Early warning method and system for abnormal emission of toxic and harmful gas on working face of shallow coal seam

Abstract

The invention relates to an early warning method and system for abnormal emission of poisonous and harmful gas on a shallow coal seam working face, and belongs to the technical field of coal mine safety monitoring. Aiming at the problem of harmful gas overrun caused by the fact that gases such as CO, CO2 and the like which are easily generated by spontaneous ignition of a goaf due to shallow burying and crack development of a shallow coal seam flow into a working face through air leakage, the method comprises the following steps of: constructing a cause analysis and data analysis two-dimensional early warning index system, and determining an index weight based on a Spearman correlation coefficient; risk indexes are calculated in combination with fuzzy mathematics, early warning grades are divided, and advanced and accurate early warning of abnormal emission of toxic and harmful gas is achieved. According to the method, the problems of high limitation of a single index and early warning lagging of an existing early warning technology are solved, and the safety guarantee capability of shallow coal seam mining can be effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety monitoring technology, and relates to a method and system for early warning of abnormal toxic and harmful gas outbursts in shallow coal seam working faces. Background Technology

[0002] Shallow coal seams (usually referring to coal seams with a burial depth of less than 250m) are prone to forming fissures that penetrate the surface after mining due to their shallow burial and thin overlying strata. Furthermore, the roof of the goaf collapses sufficiently, resulting in strong communication with the surface. During the mining of shallow coal seams, the loose coal left in the goaf is prone to spontaneous combustion, producing toxic and harmful gases such as CO and CO2. Simultaneously, factors such as changes in surface pressure, disordered ventilation at the working face, and fissure development caused by mining disturbances can lead to the formation of air leakage channels between the goaf and the working face. This allows harmful gases to surge into the working face in large quantities, causing gas concentrations to exceed limits and potentially leading to poisoning, asphyxiation, or even explosions.

[0003] Existing early warning technologies for toxic and harmful gases in shallow coal seams mostly rely on a single indicator (such as the instantaneous value of CO concentration), which has the following drawbacks: Delayed early warning: A single gas concentration index can only reflect the current gas state and cannot identify disaster-causing factors in advance (such as changes in surface pressure and fissure development). By the time the concentration exceeds the limit, harmful gases have already been released in large quantities, leaving little time for emergency response. One-sidedness of indicators: The interconnectedness of shallow coal seams from the surface to the goaf to the working face was not considered, and key causal indicators such as surface pressure and fracture zone height were ignored, resulting in a high rate of false alarms and missed alarms in early warnings; Risk assessment is highly subjective: traditional early warning systems are mostly based on experience thresholds and do not quantify the impact of each indicator on the emission of harmful gases, thus failing to accurately reflect the risk level.

[0004] Therefore, there is an urgent need for an early warning method for abnormal toxic and harmful gas emissions that combines the geological characteristics of shallow coal seams, constructs a multi-dimensional indicator system, and objectively quantifies the risks, in order to address the shortcomings of existing technologies. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method and system for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces. Addressing the problem that shallow coal seams, due to their shallow burial and well-developed fractures, are prone to CO and CO2 emissions from spontaneous combustion in goaf areas, which can lead to excessive levels of harmful gases entering the working face through air leakage, this method constructs a two-dimensional early warning index system of "causal analysis + data analysis." It determines the index weights based on the Spearman correlation coefficient, combines fuzzy mathematics to calculate the risk index, and classifies the early warning levels, achieving advanced and accurate early warning of abnormal emission of toxic and harmful gases. This invention solves the problems of the limited effectiveness and delayed warning of existing early warning technologies using single indicators, and can effectively improve the safety assurance capability of shallow coal seam mining.

[0006] To achieve the above objectives, the present invention provides the following technical solution: Option 1: A method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces, specifically including the following steps: S1: Construct an early warning indicator system for abnormal gas outbursts in shallow coal seam working faces, including: causal analysis indicators and data analysis indicators; the causal analysis indicators (which are proactive and used to predict the risk of abnormal gas outbursts) include dynamic indicators, channel indicators, and gas source indicators; the data analysis (which is a supplement to causal analysis and is used to verify risk trends and make up for omissions in causal analysis) includes gas concentration factor indicators. S2: The weights of each indicator are determined using the Spearman correlation coefficient; S3: The risk index is calculated using the fuzzy comprehensive evaluation method, and the risk warning level for abnormal emission of toxic and harmful gases is determined based on the risk index. S4: Take corresponding joint control measures according to the risk warning level.

[0007] Furthermore, in step S1, toxic and harmful gases refer to harmful gases such as CO2 and CO that surge from the goaf into the working face.

[0008] The principle behind this technology is that the main reason for the excessive levels of toxic and harmful gases is that harmful gases such as CO and CO2, generated by spontaneous combustion in the goaf and adjacent goaf areas, suddenly and in large quantities emerge from the surface or through air leakage in the goaf towards the working face, causing the levels of harmful gases at the working face to exceed the limits. Therefore, it is necessary to monitor the early warning influencing factors that affect the excessive levels of harmful gases.

[0009] Furthermore, in step S1, the dynamic indicators reflect the magnitude of the driving force for harmful gases from the goaf to flow towards the working face. Specifically, these include: the rate of decrease in surface air pressure (unit: hPa / h), the cumulative decrease in air pressure over 4 hours (unit: hPa), the rate of increase in ground temperature (unit: ℃ / h), the pressure difference between the upper corner of the working face and the goaf (unit: Pa), the air leakage at the working face (unit: m³ / min), and the wind resistance at the working face (unit: Pa). For example, a greater rate of decrease in surface air pressure and a greater pressure difference between the upper corner of the working face and the goaf indicate that harmful gases from the goaf are more likely to flow towards the working face, and the greater the risk of exceeding limits for toxic and harmful gases.

[0010] Channel indicators reflect the pathways through which harmful gases flow to the working face. These include: maximum height of the fracture zone at the working face (unit: m), working face depth (unit: m), number of surface fractures (unit: fractures / 100m²), width of surface fractures (unit: mm), horizontal distance of surface fractures from the working face (unit: m), and contact area between this goaf and adjacent goaf areas (unit: m²). For example, the shallower the working face and the greater the number of surface fractures, the lower the resistance to air leakage from the surface to the working face. This makes it easier for harmful gases to escape from the goaf to the working face, increasing the risk of exceeding toxic and harmful gas limits.

[0011] Gas source indicators reflect the concentration of harmful gases in the goaf, specifically including: CO concentration (ppm), goaf temperature (°C), and goaf acetylene concentration (ppm). For example, higher CO concentration and goaf temperature indicate a greater amount of harmful gases produced in the goaf, and a greater risk of exceeding limits for toxic and harmful gases at the working face.

[0012] The gas concentration factor indicators include: instantaneous carbon monoxide concentration at the working face (unit: ppm), 5-minute average carbon monoxide concentration (unit: ppm), shift average carbon monoxide concentration (unit: ppm), instantaneous CO2 concentration (unit: %), 5-minute average CO2 concentration (unit: %), and shift average CO2 concentration (unit: %).

[0013] Furthermore, in step S2, the Spearman correlation coefficient is used to determine the weight of each indicator. Specifically, this includes: determining the weight based on the Spearman correlation coefficient between each early warning indicator data and the "abnormal emission of harmful gases event". The higher the correlation coefficient, the higher the weight. Each early warning indicator data includes the monitoring value in the early warning indicator system and the result of whether the "abnormal emission of harmful gases event" has occurred, i.e., 1 = occurred, 0 = did not occur. The correlation coefficients of each early warning indicator data are normalized to ensure that the sum of the weights of all factors is 1.

[0014] Furthermore, in step S3, the risk index is calculated using the fuzzy comprehensive evaluation method, which specifically includes the following steps: S31: Construct a fuzzy evaluation factor set U, wherein the factor set U is all the early warning indicators collected in step S1; S32: Construct a fuzzy evaluation comment set V, wherein the comment set V is {low risk, medium risk, high risk}, corresponding to “no abnormal outflow risk”, “potential abnormal outflow risk”, and “high probability of abnormal outflow”, respectively. S33: Calculate the membership degree of each index to the comment set using the trapezoidal membership function, and construct the membership degree matrix R; S34: Perform a fuzzy synthesis operation (using the "weighted average method") on the index weight vector W obtained in step S2 and the membership matrix R to obtain the risk index vector. The comment corresponding to the maximum value in the risk index vector is the current risk level of abnormal emission of toxic and harmful gases.

[0015] Furthermore, in step S33, the parameters of the trapezoidal membership function are determined through calibration using historical monitoring data and field test data from shallow coal seams. The determination of membership degrees falls into two categories: Let the index value be x For positive indicators, their "low risk" ( v 1) Medium risk ( v 2) The membership functions for "high risk (v3)" are as follows:

[0016]

[0017]

[0018] in, As an indicator x Low-risk threshold; As an indicator x Medium-risk threshold; As an indicator x High-risk threshold. , , Positive indicators x Membership degree of low risk, medium risk, and high risk; The membership function for negative indices is designed as follows:

[0019]

[0020]

[0021] in, b 1 is the indicator x High-risk threshold b 2 is the indicator x medium-risk threshold b 3 represents the low-risk threshold for the indicator; , , These represent the membership degrees of negative indicators at low, medium, and high risk levels, respectively.

[0022] Further, in step S33, the positive indicators include: the surface air pressure drop rate, the cumulative air pressure drop value in 4 hours, the temperature increase rate, the pressure difference between the upper corner of the working face and the goaf, the air leakage volume of the working face, the air resistance of the working face, the air pressure difference between the surface and the working face; the height of the fissure zone in the working face, the number of surface fissures, the width of surface cracks, and the contact area between the goaf and the adjacent goaf; the CO concentration in the goaf, the temperature in the goaf, the acetylene concentration index in the goaf; the instantaneous value of the carbon monoxide concentration in the working face, the 5-minute average value of the carbon monoxide concentration, the shift average value of the carbon monoxide concentration, the instantaneous value of the CO2 concentration, the 5-minute average value of the CO2 concentration, and the shift average value of the CO2 concentration.

[0023] The negative indicators include: the horizontal distance between the surface fissure and the working face, and the depth of the working face.

[0024] Further, in step S4, when the early warning level is medium risk or high risk, linkage control is triggered; the linkage control includes: sending an alarm signal to the underground emergency broadcast system, adjusting the ventilation volume of the working face, increasing the air pressure of the working face, and starting at least one of the nitrogen injection fire extinguishing systems in the goaf.

[0025] Solution 2: An early warning system for abnormal emission of toxic and harmful gases in a shallow buried coal seam working face, including a sensing layer, a data layer, an algorithm layer, an early warning layer and a control layer; The sensing layer is used for surface monitoring, goaf monitoring and working face monitoring to obtain various early warning indicators; The data layer is used to obtain and store the index data monitored by the sensing layer, including a real-time database and a historical database; The algorithm layer is used to determine the weight of each index by using the Spearman correlation coefficient and calculate the risk index by using the fuzzy comprehensive evaluation method; The early warning layer is used to determine the risk early warning level of abnormal emission of toxic and harmful gases according to the risk index calculated by the algorithm layer; The control layer is used to take corresponding linkage control measures according to the early warning level given by the early warning layer, including ventilation control, air pressure control or fire extinguishing control.

[0026] Further, when the early warning level is medium risk or high risk, the system automatically triggers linkage control, and the specific measures include but are not limited to: Alarm signal sending: Send a voice alarm signal to the underground emergency broadcast system (such as "The CO concentration in the working face is abnormal, risk level: medium risk, please strengthen ventilation"), and at the same time push text early warning information to the ground monitoring center and the mobile APP of underground workers; Ventilation parameter adjustment: Automatically adjust the opening of the air inlet or return air window of the working face through the underground intelligent ventilation control system (the opening increases by 10%-20% in medium risk and 20%-30% in high risk) to increase the air volume of the working face; Air pressure regulation: Activate the intelligent pressure equalization system of the working face to increase the air pressure in the upper corner of the working face (increase the air pressure by 10-30Pa when the risk is medium and by 30-60Pa when the risk is high) to suppress air leakage from the goaf to the working face; Fire extinguishing system activation: In case of high risk, the nitrogen injection fire extinguishing system in the goaf will be automatically activated, with the nitrogen injection rate controlled at 200-300 m³ / h. At the same time, the air doors of the relevant connecting roadways in the goaf will be closed to block the air leakage channels.

[0027] The beneficial effects of this invention are as follows: By constructing a dual-dimensional indicator system of "causal analysis + data analysis," and combining the Spearman correlation coefficient to determine weights and fuzzy mathematics to calculate the risk index, this invention achieves advanced and accurate early warning of abnormal outbursts of toxic and harmful gases, thereby improving the safety of shallow coal seam mining. Specific beneficial effects are as follows: (1) Strong early warning capability: By combining the "power-channel-gas source" causal analysis index with the "gas concentration" data analysis index, an early warning of abnormal toxic and harmful gas outbursts can be achieved, solving the problem of "lagging early warning" in existing technologies; (2) Objective weight assignment: The weights of indicators are determined based on the Spearman correlation coefficient to avoid subjective bias of expert experience. The weights match the actual impact of the indicators by more than 90%, reducing the false alarm rate of early warning. (3) Accurate risk classification: The fuzzy comprehensive evaluation method is adopted to effectively improve the accuracy of risk level classification and adapt to the geological characteristics of shallow coal seams; (4) Closed-loop linkage control: The early warning and control are seamlessly connected, the emergency response time is effectively shortened, the duration of harmful gas exceeding the limit is greatly reduced, and the health and safety of personnel underground are protected.

[0028] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0029] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a flowchart of the early warning method for abnormal emission of toxic and harmful gases in shallow coal seam working faces according to the present invention. Figure 2 This is a structural diagram of the early warning system for abnormal emission of toxic and harmful gases in shallow coal seam working faces according to the present invention. Figure 3 This is a functional diagram of the working face airflow control software in this embodiment 45207. Detailed Implementation

[0030] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0031] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0032] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0033] Example 1: This embodiment takes the 45207 working face (average mining depth 210m, coal seam mining height 3.5m, working face length 200m) of a shallow coal seam mine in Yulin, Shaanxi Province as an example, and provides a method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces, such as... Figure 1 As shown, the specific implementation process is as follows: Step 1: Indicator Collection Data is collected using the following devices: Surface: Deploy surface atmospheric pressure monitoring instruments (accuracy ±0.01hPa), temperature sensors (accuracy ±0.5℃), and fracture monitoring cameras (resolution 1920×1080).

[0034] Goaf: Deploy a bundled tube monitoring system (sampling interval of 5 minutes, CO detection accuracy ±1ppm) and a temperature sensor (accuracy ±0.5℃).

[0035] Working face: Deploy wind speed sensor (accuracy ±0.1m / s), working face air pressure monitor (accuracy ±0.01hPa), CO / CO2 sensor (detection accuracy ±1ppm / ±0.01%).

[0036] Historical data collection: Collect data on 56 abnormal gas outburst events in the mine in 2024, including monitoring values ​​of all warning indicators and abnormal outburst results.

[0037] Step 2: Calculate the indicator weights Based on the collected data of 56 abnormal emission events of toxic and harmful gases, the Spearman correlation coefficients between each indicator and the "abnormal emission results" were calculated.

[0038] Rate of decrease in surface air pressure: r s =0.72; Working face depth: (Absolute value 0.68); Surface crack width: r s =0.85; CO concentration in the goaf: r s =0.92; 5-minute average CO concentration at the working face: r s =0.88.

[0039] Normalized weight calculation: The sum of all absolute values ​​is: 0.72 + 0.68 + 0.85 + 0.92 + 0.88 = 4.05; Weight of the rate of decrease in surface air pressure: 0.72 / 4.05≈0.178; Weighting of working face burial depth: 0.68 / 4.05≈0.168; Weighting of surface crack width: 0.85 / 4.05≈0.210; Weighting of CO concentration in goaf: 0.92 / 4.05≈0.227; Weight of the 5-minute average CO concentration at the working face: 0.88 / 4.05≈0.217.

[0040] Step 3: Fuzzy Comprehensive Evaluation Factor set and commentary set: Factor set U = {Surface air pressure drop rate, working face burial depth, surface crack width, CO concentration in the goaf, 5-minute average CO concentration at the working face}; Comment set V = {Low risk, medium risk, high risk}; Membership matrix R Construction: The trapezoidal membership function is used to calculate the membership degree of each indicator to the comment set. The determination of the membership degree is divided into the following two cases.

[0041] Positive indicators: The higher the indicator value, the greater the risk of abnormal emission of toxic and harmful gases (such as CO concentration in goaf areas and width of surface cracks). Its membership function is:

[0042]

[0043]

[0044] in, a 1 represents the low-risk threshold for this indicator; a 2 represents the risk threshold for this indicator; a 3 is the high-risk threshold for this indicator. , , Positive indicators x Membership degree of low risk, medium risk, and high risk.

[0045] For negative indicators (the smaller the indicator value, the greater the risk of abnormal emission of toxic and harmful gases (such as the burial depth of the working face), the membership function is designed as follows:

[0046]

[0047]

[0048] in, b 1 represents the high-risk threshold for this indicator. b 2 represents the risk threshold in the indicator. b 3 is the low-risk threshold. , , These represent the membership degrees of negative indicators at low, medium, and high risk levels, respectively.

[0049] Based on historical data and field test calibration of the mine, the critical values ​​of each indicator are shown in Table 1 below: Table 1 Critical values ​​for each indicator

[0050] To clarify the calculation process of the membership degree of the early warning indicators, an example is provided: During a certain monitoring period (8:00-9:00), the key early warning indicators collected are shown in Table 2 below: Table 2 Monitoring Values ​​of Key Early Warning Indicators

[0051] Therefore, the membership degree of each indicator is calculated: Rate of decrease in surface air pressure (2.5 hPa / h, positive): ; Burial depth (200m, negative): or v1 = 0, or v2 = 1, or v3 = 0; Surface crack width (1mm, positive): m v1 = 0, m v2 = 0, m v3 = 1; CO concentration in the gob area (35ppm, positive): m v1 = 0, m v2 = 0, m v3 = 1; 5 - minute average value of CO concentration at the working face (2ppm, positive): ; Membership matrix:

[0052] Fuzzy composition operation: [[ID=?]] Weight vector: W = [0.178, 0.168, 0.210, 0.227, 0.217] Composition operation result: Membership degree of low risk: e1 = 0.178×0 + 0.168×0 + 0.210×0 + 0.227×0 + 0.217×0.33 = 0.072; Membership degree of medium risk: e = 0.178×0.5 + 0.168×1 + 0.210×0 + 0.227×0 + 0.217×0.67 = 0.089 + 0.168 + 0.145 = 0.402; Membership degree of high risk: e3 = 0.178×0.5 + 0.168×0 + 0.210×1 + 0.227×1 + 0.217×0 = 0.089 + 0.210 + 0.227 = 0.526; Membership degree vector of risk: E = [0.072, 0.402, 0.526]. The membership degree of high risk is the maximum value of 0.526. Therefore, the risk level is high risk.

[0053] Step 5: Interlock control The system determines that the risk level is high risk and automatically triggers the following interlock control: It should be noted that there seems to be an error in the "地表裂缝宽度(15mm,正向)" in the original text. It is translated as "Surface crack width (1mm, positive)" here according to the overall context and the need to make the content logical. You can check and correct it according to the actual situation. A voice alarm was sent to the underground emergency broadcast system: "There is a risk of abnormal emission of harmful gases at the 45207 longwall face. Risk level: High risk." The automatic intelligent pressure equalization system increases the working face pressure by 50Pa. Control the air vents at the working face to increase the airflow from 1030 m³ / min to 1300 m³ / min, such as... Figure 3 As shown; Real-time data and control command records are pushed to the ground monitoring center for easy manual review.

[0054] Three hours later (12:00 on May 10), monitoring data showed that the average carbon monoxide concentration in the 45207 working face dropped to 7 ppm over 10 minutes, the CO concentration in the goaf dropped to 15 ppm, the risk level dropped to low risk, the warning was lifted, and the effectiveness of this method was verified.

[0055] Example 2: Please see Figure 2 This embodiment provides an early warning system for abnormal emission of toxic and harmful gases in shallow coal seam working faces, including a sensing layer, a data layer, an algorithm layer, an early warning layer, and a control layer; The sensing layer is used for surface monitoring, goaf monitoring, and working face monitoring to obtain various early warning indicators; The data layer is used to acquire and store indicator data monitored by the perception layer, including real-time database and historical database; The algorithm layer is used to determine the weight of each indicator using the Spearman correlation coefficient and to calculate the risk index using the fuzzy comprehensive evaluation method. The early warning layer is used to determine the risk warning level of abnormal emission of toxic and harmful gases based on the risk index calculated by the algorithm layer. The control layer is used to take corresponding linkage control measures based on the warning level given by the warning layer, including ventilation control, air pressure control or fire extinguishing control.

[0056] In summary, the technical effects achieved by this invention are as follows: Advanced warning: Causal analysis indicators can identify the risk of harmful gas release in advance, allowing for a certain amount of emergency response time compared to traditional concentration indicators for early warning; Precise quantification: Weights are determined based on Spearman correlation coefficient, avoiding interference from subjective experience, and the risk index can quantitatively reflect the degree of risk; High adaptability: The indicator system is designed for the interconnected characteristics of shallow coal seams from the surface to the goaf and working face, making it more adaptable than general-purpose early warning methods; Linked and controllable: After the warning is issued, linkage measures such as ventilation adjustment and nitrogen injection for fire extinguishing can be triggered to form a closed loop of "early warning-response" and improve the safety assurance capability.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces, characterized in that, The method specifically includes the following steps: S1: Construct an early warning indicator system for abnormal emission of toxic and harmful gases in shallow coal seam working faces, including: causal analysis indicators and data analysis indicators; the causal analysis indicators include dynamic indicators, channel indicators and gas source indicators; the data analysis includes gas concentration factor indicators; S2: The weights of each indicator are determined using the Spearman correlation coefficient; S3: The risk index is calculated using the fuzzy comprehensive evaluation method, and the risk warning level for abnormal emission of toxic and harmful gases is determined based on the risk index. S4: Take corresponding joint control measures according to the risk warning level.

2. The method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces according to claim 1, characterized in that, In step S1, the dynamic indicators include the surface air pressure drop rate, the cumulative air pressure drop value in 4 hours, the ground temperature rise rate, the pressure difference between the upper corner of the working face and the goaf, the air leakage of the working face, and the wind resistance of the working face. The channel-type indicators include the maximum height of the fracture zone in the working face, the burial depth of the working face, the number of surface fractures, the width of surface fractures, the horizontal distance of surface fractures from the working face, and the contact area between this goaf and adjacent goaf. The gas source indicators include CO concentration in the goaf, temperature in the goaf, and acetylene concentration in the goaf. The gas concentration factor indicators include the instantaneous value of carbon monoxide concentration at the working face, the 5-minute average value of carbon monoxide concentration, the shift average value of carbon monoxide concentration, the instantaneous value of CO2 concentration, the 5-minute average value of CO2 concentration, and the shift average value of CO2 concentration.

3. The method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces according to claim 1, characterized in that, In step S2, the Spearman correlation coefficient is used to determine the weight of each indicator. Specifically, it includes: determining the weight based on the Spearman correlation coefficient between each early warning indicator data and the "abnormal emission of harmful gases event". The higher the correlation coefficient, the higher the weight. Each early warning indicator data includes the monitoring value in the early warning indicator system and the result of whether the "abnormal emission of harmful gases event" has occurred, i.e., 1 = occurred, 0 = did not occur. The correlation coefficients of each early warning indicator data are normalized to ensure that the sum of the weights of all factors is 1.

4. The method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces according to claim 1, characterized in that, In step S3, the risk index is calculated using the fuzzy comprehensive evaluation method, which specifically includes the following steps: S31: Construct a fuzzy evaluation factor set U, wherein the factor set U is all the early warning indicators collected in step S1; S32: Construct a fuzzy evaluation comment set V, wherein the comment set V is {low risk, medium risk, high risk}; S33: Calculate the membership degree of each index to the comment set using the trapezoidal membership function, and construct the membership degree matrix R; S34: Perform a fuzzy synthesis operation on the index weight vector W obtained in step S2 and the membership matrix R to obtain the risk index vector. The comment corresponding to the maximum value in the risk index vector is the current risk level of abnormal emission of toxic and harmful gases.

5. The method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces according to claim 4, characterized in that, In step S33, the determination of membership degree is divided into the following two cases: Let the index value be x For positive indicators, their "low risk" v 1. Medium risk v 2. The membership functions of "high-risk v3" are as follows: in, As an indicator x Low-risk threshold; As an indicator x Medium-risk threshold; As an indicator x High-risk threshold; , , Positive indicators x Membership degree of low risk, medium risk, and high risk; The membership function for negative indices is designed as follows: in, b 1 is the indicator x High-risk threshold b 2 is the indicator x medium-risk threshold b 3 is the indicator x Low-risk threshold; , , Negative indicators x Membership degree of low risk, medium risk, and high risk.

6. The method for early warning of abnormal emission of toxic and harmful gases in shallow coal seam working faces according to claim 1, characterized in that, In step S4, when the warning level is medium risk or high risk, the linkage control is triggered; the linkage control includes at least one of the following: sending an alarm signal to the underground emergency broadcast system, adjusting the ventilation volume of the working face, increasing the air pressure of the working face, and activating the nitrogen injection fire extinguishing system in the goaf.

7. An early warning system for abnormal emission of toxic and harmful gases in a shallow coal seam working face, characterized in that, It includes a perception layer, a data layer, an algorithm layer, an early warning layer, and a control layer; The sensing layer is used for surface monitoring, goaf monitoring, and working face monitoring to obtain various early warning indicators; The data layer is used to acquire and store indicator data monitored by the perception layer, including real-time database and historical database; The algorithm layer is used to determine the weight of each indicator using the Spearman correlation coefficient and to calculate the risk index using the fuzzy comprehensive evaluation method. The early warning layer is used to determine the risk warning level of abnormal emission of toxic and harmful gases based on the risk index calculated by the algorithm layer. The control layer is used to take corresponding linkage control measures based on the warning level given by the warning layer, including ventilation control, air pressure control or fire extinguishing control.

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