Quantitative evaluation and quantitative control system and method for multi-disaster coupling effect of underground space

Abstract

The application provides a kind of underground space multi-disaster coupling effect quantitative evaluation and quantitative control system and method, system: multi-source perception module, data fusion and preprocessing module, coupling risk assessment module and quantitative control decision module are sequentially connected;Method: based on multi-source perception module long-term acquisition multi-source heterogeneous monitoring data;Multi-source heterogeneous monitoring data are registered in space-time, denoising, missing value interpolation, and the calculation of disaster original intensity index;Different disaster intensity index normalization processing, the time-varying coupling factor between disasters is constructed, and the comprehensive risk index is calculated using the coupling risk assessment module;Quantitative control decision module based on comprehensive risk index carries out dynamic risk classification;Quantitative control decision module based on risk classification results and dominant disaster mode combination, calls and executes quantitative control strategy from preset strategy library.The application can realize the accurate monitoring and early warning of mine underground engineering disaster, and can automatically generate the corresponding control decision.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mine engineering safety, and particularly relates to a system and method for quantitatively evaluating and controlling coupling effects of multiple disasters in underground space. BACKGROUND

[0002] During the service period of underground engineering structures, they are continuously subjected to complex and harsh environmental effects, such as creep of surrounding rock under high ground pressure, erosion of high-permeability underground water, periodic blasting and mechanical vibration load, and structure corrosion caused by hot and humid environment. These physical, chemical and mechanical disasters do not exist in isolation, but are intertwined and promoted with each other in the time and space dimensions, forming significant coupling effects. For example, blasting vibration can aggravate the development of rock fissures, providing a channel for water penetration; the invasion of water not only softens the rock mass and reduces its strength, but also accelerates the corrosion process of supporting members, ultimately leading to systematic deterioration of the bearing capacity of the structure. The chain reaction and nonlinear superposition mechanism of multiple disasters have become the core scientific problem and technical bottleneck faced by deep mine engineering disaster prevention and control.

[0003] Currently, the risk evaluation and prevention and control technology for mine underground engineering safety still cannot effectively cope with the challenges brought by multiple disaster coupling. Existing methods mostly focus on independent analysis and control of single disaster, such as water gushing monitoring, vibration velocity evaluation or corrosion detection. This research paradigm of "divide and rule" essentially ignores the interaction mechanism between disasters, leading to significant deviation between risk evaluation results and actual safety state, making it difficult to predict the systematic risk induced by multiple disaster coupling. In terms of technology, existing models lack the ability to quantitatively describe the coupling relationship between disasters, making the evaluation process overly dependent on engineering experience and simplified assumptions. The corresponding control strategy also mostly shows passive response based on single threshold, such as blocking when encountering seepage and stopping when exceeding vibration, lacking active intervention means for the evolution law of coupled risk, making it difficult to realize the precision and foresight of safety prevention and control.

[0004] In order to break through the above technical limitations, it is necessary to provide a system and method for quantitatively evaluating and controlling coupling effects of multiple disasters in underground space. SUMMARY

[0005] In view of the problems existing in the prior art, the application provides a system and method for quantitatively evaluating and controlling coupling effects of multiple disasters in underground space. The system has simple structure and high intelligence, which can realize accurate monitoring and early warning of mine underground engineering disasters, and automatically generate corresponding control decisions, which is beneficial to ensure the safety and efficiency of mine production operation. The method has simple implementation process and low implementation cost, which can realize quantitative and collaborative precise evaluation of disasters based on the coupling effects of multiple disasters in underground space, and automatically generate corresponding control strategies.

[0006] In order to achieve the above object, the application provides a multi-disaster coupling effect quantitative evaluation and quantitative control system for underground space, comprising a multi-source perception module, a data fusion and preprocessing module, a coupling risk evaluation module and a quantitative control decision module.

[0007] The multi-source perception module comprises an osmotic pressure and flow sensor array, a vibration and impact sensor array, a structural strain sensor array and a corrosion monitoring sensor array, which are used to collect multi-source heterogeneous data of underground space environment and structural response in real time or periodically.

[0008] The data fusion and preprocessing module is used to perform time-space registration, denoising, missing value interpolation and disaster original intensity index calculation on multi-source heterogeneous monitoring data, so as to form a structural health state data set with unified time-space reference.

[0009] The coupling risk evaluation module is internally provided with a multi-disaster coupling risk quantitative model, which is used to calculate a comprehensive risk index representing the time-space superposition effect of multi-disasters based on the structural health state data set.

[0010] The quantitative control decision module is used to retrieve and generate a quantitative collaborative control scheme from a preset strategy library based on the comprehensive risk index and the dominant disaster mode combination, and then output the scheme.

[0011] As a preferred, the osmotic pressure and flow sensor array is arranged along the structure joints, cracks and potential water seepage paths; the vibration and impact sensor array is arranged on the key components and floor plates sensitive to dynamic load, and the monitoring frequency range is 0.1 Hz to 200 Hz.

[0012] As a preferred, the structural strain sensor array adopts a fiber grating sensor, which is permanently implanted in the key monitoring section and used to monitor long-term creep and instantaneous dynamic response; the corrosion monitoring sensor array adopts a macro-battery type or resistance probe type sensor, which is arranged in the structure corrosion prone area.

[0013] Further, in order to facilitate the ability to process massive time series data, the coupling risk evaluation module and the quantitative control decision module are integrated in an edge computing gateway or a cloud server; in order to facilitate automatic delivery of control instructions to the corresponding execution unit, the quantitative control decision module is interconnected with the underground space equipment management system.

[0014] In the application, the multi-source perception module simultaneously includes an osmotic pressure and flow sensor array, a vibration and impact sensor array, a structural strain sensor array and a corrosion monitoring sensor array, a multi-source heterogeneous data fusion perception system can be constructed, and then multi-dimensional parameters such as hydrogeology, dynamic response, deformation monitoring and material corrosion can be continuously collected. Through the setting of the data fusion and preprocessing module, the data preprocessing can be automatically carried out, and the original intensity index for representing different disaster types can be automatically calculated, which is beneficial to providing a high-quality data basis for subsequent risk quantification. The coupling risk assessment module is internally provided with a multi-disaster coupling risk quantification model, the nonlinear coupling relationship between disasters can be quantified based on the structural health state data set, and then the time-varying coupling effect of multi-disasters is fully considered, so that the comprehensive risk index obtained can reflect the interaction mechanism between multi-disasters, and the quantitative, collaborative and accurate evaluation of disasters is realized, and the risk assessment result is more consistent with the actual safety state. Through the setting of the quantitative control decision module, the corresponding collaborative control strategy can be generated based on the comprehensive risk index and the dominant disaster mode. Therefore, the multi-source information such as hydrology, vibration, stress and strain and corrosion is integrated into the comprehensive monitoring system through the multi-source perception module, the data fusion and preprocessing module, the coupling risk assessment module and the quantitative control decision module, and the accurate evaluation of the disaster risk of the underground space of the mine and the automatic generation of the corresponding decision are realized.

[0015] The system has simple structure and high intelligence, can realize accurate monitoring and early warning of the disaster of the underground engineering of the mine, and can automatically generate the corresponding control decision, and is beneficial to ensuring the safety and efficiency of the production operation of the mine.

[0016] The application further provides a multi-disaster coupling effect quantitative evaluation and quantitative control method and system for underground space.

[0017] Step one: collecting multi-source heterogeneous monitoring data; obtaining water pressure P w (t) and seepage flow Q w (t) through the osmotic pressure and flow sensor; obtaining vibration acceleration a(t) and impact load F(t) through the vibration and impact sensor; obtaining time-varying strain and dynamic strain through the structural strain sensor; obtaining corrosion current density I corr (t) or corrosion depth d corr (t) through the corrosion monitoring sensor;

[0018] Step two: data preprocessing and calculation of disaster original intensity index; based on water pressure P w (t) and seepage flow Q w (t), the data preprocessing and calculation of the disaster original intensity index are carried out.(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;

[0019] 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. ;

[0020] 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;

[0021] 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.

[0022] 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);

[0023] (1);

[0024] In the formula, correspond to water inrush disaster, rock burst disaster, surrounding rock creep disaster and structure corrosion disaster respectively, is the original intensity index of the first disaster at time t; , are the design upper limit and lower limit of the intensity of the first disaster respectively;

[0025] (2);

[0026] In the formula, is a smoothing coefficient, , is the change amount of the intensity of the first disaster at adjacent time, is the change amount of the intensity of the first disaster at adjacent time; is the normalized intensity index of the first disaster;

[0027] (3);

[0028] In the formula, is the initial weight of the first disaster; is the initial weight of the first disaster.

[0029] As a preferred, in step four, , and are threshold values set by historical data analysis and engineering experience, and .

[0030] As a preferred, the quantitative control strategy includes the following contents: when it is normal level, output the suggestion of executing routine inspection; when it is attention level, output the suggestion of starting targeted enhanced monitoring and performing quantitative maintenance on the identified slight defects; when it is pre-warning level, output the suggestion of starting active control measures, at the same time, change the operation model of the large mining equipment to avoid resonance, and perform accurate grouting operation based on seepage flow and water pressure calculation; when it is high-risk level, output the suggestion of immediately implementing space line restriction or closure, and starting comprehensive engineering treatment scheme, and trigger emergency response alarm action.

[0031] Further, in order to accurately reflect the coupling of multiple disasters, the time-varying coupling factor The initial value of the strength index is obtained through a multi-disaster coupling test in a laboratory, and is dynamically corrected by a system identification technology based on least squares or a neural network through a monitoring data sequence during operation to truly reflect the time-varying interaction between disasters.

[0032] Further, in order to accurately reflect the strength index of the surrounding rock creep disaster and the structure corrosion disaster, the original strength index of the surrounding rock creep disaster is obtained according to formula (4) ; the original strength index of the structure corrosion disaster is obtained according to formula (5) ;

[0033] (4);

[0034] In the formula, is a critical allowable value of time-varying strain; , , are a material constant, a stress index and a time index related to time-varying strain respectively; is a current pressure;

[0035] (5);

[0036] In the formula, is a critical allowable value of corrosion depth; , are a corrosion rate constant and a time index related to corrosion depth respectively.

[0037] The application provides a kind of underground space multi-disaster coupling effect quantitative evaluation and quantitative control method, first, through the setting of multi-source perception module, complete monitoring network can be formed, and then the acquisition of multi-source heterogeneous monitoring data can be carried out, which can ensure that the monitoring data effectively covers the four key disaster types of underground engineering (water inrush, rock burst, surrounding rock creep, structure corrosion). Then, through data preprocessing, it is convenient to carry out time and space registration, denoising, missing value interpolation and other processing, which ensures the reliability and consistency of the data. On this basis, a special original intensity index calculation model is established for each disaster, which improves the accuracy of evaluation. Furthermore, in view of the complex coupling problem of multiple disasters in the long-term service process of mine underground space, such as groundwater seepage, blasting and mechanical vibration, impact load, surrounding rock time creep and structure material corrosion, the strength index and time-varying coupling factor are innovatively constructed to build a multi-disaster coupling risk quantitative model, which can explicitly quantify the nonlinear coupling relationship between disasters, and fully consider the time-varying coupling effect of multi-disaster in the risk assessment process. It can accurately reflect the mutual influence relationship between disasters, and can effectively reflect the coupling effect between different disaster types, which is more in line with the engineering practice. Through the introduction of time-varying coupling factor, the spatio-temporal evolution law of disaster chain under the action of water-force-chemical multi-field coupling can be accurately characterized, which is beneficial to make the risk assessment results more consistent with the actual safety state. Then, based on the multi-level threshold division of dynamic risk index, a hierarchical early warning mechanism from normal monitoring to emergency response is formed. This mechanism can not only adjust the threshold according to the specific engineering characteristics through dynamic risk grading, but also has good flexibility, and can facilitate understanding and implementation of the corresponding control strategy through clear grade division. Finally, different control strategies are generated corresponding to different risk levels, which avoids the occurrence of overreaction or insufficient response. At the same time, the specific measures of different early warning levels reflect the quantitative characteristics. According to the quantitative evaluation results, the quantitative control strategy accurately matched with the risk level is generated, which is helpful to realize intelligent decision and linkage response from routine monitoring to precise vibration suppression, quantitative water stop and emergency reinforcement, and is beneficial to reduce the risk to the minimum, so as to form a complete "monitoring-evaluation-warning-control" closed-loop technical system.

[0038] The application realizes the technical leap from qualitative analysis to quantitative evaluation of multi-disaster coupling effect, solves the technical problems of unclear disaster coupling mechanism and lack of quantitative basis for control measures in traditional methods, and provides a systematic solution from precise perception to intelligent decision for mine underground engineering safety. Compared with the prior art, the application has the following advantages:

[0039] 1) Establish an explicit quantitative evaluation method for multi-disaster coupling effect: Break through the limitation of traditional single disaster independent analysis, for the first time, the nonlinear interaction between water, vibration, creep, corrosion and other disasters is introduced into the risk assessment system, through the introduction of time-varying coupling factor, the scientific characterization of the spatio-temporal superposition effect of multi-disaster is realized, and the theoretical basis for risk assessment is provided.

[0040] 2) Build a grading early warning mechanism based on dynamic risk index: Innovatively integrate multi-source monitoring data and coupling model, establish four-level risk grade division standard, realize accurate identification and early warning of mine underground space safety state, significantly improve the pertinence and timeliness of risk control.

[0041] 3) Propose a quantitative synergistic control strategy generation method: According to the combination of dynamic risk assessment results and dominant disaster mode, a synergistic control scheme containing vibration suppression, water prevention, corrosion prevention and other measures is automatically generated, which solves the problem of poor pertinence and poor synergy of traditional single control measures, and realizes the change from passive response to active intervention.

[0042] 4) Form an integrated closed-loop technology system of evaluation and control: Through monitoring data feedback and model parameter self-learning update, the continuous optimization of risk assessment accuracy and control effect is realized, providing dynamic safety protection for mine underground engineering in the whole cycle, effectively improving the long-term operation safety level of the project.

[0043] The method has simple implementation process and low implementation cost, which can realize quantitative and synergistic precise evaluation of disasters based on multi-disaster coupling effect of underground space, and automatically generate corresponding control strategies. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is the principle block diagram of the system part in the application;

[0045] Figure 2 is the flow chart of the method part in the application. DETAILED DESCRIPTION

[0046] The application will be further described below with reference to the accompanying drawings.

[0047] As Figure 1 shown, the application provides a quantitative evaluation and quantitative control system for multi-disaster coupling effect of underground space, which comprises a multi-source perception module, a data fusion and preprocessing module, a coupling risk assessment module and a quantitative control decision module.

[0048] The multi-source perception module comprises a seepage pressure and flow sensor array, a vibration and impact sensor array, a structure strain sensor array and a corrosion monitoring sensor array, which is used for real-time or periodic acquisition of multi-source heterogeneous data of underground space environment and structure response;

[0049] The data fusion and preprocessing module is used for spatio-temporal registration, denoising, missing value interpolation and calculation of disaster original intensity index of multi-source heterogeneous monitoring data, forming a unified spatio-temporal reference structural health state data set;

[0050] The coupling risk assessment module is built-in multi-disaster coupling risk quantification model, which is used to calculate the comprehensive risk index representing the spatio-temporal superposition effect of water, vibration, impact, blasting, surrounding rock creep and structure corrosion based on the structural health state data set;

[0051] The quantitative control decision module is used to retrieve and generate quantitative collaborative control scheme from the preset strategy library based on the comprehensive risk index and dominant disaster mode combination, and output.

[0052] As a preferred, the osmotic pressure and flow sensor array is arranged along the structure joint, crack and potential water seepage path; the vibration and impact sensor array is arranged on the key components and floor sensitive to dynamic load, and the monitoring frequency range is 0.1 Hz to 200 Hz.

[0053] As a preferred, the structure strain sensor array adopts fiber grating sensor, which is permanently implanted in the key monitoring section for monitoring long-term creep and instantaneous dynamic response; the corrosion monitoring sensor array adopts macro-battery type or resistance probe type sensor, which is arranged in the structure corrosion prone area.

[0054] In order to facilitate the ability to process massive time series data, the coupling risk assessment module and the quantitative control decision module are integrated in the edge computing gateway or the cloud server.

[0055] In order to facilitate the automatic issuance of control instructions to the corresponding execution unit, the quantitative control decision module is interconnected with the underground space equipment management system.

[0056] In the present application, the multi-source perception module simultaneously includes an osmotic pressure and flow sensor array, a vibration and impact sensor array, a structural strain sensor array, and a corrosion monitoring sensor array, so that a multi-source heterogeneous data fusion perception system can be constructed, and multi-dimensional parameters such as hydrogeology, dynamic response, deformation monitoring, and material corrosion can be continuously collected. Through the setting of the data fusion and preprocessing module, the data preprocessing can be automatically performed, and the original intensity index for representing different disaster types can be automatically calculated, which is beneficial to providing a high-quality data basis for subsequent risk quantification. The coupling risk assessment module is internally provided with a multi-disaster coupling risk quantification model, which can quantitatively express the nonlinear coupling relationship between disasters based on the structural health state data set, and fully consider the time-varying coupling effect of multi-disasters, so that the comprehensive risk index obtained can reflect the interaction mechanism between multi-disasters, and the quantitative, collaborative and accurate evaluation of disasters can be realized, and the risk assessment result can be more consistent with the actual safety state. Through the setting of the quantitative control decision module, the corresponding collaborative control strategy can be generated based on the comprehensive risk index and the dominant disaster mode. Thus, the multi-source perception module, the data fusion and preprocessing module, the coupling risk assessment module and the quantitative control decision module are combined to form a comprehensive monitoring system integrating multi-source information such as hydrology, vibration, stress and strain, and corrosion, which is beneficial to realizing the accurate evaluation of the disaster risk of the underground space of the mine and the automatic generation of the corresponding decision.

[0057] The system has simple structure and high intelligence, and can realize accurate monitoring and early warning of the disaster of the underground engineering of the mine, and automatically generate the corresponding control decision, which is beneficial to ensuring the safety and efficiency of the production operation of the mine.

[0058] As shown in Figure 2 , the present application further provides a method for quantitatively evaluating and controlling the coupling effect of multi-disasters in underground space, which adopts a system for quantitatively evaluating and controlling the coupling effect of multi-disasters in underground space, and comprises the following steps:

[0059] Step 1: collecting multi-source heterogeneous monitoring data based on the multi-source perception module for a long time;

[0060] obtaining the water pressure P w (t) and the seepage flow Q w (t) through the osmotic pressure and flow sensor; obtaining the vibration acceleration a(t) and the impact load F(t) through the vibration and impact sensor; obtaining the time-varying strain and the dynamic strain through the structural strain sensor; and obtaining the corrosion current density I corr (t) or the corrosion depth d corr (t) through the corrosion monitoring sensor;

[0061] Step 2: performing data preprocessing and calculating the disaster original intensity index by using the data fusion and preprocessing module;

[0062] 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. ;

[0063] 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;

[0064] Step 3: Calculate the comprehensive risk index based on the disaster intensity index and time-varying coupling factor using the coupled risk assessment module. ;

[0065] 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 ;

[0066] (1);

[0067] 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;

[0068] 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). A disaster and a first A time-varying coupling factor of dynamic correlation strength of a disaster in time dimension , and dynamically updated;

[0069] (2);

[0070] In the formula, is a smoothing coefficient, , is a change amount of the intensity of the first disaster at adjacent time; is a change amount of the intensity of the first disaster at adjacent time; is a normalized intensity index of the first disaster;

[0071] S33: In order to realize quantitative, synergistic and accurate assessment of risk under the coupling effect of multiple disasters, a multi-disaster coupling risk quantitative model is constructed according to formula (3), and the comprehensive risk index at time t is calculated ;

[0072] (3);

[0073] In the formula, is an initial weight of the first disaster, which is determined by an analytic hierarchy process or an entropy weight method, and satisfies ; is a time-varying coupling factor of the first disaster to the first disaster at time t; is an initial weight of the first disaster, which is determined by an analytic hierarchy process or an entropy weight method, and satisfies ;

[0074] Step four: using a quantitative control decision module to perform dynamic risk classification based on the comprehensive risk index ;

[0075] Risk threshold values , and are set from low to high, when , it is determined as a normal level (I level); when , it is determined as an attention level (II level); when , it is determined as a warning level (III level); and when , it is determined as a high-risk level (IV level);

[0076] Step five: generating quantitative control strategies based on the risk classification results and the dominant disaster mode by using the quantitative control decision module;

[0077] Based on the combination of risk classification results and dominant disaster mode, the quantitative control strategy matching the risk level is called and generated from the preset strategy library, and the quantitative control strategy is executed;

[0078] The quantitative control strategy includes the following contents: when it is normal, it outputs the suggestion of executing routine inspection; when it is attention, it outputs the suggestion of starting targeted enhanced monitoring and performing quantitative maintenance on the identified minor defects; when it is pre-warning, it outputs the suggestion of starting active control measures, including but not limited to deploying or activating the preset parameter active / semi-active damping system on the vibration propagation path, changing the operation model of large mining equipment such as internal ventilation and pump group to avoid resonance, and performing precise grouting operation (quantitative water stop) based on seepage flow and water pressure calculation; when it is high risk, it outputs the suggestion of immediately implementing space line restriction or closure and starting comprehensive engineering treatment scheme, and triggers the emergency response alarm action.

[0079] As a preferred, in step four, 、 and are threshold values set by historical data analysis and engineering experience, and .

[0080] In order to make the multi-disaster coupling risk quantification model have self-learning and updating ability, and to ensure the reliability and stability during long-term service, in step three, the latest multi-source heterogeneous monitoring data and control effect feedback data are used every 1-2 years to update and calibrate the coupling factor matrix and the weight in the multi-disaster coupling risk quantification model through machine learning algorithm, so as to realize the continuous optimization of the multi-disaster coupling risk quantification model. By updating and calibrating the coupling weight between disasters, the interaction of water disaster, vibration, creep and corrosion disasters can be accurately quantified and characterized.

[0081] In order to accurately reflect the coupling of multi-disaster, the initial value of the time-varying coupling factor is obtained through laboratory simulation of multi-disaster coupling test, of course, it can also be obtained through historical data mining or machine learning, and during operation, the dynamic correction is carried out by using the system identification technology based on least square method or neural network through monitoring data sequence, so as to truly reflect the time-varying interaction between disasters.

[0082] In order to accurately reflect the strength index of surrounding rock creep disaster and structure corrosion disaster, the original strength index of surrounding rock creep disaster is obtained according to formula (4); the original strength index of structure corrosion disaster is obtained according to formula (5) ;

[0083] (4);

[0084] wherein, is a critical allowable value of time-varying strain; , , are material constants, stress exponent and time exponent related to time-varying strain, respectively; is a current pressure;

[0085] (5);

[0086] wherein, is a critical allowable value of corrosion depth; , are corrosion rate constant and time exponent related to corrosion depth, respectively.

[0087] The application provides a kind of underground space multi-disaster coupling effect quantitative evaluation and quantitative control method, first, through the setting of multi-source perception module, complete monitoring network can be formed, and then the acquisition of multi-source heterogeneous monitoring data can be carried out, which can ensure that the monitoring data effectively covers the four key disaster types of underground engineering (water inrush, rock burst, surrounding rock creep, structure corrosion). Then, through data preprocessing, it is convenient for time and space registration, denoising, missing value interpolation and other processing, which ensures the reliability and consistency of data. By empirical mode decomposition, long-term slow change and short-term dynamic response can be effectively distinguished, which is convenient for analysis. On this basis, a special original intensity index calculation model is established for each disaster, which improves the accuracy of evaluation. Furthermore, in view of the complex coupling problem of mine underground space in long-term service process, such as groundwater seepage, blasting and mechanical vibration, impact load, surrounding rock time creep and structure material corrosion, multi-disaster coupling risk quantitative model is innovatively constructed based on intensity index and time-varying coupling factor, which can explicitly quantify the nonlinear coupling relationship between disasters, and then the multi-disaster dynamic risk evaluation model considering time-varying coupling effect can accurately reflect the mutual influence relationship between disasters, and can fully reflect the coupling effect of different disaster types, which is more in line with engineering practice. Through the introduction of time-varying coupling factor, the spatio-temporal evolution law of disaster chain under the action of water-force-chemical multi-field coupling can be accurately characterized. Then, based on the multi-level threshold division of dynamic risk index, a hierarchical early warning mechanism from normal monitoring to emergency response is formed. This mechanism can not only adjust the threshold according to the specific engineering characteristics through dynamic risk grading, but also has good flexibility, and can be easily understood and executed through clear grade division. Finally, different control strategies are generated corresponding to different risk levels, which avoids the occurrence of overreaction or insufficient response. At the same time, the specific measures of different warning levels reflect the quantitative characteristics, and the quantitative control strategy accurately matched with risk level is generated according to the quantitative evaluation result, which is helpful to realize intelligent decision and linkage response from routine monitoring to precise vibration suppression, quantitative water stop and emergency reinforcement, and is conducive to reducing risk to the minimum, so as to form a complete "monitoring-evaluation-warning-control" closed-loop technical system.

[0088] The present application realizes the technical leap from qualitative analysis to quantitative evaluation of multi-disaster coupling effect, solves the technical difficulties of unclear disaster coupling mechanism and lack of quantitative basis for control measures in traditional methods, and provides a systematic solution from accurate perception to intelligent decision for mine underground engineering safety. The present application breaks through the technical limitations of traditional single disaster isolated evaluation and passive prevention and control, solves the key technical difficulties of difficult quantitative characterization of multi-disaster coupling effect, lack of systematic risk assessment and lack of targeted synergy of control measures, and provides a systematic solution for long-term safe operation of mine underground engineering. Using the method can help to realize intelligent decision from conventional inspection to accurate vibration suppression, quantitative water stopping and emergency reinforcement, and can provide a systematic solution from "perception and early warning" to "accurate control" for mine underground engineering safety.

Claims

1. A method for quantitative assessment and control of the coupling effect of multiple hazards in underground space, comprising a quantitative assessment and control system for the coupling effect of multiple hazards in underground space, the control system 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 impact sensors, an array of structural strain sensors, and an array of corrosion monitoring sensors, used to collect multi-source heterogeneous monitoring data of 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 intensity index of hazards on the multi-source heterogeneous monitoring data to form a structural health status dataset with a unified spatiotemporal benchmark; the coupling risk assessment module has a built-in multi-hazard coupling risk quantification model, used to calculate a comprehensive risk index characterizing 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 a quantitative collaborative control scheme from a preset strategy library based on a comprehensive risk index and a combination of dominant disaster modes, and then output the scheme; it is characterized by including 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 original 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 ; Calculate the first according to formula (1) 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; , The first The design of disaster intensity allows for upper and lower limits; S32: Construction Characterization disasters and the first Time-varying coupling factor of the dynamic correlation intensity of various disasters in the time dimension And dynamically update it; construct the time-varying coupling factor according to formula (2). ; （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: Construct a multi-hazard coupled risk quantification model and calculate the comprehensive risk index at time t. A multi-hazard coupled risk quantification model is constructed based on formula (3); （3）； In the formula, For the first Initial weights for different types of disasters; For the first Initial weights for different types of disasters; 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.

2. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 1, characterized in that, In step four, , and The threshold was set based on historical data analysis and engineering experience, and .

3. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 1, 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.

4. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 1, 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.

5. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 1, 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.

6. The method for quantitative assessment and control of 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.

7. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 1, 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.

8. The method for quantitative assessment and control of the coupling effect of multiple disasters in underground space according to claim 1, 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.

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