Underground space multi-disaster coupling effect quantitative evaluation and quantitative control system and method

Abstract

The invention provides an underground space multi-disaster coupling effect quantitative evaluation and quantitative control system and method. The system is characterized in that a multi-source sensing module, a data fusion and preprocessing module, a coupling risk evaluation module and a quantitative control decision module are connected in sequence; the method comprises the following steps: collecting multi-source heterogeneous monitoring data for a long time based on the multi-source sensing module; performing space-time registration, denoising and missing value interpolation on the multi-source heterogeneous monitoring data, and calculating an original disaster intensity index; performing normalization processing on different disaster intensity indexes, constructing a time-varying coupling factor between disasters, and calculating a comprehensive risk index by using a coupling risk assessment module; the quantitative control decision module performs dynamic risk grading based on the comprehensive risk index; and the quantitative control decision module calls and executes a quantitative control strategy from a preset strategy library based on the risk grading result and the dominant disaster mode combination. According to the invention, accurate monitoring and early warning of mine underground engineering disasters can be realized, and corresponding control decisions can be automatically generated.

Description

Technical Field

[0001] This invention belongs to the field of mine engineering safety technology, specifically a quantitative assessment and control system and method for the coupling effect of multiple disasters in underground space. Background Technology

[0002] During their service life, underground engineering structures continuously face complex and harsh environmental effects, such as creep of surrounding rock under high ground pressure, erosion by groundwater with high osmotic pressure, periodic blasting and mechanical vibration loads, and structural corrosion caused by humid and hot environments. These physical, chemical, and mechanical hazards do not exist in isolation, but rather intertwine and promote each other in both time and space, forming a significant coupling effect. For example, blasting vibrations can exacerbate the development of fissures in the surrounding rock, providing channels for water infiltration; while water intrusion not only softens the rock mass and reduces its strength, but also accelerates the corrosion process of support components, ultimately leading to a systemic deterioration of the structure's load-bearing capacity. This chain reaction and nonlinear superposition mechanism of multiple hazards has become a core scientific problem and technical bottleneck in the prevention and control of disasters in deep mining engineering.

[0003] Currently, risk assessment and control technologies for underground mining engineering safety still struggle to effectively address the challenges posed by the coupling of multiple hazards. Existing methods largely focus on the independent analysis and control of single hazards, such as separately monitoring water inflow, assessing vibration velocity, or detecting corrosion. This "divide and conquer" research paradigm essentially ignores the interaction mechanisms between hazards, leading to significant discrepancies between risk assessment results and actual safety conditions, and making it difficult to predict systemic risks induced by the coupling of multiple hazards. Technically, existing models lack the ability to quantitatively describe the coupling relationships between hazards, causing the assessment process to rely excessively on engineering experience and simplistic assumptions. Corresponding control strategies are also mostly passive responses based on single thresholds, such as blocking seepage or stopping operations when vibration exceeds a certain level, lacking proactive intervention methods targeting the evolution of coupled risks, making it difficult to achieve precise and forward-looking safety control.

[0004] To overcome the aforementioned technical limitations, there is an urgent need to provide a quantitative assessment and control system and method for the multi-hazard coupling effects in underground mining spaces. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a quantitative assessment and control system and method for the coupling effect of multiple hazards in underground space. This system has a simple structure and a high degree of intelligence, enabling accurate monitoring and early warning of hazards in underground mining projects and automatically generating corresponding control decisions, thus ensuring the safety and efficiency of mining operations. The method is simple to implement and has low implementation costs. It can achieve quantitative and coordinated accurate assessment of hazards based on the coupling effect of multiple hazards in underground space and automatically generate corresponding control strategies.

[0006] To achieve the above objectives, the present invention provides a quantitative assessment and control system for the coupling effects of multiple disasters in underground space, comprising a multi-source sensing module, a data fusion and preprocessing module, a coupling risk assessment module, and a quantitative control decision module; The multi-source sensing module includes an array of seepage pressure and flow sensors, an array of vibration and shock sensors, an array of structural strain sensors, and an array of corrosion monitoring sensors, which are used to collect multi-source heterogeneous data on the underground space environment and structural response in real time or periodically. The data fusion and preprocessing module is used to perform spatiotemporal registration, denoising, missing value imputation, and calculation of the original disaster intensity index on multi-source heterogeneous monitoring data to form a structural health status dataset with a unified spatiotemporal benchmark. The coupled risk assessment module has a built-in multi-hazard coupled risk quantification model, which is used to calculate a comprehensive risk index representing the spatiotemporal superposition effect of multiple hazards based on the structural health status dataset. The quantitative control decision module is used to retrieve and generate quantitative collaborative control schemes from a preset strategy library based on the comprehensive risk index and the combination of dominant disaster modes, and then output them.

[0007] As a preferred embodiment, the pressure and flow sensor array is deployed along structural joints, cracks, and potential seepage paths; the vibration and impact sensor array is deployed on key components and the base plate that are sensitive to dynamic loads, with a monitoring frequency range of 0.1Hz to 200Hz.

[0008] As a preferred embodiment, the structural strain sensor array employs fiber optic grating sensors, permanently embedded in key monitoring sections, for monitoring long-term creep and instantaneous dynamic response; the corrosion monitoring sensor array employs macrocell-type or resistance probe-type sensors, deployed in areas of the structure prone to corrosion.

[0009] Furthermore, to facilitate the processing of massive amounts of time-series data, the coupled risk assessment module and the quantitative control decision module are integrated into an edge computing gateway or cloud server; to facilitate the automatic distribution of control commands to the corresponding execution units, the quantitative control decision module is interconnected with the underground space equipment management system.

[0010] In this invention, the multi-source sensing module simultaneously includes arrays of seepage pressure and flow sensors, vibration and impact sensors, structural strain sensors, and corrosion monitoring sensors, enabling the construction of a multi-source heterogeneous data fusion sensing system. This system can continuously collect multi-dimensional parameters such as hydrogeological data, dynamic response data, deformation monitoring data, and material corrosion data. The data fusion and preprocessing module facilitates automated data preprocessing and automatically calculates the original intensity indices characterizing different disaster types, providing a high-quality data foundation for subsequent risk quantification. The coupled risk assessment module incorporates a multi-hazard coupled risk quantification model, explicitly quantifying the nonlinear coupling relationships between disasters based on structural health status datasets. This fully considers the time-varying coupling effects of multiple disasters, ensuring that the obtained comprehensive risk index reflects the interaction mechanisms between multiple disasters. This achieves quantitative and coordinated accurate disaster assessment, making the risk assessment results more consistent with the actual safety status. The quantitative control decision module generates corresponding coordinated control strategies based on the comprehensive risk index and the dominant disaster mode. Therefore, a comprehensive monitoring system integrating multi-source information such as hydrology, vibration, stress-strain and corrosion has been constructed by multi-source sensing module, data fusion and preprocessing module, coupled risk assessment module and quantitative control decision module, which is conducive to the accurate assessment of disaster risks in underground space of mines and the automatic generation of corresponding decisions.

[0011] The system has a simple structure and a high degree of intelligence. It can accurately monitor and warn of disasters in underground mining projects and automatically generate corresponding control decisions, which helps to ensure the safety and efficiency of mining operations.

[0012] This invention also provides a method for quantitative assessment and control of the coupling effects of multiple hazards in underground space, employing a quantitative assessment and control system for the coupling effects of multiple hazards in underground space, comprising the following steps: Step 1: Collect multi-source heterogeneous monitoring data; obtain water pressure P through osmosis and flow sensors. w (t) and seepage flow rate Q w (t); vibration acceleration a(t) and impact load F(t) are obtained through vibration and impact sensors; time-varying strain is obtained through structural strain sensors. and dynamic strain Corrosion current density I is obtained through corrosion monitoring sensors. corr (t) or corrosion depth d corr (t); Step 2: Data preprocessing and calculation of the original disaster intensity index; based on water pressure P w (t) and seepage flow rate Q w(t) to obtain the original intensity index of water inrush disaster, and based on vibration acceleration a(t) and impact load F(t) to obtain the original intensity index of rockburst disaster, based on time-varying strain. and dynamic strain The original strength index of the surrounding rock creep was obtained based on the corrosion current density I. corr (t) or corrosion depth d corr (t) Obtain the original strength index of structural corrosion; Step 3: Calculate the comprehensive risk index based on the disaster intensity index and the time-varying coupling factor. S31: Calculation of the first strength index based on the original strength index Normalized intensity index of various disasters S32: Construction of the characterization of the first disasters and the first Time-varying coupling factor of the dynamic correlation intensity of various disasters in the time dimension S33: Construct a multi-hazard coupled risk quantification model and calculate the comprehensive risk index at time t. ; Step 4: Based on the comprehensive risk index Implement dynamic risk classification; set risk thresholds sequentially from low to high. , and ,when When, it is judged as normal level; when When, it is judged as a level of attention; when When, it is determined to be at the warning level; when At that time, it was determined to be at a high-risk level; Step 5: Generate quantitative control strategies based on risk classification results and dominant disaster patterns; based on the risk classification results and the combination of dominant disaster patterns, call and generate quantitative control strategies that match the risk level from the preset strategy library, and execute the quantitative control strategies.

[0013] Furthermore, in order to effectively eliminate dimensional differences, in step S31 of step three, the first step is calculated according to formula (1). Normalized intensity index of various disasters In order to accurately quantify the dynamic correlation strength of each disaster in the time dimension, in step S32 of step three, a time-varying coupling factor is constructed according to formula (2). In order to achieve quantitative and coordinated accurate assessment of risks under the coupling effect of multiple disasters, in step S33 of step three, a multi-disaster coupling risk quantification model is constructed according to formula (3); (1); In the formula, These correspond to water inrush disasters, rock burst disasters, surrounding rock creep disasters, and structural corrosion disasters, respectively. For the first The original intensity index of the disaster at time t; , The first The design of disaster intensity allows for upper and lower limits; (2); In the formula, For smoothing coefficients, , For adjacent time periods The change in the intensity of a disaster For adjacent time periods The change in the intensity of a type of disaster; For the first Normalized intensity index of a type of disaster; (3); In the formula, For the first Initial weights for different types of disasters; For the first The initial weight of each type of disaster.

[0014] As a preferred option, in step four, , and The threshold was set based on historical data analysis and engineering experience, and .

[0015] As a preferred approach, the quantitative control strategy includes the following: When the level is normal, it outputs a suggestion to perform routine inspections; when the level is alert, it outputs a suggestion to initiate targeted enhanced monitoring and perform quantitative maintenance on identified minor defects; when the level is warning, it outputs a suggestion to initiate proactive control measures, while simultaneously controlling the change of the operating model of large mining equipment to avoid resonance and performing precise grouting operations based on seepage flow and water pressure calculations; when the level is high-risk, it outputs a suggestion to immediately implement space restriction or closure, initiate a comprehensive engineering treatment plan, and trigger an emergency response alarm.

[0016] Furthermore, in order to accurately reflect the coupling of multiple disasters, a time-varying coupling factor is needed. The initial values ​​were obtained through laboratory simulation of multi-hazard coupling experiments, and during operation, the data sequences were monitored and dynamically corrected using system identification technology based on least squares or neural networks to truly reflect the time-varying interactions between disasters.

[0017] Furthermore, in order to accurately reflect the intensity index of rock creep disaster and structural corrosion disaster, the original intensity index of rock creep disaster is obtained according to formula (4). The original strength index of structural corrosion disaster is obtained according to formula (5). ; (4); In the formula, This represents the critical allowable value for time-varying strain; , , These are the material constants, stress exponents, and time exponents related to time-varying strain, respectively. For the current pressure; (5); In the formula, This represents the critical allowable value for corrosion depth. , These are the corrosion rate constant and time exponent, which are related to the corrosion depth, respectively.

[0018] This invention provides a method for quantitative assessment and control of the coupling effects of multiple hazards in underground space. First, by setting up multi-source sensing modules, a complete monitoring network can be formed, enabling the collection of multi-source heterogeneous monitoring data. This ensures that the monitoring data effectively covers the four key hazard types in underground engineering (water inrush, rock bursts, surrounding rock creep, and structural corrosion). Next, data preprocessing facilitates spatiotemporal registration, noise reduction, and missing value interpolation, ensuring data reliability and consistency. Based on this, a dedicated original intensity index calculation model is established for each hazard, improving the accuracy of the assessment. Furthermore, addressing the complex coupling problem of multiple hazards interacting and exacerbating each other in underground mine spaces during long-term service, such as groundwater seepage, blasting and mechanical vibration, impact loads, aging creep of surrounding rock, and corrosion of structural materials, a multi-hazard coupling risk quantification model was innovatively constructed based on the intensity index and time-varying coupling factor. This model can explicitly quantify the nonlinear coupling relationship between hazards, thus fully considering the time-varying coupling effect of multiple hazards in the risk assessment process. It can accurately reflect the mutual influence between hazards in real time and effectively reflect the coupling effect between different types of hazards, which is more in line with engineering practice. By introducing the time-varying coupling factor, the spatiotemporal evolution law of the hazard chain under the coupling effect of water-mechanical-chemical fields can be accurately characterized, which helps to make the risk assessment results more consistent with the actual safety status. Then, based on the multi-level threshold division of the dynamic risk index, a graded early warning mechanism from normal monitoring to emergency response is formed. This mechanism, through dynamic risk classification, can not only adjust the threshold according to the specific engineering characteristics, which has good flexibility, but also facilitates the understanding and implementation of corresponding control strategies through clear level division. Finally, by generating different control strategies corresponding to different risk levels, the occurrence of overreaction or underresponse is avoided. At the same time, the specific measures for different warning levels reflect quantitative characteristics. The quantitative control strategies generated based on the quantitative assessment results and accurately matched with the risk level help to achieve intelligent decision-making and linkage response from routine monitoring to precise vibration suppression, quantitative water stoppage and emergency reinforcement, which helps to minimize risks and thus form a complete closed-loop technical system of "monitoring-assessment-early warning-control".

[0019] This invention represents a technological leap from qualitative analysis to quantitative assessment of the coupling effects of multiple hazards, solving the technical challenges of unclear hazard coupling mechanisms and a lack of quantitative basis for control measures in traditional methods. It provides a systematic solution for underground mine engineering safety, encompassing precise perception and intelligent decision-making. Compared with existing technologies, this invention has the following advantages: 1) An explicit quantitative assessment method for the coupling effect of multiple disasters was established: breaking through the limitations of traditional independent analysis of single disasters, the nonlinear interaction between disasters such as water, vibration, creep, and corrosion was incorporated into the risk assessment system for the first time. By introducing time-varying coupling factors, a scientific characterization of the spatiotemporal superposition effect of multiple disasters was achieved, providing a theoretical basis for risk assessment.

[0020] 2) A hierarchical early warning mechanism based on dynamic risk index was constructed: multi-source monitoring data and coupled models were innovatively integrated to establish a four-level risk level classification standard, which enabled accurate identification and early warning of the safety status of underground space in mines, and significantly improved the pertinence and timeliness of risk control.

[0021] 3) A quantitative collaborative control strategy generation method is proposed: Based on the dynamic risk assessment results and the combination of dominant disaster modes, a collaborative control scheme including vibration suppression, waterproofing, and corrosion prevention measures is automatically generated, which solves the problems of insufficient targeting and poor coordination of traditional single control measures and realizes the transformation from passive response to active intervention.

[0022] 4) A closed-loop technical system integrating assessment and control has been formed: through monitoring data feedback and self-learning updates of model parameters, the accuracy of risk assessment and control effect have been continuously optimized, providing dynamic safety assurance for underground mining projects throughout the entire cycle and effectively improving the long-term operational safety level of the project.

[0023] This method is simple to implement and has low implementation costs. It can achieve quantitative and coordinated accurate assessment of disasters based on the coupling effect of multiple disasters in underground space, and can automatically generate corresponding control strategies. Attached Figure Description

[0024] Figure 1 This is a principle block diagram of the system part of this invention; Figure 2 This is a flowchart of the method section of this invention. Detailed Implementation

[0025] The invention will now be further described with reference to the accompanying drawings.

[0026] like Figure 1 As shown, the present invention provides a quantitative assessment and control system for the coupling effect of multiple disasters in underground space, including a multi-source sensing module, a data fusion and preprocessing module, a coupling risk assessment module, and a quantitative control decision module; The multi-source sensing module includes an array of seepage pressure and flow sensors, an array of vibration and shock sensors, an array of structural strain sensors, and an array of corrosion monitoring sensors, which are used to collect multi-source heterogeneous data on the underground space environment and structural response in real time or periodically. The data fusion and preprocessing module is used to perform spatiotemporal registration, denoising, missing value imputation, and calculation of the original disaster intensity index on multi-source heterogeneous monitoring data to form a structural health status dataset with a unified spatiotemporal benchmark. The coupled risk assessment module has a built-in multi-hazard coupled risk quantification model, which is used to calculate a comprehensive risk index based on the structural health status dataset, representing the spatiotemporal superposition effect of multiple hazards such as water, vibration, impact, blasting, surrounding rock creep and structural corrosion. The quantitative control decision module is used to retrieve and generate quantitative collaborative control schemes from a preset strategy library based on the comprehensive risk index and the combination of dominant disaster modes, and then output them.

[0027] As a preferred embodiment, the pressure and flow sensor array is deployed along structural joints, cracks, and potential seepage paths; the vibration and impact sensor array is deployed on key components and the base plate that are sensitive to dynamic loads, with a monitoring frequency range of 0.1Hz to 200Hz.

[0028] As a preferred embodiment, the structural strain sensor array employs fiber optic grating sensors, permanently embedded in key monitoring sections, for monitoring long-term creep and instantaneous dynamic response; the corrosion monitoring sensor array employs macrocell-type or resistance probe-type sensors, deployed in areas of the structure prone to corrosion.

[0029] To facilitate the processing of massive amounts of time-series data, the coupled risk assessment module and quantitative control decision module are integrated into an edge computing gateway or cloud server.

[0030] To facilitate the automatic issuance of control commands to the corresponding execution units, the quantitative control decision module is interconnected with the underground space equipment management system.

[0031] In this invention, the multi-source sensing module simultaneously includes arrays of seepage pressure and flow sensors, vibration and impact sensors, structural strain sensors, and corrosion monitoring sensors, enabling the construction of a multi-source heterogeneous data fusion sensing system. This system can continuously collect multi-dimensional parameters such as hydrogeological data, dynamic response data, deformation monitoring data, and material corrosion data. The data fusion and preprocessing module facilitates automated data preprocessing and automatically calculates the original intensity indices characterizing different disaster types, providing a high-quality data foundation for subsequent risk quantification. The coupled risk assessment module incorporates a multi-hazard coupled risk quantification model, explicitly quantifying the nonlinear coupling relationships between disasters based on structural health status datasets. This fully considers the time-varying coupling effects of multiple disasters, ensuring that the obtained comprehensive risk index reflects the interaction mechanisms between multiple disasters. This achieves quantitative and coordinated accurate disaster assessment, making the risk assessment results more consistent with the actual safety status. The quantitative control decision module generates corresponding coordinated control strategies based on the comprehensive risk index and the dominant disaster mode. Therefore, a comprehensive monitoring system integrating multi-source information such as hydrology, vibration, stress-strain and corrosion has been constructed by multi-source sensing module, data fusion and preprocessing module, coupled risk assessment module and quantitative control decision module, which is conducive to the accurate assessment of disaster risks in underground space of mines and the automatic generation of corresponding decisions.

[0032] The system has a simple structure and a high degree of intelligence. It can accurately monitor and warn of disasters in underground mining projects and automatically generate corresponding control decisions, which helps to ensure the safety and efficiency of mining operations.

[0033] like Figure 2 As shown, the present invention also provides a method for quantitative assessment and control of the coupling effect of multiple hazards in underground space, which employs a quantitative assessment and control system for the coupling effect of multiple hazards in underground space, including the following steps: Step 1: Long-term collection of multi-source heterogeneous monitoring data based on multi-source sensing modules; Water pressure P is obtained through a osmotic pressure and flow sensor. w (t) and seepage flow rate Q w (t); vibration acceleration a(t) and impact load F(t) are obtained through vibration and impact sensors; time-varying strain is obtained through structural strain sensors. and dynamic strain Corrosion current density I is obtained through corrosion monitoring sensors. corr (t) or corrosion depth d corr (t); Step 2: Use the data fusion and preprocessing module to preprocess the data and calculate the original disaster intensity index; First, the data fusion and preprocessing module is used to perform spatiotemporal registration, denoising, and missing value imputation on the multi-source heterogeneous monitoring data. Then, empirical mode decomposition is performed on the multi-source heterogeneous monitoring data to separate the long-term trend term and short-term fluctuation term. The long-term trend term includes water pressure P. w (t), permeation flow rate Q w (t), time-varying strain Corrosion current density I corr (t) and corrosion depth d corr (t), the short-term fluctuation term includes vibration acceleration a(t), impact load F(t) and dynamic strain. ; Next, based on water pressure P w (t) and seepage flow rate Q w (t) to obtain the original intensity index of water inrush disaster, and based on vibration acceleration a(t) and impact load F(t) to obtain the original intensity index of rockburst disaster, based on time-varying strain. and dynamic strain The original strength index of the surrounding rock creep was obtained based on the corrosion current density I. corr (t) or corrosion depth d corr (t) Obtain the original strength index of structural corrosion; Step 3: Calculate the comprehensive risk index based on the disaster intensity index and time-varying coupling factor using the coupled risk assessment module. ; S31: In order to effectively eliminate the difference in dimensions, the first intensity index is calculated according to formula (1) based on the original intensity index. Normalized intensity index of various disasters ; (1); In the formula, These correspond to water inrush disasters, rock burst disasters, surrounding rock creep disasters, and structural corrosion disasters, respectively. For the first The original intensity index of the disaster at time t (water pressure P) w and time-varying strain ε of vibration acceleration a c ); , The first The design of disaster intensity allows for upper and lower limits; S32: In order to accurately quantify the dynamic correlation strength of each disaster in the time dimension, a characterization of the first disaster is constructed according to formula (2). disasters and the first Time-varying coupling factor of the dynamic correlation intensity of various disasters in the time dimension And it is dynamically updated; (2); In the formula, For smoothing coefficients, , For adjacent time periods The change in the intensity of a disaster For adjacent time periods The change in the intensity of a type of disaster; For the first Normalized intensity index of a type of disaster; S33: In order to achieve quantitative and coordinated accurate assessment of risks under the coupling effect of multiple disasters, a multi-hazard coupling risk quantification model is constructed according to formula (3), and the comprehensive risk index at time t is calculated. ; (3); In the formula, For the first The initial weights of the various disasters are determined using the analytic hierarchy process (AHP) or the entropy weight method, and satisfy the following conditions: ; For time t, the first The first type of disaster The time-varying coupling factor of various disasters; For the first The initial weights of the various disasters are determined using the analytic hierarchy process (AHP) or the entropy weight method, and satisfy the following conditions: ; Step 4: Utilize the quantitative control decision-making module based on the comprehensive risk index Conduct dynamic risk classification; Set risk thresholds sequentially from low to high. , and ,when When, it is judged as normal level (Level I); when When, it is judged as Level II (attention level); when When, it is determined to be a warning level (Level III); when At that time, it was determined to be a high-risk level (Level IV); Step 5: Utilize the quantitative control decision-making module to generate quantitative control strategies based on risk classification results and dominant disaster patterns; Based on the risk classification results and the combination of dominant disaster modes, a quantitative control strategy matching the risk level is generated by calling from the preset strategy library, and the quantitative control strategy is executed. The quantitative control strategy includes the following: When the level is normal, it outputs a suggestion to perform routine inspections; when the level is alert, it outputs a suggestion to initiate targeted enhanced monitoring and perform quantitative maintenance on identified minor defects; when the level is warning, it outputs a suggestion to initiate active control measures, including but not limited to deploying or activating active / semi-active damping systems with preset parameters along the vibration propagation path, while controlling changes to the operating models of large mining equipment such as internal ventilation and pump sets to avoid resonance, and performing precise grouting operations (quantitative water stopping) based on seepage flow and water pressure calculations; when the level is high-risk, it outputs a suggestion to immediately implement space restriction or closure, initiate a comprehensive engineering treatment plan, and trigger an emergency response alarm.

[0034] As a preferred option, in step four, , and The threshold was set based on historical data analysis and engineering experience, and .

[0035] To enable the multi-hazard coupled risk quantification model to have self-learning and updating capabilities, and to ensure its reliability and stability during long-term service, in step three, every 1-2 years, the latest multi-source heterogeneous monitoring data and control effect feedback data are used to update the coupling factor matrix through machine learning algorithms. The weights in the multi-hazard coupled risk quantification model are updated and calibrated to achieve continuous optimization of the model. By updating and calibrating the coupling weights between hazards, accurate quantitative characterization of the interactions between hazards such as water damage, vibration, creep, and corrosion can be achieved.

[0036] In order to accurately reflect the coupling of multiple disasters, a time-varying coupling factor is needed. The initial values ​​are obtained through laboratory simulation of multi-hazard coupling experiments. Alternatively, they can be obtained through historical data mining or machine learning. During operation, the values ​​are dynamically corrected by monitoring data sequences and using system identification technology based on least squares or neural networks to truly reflect the time-varying interactions between disasters.

[0037] To accurately reflect the intensity indices of rock creep and structural corrosion, the original intensity index of rock creep is obtained according to formula (4). The original strength index of structural corrosion disaster is obtained according to formula (5). ; (4); In the formula, This represents the critical allowable value for time-varying strain; , , These are the material constants, stress exponents, and time exponents related to time-varying strain, respectively. For the current pressure; (5); In the formula, This represents the critical allowable value for corrosion depth. , These are the corrosion rate constant and time exponent, which are related to the corrosion depth, respectively.

[0038] This invention provides a method for quantitative assessment and control of the coupling effects of multiple hazards in underground space. First, by setting up multi-source sensing modules, a complete monitoring network can be formed, enabling the collection of multi-source heterogeneous monitoring data. This ensures that the monitoring data effectively covers the four key hazard types in underground engineering (water inrush, rock bursts, surrounding rock creep, and structural corrosion). Next, data preprocessing facilitates spatiotemporal registration, noise reduction, and missing value imputation, ensuring data reliability and consistency. Empirical mode decomposition effectively distinguishes between long-term slow changes and short-term dynamic responses, facilitating differentiated analysis. Based on this, a dedicated original intensity index calculation model is established for each hazard, improving the accuracy of the assessment. Furthermore, addressing the complex coupling problem of multiple hazards interacting and exacerbating each other in underground mine spaces during long-term service, such as groundwater seepage, blasting and mechanical vibration, impact loads, aging creep of surrounding rock, and corrosion of structural materials, a multi-hazard coupling risk quantification model was innovatively constructed based on the intensity index and time-varying coupling factor. This model can explicitly quantify the nonlinear coupling relationship between hazards, and thus fully consider the time-varying coupling effect of the multi-hazard dynamic risk assessment model. It can accurately reflect the mutual influence between hazards in real time and fully reflect the coupling effect of different hazard types, which is more in line with engineering practice. By introducing the time-varying coupling factor, the spatiotemporal evolution law of the hazard chain under the coupling effect of water-mechanical-chemical multi-fields can be accurately characterized. Then, based on the multi-level threshold division of the dynamic risk index, a graded early warning mechanism from normal monitoring to emergency response is formed. This mechanism, through dynamic risk classification, can not only adjust the threshold according to the specific engineering characteristics, which has good flexibility, but also facilitates the understanding and implementation of corresponding control strategies through clear level division. Finally, by generating different control strategies corresponding to different risk levels, the occurrence of overreaction or underresponse is avoided. At the same time, the specific measures for different early warning levels reflect quantitative characteristics. The quantitative control strategies generated based on the quantitative assessment results and accurately matched with the risk level help to achieve intelligent decision-making and linkage response from routine monitoring to precise vibration suppression, quantitative water stoppage and emergency reinforcement, which helps to minimize risks and thus form a complete closed-loop technical system of "monitoring-assessment-early warning-control".

[0039] This invention represents a technological leap from qualitative analysis to quantitative assessment of multi-hazard coupling effects, solving the technical challenges of unclear hazard coupling mechanisms and a lack of quantitative basis for control measures in traditional methods. It provides a systematic solution for mine underground engineering safety, from precise perception to intelligent decision-making. This invention overcomes the limitations of traditional isolated assessment and passive prevention of single hazards, addressing key technical challenges such as the difficulty in quantifying multi-hazard coupling effects, the lack of systemic risk assessment, and the lack of targeted and coordinated control measures. It provides a systematic solution for the long-term safe operation of mine underground engineering, facilitating intelligent decision-making from routine inspections to precise vibration suppression, quantitative water sealing, and emergency reinforcement. This provides a systematic solution for mine underground engineering safety, from "perception and early warning" to "precise control."

Claims

1. A quantitative assessment and control system for the coupling effects of multiple hazards in underground space, characterized in that, It includes a multi-source sensing module, a data fusion and preprocessing module, a coupled risk assessment module, and a quantitative control decision module; The multi-source sensing module includes an array of seepage pressure and flow sensors, an array of vibration and shock sensors, an array of structural strain sensors, and an array of corrosion monitoring sensors, which are used to collect multi-source heterogeneous data on the underground space environment and structural response in real time or periodically. The data fusion and preprocessing module is used to perform spatiotemporal registration, denoising, missing value imputation, and calculation of the original disaster intensity index on multi-source heterogeneous monitoring data to form a structural health status dataset with a unified spatiotemporal benchmark. The coupled risk assessment module has a built-in multi-hazard coupled risk quantification model, which is used to calculate a comprehensive risk index representing the spatiotemporal superposition effect of multiple hazards based on the structural health status dataset. The quantitative control decision module is used to retrieve and generate quantitative collaborative control schemes from a preset strategy library based on the comprehensive risk index and the combination of dominant disaster modes, and then output them.

2. The quantitative assessment and control system for the coupling effect of multiple disasters in underground space according to claim 1, characterized in that, The pressure and flow sensor array is deployed along structural joints, cracks, and potential seepage paths; the vibration and impact sensor array is deployed on key components and the base plate that are sensitive to dynamic loads, with a monitoring frequency range of 0.1Hz to 200Hz.

3. The quantitative assessment and control system for the coupling effect of multiple disasters in underground space according to claim 2, characterized in that, The structural strain sensor array uses fiber optic grating sensors, which are permanently embedded in key monitoring sections to monitor long-term creep and instantaneous dynamic response; the corrosion monitoring sensor array uses macrocell-type or resistance probe-type sensors, which are deployed in the easily corroded areas of the structure.

4. The quantitative assessment and control system for the coupling effect of multiple disasters in underground space according to claim 3, characterized in that, The coupled risk assessment module and the quantitative control decision module are integrated into an edge computing gateway or a cloud server; the quantitative control decision module is interconnected with the underground space equipment management system.

5. A method for quantitative assessment and control of the coupling effect of multiple hazards in underground space, employing the quantitative assessment and control system for the coupling effect of multiple hazards in underground space as described in claim 4, characterized in that, Includes the following steps: Step 1: Collect multi-source heterogeneous monitoring data; obtain water pressure P through osmosis and flow sensors. w (t) and seepage flow rate Q w (t); vibration acceleration a(t) and impact load F(t) are obtained through vibration and impact sensors; time-varying strain is obtained through structural strain sensors. and dynamic strain Corrosion current density I is obtained through corrosion monitoring sensors. corr (t) or corrosion depth d corr (t); Step 2: Data preprocessing and calculation of the original disaster intensity index; based on water pressure P w (t) and seepage flow rate Q w (t) to obtain the original intensity index of water inrush disaster, and based on vibration acceleration a(t) and impact load F(t) to obtain the original intensity index of rockburst disaster, based on time-varying strain. and dynamic strain The original strength index of the surrounding rock creep was obtained based on the corrosion current density I. corr (t) or corrosion depth d corr (t) Obtain the original strength index of structural corrosion; Step 3: Calculate the comprehensive risk index based on the disaster intensity index and the time-varying coupling factor. S31: Calculation of the first strength index based on the original strength index Normalized intensity index of various disasters S32: Construction of the characterization of the first disasters and the first Time-varying coupling factor of the dynamic correlation intensity of various disasters in the time dimension S33: Construct a multi-hazard coupled risk quantification model and calculate the comprehensive risk index at time t. ; Step 4: Based on the comprehensive risk index Implement dynamic risk classification; set risk thresholds sequentially from low to high. , and ,when When, it is judged as normal level; when When, it is judged as a level of attention; when When, it is determined to be at the warning level; when At that time, it was determined to be of a high-risk level; Step 5: Generate quantitative control strategies based on risk classification results and dominant disaster patterns; based on the risk classification results and the combination of dominant disaster patterns, call and generate quantitative control strategies that match the risk level from the preset strategy library, and execute the quantitative control strategies.

6. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 5, characterized in that, In step S31 of step three, the first step is calculated according to formula (1). Normalized intensity index of various disasters ; In step S32 of step three, the time-varying coupling factor is constructed according to formula (2). ; In step S33 of step three, a multi-hazard coupling risk quantification model is constructed according to formula (3); （1）； In the formula, These correspond to water inrush disasters, rock burst disasters, surrounding rock creep disasters, and structural corrosion disasters, respectively. For the first The original intensity index of the disaster at time t; , The first The design of disaster intensity allows for upper and lower limits; （2）； In the formula, For smoothing coefficients, , For adjacent time periods The change in the intensity of a disaster For adjacent time periods The change in the intensity of a type of disaster; For the first Normalized intensity index of a type of disaster; （3）； In the formula, For the first Initial weights for different types of disasters; For the first The initial weight of each type of disaster.

7. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 6, characterized in that, In step four, , and The threshold was set based on historical data analysis and engineering experience, and .

8. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 7, characterized in that, In step five, the quantitative control strategy includes the following: when the level is normal, output a suggestion to perform routine inspections; when the level is alert, output a suggestion to initiate targeted enhanced monitoring and perform quantitative maintenance on identified minor defects. When the warning level is reached, the system will output a suggestion to initiate active control measures. At the same time, it will control the operation model of large mining equipment to change to avoid resonance and perform precise grouting operations based on seepage flow and water pressure calculations. When the high-risk level is reached, the system will output a suggestion to immediately implement space restriction or closure, initiate a comprehensive engineering treatment plan, and trigger an emergency response alarm.

9. A method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 8, characterized in that, In step three, the time-varying coupling factor The initial values ​​were obtained through laboratory simulation of multi-hazard coupling experiments, and during operation, the data sequences were monitored and dynamically corrected using system identification technology based on least squares or neural networks to truly reflect the time-varying interactions between disasters.

10. A method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 9, characterized in that, The original strength index of the surrounding rock creep disaster is obtained according to formula (4). The original strength index of structural corrosion disaster is obtained according to formula (5). ; （4）； In the formula, This represents the critical allowable value for time-varying strain; , , These are the material constants, stress exponents, and time exponents related to time-varying strain, respectively. For the current pressure; （5）； In the formula, This represents the critical allowable value for corrosion depth. , These are the corrosion rate constant and time exponent, which are related to the corrosion depth, respectively.

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