

[0068] Specifically, the data acquisition module 210 is used to real-time monitor the gas concentration and distribution in the gob and the working face through distributed gas sensors; the barrier module 220 is used to dynamically form a continuous water curtain barrier at the gob boundary according to the gas concentration and distribution data, form an adjustable wind curtain barrier outside the water curtain barrier, and real-time monitor the water curtain barrier and the wind curtain barrier; the barrier strength calculation module 230 is used to calculate the barrier strength index based on the gas concentration data and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; the adjustment instruction generation module 240 is used to generate control parameter adjustment instructions corresponding to the water curtain barrier and the wind curtain barrier respectively based on the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the wind curtain barrier, based on the fuzzy rule base and the MLP neural network; the parameter adjustment module 250 is used to control the water curtain barrier and the wind curtain barrier to adjust the operation parameters respectively according to the corresponding control parameter adjustment instructions, so as to realize the dual adaptive barrier for gob gas.

[0069] Figure 3FIG. 0 is a schematic structural diagram of a terminal 300 provided by an embodiment of the present invention, and the terminal 300 can be used to execute the method for adaptively shielding gob gas provided by the embodiment of the present invention.

[0070] Among them, the terminal 300 may include: a processor 310, a memory 320, and a communication module 330. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation to the present invention. It can be a bus structure, a star structure, and may also include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0071] Among them, the memory 320 can be used to store the execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can execute some or all of the steps in the above method embodiments.

[0072] The processor 310 is the control center of the storage terminal, connecting various parts of the entire electronic terminal through various interfaces and lines. By running or executing the software programs and / or modules stored in the memory 320, and calling the data stored in the memory, it executes various functions of the electronic terminal and / or processes data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or can be composed of connecting multiple packaged ICs with the same or different functions. For example, the processor 310 may only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single arithmetic core or can include multiple arithmetic cores.

[0073] The communication module 330 is used to establish a communication channel, so that the storage terminal can communicate with other terminals. Receive user data sent by other terminals or send user data to other terminals.

[0074] The present invention also provides a computer storage medium. The computer storage medium can store a program which, when executed, can include some or all of the steps in the various embodiments provided by the present invention. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), or the like.

[0075] Those skilled in the art can clearly understand that the technologies in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the part that makes contributions to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes, and includes several instructions for causing a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0076] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the descriptions in the method embodiments.

[0077] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or modules can be in electrical, mechanical, or other forms.

[0078] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they can be located in one place, or can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, in each embodiment of the present invention, each functional module may be integrated into one processing module, may exist physically alone for each module, or two or more modules may be integrated into one module.

[0080] Although the present invention has been described in detail by referring to the accompanying drawings and in conjunction with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope of the present invention. / Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, and all should be covered within the protection scope of the present invention.

Claims

1. An adaptive barrier method for goaf gas, characterized in that: include: Step S1, real-time monitoring of gas concentration and distribution in goaf and working face by distributed gas sensors; Step S2: dynamically forming a continuous water curtain barrier at the boundary of the goaf according to the gas concentration and distribution data, forming a wind curtain barrier with adjustable wind direction outside the water curtain barrier, and performing real-time monitoring on the water curtain barrier and the wind curtain barrier; Step S3, calculating the barrier strength index based on the gas concentration data and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; Step S4, generating control parameter adjustment instructions corresponding to the water curtain barrier and the wind curtain barrier respectively based on the fuzzy rule base and the MLP neural network according to the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; Step S5: the water curtain barrier and the wind curtain barrier adjust their operating parameters respectively according to the corresponding control parameter adjustment instructions to realize a dual adaptive barrier for the gas in the goaf.

2. The adaptive barrier method for goaf gas according to claim 1, characterized in that: Step S1 specifically includes: Deploy a distributed gas sensor network at key locations of the goaf boundary and working face, and arrange the distributed gas sensor network in a honeycomb topology structure; Synchronously obtain the real-time gas concentration data of each sensor, perform Kalman filter denoising on the raw gas concentration data, and generate a dynamic gas concentration sequence with a timestamp; The denoised gas concentration data corresponding to multiple nodes are integrated, and a three-dimensional gas concentration distribution model is constructed through a preset spatial interpolation algorithm. A gas concentration heat map covering the goaf and working face is generated in real time, and the data is updated at a preset frequency.

3. The adaptive barrier method for goaf gas according to claim 2, characterized in that: The control method of the water curtain barrier and the wind curtain barrier in step S2 includes: Divide the generated gas concentration heat map into a number of grid units, and determine whether the gas concentration corresponding to the grid unit exceeds a first preset concentration threshold; If the gas concentration of any grid unit exceeds the first preset concentration threshold, the water curtain barrier and wind curtain barrier linkage start instruction is triggered; If the gas concentrations of several adjacent grid cells simultaneously exceed the first preset concentration threshold, the corresponding area is determined to be a diffusion risk area, and the nozzle group at the corresponding coordinates in the water curtain barrier and the fan array at the corresponding coordinates in the wind curtain barrier are started; Real-time monitoring of water curtain barriers and wind curtain barriers.

4. The adaptive barrier method for goaf gas according to claim 3, characterized in that: The formula for calculating the barrier strength index in step S3 is: ； in, is the barrier strength index, is the current gas concentration, is the target gas concentration, is the current water curtain flow, is the target water curtain flow rate, is the current wind curtain pressure, is the target air curtain pressure, , , is the weight coefficient.

5. The adaptive barrier method for goaf gas according to claim 4, characterized in that: Step S4 specifically includes: Convert the input parameters into corresponding fuzzy linguistic variables, the input parameters include barrier strength index BI and monitored gas concentration, concentration change rate, ambient temperature, and ambient humidity; According to the predefined fuzzy rules, the fuzzy values ​​of the input parameters are matched to generate fuzzy outputs; Input the input parameters to the trained MLP neural network to calculate the corresponding adjustment amount of the water curtain barrier and the wind curtain barrier; The calculation formula of the water curtain barrier adjustment amount in the MLP neural network is: ； in, It is the MLP neural network output of the water curtain barrier; is the mapping function of the water curtain barrier in the MLP neural network; is the input gas concentration data; is the input gas concentration change rate; is the ambient temperature, is the ambient humidity; The calculation formula of the wind curtain barrier adjustment amount in the MLP neural network is: ； in, The MLP neural network output of the wind curtain barrier; is the mapping function of the wind curtain barrier in the MLP neural network; Combining the fuzzy output and the MLP neural network output, the final water curtain barrier and wind curtain barrier adjustment amount are calculated; The calculation formula for the final adjustment of the water curtain barrier is: ； in, is the final adjustment amount of the water curtain barrier; It is the fuzzy output of the water curtain barrier; is the weight of the fuzzy output; is the weight output by the MLP neural network; The calculation formula for the final adjustment of the wind curtain barrier is: ； in, is the final adjustment amount of the wind curtain barrier; is the fuzzy output of the wind curtain barrier; According to the calculated final adjustment amount, control parameter adjustment instructions corresponding to the water curtain barrier and the wind curtain barrier are generated.

6. The adaptive barrier method for goaf gas according to claim 5, characterized in that: The predefined fuzzy rules are: When the gas concentration is higher than a second preset concentration threshold and the gas concentration change rate is positive, increasing the water curtain strength and the wind curtain strength; When the gas concentration is lower than the second preset concentration threshold and the gas concentration change rate is negative, the water curtain intensity is reduced and the wind curtain intensity is lowered.

7. The adaptive barrier method for goaf gas according to claim 5, characterized in that: Step S5 specifically includes: The local controllers of the water curtain barrier and the wind curtain barrier receive the corresponding control parameter adjustment instructions and parse the control parameters in the instructions; the control parameters of the water curtain barrier include the spray angle, flow rate and atomization degree of the nozzle; the control parameters of the wind curtain barrier include the wind speed and wind direction of the fan; Perform CRC check on the received control parameter adjustment instruction, and trigger the preset retransmission mechanism if the check fails; After successful verification, the water curtain barrier and wind curtain barrier are controlled to operate according to the control parameters.

8. An adaptive barrier system for goaf gas, characterized in that: include: Data acquisition module, used to monitor the gas concentration and distribution in goaf and working face in real time through distributed gas sensors; The barrier module is used to dynamically form a continuous water curtain barrier at the boundary of the goaf area according to the gas concentration and distribution data, form a wind curtain barrier with adjustable wind direction outside the water curtain barrier, and conduct real-time monitoring of the water curtain barrier and the wind curtain barrier; A barrier strength calculation module is used to calculate the barrier strength index based on the gas concentration data and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; An adjustment instruction generation module is used to generate control parameter adjustment instructions corresponding to the water curtain barrier and the wind curtain barrier respectively based on the fuzzy rule base and the MLP neural network according to the calculated barrier strength index and the real-time monitoring data of the water curtain barrier and the wind curtain barrier; The parameter adjustment module is used to control the water curtain barrier and the wind curtain barrier to adjust the operating parameters according to the corresponding control parameter adjustment instructions, so as to realize a dual adaptive barrier for the gas in the goaf.

9. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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