Mine operation safety early warning method and system

Abstract

The invention provides a mine operation safety early warning method and system, and relates to the technical field of mine safety monitoring. Through multi-source data fusion, dynamic scene portrait construction, machine learning risk identification and a grading early warning response mechanism, the technical problem that information integration is difficult due to fragmentation of a traditional monitoring means, stiffness of a static threshold value alarm mechanism and data island is solved, and the mine safety early warning method has the remarkable advantage of improving mine safety early warning precision and timeliness.

Description

Technical Field

[0001] This application relates to the field of mine safety monitoring technology, and in particular to a mine operation safety early warning method and system. Background Technology

[0002] In the field of mine safety monitoring, existing technologies face numerous bottlenecks. Traditional monitoring methods primarily rely on single sensors to monitor specific risk factors, such as using vibration sensors to detect rock strata stability or gas sensors to monitor methane concentration. This fragmented monitoring approach struggles to comprehensively cover the complex and ever-changing safety risks in mine operations. Existing automated systems generally employ static threshold alarm mechanisms, which cannot adapt to the dynamic changes in mine geological conditions, environmental parameters, and operational status, resulting in high false alarm and missed alarm rates. While video surveillance systems can acquire on-site images, their reliance on manual analysis leads to issues such as response delays and strong subjectivity, making effective early warning difficult in the event of emergencies. Existing patented technologies, such as CN110456800A, while constructing an IoT monitoring framework, lack the ability to intelligently integrate and analyze multi-source data; the equipment fault early warning scheme proposed in US20190180654A1 focuses excessively on a single equipment dimension, failing to establish a correlation analysis model between the geological environment, equipment status, and personnel behavior. These technical solutions generally suffer from data silos, with inconsistent data formats and incompatible communication protocols among monitoring systems, resulting in the inability to effectively integrate safety information. Furthermore, existing early warning models lack self-learning capabilities and cannot dynamically optimize based on real-time operational data. Early warning response mechanisms often remain at the post-event processing level, failing to achieve proactive risk prediction and prevention. To address these issues, existing technologies urgently need improvement. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for early warning of mine operation safety, which has the advantages of comprehensively covering multi-dimensional safety risk elements in mine operation, dynamically adapting to complex environmental changes, significantly reducing false alarm and missed alarm rates, and realizing proactive risk prediction and prevention.

[0004] Firstly, the mine operation safety early warning method provided in this application adopts the following technical solution: A method for early warning of safety in mining operations includes: Collect multi-source real-time data of the mining operation environment, including geological monitoring data, equipment operation status data, environmental meteorological data, and personnel operation behavior data; Based on the mining operation environment, the corresponding scene factors are matched in a preset database to generate a target mine profile; Based on the target mine profile, processing methods are determined to combine the multi-source real-time data for preprocessing and data fusion to generate a standardized operational safety dataset; The standardized operation safety dataset is analyzed based on a machine learning model to identify potential safety risk patterns in the operation process and generate safety risk indicators. Based on the comparison results between the safety risk indicators and the preset safety thresholds, an operation safety early warning signal is generated; and the operation safety early warning signal is output to the user interface or external execution system to trigger real-time early warning response actions, including personnel warning, equipment shutdown or emergency dispatch.

[0005] Optionally, the collection of multi-source real-time data includes: Data is collected through a sensor network deployed in the mining operation area. The sensor network includes at least one of vibration sensors, gas concentration sensors, temperature sensors, humidity sensors, high-definition cameras, UWB positioning tags, and wearable devices. The wearable devices integrate heart rate sensors and motion sensors to monitor the physiological state and behavioral trajectory of personnel in real time.

[0006] Optionally, the step of matching corresponding scene factors in a preset database based on the mining operation environment to generate a target mine profile includes: Based on mine type, geological structure, historical accident data, and operation stage, matching scenario factors are retrieved from a preset database. These scenario factors include rock stratum stability parameters, gas outburst patterns, equipment aging coefficients, and personnel skill levels. A dynamically updated target mine profile is then generated through a data fusion algorithm.

[0007] Optionally, the preprocessing and data fusion include: The data is cleaned, denoised, and normalized. Then, a time-series data fusion algorithm is used to integrate multi-source data into a time-series dataset with a unified format. The human operation behavior data is used for behavior recognition and classification through computer vision algorithms, including violation detection and fatigue state analysis.

[0008] Optionally, the machine learning model includes a deep learning neural network, a random forest algorithm, or a support vector machine. The model is trained using historical mining accident data and its parameters are updated periodically with new data to identify at least one of the following: collapse risk, gas outburst risk, equipment failure risk, or personnel violation risk. Optionally, the generation of the operation safety warning signal includes: The system classifies safety risk indicators according to their severity, generating different levels of early warning signals, including low-level warnings, medium-level alarms, and high-level emergency alarms. The high-level emergency alarm automatically triggers equipment shutdown, personnel evacuation, and emergency dispatch protocols.

[0009] Optionally, the action to trigger a real-time early warning response includes: The system automatically sends early warning notifications to the mobile terminals of the workers, activates the underground emergency broadcast system, controls relevant equipment to automatically shut down, or dispatches rescue resources. The response actions are dynamically adjusted based on the early warning level and the target mine profile.

[0010] Optionally, the method further includes a feedback learning step: Collect data on early warning response results, including response efficiency and accident mitigation effects, and use this data to adjust machine learning model parameters, preset safety thresholds, or scenario factor databases to achieve adaptive operation safety early warning optimization.

[0011] Optionally, after collecting multi-source real-time data, a data verification step is also included: real-time quality checks are performed on the collected data, including integrity verification, outlier detection, and consistency verification, to ensure data reliability; Invalid data is automatically discarded or marked, triggering a re-collection or data repair mechanism. Data verification is performed through lightweight machine learning algorithms or rule engines to adapt to the dynamic changes in the mining environment. Secondly, this application provides a mine operation safety early warning system, including: The data acquisition module is used to collect multi-source real-time data of the mining operation environment, including geological monitoring data, equipment operation status data, environmental meteorological data, and personnel operation behavior data. The profile generation module is used to match corresponding scene factors in a preset database based on the mining operation environment to generate a profile of the target mine. The dataset generation module is used to determine the processing methods based on the target mine profile, and to combine the multi-source real-time data for preprocessing and data fusion to generate a standardized operation safety dataset. The risk indicator module is used to analyze the standardized operation safety dataset based on a machine learning model to identify potential safety risk patterns in the operation process and generate safety risk indicators. The output module is used to generate a work safety early warning signal based on the comparison result between the safety risk index and the preset safety threshold; and to output the work safety early warning signal to the user interface or external execution system to trigger real-time early warning response actions, including personnel warning, equipment shutdown or emergency dispatch.

[0012] In summary, this application solves the technical problems of fragmented traditional monitoring methods, rigid static threshold alarm mechanisms, and difficulties in information integration caused by data silos by using multi-source data fusion, dynamic scene profiling, machine learning risk identification, and hierarchical early warning response mechanisms. It has significant advantages in improving the accuracy and timeliness of mine safety early warning. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating the first embodiment of the mine operation safety early warning method of this application; Figure 2 This is a structural block diagram of the first embodiment of the mine operation safety early warning system of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0015] This application provides a method for early warning of safety in mining operations, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the mine operation safety early warning method of this application.

[0016] In this embodiment, the mine operation safety early warning method includes the following steps: Step S10: Collect multi-source real-time data of the mining operation environment, including geological monitoring data, equipment operation status data, environmental meteorological data, and personnel operation behavior data.

[0017] Step S20: Match the corresponding scene factors in the preset database according to the mining operation environment to generate a target mine profile.

[0018] Step S30: Based on the target mine profile, determine the processing method to combine the multi-source real-time data for preprocessing and data fusion to generate a standardized operation safety dataset.

[0019] Step S40: Analyze the standardized operation safety dataset based on the machine learning model to identify potential safety risk patterns in the operation process and generate safety risk indicators; Step S50: Based on the comparison result between the safety risk index and the preset safety threshold, generate an operation safety early warning signal; and output the operation safety early warning signal to the user interface or external execution system to trigger real-time early warning response actions, including personnel warning, equipment shutdown or emergency dispatch.

[0020] In traditional mine safety monitoring systems, insufficient integration capabilities of multi-source heterogeneous data lead to blind spots in risk identification. Static threshold mechanisms cannot adapt to dynamic environmental changes, and excessive reliance on manual intervention causes response delays. For example, a deep mine uses independently deployed vibration sensors, gas concentration sensors, and video monitoring systems. Geological monitoring data and equipment operation data are stored on different platforms, and environmental meteorological data is not linked to personnel location information. When micro-seismic events in the rock strata and ventilation system malfunctions occur simultaneously, the system fails to identify the combined risk due to the lack of a multi-dimensional data fusion mechanism, triggering only a single parameter exceeding the limit alarm. Personnel violations rely on visual identification by monitors, and fatigue detection lags behind actual physiological changes, resulting in escalating risks not being detected in a timely manner. If the above problems are not solved, the risks of multi-factor coupling will not be effectively predicted, the abnormal operating status of key equipment may trigger a chain of failures, the lag in the identification of personnel behavior risks will increase the probability of accidents, and the static early warning mechanism will generate a large number of false alarms when faced with complex geological conditions, ultimately leading to the failure of the safety protection system.

[0021] Faced with the aforementioned problems, this embodiment first considers how to break down data silos and achieve multi-dimensional risk identification. In traditional methods, geological monitoring, equipment operation, environmental meteorology, and personnel behavior data are scattered and independent, making it difficult to capture complex risks. To address this, this embodiment attempts to use multi-source real-time data acquisition and fusion as a breakthrough, eliminating data heterogeneity through unified preprocessing, and exploring a dynamic scene matching mechanism to replace static thresholds. Furthermore, this embodiment recognizes that simply increasing the number of sensors cannot solve the fundamental problem; it is necessary to dynamically adjust the analysis model in conjunction with mine profiles. Therefore, scene factor matching is introduced to generate target mine profiles, enabling data processing methods to automatically adapt to environmental characteristics. In addition, the problem of delayed response in traditional manual analysis prompts this embodiment to adopt machine learning models to achieve automated risk pattern identification, and to form a closed-loop control system by directly linking early warning signals to the execution system.

[0022] In response, this embodiment proposes a mine operation safety early warning method, comprising: collecting multi-source real-time data of the mine operation environment, including geological monitoring data, equipment operating status data, environmental meteorological data, and personnel operation behavior data; matching corresponding scene factors in a preset database based on the mine operation environment to generate a target mine profile; determining processing methods based on the target mine profile to perform preprocessing and data fusion in combination with the multi-source real-time data to generate a standardized operation safety dataset; analyzing the standardized operation safety dataset based on a machine learning model to identify potential safety risk patterns in the operation process and generate safety risk indicators; generating an operation safety early warning signal based on the comparison results of the safety risk indicators and preset safety thresholds; and outputting the operation safety early warning signal to a user interface or external execution system to trigger real-time early warning response actions, including personnel warning, equipment shutdown, or emergency dispatch.

[0023] Multi-source real-time data refers to various types of dynamic information synchronously acquired from the mining environment. Specifically, this can be achieved by using vibration sensors, gas concentration sensors, temperature sensors, high-definition cameras, and wearable devices to collect geological, equipment, environmental, and personnel behavior data. Its function is to break down data silos from single sensors or independent systems in existing technologies, achieving cross-dimensional data integration. Scene factors refer to a set of parameters used to describe the dynamic characteristics of the mine. Specifically, this can be generated by matching data stored in a pre-set database, such as rock strata stability parameters, gas emission patterns, equipment aging coefficients, and personnel skill levels. Its function is to address the problem that traditional static thresholds cannot adapt to different mine types, geological structures, and operational stages. Preprocessing and data fusion refer to cleaning, format conversion, and correlation analysis of multi-source data. Specifically, this can be achieved by using time-series data fusion algorithms to integrate data of different frequencies and formats into a unified time-series dataset. Its function is to eliminate sensor noise and establish spatiotemporal correlations between data, providing standardized input for subsequent risk analysis. Machine learning models refer to the algorithmic architecture used to identify complex risk patterns. Specifically, this can be achieved using deep learning neural networks or random forest algorithms trained on historical accident data. Its function lies in dynamically learning the coupling relationships of multiple factors, surpassing traditional single-threshold alarm mechanisms, and simultaneously detecting complex risks such as landslides, gas outbursts, equipment failures, and personnel violations. The safety risk index refers to the quantified risk level assessment result, specifically achieved by comparing the probability values ​​output by the model with preset dynamic thresholds to generate tiered early warning signals. Its function is to transform abstract risks into actionable early warning levels, solving the problem of delayed response in existing technologies. Real-time early warning response actions refer to control commands triggered by the early warning signal, specifically implemented by directly driving emergency broadcasts, equipment shutdowns, or rescue dispatch systems through interface protocols. Its function is to establish a closed loop from risk identification to automatic handling, improving response timeliness. The core innovation of this embodiment lies in constructing a digital profile of the mine through multi-source data fusion and dynamic scene matching, combining it with machine learning models to achieve multi-dimensional risk coupling analysis, and triggering proactive responses based on a tiered early warning mechanism. This solution is the first to incorporate geological, equipment, environmental, and personnel behavior data into a unified analysis framework, adjusting model parameters through dynamic scene factors, and simultaneously covering multi-level response strategies such as equipment shutdown and personnel evacuation, fundamentally solving the problems of data fragmentation, model rigidity, and passive response in existing technologies.

[0024] The working process and principle of this embodiment are as follows: First, a sensor network deployed in the mining operation area collects multi-source real-time data, including geological monitoring data, equipment operating status data, environmental meteorological data, and personnel operation behavior data. This data originates from vibration sensors, gas concentration sensors, temperature and humidity sensors, high-definition cameras, UWB positioning tags, and wearable devices, among others.

[0025] Next, the system matches corresponding scenario factors in a pre-set database based on the mining operation environment to generate a target mine profile. Scenario factors include rock strata stability parameters, gas emission patterns, equipment aging coefficients, and personnel skill levels. Through data fusion algorithms, the system generates dynamically updated target mine profiles.

[0026] Based on the target mine profile, the system determines appropriate data processing methods to preprocess and fuse multi-source real-time data. Preprocessing includes data cleaning, noise reduction, and normalization. Data fusion employs a time-series data fusion algorithm to integrate multi-source data into a unified time-series dataset. For personnel work behavior data, the system uses computer vision algorithms for behavior recognition and classification, including violation detection and fatigue state analysis.

[0027] The processed data forms a standardized operational safety dataset, which is then input into a machine learning model for analysis. This model can be a deep learning neural network, a random forest algorithm, or a support vector machine, and is trained using historical mining accident data. The purpose of the model analysis is to identify potential safety risk patterns during operations and generate safety risk indicators.

[0028] The system compares the generated safety risk indicators with preset safety thresholds and generates operational safety warning signals based on the comparison results. These warning signals are categorized into different levels according to the severity of the risk, including low-level warnings, medium-level alarms, and high-level emergency alarms.

[0029] Finally, the system outputs operational safety warning signals to the user interface or external execution system, triggering real-time warning response actions. These actions include sending warning notifications to operators' mobile terminals, activating the underground emergency broadcast system, controlling the automatic shutdown of relevant equipment, or dispatching rescue resources. The response actions are dynamically adjusted based on the warning level and the target mine profile.

[0030] In its implementation, a sensor network was deployed in an underground coal mine to collect real-time data from multiple sources. Vibration sensors were installed on the roof and sides of the roadways to monitor micro-seismic activity of the rock strata; gas concentration sensors were deployed in the coal mining face and return airway to detect the concentration of harmful gases such as methane and carbon monoxide in real time; temperature and humidity sensors were distributed in the main roadways to monitor changes in environmental parameters; high-definition cameras were installed in key work areas to capture personnel behavior in real time; UWB positioning tags were integrated into miners' headlamps to track personnel locations; and wearable devices such as smart bracelets monitored miners' heart rate and movement status.

[0031] The system matches relevant scenario factors from a pre-set database based on information such as the coal mine's geological conditions, mining depth, and gas level. For example, it selects a rock stratum stability parameter model suitable for deep, high-stress conditions, matches it with a gas emission mode prediction algorithm for high-gas mines, calculates equipment aging coefficients based on equipment service life, and determines skill levels based on personnel training records. Through data fusion algorithms, a dynamic profile of the mine is generated.

[0032] Based on the target mine profile, the system selects appropriate data processing methods. Spectral analysis is performed on vibration data to remove environmental noise; moving average filtering is applied to gas concentration data; and image enhancement and target detection preprocessing are performed on video data. A time-series data fusion algorithm is used to align data from different sources onto a unified timeline. A deep learning-based behavior recognition algorithm is applied to the video data to identify abnormal behaviors such as violations and fatigue.

[0033] The processed data forms a standardized operational safety dataset, which is then input into a pre-trained deep neural network model. This model, trained on historical accident data, can identify various potential risk patterns, such as roof collapse, gas accumulation, equipment failure, and personnel violations. The model outputs safety risk indicators, including probability scores for various risks.

[0034] The system compares risk indicators with preset thresholds. For example, a high-level alarm is triggered when the risk of roof collapse exceeds 80%, a medium-level alarm is triggered when the risk of gas accumulation exceeds 60%, and a low-level warning is triggered when the risk of equipment failure exceeds 40%. Based on the comparison results, a warning signal of the corresponding level is generated.

[0035] A high-level alarm will trigger automatic shutdown of equipment at the work site, activate the emergency broadcast system to notify personnel to evacuate, and send an emergency rescue request to the dispatch center. An intermediate-level alarm will push alarm information to on-site management personnel via mobile terminals, requesting control measures to be taken. A low-level alarm will be recorded in the system log to remind relevant personnel to pay attention.

[0036] Through the above-described scheme, this embodiment achieves real-time acquisition and fusion analysis of multi-source heterogeneous data, breaking the data silo problem in traditional mine safety monitoring systems. By dynamically matching scenarios and generating target mine profiles, the system can adaptively adjust data processing and analysis strategies according to specific mine environmental characteristics, overcoming the limitations of static threshold mechanisms. The introduction of machine learning models for automated risk identification significantly improves the ability to predict complex risks and reduces reliance on human experience. By directly linking early warning signals to the execution system, a closed-loop control is formed, significantly shortening the time from risk identification to response measure execution. This comprehensive, intelligent, and real-time safety early warning method effectively improves the level of mine operation safety management and reduces the probability of accidents.

[0037] In some of the solutions described above in this embodiment, multi-source real-time data of the mining operation environment is proposed to support safety early warning. However, in the data acquisition process, relying solely on a single type of sensor or failing to cover real-time personnel status monitoring results in insufficient data dimensions, which cannot fully reflect the dynamic changes in the operation environment and affect the accuracy of subsequent risk analysis.

[0038] This embodiment further proposes to collect data through a sensor network deployed in the mining operation area. The sensor network includes at least one of vibration sensors, gas concentration sensors, temperature sensors, humidity sensors, high-definition cameras, UWB positioning tags, and wearable devices. The wearable devices integrate heart rate sensors and motion sensors to monitor the physiological state and behavioral trajectory of personnel in real time.

[0039] Vibration sensors are installed on the tunnel roof or rock face to detect micro-vibration signals in the rock strata; gas concentration sensors are distributed in ventilation tunnels and working faces to continuously monitor methane and carbon monoxide concentrations; temperature and humidity sensors are deployed in areas with dense equipment and personnel movement channels to collect environmental temperature and humidity changes; high-definition cameras are deployed at key work nodes to capture personnel operation behaviors through image acquisition; UWB positioning tags are embedded in safety helmets or work clothes to track personnel location coordinates in real time; heart rate sensors built into wearable devices collect personnel heart rate data through contact electrodes, and motion sensors record limb movement frequencies through accelerometers. All of these sensors are connected to the central data acquisition module via wired or wireless communication protocols, forming a heterogeneous data stream.

[0040] Specifically, vibration sensors capture rock vibration waveforms at a sampling rate of 10-1000Hz and identify abnormal vibration patterns through spectrum analysis; gas concentration sensors update concentration values ​​every 5 seconds and correct gas diffusion models by combining temperature and humidity data; high-definition cameras capture video streams at 30fps and extract personnel posture features through edge computing devices; UWB positioning tags update location coordinates in real time with 0.1-meter accuracy to construct a personnel movement heatmap; wearable devices' heart rate sensors continuously monitor heart rate variability index, and motion sensors identify abnormal movements such as falls and running through three-axis acceleration data. After time-stamping, multi-source data forms a four-dimensional dataset containing geological activity, environmental conditions, equipment operation, and personnel behavior. For example, when a vibration sensor detects a 0.5Hz low-frequency vibration with an amplitude exceeding 2mm, it simultaneously retrieves gas concentration data and personnel location information for that area to determine whether to trigger a coordinated early warning. When the heart rate collected by the wearable device suddenly increases to above 120bpm and is accompanied by irregular movement trajectories, it is automatically marked as a personnel stress state, triggering a behavioral intervention mechanism.

[0041] In practice, a sensor network is deployed in the mining area to collect real-time data from multiple sources. This sensor network includes vibration sensors, gas concentration sensors, temperature sensors, humidity sensors, high-definition cameras, UWB positioning tags, and wearable devices. The wearable devices integrate heart rate and motion sensors to monitor personnel's physiological state and behavioral patterns in real time.

[0042] Specifically, vibration sensors are installed on the walls and roof of mine roadways to detect micro-seismic activity in the rock strata. Gas concentration sensors are placed at key points in the mining faces and ventilation systems to monitor the concentration of harmful gases such as methane and carbon monoxide. Temperature and humidity sensors are distributed throughout the work areas to collect environmental parameters in real time. High-definition cameras are installed in key areas, such as mining faces and transport roadways, for video surveillance. UWB positioning tags are distributed to each worker for precise location tracking. Wearable devices, in the form of smart bracelets, are worn with built-in heart rate and accelerometer sensors to monitor personnel's physiological indicators and movement status.

[0043] These sensors are connected to a central data processing system via wired or wireless networks. The data acquisition frequency is dynamically adjusted according to the specific monitoring object; for example, vibration data is collected 100 times per second, gas concentration is collected once per minute, and video data is transmitted in real time. The system adopts a distributed architecture, with preliminary data processing and compression performed at edge nodes to reduce the burden on network transmission.

[0044] Through the above technical solutions, this embodiment achieves comprehensive and multi-dimensional real-time monitoring of the mining operation environment. The sensor network covers key factors such as geology, equipment, environment, and personnel, providing rich data sources to support subsequent analysis. The introduction of wearable devices enables more refined monitoring of personnel status, helping to detect potential risks such as fatigue and heatstroke early. The fusion of multi-source data lays the foundation for a comprehensive assessment of operational safety, improving the accuracy and timeliness of early warnings.

[0045] In some of the solutions described above in this embodiment, if only single-dimensional data or static scene factors are relied upon when generating the target mine profile, the mine profile may not match the dynamic changes in the actual working environment, thereby affecting the accuracy of subsequent safety risk assessment. This embodiment further proposes to retrieve matching scene factors from a preset database based on mine type, geological structure, historical accident data, and operation stage, and generate dynamically updated target mine profiles through data fusion algorithms. The scenario factors include rock strata stability parameters, gas outburst patterns, equipment aging coefficients, and personnel skill levels. Mine types are classified by ore body morphology and mining methods, such as open-pit or underground mines; geological structures are defined by strata distribution and fault characteristics; historical accident data covers accident types, locations, and causes; and operational stages are divided into different processes such as tunneling, support, and transportation. The data fusion algorithm uses Kalman filtering or Bayesian networks to dynamically integrate multi-source scenario factors according to their weights. Specifically, when the mine type is underground, the pre-set database prioritizes matching high gas outburst patterns and complex rock strata stability parameters; geological structure data is updated with the historical distribution of rock strata stability parameters through borehole exploration results; historical accident data is combined with spatiotemporal characteristics to screen similar accident patterns, such as the correlation between collapse accidents and rock strata fissures in specific areas; when switching operational phases, equipment aging coefficients are recalculated based on runtime and maintenance records. The data fusion algorithm adjusts the fusion weights of various scenario factors according to the real-time operational phase; for example, the weight of rock strata stability parameters is increased during the support phase, while the equipment aging coefficient is emphasized during the transportation phase. Through dynamically updated target mine profiles, the multidimensional risk characteristics of the current mining environment can be more accurately reflected, providing reliable input for subsequent safety risk analysis.

[0046] In practice, based on mine type, geological structure, historical accident data, and operational stage, matching scenario factors are retrieved from a pre-set database. Scenario factors include rock strata stability parameters, gas emission patterns, equipment aging coefficients, and personnel skill levels. A dynamically updated target mine profile is generated through data fusion algorithms.

[0047] Specifically, the first step is to select a suitable subset of the database based on the type of mine (e.g., coal mine, metallic mine, or non-metallic mine). Further, applicable rock strata stability parameters are selected based on geological structure information (e.g., rock strata distribution, fault location). Then, combined with historical accident data, gas outburst patterns and equipment aging coefficients are extracted. For example, for coal mines, data on gas outburst accidents occurring in the past five years can be selected to analyze outburst frequency and intensity, generating a gas outburst risk model.

[0048] As a preferred implementation method, the equipment aging coefficient is calculated by analyzing equipment operating time, maintenance records, and failure rates. Personnel skill levels are assessed based on years of service, training records, and operational assessment results. These scenario factors are integrated into a comprehensive target mine profile using data fusion algorithms, such as Bayesian networks or decision tree ensemble methods.

[0049] This profile is stored as a digital model and includes risk indicators across multiple dimensions. It is continuously updated with real-time data, reflecting the mine's current status and potential risks. For example, when new geological changes or equipment updates are detected, relevant parameters are automatically adjusted to ensure the profile's timeliness and accuracy.

[0050] Through the above technical solution, this embodiment achieves a comprehensive and dynamic assessment of the mining environment. By integrating multi-dimensional data, the generated target mine profile accurately reflects the current safety status and potential risks of the mine. This method overcomes the limitations of traditional single data sources, improving the accuracy and comprehensiveness of risk assessment. The dynamic update mechanism ensures that the profile always reflects the latest mine status, providing a reliable basis for subsequent safety early warning and decision-making. Furthermore, through a standardized data fusion process, this method can adapt to mines of different types and sizes, improving the system's versatility and scalability.

[0051] In some of the solutions described above in this embodiment, there are problems such as large differences in the formats of multi-source data and inconsistent timing during the preprocessing and data fusion process. This makes it difficult for the fused dataset to accurately reflect the overall safety status of the mining operation environment, affecting the input quality of the subsequent risk identification model.

[0052] This embodiment further proposes preprocessing and data fusion, including performing data cleaning, denoising, and normalization, and using a time-series data fusion algorithm to integrate multi-source data into a time-series dataset in a unified format. Among them, personnel operation behavior data is used for behavior recognition and classification through computer vision algorithms, including violation operation detection and fatigue state analysis.

[0053] The data cleaning process involves filtering out missing values ​​or outliers exceeding the physical measurement range using a rule engine; noise reduction employs sliding window mean filtering to eliminate high-frequency interference in sensor signals; and normalization linearly maps sensor data of different dimensions to the [0,1] interval. The time-series data fusion algorithm uses dynamic time warping to align timestamps from multiple data sources, generating a standardized time-series dataset with one sample point per minute. The computer vision algorithm extracts human posture features from video frames using a pre-trained convolutional neural network model, classifies these behaviors based on skeleton keypoint trajectories into safe operations, unauthorized climbing, or tool misoperation, and determines fatigue levels through eye closure frequency and head posture changes.

[0054] Specifically, the raw acceleration data collected by the vibration sensor is filtered through a sliding window and synchronized to a unified time base with the normalized value from the temperature sensor. Gas concentration data, after clearing abnormal peaks, is aligned with equipment operating current data through dynamic time warping. The video stream from the downhole camera, after detecting moving targets using the frame difference method, is input into a convolutional neural network to extract the safety helmet wearing status and tool usage posture of the workers, outputting behavioral classification labels. All preprocessed data is integrated into a structured table by timestamp, with each row containing geological parameters, equipment status, environmental indicators, and personnel behavior codes at the same time. This dataset is input into a machine learning model through a unified data interface, enabling the model to simultaneously capture the correlation between abnormal equipment vibration, sudden changes in gas concentration, and personnel violations, improving the accuracy of landslide risk prediction.

[0055] In practice, the preprocessing and data fusion steps include data cleaning, noise reduction, and normalization, followed by the use of time-series data fusion algorithms to integrate multi-source data into a unified time-series dataset. Specifically, personnel work behavior data is used for behavior recognition and classification via computer vision algorithms, including violation detection and fatigue analysis.

[0056] Specifically, the data cleaning process begins with outlier detection on the raw data, removing data points that significantly deviate from the normal range. For example, for gas concentration data, if a reading suddenly increases to more than 10 times the normal value at a certain moment, it is considered an outlier and deleted. Next, data completion is performed; for missing data within a short period, linear interpolation is used to fill in the gaps.

[0057] In the denoising process, for vibration sensor data, wavelet transform denoising algorithm is applied, the db4 wavelet basis function is selected, the signal is decomposed into three levels, and the high-frequency coefficients are processed by the soft thresholding method to eliminate the influence of random noise.

[0058] The normalization process employs the min-max standardization method to uniformly map data of different dimensions to the [0,1] interval. The time-series data fusion algorithm uses the Dynamic Time Warping (DTW) method to align sensor data with different sampling frequencies onto a unified time axis. For example, temperature and humidity data sampled at 1Hz are time-aligned with vibration data sampled at 10Hz to generate a unified multidimensional time-series dataset with a 10Hz sampling rate.

[0059] For personnel work behavior data, deep learning-based computer vision algorithms are used for processing. Specifically, the YOLOv5 object detection model is used to analyze the video stream in real time to identify the position and posture of the workers. Then, a behavior recognition model based on a Long Short-Term Memory (LSTM) network is used to classify continuous posture sequences and identify violations, such as not wearing a safety helmet or loitering in prohibited areas.

[0060] Furthermore, by analyzing facial features and eye movement data, a support vector machine (SVM) algorithm is used to determine fatigue levels. The model input includes features such as blink frequency and PERCLOS value (percentage of time spent with eyes closed), and outputs a classification result indicating the degree of fatigue.

[0061] Through the above technical solutions, this embodiment achieves effective integration and standardized processing of multi-source heterogeneous data. Data cleaning and denoising improve the quality and reliability of the original data, while normalization processing makes different types of data comparable. The time-series data fusion algorithm solves the problem of asynchronous time among multi-source data, providing a unified data foundation for subsequent safety risk analysis. The application of computer vision algorithms expands personnel behavior data from simple location information to richer behavioral pattern analysis, enhancing the system's ability to identify human-related risk factors. The comprehensive application of these technical means significantly improves the data processing capabilities and analytical accuracy of the mine operation safety early warning system, laying the foundation for more accurate and comprehensive safety risk assessment.

[0062] In some of the solutions described above in this embodiment, although a standardized operational safety dataset is generated through data fusion, traditional analysis methods are difficult to dynamically adapt to the complex changes in the mining environment, resulting in insufficient accuracy in risk identification and an inability to cover multiple types of risk factors.

[0063] This embodiment further proposes a machine learning model including a deep learning neural network, a random forest algorithm, or a support vector machine. The model is trained using historical mining accident data and its parameters are updated periodically with new data to identify at least one of the following: collapse risk, gas outburst risk, equipment failure risk, or personnel violation operation risk.

[0064] Deep learning neural networks extract deep features from data through multi-layer nonlinear transformations, making them suitable for handling complex correlation patterns between geological monitoring data and equipment operation data. Random forest algorithms employ a multi-decision tree ensemble strategy, effectively handling the coupling relationship between high-dimensional environmental meteorological data and personnel behavior data. Support vector machines solve risk classification problems under small sample conditions through kernel function mapping. During model training, historical accident data is used to construct positive and negative sample sets, and model parameters are optimized through backpropagation or feature importance assessment. In the parameter update phase, model retraining is triggered at preset intervals or data increment thresholds to ensure the model continuously adapts to dynamic factors such as changes in rock structure and equipment aging processes. During risk identification, the model outputs probabilistic risk assessment values ​​for standardized datasets, triggering corresponding early warning signals when the probability of landslide risk exceeds a preset threshold.

[0065] Specifically, after inputting a standardized operational safety dataset, the deep learning neural network first performs convolution operations on the time-series data to capture the spatiotemporal correlation features between geological displacement and equipment vibration. The random forest algorithm further ranks the importance of features from multiple sources, filtering out strongly correlated factors between sudden changes in gas concentration and abnormal personnel positioning. The support vector machine then performs classification decisions on small sample data near the equipment temperature threshold to identify potential failure modes. The model automatically incorporates new operational data every 24 hours, adjusting classification boundary parameters through online learning algorithms. For example, when a new hydrogen sulfide gas leak occurs underground, the model automatically strengthens the correlation weight between gas concentration and ventilation equipment status in the next training cycle, reducing the response time for subsequent similar risk identification to within 3 seconds.

[0066] In practice, machine learning models include deep learning neural networks, random forest algorithms, or support vector machines. The models are trained using historical mining accident data and their parameters are periodically updated with new data to identify at least one of the following risks: landslide risk, gas outburst risk, equipment failure risk, or personnel misconduct risk.

[0067] For example, a deep learning neural network can employ a multi-layer convolutional neural network structure. The input layer receives a standardized job safety dataset, the hidden layers contain multiple convolutional and pooling layers to extract data features, and the output layer uses the softmax function to output the risk class probability. Model training uses the backpropagation algorithm, the loss function is cross-entropy, and the optimizer is Adam.

[0068] The Random Forest algorithm can construct an ensemble model consisting of multiple decision trees, each of which is trained independently and votes to predict the risk category. The splitting criterion for the decision trees is the Gini coefficient, the number of trees is set to 100-500, and the maximum depth is limited to 10-20 layers.

[0069] The support vector machine model can use a radial basis function kernel, and the penalty parameter C and the kernel parameter gamma are optimized through grid search. For multi-class risk identification, a one-to-many strategy is used to construct multiple binary classifiers.

[0070] The model training data includes historical landslide accidents, gas outburst events, equipment failure records, and cases of personnel violations, with each data point labeled with its corresponding risk type. During training, k-fold cross-validation is used to evaluate model performance, and the model parameters with the highest F1 scores are selected.

[0071] The model is updated periodically using incremental learning, retraining weekly or monthly based on newly collected data. A sliding window technique is used during the update process, retaining data from the most recent N months as the training set to adapt to the dynamic changes in the mining environment.

[0072] The risk identification results are output as probability values ​​for various risks, and a warning is triggered based on preset thresholds. For example, when the probability of a landslide exceeds 0.8, the system generates a high-level warning signal.

[0073] Through the above technical solution, this embodiment achieves intelligent and dynamic identification of safety risks in mining operations. The machine learning model can extract complex features from multi-source data and capture potential risk patterns, significantly improving early warning accuracy compared to traditional fixed threshold methods. A regular update mechanism allows the model to adapt to changes in the mining environment and continuously optimize the early warning effect. The comprehensive identification of multiple risk types provides comprehensive decision support for safety management and effectively reduces the probability of safety accidents.

[0074] In some of the solutions described above in this embodiment, although the comparison results between the safety risk indicators and the preset safety thresholds can trigger warning signals, there is a lack of detailed classification of the severity of the risks, which makes it impossible for the response actions to accurately match the actual risk level, and may cause problems such as over-response for low-level risks or under-response for high-level risks.

[0075] This embodiment further proposes that the generation of operational safety early warning signals includes: classifying the severity of safety risk indicators and generating early warning signals of different levels, including low-level warnings, medium-level alarms and high-level emergency alarms, wherein the high-level emergency alarm automatically triggers equipment shutdown, personnel evacuation and emergency dispatch protocols.

[0076] The safety risk indicators are divided into three independent ranges, each corresponding to a different warning level. A low-level warning corresponds to an indicator value within 10%-30% of the preset safety threshold; a medium-level alarm corresponds to 30%-70%; and a high-level emergency alarm corresponds to values ​​above 70%. Each warning level is bound to a specific execution protocol. For example, when a high-level alarm activates a shutdown command, the PLC controller directly cuts off the equipment's power circuit, simultaneously triggering the underground broadcast system to play a pre-recorded evacuation instruction audio file.

[0077] Specifically, when the standardized operational safety dataset is input into the machine learning model, the output safety risk index values ​​are mapped to graded ranges in real time. If the index value exceeds the high-level threshold, the system immediately calls the equipment control interface to send a shutdown command. This command is transmitted to the equipment controller via the industrial bus to execute a hard-wired power-off operation. Simultaneously, when the emergency dispatch protocol is activated, the coordinates of the nearest available rescue team in the rescue resource database are extracted, and the dispatch command is sent to the vehicle-mounted terminal of the rescue vehicle via the wireless communication module. During this process, the binding relationship between the warning level and the response action is achieved through a dynamic configuration table. This configuration table is adaptively adjusted based on the geological structure parameters and historical accident data in the target mine profile. For example, in mines with active gas outburst patterns, the trigger threshold for high-level alarms is automatically lowered by 15%.

[0078] In practice, when generating operational safety early warning signals, the severity of safety risk indicators is categorized to generate different levels of warning signals. These warning signals include low-level warnings, medium-level alarms, and high-level emergency alarms. The high-level emergency alarm automatically triggers equipment shutdown, personnel evacuation, and emergency dispatch protocols.

[0079] Specifically, the safety risk indicators are derived by analyzing standardized operational safety datasets using machine learning models. Three preset safety thresholds correspond to low, medium, and high levels, respectively. When the safety risk indicator is below the first threshold, a low-level warning is generated; when the safety risk indicator is between the first and second thresholds, a medium-level alarm is generated; and when the safety risk indicator is above the second threshold, a high-level emergency alarm is generated.

[0080] For example, for the safety risk indicator of methane concentration, the threshold for a low-level warning is set at 0.5%, the threshold for a medium-level alarm is 1%, and the threshold for a high-level emergency alarm is 1.5%. When the methane concentration is detected to reach 0.7%, the system generates a medium-level alarm; when the methane concentration exceeds 1.5%, a high-level emergency alarm is immediately triggered.

[0081] Furthermore, the advanced emergency alert triggers a series of automated response measures. These include: immediately cutting off the power supply to the affected area and stopping all machinery; activating emergency lighting and ventilation systems; issuing evacuation instructions to all personnel via broadcast systems and personal communication devices; and simultaneously, the dispatch system automatically assigns the nearest rescue teams to the incident site and prepares necessary emergency supplies.

[0082] Therefore, the early warning mechanism in this embodiment realizes full-process automation from risk identification to emergency response, which greatly improves the efficiency and reliability of mine safety management.

[0083] Through the above technical solution, this embodiment achieves accurate classification and rapid response to safety risks in mining operations. By comparing safety risk indicators with multi-level preset thresholds, the system can more accurately assess the severity of the current safety situation, avoiding over-warning or under-response that may occur with traditional single-threshold methods. Especially in high-risk situations, advanced emergency alarms can trigger a series of automated emergency measures, minimizing human delays and improving emergency response speed. This graded early warning and automatic response mechanism significantly enhances the initiative and effectiveness of mine safety management, helps to prevent and control potential safety accidents in a timely manner, and protects the lives of mine workers.

[0084] In some of the solutions described above in this embodiment, the mechanism for triggering real-time early warning response actions adopts a fixed execution strategy, which cannot be dynamically adapted according to the differences in actual early warning levels and mining operation scenarios. This may result in excessive or insufficient response measures, affecting the early warning effect and the continuity of operations. This embodiment further proposes triggering real-time early warning response actions including: automatically sending early warning notifications to the mobile terminals of operators, activating the underground emergency broadcast system, controlling relevant equipment to automatically shut down or dispatching rescue resources, and dynamically adjusting the response actions based on the early warning level and the target mine profile. The system automatically pushes early warning notifications to mobile terminals worn by workers via a pre-set communication protocol. Terminal devices include explosion-proof phones or smart bracelets. The activation of the underground emergency broadcast system is linked to the early warning level; higher-level alarms trigger full-area broadcast coverage. Equipment shutdown commands are issued to associated mining machines, conveyor belts, or ventilation equipment via the industrial control network. Rescue resource scheduling generates optimal paths based on the roadway layout, personnel distribution, and equipment locations recorded in the target mine profile. Dynamic adjustment logic is implemented through a pre-set strategy table, which links early warning levels with response actions. Simultaneously, it incorporates geological stability parameters, equipment operating status, and personnel skill levels from the target mine profile to adjust the execution intensity or priority of response actions in real time. Specifically, when a low-level warning is generated, a vibration alert is sent only to the mobile devices of personnel in the relevant area, and a local broadcast reminder is initiated. If the target mine profile shows a trend of expanding rock fissures in the area, the broadcast frequency is automatically increased. When an intermediate alarm is triggered, equipment shutdown instructions are limited to specific high-risk equipment, and detailed evacuation instructions are sent only to inexperienced operators based on personnel skill level data in the profile. When a high-level emergency alarm occurs, the emergency broadcast system switches to full-power mode, the equipment shutdown range is expanded to the associated power supply system, and the rescue path is dynamically adjusted based on real-time updated tunnel collapse data in the profile. The execution parameters of the response actions are matched with the severity of the risk by comparing the warning level with the dynamic risk factors in the profile in real time, avoiding resource waste or response delays caused by fixed response strategies.

[0085] In practice, the system automatically sends early warning notifications to workers' mobile devices, activates the underground emergency broadcast system, and controls relevant equipment to automatically shut down or dispatches rescue resources. The early warning response actions are dynamically adjusted based on the early warning level and the target mine profile.

[0086] Specifically, once the system generates a work safety warning signal, it first pushes the warning information to the smartwatches or mobile applications worn by workers via a wireless network. The warning information includes the risk type, location, and suggested actions. Simultaneously, the system activates the underground emergency broadcast system, playing pre-recorded voice alarms through speakers. For high-level warnings, the system automatically sends shutdown commands to relevant equipment, such as cutting off the power to the coal mining machine or shutting down the conveyor belt. Furthermore, the system will invoke pre-set emergency plans based on the warning level and mine profile, such as automatically notifying the ground command center to dispatch rescue teams. The specific execution method of the warning response will be adjusted according to the real-time updated target mine profile; for example, in areas with high methane concentrations, the system will prioritize activating ventilation equipment rather than evacuating personnel.

[0087] Through the above technical solution, this embodiment achieves rapid response and precise execution of mine operation safety early warnings. This reduces human judgment and operational delays, improving the real-time performance and effectiveness of the early warning system. Furthermore, by combining the early warning response with dynamically updated mine profiles, the pertinence and adaptability of early warning measures are enhanced, unnecessary production interruptions are avoided, and key risks are ensured to be addressed promptly.

[0088] In some of the solutions described above in this embodiment, the adjustment of machine learning model parameters, preset safety thresholds, and scene factor databases relies on human experience, which makes it impossible for the system to dynamically optimize based on the actual warning effect, thus affecting the accuracy and adaptability of the warning.

[0089] This embodiment further proposes a feedback learning step: collecting data on early warning response results, including response efficiency and accident mitigation effects, and using this data to adjust machine learning model parameters, preset safety thresholds, or scenario factor databases to achieve adaptive operation safety early warning optimization.

[0090] The feedback learning step utilizes a data acquisition module to capture real-time data on equipment downtime, personnel evacuation route optimization, and rescue resource scheduling matching as response efficiency indicators. It also records the deviation between the actual and predicted impact range of the accident as a mitigation performance indicator. The adjustment mechanism employs an incremental learning algorithm to update model parameters online, dynamically calibrating safety thresholds using a sliding window statistical method, and optimizing parameter weights in the scenario factor database based on association rule mining technology. A bidirectional communication link is established between the data acquisition module and the preprocessing module to ensure that the timestamps of the feedback data are aligned with the original multi-source data.

[0091] Specifically, when the system generates a high-level emergency alarm and triggers equipment shutdown, the data acquisition module records the time difference from alarm triggering to complete equipment shutdown as a response efficiency indicator. Simultaneously, it tracks personnel evacuation trajectories using location tags to calculate path deviation rates. Accident mitigation effectiveness is quantified by comparing the area difference between the predicted collapse area and the actual collapse area. This data, after cleaning, is input into the incremental learning module, which uses an online gradient descent algorithm to update the neural network weight parameters, with the update magnitude automatically adjusted based on historical warning accuracy. The safety threshold calibration module uses warning records from the past 30 days as a sliding window to calculate the standard deviation of each risk indicator and reset the threshold boundaries. The scenario factor database uses the Apriori algorithm to mine the correlation rules between equipment aging coefficients and gas emission patterns, dynamically adjusting the weight allocation of rock strata stability parameters. This closed-loop optimization mechanism enables the warning model to adapt to dynamic changes in the mining environment; for example, it automatically increases the influence factor of humidity parameters in the risk prediction model during the rainy season, thereby enhancing the adaptive capability of the warning system.

[0092] In practical implementation, after generating safety warning signals and triggering response actions, the system automatically collects warning response result data, specifically including the execution delay of equipment shutdown commands, the optimization degree of personnel evacuation routes, and the efficiency of rescue resource dispatch. This data is transmitted to the central processing unit through a pre-defined feedback interface. The response efficiency index is quantified as time-series data, and the accident mitigation effect is converted into a quantitative score using an accident loss assessment model. The feedback learning module employs an incremental learning mechanism, merging newly collected response result data with historical training sets weekly, and updating the weight parameters of the deep learning neural network using a stochastic gradient descent algorithm. Simultaneously, the pre-defined safety threshold is dynamically adjusted based on the gas concentration fluctuation trend over the past three months, and the rock strata stability parameters in the scene factor database are periodically calibrated using ground-penetrating radar scan data, with a calibration cycle set to 24 hours. Through the above technical solutions, this embodiment achieves continuous optimization of early warning model parameters and safety thresholds, effectively solving the technical deficiency of static early warning systems in adapting to dynamic changes in the mining environment. By inputting actual response data back into the machine learning model, the accuracy of landslide risk prediction is enhanced. Simultaneously, adjusting the scenario factor database based on real accident handling results improves the adaptability of equipment failure risk identification. Furthermore, through an automated feedback learning mechanism, the threshold update delay caused by manual intervention is reduced, enabling the system to autonomously adapt to the safety management needs of different geological conditions and operational stages.

[0093] In some of the solutions described above in this embodiment, the collected multi-source real-time data may be of unstable quality due to the complexity of the mining environment or sensor failure, such as missing data, noise, or logical contradictions. If used directly for subsequent analysis, it may lead to misjudgment or delayed early warning. This embodiment further proposes to add a data verification step after collecting multi-source real-time data: real-time quality checks are performed on the collected data, including integrity verification, outlier detection and consistency verification, to ensure data reliability; invalid data is automatically discarded or marked, and a re-collection or data repair mechanism is triggered, wherein data verification is performed through a lightweight machine learning algorithm or rule engine to adapt to the dynamic changes of the mining environment. The system employs several key functionalities: integrity verification checks the completeness of data fields, such as whether sensor data packets are missing timestamps or measurement value fields; outlier detection uses statistical methods or predefined rules to identify data exceeding reasonable ranges, such as temperature sensor readings exceeding the physical limits of the mining environment; and consistency verification compares logical relationships across data sources, such as whether personnel location data matches video surveillance trajectories. Once invalid data is marked, the system automatically calls backup sensors to re-acquire data or triggers a data interpolation algorithm to repair missing parts. Lightweight machine learning algorithms utilize online learning models, such as incremental decision trees, to dynamically update anomaly detection rules to match environmental changes; the rule engine performs rapid verification based on predefined logical chains, for example, if vibration sensor data suddenly increases but geological monitoring data remains unchanged, it is determined to be equipment interference noise. Specifically, the data verification step performs real-time quality checks before the data enters the preprocessing flow. Integrity verification first scans the data packet structure; if key fields are missing, a data missing marker is generated, triggering a retransmission request for the corresponding sensor. The outlier detection module uses a sliding window to statistically analyze recent data distribution, identifying data points deviating from the mean by more than three standard deviations, or matches predefined anomaly patterns using a rule engine, such as a 50% surge in gas concentration within ten seconds. The consistency verification module aligns the timestamps of multi-source data and checks logical correlations, such as whether personnel location data and wearable device heart rate data abruptly change within the same time period. If data is deemed invalid, the system automatically isolates it in a cache and prioritizes supplementing it with backup data sources; if supplementation is not possible, an interpolation algorithm is initiated to generate alternative values ​​based on historical data. Lightweight machine learning algorithms update model parameters online, such as retraining anomaly detection thresholds hourly, to address the impact of underground temperature and humidity fluctuations on sensor accuracy; the rule engine dynamically adjusts verification rules based on the mine profile, such as increasing the anomaly sensitivity of vibration data in unstable rock formations. Through the above mechanism, data quality is effectively controlled, thereby ensuring the reliability of input to subsequent risk analysis models and reducing false alarms or response delays caused by data errors.

[0094] In practical implementation, after vibration sensors deployed in the underground mining environment collect rock displacement data, the data verification module immediately initiates a real-time quality inspection process. Integrity verification first confirms whether the data packet contains the preset six-dimensional parameters; missing dimensions are automatically marked as incomplete data. Outlier detection uses the isolated forest algorithm to analyze outliers in 1000 continuously collected samples. When more than 15% of the samples in a batch deviate from the normal distribution range, the batch is determined to have a systematic anomaly. Consistency verification checks the logical relationships of the data through a rule engine; for example, the vibration amplitude value corresponding to the equipment operating status being "stopped" should not exceed 0.5 mm / s². When invalid data is detected, the system automatically discards the data segment and activates the data acquisition channel of the backup sensor. Simultaneously, it sends a data repair command to the edge computing node, using a linear interpolation algorithm to complete the data for missing time points. Through the above technical solution, this embodiment effectively solves the risk of misjudgment caused by data quality problems in traditional mine monitoring systems. The multi-dimensional real-time verification mechanism ensures that the data input into the safety early warning model has integrity and logical consistency. The functions of rapid removal and automatic repair of abnormal data significantly reduce false alarms caused by sensor failure or environmental interference. At the same time, the dynamically triggered data re-collection mechanism ensures the continuous and reliable operation of the monitoring system.

[0095] In some of the solutions described above in this embodiment, the data verification step uses fixed rules for quality checks, which is difficult to adapt to the dynamic data fluctuations of sensor networks in the mining environment caused by geological activities, equipment vibrations or extreme weather. This may lead to misjudgment of valid data or missed detection of outliers, thereby affecting the reliability of safety warnings.

[0096] This embodiment further proposes that after collecting multi-source real-time data, a data verification step is also included: real-time quality checks are performed on the collected data, including integrity verification, outlier detection, and consistency verification, to ensure data reliability; invalid data is automatically discarded or marked, and a re-collection or data repair mechanism is triggered, wherein data verification is performed through lightweight machine learning algorithms or rule engines to adapt to the dynamic changes in the mining environment.

[0097] The integrity verification is achieved by checking the missing data field rate and sampling frequency deviation. For example, a data missing flag is triggered when the vibration sensor fails to upload data for three consecutive sampling cycles. Outlier detection uses the isolated forest algorithm to analyze outliers in the sensor data. An anomaly is identified when the gas concentration reading deviates from three standard deviations of the median value of sensors in the same area. Consistency verification is achieved by checking the correlation between cross-sensor data. For example, the equipment operating current data and vibration amplitude data are matched in time series. A verification alarm is triggered when the trends of the two data show logical conflicts. The data repair mechanism includes two modes: automatic retransmission request and interpolation compensation. When UWB positioning data is abnormal, the location information of the nearest timestamp is used for linear interpolation first. The lightweight machine learning algorithm uses a micro neural network model deployed on edge computing devices. Its input layer receives the statistical features of the sensor data, and the output layer generates a quality score. The rule engine has a built-in multi-condition judgment matrix that dynamically adjusts the verification threshold according to the mine profile. For example, it automatically relaxes the fluctuation tolerance range of the humidity sensor under heavy rain weather conditions.

[0098] Specifically, the data verification step receives raw sensor data streams in real time through edge computing nodes. First, integrity checks are performed to identify missing fields due to network latency or equipment failure. Then, a lightweight machine learning model is used to extract data feature vectors, combined with an isolated forest algorithm to detect abnormal patterns. For consistency verification of multi-source data, a sliding time window is used to perform cross-dimensional correlation analysis of equipment status and environmental parameters. When a sudden increase in equipment vibration is detected without a corresponding increase in current, it is automatically marked as suspicious data. Invalid data is handled according to preset strategies: short-term, occasional anomalies trigger an automatic re-sampling mechanism, sending a retransmission command to the sensor nodes; persistent anomalies activate the data repair process, using historical data trend prediction for interpolation compensation. The rule engine dynamically adjusts the verification threshold based on the geological activity level in the mine profile. For example, when the rock strata stability parameter is below a critical value, the anomaly detection sensitivity of the vibration sensor is automatically increased. This verification mechanism, through collaborative processing between the edge and cloud, optimizes data quality in dynamic environments while ensuring real-time performance, effectively eliminating the risk of false alarms caused by sensor drift or transient interference.

[0099] In practice, after vibration sensors and gas concentration sensors deployed in the mining area collect data, real-time quality checks are performed by a rule engine deployed on edge computing nodes. Integrity verification first checks whether the number of fields in each sensor data packet conforms to the protocol specifications. Outlier detection uses a sliding window method to calculate the standard deviation threshold of the most recent 5 minutes of data; data points exceeding this threshold are marked as suspicious. The consistency verification module compares temperature sensor readings with baseline data from an environmental meteorological station; a data repair request is triggered when the difference exceeds ±3℃. When a gas sensor misses three consecutive sampling values, the data acquisition channel of the backup sensor is automatically activated, and a device maintenance request is sent to the control center via the LoRa communication protocol.

[0100] Through the above technical solution, this embodiment effectively solves the problem of inaccurate early warnings caused by unreliable data acquisition in dynamic mining environments. A multi-layered data quality verification mechanism can proactively identify and repair abnormal data during the data input stage, preventing erroneous data from entering subsequent fusion analysis processes. The adoption of a hybrid verification strategy combining rule engines and machine learning ensures both the real-time nature of basic verification and adapts to complex operating conditions through dynamic threshold adjustments, thereby significantly improving the anti-interference capability and decision-making accuracy of the safety early warning system.

[0101] Reference Figure 2 , Figure 2 This is a structural block diagram of the first embodiment of the mine operation safety early warning system of this application.

[0102] like Figure 2 As shown in the embodiments of this application, the mine operation safety early warning system includes: Data acquisition module 10 is used to collect multi-source real-time data of the mining operation environment, including geological monitoring data, equipment operation status data, environmental meteorological data and personnel operation behavior data; The portrait generation module 20 is used to match the corresponding scene factors in the preset database according to the mining operation environment to generate a target mine portrait. The dataset generation module 30 is used to determine the processing methods based on the target mine profile, and to perform preprocessing and data fusion in combination with the multi-source real-time data to generate a standardized operation safety dataset. The risk indicator module 40 is used to analyze the standardized operation safety dataset based on a machine learning model to identify potential safety risk patterns in the operation process and generate safety risk indicators. The output module 50 is used to generate a work safety early warning signal based on the comparison result between the safety risk index and the preset safety threshold; and to output the work safety early warning signal to the user interface or external execution system to trigger real-time early warning response actions, including personnel warning, equipment shutdown or emergency dispatch.

[0103] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0104] This embodiment solves the technical problems of fragmented traditional monitoring methods, rigid static threshold alarm mechanisms, and difficulties in information integration caused by data silos by multi-source data fusion, dynamic scene profiling construction, machine learning risk identification, and hierarchical early warning response mechanism. It has significant advantages in improving the accuracy and timeliness of mine safety early warning.

[0105] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0106] In addition, for technical details not described in detail in this embodiment, please refer to the method for mine operation safety early warning provided in any embodiment of this application, which will not be repeated here.

[0107] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0108] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A mine operation safety early warning method, characterized in that, The method comprises: collecting multi-source real-time data of the mine operation environment, including geological monitoring data, equipment operation state data, environmental meteorological data and personnel operation behavior data; matching corresponding scene factors in a preset database according to the mine operation environment to generate a target mine image; determining a processing means according to the target mine image to combine the multi-source real-time data for preprocessing and data fusion to generate a standardized operation safety data set; analyzing the standardized operation safety data set based on a machine learning model to identify potential safety risk patterns in the operation process and generate safety risk indicators; generating an operation safety warning signal according to the comparison result of the safety risk indicators and the preset safety threshold; and outputting the operation safety warning signal to a user interface or an external execution system to trigger real-time warning response actions, including personnel warning, equipment shutdown or emergency dispatch.

2. The method of claim 1, wherein, The collection of multi-source real-time data includes: collecting data through a sensor network deployed in the mine operation area, the sensor network including at least one of a vibration sensor, a gas concentration sensor, a temperature sensor, a humidity sensor, a high-definition camera, a UWB positioning tag and a wearable device, wherein the wearable device integrates a heart rate sensor and a motion sensor for real-time monitoring of personnel physiological state and behavior trajectory.

3. The method of claim 1, wherein, The matching of corresponding scene factors in a preset database according to the mine operation environment to generate a target mine image includes: retrieving matching scene factors from a preset database based on mine type, geological structure, historical accident data and operation stage, the scene factors including rock stability parameters, gas emission patterns, equipment aging coefficients and personnel skill levels, and generating a dynamically updated target mine image through a data fusion algorithm.

4. The method of claim 1, wherein, The preprocessing and data fusion include: performing data cleaning, denoising and normalization processing, and using a time series data fusion algorithm to integrate multi-source data into a unified format time series data set, wherein personnel operation behavior data is identified and classified through computer vision algorithms, including violation detection and fatigue state analysis.

5. The method of claim 1, wherein, The machine learning model includes a deep learning neural network, a random forest algorithm or a support vector machine, the model is trained through historical mine operation accident data, and the model parameters are updated regularly using new data to identify at least one of collapse risk, gas outburst risk, equipment failure risk or personnel violation operation risk.

6. The method of claim 1, wherein, The generation of operation safety warning signal includes: grading according to the severity of the safety risk indicators to generate different levels of warning signals, including low-level warning, medium-level alarm and high-level emergency alarm, wherein the high-level emergency alarm automatically triggers equipment shutdown, personnel evacuation and emergency dispatch protocol.

7. The method of claim 6, wherein, The triggering of real-time warning response actions includes: automatically sending warning notifications to the mobile terminals of the operating personnel, starting the underground emergency broadcasting system, controlling the automatic shutdown of related equipment or dispatching rescue resources, the response actions are dynamically adjusted based on the warning level and the target mine image.

8. The method of claim 1, wherein, The method further comprises a feedback learning step: Data of early warning response results, including response efficiency and accident mitigation effect, are collected and used to adjust machine learning model parameters, preset safety threshold or scene factor database, to achieve adaptive job safety early warning optimization.

9. The method of claim 1, wherein, After collecting the multi-source real-time data, a data verification step is further included: real-time quality inspection is performed on the collected data, including integrity check, outlier detection and consistency verification, to ensure data reliability; Invalid data is automatically discarded or marked, and triggers a re-collection or data repair mechanism, wherein data verification is performed by a lightweight machine learning algorithm or a rule engine to adapt to the dynamic changes of the mine environment.

10. A mine operation safety early warning system, characterized in that, Comprise: a data collection module for collecting multi-source real-time data of a mine working environment, the multi-source real-time data including geological monitoring data, equipment operating state data, environmental meteorological data and personnel working behavior data; an image generation module for matching corresponding scene factors in a preset database according to the mine working environment to generate a target mine image; a data set generation module for determining a processing means according to the target mine image to combine the multi-source real-time data for preprocessing and data fusion to generate a standardized job safety data set; a risk index module for analyzing the standardized job safety data set based on a machine learning model to identify potential safety risk patterns in the working process and generate safety risk indexes; an output module for generating a job safety early warning signal according to the comparison result of the safety risk indexes and a preset safety threshold; and output the job safety early warning signal to a user interface or an external execution system to trigger real-time early warning response actions, including personnel warning, equipment shutdown or emergency dispatch.

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