Intelligent safety early warning system for multi-source data fusion of mining environment

Abstract

The invention discloses an intelligent safety early warning system for multi-source data fusion of a mining environment, and relates to the technical field of mine safety monitoring and early warning. According to the system, data are collected through a multi-source sensor, a virtual sample with authenticity scores is generated by using a two-way calibration digital twin model, and a training set is expanded through progressive training; dynamically adjusting the data weight by adopting a three-layer adaptive fusion mechanism so as to predict and compensate missing information in a cross-modal manner; the method comprises the following steps: respectively constructing a special identification network for sudden and gradual change disasters, and modeling spatial propagation features by combining a graph convolutional network; a progressive threshold value is set according to the disaster evolution stage, four-stage early warning is formed, and an emergency plan is linked to achieve closed-loop response; the system also supports cross-scene deployment through feedback annotation and incremental learning optimization model and by means of knowledge management and transfer learning. According to the method, the problems of scarcity of samples, insufficient fusion robustness and low recognition accuracy are relieved, and the early warning intelligence and the self-adaptive capability are improved.

Description

Technical Field

[0001] This invention relates to the field of early warning system technology, specifically to an intelligent safety early warning system that integrates multi-source data on mining environment. Background Technology

[0002] In the fields of intelligent industry and safe production, mines, as typical high-risk work environments, are at a critical stage of transitioning from traditional manual monitoring to intelligent early warning systems. Currently, early identification and warning of major disasters such as gas outbursts and roof collapses mainly rely on the deep fusion and intelligent analysis of data collected by multi-source heterogeneous sensors underground. This has become an important technical approach to ensure the safety of workers. However, the practical application effectiveness of existing intelligent monitoring systems is still constrained by a series of inherent problems.

[0003] However, existing systems face a key bottleneck in practical applications: the extreme scarcity of major accident samples. For example, the limited number of actual cases of disasters such as gas outbursts makes it difficult to collect a sufficient number of samples to train effective deep learning models. To compensate for the lack of real data, digital twin technology is often used to generate simulation data. However, traditional digital twin modeling methods are usually one-way, and the generated simulation data often deviates significantly from the real downhole environment. On the other hand, existing technologies lack an effective mechanism for quantitatively evaluating the credibility of simulation data itself. Furthermore, when using simulation data for model training, there is a lack of effective mechanisms to distinguish and utilize data of different credibility levels, affecting training effectiveness. These shortcomings collectively result in low accuracy for rare disaster types in the trained disaster identification models, and weak generalization ability, making it difficult to adapt to complex and changing real-world conditions. Summary of the Invention

[0004] This invention proposes an intelligent safety early warning system based on multi-source data fusion for mining environments. Its objectives are: 1. To overcome the difficulties in training deep learning models due to the scarcity of real-world samples from major disasters. 2. To address the discrepancies between traditional digital twin simulation data and the real environment, as well as the lack of credibility assessment. 3. To improve the accuracy and generalization ability of disaster identification models for rare disaster types.

[0005] The technical solution of this invention is as follows:

[0006] A smart safety early warning system for mining environment that integrates multi-source data includes a data acquisition module, a two-way calibration module, a progressive training module, an adaptive fusion module, an intelligent identification module, a graded early warning module, an emergency response module, a feedback optimization module, and a knowledge management module connected in sequence.

[0007] The data acquisition module includes a network of downhole gas, pressure, microseismic and environmental sensors. After noise filtering, time synchronization and anomaly removal, the data quality is quantitatively scored and then a standardized data stream is output to the bidirectional calibration module.

[0008] The bidirectional calibration module constructs a three-dimensional digital twin model based on mine geological and ventilation parameters, generates virtual data through fluid dynamics simulation, optimizes model parameters in reverse with real data as the target value, assigns a realism score to the simulation data, and outputs virtual samples with realism scores to the progressive training module.

[0009] The progressive training module divides the training process into three stages. The first stage trains the basic network with real samples. The second stage adds highly realistic simulated samples to expand the training set. The third stage introduces simulated samples with medium ratings and sets loss weights proportional to the ratings to obtain the trained feature extraction network and recognition network, which are then output to the adaptive fusion module and intelligent recognition module.

[0010] The adaptive fusion module receives a standardized data stream and a trained feature extraction network, performs three-layer fusion of data layer, feature layer and decision layer, dynamically adjusts weights according to real-time quality score, compensates for missing data through cross-modal prediction network, and outputs fused features to intelligent recognition module.

[0011] The intelligent identification module uses a combination of convolutional and recurrent networks to capture abrupt changes for sudden disasters based on fused features. For gradual disasters, a multi-head attention encoder is used to process long time series. Graph convolutional networks are used to model spatial propagation and output disaster classification, probability, and confidence to the hierarchical early warning module.

[0012] The graded early warning module calculates the risk value by comprehensively considering disaster probability, confidence level, and on-site conditions. It sets progressive thresholds based on the incubation period, development period, and outbreak period to form a four-level early warning mechanism and provides cost-benefit analysis. It also generates a decision report and sends it to the emergency response module.

[0013] The emergency response module automatically triggers linkage measures according to the warning level in the decision report, retrieves emergency plan templates, optimizes resource scheduling paths, continuously monitors the effects, and supports response upgrades.

[0014] The feedback optimization module verifies and labels the early warning results, records the basis for manual judgment, uses active learning to screen key samples, and updates model parameters and thresholds through incremental learning.

[0015] The knowledge management module establishes a hierarchical sample library, implements version control to support historical backtracking, enables rapid deployment across scenarios through transfer learning, sets up a periodic recalibration mechanism, and maintains a dynamically updated knowledge graph.

[0016] As a further improvement to the intelligent safety early warning system that integrates multi-source data of the mining environment: the data acquisition module includes a sensor data acquisition unit and a data preprocessing unit;

[0017] The distributed sensor network of the sensor data acquisition unit includes sensors for gas concentration, roof pressure, microseismic detection, and environmental parameters. It performs protocol parsing, format conversion, and adds timestamps, sensor numbers, and spatial coordinates to the acquired data.

[0018] The data preprocessing unit sequentially performs noise filtering, time calibration, anomaly identification, and quality scoring. The quality score is calculated based on data integrity, time continuity, numerical stability, and equipment reliability, and outputs a standardized data stream containing measurement values, score values, timestamps, and sensor identifiers.

[0019] As a further improvement to the intelligent safety early warning system that integrates multi-source data of the mining environment, the bidirectional calibration module includes a digital twin modeling unit and an authenticity assessment unit.

[0020] The digital twin modeling unit constructs a three-dimensional model based on geological exploration data, tunnel structure, and ventilation system parameters, including a gas diffusion and surrounding rock stress simulation model, and generates a virtual monitoring dataset.

[0021] The authenticity assessment unit uses a hybrid intelligent inversion algorithm to optimize the permeability coefficient, elastic modulus and geostress parameters of the simulation model with real data, establishes an evaluation system for statistical distribution similarity, temporal correlation and frequency domain matching degree, and outputs the authenticity score to the progressive training module.

[0022] As a further improvement to the intelligent safety early warning system that integrates multi-source data of the mining environment: the progressive training module includes a sample organization unit and a network training unit;

[0023] The sample organization unit receives real and virtual data, and divides them into high-fidelity and medium-fidelity sample sets according to the scores. The first stage uses only real samples, the second stage mixes in high-fidelity samples, and the third stage introduces medium-fidelity samples and calculates the loss weight coefficient.

[0024] The network training unit constructs a deep neural network that includes feature extraction and disaster recognition. It is trained in stages and a weighted cross-entropy function is used in the third stage. After each stage, the performance is evaluated and calibration is triggered when the performance degrades. After training is completed, the output is sent to the fusion module and the recognition module.

[0025] As a further improvement to the intelligent safety early warning system that integrates multi-source data of the mining environment: the adaptive fusion module includes a data fusion unit and an adjustment and compensation unit;

[0026] The data fusion unit receives a standardized data stream and a feature extraction subnetwork, and performs three layers of processing: data layer weighted fusion, feature layer attention fusion, and decision layer evidence fusion.

[0027] The compensation unit monitors the real-time quality score. When the score drops below the threshold, a decay function is used to reduce the weight of the corresponding data source. A cross-modal prediction network is established. When modal data is detected to be missing, the estimated value and confidence level are output for compensation. The compensated data is returned and re-fused before being output to the intelligent recognition module.

[0028] As a further improvement to the intelligent safety early warning system that integrates multi-source data on the mining environment: the intelligent identification module includes a feature identification unit and a spatial modeling unit;

[0029] The feature recognition unit classifies disasters into short-term sudden types and long-term gradual types, and constructs a hybrid structure of convolutional neural network combined with long short-term memory network and multi-head attention encoder to extract features, which are then processed in parallel and spliced ​​together for output.

[0030] The spatial modeling unit constructs a graph structure with monitoring points as nodes and spatial adjacency or ventilation connectivity as edges. It uses a graph convolutional network to aggregate the features of adjacent nodes to model spatial diffusion. It outputs the prediction probability through global pooling and classification networks. It calculates the confidence level by combining the probability distribution entropy value, data quality score and historical accuracy, and outputs it to the hierarchical early warning module.

[0031] As a further improvement to the intelligent safety early warning system that integrates multi-source data on the mining environment, the hierarchical early warning module includes a risk assessment unit and an early warning decision unit.

[0032] The risk assessment unit receives disaster classification, probability and confidence level, and calculates risk value using a multi-factor weighting method in combination with operating parameters and historical data and normalizes it to 0 to 100. Based on the data change trend, the duration and magnitude of the anomaly, the evolution stage is determined to be the incubation period, development period or outbreak period.

[0033] The early warning decision-making unit determines four levels—blue alert, yellow warning, orange alert, and red danger—based on progressive thresholds, matches response plans, calculates cost-benefit ratios, and generates a decision report containing the early warning level, response plan, and cost analysis, which is then sent to the emergency response module.

[0034] As a further improvement to the intelligent safety early warning system that integrates multi-source data on the mining environment, the emergency response module includes a contingency plan scheduling unit and an effect evaluation unit.

[0035] The contingency plan dispatch unit triggers linkage measures based on the warning level, retrieves contingency plan templates from the emergency knowledge base using a multi-dimensional matching algorithm, selects the template with the highest comprehensive score and modifies it according to the actual situation, and uses a path planning algorithm to optimize the dispatching paths of emergency supplies, equipment, personnel and vehicles.

[0036] The effectiveness evaluation unit acquires sensor measurements, personnel evacuation progress, material arrival status, and equipment operating status. It establishes a risk control, execution efficiency, and secondary risk assessment indicator system, uses a sliding window to continuously calculate indicators, and maintains, optimizes, or upgrades the response based on changes in risk values, forming a closed loop.

[0037] As a further improvement to the intelligent safety early warning system that integrates multi-source data of the mining environment, the feedback optimization module includes a verification and annotation unit and an incremental learning unit.

[0038] The verification and labeling unit labels the early warning results as true positive, false positive, true negative and false negative and records the reasons for misjudgment and the basis for judgment. Active learning is used to filter boundary samples and conflict samples.

[0039] The incremental learning unit updates network weights through online learning, increases the loss weight and training weight for false positive and false negative samples respectively, adjusts the fusion weights according to the reasons for misjudgment, adjusts the warning threshold using ROC curve analysis, transforms manual judgment into a rule base, and updates parameters to feed back to each module.

[0040] As a further improvement to the intelligent safety early warning system that integrates multi-source data on the mining environment: the knowledge management module includes a knowledge storage unit and a transfer learning unit;

[0041] The knowledge storage unit establishes a hierarchical sample library, which is divided into three levels according to importance and representativeness: core samples are permanently stored, regular samples are periodically cleaned up, and redundant samples retain summaries. Version control is implemented to manage model parameters and support historical backtracking. A knowledge graph containing disaster types, symptom characteristics, and response measures is constructed.

[0042] The transfer learning unit achieves cross-working face deployment through fine-tuning strategies and cross-mine deployment through domain adaptation. It sets a periodic recalibration mechanism to trigger incremental training when the performance is 90% to 95% below the baseline, and continuously monitors performance indicators and plots trend curves.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. This invention effectively alleviates the training challenge of deep learning models in the field of mine safety early warning due to the scarcity of real samples from major disasters by introducing a bidirectionally calibrated digital twin model and a progressive training strategy. Specifically, the bidirectional calibration module optimizes simulation model parameters based on real monitoring data, ensuring that the generated virtual samples closely approximate the real environment in terms of statistical distribution, temporal correlation, and frequency domain features, and assigns a quantified authenticity score to each virtual sample. The progressive training module divides the training process into three stages based on this score, gradually introducing virtual samples of varying authenticity, and finally employing a weighted loss function proportional to the score for training in the final stage. This combination of techniques allows the model to effectively expand the training scale from hundreds to thousands of samples while ensuring the learning of reliable real features, thereby significantly improving the model's accuracy and generalization ability in identifying rare disaster types.

[0045] 2. This invention constructs a three-layer adaptive fusion architecture comprising a data layer, a feature layer, and a decision layer, and combines it with a cross-modal prediction network to significantly improve the robustness of multi-source heterogeneous data fusion and system availability. The system achieves millisecond-level precision in multi-source data alignment through a preprocessing process. The adaptive fusion module dynamically adjusts the fusion weights of each data source based on the real-time quality score provided by the data acquisition module, automatically reducing the weight contribution when the data quality of a certain sensor deteriorates. Simultaneously, by learning the intrinsic correlations between different monitoring modalities through the cross-modal prediction network, when missing data in a specific modality is detected, the missing value and its confidence level can be estimated based on data from other normal modalities, and compensating fusion can be performed. This design enables the system to maintain basic monitoring and early warning functions even when some sensors malfunction or data quality temporarily deteriorates, effectively avoiding the overall system performance degradation caused by single-point failures.

[0046] 3. This invention designs a dedicated intelligent identification network structure for different evolution timescales of mine disasters and introduces a graph convolutional network to model the spatial propagation process of disasters, thereby improving the accuracy and reliability of disaster identification. For short-term, sudden disasters such as gas outbursts, a hybrid structure combining convolutional neural networks and long short-term memory networks is used to capture abrupt change patterns in the data; for long-term, gradual disasters such as continuous roof subsidence, a multi-head attention encoder is used to process long-term time-series data to learn key trends in slow evolution. In addition, the spatial modeling unit constructs monitoring points as a graph structure and uses a graph convolutional network to aggregate the feature information of adjacent nodes, thus considering both the temporal evolution and spatial diffusion characteristics of disasters in the identification process, further reducing the possibility of false alarms and missed alarms.

[0047] 4. The present invention establishes a multi-factor risk quantification assessment and progressive threshold warning system based on the disaster evolution stage through the hierarchical warning module, and combines cost-benefit analysis to improve the timeliness, pertinence and scientificity of warning decisions. This module comprehensively considers the disaster occurrence probability, prediction confidence level, on-site working conditions and historical data, calculates the normalized comprehensive risk value through the multi-factor weighting method, and determines whether the disaster is in the latent period, development period or outbreak period according to the data change trend. Progressive warning thresholds are set for different evolution stages to form a four-level warning mechanism from blue attention to red danger, and corresponding response plans are matched for each warning level and accompanied by cost-benefit analysis, thus providing a clear, quantitative and economically reasonable action basis for emergency decision-making.

[0048] 5. The present invention enables the system to have the capabilities of continuous self-optimization and rapid cross-scene deployment by constructing a closed-loop emergency response mechanism from warning trigger, plan scheduling to effect evaluation, and integrating the feedback optimization and knowledge management modules. The emergency response module automatically triggers linkage measures according to the warning level, shortens the emergency response start time from the minute level to the second level, and optimizes the resource scheduling path based on the path planning algorithm to improve efficiency; the effect evaluation unit continuously monitors the disposal effect through a sliding window and dynamically adjusts the response strategy to form a disposal closed-loop. The feedback optimization module manually verifies and annotates the warning results, uses active learning to screen key samples, and online updates the model parameters and warning thresholds through incremental learning, enabling the system to have the ability of adaptive evolution and ensuring stable long-term operation performance. The knowledge management module maintains the system knowledge through hierarchical storage and version control, and realizes the rapid adaptation and deployment of the model in a new working face or new mine by means of transfer learning technology, thus significantly reducing the long-term operation and maintenance costs, promotion threshold and new scene deployment cycle of the system, and ensuring its continuous and stable performance.

[0049] In summary, through the coordination of the above-mentioned multiple core technologies, the present invention provides a complete, efficient and evolvable system solution for the intelligentization and practical application of mine safety warning technology from high-quality data acquisition and enhancement, robust feature fusion, accurate spatio-temporal identification, scientific hierarchical warning to intelligent closed-loop response and self-optimization. Brief Description of the Drawings

[0050] Figure 1 It is the architecture diagram of the intelligent safety warning system for multi-source data fusion in the mine mining environment. Detailed Embodiment

[0051] The technical solution of the present invention will be described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0052] Such as Figure 1A smart safety early warning system for mining environment, which integrates multi-source data, includes a data acquisition module, a two-way calibration module, a progressive training module, an adaptive fusion module, an intelligent identification module, a graded early warning module, an emergency response module, a feedback optimization module, and a knowledge management module connected in sequence.

[0053] The data acquisition module includes a network of downhole gas, pressure, microseismic, and environmental sensors. After noise filtering, time synchronization, and anomaly removal, the data quality is quantified and scored before outputting a standardized data stream.

[0054] The two-way calibration module constructs a three-dimensional digital twin model based on mine geological and ventilation parameters, generates virtual data through fluid dynamics simulation, and optimizes the model parameters in reverse with real data as the target value, thus assigning a realism score to the simulation data.

[0055] The progressive training module divides the training process into three stages. The first stage trains the base network with real samples. The second stage adds highly realistic simulation samples to expand the training set. The third stage introduces simulation samples with moderate ratings and sets loss weights that are proportional to the ratings.

[0056] The adaptive fusion module implements three-layer fusion of data layer, feature layer and decision layer, dynamically adjusts weights based on real-time quality score, and compensates for missing data through cross-modal prediction network.

[0057] The intelligent identification module uses a combination of convolutional and recurrent networks to capture abrupt changes in sudden disasters, and a multi-head attention encoder to process long time series for gradual disasters. It also uses graph convolutional networks to model spatial propagation and outputs disaster classification, probability, and confidence.

[0058] The graded early warning module calculates risk values ​​by comprehensively considering disaster probability, confidence level, and on-site conditions. It sets progressive thresholds based on the incubation period, development period, and outbreak period to form a four-level early warning mechanism and provides cost-benefit analysis.

[0059] The emergency response module automatically triggers coordinated measures based on the warning level, retrieves emergency plan templates, optimizes resource scheduling paths, continuously monitors the effects, and supports response upgrades.

[0060] The feedback optimization module verifies and labels the early warning results, records the basis for manual judgment, uses active learning to screen key samples, and updates model parameters and thresholds through incremental learning.

[0061] The knowledge management module establishes a hierarchical sample library, implements version control to support historical backtracking, enables rapid deployment across scenarios through transfer learning, sets up a periodic recalibration mechanism, and maintains a dynamically updated knowledge graph.

[0062] Specifically, the data acquisition module achieves simultaneous monitoring of multi-dimensional physical quantities by deploying a network of multiple types of sensors, including gas, pressure, microseismic, and environmental sensors, thus solving the problem of incomplete information from a single data source. The execution of wavelet transform noise filtering, network time protocol synchronization, statistical anomaly removal, and quality quantification scoring ensures data alignment accuracy at the millisecond level, and the output standardized data stream provides a high-quality data foundation for subsequent intelligent analysis.

[0063] A two-way calibration module constructs a 3D digital twin model to generate virtual monitoring data, solving the problem of scarce training samples. Particle swarm optimization or genetic algorithms are used to adjust simulation parameters using real data, achieving accurate mapping of the model to the actual environment. A multi-dimensional evaluation system calculates an authenticity score for each simulation data point, ensuring the credibility of supplementary samples and providing a reliable source of data for model training.

[0064] The progressive training module's three-stage training process balances the high reliability of real samples with the numerical advantage of simulated samples. The first stage uses real samples to train the underlying network and solidify its basic features. The second stage introduces high-quality simulated samples to train the mid-layer network and improve its generalization ability. The third stage sets loss weights based on realism scores to control the impact of low-quality samples, making it possible to fully train the deep network.

[0065] The adaptive fusion module implements a three-layer fusion architecture to extract complementary information from multi-source data at different levels of abstraction. The data layer dynamically calculates weights based on quality scores, the feature layer highlights key features through an attention mechanism, and the decision layer synthesizes sub-model predictions based on evidence theory. The designed cross-modal prediction network ensures that basic functions are maintained even when some sensors fail, avoiding single-point failures that lead to overall system failure and significantly enhancing system robustness.

[0066] The intelligent recognition module designs dedicated recognition networks for different timescales of mine disaster evolution. For short-term sudden disasters, a convolutional neural network combined with a long short-term memory network is used to identify abrupt change patterns. For long-term gradual disasters, a multi-head attention encoder is used to capture long-distance dependencies. A graph convolutional network is introduced to model the spatial propagation process, which makes up for the shortcomings of traditional methods that ignore spatial correlations and significantly improves the recognition accuracy.

[0067] The risk value calculation model established by the tiered early warning module comprehensively considers the probability of occurrence, prediction confidence, on-site conditions, and historical statistics, avoiding misjudgment based on a single indicator through weighted summation. Based on disaster evolution theory, it divides the disaster into three stages: latent period, development period, and outbreak period, and sets progressive thresholds to establish a four-level early warning mechanism with corresponding progressively escalating response measures. The provided cost-benefit analysis supports optimal resource allocation, improving the timeliness of early warnings and the scientific basis of decision-making.

[0068] After receiving the warning level, the emergency response module automatically triggers corresponding linkage measures, shortening the response start time to the second level. Retrieving matching plan templates from the emergency knowledge base reduces the time and errors of ad hoc formulation, and the shortest path algorithm is used to optimize resource scheduling to improve efficiency. Continuously monitor the on-site data to evaluate the disposal effect, adjust the response measures according to the change of risk value, and form a closed-loop mechanism from warning to response and then to evaluation.

[0069] The feedback optimization module requires professionals to verify and annotate the warning results and fill in the reasons for misjudgment, establishing a comparison relationship between manual supervision and system output. The active learning algorithm is used to preferentially screen boundary samples and conflict samples for manual annotation to reduce the workload. Incremental updates are performed through the online learning algorithm, and different training strategies are adopted for false positive and false negative samples to achieve the continuous optimization and adaptive evolution of the system.

[0070] The hierarchical sample library established by the knowledge management module divides samples into three levels: core, regular, and redundant according to importance and representativeness, improving storage efficiency. The implemented version control mechanism supports the rollback of historical versions and provides the ability to recover from failures. Rapid deployment across working faces and mines is achieved through transfer learning, and the set periodic recalibration mechanism ensures stable long-term operation performance. The maintained knowledge graph realizes the structured storage and continuous accumulation of knowledge.

[0071] This invention constructs a complete technical system encompassing data acquisition, sample expansion, model training, feature fusion, intelligent recognition, hierarchical early warning, emergency response, feedback optimization, and knowledge management, addressing several technical bottlenecks in existing mine safety early warning systems. Regarding data acquisition, multi-dimensional, high-quality data acquisition is achieved through a multi-type sensor network and a systematic preprocessing workflow, with data alignment accuracy reaching millisecond levels. For training samples, a novel bidirectional calibration digital twin model is introduced to generate high-quality virtual samples, and a progressive training strategy expands the effective training samples from hundreds to thousands, overcoming the small-sample dilemma faced by deep learning in the mine safety field. In feature fusion, a three-layer adaptive fusion architecture fully leverages complementary information at different levels of abstraction, and a cross-modal prediction network ensures the system maintains basic functionality even when some sensors fail, significantly improving fusion effectiveness and system availability. For disaster recognition, a dedicated network architecture designed for different evolution timescales, combined with spatial propagation modeling, significantly improves recognition accuracy and reduces false alarm and false negative rates. In early warning decision-making, a progressive threshold system based on disaster evolution stages and cost-benefit analysis enhances the timeliness, relevance, and scientific rigor of early warnings. In terms of emergency response, the automated linkage triggering mechanism reduces response start time from minutes to seconds, the path optimization algorithm improves resource scheduling efficiency, and the closed-loop evaluation mechanism ensures effective handling. Regarding system optimization, manual verification feedback and online incremental learning mechanisms enable the system to adaptively evolve, maintaining stable performance after long-term operation. In terms of system deployment, transfer learning technology and version control mechanisms significantly reduce deployment costs and cycles in new scenarios, substantially improving the system's maintainability and scalability, providing a complete solution for the intelligent and practical application of mine safety early warning technology.

[0072] like Figure 1 The data acquisition module includes a sensor data acquisition unit and a data preprocessing unit.

[0073] The distributed sensor network of the sensor data acquisition unit includes sensors for gas concentration, roof pressure, microseismic detection, and environmental parameters. It performs protocol parsing, format conversion, and adds timestamps, sensor numbers, and spatial coordinates to the collected data.

[0074] The data preprocessing unit sequentially performs noise filtering, time calibration, anomaly identification, and quality scoring. The quality score is calculated based on data integrity, time continuity, numerical stability, and equipment reliability, and outputs a standardized data stream containing measurement values, score values, timestamps, and sensor identifiers.

[0075] In this embodiment, the sensor data acquisition unit deploys a distributed network of gas concentration sensors, roof pressure sensors, microseismic sensors, and environmental parameter sensors in the downhole operating area. This enables simultaneous monitoring of multiple physical quantities such as downhole gas composition, rock stress, seismic activity, and temperature and humidity, solving the technical problem of incomplete environmental perception caused by traditional monitoring systems focusing on only a single or a few parameters. Various sensors continuously collect monitoring data according to a preset sampling cycle and transmit it in real time via wired or wireless means, ensuring the continuity and timeliness of the data stream. This avoids the limitations of long monitoring intervals and slow data updates inherent in manual periodic inspections, completing the technological transformation from decentralized single-point monitoring to multi-dimensional collaborative sensing.

[0076] Protocol parsing and format conversion were performed on the raw sensor data received from different manufacturers and with different communication protocols, which unified the representation of heterogeneous data. Each data record was given a precise timestamp, a unique sensor number, and three-dimensional spatial coordinates including the tunnel name and installation location. A spatiotemporal identification system for the data was established, and the conversion from scattered heterogeneous raw signals to unified structured data was completed.

[0077] The data preprocessing unit sequentially performs four processing steps: noise removal, time calibration, anomaly identification, and quality scoring. Noise removal uses digital filtering algorithms such as wavelet transform or Kalman filtering to eliminate electromagnetic interference and mechanical vibration noise. Time calibration maps the timestamps of each sensor to a unified global time reference through a network time protocol. Anomaly identification automatically marks abnormal measurements that exceed the normal range based on statistical distribution tests. Quality scoring calculates a comprehensive score based on data integrity, time continuity, numerical stability, and equipment reliability. The output standardized data stream includes the measured value, score value, timestamp, and sensor identifier.

[0078] The data acquisition module, through the deployment of a multi-type sensor network, achieves comprehensive perception of multiple physical fields such as gas, stress, vibration, and environment, significantly improving the integrity of downhole environmental information and the coverage of monitoring dimensions. A systematic preprocessing process eliminates electromagnetic and mechanical interference through noise filtering, achieves millisecond-level time alignment accuracy for multi-source data through network time protocols, automatically removes abnormal data caused by sensor malfunctions through statistical methods and the isolated forest algorithm, and assigns a quality score index based on completeness, continuity, stability, and reliability to each data point through a comprehensive scoring system, improving the usability of the raw data to meet the input requirements of deep learning algorithms. The output standardized data stream establishes a complete data identification system including measured values, quality scores, precise timestamps, and three-dimensional spatial coordinates, providing subsequent adaptive fusion modules with formatted, complete, and quality-controllable data input. This enables the effective implementation of deep learning-based multi-source fusion and intelligent identification, laying a solid data foundation for the accurate operation of the entire early warning system and significantly improving the system's environmental perception capabilities and data processing quality.

[0079] like Figure 1 The two-way calibration module includes a digital twin modeling unit and a realism assessment unit.

[0080] The digital twin modeling unit constructs a three-dimensional model based on geological exploration data, tunnel structure, and ventilation system parameters, including gas diffusion and surrounding rock stress simulation models, and generates a virtual monitoring dataset.

[0081] The authenticity assessment unit uses a hybrid intelligent inversion algorithm to optimize the permeability coefficient, elastic modulus and geostress parameters of the simulation model with real data, establishes an evaluation system for statistical distribution similarity, temporal correlation and frequency domain matching degree, and outputs the authenticity score to the progressive training module.

[0082] In this embodiment, the digital twin modeling unit constructs a three-dimensional digital twin model based on mine geological exploration data, roadway structural parameters, and ventilation system configuration. This model includes a gas diffusion simulation model and a surrounding rock stress simulation model, achieving a virtual mapping of the real underground physical environment and solving the problem of insufficient sample size caused by relying on actual disaster accident data for training. The gas diffusion model solves for the gas concentration distribution at each node based on fluid dynamics equations, while the surrounding rock stress model uses the finite element method to calculate the node displacement and stress fields. By changing the combination of input parameters, a virtual monitoring dataset covering normal operating conditions, potential hazards, and pre-disaster conditions is generated, providing a sufficient number of diverse supplementary samples for model training.

[0083] The authenticity assessment unit receives virtual monitoring datasets and real monitoring data. It establishes a two-way calibration mechanism between the simulation model and the real environment through a hybrid intelligent inversion algorithm, solving the problem of large deviations in simulation results caused by inaccurate parameter settings in traditional one-way simulations. Using genetic algorithms or particle swarm optimization, real data is used as target values ​​to inversely adjust physical parameters such as coal seam permeability coefficient, rock mass elastic modulus, and geostress distribution. The optimized parameters are then fed back to update the simulation model.

[0084] The established evaluation system quantifies the authenticity of virtual data from four dimensions: statistical distribution similarity, temporal correlation, frequency domain matching degree, and extreme value frequency. The authenticity score is calculated and attached to the virtual data record to complete the quantitative evaluation of the credibility of virtual samples, providing quality-labeled expanded samples for the progressive training module.

[0085] The bidirectional calibration module constructs a digital twin model based on physical equations and generates virtual monitoring data using numerical simulation methods. This expands the available training samples from hundreds to thousands or even tens of thousands, making it possible to fully train complex models such as deep convolutional networks and recurrent neural networks, and significantly improving the model's feature learning capabilities.

[0086] The specific set of inversion parameters is as follows:

[0087]

[0088] in, Let be the permeability coefficient of the surrounding rock. For elastic modulus, This represents the initial ground stress.

[0089] The objective function is defined as follows:

[0090] The goal is to minimize the combined error between real monitoring data and simulation output.

[0091]

[0092] in, For the simulation output of the gas pressure field, This provides real monitoring data of the gas pressure field. This outputs the simulation results for the surrounding rock stress field. This provides real monitoring data of the surrounding rock stress field. This is the output of the time-series simulation of gas concentration. This provides real-time monitoring data for gas concentration. , as well as All are normalized weights.

[0093] Statistical distribution similarity was calculated using the JSD method, temporal correlation was evaluated using the Pearson correlation coefficient and DTW distance, and frequency domain matching was calculated using power similarity.

[0094] The introduced two-way calibration mechanism uses a hybrid intelligent inversion algorithm to optimize the simulation model parameters using real monitoring data, so that the simulation model accurately reflects the real physical characteristics of a specific mine. The generated virtual data is highly consistent with the real data in terms of statistical characteristics, time series patterns and frequency domain characteristics, thus ensuring the credibility of the virtual samples.

[0095] The authenticity score is used to characterize the degree of consistency between monitoring data or virtual data and real operating conditions. It is an important parameter used within the system for data quality control and training scheduling. The authenticity score is expressed in numerical form, and its value range is limited to a preset interval. It is usually a continuous value between the lowest and highest confidence levels, where a larger value indicates higher authenticity and reliability of the data.

[0096] For each data sample processed, the system generates a corresponding authenticity score. This score, as an additional attribute of the sample, is stored and transmitted along with the sample data itself, enabling subsequent modules to synchronously obtain its credibility information when using the data. In addition to the overall score, the system can also retain the evaluation results of each sub-dimension internally for subsequent analysis and adjustment, but the overall authenticity score is the primary output to ensure a concise and consistent data format.

[0097] During data transmission, the authenticity score is generated by the authenticity assessment unit in the two-way calibration module. After the score is generated, it is synchronously transmitted to the progressive training module and other relevant functional modules via the system's internal data transmission interface, along with the sample data. This transmission process is automatic and does not rely on manual intervention, ensuring that the score information remains consistent across all modules of the system.

[0098] In the progressive training module, the authenticity score serves as one of the important bases for sample scheduling and training control. On the one hand, the training module manages samples hierarchically based on the authenticity score, using data with different levels of credibility for different training stages. On the other hand, the authenticity score is also used to adjust the degree of influence of samples during the training process, so that data with higher authenticity plays a greater role in updating model parameters, while data with lower authenticity only plays an auxiliary and supplementary role.

[0099] In the early warning and decision-making modules, the authenticity score is used to help determine the reliability of the identification results. When the system outputs early warning information, the authenticity score of the corresponding data can be combined to comprehensively evaluate the results, thereby improving the credibility of the early warning conclusions and providing a reference for subsequent decision-making.

[0100] The authenticity score is continuously updated as the system runs. When subsequent modules detect data deviations or misjudgments through the feedback mechanism, the relevant information is fed back to the calibration module to adjust the scoring calculation process. This allows the authenticity score to be continuously corrected as the environment changes and the system learns, thus forming a closed-loop mechanism for continuous optimization.

[0101] The established multi-dimensional authenticity evaluation system calculates a quantitative score for each piece of virtual data, enabling the progressive training module to adopt differentiated training strategies for virtual samples of different qualities based on the score value. High-authenticity samples receive greater weight, avoiding the negative impact of low-quality virtual samples. While expanding the number of samples, it ensures training quality and solves the core bottleneck of scarce training samples in the field of mine safety that restricts the application of deep learning. It also provides data support for the intelligent recognition module to adopt an advanced deep neural network architecture.

[0102] like Figure 1 The progressive training module includes a sample organization unit and a network training unit.

[0103] The sample organization unit receives real and virtual data, and divides them into high-fidelity and medium-fidelity sample sets according to the scores. The first stage uses only real samples, the second stage mixes in high-fidelity samples, and the third stage introduces medium-fidelity samples and calculates the loss weight coefficient.

[0104] The network training unit constructs a deep neural network that includes feature extraction and disaster recognition. It is trained in stages and a weighted cross-entropy function is used in the third stage. After each stage, the performance is evaluated and calibration is triggered when the performance degrades. After training is completed, the output is sent to the fusion module and the recognition module.

[0105] In this embodiment: The sample organization unit receives real monitoring data samples and virtual monitoring datasets with additional authenticity scores. By setting a scoring threshold, the virtual data is divided into high-authenticity sample sets and medium-authenticity sample sets, establishing a hierarchical management mechanism based on sample quality. This solves the problem of low-quality samples interfering with model learning caused by the mixing of samples of different quality in traditional training methods. Differentiated training sets are organized for three training stages. The first stage contains only real samples to ensure that the model learns real disaster characteristics. The second stage expands the quantity by mixing real samples with high-authenticity virtual samples while ensuring quality. The third stage introduces medium-authenticity virtual samples and calculates a loss weight coefficient proportional to the authenticity score for each sample, achieving a smooth transition from small-scale high-quality to large-scale diversification.

[0106] The network training unit constructs a deep neural network model comprising a feature extraction subnetwork and a disaster recognition subnetwork, implementing a three-stage progressive training strategy to fully leverage the role of samples of varying quality. The first stage uses real samples to train the bottom convolutional and pooling layers of the feature extraction subnetwork and fixes the parameters, ensuring the model accurately grasps the features of real data. The second stage loads the parameters from the first stage and introduces highly realistic virtual samples to expand and train the middle layers, enhancing the model's adaptability to different working conditions. The third stage uses a weighted cross-entropy loss function combined with loss weight coefficients for training. Real samples and highly realistic virtual samples contribute significantly to model optimization, while moderately realistic virtual samples increase diversity but limit negative impacts. Performance is evaluated after each stage, and an iterative calibration mechanism is triggered when metrics decline.

[0107] Samples with higher authenticity scores carry a larger weight in loss calculations, and their errors have a stronger influence on the direction of model optimization. Samples with relatively lower authenticity scores participate in training with smaller weights, only playing a supplementary and constraining role in the model within a limited scope. In this way, a positive correlation is achieved between sample credibility and the strength of training influence, avoiding mutual interference between samples of different qualities during training.

[0108] In the weighted loss mechanism employed in the third stage, the system first calculates the classification error for each sample, then adjusts the error based on the corresponding loss weights, ultimately forming the overall training loss used for backpropagation. This process does not change the model's original training objective or optimization direction; rather, by adjusting the proportion of different sample errors in the total loss, it makes the model's parameter updates more focused on real samples and highly realistic virtual samples, thereby improving the stability and reliability of the training process.

[0109] During the backpropagation phase, the loss weights directly affect the impact of sample errors on the magnitude of network parameter updates, allowing highly realistic samples to play a dominant role in gradient updates, while moderately realistic virtual samples participate in parameter adjustments with only a small margin. This mechanism ensures that the model primarily learns from highly reliable data, while simultaneously absorbing diverse information provided by virtual samples without significantly introducing noise.

[0110] Through the above design, samples of different realism levels form a clear division of labor in the third stage of training: real samples are used to consolidate the model's accurate representation of real disaster characteristics, high realism virtual samples expand the training scale and enhance the model's adaptability to complex working conditions while ensuring credibility, and medium realism virtual samples are mainly used to enrich the data distribution, while their potential uncertainty is effectively limited to a controllable range.

[0111] When the model performance evaluation results show a downward trend during training, the system will trigger a calibration mechanism to re-evaluate the authenticity score of the virtual samples and simultaneously adjust the loss weights of the corresponding samples in subsequent training. This dynamic adjustment method reduces the negative impact of abnormal samples on model training without manual intervention, further improving the overall adaptability and operational stability of the system.

[0112] By introducing a weighted loss strategy based on authenticity scoring into the progressive training module, the model can prioritize learning real and reliable data features when the number of training samples is limited, while making reasonable use of virtual samples to expand the training scale. This effectively improves the model's feature learning ability and disaster identification performance while ensuring training quality, and solves the technical problems caused by insufficient and uneven quality of training samples for deep learning models in the field of mine safety.

[0113] The three-stage progressive training strategy designed in the progressive training module realizes a progressive training path from high-quality small-scale to high-quality large-scale. In the first stage, only real samples are used to train the bottom feature extraction network. In the second stage, highly realistic virtual samples are introduced to train the middle layer network. In the third stage, moderately realistic virtual samples are added and a weighted loss function is set. This ensures that the model learns real and reliable feature representations and makes full use of virtual samples to expand the data scale, thus achieving effective training of deep neural networks under the condition of limited training samples.

[0114] The progressive training module establishes a weighted training mechanism based on authenticity scoring, which calculates a loss weight coefficient proportional to the credibility of each sample. This allows high-quality samples to play a leading role in gradient updates, while low-quality samples play only an auxiliary role. This effectively controls the negative impact of low-quality samples while expanding the training set size.

[0115] By combining high-quality virtual samples generated by the bidirectional calibration module with real samples, the effective training samples are expanded from hundreds to thousands. With the help of progressive training strategies and weighted loss functions, deep neural networks containing multi-layer convolution and recurrent structures can be fully trained, which greatly improves the model's feature learning ability and disaster identification accuracy, and solves the key technical bottleneck of insufficient training samples in deep learning applications in the field of mine safety.

[0116] like Figure 1 The adaptive fusion module includes a data fusion unit and an adjustment and compensation unit.

[0117] The data fusion unit receives a standardized data stream and a feature extraction subnetwork, and performs three layers of processing: data layer weighted fusion, feature layer attention fusion, and decision layer evidence fusion.

[0118] The compensation unit monitors the real-time quality score. When the score drops below the threshold, a decay function is used to reduce the weight of the corresponding data source. A cross-modal prediction network is established. When modal data is detected to be missing, the estimated value and confidence level are output for compensation. The compensated data is returned and re-fused before being output to the intelligent recognition module.

[0119] In this embodiment: The data fusion unit receives a standardized data stream and a feature extraction sub-network, and performs a three-layer fusion process consisting of a data layer, a feature layer, and a decision layer. This achieves deep integration of multi-source heterogeneous data and solves the problem of insufficient information utilization in traditional single-layer fusion methods. Data layer fusion assigns dynamic weight coefficients to each sensor based on its quality score for weighted fusion, overcoming the limitation of fixed-weight fusion in handling sensor performance fluctuations. Feature layer fusion automatically assigns attention weights by learning the correlation between features through an attention mechanism network. The weighted summation output of the comprehensive feature vector highlights key information and suppresses redundant information. Decision layer fusion uses evidence theory or a weighted voting method to comprehensively judge the prediction results of multiple early warning sub-models, reducing the risk of misjudgment based on a single modality.

[0120] The compensation unit receives fusion results and real-time quality scores, continuously monitors the quality change trends and fluctuations of each data source, and establishes a dynamic adjustment mechanism for fusion parameters, solving the problem that fixed fusion parameters cannot adapt to changes in sensor performance. When the quality score of a data source drops beyond a preset threshold, a new fusion weight coefficient is calculated using an exponential decay function or a piecewise linear function and fed back to the data fusion unit for updating. The established cross-modal prediction network learns the correlation between different monitoring modalities. When a missing sensor data for a certain modality is detected, the estimated value and confidence level of the missing modality are output based on other normal modal data. Compensated data is returned to the data fusion unit for re-fusion, ensuring that the system can maintain basic monitoring and early warning functions even when some sensors fail.

[0121] The adaptive fusion module's three-layer fusion architecture performs fusion processing at the data layer, feature layer, and decision layer. Data layer fusion eliminates measurement noise, feature layer fusion highlights key features, and decision layer fusion integrates multiple judgment conclusions. This multi-layer fusion fully explores complementary information at different levels of abstraction, significantly improving the accuracy and reliability of the fusion results.

[0122] The adaptive fusion module establishes an adaptive weight adjustment mechanism that dynamically calculates the fusion weight based on the real-time quality score. When the quality score decreases, the weight of the corresponding data source is automatically reduced, and when the quality score recovers, the weight is increased accordingly. This dynamic adjustment mechanism enables the fusion process to adapt to changes in data quality in real time, ensuring the stability of the fusion results.

[0123] The cross-modal prediction network introduced by the adaptive fusion module utilizes the physical correlation and statistical correlation between different monitoring modes to make intelligent inferences and compensations based on other modal data when data for a certain modality is missing. This enables the system to maintain degraded operation even when some sensors fail, avoiding complete functional failure caused by single-point failures. It significantly improves the system's fault tolerance and availability, ensuring the continuous and reliable operation of the early warning system in complex downhole environments.

[0124] The cross-modal prediction network adopts a multi-input, single-objective prediction structure. Its input consists of multiple monitoring modalities currently in normal working condition. These data have undergone uniform standardization processing before entering the network, ensuring consistency in time scale and spatial location. The network extracts the intrinsic connections between various monitoring information by comprehensively modeling the relationships between different modalities, which is used to infer the possible values ​​of missing modalities in the current state.

[0125] The network output consists of two parts: one part is the estimation result of the missing mode, which characterizes the predicted state of the mode under the current operating conditions; the other part is the confidence information corresponding to the estimation result, which reflects the reliability of the prediction result. These two types of information are generated synchronously in the same prediction process, maintaining a semantic association between the estimation result and its confidence level.

[0126] The cross-modal prediction network was trained offline before system deployment, using historical complete monitoring data and calibrated virtual monitoring data. During training, a simulated data missing scenario was constructed by selectively masking a certain monitoring modality from the complete data. The remaining modal data served as input, while the original complete data served as a reference target. This allowed the network to gradually learn the correlation patterns and trends between different monitoring modalities. In this way, the network can make reasonable inferences about missing modalities based on existing modal information during actual operation.

[0127] During system operation, the adaptive fusion module continuously monitors the data status of each monitored modality. When a modality is detected to have missing data or abnormal quality within the current fusion cycle, that modality will temporarily not participate in the fusion process directly, and a cross-modal prediction network will be triggered to perform compensation calculations. The network takes other available modality data as input, outputs the estimation results and confidence scores of the missing modality, and feeds this compensation information back to the data fusion unit.

[0128] Compensation data does not simply replace missing data; instead, it participates in the subsequent fusion process in the form of a confidence level indicator. During the fusion process, the system dynamically adjusts the participation level of compensation data based on the confidence level of the compensation results and the real-time quality score of the corresponding data source. When the confidence level is high, the compensation data has a higher weight in the fusion result; when the confidence level is low, its influence is automatically limited to a smaller range, thereby avoiding excessive interference from uncertain compensation in the fusion result.

[0129] By introducing a cross-modal prediction network and constructing a corresponding compensation loop, the adaptive fusion module can make full use of the correlation information between different monitoring modes, maintain basic monitoring and early warning capabilities even when data is incomplete, effectively avoid system function interruption caused by single mode failure, and significantly improve the system's stability, fault tolerance and continuous operation capability in complex downhole environments.

[0130] like Figure 1 The intelligent recognition module includes a feature recognition unit and a spatial modeling unit.

[0131] The feature recognition unit classifies disasters into short-term sudden types and long-term gradual types, and constructs a hybrid structure of convolutional neural network combined with long short-term memory network and multi-head attention encoder to extract features, which are then processed in parallel and spliced ​​together for output.

[0132] The spatial modeling unit constructs a graph structure with monitoring points as nodes and spatial adjacency or ventilation connectivity as edges. It uses a graph convolutional network to aggregate the features of adjacent nodes to model spatial diffusion. It outputs the prediction probability through global pooling and classification networks. It calculates the confidence level by combining the probability distribution entropy value, data quality score and historical accuracy, and outputs it to the hierarchical early warning module.

[0133] In this embodiment: The feature recognition unit receives comprehensive feature vectors and fused measurement values. Based on the differences in the time scale of mine disaster evolution, disasters are divided into two categories: short-term sudden disasters and long-term gradual disasters. Dedicated recognition networks are designed for different types, solving the problem of unsatisfactory recognition results caused by the traditional method using a uniform model to handle all disaster types. For short-term sudden disasters such as gas outbursts and roof collapses, a hybrid structure combining convolutional neural networks and long short-term memory networks is constructed to extract local temporal features and mutation patterns, identifying abrupt fluctuations and abnormal mutation signals in the monitoring data. For long-term gradual disasters such as gas exceedances and continuous roof subsidence, a multi-head attention encoder is constructed to process monitoring data over a longer time span, automatically learning key turning points and slow changing trends in the evolution process.

[0134] The spatial modeling unit receives feature representation vectors and constructs a graph structure with sensor monitoring points as nodes and spatial adjacency or ventilation connectivity as edges, mapping the underground roadway network into a graph topology. This solves the problem of traditional methods focusing only on single-point temporal features while neglecting spatial correlations. A graph convolutional network is used to aggregate the feature information of each node and its neighboring nodes, modeling the spatial propagation and diffusion process of disasters and improving the ability to identify spatially correlated disasters. Global pooling is used to aggregate all node features into a graph-level global feature representation, which is then output as a predicted probability for each disaster type via a multi-layer fully connected classification network. The prediction confidence is calculated by comprehensively considering the probability distribution entropy, input data quality score, and historical model accuracy, providing comprehensive risk assessment information for scientific decision-making.

[0135] The spatial modeling unit treats underground sensor monitoring points as an interconnected whole system. By constructing a spatial correlation model that reflects the actual tunnel structure and ventilation relationship, it characterizes the propagation characteristics of disasters. Each monitoring point is abstracted as a node in a network. Whether a relationship is established between nodes is determined comprehensively based on their relative position in the underground tunnel and the existence of a clear ventilation connection path.

[0136] When two monitoring points are located in adjacent roadways or the same working area, and there is no obvious structural isolation between them, they are considered to be directly related in space. This relationship reflects the possibility of disaster spreading within a local area. For monitoring points that are not spatially adjacent but are in the same ventilation circuit or connected by airflow channels, a similar relationship is established to describe the objective situation of disaster propagation along the ventilation path. By simultaneously considering both spatial location and ventilation conditions, the constructed spatial relationship can more closely reflect actual underground working conditions.

[0137] The correlation strength between different monitoring points is differentiated based on factors such as spatial distance, ventilation path length, and ventilation condition stability. Monitoring points that are spatially close or have a more direct ventilation connection have a relatively high correlation; monitoring points that are farther away or have a weaker ventilation influence have a correspondingly lower correlation. In this way, the spatial modeling results reflect both the overall structure and retain differentiated characteristics.

[0138] The spatial modeling process employs a graph structure approach based on adjacency relationships for feature aggregation. This ensures that each monitoring point, when updating its status, considers not only its own monitoring information but also the changes in surrounding related monitoring points. Through multi-level feature aggregation, the spatial perception range is gradually expanded, enabling the feature representation of monitoring points to reflect the propagation and impact of disasters in local areas and over a wider area. While ensuring the effectiveness of spatial modeling, the network size and computational complexity are kept within a reasonable range to meet the real-time requirements of mine monitoring scenarios.

[0139] In terms of feature fusion, feature extraction in the temporal dimension and spatial relationship modeling are not performed simultaneously, but rather in stages and collaboratively. First, the feature recognition unit extracts temporal feature representations that reflect the disaster evolution process from the monitoring data. Subsequently, these temporal features are introduced as node attributes into the spatial modeling process, ensuring that each monitoring point possesses temporal evolution information during spatial correlation modeling. Through feature interaction between adjacent monitoring points, temporal features are further supplemented and constrained in the spatial dimension, achieving an organic combination of temporal variation patterns and spatial propagation characteristics.

[0140] After spatial correlation modeling, the characteristics of each monitoring point not only reflect the changes in the state of a single point, but also include its spatial influence relationship within the overall underground structure. Finally, by summarizing and processing the characteristics of all monitoring points, an overall feature representation is formed for disaster type identification and risk assessment, enabling the identification results to comprehensively reflect the temporal process and spatial diffusion characteristics of disaster occurrence and development.

[0141] The intelligent identification module designs dedicated identification networks based on the differences in disaster evolution time scales. For short-term sudden disasters, a hybrid structure combining convolutional neural networks and long short-term memory networks is used to extract mutation features. For long-term gradual disasters, a multi-head attention encoder is used to learn the evolution trend. The precise matching between the dedicated network architecture and the disaster characteristics significantly improves the identification accuracy of various types of disasters and reduces the false alarm rate and missed alarm rate.

[0142] By introducing graph convolutional networks to model the graph structure of underground roadway networks, and by aggregating monitoring information from adjacent nodes to model the spatial diffusion pattern of disasters, the identification process considers both temporal evolution and spatial propagation dimensions simultaneously, which is more in line with the objective laws of disaster occurrence and development, and improves the ability to identify spatially related disasters and the accuracy of predicting their impact range.

[0143] The output prediction confidence level comprehensively considers three dimensions: probability distribution entropy, data quality score, and historical accuracy. It provides decision-makers with a quantitative indicator of the reliability of the prediction results, enabling them to take differentiated response measures based on the confidence level. This improves the scientificity and rationality of emergency decision-making and provides a reliable basis for the graded early warning module to formulate accurate early warning plans.

[0144] like Figure 1 The tiered early warning module includes a risk assessment unit and an early warning decision-making unit.

[0145] The risk assessment unit receives disaster classification, probability, and confidence level, and calculates the risk value using a multi-factor weighted method in combination with operating parameters and historical data, normalizing it to 0 to 100. Based on the data change trend, duration and magnitude of the anomaly, it determines the evolution stage as the incubation period, development period or outbreak period.

[0146] The early warning decision-making unit determines four levels—blue alert, yellow warning, orange alert, and red danger—based on progressive thresholds, matches response plans, calculates cost-benefit ratios, and generates a decision report containing the early warning level, response plan, and cost analysis, which is then sent to the emergency response module.

[0147] In this embodiment, the risk assessment unit receives disaster classification results, occurrence probability, and prediction confidence level. Combining these with on-site operating parameters and historical statistical data, it calculates the comprehensive risk value using a multi-factor weighted scoring method. This addresses the problem of traditional methods relying solely on a single indicator, leading to a one-sided assessment. The included scoring factors include disaster occurrence probability, prediction confidence level, disaster severity, personnel exposure level, and emergency response capability. These five factors comprehensively characterize the disaster risk situation from different dimensions. The comprehensive risk value is obtained by weighted summation and normalization to a value range of 0 to 100. Simultaneously, the unit analyzes the changing trends, duration, and magnitude of monitoring data to comprehensively determine whether the disaster evolution stage is latent, development, or outbreak, consistent with the objective laws governing disaster occurrence.

[0148] The probability of disaster occurrence is provided by the intelligent identification module, reflecting the likelihood of a disaster occurring under current conditions. Prediction confidence describes the reliability of the identification results, reflecting the stability of the model's judgment. Disaster severity is assessed by combining the disaster type and its potential impact on personnel, equipment damage, and production, reflecting the intensity of the consequences should a disaster occur. Personnel exposure is comprehensively judged by analyzing the distribution of personnel within the work area and the relationship between personnel and potential hazards, measuring the degree of risk faced by personnel. Emergency response capability is assessed by combining the configuration of on-site emergency facilities, the completeness of the response procedures, and the historical effectiveness of responses, reflecting the ability to control and mitigate risks after a disaster occurs.

[0149] Before being incorporated into the comprehensive assessment, all the aforementioned factors are processed according to a unified evaluation standard to ensure they can be compared and integrated within the same framework. Based on this, the risk assessment unit performs a comprehensive calculation on each factor according to preset weights, generating a risk score that reflects the current overall risk situation.

[0150] The weights of each factor are not fixed but determined based on mine safety management experience and historical operational data. During the initial system operation phase, weights can be configured according to existing management standards and expert experience to ensure that the assessment results meet actual safety management needs. As the system operates for longer periods, the weights are adjusted appropriately by comparing historical warning results with actual events, making the risk assessment results closer to the true risk level. Weights can also be differentiated under different disaster types or operating conditions to improve the adaptability and relevance of the assessment model.

[0151] After completing the comprehensive scoring, the risk assessment unit performs a unified normalization process on the scoring results, mapping the final risk value to a fixed numerical range. This processing method determines a reasonable value range by referring to historical data and maps the current score proportionally to a uniform scale, making the risk value highly readable and comparable, facilitating subsequent classification of warning levels and threshold determination.

[0152] Through the aforementioned multi-factor weighting and normalization mechanism, the risk assessment unit can provide a comprehensive, stable, and intuitive quantitative expression of disaster risk, providing a reliable basis for the graded early warning module to make reasonable decisions based on risk level and evolution stage.

[0153] The early warning decision-making unit receives the comprehensive risk value and evolution stage, and maps the risk value to four warning levels according to a progressive threshold system, solving the problem that traditional fixed threshold early warnings cannot adapt to the dynamic evolution of disasters. Different threshold ranges are set for the three evolution stages: latent period, development period, and outbreak period. Blue alert corresponds to low risk in the latent period and suggests strengthening monitoring; yellow alert corresponds to medium risk in the development period and suggests adjusting parameters; orange alert corresponds to high risk in the development period and suggests stopping operations; and red danger corresponds to extremely high risk in the outbreak period and suggests emergency evacuation. Based on the warning level, a response plan is matched and the cost-benefit ratio is calculated. An early warning decision report, including the warning level, recommended plan, execution time limit, and cost analysis, is generated and sent to the emergency response module.

[0154] The multi-factor weighted scoring method established by the graded early warning module comprehensively considers five dimensions: disaster probability, confidence level, severity, personnel exposure, and emergency response capability. By calculating the comprehensive risk value through weighted summation, it achieves a comprehensive quantitative assessment of disaster risk, significantly improving the accuracy and scientific nature of risk judgment.

[0155] The introduction of disaster evolution stage determination divides the evolution process into latent period, development period, and outbreak period based on the changing trend of monitoring data, the duration of abnormality, and the magnitude of abnormality. Progressive thresholds are set for different stages, enabling early warning decisions to adapt to the dynamic development of disasters and improving the timeliness and pertinence of early warnings.

[0156] The system automatically matches response plans based on the warning level and calculates the economic rationality of the plans through cost-benefit analysis. This provides decision-makers with complete decision support, including specific measures, execution timelines, and economic analysis. Under the condition of limited emergency resources, it achieves optimal resource allocation, improves the scientific nature and execution efficiency of emergency decision-making, and significantly shortens the decision-making time from risk identification to response implementation.

[0157] like Figure 1 The emergency response module includes a contingency plan scheduling unit and an effectiveness evaluation unit.

[0158] The contingency plan dispatch unit triggers linkage measures based on the warning level, retrieves contingency plan templates from the emergency knowledge base using a multi-dimensional matching algorithm, selects the template with the highest comprehensive score and modifies it according to the actual situation, and uses a path planning algorithm to optimize the dispatching routes of emergency supplies, equipment, personnel and vehicles.

[0159] The effectiveness evaluation unit acquires sensor measurements, personnel evacuation progress, material arrival status, and equipment operating status. It establishes a risk control, execution efficiency, and secondary risk assessment indicator system, uses a sliding window to continuously calculate indicators, and maintains, optimizes, or upgrades the response based on changes in risk values, forming a closed loop.

[0160] In this embodiment: the contingency plan dispatch unit receives early warning decision reports and automatically triggers corresponding linkage measures according to the early warning level. A blue alert triggers encrypted monitoring and personnel reminders; a yellow alert triggers on-site inspections and parameter adjustments; an orange alert triggers work stoppage and personnel evacuation; and a red alert triggers emergency evacuation and area closure. This automated linkage mechanism shortens the response initiation time from several minutes of traditional manual decision-making to several seconds, solving the problem of response delays caused by reliance on manual decision-making in traditional emergency responses. Emergency plan templates are retrieved from the emergency knowledge base using a multi-dimensional matching algorithm. Matching dimensions include disaster type consistency, operational condition similarity, resource availability, and historical effect evaluation. The template with the highest comprehensive score is selected as the basic plan, and parameters are adjusted according to the current specific situation. The shortest path algorithm is used to optimize the emergency resource dispatch path.

[0161] The effectiveness evaluation unit receives emergency plans and dispatch schemes, and acquires sensor measurements, personnel evacuation progress, material arrival status, and equipment operating status in real time from the data acquisition module. This establishes a comprehensive monitoring mechanism for the emergency response process, addressing the problem of untimely adjustments to response measures due to the lack of process monitoring and effectiveness evaluation in traditional emergency management. The established evaluation index system includes risk control effectiveness, response execution efficiency, and secondary risk indicators. A sliding time window technique is used to continuously calculate the values ​​of each indicator and track the dynamic effect of the emergency response in real time. When the risk value continuously decreases to a safe threshold, the response is deemed effective, and the current measures are maintained. When the risk value remains stable, the plan is optimized. When the risk value increases or secondary risks exceed limits, a response escalation mechanism is triggered and feedback is sent to the plan dispatch unit, forming a closed-loop control mechanism.

[0162] The emergency response module's automatic linkage triggering mechanism directly activates corresponding response measures based on the warning level, reducing the response activation time to the second level, significantly improving the timeliness of emergency response, and providing technical support for gaining valuable handling time.

[0163] By storing a large amount of historical emergency plans and response experience in an emergency knowledge base, and employing a multi-dimensional matching algorithm to quickly retrieve the most suitable plan template and adjust parameters according to actual conditions, the quality of emergency plans is significantly improved, ensuring both scientific rigor and completeness while adapting to the specific characteristics of each working situation. Furthermore, a path planning algorithm is used to optimize emergency resource scheduling, minimizing scheduling costs while meeting time constraints, thus achieving efficient allocation of emergency resources.

[0164] The emergency response module establishes an effectiveness evaluation system that continuously assesses response effectiveness through real-time monitoring of multi-dimensional indicators. When the effect is not ideal, it automatically triggers plan optimization or response upgrade, forming a closed-loop management mechanism from early warning to response to evaluation. This ensures the dynamic adaptability and ultimate effectiveness of emergency response, and significantly improves the success rate of disaster response and the level of intelligence in emergency management.

[0165] like Figure 1 The feedback optimization module includes a validation annotation unit and an incremental learning unit.

[0166] The verification and labeling unit labels the early warning results as true positive, false positive, true negative, and false negative, and records the reasons for misjudgment and the basis for judgment. It also uses active learning to filter boundary samples and conflict samples.

[0167] The incremental learning unit updates network weights through online learning, increases the loss weight and training weight for false positive and false negative samples respectively, adjusts the fusion weights according to the reasons for misjudgment, adjusts the warning threshold using ROC curve analysis, transforms manual judgment into a rule base, and updates parameters to feed back to each module.

[0168] In this embodiment: the verification and annotation unit receives the early warning results, and a human compares the early warning results with the actual occurrence within a specified observation window, labeling the early warning results as four categories: true positive, false positive, true negative, or false negative. This establishes a comparison between human verification and system output, solving the problem of uncorrectable errors caused by the lack of verification feedback mechanisms in traditional systems. For false positive and false negative cases, a detailed analysis of the reasons for misjudgment is required. When there is a difference between human judgment and system prediction, the basis for judgment is recorded, transforming implicit human knowledge into recordable and usable explicit knowledge. An active learning algorithm is used to calculate the uncertainty score and representativeness score of each sample, prioritizing boundary samples near the decision boundary and conflict samples with prediction conflicts for submission for human annotation, reducing the annotation workload and increasing the value of the labeled data.

[0169] Boundary samples refer to samples where the system's prediction results are at the critical point between different judgment outcomes when making risk assessments. These samples typically exhibit unstable judgment results, and the system lacks a clear bias towards their respective risk levels, reflecting insufficient discriminative ability of the model under such conditions or feature combinations. Manually verifying boundary samples helps the system correct its judgment boundaries and improve recognition accuracy.

[0170] Conflict samples refer to those where the system's warning conclusions differ significantly from manual verification results or subsequent actual situations. These samples often reveal problems such as insufficient model understanding of specific scenarios or inappropriate rule application, serving as crucial evidence for locating system biases and improving judgment logic. In-depth analysis of conflict samples can provide clear direction for subsequent parameter adjustments.

[0171] During the sample selection process, the system comprehensively evaluates the judgment stability and typicality of each sample. Judgment stability reflects the consistency of the system's prediction results for that sample; when the prediction results are close to the warning threshold or show significant fluctuations under similar conditions, the sample is considered to have high uncertainty. Typicality measures the representativeness of the sample in recent data, reflecting a common type of working condition or risk pattern. By comprehensively considering these two factors, limited manual annotation resources are prioritized for the samples with the highest learning value, avoiding invalid or duplicate annotations.

[0172] After completing the verification annotation and misjudgment analysis, the incremental learning unit makes targeted adjustments to the system parameters based on different types of misjudgments. When frequent false positives are detected, the system appropriately reduces its sensitivity to relevant information sources or features; when false negatives occur, it correspondingly strengthens its focus on and learning intensity of key features. These adjustments mainly affect the dynamic weight configuration in the adaptive fusion module, enabling the system to reallocate the influence ratio of different information sources and features based on actual operating results.

[0173] The parameter update process employs a unified feedback mechanism. After completing a full verification, analysis, and adjustment cycle, the incremental learning unit distributes the new parameter configuration to the corresponding module via the system management interface, and the new configuration takes effect within a preset update period. The entire update process does not interrupt system operation or affect the continuous execution of the current early warning task, thus achieving smooth iteration.

[0174] The incremental learning unit receives the validation labeled dataset and knowledge records, and uses an online learning algorithm to incrementally update the deep neural network. A small learning rate is set to avoid excessive perturbation of the original model parameters by new data, maintaining the stability of learned knowledge while absorbing new knowledge. This solves the problem of high time costs caused by the need for full retraining on traditional offline training. Differentiated training strategies are adopted for different types of errors: increasing the loss weight for false positives reduces sensitivity and false alarms, while increasing the training weight for false negatives improves recognition ability. Based on the cause of misjudgment, Bayesian optimization or grid search is used to adjust the fusion weight coefficients and attention weight parameters. ROC curves are plotted based on the true positive rate and false positive rate to select the optimal threshold. The manual judgment criteria are transformed into a rule base, and contextual features are extracted.

[0175] The feedback optimization module's manual verification and annotation mechanism requires manual verification of the authenticity of the warning results and recording the reasons for misjudgments. This enables timely detection of system errors and provides clear directions for improvement, significantly enhancing the system's reliability and maintainability.

[0176] An online incremental learning algorithm is employed, using validation labeled data to update the model in small steps. This continuously improves model performance without requiring full data and large-scale retraining, enabling rapid iteration and low-cost optimization. Differentiated strategies are adopted to reduce sensitivity and improve recognition ability for false positives and false negatives, respectively, accurately correcting specific biases in the model and improving the targeting and effectiveness of optimization.

[0177] By transforming human judgment criteria into rule bases and features, implicit human knowledge is made explicit and systematized, enabling deep integration of artificial intelligence and human experience. This constructs a complete feedback optimization loop from early warning verification, cause analysis, parameter optimization to knowledge accumulation, giving the system continuous learning and adaptive evolution capabilities, and significantly improving the system's intelligence level and long-term operational stability.

[0178] like Figure 1 The knowledge management module includes a knowledge storage unit and a transfer learning unit.

[0179] The knowledge storage unit establishes a hierarchical sample library, which is divided into three levels according to importance and representativeness: core samples are permanently stored, regular samples are periodically cleaned up, and redundant samples retain summaries. Version control is implemented to manage model parameters and support historical backtracking. A knowledge graph containing disaster types, symptom characteristics, and response measures is constructed.

[0180] The transfer learning unit achieves cross-working face deployment through fine-tuning strategies and cross-mine deployment through domain adaptation. It sets a periodic recalibration mechanism to trigger incremental training when the performance is 90% to 95% below the baseline, and continuously monitors performance indicators and plots trend curves.

[0181] In this embodiment, the knowledge storage unit establishes a hierarchical sample library to store original monitoring data, feature data, labeled samples, and case samples. Based on importance and representativeness scores, the samples are divided into three levels: core samples, regular samples, and redundant samples. Core samples, including rare disaster cases and typical feature samples, are permanently stored. Regular samples are periodically cleaned up to free up storage space, and redundant samples only retain statistical summaries. This hierarchical storage strategy effectively controls storage costs while ensuring that key data is not lost, solving the problems of wasted storage resources and low retrieval efficiency caused by the indiscriminate storage of all data in traditional systems. The implemented model version control mechanism uses a three-stage numbering system to uniformly manage model weight parameters, fusion parameters, and early warning thresholds, supporting historical version queries and backtracking, ensuring the stability of system operation.

[0182] The transfer learning unit selects a stable base model from the model repository and, through a fine-tuning strategy, maintains the weights of the underlying feature extraction network while adjusting only the weights of the top-level classification network to achieve cross-working-face deployment. This avoids the problem of needing a large amount of data and time to train a model from scratch in a new working face, significantly reducing system deployment costs and cycle time. A domain adaptation strategy is used to introduce a domain discriminator for adversarial training to learn domain-invariant features, enabling cross-mine deployment and overcoming the performance degradation caused by differences in geological conditions and processes between different mines. A periodic recalibration mechanism automatically triggers incremental training when key indicators fall below 90% to 95% of the baseline performance, continuously monitoring five performance indicators: early warning accuracy, true positive rate, false positive rate, response time, and resource utilization, and plotting trend curves.

[0183] The knowledge management module establishes a hierarchical sample library that implements differentiated storage strategies based on the importance and representativeness of the samples. Core samples are permanently stored to ensure that key knowledge is not lost, regular samples are cleaned up periodically to release storage space, and redundant samples are only retained as summaries to save storage resources. This significantly reduces storage costs and improves retrieval efficiency while ensuring data integrity.

[0184] The implemented model version control mechanism establishes a version record for each parameter update and supports historical version rollback. When a problem occurs in a new version, it can be immediately restored to a stable version, ensuring the continuity and reliability of system operation. The introduced transfer learning technology utilizes existing models to quickly deploy in new scenarios through fine-tuning and domain adaptation strategies. Adaptation can be completed with only a small amount of labeled data, shortening the deployment cycle from months to weeks or even days, significantly reducing deployment costs and accelerating the promotion process.

[0185] The established periodic recalibration mechanism and performance monitoring mechanism continuously track key performance indicators. When the performance drops to the threshold, incremental training is automatically triggered or an alarm is issued to ensure that the system always maintains a good operating state, which greatly improves the long-term availability and maintainability of the system.

[0186] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0187] It should be noted that, as will be apparent to those skilled in the art, the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics thereof. The scope of the present invention is defined by the claims rather than the foregoing description.

Claims

1. An intelligent safety early warning system for mining environment based on multi-source data fusion, characterized in that: It includes a data acquisition module, a two-way calibration module, a progressive training module, an adaptive fusion module, an intelligent recognition module, a graded early warning module, an emergency response module, a feedback optimization module, and a knowledge management module, which are connected in sequence. The data acquisition module includes a network of downhole gas, pressure, microseismic and environmental sensors. After noise filtering, time synchronization and anomaly removal, the data quality is quantitatively scored and then a standardized data stream is output to the bidirectional calibration module. The bidirectional calibration module constructs a three-dimensional digital twin model based on mine geological and ventilation parameters, generates virtual data through fluid dynamics simulation, optimizes model parameters in reverse with real data as the target value, assigns a realism score to the simulation data, and outputs virtual samples with realism scores to the progressive training module. The progressive training module divides the training process into three stages. The first stage trains the basic network with real samples. The second stage adds highly realistic simulated samples to expand the training set. The third stage introduces simulated samples with medium ratings and sets loss weights proportional to the ratings to obtain the trained feature extraction network and recognition network, which are then output to the adaptive fusion module and intelligent recognition module. The adaptive fusion module receives a standardized data stream and a trained feature extraction network, performs three-layer fusion of data layer, feature layer and decision layer, dynamically adjusts weights according to real-time quality score, compensates for missing data through cross-modal prediction network, and outputs fused features to intelligent recognition module. The intelligent identification module uses a combination of convolutional and recurrent networks to capture abrupt changes for sudden disasters based on fused features. For gradual disasters, a multi-head attention encoder is used to process long time series. Graph convolutional networks are used to model spatial propagation and output disaster classification, probability, and confidence to the hierarchical early warning module. The graded early warning module calculates the risk value by comprehensively considering disaster probability, confidence level, and on-site conditions. It sets progressive thresholds based on the incubation period, development period, and outbreak period to form a four-level early warning mechanism and provides cost-benefit analysis. It also generates a decision report and sends it to the emergency response module. The emergency response module automatically triggers linkage measures according to the warning level in the decision report, retrieves emergency plan templates, optimizes resource scheduling paths, continuously monitors the effects, and supports response upgrades. The feedback optimization module verifies and labels the early warning results, records the basis for manual judgment, uses active learning to screen key samples, and updates model parameters and thresholds through incremental learning. The knowledge management module establishes a hierarchical sample library, implements version control to support historical backtracking, enables rapid deployment across scenarios through transfer learning, sets up a periodic recalibration mechanism, and maintains a dynamically updated knowledge graph.

2. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The data acquisition module includes a sensor data acquisition unit and a data preprocessing unit; The distributed sensor network of the sensor data acquisition unit includes sensors for gas concentration, roof pressure, microseismic detection, and environmental parameters. It performs protocol parsing, format conversion, and adds timestamps, sensor numbers, and spatial coordinates to the acquired data. The data preprocessing unit sequentially performs noise filtering, time calibration, anomaly identification, and quality scoring. The quality score is calculated based on data integrity, time continuity, numerical stability, and equipment reliability, and outputs a standardized data stream containing measurement values, score values, timestamps, and sensor identifiers.

3. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The bidirectional calibration module includes a digital twin modeling unit and an authenticity assessment unit; The digital twin modeling unit constructs a three-dimensional model based on geological exploration data, tunnel structure, and ventilation system parameters, including a gas diffusion and surrounding rock stress simulation model, and generates a virtual monitoring dataset. The authenticity assessment unit uses a hybrid intelligent inversion algorithm to optimize the permeability coefficient, elastic modulus and geostress parameters of the simulation model with real data, establishes an evaluation system for statistical distribution similarity, temporal correlation and frequency domain matching degree, and outputs the authenticity score to the progressive training module.

4. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The progressive training module includes a sample organization unit and a network training unit; The sample organization unit receives real and virtual data, and divides them into high-fidelity and medium-fidelity sample sets according to the scores. The first stage uses only real samples, the second stage mixes in high-fidelity samples, and the third stage introduces medium-fidelity samples and calculates the loss weight coefficient. The network training unit constructs a deep neural network that includes feature extraction and disaster recognition. It is trained in stages and a weighted cross-entropy function is used in the third stage. After each stage, the performance is evaluated and calibration is triggered when the performance degrades. After training is completed, the output is sent to the fusion module and the recognition module.

5. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The adaptive fusion module includes a data fusion unit and an adjustment and compensation unit; The data fusion unit receives a standardized data stream and a feature extraction subnetwork, and performs three layers of processing: data layer weighted fusion, feature layer attention fusion, and decision layer evidence fusion. The compensation unit monitors the real-time quality score. When the score drops below the threshold, a decay function is used to reduce the weight of the corresponding data source. A cross-modal prediction network is established. When modal data is detected to be missing, the estimated value and confidence level are output for compensation. The compensated data is returned and re-fused before being output to the intelligent recognition module.

6. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The intelligent recognition module includes a feature recognition unit and a spatial modeling unit; The feature recognition unit classifies disasters into short-term sudden types and long-term gradual types, and constructs a hybrid structure of convolutional neural network combined with long short-term memory network and multi-head attention encoder to extract features, which are then processed in parallel and spliced ​​together for output. The spatial modeling unit constructs a graph structure with monitoring points as nodes and spatial adjacency or ventilation connectivity as edges. It uses a graph convolutional network to aggregate the features of adjacent nodes to model spatial diffusion. It outputs the prediction probability through global pooling and classification networks. It calculates the confidence level by combining the probability distribution entropy value, data quality score and historical accuracy, and outputs it to the hierarchical early warning module.

7. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The hierarchical early warning module includes a risk assessment unit and an early warning decision unit; The risk assessment unit receives disaster classification, probability and confidence level, and calculates risk value using a multi-factor weighting method in combination with operating parameters and historical data and normalizes it to 0 to 100. Based on the data change trend, the duration and magnitude of the anomaly, the evolution stage is determined to be the incubation period, development period or outbreak period. The early warning decision-making unit determines four levels—blue alert, yellow warning, orange alert, and red danger—based on progressive thresholds, matches response plans, calculates cost-benefit ratios, and generates a decision report containing the early warning level, response plan, and cost analysis, which is then sent to the emergency response module.

8. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The emergency response module includes a contingency plan scheduling unit and an effectiveness evaluation unit; The contingency plan dispatch unit triggers linkage measures based on the warning level, retrieves contingency plan templates from the emergency knowledge base using a multi-dimensional matching algorithm, selects the template with the highest comprehensive score and modifies it according to the actual situation, and uses a path planning algorithm to optimize the dispatching paths of emergency supplies, equipment, personnel and vehicles. The effectiveness evaluation unit acquires sensor measurements, personnel evacuation progress, material arrival status, and equipment operating status. It establishes a risk control, execution efficiency, and secondary risk assessment indicator system, uses a sliding window to continuously calculate indicators, and maintains, optimizes, or upgrades the response based on changes in risk values, forming a closed loop.

9. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The feedback optimization module includes a verification annotation unit and an incremental learning unit; The verification and labeling unit labels the early warning results as true positive, false positive, true negative and false negative and records the reasons for misjudgment and the basis for judgment. Active learning is used to filter boundary samples and conflict samples. The incremental learning unit updates network weights through online learning, increases the loss weight and training weight for false positive and false negative samples respectively, adjusts the fusion weights according to the reasons for misjudgment, adjusts the warning threshold using ROC curve analysis, transforms manual judgment into a rule base, and updates parameters to feed back to each module.

10. The intelligent safety early warning system for multi-source data fusion in mining environment as described in claim 1, characterized in that: The knowledge management module includes a knowledge storage unit and a transfer learning unit; The knowledge storage unit establishes a hierarchical sample library, which is divided into three levels according to importance and representativeness: core samples are permanently stored, regular samples are periodically cleaned up, and redundant samples retain summaries. Version control is implemented to manage model parameters and support historical backtracking. A knowledge graph containing disaster types, symptom characteristics, and response measures is constructed. The transfer learning unit achieves cross-working face deployment through fine-tuning strategies and cross-mine deployment through domain adaptation. It sets a periodic recalibration mechanism to trigger incremental training when the performance is 90% to 95% below the baseline, and continuously monitors performance indicators and plots trend curves.

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