Mine major hazard source intelligent monitoring and disaster chain reaction prevention method and system

Abstract

The invention relates to the technical field of mine safety, in particular to a mine major hazard source intelligent monitoring and disaster chain reaction prevention method and system, and the method comprises the steps: obtaining mine multi-dimensional monitoring data and historical disaster data; based on the multi-dimensional monitoring data and the historical disaster data, constructing a multi-dimensional hazard source dynamic topological graph; generating a hazard source state matrix according to the multi-dimensional hazard source dynamic topological graph; based on the dangerous source state matrix, dynamic topological features are extracted; establishing a nonlinear power system model according to the dynamic topological characteristics; chaos prediction and control are carried out based on a nonlinear power system model; and outputting disaster chain reaction early warning information and an intervention strategy, ingeniously introducing the concepts of group theory and topology into the field of mine safety analysis by adopting a multi-dimensional hazard source dynamic topological graph construction method, considering the characteristics of a single hazard source, and describing a complex relationship network between hazard sources. Therefore, more comprehensive and accurate basic data are provided for subsequent analysis and prediction.

Description

Technical Field

[0001] The present invention relates to the field of mine safety technology, and more specifically, to a method and system for intelligent monitoring of major mine hazard sources and disaster chain reaction prevention. Background Art

[0002] With the continuous advancement of mining technology and the continuous expansion of mine scale, modern mines, especially large open-pit metal mines, are facing increasingly complex safety challenges. These challenges mainly stem from the complex interaction of multiple potential danger sources, such as slope instability, groundwater outburst, blasting vibration, etc. Traditional mine safety management methods are unable to cope with these complex safety issues.

[0003] At present, the closest existing technologies mainly include two categories: traditional methods based on geomechanical analysis and data-driven intelligent prediction methods. The former mainly relies on expert experience and simple numerical simulations for prediction. Although it has strong interpretability, it is often difficult to fully consider the complex interactions between multiple sources of danger, resulting in insufficient accuracy and timeliness of early warning. The latter, such as the use of deep learning methods such as long short-term memory networks (LSTM) for time series prediction, although it performs well in processing large amounts of historical data, is often regarded as a "black box" model, lacks understanding of the underlying physical mechanisms, and is difficult to cope with sudden changes and emerging dangerous patterns in the mining environment.

[0004] Traditional methods for monitoring mine hazards rely on static models and single-point sensor data. For example, in the paper "Evolution of theuranium mineralisation in the Zoujiashan deposit, Xiangshan ore field: Implications for the genesis of volcanic-related hydrothermal U deposits in South China" published by Bonnetti et al. (2020) in Ore Geology Reviews, geological analysis methods were used to study the genesis of ore deposits and their dynamic evolution (Bonnetti et al., 2020). Although the study revealed the evolution trend of hazardous areas, its method mainly focused on the study of long-term geological changes in mining areas, ignoring the need for real-time monitoring, and failed to provide support for early warning and intervention of complex disaster chain reactions.

[0005] Furthermore, some scholars have tried to introduce dynamic feature analysis and spatial topological models. Tang and Mao (2024) studied dynamic spatial features and their evolution laws in their paper "The Humanistic Process and Spatial Practice of Chinese Zhenshan Worship" published in "Religions" (Tang & Mao, 2024). However, this study mainly focuses on spatial dynamic modeling in the humanities field, and its algorithms and technical methods are difficult to directly apply to the monitoring needs of complex mining systems. At the same time, it lacks systematic analysis and control strategies for nonlinear dynamics.

[0006] In addition, Yuan (2024) proposed a traffic environment management strategy based on dynamic optimization in his paper "Investigation and Optimization Strategies for Comprehensive Management of Transportation around Primary and Secondary Schools" published in Journal of Social Science and Humanities (Yuan, 2024). Although his method has certain reference value in optimizing the performance of dynamic systems, it still has great limitations in dealing with the complex multidimensional interactions of mine hazards and disaster chain reactions.

[0007] The main technical problems of these existing methods include:

[0008] 1. It is difficult to effectively capture and characterize the complex interactions between multiple hazard sources;

[0009] 2. The accuracy and timeliness of early warnings are insufficient, especially for potential disaster chain reactions;

[0010] 3. The system responds slowly, making it difficult to process and analyze real-time monitoring data in a timely manner;

[0011] 4. Hazard identification is incomplete, resulting in the risk of underreporting;

[0012] 5. Lack of ability to adapt to dynamic changes in the mining environment. Summary of the invention

[0013] The present invention aims to solve the above technical problems and proposes a new method for intelligent monitoring of major mine hazards and prevention of disaster chain reactions. This method innovatively integrates advanced technologies such as multidimensional data analysis, dynamic topology map construction, nonlinear dynamic system modeling and chaos predictive control to achieve intelligent monitoring of major mine hazards and effective prevention of disaster chain reactions.

[0014] The present invention provides a method for intelligent monitoring of major mine hazards and prevention of disaster chain reactions, including:

[0015] The acquisition steps include:

[0016] Obtain multi-dimensional monitoring data and historical disaster data of mines;

[0017] Processing steps include:

[0018] Based on the multi-dimensional monitoring data and the historical disaster data, construct a multi-dimensional hazard source dynamic topology map;

[0019] Generate a hazard source state matrix according to the multi-dimensional hazard source dynamic topology map;

[0020] Extracting dynamic topological features based on the hazard source state matrix;

[0021] According to the dynamic topological characteristics, a nonlinear dynamic system model is established;

[0022] Based on the nonlinear dynamic system model, chaos prediction and control are performed;

[0023] Output steps include:

[0024] Output disaster chain reaction warning information and intervention strategies.

[0025] Preferably, the construction of a multi-dimensional dynamic topological map of hazard sources specifically includes:

[0026] Define the hazard source set V and the relationship set E between hazard sources;

[0027] Building group structure in For group operations;

[0028] Defining Group Operations So that for any The result is the status after the hazard source interacts.

[0029] Preferably, generating the hazard source state matrix specifically includes:

[0030] Define the state matrix S = [s ij ] n×n ,in

[0031] Construct a mapping function φ:V→R to map the group operation result to the real number domain;

[0032] calculate where f k is a function describing the characteristics of the hazard source, w kis the corresponding weight.

[0033] Preferably, the extracting of dynamic topological features specifically includes:

[0034] Defining a simplicial complex sequence Where K t is a simplicial complex at time t;

[0035] Calculate continuous coherence Among them, H * represents the homology group of all dimensions.

[0036] Preferably, the establishing of the nonlinear dynamic system model specifically includes:

[0037] Constructing differential equations

[0038] in, is the system state vector, F is a nonlinear function, and β is the function that converts topological features into system parameters.

[0039] Preferably, the chaos prediction and control specifically includes:

[0040] Calculate Lyapunov exponent

[0041] Constructing the governing equations

[0042] Among them, u(λ) is the control function based on Lyapunov exponent.

[0043] Preferably, the data preprocessing step is also included:

[0044] Performing denoising and standardization processing on the multi-dimensional monitoring data;

[0045] The historical disaster data are temporally and spatially aligned and normalized.

[0046] As a preferred method, the model optimization step is also included:

[0047] Based on real-time monitoring data, dynamically update the multi-dimensional hazard source dynamic topology map;

[0048] The nonlinear dynamic system model is continuously optimized using a sliding time window technique.

[0049] Preferably, the output of disaster chain reaction warning information and intervention strategies specifically includes:

[0050] Generate graded warning information, including warning level, impact scope and duration;

[0051] Develop a multi-level intervention strategy, including immediate intervention measures and long-term prevention and control plans.

[0052] The intelligent monitoring system for major mine hazards and the disaster chain reaction prevention system for executing the method comprises:

[0053] Data acquisition module, used to obtain multi-dimensional monitoring data and historical disaster data of mines;

[0054] A topological map construction module, used to construct a multi-dimensional hazard source dynamic topological map based on the multi-dimensional monitoring data and the historical disaster data;

[0055] A state matrix generation module, used to generate a hazard source state matrix according to the multi-dimensional hazard source dynamic topology map;

[0056] A feature extraction module, used for extracting dynamic topological features based on the hazard source state matrix;

[0057] A power system modeling module, used for establishing a nonlinear power system model according to the dynamic topological characteristics;

[0058] A chaos prediction and control module, used for performing chaos prediction and control based on the nonlinear dynamic system model;

[0059] The output module is used to output disaster chain reaction warning information and intervention strategies.

[0060] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0061] First, the method for constructing a multi-dimensional hazard source dynamic topological map proposed in this invention cleverly introduces the concepts of group theory and topology into the field of mine safety analysis, effectively solving the problem that traditional methods are difficult to fully capture the interaction of multiple hazard sources. This method not only considers the characteristics of a single hazard source, but also describes the complex relationship network between hazard sources, thereby providing more comprehensive and accurate basic data for subsequent analysis and prediction.

[0062] Secondly, the nonlinear dynamic system modeling and chaos predictive control technology introduced in this invention effectively solves the limitations of traditional linear models in dealing with complex mining environments. By converting topological features into dynamic system parameters, this method can more accurately describe and predict the dynamic changes of the mining environment, greatly improving the accuracy and lead time of early warning. In particular, this method shows significant advantages in predicting potential disaster chain reactions.

[0063] In addition, the method of the present invention has also made breakthrough progress in terms of system response speed and hazard source identification integrity. By optimizing algorithm design and introducing parallel computing technology, the method can complete the entire process from data input to warning output in a short time, providing timely and effective decision support for mine safety management. At the same time, the comprehensive hazard source identification capability based on dynamic topological feature extraction technology greatly reduces the risk of underreporting.

[0064] Finally, the various steps of the method of the present invention show excellent complementarity, superposition and synergy. For example, the multi-dimensional hazard source dynamic topology map provides high-quality input data for nonlinear dynamic system modeling, while the chaos predictive control technology further improves the system's predictive ability. This close collaboration between the steps not only improves the effect of the overall method, but also enhances the system's ability to adapt to dynamic changes in the mining environment.

[0065] In summary, the method for intelligent monitoring of major mine hazards and disaster chain reaction prevention proposed by the present invention has significantly improved the level of mine safety management by innovatively solving the key problems existing in the prior art. This method can not only warn of potential hazards more accurately and timely, but also respond quickly to new monitoring data and comprehensively identify various potential hazards, providing strong technical support for protecting the lives of miners and promoting the sustainable development of mines. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The present invention is a flow chart of the method.

[0067] Figure 2 It is a logic block diagram of the data acquisition module of the present invention.

[0068] Figure 3 It is a logic block diagram of the state matrix generation module of the present invention.

[0069] Figure 4 It is a logic block diagram of the chaos prediction and control module of the present invention.

[0070] Figure 5 It is a diagram of the dynamic evolution process of the hazard source topological map of the present invention.

[0071] Figure 6 It is the topological feature extracted by continuous coherence of the present invention.

[0072] Figure 7 It is a thermal diagram of the hazard source state matrix of the present invention.

[0073] Figure 8 It is the curve of the change of Lyapunov exponent over time of the present invention.

[0074] Fig. 9This is a comparison diagram of disaster propagation before and after the control of the present invention. DETAILED DESCRIPTION

[0075] Please refer to Figure 1-9 The present invention provides a method and system for intelligent monitoring of major hazardous sources in mines and prevention of disaster chain reactions. The method realizes intelligent monitoring of major hazardous sources in mines and effective prevention of disaster chain reactions by innovatively integrating advanced technologies such as multidimensional data analysis, dynamic topology map construction, nonlinear dynamic system modeling and chaos predictive control.

[0076] Specifically, the method of the present invention comprises the following steps:

[0077] First, in the acquisition step, the method acquires multidimensional monitoring data and historical disaster data of the mine. In the metal mine environment, data acquisition is the cornerstone of the entire system. Multidimensional monitoring data of the mine includes geological structure data, rock stress data, groundwater data, gas concentration data, temperature and humidity data, etc. Historical disaster data contains detailed records of various types of mine disasters that occurred in the past, such as time, location, type, severity and other information. The acquisition and preprocessing of these data are crucial to the accuracy of subsequent analysis. Geological structure data obtains geological structure information of the mine through geological exploration, drilling sampling, three-dimensional laser scanning and other means to understand the distribution of ore bodies, fault locations, rock layer thickness, etc. Rock stress data uses strain gauges, pressure sensors, acoustic emission instruments and other equipment to monitor stress changes inside the rock and identify potential rock burst risks. Groundwater hydrological data monitors groundwater level, water quality and water flow velocity through water level meters, water quality sensors, flow meters and other equipment to evaluate the impact of groundwater on mine stability. Gas concentration data Although there are relatively few gas problems in metal mines, some metal mines (such as gold mines) may still have a small amount of gas leakage, and the gas concentration needs to be monitored to prevent explosion accidents. Temperature and humidity data monitor the temperature and humidity in the mine through temperature and humidity sensors, evaluate ventilation conditions, and prevent the impact of high temperature and high humidity environments on equipment and personnel. Wavelet transform or Kalman filtering and other methods are used to remove noise in monitoring data to ensure the accuracy and reliability of the data. The Z-score method is used to standardize different types of monitoring data to eliminate dimensional differences so that different data can be analyzed uniformly. For example: historical disaster data is mapped to a unified space-time coordinate system to ensure that data from different periods and locations can be compared and analyzed. For historical disaster data, the minimum-maximum scaling method is used to map it to the [0,1] interval, which is convenient for subsequent analysis and comparison. Through comprehensive acquisition and preprocessing of multi-source data, the present invention can obtain high-quality and reliable input data, which provides a solid foundation for subsequent multi-dimensional hazard source dynamic topology map construction, state matrix generation, feature extraction and other steps. This helps to improve the robustness and prediction accuracy of the system and ensure the effectiveness of mine safety management.

[0078] The hazards in mines include not only single geological structures or equipment failures, but also complex interactions between multiple hazards. Traditional static models are difficult to capture these dynamic changes, so it is necessary to introduce dynamic topological maps to describe the relationship between hazard sources and their changes over time.

[0079] Next, in the processing step, the method first constructs a multidimensional hazard source dynamic topology map based on the acquired multidimensional monitoring data and historical disaster data. This step is one of the core innovations of the present invention. Specifically, the method defines the hazard source set V and the relationship set E between hazard sources, and constructs a group structure in This modeling method based on group theory can effectively capture the complex interaction between mine hazards.

[0080] The hazard source set V represents all possible hazard sources, such as rock burst, groundwater infiltration, equipment failure, etc.

[0081] The relationship set E between hazard sources represents the interaction between hazard sources, such as rock burst may lead to increased groundwater infiltration, equipment failure may cause fire, etc.

[0082] Group Operation is defined as follows:

[0083]

[0084] Among them, v i 、v j and v k For the source of danger, e ij is the relationship between the hazard sources. This means that when two hazard sources v i and v j When interactions occur, new sources of danger may arise k , or change the status of the existing hazard source. If there is no direct relationship between the two, keep the original status unchanged.

[0085] This definition allows us to simulate the interaction between hazards. For example, when two hazards interact, new hazards may be generated or the status of existing hazards may be changed. Through group theory modeling, the present invention can effectively capture the complex interactions between multiple hazards in mines and avoid the limitations of traditional static models. For example, in metal mines, rock bursts may lead to increased groundwater infiltration, which in turn may cause equipment failure or collapse. This chain reaction can be simulated through group operation. Modeling.

[0086] The group structure G not only describes the current hazard source relationship, but also can reflect the dynamic changes of the hazard source relationship by updating the E set, thereby realizing real-time monitoring and early warning of the mine environment.

[0087] Based on the construction of the multi-dimensional dynamic topological map of hazardous sources, this method further generates a hazardous source state matrix. This step realizes the quantitative representation of the hazardous source state by mapping the results of group operations to the real number domain. The hazardous source state matrix is ​​used to quantify the interactions between various hazardous sources and convert them into a computable form. By mapping the results of group operations to the real number domain, the state changes of hazardous sources can be more intuitively represented.

[0088] Specifically, we define the state matrix S = [s ij ] n×n ,in

[0089] Among them, s ij Indicates the source of danger i and v j The state value after interaction, φ is a mapping function that maps the group operation result to the real number domain.

[0090] The mapping function φ:V→R is defined as follows:

[0091]

[0092] Here, f k is a function that describes the characteristics of the hazard source, w k are the corresponding weights. For example, f1 may represent the severity of the hazard source, f2 may represent its impact range, and w1 and w2 reflect the relative importance of these features in the overall assessment. By adjusting these weights, we can flexibly adapt to different types of mine environments and safety management strategies.

[0093] Through the state matrix S, the present invention can quantify the complex interaction of hazard sources into specific values, which is convenient for subsequent mathematical analysis and model construction. For example, in a metal mine, the severity, impact range, duration and other characteristics of rock burst can be obtained through f1, f2, ..., f m To describe, and the weight w k It can be adjusted according to actual conditions to ensure the flexibility and adaptability of the model. The state matrix not only takes into account the characteristics of individual hazard sources, but also captures the interactions between them, thereby achieving a multi-dimensional assessment of the mining environment. For example, rock bursts not only directly affect the safety of the mining face, but may also trigger chain reactions such as groundwater infiltration and equipment failure. These interactions can be comprehensively evaluated through the state matrix S.

[0094] Preferably, in one embodiment of the present invention, the weight w k The value of can be determined by expert evaluation combined with machine learning methods. For example, we can set w1 = 0.6, w2 = 0.4, which means that we believe that the severity of the hazard source is slightly more important than its impact range. Of course, the specific values ​​of these weights should be adjusted and optimized according to the actual situation.

[0095] The simplicial complex and persistent homology theory in algebraic topology provide powerful tools for extracting dynamic topological features. By defining the simplicial complex sequence And calculate its continuous coherence It can capture the complex dynamic characteristics of hazardous source systems evolving over time.

[0096] Next, the method extracts dynamic topological features based on the generated hazard source state matrix. This step introduces advanced concepts from algebraic topology, allowing us to capture the complex dynamic features of the hazard source system evolving over time.

[0097] The definition of simplicial complex sequence κ is:

[0098] K t is a simple complex at time t,

[0099] Among them, K t It is a simplicial complex at time t, representing the topological structure of the hazard source system at a certain moment. The topological characteristics of this sequence can be analyzed:

[0100] By calculating the continuous coherence We can analyze the topological characteristics of this sequence. Here, H * Representing the homology group of all dimensions, it is possible to capture the topological characteristics of the system, such as connectivity and ring structure. This approach allows us to track the changes in the topological structure of the hazard source system and thus identify potential hazard patterns and trends.

[0101] Through continuous coherence, the present invention can track the long-term evolution trend of the topological structure of the hazard source system and identify potential hazard patterns and trends. For example, in metal mines, certain persistent coherence classes may correspond to long-term stable hazard source structures, such as the "ring"-shaped groundwater infiltration area formed around the mining face, which may indicate areas that need special attention.

[0102] The sudden appearance or disappearance of a coherent class may indicate a significant change in the hazard source system, such as the intensification of groundwater infiltration caused by rock bursts, which leads to the destruction of the original stable structure. Through continuous coherence, these short-term mutations can be discovered in time and preventive measures can be taken in advance.

[0103] For example, in practical applications, we may find that some persistent homology classes may correspond to the stable hazard source structure that exists for a long time in the mine. However, the sudden appearance or disappearance of homology classes may indicate that the hazard source system has changed significantly and needs special attention.

[0104] Through the above steps, the method of the present invention realizes comprehensive, dynamic and accurate modeling of major hazards in mines. This method not only takes into account the individual characteristics of each hazard source, but also captures the complex interactions between them and their evolution over time. This provides a solid foundation for subsequent prediction and prevention, and helps to improve the efficiency and accuracy of mine safety management.

[0105] In the next step, the method will establish a nonlinear dynamic system model based on the extracted dynamic topological features, and perform chaos prediction and control. These steps will be described in detail in the subsequent description. On the basis of constructing a multidimensional hazard source dynamic topological map and generating a hazard source state matrix, the method of the present invention further extracts dynamic topological features. This step introduces the advanced concept of algebraic topology, so that the method can capture the complex dynamic characteristics of the hazard source system evolving over time.

[0106] Specifically, this method defines the simplicial complex sequence Where K t is a simplicial complex at time t. By computing the continuous homology This method analyzes the topological characteristics of this sequence. Here, H * Represents the homology group of all dimensions. This approach allows tracking changes in the topology of the hazardous source system and thus identifying potentially hazardous patterns and trends.

[0107] Preferably, in one embodiment of the present invention, persistent homology barcodes can be used to visualize the evolution of these topological features. Long bars in the barcode represent persistent topological features, while short bars represent transient features. For example, in a mining environment, a persistent 1D homology class may correspond to a "ring" structure formed around a dangerous area, which may indicate an area that requires special attention.

[0108] Nonlinear dynamic system theory is used to describe the dynamic behavior of hazardous source systems, especially in the presence of complex interactions and nonlinear effects. By constructing differential equations, the evolution of the system can be simulated and the future state can be predicted.

[0109] Based on the extracted dynamic topological features, this method further establishes a nonlinear dynamic system model. This step describes the dynamic behavior of the hazard source system by constructing differential equations. Specifically, this method constructs the following differential equations:

[0110]

[0111] in, is the system state vector, F is a nonlinear function, and β is a function that converts topological features into system parameters. This modeling method allows the previously extracted topological feature information to be incorporated into the dynamic system, thereby more accurately describing the dynamic behavior of the mine hazard source system.

[0112] In a preferred embodiment of the present invention, the function F can be in the form of a polynomial to represent the nonlinear characteristics of the system. For example:

[0113]

[0114] Among them, A, B, C, D, E are coefficient matrices and ω is the natural frequency of the system. This form allows the expression of complex nonlinear dynamics, including interactions between states (quadratic terms), saturation effects (cubic terms), and periodic external influences (sinusoidal terms). The design of the function β is the key to converting topological features into parameters of the dynamical system. In practical applications, β can be designed as a function of the Betti number of the homology group. For example:

[0115]

[0116] Here, b i is the Betti number of the i-th homology group, α i is the corresponding weight coefficient. In this way, topological features can directly affect the dynamic behavior of the system.

[0117] Through the nonlinear dynamic system model, the present invention can more accurately describe the dynamic behavior of the mine hazard source system, especially in the presence of complex interactions and nonlinear effects. For example, in metal mines, the interactions between hazard sources such as rock bursts, groundwater infiltration, and equipment failures may be nonlinear. Traditional linear models are difficult to capture these complex dynamics, while nonlinear dynamic system models can better describe these phenomena.

[0118] Based on the nonlinear dynamic system model, the future state of the system can be predicted and potential risks can be identified in advance. For example, by simulating the evolution of the system, the time and intensity of rock bursts can be predicted, thereby providing early warning information for mine managers to take corresponding preventive measures.

[0119] Chaos theory is used to predict and control the long-term behavior of a system, especially when the system has chaotic characteristics. By calculating the Lyapunov exponent, the degree of chaos in the system can be assessed and corresponding control strategies can be designed.

[0120] On the basis of establishing a nonlinear dynamic system model, the method of the present invention further performs chaos prediction and control. This step uses chaos theory to predict and control the long-term behavior of the system. Specifically, the method first calculates the Lyapunov exponent:

[0121]

[0122] in, It is the evolution of a small perturbation of the initial state at time t. The Lyapunov exponent provides a measure of the degree of chaos in the system, and a positive value indicates that the system has chaotic characteristics.

[0123] Based on the calculated Lyapunov exponent, this method constructs the governing equation:

[0124]

[0125] Here, u(λ) is a control function based on the Lyapunov exponent. This control method allows to prevent the system from entering an unstable state while maintaining the complex dynamics of the system.

[0126] In a preferred embodiment of the present invention, the control function u(λ) can be designed as:

[0127]

[0128] Here, K is the control gain and λ0 is the desired Lyapunov exponent value. In this way, the system can be maintained in a controlled chaotic state, which not only maintains the complexity of the system but also avoids completely unpredictable behavior.

[0129] Through chaos theory, the present invention can predict the long-term behavior of the system, especially when the system has chaotic characteristics. For example, in metal mines, the occurrence of rock bursts may be random, but through chaos prediction, potential rock burst risks can be identified in advance, providing early warning information for mine managers.

[0130] By designing a control strategy based on the Lyapunov exponent, the present invention can prevent the system from entering an unstable state while maintaining the complex dynamics of the system. For example, when the Lyapunov exponent of the system exceeds a certain threshold, the chaotic behavior of the system can be suppressed by adjusting the control gain K to ensure the safety of the mining environment.

[0131] Preferably, the method of the present invention further comprises a data preprocessing step. In this step, the acquired multidimensional monitoring data is subjected to denoising and standardization, and the historical disaster data is subjected to spatiotemporal alignment and normalization. These preprocessing operations help to improve the accuracy and reliability of subsequent analysis.

[0132] For example, for multi-dimensional monitoring data, wavelet transform can be used for denoising. Standardization can use the Z-score method, that is:

[0133]

[0134] Among them, μ is the data mean and σ is the standard deviation. This processing method can eliminate the dimensional differences between different types of data, making subsequent analysis more reasonable. For historical disaster data, spatiotemporal alignment can be achieved by mapping all events into a unified spatiotemporal coordinate system. Normalization can use the minimum-maximum scaling method:

[0135]

[0136] This processing can map all data to the [0,1] interval, which is convenient for subsequent analysis and comparison.

[0137] Through the above steps, the method of the present invention realizes comprehensive, dynamic and accurate modeling, prediction and control of major hazards in mines. This method not only takes into account the individual characteristics of each hazard source, but also captures the complex interactions between them and their evolution over time. By introducing advanced mathematical tools and theories such as algebraic topology, nonlinear dynamic system theory and chaos theory, this method can better understand and predict the complex dynamic behavior in the mining environment, thereby providing strong support for the formulation of effective disaster prevention strategies.

[0138] The method of the present invention also includes a model optimization step, which is intended to improve the adaptability and prediction accuracy of the system. Specifically, the method dynamically updates the multi-dimensional hazard source dynamic topology map based on real-time monitoring data. This real-time update mechanism enables the system to capture changes in the mining environment in a timely manner, thereby maintaining the timeliness and accuracy of the model.

[0139] In a preferred embodiment of the present invention, the dynamic update process uses a sliding time window technology. For example, a 24-hour time window can be set to update the topology map every 1 hour. This method can ensure the sensitivity of the model to the latest situation while maintaining a certain computational efficiency. During the update process, new monitoring data will be added to the data set, and outdated data will be removed, thereby maintaining the freshness of the data set.

[0140] At the same time, this method uses the sliding time window technology to continuously optimize the nonlinear dynamic system model. This optimization process can be achieved by adjusting the model parameters, for example:

[0141]

[0142] Among them, θ tis the model parameter at the current moment, η is the learning rate, L(θ t ) is the loss function. In this way, the model can continuously adapt to new data and improve prediction accuracy.

[0143] After completing the above processing steps, the method of the present invention enters the output step, including outputting disaster chain reaction warning information and intervention strategies. This step converts the analysis results of the previous links into actionable warning information and specific intervention measures, which is the embodiment of the ultimate goal and practical application value of the method.

[0144] Specifically, this method generates graded warning information, including warning level, impact range and duration. The warning level can be divided into four levels: level 1 (red), level 2 (orange), level 3 (yellow) and level 4 (blue), corresponding to extremely serious, serious, large and general levels of danger respectively. The impact range is determined according to the spatial distribution of the hazard source and the potential impact area, and can be accurate to the specific mining area or working face. The duration prediction is based on the evolution results of the nonlinear dynamic system model.

[0145] For example, a typical warning message might be: "Level 2 (orange) warning, expected to affect A3-B2 mining area, duration 48 hours ± 6 hours." This clear and specific warning information can help mine managers quickly understand the current dangerous situation and take corresponding measures.

[0146] At the same time, this approach also develops multi-level intervention strategies, including immediate intervention measures and long-term prevention and control plans. Immediate intervention measures target the current emergency situation and may include evacuating personnel, closing specific areas, and increasing monitoring frequency. Long-term prevention and control plans focus on systematically reducing risks and may include improving mining methods, upgrading monitoring equipment, and strengthening employee training.

[0147] In a preferred embodiment of the present invention, the formulation of the intervention strategy can adopt a multi-objective optimization method, taking into account both safety improvement and economic cost. For example, the Pareto optimization method can be used to find the best balance between safety and cost.

[0148] Finally, the present invention also provides a system for intelligent monitoring of major hazardous sources in mines and prevention of disaster chain reactions, the system comprising a plurality of functional modules, each module corresponding to a key step in the method.

[0149] Specifically, the system includes:

[0150] The data acquisition module 1 is used to acquire multi-dimensional monitoring data and historical disaster data of the mine. This module can integrate various sensors and database interfaces to achieve real-time collection of multi-source data and efficient reading of historical data.

[0151] The topological map construction module 2 is used to construct a multi-dimensional hazard source dynamic topological map based on the multi-dimensional monitoring data and the historical disaster data. This module implements the group theory modeling process in this method and is one of the core components of the system.

[0152] The state matrix generation module 3 is used to generate the hazard source state matrix according to the multi-dimensional hazard source dynamic topology map. This module converts the information in the topology map into a quantifiable state representation, laying the foundation for subsequent analysis.

[0153] The feature extraction module 4 is used to extract dynamic topological features based on the hazard source state matrix. This module implements continuous coherent calculation and can capture the dynamic topological features of the system.

[0154] The power system modeling module 5 is used to establish a nonlinear power system model according to the dynamic topological characteristics. This module converts the topological characteristics into power system parameters and constructs differential equations that describe the dynamic behavior of the system.

[0155] The chaos prediction and control module 6 is used to perform chaos prediction and control based on the nonlinear dynamic system model. This module implements the calculation of the Lyapunov exponent and the control strategy based on chaos theory.

[0156] Output module 7 is used to output disaster chain reaction warning information and intervention strategies. This module converts the system's analysis results into actionable warning information and specific measures.

[0157] Through the collaborative work of these functional modules, the system can realize intelligent monitoring of major hazardous sources in mines and effective prevention of disaster chain reactions, providing strong technical support for mine safety management.

[0158] In order to verify the effectiveness and superiority of the intelligent monitoring of major mine hazard sources and disaster chain reaction prevention method proposed by the present invention in metal mines, the present invention selected a large open-pit copper mine as an experimental base and conducted a one-year field test. The copper mine is located in an area with complex geological structures and has a variety of potential hazard sources, such as slope instability, groundwater outburst, blasting vibration, etc., which provides an ideal test environment for the application of the method of the present invention.

[0159] This experiment collected multi-dimensional monitoring data including geological structure, rock stress, groundwater, blasting vibration, and historical disaster records in the past 10 years. Example 1 adopts the complete method proposed by the present invention, including the construction of multi-dimensional hazard source dynamic topological map, dynamic topological feature extraction, nonlinear dynamic system modeling and chaos prediction control and other core technologies. Comparative Example 1 adopts traditional geomechanical analysis methods, mainly relying on expert experience and simple numerical simulation for prediction. Comparative Example 2 adopts a common data-driven method and uses a long short-term memory network (LSTM) for time series prediction.

[0160] This experiment focuses on the following key indicators: warning accuracy, warning lead time, system response speed and hazard source identification integrity. The testing standards and methods for these indicators are as follows:

[0161] The early warning accuracy index is evaluated by counting the correspondence between the early warnings issued by the system and the actual disaster events that occurred within a year. The early warning lead time is measured by calculating the average time interval between the system issuing the early warning and the actual occurrence of the disaster. The system response speed refers to the time required from receiving new monitoring data to outputting updated early warning information. The completeness of hazard source identification is evaluated by comparing the number of hazard sources identified by the system with the actual number of hazard sources manually marked.

[0162] The following is a comparison of the performance of the three methods on these indicators:

[0163] index Example 1 Comparative Example 1 Comparative Example 2 Early warning accuracy 94.2％ 78.5％ 86.3％ Warning lead time 72 hours 24 hours 48 hours System response speed 5 minutes 2 hours 30 minutes Hazard identification completeness 97.8％ 82.1％ 89.5％

[0164] It can be clearly seen from the above results that the method proposed in the present invention (Example 1) is significantly superior to the traditional geomechanical analysis method (Comparative Example 1) and the common data-driven method (Comparative Example 2) in all indicators.

[0165] First, in terms of early warning accuracy, the method of the present invention achieved a high accuracy of 94.2%, which fully reflects the advantages of the multi-dimensional hazard source dynamic topological map. The map not only considers the characteristics of a single hazard source, but also captures the complex interactions between different hazard sources, so as to more comprehensively and accurately evaluate the overall safety status of the mine.

[0166] Secondly, in terms of early warning time, the method of the present invention can issue an early warning 72 hours in advance on average, which is 48 hours earlier than the traditional method. This significant improvement is mainly due to the nonlinear dynamic system modeling and chaos predictive control technology introduced by the present invention. These advanced mathematical tools enable the system to better capture and predict the complex dynamic changes in the mining environment, thereby achieving earlier early warning.

[0167] In terms of system response speed, the method of the present invention can complete the entire process from data input to warning output in just 5 minutes. This rapid response capability is particularly important in mine safety management. In contrast, traditional methods often require a team of experts to conduct long-term analysis and discussion. Although general data-driven methods are faster than traditional methods, they still have efficiency bottlenecks when processing high-dimensional and high-complexity data.

[0168] Finally, in terms of completeness of hazard identification, the method of the present invention achieved a high level of 97.8%. This means that the system can identify almost all potential hazard sources, greatly reducing the risk of underreporting. This outstanding performance is mainly due to the dynamic topological feature extraction technology proposed by the present invention, which can extract key topological features from complex mining environments, thereby comprehensively characterizing various potential hazards.

[0169] It is worth noting that although Comparative Example 2 (LSTM method) performs well in some indicators, it still cannot comprehensively consider the dynamic complexity of the mining environment and the interaction between hazard sources like the method of the present invention. This also once again confirms the importance of the innovative points proposed by the present invention, such as the multi-dimensional hazard source dynamic topology map and nonlinear dynamic system modeling.

[0170] In summary, the method for intelligent monitoring of major mine hazards and disaster chain reaction prevention proposed by the present invention has shown significant superiority in practical applications. It can not only warn of potential hazards more accurately and timely, but also respond quickly to new monitoring data and comprehensively identify various potential hazard sources. This comprehensive improvement in performance will greatly enhance the safety management capabilities of mines, effectively reduce the risk of disasters, and ensure the safety of miners and the sustainable development of mines.

[0171] Figure 5 The figure shows the change of dynamic interaction intensity of mine hazard sources over time, which is manifested as the dynamic interaction pattern between hazard sources. The peak value corresponds to the triggering moment of potential disaster chain reaction, and the valley value represents the stable stage of the system. The present invention accurately captures the changing trend of complex interactions between hazard sources through dynamic topological map construction. Compared with the traditional static map method, the dynamic model can monitor the changes of potential disaster points in real time and provide a basis for early warning.

[0172] Figure 6 The number of 0-dimensional and 1-dimensional homology groups is shown in the figure, which reflects the dynamic changes of connected components and hole structures in the system respectively. The reduction of 0-dimensional homology groups indicates that the hazard sources are gradually gathering into risk clusters; while 1-dimensional homology groups reveal the path structure of potential disaster chain reactions. By extracting topological features using continuous homology technology, the law of dynamic evolution of hazard sources can be intuitively quantified. This method has more theoretical depth than traditional statistical indicators and can accurately identify key risk nodes in complex topological relationships.

[0173] hot Figure 7 Different colors represent the status of the hazard source, the red area is a high-risk state, and the blue area is a low-risk state. The heat map reflects the state coupling characteristics between hazard sources, and the risk gradually spreads from the local to the whole. The present invention comprehensively quantifies the interaction intensity and risk distribution of hazard sources through state matrix analysis. The visualization results of the heat map provide a scientific basis for the formulation of intervention strategies, and can achieve rapid and accurate risk positioning.

[0174] Figure 8 The red curve in the middle reflects the dynamic change of the Lyapunov exponent. A positive value of the exponent indicates that the system enters a chaotic state, and a negative value indicates that the system tends to be stable. The present invention successfully pulls the system back from a chaotic state to a stable state through the control strategy u(λ). The present invention effectively suppresses the spread of the disaster chain reaction through a chaos control method based on the Lyapunov exponent. Compared with the uncontrolled state, this strategy significantly improves the stability of the system and reduces the probability of secondary disasters.

[0175] Columnar Fig. 9 The comparison of the number of disaster propagation paths before control (orange) and after control (green) is shown. The number of paths after control is greatly reduced, indicating that the control strategy of the present invention effectively blocks the disaster chain reaction. The present invention significantly reduces the number of disaster propagation paths through nonlinear dynamic modeling and control strategy optimization. This effect not only mitigates the consequences of disasters, but also improves the efficiency and effectiveness of emergency management.

[0176] The method of the present invention demonstrates the following beneficial effects through dynamic topological map construction, continuous coherent feature extraction, nonlinear dynamics modeling and chaos control strategy: dynamic monitoring of interactive changes of dangerous sources, timely identification of potential risks, quantification of system structure changes using topological features, and location of key risk points.

[0177] The chaos control strategy effectively reduces the chaotic state of the system and enhances stability. The visualization results provide a scientific basis for disaster warning and intervention strategies, facilitating decision-making.

[0178] The above effects fully reflect the theoretical innovation and practical application value of the method of the present invention, and provide an efficient and reliable solution for intelligent monitoring and disaster prevention of major hazardous sources in mines.

[0179] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, replacement, and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligent monitoring of major hazardous sources in mines and prevention of disaster chain reactions, characterized in that: include: The acquisition steps include: Obtain multi-dimensional monitoring data and historical disaster data of mines; Processing steps include: Based on the multi-dimensional monitoring data and the historical disaster data, construct a multi-dimensional hazard source dynamic topology map; Generate a hazard source state matrix according to the multi-dimensional hazard source dynamic topology map; Extracting dynamic topological features based on the hazard source state matrix; According to the dynamic topological characteristics, a nonlinear dynamic system model is established; Based on the nonlinear dynamic system model, chaos prediction and control are performed; Output steps include: Output disaster chain reaction warning information and intervention strategies.

2. The method according to claim 1, characterized in that The construction of a multi-dimensional hazard source dynamic topology map specifically includes: Define the hazard source set V and the relationship set E between hazard sources; Building group structure in For group operations; Defining Group Operations So that for any v i ,v j ∈V, The result is the status after the hazard source interacts.

3. The method according to claim 2, characterized in that The generating of the hazard source status matrix specifically includes: Define the state matrix s = [s ij ] n×n ,in Construct a mapping function φ:V→R to map the group operation result to the real number domain; calculate where f k is a function describing the characteristics of the hazard source, w k is the corresponding weight.

4. The method according to claim 3, characterized in that The extracting of dynamic topological features specifically includes: Defining a simplicial complex sequence Where K t is a simplicial complex at time t; Calculate continuous coherence Among them, H * represents the homology group of all dimensions.

5. The method according to claim 4, characterized in that The establishment of the nonlinear dynamic system model specifically includes: Constructing differential equations in, is the system state vector, F is a nonlinear function, and β is the function that converts topological features into system parameters.

6. The method according to claim 5, characterized in that The chaos prediction and control specifically includes: Calculate Lyapunov exponent Constructing the governing equations Among them, u(λ) is the control function based on Lyapunov exponent.

7. The method according to claim 1, characterized in that It also includes data preprocessing steps: Performing denoising and standardization processing on the multi-dimensional monitoring data; The historical disaster data are temporally and spatially aligned and normalized.

8. The method according to claim 1, characterized in that It also includes a model optimization step: Based on real-time monitoring data, dynamically update the multi-dimensional hazard source dynamic topology map; The nonlinear dynamic system model is continuously optimized using a sliding time window technique.

9. The method according to claim 1, characterized in that: The output of disaster chain reaction warning information and intervention strategies specifically includes: Generate graded warning information, including warning level, impact scope and duration; Develop a multi-level intervention strategy, including immediate intervention measures and long-term prevention and control plans.

10. A system for intelligent monitoring of major mine hazards and disaster chain reaction prevention that implements the method described in any one of claims 1 to 9, characterized in that: include: Data acquisition module, used to obtain multi-dimensional monitoring data and historical disaster data of mines; A topological map construction module, used to construct a multi-dimensional hazard source dynamic topological map based on the multi-dimensional monitoring data and the historical disaster data; A state matrix generation module, used to generate a hazard source state matrix according to the multi-dimensional hazard source dynamic topology map; A feature extraction module, used for extracting dynamic topological features based on the hazard source state matrix; A power system modeling module, used for establishing a nonlinear power system model according to the dynamic topological characteristics; A chaos prediction and control module, used for performing chaos prediction and control based on the nonlinear dynamic system model; The output module is used to output disaster chain reaction warning information and intervention strategies.

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