Gas accumulation concentration gas sensing intelligent early warning system

Abstract

The present application belongs to the technical field of gas sensor and safety monitoring, and specifically discloses a gas accumulation concentration gas sensing intelligent early warning system. The system comprises: a heterogeneous sensing array module for collecting original data through multi-physical field coupling; an edge intelligent processing module for local fusion and feature extraction; a dynamic risk quantification module for calculating real-time risk index and future trend based on a space-time evolution model; an adaptive early warning decision module for generating graded early warning instructions according to the comparison of the risk index and the threshold value; and a system self-calibration and health management module for online monitoring and calibration of the sensing unit. Through the above scheme, full-dimensional perception, forward-looking prediction and automatic graded response of the gas accumulation risk are realized, and the real-time, accuracy and reliability of the early warning are improved.

Description

Technical Field

[0001] This invention belongs to the field of gas sensor and safety monitoring technology, specifically relating to an intelligent early warning system for gas accumulation concentration sensing. Background Technology

[0002] In the field of mine safety production, the monitoring and early warning of gas disasters is a crucial technical link in ensuring the safety of underground workers and the stable operation of production systems. As a flammable and explosive gas, the abnormal accumulation of gas in mine roadways and working faces is a major cause of serious safety accidents. Therefore, real-time and accurate monitoring and risk warning of gas concentrations are of paramount importance. Gas concentration monitoring systems based on gas sensing technology are a key technological direction for achieving this goal. These systems aim to collect real-time gas concentration data in the environment through the deployment of sensor networks, and use this data to conduct safety status assessments and risk warnings.

[0003] Current technologies primarily rely on fixed or portable single-point gas sensors for concentration monitoring, and these systems generally suffer from several problems. Single-parameter detection modes cannot fully reflect the complex stress-gas coupling dynamics in deep mining environments, resulting in incomplete and unrepresentative monitoring data. The response speed of traditional systems is limited by the sensor's own performance and data transmission mechanism, exhibiting significant lag when gas concentration changes rapidly, making it difficult to provide timely information for emergency decision-making. Sensors are prone to drift and failure in harsh underground environments with high temperatures, high humidity, and dust interference, resulting in high monitoring error rates and compromising the accuracy and reliability of early warnings. The combination of these problems makes existing monitoring methods inadequate for meeting the urgent needs of modern, high-intensity mining operations for advanced and precise gas disaster prevention and control. Therefore, constructing an intelligent early warning system capable of real-time perception, intelligent prediction, and tiered response has become a pressing technical challenge. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent early warning system for gas accumulation concentration sensing, in order to solve the problems of limited monitoring, delayed response, poor environmental adaptability, and insufficient early warning accuracy of existing single-point sensors.

[0005] This invention provides an intelligent early warning system for gas accumulation concentration sensing, comprising: Heterogeneous sensor array modules are used to be deployed in downhole monitoring areas to collect raw environmental data using multi-physics field coupled sensing. The edge intelligence processing module is used to perform localized fusion and feature extraction on the raw environmental data from the heterogeneous sensor array module, and generate standardized multi-source feature vectors. The dynamic risk quantification module is used to receive multi-source feature vectors from the edge intelligent processing module and calculate the real-time risk index and future risk trend of the current monitoring area based on the preset spatiotemporal evolution model and risk quantification algorithm. The adaptive early warning decision module is used to compare the real-time risk index output by the dynamic risk quantification module with the preset multi-level risk thresholds, and generate and execute corresponding graded early warning instructions and handling strategies based on the comparison results and future risk trends. The system self-calibration and health management module is used to continuously monitor the working status and performance indicators of each sensing unit in the heterogeneous sensing array module, and to perform online calibration and fault diagnosis of the sensing units based on the monitoring data driven by the preset adaptive calibration algorithm.

[0006] Preferably, the heterogeneous sensing array module is composed of multiple sensing units in a spatial topology, and each sensing unit integrates at least three different types of sensors. The three types of sensors are a gas concentration sensor, a micro-vibration sensor, and a temperature and humidity composite sensor. The gas concentration sensor uses an optical sensing probe based on tuned laser absorption spectroscopy technology. The micro-vibration sensor is a wideband accelerometer; The temperature and humidity composite sensor uses a digital integrated chip; all sensing units are connected to the edge intelligent processing module via industrial Ethernet or intrinsically safe wireless mesh network.

[0007] Preferably, the edge intelligence processing module is built into the local gateway device of each sensor unit cluster, and its data processing flow is as follows: The raw concentration data from the gas concentration sensor were preprocessed by moving average filtering and outlier removal; the raw vibration signal from the microseismic sensor was analyzed by wavelet packet transform, and the energy distribution characteristics of the signal in 8 different frequency bands were extracted as microseismic feature vectors. The preprocessed concentration data, extracted microseismic feature vectors, and temperature and humidity data are fused at the feature level after being aligned with the timestamps to form a multi-source feature vector containing at least 12 dimensions. The multi-source feature vector is standardized to have a mean of 0 and a variance of 1, and then packaged into a standardized data packet and uploaded to the dynamic risk quantification module.

[0008] Preferably, the dynamic risk quantification module runs on the server cluster of the well monitoring center and includes a spatiotemporal evolution prediction model based on long short-term memory networks. The spatiotemporal evolution prediction model uses historical multi-source feature vector sequences as training inputs and gas concentration measured values ​​sequences over a future period as training labels, and trains the model through supervised learning. During the early warning phase, the module receives multi-source feature vectors from the current and historical periods, inputs them into the trained spatiotemporal evolution prediction model, and outputs a sequence of predicted gas concentration values ​​for the next 5, 15, and 30 minutes.

[0009] Preferably, the dynamic risk quantification module has a built-in risk quantifier; The calculation logic of the risk quantifier is as follows: The real-time gas concentration value, concentration change rate, microseismic activity intensity index, and predicted concentration sequence are assigned weights of 0.4, 0.2, 0.2, and 0.2 respectively, and a real-time risk index ranging from 0 to 100 is calculated by weighted summation.

[0010] Preferably, the adaptive early warning decision module makes automated decisions based on the real-time risk index output by the dynamic risk quantification module and the preset 3-level risk threshold; The three risk thresholds include a level 1 risk threshold of 30, a level 2 risk threshold of 60, and a level 3 risk threshold of 85. When the real-time risk index is less than the Level 1 risk threshold, the system maintains normal monitoring status. When the real-time risk index reaches or exceeds the Level 1 risk threshold but is less than the Level 2 risk threshold, the system generates a blue warning command, triggers the local audible and visual alarm to provide intermittent alerts, and sends warning information to the area manager. When the real-time risk index reaches or exceeds the Level 2 risk threshold but is less than the Level 3 risk threshold, the system generates a yellow warning command, triggers the local audible and visual alarm to continuously alarm, automatically cuts off the power supply to non-intrinsically safe equipment in the risk area, and increases the data upload frequency to once per second. When the real-time risk index reaches or exceeds the Level 3 risk threshold, the system generates a red warning instruction. In addition to executing all actions required for a yellow warning, it immediately activates the mine-wide emergency broadcast, forcibly issues personnel evacuation instructions, and coordinates with the ventilation system to execute the forced ventilation plan.

[0011] Preferably, the workflow of the system self-calibration and health management module includes a status monitoring closed loop and an active calibration closed loop; The condition monitoring closed loop continuously collects the output baseline value, historical response time data, and operating temperature data of each gas concentration sensor, and inputs them into the fault diagnosis model based on support vector machine, outputting the health score and potential fault type of each sensor. The active calibration closed loop is triggered under two conditions: the first condition is that the system detects that the health score of a certain sensor is continuously lower than a preset threshold for 10 minutes. The second condition is that the system performs a routine calibration every 24 hours according to a preset cycle.

[0012] Preferably, the calibration process of the active calibration closed loop is as follows: The control unit integrates a standard gas release device to release standard methane gas of known concentration into the vicinity of the gas concentration sensor probe. Record the sensor's response value to the standard gas, compare it with the standard value, and calculate the current calibration coefficient; This calibration coefficient is used to compensate and correct subsequent measurements from the sensor in real time.

[0013] Preferably, the spatiotemporal evolution prediction model is enhanced using an attention mechanism; The attention mechanism is configured to dynamically calculate the importance weights of data at different time points in the historical sequence to the current prediction when the spatiotemporal evolution prediction model processes multi-source feature vector sequences. The spatiotemporal evolution prediction model first calculates the correlation score between the current hidden state and all historical hidden states, and then transforms these scores into attention weights through a normalized exponential function. The context vector of the spatiotemporal evolution prediction model is a weighted sum of historical hidden states, which is determined by attention weights.

[0014] Preferably, the spatial topology of the heterogeneous sensor array module is optimized and deployed based on the geometric characteristics of the underground roadway and the simulation results of the ventilation flow field; For the tunnel excavation face, the sensing units are arranged in a linear array with a unit spacing of 10 meters. For the intersection of goaf and roadway, the sensing units are arranged in a mesh array; The preferred deployment location is within 0.5 to 1 meter below the top plate.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention changes the passive and one-sided mode of traditional gas monitoring by constructing a system-level solution that integrates heterogeneous sensing, edge intelligence, dynamic risk quantification and adaptive decision-making.

[0016] 2. The system employs a multi-physics heterogeneous sensor array integrating gas, micro-vibration, temperature, and humidity sensors, achieving full-dimensional perception of the gas accumulation and breeding environment, thus solving the problem of insufficient representativeness of single-parameter detection. The edge intelligent processing module integrates raw data and performs feature extraction in advance, reducing data transmission volume and improving system response speed, overcoming the problem of delayed early warning.

[0017] 3. The dynamic risk quantification module, based on long short-term memory networks and attention mechanisms, enables accurate prediction of the spatiotemporal evolution trend of gas concentration and calculation of the comprehensive risk index, thus providing a forward-looking basis for early warning decisions.

[0018] 4. The adaptive early warning decision-making module, based on clear multi-level thresholds and well-defined handling strategies, achieves a fully automated closed loop from risk perception to control execution, improving the timeliness and effectiveness of emergency response.

[0019] 5. The system self-calibration and health management module ensures the long-term reliability and accuracy of sensor data in harsh environments through online monitoring and active calibration, thus guaranteeing the stable operation of the entire early warning system from the source. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of dynamic risk quantification based on multi-source feature fusion and spatiotemporal evolution prediction in this invention. Figure 3 This is a schematic diagram of the multi-level interaction relationship and data flow between the heterogeneous sensor array module and the edge intelligent processing module in this invention; Figure 4 This is a logical flow diagram of the adaptive early warning decision module based on multi-level risk thresholds in this invention; Figure 5 This is a schematic diagram of the dual closed-loop principle framework of the system self-calibration and health management module in this invention, which combines status monitoring and active calibration. Detailed Implementation

[0021] Example 1: The intelligent early warning system for gas accumulation concentration sensing of the present invention includes a heterogeneous sensor array module, an edge intelligent processing module, a dynamic risk quantification module, an adaptive early warning decision module, and a system self-calibration and health management module. The modules achieve highly reliable, low-latency data interaction and command coordination through industrial Ethernet or intrinsically safe wireless mesh networks, jointly forming a closed-loop early warning system with full-dimensional perception, localized intelligence, dynamic risk assessment, adaptive response, and self-maintenance capabilities. Please refer to the appendix. Figure 1 The diagram clearly shows the logical connections and data flow between the five modules mentioned above.

[0022] The heterogeneous sensor array module is deployed in various key monitoring areas underground, including but not limited to areas prone to gas accumulation such as roadway excavation faces, goaf boundaries, roadway intersections, and return airways. This heterogeneous sensor array module consists of multiple sensing units arranged according to a spatial topology. Each sensing unit integrates three different types of sensors: a gas concentration sensor, a microseismic sensor, and a temperature and humidity composite sensor. The gas concentration sensor uses an optical sensing probe based on tuned laser absorption spectroscopy technology, with a measurement accuracy better than 1 part per million and a response time of less than 2 seconds, maintaining high stability in complex dusty and high-humidity environments. The microseismic sensor uses a broadband accelerometer with a frequency response range covering 1 Hz to 1000 Hz, used to capture microseismic event signals released by rock mass due to stress concentration or fracture in real time, providing a basis for judging the stability of the surrounding rock.

[0023] The temperature and humidity composite sensor uses a digital integrated chip to simultaneously output ambient temperature and relative humidity data. The temperature measurement range is -40 degrees Celsius to 85 degrees Celsius, and the humidity measurement range is 0 to 100% relative humidity. Its output data is used to compensate for the temperature and humidity drift effect in gas concentration measurement and to help determine whether the local microclimate is conducive to gas accumulation.

[0024] All sensing units establish bidirectional communication links with the edge intelligent processing module via industrial Ethernet or intrinsically safe wireless mesh networks, forming a redundant topology. When a communication path fails, the system can automatically switch to a backup path, ensuring continuous data transmission. (See attached...) Figure 3 As shown, the heterogeneous sensor array module continuously outputs three types of raw data streams to the edge intelligent processing module: the first type is the gas concentration time series, sampled once per second; the second type is the micro-vibration signal, sampled 2000 times per second; and the third type is temperature and humidity data, sampled 0.5 times per second. These data are aligned in timestamps using a high-precision time synchronization protocol to ensure the timing consistency of subsequent fusion processing.

[0025] The edge intelligent processing module is built into the local gateway device corresponding to each sensor unit cluster. Its hardware platform is typically an embedded computing device with a real-time operating system and a neural network acceleration unit. This edge intelligent processing module performs localized preprocessing, feature extraction, and feature-level fusion on the raw environmental data from the heterogeneous sensor array modules, ultimately generating standardized multi-source feature vectors and uploading them to the dynamic risk quantification module. Its data processing flow consists of four stages.

[0026] The first stage involves moving average filtering and outlier removal of gas concentration data. The system uses a moving average filter with a window length of 5 seconds to smooth the original concentration sequence to suppress high-frequency noise interference. Simultaneously, a dynamic outlier detection threshold is set: if the absolute difference between the current concentration value and the previous concentration value is greater than 0.5% of the volume concentration, it is considered an abnormal jump, the data point is marked as invalid, and replaced by interpolation of the preceding and following valid values, preventing false high-concentration alarms caused by instantaneous sensor interference or communication errors.

[0027] The second stage involves wavelet packet transform analysis of the microseismic signal. The original microseismic vibration signal is first subjected to an anti-aliasing low-pass filter with a cutoff frequency set to 1000 Hz. The signal is then input into a three-layer wavelet packet decomposition tree, using the Daubechies-4 wavelet basis function to decompose the signal energy into eight non-overlapping frequency bands with center frequencies of 62.5 Hz, 187.5 Hz, 312.5 Hz, 437.5 Hz, 562.5 Hz, 687.5 Hz, 812.5 Hz, and 937.5 Hz. The energy value of the wavelet coefficients within each frequency band is calculated, i.e., the root mean square of the sum of squares of the coefficients, forming an 8-dimensional microseismic feature vector. This feature vector characterizes the energy distribution pattern of microseismic events, thus distinguishing between tectonic microseismic events, blasting vibrations, and equipment operating noise.

[0028] The third stage involves the time alignment and fusion of multi-source features. Using the timestamps of the gas concentration data as a benchmark, the system performs linear interpolation on the microseismic feature vector and the temperature and humidity data to ensure precise alignment along the time axis. The fused feature vector contains the following 12 dimensions: current gas concentration value, concentration change rate over the past 30 seconds, current temperature value, current relative humidity value, and energy values ​​for eight microseismic frequency bands. This 12-dimensional vector constitutes a complete description of the current local environmental state.

[0029] The fourth stage is feature standardization. The system performs Z-score standardization on the feature values ​​of each dimension, that is, subtracting the mean of that dimension within the historical window and dividing by its standard deviation, so that the mean of each dimension of the standardized feature vector is 0 and the variance is 1. The standardized data is packaged into a fixed-length data packet containing a timestamp, device identifier, 12-dimensional feature vector and checksum, and uploaded to the dynamic risk quantification module through a secure encrypted channel. The data upload frequency is dynamically adjusted according to the system's operating status: once every 10 seconds under normal conditions; when a yellow or red alert is triggered, the frequency increases to once per second.

[0030] The dynamic risk quantification module is deployed in the server cluster of the well monitoring center, and includes a spatiotemporal evolution prediction model based on Long Short-Term Memory (LSTM) networks and incorporating an attention mechanism. Please refer to the appendix. Figure 2The figure details the internal structure and data processing logic of the dynamic risk quantification module. The model's input is a sequence of standardized multi-source feature vectors for N consecutive time steps, where N is 60, corresponding to a 10-minute historical observation window (with a step size of 10 seconds). The model's output is the predicted gas concentration values ​​for the next three time points, corresponding to the concentration levels 5 minutes, 15 minutes, and 30 minutes later, respectively.

[0031] The training process of this spatiotemporal evolution prediction model adopts a supervised learning paradigm. The training dataset consists of historical multi-source feature vector sequences and their corresponding future measured gas concentration sequences. The loss function uses mean squared error (MSE), and the model parameters are optimized through backpropagation. To improve the model's sensitivity to key historical events, an attention mechanism is introduced.

[0032] At each time step of the LSTM network The model calculates the current hidden state. With all hidden states in history Correlation score between , For the first A historical hidden state, , For learnable weight matrix, This serves as the context vector. Subsequently, the scores are transformed into attention weights using a normalized exponential function. , For the first Each relevance score. Finally, the context vector. After being stitched into the current hidden state, it is used to generate the final prediction output. This mechanism allows the model to automatically focus on historical segments most relevant to the current risk state, such as recent peaks in microseismic activity or periods of rapid concentration increases, thereby improving prediction accuracy.

[0033] After completing the concentration prediction, the dynamic risk quantification module further calculates the real-time risk index. The real-time risk index is derived by weighted summation of four key factors, calculated using the following formula: ; This is the normalized value of the current gas concentration (mapped to the 0-100 range). This is the normalized value of the rate of change of concentration. This is the normalized value of the microseismic activity intensity index (obtained by mapping the 8-dimensional microseismic eigenvectors after dimensionality reduction through principal component analysis). This is the normalized value for the predicted concentration over the next 30 minutes. All normalization operations are dynamically adjusted based on historical extreme values ​​to ensure that the index changes continuously between 0 and 100. The higher the value, the greater the probability of gas accumulation or even a gas outburst in the current area.

[0034] The adaptive early warning decision module receives the real-time risk index R output by the dynamic risk quantification module and compares it with the preset three-level risk thresholds, then executes the corresponding graded early warning and response strategies. Please refer to the appendix. Figure 4 The diagram illustrates the complete decision-making logic process. The system sets the risk threshold to 30 for Level 1, 60 for Level 2, and 85 for Level 3.

[0035] when At this time, the system is determined to be in a normal state, performing only routine data recording and visualization without triggering any alarms or control actions. During this period, the edge intelligent processing module maintains a data upload frequency of 10 seconds per upload, minimizing system resource consumption.

[0036] when When the system determines the situation to be low-risk, it generates a blue alert. This blue alert triggers the following actions: local audible and visual alarms deployed in the monitoring area activate an intermittent alert mode, lighting up for 1 second and turning off for 2 seconds, lasting for 5 minutes; simultaneously, the system pushes alert information to the mobile terminals of the shift leaders or safety officers in the monitoring area via the mine dispatch communication network. The information includes the risk index, current concentration, microseismic activity overview, and recommended measures. This stage aims to alert personnel to potential risks without interrupting normal production operations.

[0037] when Upon arrival, the system determined the situation to be of medium risk and generated a yellow alert. This yellow alert triggered a stronger response: the local audible and visual alarms switched to continuous alarm; the system automatically sent a power-off command to the regional power distribution controller, cutting off power to all non-intrinsically safe electrical equipment within the area, retaining power only to intrinsically safe communication and monitoring equipment; simultaneously, the data upload frequency of the edge intelligent processing module immediately increased to once per second to support more frequent risk reassessments. Furthermore, the system pushed a highlighted alarm to the dispatch center's large screen and recorded the complete contextual data of the event for post-event analysis.

[0038] when When the system determines the situation to be high-risk, it generates a red alert. This red alert, in addition to executing all actions required for a yellow alert, further activates the entire mine's emergency response mechanism: First, it activates the entire mine's emergency broadcast system, continuously playing a mandatory evacuation order stating, "The gas risk in area XX is extremely high; please evacuate immediately along the escape route." Second, it sends a forced ventilation command to the central ventilation control system, requiring it to automatically increase the area's ventilation volume to 150% of its design maximum within 5 minutes and maintain this level for at least 30 minutes. Third, the system automatically locks the location tags of all personnel in the area, tracks the evacuation progress in real time, and sends targeted voice reminders to the nearest base station where personnel who have not evacuated in time are located. Finally, the system reports the event level, location, risk index, and executed actions to the provincial mine safety supervision platform, achieving cross-level coordination.

[0039] All warning commands are reliably transmitted through a message queue mechanism to ensure that commands are not lost or duplicated. The system also has a built-in command execution feedback mechanism: if an action does not receive execution confirmation within 5 seconds, the command is automatically resent and highlighted in red on the scheduling interface for manual verification.

[0040] The system self-calibration and health management module is responsible for ensuring the long-term reliability of the entire sensor network. Please refer to the appendix. Figure 5 The system's self-calibration and health management module adopts a dual closed-loop control architecture, which includes a status monitoring closed loop and an active calibration closed loop.

[0041] A closed-loop condition monitoring system continuously runs in the background, collecting key performance indicators (KPIs) for each gas concentration sensor every 5 minutes. These KPIs include: output baseline value, average response time of the last 100 responses, operating temperature, and reading deviation from neighboring sensors. These KPIs are then fed into a pre-trained support vector machine-based fault diagnosis model. This model is trained offline using a large number of historical fault samples, including zero-point drift, sensitivity decay, response hysteresis, and complete failure, and outputs a health score (0-100) and the most likely fault type for each sensor. A health score below 80 is considered performance degradation.

[0042] The active calibration closed loop is triggered under two conditions. The first condition is performance degradation triggering: when the health score of a sensor is less than 80 points for 10 consecutive minutes, the system automatically initiates the calibration process. The second condition is periodic triggering: regardless of the health status, the system performs a routine calibration on all sensors every 24 hours to prevent latent drift.

[0043] The calibration process is as follows: The system first sends a command to the target sensing unit to activate its integrated standard gas release device. This device contains a miniature gas reservoir filled with 1.0% methane standard gas by volume. After the release valve opens, the standard gas diffuses around the optical probe within 0.5 seconds, creating a stable testing environment. The sensor continuously outputs a response value for the next 5 seconds, and the system records the steady-state output value. Ideally, It should equal 1.0%. Calibration factor. It was calculated. After that, all the original measurements from the sensor were... All passed Real-time compensation and correction are performed to obtain the final measured value after calibration and correction. The timestamp of the calibration event, , The health scores before and after calibration are written into the system log database to build sensor lifespan curves and drift trend models.

[0044] The system also has cross-validation capabilities. When a sensor triggers calibration, its two neighboring sensors are temporarily designated as reference sources, and their readings are used to verify the reasonableness of the calibrated data. If the calibrated reading deviates from the expected value of the neighboring sensors under the same environment by more than 15%, the calibration is deemed a failure, the system marks the sensor as "pending maintenance," and increases the uncertainty weight of its data in risk calculation, reducing its impact on the final risk index.

[0045] Regarding the spatial deployment strategy of the heterogeneous sensor array modules, the system is optimized based on the geometric characteristics of the underground roadway and the simulation results of the ventilation flow field. For straight roadway excavation faces, the sensor units are arranged in a linear array with the unit spacing strictly controlled at 10 meters to ensure that changes in gas concentration gradient can be fully captured. For complex areas such as goaf areas, roadway intersections, or chambers, a mesh array is used with a node spacing of no more than 8 meters to form a monitoring grid covering the entire three-dimensional space. All deployment locations are determined through prior CFD (Computational Fluid Dynamics) simulations to avoid return air dead zones, equipment heat dissipation vents, and water-spraying areas. The installation height is uniformly set within 0.5 to 1 meter below the roof, as gas density is lower than air and easily accumulates in this area; this installation height maximizes early warning sensitivity.

[0046] In summary, the intelligent early warning system for gas accumulation concentration sensing described in this embodiment constructs a complete intelligent closed loop from perception to decision-making, execution, and maintenance through multi-physics heterogeneous sensing, edge intelligent feature fusion, spatiotemporal risk prediction based on attention-enhanced LSTM, multi-level adaptive early warning linkage, and a dual-closed-loop self-calibration mechanism. The system not only solves the one-sidedness of traditional single-point monitoring but also transforms gas disaster prevention and control from "post-event response" to "pre-event prevention" through proactive prediction and automated response, thereby improving the intelligent level and inherent safety capabilities of mine safety production.

[0047] Example 2: Building upon Example 1, this example further introduces virtual mapping and simulation capabilities based on digital twins to enhance the system's decision-making robustness under extreme conditions. Specifically, the system constructs a digital twin at the surface monitoring center that is completely synchronized with the physical mine. This digital twin not only includes static information such as roadway geometry, ventilation network topology, and equipment layout, but also receives dynamic environmental data streams from heterogeneous sensor array modules in real time, forming a virtual-real fusion mirror system.

[0048] When the real-time risk index calculated by the dynamic risk quantification module approaches the Level 3 risk threshold (i.e.) When a situation arises, the system automatically activates the emergency simulation engine within the digital twin. This engine, based on computational fluid dynamics and multiphase flow theory, constructs a transient simulation model of gas diffusion and accumulation. Model inputs include current concentration, wind speed, temperature, microseismic activity intensity, and tunnel cross-sectional parameters from each sensor unit. Initial conditions are set by multi-source feature vectors provided by the edge intelligent processing module. Within 10 seconds, the simulation engine completes predictions of the three-dimensional diffusion path, concentration distribution cloud map, and evolution of high-risk areas for the gas cloud over the next 30 minutes.

[0049] The simulation results are fed back to the adaptive early warning decision-making module in real time. If the simulation shows that the red warning area will expand to adjacent roadways within 10 minutes, the system will raise the warning level of the adjacent area to yellow in advance and pre-activate its ventilation enhancement plan. If the simulation shows that the current strong exhaust strategy is insufficient to reduce the concentration to a safe level within 15 minutes, the system will automatically recommend an optimized solution to the dispatcher, such as increasing the number of local fans or adjusting the opening of the dampers, and highlight the recommended operation path on the human-machine interface.

[0050] The digital twin is also used for fault scenario simulation in the system's self-calibration and health management modules. When a sensor is diagnosed as potentially faulty, the system simulates a scenario of complete sensor failure in the twin to assess its impact on the overall risk assessment accuracy. If the impact exceeds a preset tolerance (e.g., a risk index deviation greater than 10 points), the system immediately raises the calibration priority and triggers activation commands for redundant sensors in the physical world to ensure comprehensive monitoring coverage.

[0051] The digital twin enhancement mechanism does not change the original hardware architecture, but only adds high-performance simulation computing nodes at the software level, seamlessly integrating with existing modules through APIs. This enables the system to not only have real-time response capabilities, but also forward-looking intelligence such as hypothesis analysis and contingency plan simulation, improving its overall ability to cope with gas disasters in complex, dynamic, and highly uncertain underground environments.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A gas accumulation concentration sensing intelligent early warning system, characterized in that, include: Heterogeneous sensor array modules are used to be deployed in downhole monitoring areas to collect raw environmental data using multi-physics field coupled sensing. The edge intelligence processing module is used to perform localized fusion and feature extraction on the raw environmental data from the heterogeneous sensor array module, and generate standardized multi-source feature vectors. The dynamic risk quantification module is used to receive multi-source feature vectors from the edge intelligent processing module and calculate the real-time risk index and future risk trend of the current monitoring area based on the preset spatiotemporal evolution model and risk quantification algorithm. The adaptive early warning decision module is used to compare the real-time risk index output by the dynamic risk quantification module with the preset multi-level risk thresholds, and generate and execute corresponding graded early warning instructions and handling strategies based on the comparison results and future risk trends. The system self-calibration and health management module is used to continuously monitor the working status and performance indicators of each sensing unit in the heterogeneous sensing array module, and to perform online calibration and fault diagnosis of the sensing units based on the monitoring data driven by the preset adaptive calibration algorithm.

2. The intelligent early warning system for gas accumulation concentration sensing according to claim 1, characterized in that, The heterogeneous sensing array module consists of multiple sensing units arranged in a spatial topology, and each sensing unit integrates at least three different types of sensors. The three types of sensors are a gas concentration sensor, a micro-vibration sensor, and a temperature and humidity composite sensor. The gas concentration sensor uses an optical sensing probe based on tuned laser absorption spectroscopy technology. The micro-vibration sensor is a wideband accelerometer; The temperature and humidity composite sensor uses a digital integrated chip; all sensing units are connected to the edge intelligent processing module via industrial Ethernet or intrinsically safe wireless mesh network.

3. The intelligent early warning system for gas accumulation concentration sensing according to claim 2, characterized in that, The edge intelligence processing module is built into the local gateway device of each sensor unit cluster, and its data processing flow is as follows: The raw concentration data from the gas concentration sensor were preprocessed by moving average filtering and outlier removal; the raw vibration signal from the microseismic sensor was analyzed by wavelet packet transform, and the energy distribution characteristics of the signal in 8 different frequency bands were extracted as microseismic feature vectors. The preprocessed concentration data, extracted microseismic feature vectors, and temperature and humidity data are fused at the feature level after being aligned with the timestamps to form a multi-source feature vector containing at least 12 dimensions. The multi-source feature vector is standardized to have a mean of 0 and a variance of 1, and then packaged into a standardized data packet and uploaded to the dynamic risk quantification module.

4. The intelligent early warning system for gas accumulation concentration sensing according to claim 3, characterized in that, The dynamic risk quantification module runs on the server cluster of the well monitoring center and includes a spatiotemporal evolution prediction model based on long short-term memory networks. The spatiotemporal evolution prediction model uses historical multi-source feature vector sequences as training inputs and gas concentration measured values ​​sequences over a future period as training labels, and trains the model through supervised learning. During the early warning phase, the dynamic risk quantification module receives multi-source feature vectors from the current and historical periods, inputs them into the trained spatiotemporal evolution prediction model, and outputs a sequence of predicted gas concentration values ​​for the next 5, 15, and 30 minutes.

5. The intelligent early warning system for gas accumulation concentration sensing according to claim 4, characterized in that, The dynamic risk quantification module has a built-in risk quantifier. The calculation logic of the risk quantifier is as follows: The real-time gas concentration value, concentration change rate, microseismic activity intensity index, and predicted concentration sequence are assigned weights of 0.4, 0.2, 0.2, and 0.2 respectively, and a real-time risk index ranging from 0 to 100 is calculated by weighted summation.

6. The intelligent early warning system for gas accumulation concentration sensing according to claim 5, characterized in that, The adaptive early warning decision module makes automatic decisions based on the real-time risk index output by the dynamic risk quantification module and the preset 3-level risk threshold. The three risk thresholds include a level 1 risk threshold of 30, a level 2 risk threshold of 60, and a level 3 risk threshold of 85. When the real-time risk index is less than the Level 1 risk threshold, the system maintains normal monitoring status. When the real-time risk index reaches or exceeds the Level 1 risk threshold but is less than the Level 2 risk threshold, the system generates a blue warning command, triggers the local audible and visual alarm to provide intermittent alerts, and sends warning information to the area manager. When the real-time risk index reaches or exceeds the Level 2 risk threshold but is less than the Level 3 risk threshold, the system generates a yellow warning command, triggers the local audible and visual alarm to continuously alarm, automatically cuts off the power supply to non-intrinsically safe equipment in the risk area, and increases the data upload frequency to once per second. When the real-time risk index reaches or exceeds the Level 3 risk threshold, the system generates a red warning instruction. In addition to executing all actions required for a yellow warning, it immediately activates the mine-wide emergency broadcast, forcibly issues personnel evacuation instructions, and coordinates with the ventilation system to execute the forced ventilation plan.

7. The intelligent early warning system for gas accumulation concentration sensing according to claim 6, characterized in that, The workflow of the system self-calibration and health management module includes a status monitoring closed loop and an active calibration closed loop. The condition monitoring closed loop continuously collects the output baseline value, historical response time data, and operating temperature data of each gas concentration sensor, and inputs them into the fault diagnosis model based on support vector machine, outputting the health score and potential fault type of each sensor. The active calibration closed loop is triggered under two conditions: the first condition is that the system detects that the health score of a certain sensor is continuously lower than a preset threshold for 10 minutes. The second condition is that the system performs a routine calibration every 24 hours according to a preset cycle.

8. The intelligent early warning system for gas accumulation concentration sensing according to claim 7, characterized in that, The calibration process of the active calibration closed loop is as follows: The control unit integrates a standard gas release device to release standard methane gas of known concentration into the vicinity of the gas concentration sensor probe. Record the sensor's response value to the standard gas, compare it with the standard value, and calculate the current calibration coefficient; This calibration coefficient is used to compensate and correct subsequent measurements from the sensor in real time.

9. The intelligent early warning system for gas accumulation concentration sensing according to claim 8, characterized in that, The spatiotemporal evolution prediction model is enhanced using an attention mechanism; The attention mechanism is configured to dynamically calculate the importance weights of data at different time points in the historical sequence to the current prediction when the spatiotemporal evolution prediction model processes multi-source feature vector sequences. The spatiotemporal evolution prediction model first calculates the correlation score between the current hidden state and all historical hidden states, and then transforms these scores into attention weights through a normalized exponential function. The context vector of the spatiotemporal evolution prediction model is a weighted sum of historical hidden states, which is determined by attention weights.

10. The intelligent early warning system for gas accumulation concentration sensing according to claim 9, characterized in that, The spatial topology of the heterogeneous sensor array module is optimized and deployed based on the geometric characteristics of the underground roadway and the simulation results of the ventilation flow field. For the tunnel excavation face, the sensing units are arranged in a linear array with a unit spacing of 10 meters. For the intersection of goaf and roadway, the sensing units are arranged in a mesh array; The preferred deployment location is within 0.5 to 1 meter below the top plate.

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