Coal mine safety data comprehensive analysis and early warning system

Abstract

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine safety monitoring, and in particular to a coal mine safety data comprehensive analysis and early warning system. Background Art

[0002] Coal mine production environments are highly dangerous, complex, and dynamic, posing numerous challenges to underground safety monitoring. Traditional monitoring systems suffer from data isolation. Multi-source data, such as underground personnel location, equipment operation, and environmental monitoring, comes from sensors and equipment from different manufacturers and using different protocols. Heterogeneous data formats and inconsistent interface standards make it difficult to effectively integrate data and form a global understanding of the safety situation. For example, environmental monitoring data (such as gas concentration and dust content) and equipment operation data (such as fan speed and hoist load) are independent of each other, making it difficult to identify potential risks through correlation analysis.

[0003] At the visualization level, traditional two-dimensional flat monitoring interfaces cannot truly reflect the complex spatial structure and dynamic changes in underground mines. Coal mines have a complex network of tunnels, complex geological structures, and equipment distribution has strong spatial characteristics. Two-dimensional images cannot accurately represent the distribution and evolution of monitoring data in three dimensions, making it difficult for monitoring personnel to quickly locate risk areas, hindering the efficiency of emergency response.

[0004] In terms of risk assessment, traditional methods are mostly based on single parameter threshold alarms, lacking dynamic analysis of risk transmission paths and diffusion trends. For example, when the gas concentration in a certain area exceeds the standard, it is impossible to calculate the probability of the risk spreading to surrounding areas and the scope of impact in real time, making it difficult to predict the development of dangerous situations in advance, which can easily lead to the expansion of accidents. In addition, there is a lack of effective linkage mechanisms between subsystems (such as ventilation systems, drainage systems, and transportation systems). When a single system alarms, cross-system collaborative disposal cannot be triggered, resulting in a single emergency response measure and difficulty in achieving rapid risk control.

[0005] In terms of model prediction, traditional risk prediction models often use statistical methods with fixed parameters, which are unable to adapt to the dynamic changes in the underground environment. As mining progresses, geological conditions, equipment status, and other factors constantly change. Fixed models are unable to accurately capture the real-time characteristics of accident evolution, resulting in reduced prediction accuracy and an inability to provide reliable decision support for safety management.

[0006] In terms of data storage and processing, traditional systems have a decentralized data storage architecture, lacking centralized management of raw monitoring data and model training data, resulting in inefficient data retrieval and analysis. Furthermore, faced with massive amounts of real-time data, traditional central processing units lack the computing power and scheduling efficiency to achieve real-time processing and dynamic response for multi-module data.

[0007] While existing technologies have seen some improvements in data integration and visualization, they still have significant flaws. Some systems introduce data conversion interfaces to enable heterogeneous data collection, but these only address the data format conversion level. They lack in-depth modeling of the temporal and spatial correlations of data and are unable to construct dynamic topological networks to reflect the inherent connections between data. While three-dimensional visualization technology has been upgraded from two-dimensional to three-dimensional, it mostly uses static model rendering and lacks a dynamic rendering mechanism based on a spatiotemporal reference axis. This makes it impossible to achieve continuous visualization of monitoring data in both temporal and spatial dimensions, making it difficult to present the evolution of risks.

[0008] In the area of ​​risk assessment, some systems have attempted to incorporate graph theory algorithms to analyze risk propagation. However, these systems rely solely on static network structures (such as tunnel layouts), ignoring the dynamic impact of environmental parameter fluctuations and equipment operating conditions, leading to inaccurate calculations of risk propagation paths. Regarding linkage control, existing systems primarily implement threshold-triggered responses within a single system, while cross-system linkages remain limited to simple logical linkages. These systems lack a multi-level response mechanism based on the correlation of multi-system alarms, making it impossible to dynamically adjust response plans based on risk levels.

[0009] In terms of model training, existing technologies often use a single neural network structure, making it difficult to simultaneously integrate the dual impacts of equipment degradation characteristics and environmental anomaly patterns. Furthermore, model updates are often performed in offline batches, which cannot adapt to the dynamic changes in real-time data streams, resulting in insufficient timeliness and accuracy of the prediction models. Furthermore, existing systems lack decision support capabilities for different management levels and are unable to provide differentiated safety management information to grassroots monitoring personnel and senior managers based on multi-resolution analysis of risk briefings. Summary of the Invention

[0010] The purpose of the present invention is to provide a coal mine safety data comprehensive analysis and early warning system to solve the problems raised in the above background technology.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a coal mine safety data comprehensive analysis and early warning system, the system comprising:

[0012] The data integration module is used to build a multi-source heterogeneous data acquisition channel for coal mines, and to perform dynamic topological modeling on real-time data of underground personnel positioning, equipment operation, and environmental monitoring based on preset spatiotemporal association rules;

[0013] 3D visualization module, used to load 3D geological structure models and equipment layout spatial data, establish a dynamic rendering mechanism based on time and space reference axes to achieve stereoscopic projection of monitoring data;

[0014] The risk assessment module is used to calculate the risk propagation path of each monitoring node through spatiotemporal correlation analysis and generate a dangerous situation map of the underground area based on the dynamic topology network;

[0015] The linkage control module is used to establish a two-way mapping relationship between equipment control instructions and alarm trigger conditions, forming a multi-level response mechanism for cross-system linkage;

[0016] The model training module is used to build an accident evolution feature library and optimize the parameter weights of the risk prediction model through a progressive learning algorithm;

[0017] A central processing unit, configured to receive the topological network data from the data integration module, drive the rendering engine of the 3D visualization module, invoke the graph generation algorithm of the risk assessment module, activate the response mechanism of the linkage control module, and schedule the optimization process of the model training module;

[0018] The multi-source heterogeneous data acquisition channel includes a preset device communication protocol conversion interface and / or a data cleaning rule library.

[0019] Preferably, the calculating of the risk propagation path of each monitoring node by the spatiotemporal correlation analysis method includes:

[0020] The underground space is divided into several dynamic analysis units, and a risk transfer intensity matrix between units is established based on the spatiotemporal variation curves of equipment operating parameters and environmental monitoring values.

[0021] Determine the initial risk unit according to the preset risk threshold, and traverse the associated paths in the risk transfer intensity matrix through a breadth-first search algorithm;

[0022] Calculate the propagation delay and energy attenuation coefficient of each associated path, and generate a risk diffusion probability distribution map based on device status parameters;

[0023] When the risk diffusion probability of an adjacent unit exceeds a preset propagation threshold, the unit is marked as a secondary risk source and the danger situation map is updated.

[0024] Preferably, the dynamic topology network includes static connection relationships and dynamic influence relationships; the static connection relationships are determined by the underground tunnel layout and the physical connection relationship of the equipment; and the dynamic influence relationships are determined by the correlation pattern between environmental parameter fluctuations and equipment operating status.

[0025] Preferably, the establishment of a dynamic rendering mechanism based on a time-space reference axis includes:

[0026] Expand the time dimension of the three-dimensional coordinate system into an independent time series axis, and establish a mapping relationship between the timestamp and spatial coordinates of the monitoring data;

[0027] Set the benchmark time slice according to the equipment operation cycle, and perform multiple sampling and interpolation calculations on the spatial data in each slice;

[0028] The layered rendering technology is used to overlay and display the monitoring data of different time slices to form a temporally and spatially continuous visualization scene.

[0029] Preferably, the multi-level response mechanism includes a basic response layer and an enhanced response layer; the basic response layer triggers a preset handling process according to a single alarm signal; and the enhanced response layer initiates a composite handling solution based on the correlation of multiple system alarms.

[0030] Preferably, the system further comprises a decision support module connected to the central processing unit;

[0031] The decision support module is used to extract feature vectors of historical accident cases and generate a set of response strategies for the current risk scenario through a similarity matching algorithm.

[0032] Preferably, the optimizing the risk prediction model by the progressive learning algorithm includes:

[0033] Build a dual-channel neural network that includes equipment degradation characteristics and environmental anomaly patterns;

[0034] The real-time monitoring data stream is divided into training set and validation set, and a sliding time window is used for incremental model update;

[0035] The error gradient of each network node is calculated through the back propagation algorithm, and the weight is corrected based on historical accident data.

[0036] Preferably, the central processing unit performs multi-resolution analysis on the hazard situation map to generate risk briefings for different management levels.

[0037] Preferably, the system further comprises a storage cluster connected to the central processing unit, wherein the storage cluster is used to store original monitoring data streams, dynamic topology network parameters, hazard situation maps and model training process data.

[0038] Preferably, the system further comprises a communication gateway connected to the central processing unit, wherein the communication gateway is used to convert the warning information into a standard industrial protocol data packet and transmit it to the ground dispatching center through multiple redundant channels.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] This invention significantly improves the intelligence level of coal mine safety monitoring and the effectiveness of emergency management through multi-module collaboration and innovative technical architecture. The data integration module constructs a multi-source heterogeneous data acquisition channel. Through the preset equipment communication protocol conversion interface and data cleaning rule library, it realizes the standardized acquisition and spatiotemporal correlation modeling of multi-source data such as underground personnel positioning, equipment operation, and environmental monitoring. This design breaks the traditional data silos and transforms discrete data into a network structure with spatiotemporal correlation through dynamic topology modeling, providing a comprehensive and accurate data foundation for subsequent risk assessment, enabling monitoring personnel to grasp the underground safety situation from a global perspective.

[0041] The 3D visualization module innovatively establishes a dynamic rendering mechanism based on a spatiotemporal reference axis, extending the time dimension of the 3D coordinate system into an independent time-series axis. Through mapping timestamps to spatial coordinates, multiple sampling and interpolation calculations, and layered rendering techniques, it enables continuous dynamic presentation of monitoring data across the spatiotemporal dimensions. Compared to traditional static 3D models, this technology can intuitively display the temporal trends of parameters such as gas concentration and equipment temperature, as well as their diffusion processes in 3D space. This helps monitoring personnel quickly locate risk sources and predict their evolutionary direction, significantly improving the visualization accuracy and decision-making efficiency of safety monitoring.

[0042] The risk assessment module uses spatiotemporal correlation analysis and a breadth-first search algorithm to construct a risk transmission intensity matrix and generate a hazard situation map. This module not only considers static connections such as underground tunnel layout but also incorporates the dynamic influence of environmental parameter fluctuations and equipment operating status to accurately calculate risk propagation paths, propagation delays, and diffusion probabilities. When the risk diffusion probability exceeds a threshold, secondary risk sources are automatically flagged and the map updated, enabling dynamic risk tracking and early warning. This provides a scientific risk prediction tool for coal mine safety management, effectively reducing the probability of accidents.

[0043] The linkage control module establishes a bidirectional mapping relationship between device control instructions and alarm trigger conditions, forming a multi-level response mechanism consisting of a basic response layer and an enhanced response layer. The basic response layer quickly triggers a preset response process for a single alarm, such as automatically starting a ventilator when gas exceeds the standard. The enhanced response layer initiates a complex response plan based on the correlation of multiple system alarms. For example, when abnormal gas concentration and ventilator failure are detected simultaneously, the backup ventilation system is activated and an evacuation order is triggered. This layered response mechanism achieves an upgrade from single-device control to cross-system collaborative response, significantly improving the targeted and effective nature of emergency response.

[0044] The model training module achieves deep learning of equipment degradation characteristics and environmental anomaly patterns by constructing a dual-channel neural network and an incremental update mechanism using a sliding time window. By combining a backpropagation algorithm with historical accident data to optimize model parameter weights, the risk prediction model can adapt to changes in the underground environment in real time, significantly improving prediction accuracy. The decision support module generates a set of response strategies for current risk scenarios based on feature vector matching of historical accident cases, providing managers with scientific and intelligent decision-making references and addressing the shortcomings of traditional empirical decision-making.

[0045] The central processing unit, the core of the system, centrally dispatches data and algorithms across all modules, enabling automated management of the entire process, from data collection and visualization to risk assessment and emergency response. Its multi-resolution analysis function generates customized risk briefings for different management levels, meeting the differentiated needs of grassroots real-time monitoring and senior-level strategic decision-making. The storage cluster and communication gateway design ensure secure data storage and reliable transmission. Multiple redundant channels ensure that early warning information reaches the ground dispatch center in a timely manner, providing stable technical support for coal mine safety management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a working principle diagram of the coal mine safety data comprehensive analysis and early warning system of the present invention;

[0047] Figure 2 A flowchart for risk transmission pathway analysis;

[0048] Figure 3 It is a composition diagram of a dynamic topology network;

[0049] Figure 4 Mechanism diagram rendered for the time-space reference axis. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] See also Figures 1-4 The present invention relates to a coal mine safety data comprehensive analysis and early warning system, which includes:

[0052] Data integration module, 3D visualization module, risk assessment module, linkage control module, model training module, and central processing unit. Each module works collaboratively through the central processing unit.

[0053] The data integration module constructs a multi-source heterogeneous data acquisition channel for coal mines, which contains a preset device communication protocol conversion interface and / or a data cleaning rule library. Specifically, through the device communication protocol conversion interface, various communication protocols such as Modbus and OPCUA used by different manufacturers and different types of equipment (such as personnel positioning equipment, environmental monitoring equipment, production equipment, etc.) in the mine can be converted to achieve unified collection of real-time data such as underground personnel positioning, equipment operation, and environmental monitoring. At the same time, the data cleaning rule library is used to perform deduplication, error correction, and completion on the collected raw data to ensure the accuracy and completeness of the data. After completing data collection and cleaning, these real-time data are dynamically topologically modeled according to the preset spatiotemporal association rules, and the data are organized according to the time and space dimensions to construct a topological model that can reflect the spatiotemporal association relationship of various elements in the mine.

[0054] The 3D visualization module loads the 3D geological structure model and equipment layout spatial data, establishing a dynamic rendering mechanism based on a time-space reference axis to achieve stereoscopic projection of monitoring data. The 3D geological structure model, constructed using geological exploration data, accurately depicts underground geological structures such as coal seams, tunnels, and faults. The equipment layout spatial data defines the specific location of each device within the underground space. The dynamic rendering mechanism expands the time dimension of the 3D coordinate system into an independent time-series axis, mapping the monitoring data's timestamp to its spatial coordinates, enabling accurate location of each piece of monitoring data in both time and space. Reference time slices are set based on the equipment's operating cycle. For example, for equipment with a one-hour operating cycle, each minute can be used as a time slice. Spatial data within each slice is multi-sampled and interpolated to ensure data continuity and smoothness. Using layered rendering technology, monitoring data from different time slices are overlaid and displayed, creating a temporally and spatially continuous visualization scene, allowing operators to intuitively observe how the underground environment and equipment operating status change over time.

[0055] The risk assessment module calculates the risk propagation paths for each monitoring node using a spatiotemporal correlation analysis method, generating a hazard map of the underground area based on a dynamic topological network. The dynamic topological network consists of static connectivity relationships and dynamic influence relationships. Static connectivity relationships are determined by the underground tunnel layout and the physical connections between equipment, such as tunnel orientation, branching, and the physical connections between equipment. Dynamic influence relationships are determined by the correlation patterns between environmental parameter fluctuations and equipment operating status, such as the correlation between increased gas concentration and changes in ventilation equipment operating status. To calculate the risk propagation paths, the underground space is first divided into several dynamic analysis units. Based on the spatiotemporal variation curves of equipment operating parameters and environmental monitoring values, a risk transmission intensity matrix is ​​established between each unit. This matrix reflects the strength of risk transmission between different units. The initial risk unit is then determined based on a preset risk threshold. A breadth-first search algorithm is used to traverse the associated paths in the risk transmission intensity matrix to identify possible risk propagation pathways. The propagation delay and energy attenuation coefficient of each associated path are then calculated. Combined with equipment status parameters, a risk diffusion probability distribution map is generated, clearly demonstrating the potential for risk spread within the underground space. When the risk diffusion probability of an adjacent unit exceeds the preset propagation threshold, the unit is marked as a secondary risk source and the hazard situation map is updated to detect new risk points in a timely manner.

[0056] The linkage control module establishes a bidirectional mapping relationship between device control instructions and alarm trigger conditions, forming a multi-level response mechanism with cross-system linkage. The multi-level response mechanism consists of a basic response layer and an enhanced response layer. The basic response layer triggers a preset response process based on a single alarm signal. For example, when the gas concentration in a certain area exceeds the alarm threshold, the ventilation equipment in that area is automatically activated. The enhanced response layer initiates a complex response plan based on the correlation of multiple system alarms. For example, when elevated gas concentration, abnormal equipment temperature, and smoke signals are detected simultaneously, not only the ventilation equipment is activated, but also the sprinkler fire extinguishing system is triggered, the power supply to the relevant area is cut off, and a series of linkage operations are carried out to more effectively deal with complex and dangerous situations.

[0057] The model training module constructs an accident evolution feature library and optimizes the parameter weights of the risk prediction model through a progressive learning algorithm. The accident evolution feature library collects relevant data on historical coal mine accidents and extracts characteristic information such as equipment operating characteristics and environmental parameter changes before the accident. The progressive learning algorithm constructs a dual-channel neural network that incorporates equipment degradation characteristics and environmental anomaly patterns. It divides the real-time monitoring data stream into a training set and a validation set, and uses a sliding time window for incremental model updates, enabling the model to continuously adapt to new data and the changing underground environment. The backpropagation algorithm calculates the error gradient of each network node and uses historical accident data to modify the weights, improving the accuracy and reliability of the risk prediction model.

[0058] The central processing unit, the core of the system, receives topological network data from the data integration module and drives the rendering engine of the 3D visualization module, enabling real-time updates of the 3D visualization scene. It also invokes the risk assessment module's graph generation algorithm to produce accurate hazard maps. It activates the linkage control module's response mechanism to ensure timely initiation of appropriate measures in the event of a hazard. It also schedules the optimization process of the model training module to ensure continuous optimization of the risk prediction model. Furthermore, the central processing unit performs multi-resolution analysis of the hazard maps and generates corresponding risk briefings based on the needs of different management levels, providing strong support for management decision-making.

[0059] The present invention will be further described below in conjunction with Examples 1 to 5:

[0060] Example 1:

[0061] The system's dynamic topology network is constructed as follows: The dynamic topology network includes static connection relationships and dynamic influence relationships. The two together constitute a network model that can reflect the complex underground environment in real time, providing an infrastructure for risk assessment and early warning.

[0062] Static connectivity is determined by the underground tunnel layout and the physical connectivity of equipment. The underground tunnel layout is derived from geological exploration data and engineering drawings from the mine design phase. This includes information on geometric parameters such as the tunnel's spatial orientation, branching structure, length, width, and height, as well as connectivity between tunnels. For example, the spatial relationships between different types of tunnels, such as main haul tunnels, return air tunnels, and working face tunnels, are transformed into nodes and edges in a network using 3D modeling techniques. Nodes represent key locations in the tunnel (such as intersections and endpoints), while edges represent actual travel paths. Edge weights can be set to tunnel length or travel time. Physical connectivity is determined by engineering records of equipment installation and deployment. This includes the physical installation locations and connection routes of various production equipment (such as shearers, face conveyors, and belt conveyors), safety monitoring equipment (such as gas sensors and temperature sensors), and communication equipment (such as switches and routers). For example, a shearer is connected to the face conveyor's control box via a cable. This connection is represented in the static topology network as a physical edge between the two nodes. Edge attributes include parameters such as the cable model and transmission rate. Through the above method, the physical structure of underground tunnels and equipment is transformed into a static topological network to form a fixed spatial association framework.

[0063] Dynamic influence relationships are determined by the correlation patterns between environmental parameter fluctuations and equipment operating status. Environmental parameters include real-time monitoring data such as gas concentration, carbon monoxide concentration, dust concentration, temperature, humidity, wind speed, and roof pressure. Equipment operating status parameters include equipment speed, current, voltage, power, vibration frequency, and operating hours. The dynamic influence relationship is constructed as follows: First, historical monitoring data is collected to form a dataset containing time series of environmental parameters and equipment operating status. Then, data mining algorithms (such as correlation analysis, regression analysis, and Granger causality tests) are used to identify correlation patterns between environmental parameters and equipment operating status. For example, analysis revealed that when gas concentration exceeds 0.5%, the operating power of ventilation equipment increases significantly, with a time lag of 5-10 minutes. This indicates that the increased gas concentration is one of the factors contributing to the change in ventilation equipment power. Consequently, a dynamic influence edge is established in the dynamic topology network between the gas concentration monitoring node and the ventilation equipment operating status node, with the edge weight being the correlation coefficient or causal strength value. For example, when roof pressure suddenly increases, the data from anchor bolt stress sensors in nearby areas will experience abnormal fluctuations simultaneously. The two are strongly correlated, allowing for the establishment of dynamic influence edges between corresponding nodes. Furthermore, dynamic influence relationships must also consider the interactions between equipment operating states. For example, changes in the operating state of a shearer (such as shutdown or speed regulation) can affect the load state of a scraper conveyor through the transmission system, leading to changes in parameters such as its current and speed. This inter-equipment linkage effect is identified through time-series analysis of historical operating data, and corresponding dynamic influence edges are constructed within the topological network.

[0064] In the actual operation of a dynamic topology network, static connection relationships remain relatively stable as the basic framework and are only updated when the tunnel layout is adjusted or the physical location of the equipment changes. Dynamic influence relationships are continuously adjusted based on real-time monitoring data. For example, when the temperature in a certain area suddenly rises, the system automatically analyzes the relationship between this temperature change and the operating status of the surrounding equipment. If it is found to be time-synchronized with the abnormal increase in current of a nearby motor, the dynamic influence edge weight between the temperature node and the motor operating status node is increased. If subsequent monitoring shows that the temperature increase is due to insufficient local ventilation and has nothing to do with the equipment, the corresponding influence edge is weakened or removed. This dynamic adjustment mechanism enables the topology network to reflect the true relationship between the underground environment and the equipment status in real time, improving the accuracy of risk assessment.

[0065] The construction of a dynamic topological network also requires consideration of the integration of time and space. In the spatial dimension, the underground is divided into several physical areas based on the tunnel layout. The equipment and environmental monitoring nodes within each area form a subnetwork, and the subnetworks are interconnected through tunnel edges. In the temporal dimension, a timestamp attribute is set for each node and edge to record the acquisition time of the monitoring data and the update time of the associated relationship, thus forming a dynamic topological structure coupled in time and space. For example, the operating status of a device node may be affected by different environmental parameters at different time periods. The timestamp can be used to trace the historical changes in its dynamic influence relationship.

[0066] Furthermore, dynamic topology networks support hierarchical modeling, enabling the construction of network models at different levels based on different application scenarios and analysis requirements. For example, short-term risk warnings focus on the real-time dynamic impact of equipment operating status and adjacent environmental parameters; long-term trend analysis emphasizes the slowly changing correlation patterns between environmental parameters and equipment degradation characteristics. This hierarchical modeling allows the system to more flexibly adapt to security management needs at different levels.

[0067] Example 2:

[0068] The implementation process of the dynamic rendering mechanism based on the time-space reference axis is as follows: This mechanism organically integrates the time dimension and the space dimension to construct a unified time-space visualization framework, so that monitoring data can be dynamically presented over time in three-dimensional space, providing operators with an immersive underground environment monitoring experience.

[0069] The time dimension of the three-dimensional coordinate system is expanded into an independent time-series axis, establishing a mapping relationship between the timestamp and spatial coordinates of the monitoring data. In addition to the traditional three-dimensional spatial coordinate system (X, Y, Z axes), an independent time axis (T axis) is introduced to form a four-dimensional spatiotemporal coordinate system (X, Y, Z, T). For each piece of monitoring data (such as personnel location data, equipment operating parameters, and environmental monitoring values), its timestamp (accurate to the millisecond level) is recorded synchronously during acquisition. Three-dimensional spatial coordinates are assigned based on the data's corresponding physical location (such as equipment installation coordinates or sensor deployment location). For example, if a gas sensor is deployed at the coordinates (100, 200, 5) underground, the gas concentration data collected at 14:00:05 corresponds to the point (100, 200, 5, 14:00:05) in the spatiotemporal coordinate system. Through this mapping relationship, all monitoring data can be accurately located in the four-dimensional spatiotemporal coordinate system, forming a spatiotemporal data point cloud.

[0070] Based on the equipment's operating cycle, a benchmark time slice is set, and multiple sampling and interpolation calculations are performed on the spatial data within each slice. The equipment operating cycle is determined by the operating characteristics of each piece of equipment. For example, the coal cutting cycle of a shearer is typically 30 minutes to 2 hours. The start-stop cycle of ventilation equipment may be related to shifts or changes in environmental parameters. The sampling period of sensors is generally on the order of seconds or minutes. Taking a shearer as an example, if its operating cycle is one hour, the hour can be divided into several benchmark time slices, such as one slice per minute (i.e., each slice is one minute long). Within each slice, multiple sampling is performed on equipment operating parameters (such as the shearer's hauling speed, cutting power, and drum speed) and environmental monitoring data (such as gas concentration, dust concentration, and temperature in the area). Multiple sampling refers to collecting data within a single slice at an interval higher than the normal sampling frequency. For example, if the normal sampling frequency is once per minute, multiple sampling can be increased to five or ten times per minute to obtain more dense spatiotemporal data points and avoid data omissions. Interpolation methods such as linear, polynomial, and spline interpolation are used to handle gaps or outliers in the sampled data. For example, if the temperature values ​​collected at the 10th and 50th seconds in a slice are 25°C and 28°C, respectively, and the data at the 30th second is missing, linear interpolation is used to calculate the temperature value at the 30th second as 26.5°C, thus ensuring data continuity in the time dimension.

[0071] The layered rendering technology is used to overlay and display the monitoring data of different time slices to form a spatiotemporal continuous visualization scene. The layered rendering technology is based on the timeline sequence, and the monitoring data of each benchmark time slice is rendered as an independent layer. Each layer contains all the spatial data points and their attribute information (such as numerical value, status color, etc.) within the slice time. For example, the monitoring data of the first slice (14:00-14:01) is rendered as layer 1, the data of the second slice (14:01-14:02) is rendered as layer 2, and so on. In the three-dimensional visualization interface, these layers are superimposed from front to back in chronological order. The operator can choose to display a single layer at a certain moment by sliding the timeline control or entering a specific time point, or display multiple continuous layers at the same time to observe the data change trend over time. To enhance the visualization effect, different types of data are rendered in different ways: personnel positioning data can be represented by a three-dimensional model (such as a human icon), and its color can reflect the personnel's working status (such as green for normal operation and red for inactivity timeout); equipment operating parameters are superimposed on the equipment's three-dimensional model in the form of dashboards, curve charts, etc., and the parameter values ​​are displayed in real time; environmental monitoring data uses volume rendering or isosurface rendering technology. For example, gas concentration data is displayed through a translucent gas cloud model, and the higher the concentration, the darker the color (such as blue for low concentration and red for high concentration).

[0072] During the rendering process, the temporal and spatial consistency of the data and the optimization of rendering performance must also be considered. Temporal and spatial consistency ensures that the position and attribute changes of the same monitoring point in different time slices conform to the laws of physics. For example, the running trajectory of a certain device should remain continuous in adjacent slices without jump-like displacement. Rendering performance optimization is achieved through data compression and level of detail (LOD) technology: for historical time slice data, lossy compression algorithms (such as wavelet compression) are used to reduce the amount of data and improve rendering efficiency; for monitoring data from distant or secondary areas, the rendering detail level is automatically lowered to reduce the number of polygons, while data from close distances or key areas maintain a high level of detail to ensure that operators can clearly observe key areas.

[0073] In addition, the dynamic rendering mechanism supports interactive operations. Operators can zoom, pan, and rotate the 3D scene using a mouse, keyboard, or touch device, observing the underground situation from different perspectives. It also supports data query functionality. Clicking on any 3D model or data point will pop up a detailed information window displaying the object's real-time data, historical trends, alarm records, and other information. For example, clicking on a ventilation equipment model will display its current operating status (such as start / stop status, speed, and power), operating parameter curves for the last hour, and whether there have been any fault alarms.

[0074] The establishment of the spatiotemporal reference axis also provides a unified time reference for the integrated display of multi-source data. Different types of monitoring data (such as personnel location data, equipment data, and environmental data) may come from different acquisition systems and have different time synchronization accuracies. By calibrating the spatiotemporal reference axis, time deviations can be eliminated, ensuring that various types of data are accurately aligned in the visualization scene, making it easier for operators to comprehensively analyze the correlation between multiple factors. For example, when an abnormal increase in gas concentration occurs in a certain area, the spatiotemporal reference axis can be used to quickly locate the personnel distribution, equipment operating status, and other data in the area within the same time slice to determine the cause of the abnormality.

[0075] Example 3:

[0076] The specific composition and working mode of the multi-level response mechanism are as follows: This mechanism establishes a two-way mapping relationship between equipment control instructions and alarm triggering conditions to form a hierarchical disposal system consisting of a basic response layer and an enhanced response layer, thereby achieving accurate and efficient response to different types of alarm signals, ensuring that corresponding control measures can be initiated in time before or when a safety accident occurs in a coal mine underground, thereby reducing the risk and degree of harm of the accident.

[0077] The basic response layer triggers the preset handling process based on a single alarm signal. During the system initialization phase, independent alarm thresholds are set for each type of single safety monitoring parameter (such as gas concentration, carbon monoxide concentration, equipment temperature, roof pressure, etc.), and a corresponding preset handling process is associated with each alarm threshold. For example, when the gas concentration monitoring sensor detects real-time data C gas When the preset first-level alarm threshold T1 is exceeded, the system recognizes the single alarm signal through the logic judgment module and triggers the handling process of the basic response layer: first, a command is sent to the sound and light alarm device in the area to make it emit a sound and light alarm signal to alert on-site personnel; at the same time, a control command is sent to the ventilation control system to turn on the local ventilation fan. The command content contains parameters such as the ventilation fan's equipment number, start time, and operating power to increase the air volume in the area and reduce the gas concentration. The logical relationship of the handling process can be expressed as:

[0078] If C gas > T1, then execute {start the sound and light alarm, turn on the local ventilation fan}

[0079] Among them, C gas It indicates the real-time monitored gas concentration value, T1 indicates the preset gas level 1 alarm threshold, and the unit is %LEL (lower explosion limit percentage).

[0080] The handling process of the basic response layer is fast and targeted. Its control instructions directly correspond to a single alarm source, aiming to eliminate or reduce the impact of a single risk factor through simple and direct operations. For example, when the device temperature monitoring sensor detects a motor temperature T motor When the preset overheating alarm threshold T2 is exceeded, the basic response layer automatically triggers the shutdown protection command of the motor and sends fault warning information to the handheld terminal of the operation and maintenance personnel, including motor position, temperature abnormality value, recommended maintenance content, etc., so that the operation and maintenance personnel can conduct fault troubleshooting and processing in time.

[0081] The enhanced response layer initiates a composite disposal plan based on the correlation of multiple system alarms. Safety risks in coal mines are usually caused by the combined action of multiple interrelated factors. A single alarm signal may only be a preliminary manifestation of the risk, while when the alarm signals of multiple different systems appear simultaneously or in succession within a short period of time, it often indicates a more serious dangerous situation. The enhanced response layer establishes a correlation analysis model for the alarm signals of multiple systems, monitors the spatiotemporal distribution and logical relationship of the alarm signals of each system in real time, and determines whether the composite disposal conditions are met. For example, when the following three alarm signals are detected at the same time: (1) the gas concentration C gas Exceeds the second level alarm threshold T3 (T3>T1); (2) The carbon monoxide concentration in the area C coExceeding the warning threshold T4; (3) The nearby smoke sensor detects a smoke signal S=1 (S=1 means smoke is detected, S=0 means no smoke is detected). The system determines through the correlation analysis model that there may be a risk of gas combustion or explosion, and at this time triggers the composite disposal plan of the enhanced response layer.

[0082] The enhanced response layer's composite disposal plan includes multiple coordinated actions, each with a strict logical sequence and dependencies. Taking a gas combustion risk scenario as an example, the composite disposal plan's execution process is as follows: First, an emergency evacuation command is sent to all personnel positioning systems in the area and adjacent areas. The positioning tags worn by personnel emit audible and visual alarms and display evacuation routes. Evacuation trajectories are also annotated in real time on a three-dimensional visualization interface. Second, a partitioned power-off command is sent to the power supply control system, shutting off power to non-intrinsically safe electrical equipment in the area and potentially affected areas to prevent explosions caused by sparks. The power-off range is determined by the static topology of the tunnel layout and the dynamic impact range of the alarm area. Then, the area's automatic fire extinguishing system is activated, such as spraying extinguishing media or releasing inert gas to suppress the spread of combustion. Finally, a special alarm message is sent to the ground dispatch center, including the alarm location, risk type, and activated disposal measures, requesting support from ground rescue forces.

[0083] The analysis of multi-system alarm correlation is based on the node association relationship in the dynamic topology network. Each alarm signal corresponds to a node in the dynamic topology network. The edge between the nodes represents the correlation of the alarm signal. The edge weight is determined by factors such as the co-occurrence frequency and time lag of the alarm signal in the historical data. For example, the co-occurrence frequency of abnormal gas concentration and ventilation equipment failure signals is high, and the correlation edge weight between the two is large. When one of the signals appears, the system will automatically increase the monitoring frequency of the other signal. The trigger condition of the enhanced response layer can be expressed as:

[0084] like A composite disposal plan is initiated

[0085] Among them, n represents the number of alarm signals that appear at the same time, A i Indicates the state of the i-th alarm signal (A i =1 means triggering the alarm, A i =0 means not triggered), w i represents the association weight between the ith alarm signal and other signals, T thr Represents the preset compound action trigger threshold, which is determined by analyzing the multi-alarm correlation pattern in historical accident data.

[0086] During the execution of the enhanced response layer, the system monitors the feedback signals from each action in real time to ensure its effectiveness. For example, if the smoke signal does not diminish within a preset time after the automatic fire extinguishing system is activated, the system will automatically trigger a backup fire extinguishing plan, such as increasing the injection rate of the fire extinguishing medium or activating fire extinguishing equipment in adjacent areas. The enhanced response layer also supports manual intervention, allowing operators to manually adjust the execution order of the action plan or add other control instructions based on actual conditions, thereby increasing the flexibility of emergency response.

[0087] The basic response layer and the enhanced response layer collaborate through the central processing unit. The central processing unit receives alarm signals from each monitoring system in real time, triggers the corresponding response level based on the alarm type and relevance, and sends control instructions to each subsystem through the linkage control module. For the basic response layer, the central processing unit directly calls the preset single disposal process; for the enhanced response layer, the central processing unit first calls the risk assessment module's hazard map generation algorithm to analyze the risk propagation path and impact range of multiple alarm signals. Then, based on the analysis results, it dynamically generates a composite disposal plan to ensure the scientific and effective disposal measures.

[0088] Example 4:

[0089] The collaborative working mechanism between the decision support module and the model training module is as follows: through data sharing and algorithm linkage, the two form a closed-loop process from historical experience learning to real-time risk response, providing intelligent decision-making support for coal mine safety management.

[0090] The core function of the decision support module is to generate response strategies for current risk scenarios based on historical accident cases. This module first extracts feature vectors of typical accident cases from a historical accident database. These feature vectors encompass multidimensional data from the period leading up to the accident, including environmental parameters (such as abnormal fluctuations in gas concentration, carbon monoxide concentration, temperature, and wind speed), equipment operating status (such as sudden changes in shearer cutting power, abnormal fan speed, and sensor signal interruptions), and personnel operation records (such as illegal live work, overstaffing, and failure to follow required inspections). For example, a feature vector for a gas explosion accident might include: gas concentration continuously rising from 0.3% to 1.2% within 30 minutes before the accident; a local fan failure; personnel in the area not being evacuated promptly; and traces of sparks on nearby equipment. Feature vectors are extracted through structured parsing of accident investigation reports using natural language processing techniques, combined with time-series analysis of monitoring data. Each feature vector corresponds to a set of spatiotemporally correlated anomaly patterns.

[0091] The model training module is based on the accident evolution feature library constructed by the decision support module, and optimizes the risk prediction model through a progressive learning algorithm. Specifically, the model training module first constructs a dual-channel neural network: one channel is used to learn the equipment degradation characteristics, and the input data includes the time series of equipment operating parameters (such as the trend changes of motor current, bearing temperature, and vibration frequency); the other channel is used to learn the environmental anomaly pattern, and the input data includes the spatiotemporal distribution of environmental monitoring parameters (such as the spatial diffusion trajectory of gas concentration and the regional aggregation characteristics of roof pressure). Taking the belt conveyor fault prediction as an example, the equipment degradation feature channel identifies early signs of failure by analyzing the coupling relationship between parameters such as the roller speed fluctuation, the increase in reducer oil temperature, and the increase in conveyor belt deviation; the environmental anomaly pattern channel associates environmental factors such as the increase in dust concentration in the tunnel and the change in temperature gradient to determine whether the equipment loss is aggravated by dust accumulation or insufficient ventilation.

[0092] The real-time monitoring data stream is divided into a training set and a validation set. The training set accounts for 70%-80% and is used to update model parameters; the validation set, which accounts for 20%-30%, is used to test the model's generalization ability. Using a sliding time window technique, the latest monitoring data is continuously injected into the training set while older data outside the window is removed, enabling incremental model updates. For example, the window size is set to 7 days, and the monitoring data from the previous 7 days is automatically added to the training set at dawn each day. The model is iteratively trained to ensure that it can adapt to dynamic factors such as wear and tear of underground equipment and changing geological conditions. The backpropagation algorithm calculates the error gradient of each node in the neural network and, combined with feature vectors from historical accident data, adjusts the network weights. For example, if the model predicts an abnormal gas concentration in a certain area but does not trigger an alarm, but a gas over-limit accident actually occurs, the system backpropagates this error signal to the input layer, adjusting the connection weights between the gas concentration and ventilation equipment operating status nodes in the environmental anomaly model channel to strengthen the ability to identify the correlation between the two.

[0093] The synergy between the decision support module and the model training module is reflected in the following scenarios: When the model training module discovers through real-time data training that the prediction accuracy of a certain risk pattern is below a threshold (e.g., below 85%), it will send a data supplement request to the decision support module. The latter automatically retrieves cases with similar scenarios from the historical accident database, extracts more detailed feature vectors (such as the specific time nodes of personnel operations before the accident, equipment maintenance records, etc.), and injects them into the model training module to enhance its learning depth. For example, if the model frequently misses the combined risk prediction of "equipment overheating + increased gas concentration", the decision support module will retrieve historical accident cases where gas ignition was caused by equipment overheating. It will supplement the training data with features such as the equipment temperature rise rate, the spatiotemporal intersection of gas accumulation areas, and personnel inspection routes, helping the model establish more complex association rules.

[0094] In real-time risk response, the risk prediction results output by the model training module (e.g., a 75% probability of roof collapse in a certain area within the next two hours) serve as input parameters for the decision support module. Using a similarity matching algorithm, the decision support module retrieves the top five cases most similar to the current risk feature vector from a historical accident case library (e.g., with a similarity threshold set to 0.85). It then extracts the disposition measures recorded in these cases, such as "Immediately halt operations in the area, initiate temporary support, and evacuate personnel" and "Notify the geological team to conduct radar detection," to generate a set of response strategies. These strategies are adaptively adjusted based on real-time data such as the current underground personnel distribution and equipment status. For example, if personnel are currently working in the area, the strategy will prioritize triggering an emergency evacuation order. If the temporary support equipment is under maintenance, the strategy will automatically invoke control commands for the backup support equipment.

[0095] In addition, the decision support module supports manual annotation. If operators discover that the response strategy generated by the model is unreasonable, they can manually adjust the strategy's priority or add new handling steps. These manual intervention records are fed back into the model training module as new feature vectors, which are used to optimize the subsequent strategy generation logic. For example, in a certain scenario, the model recommends cutting off the power before evacuating personnel. However, if the evacuation route in this area requires passing through an area with energized equipment, the operator can adjust the strategy sequence to "first designate safe evacuation corridors, then cut off the power in sections." This adjustment will be recorded by the system and used as a special case to train the model's logical judgment capabilities.

[0096] Example 5:

[0097] The system's data storage and communication transmission mechanism achieves efficient storage and management of monitoring data and reliable transmission of early warning information through the collaborative work of storage clusters and communication gateways, providing basic support for the full life cycle management of coal mine safety data and cross-system interaction.

[0098] The storage cluster is connected to the central processing unit via a high-speed data bus and utilizes a distributed storage architecture consisting of multiple storage nodes. Each node is equipped with a hybrid storage medium consisting of solid-state drives (SSDs) and mechanical hard disks (HDDs). The SSDs are used to store real-time monitoring data streams and frequently accessed dynamic topology network parameters, while the HDDs are used to archive historical data and model training process data. For example, the underground personnel positioning system generates approximately 10KB of real-time location data per second. This data is cleaned by the data integration module and written to the storage cluster's SSDs in binary format in real time, ensuring millisecond-level readability by the central processing unit. Historical personnel location data from the past year is regularly migrated to the HDDs to conserve storage resources.

[0099] The raw monitoring data streams stored in the storage cluster encompass real-time data collected by all sensors and equipment underground. These include personnel location data (such as personnel ID, coordinates, movement speed, and work area), equipment operation data (such as shearer traction speed, cutting depth, motor temperature, fan start / stop status, air volume and pressure), and environmental monitoring data (such as gas concentration, carbon monoxide concentration, dust concentration, and roof pressure). This data is managed in a time series database (such as InfluxDB), supporting fast queries based on multiple dimensions, such as time range, equipment type, and monitoring area. For example, to analyze gas concentration trends at a working face over the past week, a query can specify a time range (such as "2025-05-20 to 2025-05-27"), area coordinates (such as "X = 100-200, Y = 300-400, Z = 5-10"), and parameter type (such as "gas concentration"). The system returns results within seconds and generates a trend curve.

[0100] Dynamic topology network parameter storage includes definition data of static connection relationships and dynamic impact relationships. Static connection relationship data, such as the three-dimensional coordinates of the tunnel layout, port information of the physical connection of the equipment, etc., are stored in the form of structured data tables. For example, the "tunnel table" contains fields such as the tunnel ID, starting point coordinates, end point coordinates, cross-sectional dimensions, ventilation type, etc., and the "device connection table" contains fields such as device AID, device BID, connection type (such as cable connection, wireless communication), and transmission protocol. Dynamic impact relationship parameters, such as the association weights between environmental parameters and equipment status, and the risk transfer intensity matrix, are stored in the form of key-value pairs. For example, the key is "gas concentration-ventilator power" and the value is a correlation coefficient of 0.85 (indicating a positive correlation between the two, and the larger the coefficient, the stronger the correlation). These parameters are dynamically updated with real-time monitoring data and persistently stored to ensure that the current network status can be restored after the system is restarted.

[0101] Hazard situation map data is stored in a rasterized format, dividing the underground space into 1m×1m×1m three-dimensional grid cells. Each cell records information such as the risk level (e.g., low, medium, high, or very high), the risk type (e.g., gas limit violation, equipment failure, or roof hazard), and the distance to the risk source. For example, a grid cell with coordinates (150, 350, 8) has a "high" risk level, a "gas limit violation," and a distance to the risk source of 5m. This data is loaded into the visualization scene in real time via a 3D rendering engine and identified with different colors and icons. Historical hazard situation maps are stored by timestamp and named (e.g., "202505281400.dtm"), allowing for retrospective review of underground risk distribution at any point in the past.

[0102] The data of the model training process includes the weight parameters of the neural network, the loss function values ​​of the training set and the validation set, the time window records of incremental updates, etc. For example, when training a dual-channel neural network with equipment degradation characteristics, the weight matrix after each iterative update (such as the weight W1 from the input layer to the hidden layer, the weight W2 from the hidden layer to the output layer) will be saved as a binary file, and hyperparameters such as the training round (Epoch), learning rate, and batch size (BatchSize) will be recorded to facilitate subsequent model tuning and reproduction. If the system detects a sudden increase in the model prediction error (such as exceeding the preset threshold of 15%), it can automatically roll back to the weight parameters of the previous version to avoid model failure due to abnormal data.

[0103] The communication gateway, serving as a bridge between the central processing unit and the ground dispatch center, utilizes industrial-grade edge computing equipment and features protocol conversion, data encryption, and link redundancy. Its hardware configuration includes dual gigabit Ethernet ports, 4G / 5G communication modules, and fiber optic interfaces, supporting simultaneous connection to both wired LANs and wireless communication networks. When the central processing unit generates an early warning message (e.g., gas concentration in a certain area reaches 80% of the lower explosion limit, or the risk level is "extremely high"), the communication gateway first performs protocol conversion on the message: it converts the custom data format used within the system (e.g., JSON format, containing fields such as alarm time, location, type, and level) into a standard industrial protocol data packet, such as the data packet structure corresponding to function code 0x03 (read holding register) of the ModbusTCP protocol, or the IRT (isochronous real-time) message of the PROFINET protocol, to ensure that the SCADA (supervisory control and data acquisition) system of the ground dispatch center can directly parse it.

[0104] The transmission of warning information is achieved through multiple redundant channels, including:

[0105] Wired fiber optic channels: Leveraging deployed industrial Ethernet fiber optic links underground, data is transmitted via the TCP / IP protocol. This high-bandwidth, low-latency approach is ideal for real-time warnings (such as emergency shutdown instructions). For example, when the enhanced response layer triggers a power outage command for a specific area, the command is transmitted via fiber optic channels to the power supply control subsystem at the surface dispatch center within 20 milliseconds.

[0106] Wireless Mesh Channel: In complex tunnel areas or temporary working surfaces, wireless mesh nodes (such as 802.11n-based APs) are deployed to build ad hoc networks. When wired links are interrupted, they automatically switch to wireless channels to ensure that warning information is not lost. For example, during the extension of the tunneling working surface, temporarily installed wireless mesh nodes can transmit gas over-limit alarm information in that area to the surface via multi-hop relay.

[0107] Satellite communication backup channel: In extreme situations (such as a total power outage in a mine or failure of both wired and wireless links), the satellite communication module (such as the Beidou short message module) built into the communication gateway can compress key warning information and send it to the ground emergency command center via a satellite link, ensuring communication continuity in disaster recovery scenarios.

[0108] Data is encrypted using the AES-256 algorithm during communication to prevent theft or tampering during transmission. For example, the plaintext data of a warning message (such as "Alarm location: Return airway of the No. 10 coal seam, coordinates (200, 400, 6), Risk type: Roof collapse") is encrypted using a random key generated by a key generator. Upon reaching the ground dispatch center, it is decrypted using the same key, ensuring secure data transmission.

[0109] After receiving the warning information, the ground dispatch center uses the SCADA system to visualize it, including 3D annotation of the alarm location, risk level indication, and historical trend comparison. It also automatically triggers a multi-level notification mechanism, including audio and visual alarms, SMS notifications (such as those sent to the mine manager and safety director's mobile phones), and email alerts. For example, when receiving a gas explosion warning with an "extremely high" risk level, the SCADA system immediately pops up a red alarm window, accompanied by a buzzer alarm, and simultaneously sends SMS and emails containing risk details to relevant personnel, ensuring that the information is promptly conveyed to decision-makers and executives.

[0110] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0111] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A coal mine safety data comprehensive analysis and early warning system, characterized by: include: The data integration module is used to build a multi-source heterogeneous data acquisition channel for coal mines, and to perform dynamic topological modeling on real-time data of underground personnel positioning, equipment operation, and environmental monitoring based on preset spatiotemporal association rules; 3D visualization module, used to load 3D geological structure models and equipment layout spatial data, establish a dynamic rendering mechanism based on time and space reference axes to achieve stereoscopic projection of monitoring data; The risk assessment module is used to calculate the risk propagation path of each monitoring node through spatiotemporal correlation analysis and generate a dangerous situation map of the underground area based on the dynamic topology network; The linkage control module is used to establish a two-way mapping relationship between equipment control instructions and alarm trigger conditions, forming a multi-level response mechanism for cross-system linkage; The model training module is used to build an accident evolution feature library and optimize the parameter weights of the risk prediction model through a progressive learning algorithm; A central processing unit, configured to receive the topological network data from the data integration module, drive the rendering engine of the 3D visualization module, invoke the graph generation algorithm of the risk assessment module, activate the response mechanism of the linkage control module, and schedule the optimization process of the model training module; The multi-source heterogeneous data acquisition channel includes a preset device communication protocol conversion interface and / or a data cleaning rule library.

2. The coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: Calculating the risk propagation path of each monitoring node by using the spatiotemporal correlation analysis method includes: The underground space is divided into several dynamic analysis units, and a risk transfer intensity matrix between units is established based on the spatiotemporal variation curves of equipment operating parameters and environmental monitoring values. Determine the initial risk unit according to the preset risk threshold, and traverse the associated paths in the risk transfer intensity matrix through a breadth-first search algorithm; Calculate the propagation delay and energy attenuation coefficient of each associated path, and generate a risk diffusion probability distribution map based on device status parameters; When the risk diffusion probability of an adjacent unit exceeds a preset propagation threshold, the unit is marked as a secondary risk source and the danger situation map is updated.

3. The coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The dynamic topology network includes static connection relationships and dynamic influence relationships; the static connection relationships are determined by the underground tunnel layout and the physical connection relationship of the equipment; the dynamic influence relationships are determined by the association pattern between environmental parameter fluctuations and equipment operating status.

4. The coal mine safety data comprehensive analysis and early warning system according to claim 2, characterized in that: The establishment of a dynamic rendering mechanism based on a time-space reference axis includes: Expand the time dimension of the three-dimensional coordinate system into an independent time series axis, and establish a mapping relationship between the timestamp and spatial coordinates of the monitoring data; Set the benchmark time slice according to the equipment operation cycle, and perform multiple sampling and interpolation calculations on the spatial data in each slice; The layered rendering technology is used to overlay and display the monitoring data of different time slices to form a temporally and spatially continuous visualization scene.

5. The coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The multi-level response mechanism includes a basic response layer and an enhanced response layer; the basic response layer triggers a preset handling process according to a single alarm signal; the enhanced response layer initiates a composite handling solution based on the correlation of multiple system alarms.

6. The coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: Also included is a decision support module connected to the central processing unit; The decision support module is used to extract feature vectors of historical accident cases and generate a set of response strategies for the current risk scenario through a similarity matching algorithm.

7. The coal mine safety data comprehensive analysis and early warning system according to claim 6, characterized in that: The method of optimizing the risk prediction model by using a progressive learning algorithm includes: Build a dual-channel neural network that includes equipment degradation characteristics and environmental anomaly patterns; The real-time monitoring data stream is divided into training set and validation set, and a sliding time window is used for incremental model update; The error gradient of each network node is calculated through the back propagation algorithm, and the weight is corrected based on historical accident data.

8. The coal mine safety data comprehensive analysis and early warning system according to claim 1 is characterized in that: The central processing unit performs multi-resolution analysis on the hazard situation map to generate risk briefings for different management levels.

9. The coal mine safety data comprehensive analysis and early warning system according to claim 1, characterized in that: It also includes a storage cluster connected to the central processing unit, and the storage cluster is used to store original monitoring data streams, dynamic topology network parameters, danger situation maps and model training process data.

10. The coal mine safety data comprehensive analysis and early warning system according to claim 1, characterized in that: It also includes a communication gateway connected to the central processing unit, which is used to convert the warning information into a standard industrial protocol data packet and transmit it to the ground dispatch center through multiple redundant channels.

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