Mine gas safety early warning method and system

Abstract

The present application relates to the technical field of gas safety early warning, in particular to a mine gas safety early warning method and system, comprising the following steps: collecting the concentration time series of continuous nodes and real-time roadway wind speed, combining the concentration distribution gradient difference to construct a spatial diffusion attenuation operator, generating a dynamic research limit according to the adaptive adjustment of the operator on the basic fixed alarm limit, and extracting the high-frequency fluctuation variance of the time series to execute secondary feature verification to output an over-limit early warning signal when the current concentration value approaches the limit. In the present application, a nonlinear coupling operator is constructed by using the concentration distribution gradient and fluid motion characteristics to correct the basic limit and generate a dynamic research limit, and then the high-frequency fluctuation variance is extracted to execute secondary determination, a double-layer comprehensive verification mechanism is established to integrate spatial correlation and time series fluctuation, the rigid defect caused by single-point determination deviating from diffusion effect is solved, and the risk of false alarm and missed alarm under pipe network interference is reduced.

Description

Technical Field

[0001] This invention relates to the field of gas safety early warning technology, and in particular to a method and system for early warning of gas safety in mines. Background Technology

[0002] The field of gas safety early warning technology involves real-time monitoring and hazard assessment mechanisms for changes in the concentration of harmful gases in mining environments. Traditional mine gas safety early warning methods involve installing electrochemical gas sensors at fixed nodes in various mine roadways, setting a single fixed concentration threshold, and triggering an audible and visual alarm when the current gas concentration value directly collected by the sensor exceeds this fixed threshold.

[0003] Existing technologies rely solely on fixed concentration reference limits and single-point physical sensing devices for linear determination of absolute values ​​in actual operation. This completely ignores the nonlinear diffusion coupling effect and gradient change law of gases between adjacent spatial nodes in the complex micro-ventilation network of mines. Consequently, when faced with the dynamic accumulation of hidden harmful gases and the interference of time-varying environmental fluctuations, it is impossible to implement adaptive dynamic correction of the judgment limit and secondary verification compensation based on time-series characteristics. This single judgment logic that severs spatial correlation leads to false or missed warnings of dangerous situations caused by the early warning system in the face of complex working conditions in deep mining. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for early warning of mine gas safety.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a mine gas safety early warning method, comprising the following steps: S1: Using an electrochemical probe and wind speed detector deployed on the roof of the mine roadway, the initial gas concentration time series and real-time roadway wind speed parameters of multiple continuous monitoring nodes in the ventilation network are collected simultaneously. S2: Analyze the initial gas concentration time series, obtain the spatial concentration distribution gradient difference between adjacent continuous monitoring nodes, and perform nonlinear fitting partial derivative calculations in combination with the real-time roadway wind speed parameter to construct a spatial diffusion coupling attenuation operator; S3: Based on the spatial diffusion coupling attenuation operator, perform real-time feedback weight calculation and adaptive adjustment on the basic fixed alarm limits stored in the device to calculate the dynamic concentration judgment limit; S4: Compare the current absolute time value in the initial gas concentration time series with the dynamic concentration judgment limit in real time. When the current absolute time value approaches the dynamic concentration judgment limit, separate the initial gas concentration time series and extract the high-frequency fluctuation variance value. S5: Based on the high-frequency fluctuation variance value and the spatial correlation judgment scale, a secondary feature verification and comparison operation is performed. When the actual variance exceeds the limit benchmark, an abnormal alarm mechanism is triggered, and a gas over-limit warning command signal is output.

[0006] As a further aspect of the present invention, step S1 specifically comprises: S11: Configure the working frequency of the electrochemical probe deployed on the roof of the mine roadway and the sampling cycle of the wind speed detector, establish a unified high-precision clock synchronization benchmark, and perform millisecond-level alignment processing on the original sensor electrical signals of multiple continuous monitoring nodes deployed in the complex ventilation network based on a precise timestamp matching mechanism to generate synchronous sampling data records. S12: Collect the synchronous sampling data records of each of the continuous monitoring nodes, and perform physical conversion and background noise removal on the environmental interference drift in the original sensor electrical signal according to the preset temperature compensation coefficient and humidity calibration curve to establish a single-node concentration monitoring sequence. S13: Integrate the single-node concentration monitoring sequences of each of the continuous monitoring nodes and the dynamic air velocity information output by the wind speed detector, and extract the set of monitoring values ​​within a continuous time period through an adaptive sliding window mechanism to generate the initial gas concentration time series and the real-time roadway wind speed parameter. The single-node concentration monitoring sequence refers to the concentration value sequence after removing background noise and undergoing physical conversion of temperature and humidity.

[0007] As a further aspect of the present invention, step S2 specifically comprises: S21: Analyze the initial gas concentration time series, extract the concentration difference of adjacent continuous monitoring nodes at the same physical moment according to the mine airflow direction and roadway topology, and divide the concentration difference by the physical distance between the nodes to calculate the spatial concentration distribution gradient difference. S22: Obtain the spatial concentration distribution gradient difference and the real-time roadway wind speed parameter. Based on the preset convection-diffusion equilibrium physical model, use the real-time roadway wind speed parameter as the independent variable to perform nonlinear partial derivative calculation on the spatial concentration distribution gradient difference to establish a flow field diffusion evolution trend matrix. S23: Using a pre-set roadway wall resistance correction coefficient, the elements in the flow field diffusion evolution trend matrix are weighted and averaged to extract the core feature dimension and construct the spatial diffusion coupling attenuation operator. The flow field diffusion evolution trend matrix refers to a two-dimensional matrix composed of the spatial concentration partial derivative values ​​at each monitoring node location. The core feature dimension refers to the principal component feature vector retained after dimensionality reduction based on weighted averaging.

[0008] As a further aspect of the present invention, step S3 specifically comprises: S31: Extract the basic fixed alarm limit stored inside the device, analyze the multidimensional attenuation features contained in the spatial diffusion coupling attenuation operator, calculate the dynamic compensation deviation of the gas accumulation rate through the exponential smoothing algorithm, and obtain the real-time feedback weight. S32: Using the real-time feedback weight, the basic fixed alarm limit is linearly scaled and the safety margin is compensated, and an adaptive adjustment operation is performed to determine the transient judgment threshold baseline. S33: Perform a weighted fusion operation on the transient judgment threshold baseline and the historical long-term fluctuation average to calculate the dynamic concentration judgment limit; The multidimensional attenuation characteristic refers to the projection components of the spatial diffusion coupling attenuation operator in different spatial coordinate axis directions.

[0009] As a further aspect of the present invention, step S4 specifically comprises: S41: Extract the latest data point from the initial gas concentration time series in real time as the current absolute time value, compare the current absolute time value with the dynamic concentration judgment boundary, and generate a boundary approach state indicator. S42: When the boundary approach state indicator reaches the preset safety tolerance range, separate the recent drastic change segment in the initial gas concentration time series, use fast Fourier transform to convert the recent drastic change segment from the time domain to the frequency domain, and extract the discrete fluctuation component. S43: Perform statistical dispersion analysis on the discrete fluctuation components, calculate the average expected index of the sum of squared deviations of the discrete fluctuation components from the mean center, and extract the high-frequency fluctuation variance value; The recent drastic change segment refers to the subset of values ​​in the initial gas concentration time series that are within a preset time window before the current moment.

[0010] As a further aspect of the present invention, step S5 specifically comprises: S51: Retrieve the pre-set spatial correlation judgment scale, perform a horizontal cross-comparison between the high-frequency fluctuation variance value and the feature values ​​of adjacent regional nodes, and perform the secondary feature verification and comparison operation to obtain the regional collaborative anomaly probability. S52: Convert the regional collaborative anomaly probability into an actual variance evaluation index, and determine whether the absolute value of the actual variance evaluation index exceeds the preset limit benchmark. S53: When the judgment result is that the limit benchmark is exceeded, the alarm trigger in the underlying control hardware is activated, driving the mine central control system to execute the linkage response of the regional sound and light alarm equipment and the power failure protection control logic, and outputting the gas over-limit early warning command signal. The actual variance evaluation index refers to the corrected fluctuation assessment value obtained by multiplying the local single-point variance by the regional collaborative anomaly probability.

[0011] As a further aspect of the present invention, the process of calculating the spatial concentration distribution gradient difference specifically includes: Extract the concentration values ​​of the first measuring point located on the upwind side of the roadway and the concentration values ​​of the second measuring point located on the downwind side of the roadway at the same sampling time section in the initial gas concentration time series; The three-dimensional spatial straight-line distance value between the first and second measuring points and the pre-stored roadway curvature compensation parameters are obtained by querying the mine geographic information system database. The actual airflow diffusion path length is calculated by multiplying the three-dimensional spatial straight-line distance value by the roadway curvature compensation parameters. The absolute concentration increment is obtained by subtracting the concentration value of the first measuring point from the concentration value of the second measuring point, and the absolute concentration increment is divided by the actual airflow diffusion path length to obtain the spatial concentration distribution gradient difference.

[0012] As a further aspect of the present invention, the process of obtaining the real-time feedback weight specifically includes: The temporal evolution feature component and the spatial transmission feature component contained in the spatial diffusion coupling attenuation operator are separated, and the temporal evolution feature component and the spatial transmission feature component are multiplied and fused according to a preset feature weight allocation strategy to obtain a comprehensive attenuation intensity characterization value. The deviation ratio of the comprehensive attenuation intensity characterization value relative to the standard diffusion reference value under ideal ventilation conditions is calculated, and a preset exponential smoothing constant is introduced to perform low-pass filtering on the deviation ratio to calculate the smoothing deviation amount. The smoothing deviation is mapped to a preset weight control feedback range, and the smoothing deviation is converted into a multiplicative factor through proportional-integral-derivative adjustment logic to obtain the real-time feedback weight.

[0013] As a further aspect of the present invention, the specific process of the secondary feature verification and comparison operation includes: Extract the high-frequency fluctuation variance value set of all upstream historical nodes in the ventilation branch loop where the current monitoring node is located, and calculate the Pearson correlation coefficient between the high-frequency fluctuation variance value and the high-frequency fluctuation variance value set to obtain the spatial fluctuation correlation matrix. Obtain the predefined topological distance attenuation threshold matrix in the spatial correlation determination scale, and use the topological distance attenuation threshold matrix to perform item-by-item masking filtering on the elements in the spatial fluctuation correlation matrix to filter out edge interference node data. The weighted coefficient of variation of the strongly correlated node features retained by the filter is jointly solved to calculate the degree of local regional clustering evolution, and the secondary feature verification and comparison operation is completed.

[0014] A mine gas safety early warning system, the system being used to implement the aforementioned mine gas safety early warning method, the system comprising: The parameter synchronous acquisition module is used to simultaneously acquire the initial gas concentration time series and real-time roadway wind speed parameters of multiple continuous monitoring nodes in the ventilation network using an electrochemical probe and a wind speed detector deployed on the roof of the mine roadway. The attenuation operator construction module is used to parse the initial gas concentration time series, obtain the spatial concentration distribution gradient difference between adjacent continuous monitoring nodes, and perform nonlinear fitting partial derivative calculations in combination with the real-time roadway wind speed parameter to construct a spatial diffusion coupling attenuation operator. The judgment boundary adjustment module is used to perform real-time feedback weight calculation and adaptive adjustment operations on the basic fixed alarm boundary stored in the device based on the spatial diffusion coupling attenuation operator, and to calculate the dynamic concentration judgment boundary. The high-frequency variance extraction module is used to compare the current absolute value of the initial gas concentration time series with the dynamic concentration judgment boundary in real time. When the current absolute value approaches the dynamic concentration judgment boundary, the initial gas concentration time series is separated and the high-frequency fluctuation variance value is extracted. The abnormal warning triggering module is used to perform a secondary feature verification and comparison operation based on the high-frequency fluctuation variance value and the spatial correlation judgment scale. When the actual variance exceeds the limit benchmark, the abnormal alarm mechanism is triggered and the gas over-limit warning command signal is output.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, gas concentration values ​​and real-time wind speed parameters of continuous nodes in the target roadway are collected using an electrochemical probe. A nonlinear spatial coupling constraint operator is constructed by combining the concentration distribution gradient difference between adjacent nodes and the fluid motion characteristics. Based on the operator, the basic fixed early warning limit is weighted and adaptively corrected to generate a dynamic judgment limit that fits the current working conditions. When the node concentration approaches the dynamic judgment limit, the high-frequency fluctuation variance of the concentration evolution sequence is extracted to perform a secondary judgment. A two-layer verification mechanism that integrates spatial correlation gradient and temporal fluctuation evolution is established to solve the judgment rigidity defect caused by the single-point judgment mode being decoupled from the diffusion coupling effect and reduce the risk of false early warning caused by the accumulation of hidden gas under the interference of complex pipeline network. Attached Figure Description

[0016] Figure 1 This is an overall flowchart of the mine gas safety early warning method of the present invention; Figure 2 This is a detailed flowchart of the synchronous data acquisition and sequence generation process of this invention; Figure 3 This is a detailed flowchart of the construction of the spatial diffusion coupling attenuation operator of the present invention; Figure 4 This is a detailed flowchart of the dynamic concentration judgment threshold calculation for this invention; Figure 5 This is a flowchart illustrating the process of refining the high-frequency fluctuation variance numerical extraction of the present invention. Figure 6 This is a detailed flowchart of the gas over-limit early warning command triggering method of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0018] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0019] Please see Figure 1 and Figure 2 This invention provides a technical solution: a method for early warning of mine gas safety, comprising the following steps: S1: Using an electrochemical probe and wind speed detector deployed on the roof of the mine roadway, the initial gas concentration time series and real-time roadway wind speed parameters of multiple continuous monitoring nodes in the ventilation network are collected simultaneously.

[0020] The specific steps of S1 are as follows: S11: Configure the working frequency of the electrochemical probe deployed on the roof of the mine roadway and the sampling cycle of the wind speed detector, establish a unified high-precision clock synchronization benchmark, and perform millisecond-level alignment processing on the original sensor electrical signals of multiple continuous monitoring nodes deployed in the complex ventilation network based on a precise timestamp matching mechanism to generate synchronous sampling data records. S12: Collect synchronous sampling data records from each continuous monitoring node, and perform physical conversion and background noise removal on the environmental interference drift in the original sensor electrical signal based on the preset temperature compensation coefficient and humidity calibration curve to establish a single-node concentration monitoring sequence. S13: Integrate the single-node concentration monitoring sequences of each continuous monitoring node and the dynamic air velocity information output by the wind speed detector, and extract the monitoring value set within a continuous time period through an adaptive sliding window mechanism to generate the initial gas concentration time series and real-time roadway wind speed parameters. A single-node concentration monitoring sequence refers to a concentration value sequence after removing background noise and undergoing physical conversion of temperature and humidity.

[0021] Twenty-four electrochemical probes, deployed at a distance of 300mm from the rock surface on the roof of the mine's return airway, operate at a frequency of 50Hz, meaning they sample voltage signals every 20ms. Simultaneously, twelve anemometers positioned at ventilation branch nodes operate at a sampling period of 20Hz, meaning they count wind pressure pulses every 50ms. During data acquisition, the constant-potential circuit within the electrochemical probes converts the weak current signal generated by the reaction of methane gas with the electrolyte into an analog voltage signal of 0V to 5V via an operational amplifier. This signal is then digitized into a discrete value between 0 and 65535 by a 16-bit high-precision analog-to-digital converter. By introducing a precise time protocol based on IEEE 1588, a unified high-precision clock synchronization reference is established between the ground-based central server and each underground monitoring station. A network latency compensation value of 5.2ms is set to ensure that the clock synchronization error across the entire network is controlled within 100ns. Based on a precise timestamp matching mechanism, the raw sensor signals from 24 continuously monitored nodes deployed within a complex ventilation network are aligned at the millisecond level. Each data packet is encapsulated into a real-time transmission protocol frame containing a nanosecond-level timestamp the instant it is generated. The absolute timestamp attached to each data packet is obtained, and a first-in-first-out queue of 2048 data units is established according to the chronological order. For data sources with different frequencies, a linear interpolation algorithm is used to upsample and pad the 20Hz wind speed data. The slope of change between two adjacent wind speed sampling points is calculated, and the predicted values ​​are padded at missing time points according to the 50Hz sampling interval, so that its time resolution is completely aligned with the 50Hz concentration data in the time domain. This eliminates the timing misalignment caused by transmission link fluctuations and sampling rate inconsistencies, and generates a synchronous sampling data record containing synchronous physical quantity pairs.

[0022] The aforementioned precision time protocol refers to a communication protocol used to achieve clock synchronization between nodes in a distributed network. It measures path delay through multiple message exchanges between master and slave nodes, thereby achieving sub-microsecond synchronization accuracy.

[0023] Synchronous sampling data records from each continuous monitoring node are collected and fed into the preprocessing algorithm stream. The current ambient temperature (32.5℃) and relative humidity (88%) are acquired in real time using integrated environmental sensors deployed on-site. Based on laboratory calibration data provided by the sensor manufacturer, the real-time temperature is subtracted from the standard calibration temperature (20℃) to obtain a temperature difference of 12.5℃. This temperature difference is multiplied by a preset temperature compensation coefficient of 0.0155 to calculate a thermal drift correction of 0.19375. A three-dimensional lookup table of humidity calibration curves pre-stored in the controller's read-only memory is consulted, matching the humidity influence factor of 0.925 for 88% relative humidity at 32.5℃. The initial concentration value after linear conversion of the original sensor signal by the ADC is subtracted from the thermal drift correction of 0.19375, and then divided by the humidity influence factor of 0.925 to complete the physical conversion of environmental interference drift. A moving average filter with a window length of 15 sampling points is introduced to remove background noise from the converted values, eliminating transient electromagnetic pulse interference caused by the start-up and shutdown of large underground electromechanical equipment. The arithmetic mean of all sampling points within the 15-point window is calculated, and this mean is used as the central representative value for that time period. The filter is then processed point by point to smooth out random high-frequency spikes in the signal, establishing a stable single-node concentration monitoring sequence.

[0024] The aforementioned environmental interference drift refers to the error in the measured value of an electrochemical sensor deviating from the true value due to changes in electrolyte activity or membrane permeability under non-standard temperature and humidity conditions.

[0025] This system integrates single-node concentration monitoring sequences from various continuous monitoring nodes with dynamic air velocity information output from anemometers. The initial time span of the basic adaptive sliding window is set to 60 seconds, with a sliding step of 10 seconds. The variance of the single-node concentration monitoring sequence within the current window is calculated, and it is determined in real-time whether this variance is within a stable range of 0 to 0.05. If the calculated variance is 0.062, it is determined to exceed the preset stable upper limit of 0.05, and a window contraction mechanism is activated, automatically reducing the sliding window time span from 60 seconds to 30 seconds and the sliding step from 10 seconds to 2 seconds, thereby improving the capture frequency of abrupt changes in gas outburst waveforms. The adaptive sliding window mechanism extracts a set of monitoring values ​​within a continuous time period, generating a multi-dimensional data matrix containing spatial coordinates, absolute time, corrected concentration, and real-time wind speed, which serves as the initial gas concentration time series and real-time roadway wind speed parameters. Experimental data show that after setting the sampling frequency to 50Hz and performing humidity calibration curve correction, the system's measurement accuracy in the concentration range of 0.5% to 1.5% increased from 92.5% to 98.2%, and compared with the traditional timed trigger sampling mode, the recognition delay for abnormal gas outbursts was shortened by 3.2s.

[0026] Table 1. Sensor Synchronous Acquisition and Preprocessing Parameters

[0027] As shown in Table 1, the reliability of the initial gas concentration time series at the data source is ensured by precisely setting the hardware acquisition parameters and physical compensation coefficients.

[0028] Please see Figure 1 and Figure 3 S2: Analyze the initial gas concentration time series, obtain the spatial concentration distribution gradient difference between adjacent continuous monitoring nodes, and perform nonlinear fitting partial derivative calculations based on real-time roadway wind speed parameters to construct a spatial diffusion coupling attenuation operator.

[0029] The specific steps of S2 are as follows: S21: Analyze the initial gas concentration time series, extract the concentration difference of adjacent continuous monitoring nodes at the same physical moment according to the mine airflow direction and roadway topology, and divide the concentration difference by the physical distance between the nodes to calculate the spatial concentration distribution gradient difference. The process of calculating the spatial concentration distribution gradient difference specifically includes: Extract the concentration values ​​of the first measuring point located on the upwind side of the roadway and the second measuring point located on the downwind side of the roadway at the same sampling time section in the initial gas concentration time series; The actual airflow diffusion path length is calculated by multiplying the three-dimensional spatial straight distance between the first and second measuring points and the pre-stored roadway curvature compensation parameters by querying the mine geographic information system database. Subtracting the concentration value of the first measuring point from the concentration value of the second measuring point yields the absolute concentration increment. Dividing the absolute concentration increment by the actual airflow diffusion path length yields the spatial concentration distribution gradient difference. S22: Obtain the spatial concentration distribution gradient difference and real-time roadway wind speed parameters. Based on the preset convection-diffusion equilibrium physical model, use the real-time roadway wind speed parameters as independent variables to perform nonlinear partial derivative calculations on the spatial concentration distribution gradient difference, and establish the flow field diffusion evolution trend matrix. S23: Using a pre-set roadway wall resistance correction coefficient, the elements in the flow field diffusion evolution trend matrix are weighted and averaged to extract the core feature dimensions and construct a spatial diffusion coupling attenuation operator. The flow field diffusion evolution trend matrix is ​​a two-dimensional matrix composed of the spatial concentration partial derivative values ​​at each monitoring node location; The core feature dimension refers to the principal component feature vectors retained after dimensionality reduction based on weighted averaging.

[0030] The initial gas concentration time series was analyzed, and monitoring node pairs adjacent to each other on the same ventilation flow line were extracted according to the airflow direction of the main ventilation system and the topological connection relationship of the roadway branches. The same synchronous sampling time section was locked, and the concentration values ​​of the first measuring point (0.68%) on the upwind side of the roadway and the second measuring point (0.85%) on the leeward side were extracted. A query message containing the node ID was sent to the mine's geographic information system database to obtain the three-dimensional coordinates of the first and second measuring points in the mine coordinate system. The three-dimensional spatial straight-line distance between the first and second measuring points was calculated to be 150m using the Euclidean distance formula. The pre-stored roadway curvature compensation parameter, 1.12, was retrieved; this parameter is the ratio of the actual length to the straight-line distance calculated based on the roadway laser scan point cloud data. Multiplying the three-dimensional spatial straight-line distance value of 150 by the roadway curvature compensation parameter 1.12, the actual airflow diffusion path length was calculated to be 168m. Subtracting the concentration value of the first measuring point from the concentration value of the second measuring point (0.85) yields an absolute concentration increment of 0.17%. Dividing the absolute concentration increment of 0.17 by the actual airflow diffusion path length (168) yields a spatial concentration distribution gradient difference of 0.00101.

[0031] The aforementioned roadway curvature compensation parameters refer to the correction ratio coefficients used to correct the increase in the actual airflow path caused by roadway turns and local expansion, taking into account that underground roadways are not ideal straight lines.

[0032] The spatial concentration distribution gradient difference and the real-time roadway wind speed parameter of 2.6 m / s were obtained. Based on the convective-diffusion equilibrium physical model, the real-time roadway wind speed parameter was used as the core independent variable, and nonlinear partial derivative calculations were performed on the spatial concentration distribution gradient difference. Specifically, the real-time wind speed of 2.6 was substituted into the first-order convective partial derivative term, and a nonlinear fit was performed using the aerodynamic viscosity coefficient of 0.000018. The convective dominance coefficient under different wind speeds was calculated using a simplified form of the Navier-Stokes equations in fluid dynamics. All monitoring nodes in the roadway were traversed, and the concentration partial derivative values ​​at each node were arranged according to spatial topological order to construct a 24-row by 24-column flow field diffusion evolution trend matrix. Each element in the matrix represents the dynamic rate of gas molecule diffusion from the upstream node to the rear under the given wind speed condition.

[0033] The aforementioned flow field diffusion evolution trend matrix refers to a mathematical array consisting of the set of partial derivatives describing the change of gas concentration at various points in the mine space with location under specific wind field dynamics.

[0034] The preset roadway wall resistance correction coefficient of 0.86 is retrieved from the memory. This value was determined through field-measured wind resistance experiments based on the roughness of the shotcrete support surface. The roadway wall resistance correction coefficient of 0.86 is used to weight each element in the flow field diffusion evolution trend matrix to simulate the hindering effect of the roadway boundary layer on gas diffusion. Principal component analysis (PCA) is used to reduce the dimensionality of the weighted matrix and extract the core feature dimensions. Specifically, the process includes: calculating the covariance eigenvalues ​​of the matrix, arranging them in descending order of eigenvalues, and selecting the top four principal component eigenvectors with a cumulative variance contribution rate of 90%. The original high-dimensional flow field data is mapped to a low-dimensional space composed of these four eigenvectors, constructing a spatial diffusion coupling attenuation operator that incorporates spatial geometric features and hydrodynamic characteristics. Experimental data show that after introducing a resistance correction coefficient of 0.86 and using PCA for dimensionality reduction, the spatial diffusion coupling attenuation operator achieves a 95.4% accuracy in describing the gas migration trend. Compared with the traditional simple diffusion model, its prediction bias at roadway intersections is reduced by 22%.

[0035] Please see Figure 1 and Figure 4 S3: Based on the spatial diffusion coupling attenuation operator, perform real-time feedback weight calculation and adaptive adjustment operation on the basic fixed alarm limit stored in the device, and calculate the dynamic concentration judgment limit.

[0036] The specific steps for S3 are as follows: S31: Extract the basic fixed alarm limits stored inside the equipment, analyze the multidimensional attenuation characteristics contained in the spatial diffusion coupling attenuation operator, calculate the dynamic compensation deviation of the gas accumulation rate through the exponential smoothing algorithm, and obtain the real-time feedback weight. The process of obtaining real-time feedback weights specifically includes: The temporal evolution feature component and the spatial propagation feature component contained in the spatial diffusion coupling attenuation operator are separated. The temporal evolution feature component and the spatial propagation feature component are multiplied and fused according to the preset feature weight allocation strategy to obtain the comprehensive attenuation intensity characterization value. The deviation ratio of the comprehensive attenuation intensity characterization value relative to the standard diffusion reference value under ideal ventilation conditions is calculated, and a preset exponential smoothing constant is introduced to perform low-pass filtering on the deviation ratio to calculate the smoothing deviation amount. The smoothing deviation is mapped to a preset weight control feedback range, and the smoothing deviation is converted into a multiplicative factor through proportional-integral-derivative adjustment logic to obtain the real-time feedback weight. S32: Utilize real-time feedback weights to linearly scale and compensate for safety margins on the basic fixed alarm limits, perform adaptive adjustment operations, and determine the transient judgment threshold baseline; S33: Perform a weighted fusion calculation between the transient judgment threshold baseline and the historical long-term fluctuation average to calculate the dynamic concentration judgment limit; Multidimensional attenuation characteristic refers to the projection components of the spatial diffusion coupling attenuation operator in different spatial coordinate axis directions.

[0037] The basic fixed alarm limit of 1.0% set in the non-volatile memory inside the equipment is extracted. The four-dimensional attenuation characteristics contained in the spatial diffusion coupling attenuation operator are analyzed and separated into a component representing the time-axis evolution (0.42) and a component representing the spatial transmission (0.68). The feature weight allocation strategy is set as follows: time feature weight 0.35, spatial feature weight 0.65. The time evolution feature component 0.42 is multiplied by its weight 0.35, and the spatial transmission feature component 0.68 is multiplied by its weight 0.65. The sum of these two products yields a comprehensive attenuation intensity characterization value of 0.589. The deviation ratio of the comprehensive attenuation intensity characterization value 0.589 from the standard diffusion reference value 0.75 under ideal ventilation conditions is calculated, yielding a deviation ratio of 21.47%. An exponential smoothing constant of 0.25 is introduced to perform low-pass filtering on the deviation ratio, resulting in a smoothed deviation. This smoothed deviation is then input into a proportional-integral-derivative (PID) control logic.

[0038] In this embodiment, the real-time feedback weight is calculated using the following formula. : ; in, This represents the real-time feedback weight, used to proportionally adjust alarm limits; This represents the proportionality coefficient, with a value of 1.45, which determines the response speed to the current deviation ratio. This represents the smoothing deviation, with a value of 21.47%, which serves as the error input for the feedback loop. This represents the integral coefficient, with a value of 0.18, used to eliminate the accumulation of static error in the deviation. This represents the differential coefficient, with a value of 0.08, used to predict the trend of deviation changes; This represents the adjustment time constant.

[0039] The calculated proportional, integral, and differential terms are summed to obtain a real-time feedback weight of 1.12. This real-time feedback weight is a multiplicative correction coefficient used to dynamically adjust the safety alarm threshold; its magnitude directly reflects the degree of influence of the current ventilation environment on the risk of gas accumulation.

[0040] Using a real-time feedback weight of 1.12, a linear scaling operation was performed on the basic fixed alarm threshold of 1.0%, resulting in an initial adjustment value of 1.12%. Considering the redundancy requirements of mine safety regulations, a safety margin compensation of -0.05% was introduced, and an adaptive adjustment operation was performed to determine the transient judgment threshold baseline as 1.07%. The transient judgment threshold baseline of 1.07% was weighted and fused with the average long-term fluctuation of 0.42% calculated from the historical database over the past 12 hours. The transient weight was set to 0.75, and the historical weight to 0.25. Multiplying 1.07 by 0.75 and adding 0.42 by 0.25, the dynamic concentration judgment threshold was calculated to be 0.9075%. This result shows that the system can automatically tighten or loosen the alarm threshold according to real-time airflow diffusion conditions. Experimental data shows that when the parameters... When the threshold is set to 1.45, the system's response time to concentration fluctuations caused by ventilation abnormalities is 4.5 seconds. Compared with the traditional fixed threshold method, the false alarm rate is reduced from 15 times per day to less than 2 times.

[0041] Table 2 Dynamic Threshold Adjustment and PID Calculation Data Table

[0042] As shown in Table 2, by integrating PID control logic with multi-dimensional features, a scientific transformation of alarm thresholds from static to dynamic has been achieved.

[0043] Please see Figure 1 and Figure 5 S4: In real time, compare the current absolute value of the initial gas concentration time series with the dynamic concentration judgment limit. When the current absolute value approaches the dynamic concentration judgment limit, separate the initial gas concentration time series and extract the high-frequency fluctuation variance value.

[0044] The specific steps for S4 are as follows: S41: Extract the latest data point from the initial gas concentration time series in real time as the current absolute time value, compare the current absolute time value with the dynamic concentration judgment boundary, and generate a boundary approach status indicator. S42: When the boundary approach state indicator reaches the preset safety tolerance range, separate the recent drastic change segment in the initial gas concentration time series, use fast Fourier transform to transform the recent drastic change segment from the time domain to the frequency domain, and extract the discrete fluctuation component. S43: Perform statistical dispersion analysis on discrete fluctuation components, calculate the average expected index of the sum of squared deviations of discrete fluctuation components from the mean center, and extract the high-frequency fluctuation variance value. The recent drastic change segment refers to the subset of values ​​in the initial gas concentration time series that are within a preset time window before the current moment.

[0045] The latest data point collected and uploaded by the sensor from the initial gas concentration time series is extracted in real time and marked as the current absolute value of 0.88%. The current absolute value of 0.88% is compared with the dynamic concentration judgment threshold of 0.9075% calculated in the previous step, and the difference between the two is calculated to be 0.0275%. The current value accounts for 96.97% of the judgment threshold. A safe tolerance range for threshold approach is set to 95% to 100%. If 96.97% is determined to be within the safe tolerance range, a threshold approach status flag with a logical value of "true" is generated in memory.

[0046] When the boundary approach state flag is true, a recent drastic change segment within the previous 128 seconds is separated from the real-time data stream buffer. This segment contains 6400 sampling points. A Fast Fourier Transform (FFT) based on the Cooley-Tukey algorithm is used to transform the recent drastic change segment from the time domain to the frequency domain. The processing includes: performing Hanning window weighting on the 6400 original sampling points to reduce spectral leakage; performing a 1024-point radix-2 Fast Fourier Transform operation to decompose the time-domain signal into a series of sinusoidal components of different frequencies; calculating the amplitude spectrum at each frequency point, and extracting discrete fluctuation components with a frequency range between 0.5Hz and 5.5Hz. This frequency band represents high-frequency concentration disturbances caused by abnormal gas outburst dynamics or minor damage to the coal and rock structure.

[0047] Statistical analysis was performed on the extracted discrete fluctuation components. The arithmetic mean of the amplitudes of all discrete fluctuation components within the frequency band was calculated as the mean center. For each fluctuation component, its amplitude was subtracted from the mean center to obtain the deviation. The squared deviations were then summed and divided by the total number of fluctuation components to calculate the average expected value of the sum of squared deviations. The statistical result obtained from this process was a high-frequency fluctuation variance of 0.0125. Experimental data showed that when the boundary approach indicator was activated and FFT frequency domain analysis was performed, the system could detect high-frequency fluctuation characteristics caused by sudden changes in minute gas outbursts 150 seconds before the concentration reached the alarm threshold. Compared with traditional time-domain judgment methods, its warning advance was increased by 42%.

[0048] Please see Figure 1 and Figure 6 S5: Based on the high-frequency fluctuation variance value and the spatial correlation judgment scale, a secondary feature verification and comparison operation is performed. When the actual variance exceeds the limit benchmark, an abnormal alarm mechanism is triggered, and a gas over-limit warning command signal is output.

[0049] The specific steps of S5 are as follows: S51: Retrieve the pre-set spatial correlation judgment scale, perform a horizontal cross-comparison between the high-frequency fluctuation variance value and the feature value of the adjacent regional node, perform a secondary feature verification and comparison operation, and obtain the regional coordination anomaly probability. The specific process of secondary feature verification and comparison includes: Extract the high-frequency fluctuation variance value set of all upstream historical nodes in the ventilation branch loop where the current monitoring node is located, and calculate the Pearson correlation coefficient between the high-frequency fluctuation variance value and the high-frequency fluctuation variance value set to obtain the spatial fluctuation correlation matrix. Obtain the predefined topological distance attenuation threshold matrix in the spatial correlation determination scale, and use the topological distance attenuation threshold matrix to perform item-by-item masking filtering on the elements in the spatial fluctuation correlation matrix to filter out edge interference node data. The weighted coefficient of variation of the strongly correlated node features retained by the filter is jointly solved to calculate the degree of local regional clustering evolution, and the secondary feature verification and comparison operation is completed. S52: Transform the regional collaborative anomaly probability into an actual variance evaluation index, and determine whether the absolute value of the actual variance evaluation index exceeds the pre-set limit benchmark. S53: When the judgment result is that the limit benchmark is exceeded, the alarm trigger in the underlying control hardware is activated, driving the mine central control system to execute the linkage response of the regional sound and light alarm equipment and the power failure protection control logic, and outputting the gas over-limit early warning command signal. The actual variance evaluation index refers to the corrected fluctuation assessment value obtained by multiplying the local single-point variance by the regional collaborative anomaly probability.

[0050] A pre-set spatial correlation judgment scale is retrieved, and the high-frequency fluctuation variance value of the current monitoring node (0.0125) is cross-compared with the feature values ​​of adjacent regional nodes. The high-frequency fluctuation variance value set of all upstream historical nodes in the ventilation branch loop where the current monitoring node is located is extracted, including the real-time variance data of three upstream measuring points (0.0105, 0.0118, and 0.0132). The Pearson correlation coefficient is calculated between the high-frequency fluctuation variance value of the current monitoring node (0.0125) and the high-frequency fluctuation variance value set. The calculation process includes: calculating the covariance value (0.00015) of the variance sequences of the current node and the upstream nodes, and calculating the product of their standard deviations (0.00017). Dividing the covariance by the standard deviation product yields the spatial fluctuation correlation matrix representing linear correlation, with a correlation coefficient of 0.88.

[0051] A predefined topological distance attenuation threshold matrix is ​​obtained from the spatial correlation judgment scale. The elements of this matrix are distributed exponentially according to the physical distance between nodes. For upstream nodes 50m, 100m, and 200m away from the current node, the corresponding attenuation thresholds are 0.75, 0.60, and 0.40, respectively. The elements in the spatial fluctuation correlation matrix are filtered item by item using the topological distance attenuation threshold matrix. It is determined that the coefficients 0.88 between the current node and the two preceding nodes are all greater than 0.75 and 0.60, confirming a strong correlation signal. The weighted coefficient of variation is jointly calculated for the features of the strongly correlated nodes retained after filtering, yielding a local regional clustering evolution degree value of 0.85. This local regional clustering evolution degree value of 0.85 is then converted into a regional cooperative anomaly probability.

[0052] The aforementioned regional coordinated anomaly probability refers to the probability index that confirms the gas fluctuation belongs to a real regional evolution by analyzing the correlation characteristics of fluctuations at multiple monitoring nodes within the same ventilation circuit, excluding interference from single-point sensor failures.

[0053] Multiplying the local single-point variance of the current monitoring node (0.0125) by the regional collaborative anomaly probability (0.85), the corrected actual variance evaluation index is obtained as 0.010625. The preset limit benchmark (0.008) in the system is retrieved. The actual variance evaluation index (0.010625) is compared with the limit benchmark (0.008). Since 0.010625 significantly exceeds the limit benchmark (0.008), the result is deemed anomaly. The solid-state relay alarm trigger in the underlying control hardware is activated. An over-limit warning message is sent to the mine's central control system via the fiber optic industrial ring network, driving the explosion-proof audible and visual alarm in the roadway to execute a high-frequency flashing warning. This triggers the low-voltage power distribution feeder switch in the underground substation to execute power-off protection control logic, cutting off the power supply to non-intrinsically safe equipment and outputting a gas over-limit warning command signal containing fault codes and node coordinates. Experimental data shows that after implementing the secondary feature verification and comparison operation, the probability of false alarms caused by instantaneous airflow disturbances due to the opening of underground air doors is reduced by 96.5%.

[0054] Table 3 Spatial Association Verification and Alarm Trigger Test Table

[0055] As shown in Table 3, the collaborative verification of single-point data and regional spatial correlation effectively filters out isolated sensor anomalies (such as test group 2), ensuring the high reliability of alarm command output.

[0056] A mine gas safety early warning system, used to execute the above-mentioned mine gas safety early warning method, the system comprising: The parameter synchronous acquisition module is used to simultaneously acquire the initial gas concentration time series and real-time roadway wind speed parameters of multiple continuous monitoring nodes in the ventilation network using an electrochemical probe and a wind speed detector deployed on the roof of the mine roadway. The attenuation operator construction module is used to analyze the initial gas concentration time series, obtain the spatial concentration distribution gradient difference between adjacent continuous monitoring nodes, and perform nonlinear fitting partial derivative calculations in combination with real-time roadway wind speed parameters to construct a spatial diffusion coupling attenuation operator. The judgment boundary adjustment module is used to perform real-time feedback weight calculation and adaptive adjustment operations on the basic fixed alarm boundary stored in the device based on the spatial diffusion coupling attenuation operator, and to calculate the dynamic concentration judgment boundary. The high-frequency variance extraction module is used to compare the current absolute value of the initial gas concentration time series with the dynamic concentration judgment boundary in real time. When the current absolute value approaches the dynamic concentration judgment boundary, the initial gas concentration time series is separated and the high-frequency fluctuation variance value is extracted. The abnormal warning triggering module is used to perform secondary feature verification and comparison calculations based on the high-frequency fluctuation variance value and the spatial correlation judgment scale. When the actual variance exceeds the limit benchmark, the abnormal alarm mechanism is triggered, and the gas over-limit warning command signal is output.

[0057] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A mine gas safety early warning method, characterized in that, Includes the following steps: S1: Using an electrochemical probe and wind speed detector deployed on the roof of the mine roadway, the initial gas concentration time series and real-time roadway wind speed parameters of multiple continuous monitoring nodes in the ventilation network are collected simultaneously. S2: Analyze the initial gas concentration time series, obtain the spatial concentration distribution gradient difference between adjacent continuous monitoring nodes, and perform nonlinear fitting partial derivative calculations in combination with the real-time roadway wind speed parameter to construct a spatial diffusion coupling attenuation operator; S3: Based on the spatial diffusion coupling attenuation operator, perform real-time feedback weight calculation and adaptive adjustment on the basic fixed alarm limits stored in the device to calculate the dynamic concentration judgment limit; S4: Compare the current absolute time value in the initial gas concentration time series with the dynamic concentration judgment limit in real time. When the current absolute time value approaches the dynamic concentration judgment limit, separate the initial gas concentration time series and extract the high-frequency fluctuation variance value. S5: Perform a secondary feature verification and comparison operation based on the high-frequency fluctuation variance value and the spatial correlation judgment scale. When the actual variance exceeds the limit benchmark, trigger the abnormal alarm mechanism and output the gas over-limit warning command signal.

2. The mine gas safety early warning method according to claim 1, characterized in that, The specific steps of S1 are as follows: S11: Configure the working frequency of the electrochemical probe deployed on the roof of the mine roadway and the sampling cycle of the wind speed detector, establish a unified high-precision clock synchronization benchmark, and perform millisecond-level alignment processing on the original sensor electrical signals of multiple continuous monitoring nodes deployed in the complex ventilation network based on a precise timestamp matching mechanism to generate synchronous sampling data records. S12: Collect the synchronous sampling data records of each of the continuous monitoring nodes, and perform physical conversion and background noise removal on the environmental interference drift in the original sensor electrical signal according to the preset temperature compensation coefficient and humidity calibration curve to establish a single-node concentration monitoring sequence. S13: Integrate the single-node concentration monitoring sequences of each of the continuous monitoring nodes and the dynamic air velocity information output by the wind speed detector, and extract the set of monitoring values ​​within a continuous time period through an adaptive sliding window mechanism to generate the initial gas concentration time series and the real-time roadway wind speed parameter. The single-node concentration monitoring sequence refers to the concentration value sequence after removing background noise and undergoing physical conversion of temperature and humidity.

3. The mine gas safety early warning method according to claim 1, characterized in that, The specific steps of S2 are as follows: S21: Analyze the initial gas concentration time series, extract the concentration difference of adjacent continuous monitoring nodes at the same physical moment according to the mine airflow direction and roadway topology, and divide the concentration difference by the physical distance between the nodes to calculate the spatial concentration distribution gradient difference. S22: Obtain the spatial concentration distribution gradient difference and the real-time roadway wind speed parameter. Based on the preset convection-diffusion equilibrium physical model, use the real-time roadway wind speed parameter as the independent variable to perform nonlinear partial derivative calculation on the spatial concentration distribution gradient difference to establish a flow field diffusion evolution trend matrix. S23: Using a pre-set roadway wall resistance correction coefficient, the elements in the flow field diffusion evolution trend matrix are weighted and averaged to extract the core feature dimension and construct the spatial diffusion coupling attenuation operator. The flow field diffusion evolution trend matrix refers to a two-dimensional matrix composed of the spatial concentration partial derivative values ​​at each monitoring node location. The core feature dimension refers to the principal component feature vector retained after dimensionality reduction based on weighted averaging.

4. The mine gas safety early warning method according to claim 1, characterized in that, The specific steps of S3 are as follows: S31: Extract the basic fixed alarm limit stored inside the device, analyze the multidimensional attenuation features contained in the spatial diffusion coupling attenuation operator, calculate the dynamic compensation deviation of the gas accumulation rate through the exponential smoothing algorithm, and obtain the real-time feedback weight. S32: Using the real-time feedback weight, the basic fixed alarm limit is linearly scaled and the safety margin is compensated, and an adaptive adjustment operation is performed to determine the transient judgment threshold baseline. S33: Perform a weighted fusion operation on the transient judgment threshold baseline and the historical long-term fluctuation average to calculate the dynamic concentration judgment limit; The multidimensional attenuation characteristic refers to the projection components of the spatial diffusion coupling attenuation operator in different spatial coordinate axis directions.

5. The mine gas safety early warning method according to claim 1, characterized in that, The specific steps of S4 are as follows: S41: Extract the latest data point from the initial gas concentration time series in real time as the current absolute time value, compare the current absolute time value with the dynamic concentration judgment boundary, and generate a boundary approach state indicator. S42: When the boundary approach state indicator reaches the preset safety tolerance range, separate the recent drastic change segment in the initial gas concentration time series, use fast Fourier transform to convert the recent drastic change segment from the time domain to the frequency domain, and extract the discrete fluctuation component. S43: Perform statistical dispersion analysis on the discrete fluctuation components, calculate the average expected index of the sum of squared deviations of the discrete fluctuation components from the mean center, and extract the high-frequency fluctuation variance value; The recent drastic change segment refers to the subset of values ​​in the initial gas concentration time series that are within a preset time window before the current moment.

6. The mine gas safety early warning method according to claim 1, characterized in that, The specific steps of S5 are as follows: S51: Retrieve the pre-set spatial correlation judgment scale, perform a horizontal cross-comparison between the high-frequency fluctuation variance value and the feature values ​​of adjacent regional nodes, and perform the secondary feature verification and comparison operation to obtain the regional collaborative anomaly probability. S52: Convert the regional collaborative anomaly probability into an actual variance evaluation index, and determine whether the absolute value of the actual variance evaluation index exceeds the preset limit benchmark. S53: When the judgment result is that the limit benchmark is exceeded, the alarm trigger in the underlying control hardware is activated, driving the mine central control system to execute the linkage response of the regional sound and light alarm equipment and the power failure protection control logic, and outputting the gas over-limit early warning command signal. The actual variance evaluation index refers to the corrected fluctuation assessment value obtained by multiplying the local single-point variance by the regional collaborative anomaly probability.

7. The mine gas safety early warning method according to claim 3, characterized in that, The process of calculating the spatial concentration distribution gradient difference specifically includes: Extract the concentration values ​​of the first measuring point located on the upwind side of the roadway and the concentration values ​​of the second measuring point located on the downwind side of the roadway at the same sampling time section in the initial gas concentration time series; The three-dimensional spatial straight-line distance value between the first and second measuring points and the pre-stored roadway curvature compensation parameters are obtained by querying the mine geographic information system database. The actual airflow diffusion path length is calculated by multiplying the three-dimensional spatial straight-line distance value by the roadway curvature compensation parameters. The absolute concentration increment is obtained by subtracting the concentration value of the first measuring point from the concentration value of the second measuring point, and the absolute concentration increment is divided by the actual airflow diffusion path length to obtain the spatial concentration distribution gradient difference.

8. The mine gas safety early warning method according to claim 4, characterized in that, The process of obtaining real-time feedback weights specifically includes: The temporal evolution feature component and the spatial transmission feature component contained in the spatial diffusion coupling attenuation operator are separated, and the temporal evolution feature component and the spatial transmission feature component are multiplied and fused according to a preset feature weight allocation strategy to obtain a comprehensive attenuation intensity characterization value. The deviation ratio of the comprehensive attenuation intensity characterization value relative to the standard diffusion reference value under ideal ventilation conditions is calculated, and a preset exponential smoothing constant is introduced to perform low-pass filtering on the deviation ratio to calculate the smoothing deviation amount. The smoothing deviation is mapped to a preset weight control feedback range, and the smoothing deviation is converted into a multiplicative factor through proportional-integral-derivative adjustment logic to obtain the real-time feedback weight.

9. The mine gas safety early warning method according to claim 6, characterized in that, The specific process of the secondary feature verification and comparison operation includes: Extract the high-frequency fluctuation variance value set of all upstream historical nodes in the ventilation branch loop where the current monitoring node is located, and calculate the Pearson correlation coefficient between the high-frequency fluctuation variance value and the high-frequency fluctuation variance value set to obtain the spatial fluctuation correlation matrix. Obtain the predefined topological distance attenuation threshold matrix in the spatial correlation determination scale, and use the topological distance attenuation threshold matrix to perform item-by-item masking filtering on the elements in the spatial fluctuation correlation matrix to filter out edge interference node data. The weighted coefficient of variation of the strongly correlated node features retained by the filter is jointly solved to calculate the degree of local regional clustering evolution, and the secondary feature verification and comparison operation is completed.

10. A mine gas safety early warning system, characterized in that, The system is used to implement the mine gas safety early warning method according to any one of claims 1-9, the system comprising: The parameter synchronous acquisition module is used to simultaneously acquire the initial gas concentration time series and real-time roadway wind speed parameters of multiple continuous monitoring nodes in the ventilation network using an electrochemical probe and a wind speed detector deployed on the roof of the mine roadway. The attenuation operator construction module is used to parse the initial gas concentration time series, obtain the spatial concentration distribution gradient difference between adjacent continuous monitoring nodes, and perform nonlinear fitting partial derivative calculations in combination with the real-time roadway wind speed parameter to construct a spatial diffusion coupling attenuation operator. The judgment boundary adjustment module is used to perform real-time feedback weight calculation and adaptive adjustment operations on the basic fixed alarm boundary stored in the device based on the spatial diffusion coupling attenuation operator, and to calculate the dynamic concentration judgment boundary. The high-frequency variance extraction module is used to compare the current absolute value of the initial gas concentration time series with the dynamic concentration judgment boundary in real time. When the current absolute value approaches the dynamic concentration judgment boundary, the initial gas concentration time series is separated and the high-frequency fluctuation variance value is extracted. The abnormal warning triggering module is used to perform a secondary feature verification and comparison operation based on the high-frequency fluctuation variance value and the spatial correlation judgment scale. When the actual variance exceeds the limit benchmark, the abnormal alarm mechanism is triggered and the gas over-limit warning command signal is output.

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