A method for evaluating risks of sudden geological environmental problems in a mining area

Abstract

The application discloses a kind of mine sudden geological environment problem risk assessment methods, belong to geological environment risk assessment technical field, comprising: based on source feature parameter and topographic shear strength obtains source potential energy index;Through intensity stress ratio and mining space-time footage ratio coupling nesting obtains time-dependent damage mining stress disturbance index;Source potential energy index and mining stress disturbance index are interacted to obtain sudden power instability probability index;Introduce macroscopic traffic flow congestion law, utilize dynamic evacuation characteristic parameter and geological early warning parameter to obtain perception space block hazard-affected body vulnerability index;Sudden power instability probability index and hazard-affected body vulnerability index are interacted to obtain comprehensive risk evaluation value;Finally, comprehensive risk evaluation value is compared with multilevel risk threshold and corresponding control instruction is generated.The application comprehensively considers geological disaster factor and hazard-affected body evacuation vulnerability, and can dynamically quantify the comprehensive risk of mine sudden geological environment problem.

Description

Technical Field

[0001] This invention belongs to the field of geological environment risk assessment technology, and in particular relates to a method for risk assessment of sudden geological environment problems in mining areas. Background Technology

[0002] Sudden geological environmental problems in mining areas (such as landslides, collapses, goaf subsidence, rock bursts, etc.) pose a major threat to mine safety and the surrounding ecological security.

[0003] Existing risk assessment methods often focus on a single type of disaster or a single influencing factor, for example:

[0004] In terms of landslide disaster assessment, Yunnan Normal University has published a landslide susceptibility assessment method and device (CN119988890A) that takes into account the intensity of influencing factors. This method divides slope units based on digital elevation model data and assesses the susceptibility of landslides by quantifying the influence of landslide factors. China Railway Engineering Design Consulting Group Co., Ltd. has published a method, device, equipment and medium for calculating the safe distance of landslides at spoil disposal sites (CN120030736A). This method uses the topographic features of spoil disposal sites and the physical and mechanical properties of spoil to construct a dynamic model of the landslide body and uses the law of conservation of energy to calculate the safe distance.

[0005] Regarding early warning of mining-induced geological disasters, Northeastern University has published a method for early warning of coal mine composite geological disasters based on mining-induced seismic source parameters, which provides early warning by identifying changes in microseismic source parameters caused by excavation stress disturbance (CN111915865A); China Coal Technology & Engineering Mining Research Institute Co., Ltd. and others have published a method for judging the stability of surrounding rock (CN118566464B), which introduces strength-stress ratio index, geological structure index and mining-induced index to judge the stability of surrounding rock.

[0006] The above methods assess risk from the perspective of landslide material source or mining stress, but lack a systematic consideration of the coupled effects of multiple factors such as material source supply, terrain shear strength, mining-induced damage, and rock mass rheological properties.

[0007] Regarding disaster-bearing body risk assessment, Nanjing University of Information Science and Technology has published a dynamic vulnerability assessment method and system for disaster-bearing bodies (CN113610436B). This method constructs a dynamic vulnerability assessment model by establishing a disaster database, identifying disaster characteristics, obtaining exposure characteristics of disaster-bearing bodies, and plotting disaster loss rate curves. However, this method is mainly applicable to meteorological disaster scenarios such as rainstorms and floods, and does not address the spatial congestion effect during emergency evacuation in mining areas. In other words, it does not incorporate dynamic social factors such as actual population density, congestion reduction, and effective early warning response time into the mining area risk assessment system.

[0008] In addition, existing geological environment risk assessment methods for mining areas mostly rely on a single threshold to classify risk levels, lacking a hierarchical control instruction generation mechanism based on multi-level risk thresholds, which makes it difficult to support differentiated emergency decision-making.

[0009] Therefore, there is an urgent need for a risk assessment method for sudden geological environmental problems in mining areas that can integrate geological disaster-causing factors (potential energy of material sources, stress disturbance caused by mining, rheological aging of rock mass, geological structure) with the dynamic characteristics of vulnerability of disaster-bearing bodies (personnel density, congestion reduction, evacuation routes, early warning response time), and can automatically generate graded control instructions based on multi-level risk thresholds. Summary of the Invention

[0010] To address the shortcomings of existing technologies, this invention provides a risk assessment method for sudden geological environmental problems in mining areas, thus solving the aforementioned problems.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a method for risk assessment of sudden geological environmental problems in mining areas, comprising:

[0012] S10. Obtain the source potential energy index based on the relative ratio of the source characteristic parameters and the terrain shear strength characterized by the terrain evolution characteristic parameters;

[0013] S20. By coupling and nesting the strength-stress ratio obtained based on rock mechanics characteristic parameters with the mining time-space advance ratio obtained based on mining engineering characteristic parameters, the mining-induced stress disturbance index containing time-dependent damage is obtained.

[0014] S30. Perform interactive calculations on the source potential energy index and the mining stress disturbance index to obtain the probability index of sudden dynamic instability.

[0015] S40. Obtain the dynamic evacuation characteristic parameters and geological early warning parameters within the evaluation unit. By introducing the macro traffic flow congestion law (congestion reduction coefficient), quantify the game relationship between the actual evacuation time and the effective early warning response time under crowd congestion reduction, and obtain the vulnerability index of the disaster-bearing body for sensing spatial congestion.

[0016] S50. Interact with the probability index of sudden dynamic instability and the vulnerability index of the disaster-bearing body to obtain a comprehensive risk assessment value for sudden geological environmental problems;

[0017] S60. Compare the comprehensive risk assessment value of sudden geological environmental problems with the preset multi-level risk thresholds and generate corresponding control instructions.

[0018] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0019] A further technical solution: the terrain evolution characteristic parameters include the surface terrain slope angle;

[0020] The source characteristics parameters include the actual observed thickness of the shallow loose deposits and the internal friction angle of the loose deposits;

[0021] The mining engineering characteristic parameters include the actual observed values ​​of mining thickness, actual observed values ​​of mining depth, ore layer dip angle, working face advance speed, active mining duration, and working face strike length;

[0022] The rock mass mechanical characteristic parameters include the uniaxial compressive strength of the overlying strata and the average unit weight of the overlying strata;

[0023] The dynamic evacuation characteristic parameters include the actual personnel density within the evaluation unit, the extreme crowding personnel density threshold, the average evacuation path length, and the free-state human walking speed.

[0024] The geological early warning parameters include the effective early warning response time.

[0025] Further technical solution: In step S10, the calculation logic of the material source potential energy index is as follows:

[0026] The ratio of the actual observed thickness of the shallow loose deposit to the reference critical loose deposit thickness constant is calculated to obtain the relative thickness characteristic value that characterizes the relative ratio of the source characteristic parameters;

[0027] Divide the tangent of the slope angle of the land surface by the sum of the tangent of the internal friction angle of the loose deposit and the shear stability offset constant to prevent division by zero anomaly, and obtain the shear instability ratio that characterizes the shear strength of the land surface.

[0028] Using the shear instability ratio, the thickness relative characteristic value is nonlinearly amplified exponentially to construct the material source potential energy index of the terrain coupling that introduces the terrain tangential stress.

[0029] Further technical solution: In step S20, the calculation logic for the mining stress disturbance index containing time-related damage is as follows:

[0030] Multiply the actual observed mining thickness by the cosine of the ore dip angle, and then divide by the actual observed mining depth superimposed with the burial depth offset constant to obtain the benchmark value of mining geometric disturbance.

[0031] The mining time-space advance ratio is obtained by dividing the product of the working face advance speed and the active mining duration by the working face strike length.

[0032] The strength-stress ratio is obtained by dividing the uniaxial compressive strength of the overlying strata by the product of the average unit weight of the overlying strata and the actual observed value of the mining depth.

[0033] The difference between the time-space advance ratio and the strength stress ratio is calculated to obtain the time-space damage residual characteristic value;

[0034] Using the spatiotemporal damage residual characteristic value, the mining geometric disturbance benchmark value is subjected to nonlinear exponential product amplification to obtain the mining stress disturbance index containing time-related damage.

[0035] Further technical solution: In step S30, the calculation logic of the sudden dynamic instability probability index is as follows:

[0036] The mining stress disturbance index containing time-dependent damage is calculated by power-law based on the time-dependent index characterizing the rheological properties of the rock mass, to obtain the rheological time-dependent disturbance amplification value.

[0037] The cumulative value of the comprehensive disaster intensity is obtained by multiplying the rheological time-effect disturbance amplification value, the material source potential energy index coupled with the terrain, and the penalty coefficient of the geological structure fault zone of the mining area based on the fault density.

[0038] As the aforementioned nonlinear dynamic model, the negative of the cumulative comprehensive disaster intensity value is used for nonlinear exponential decay calculation, and the convergence is limited by subtracting the decay calculation result from the numerical value 1. Within the range of , the probability index of sudden dynamic instability is obtained.

[0039] Further technical solution: In step S40, the calculation logic for the vulnerability index of the disaster-bearing body in sensing spatial congestion is as follows:

[0040] The static exposure baseline value is obtained by calculating the ratio of the actual population density within the evaluation unit to the extreme crowding population density threshold.

[0041] Based on the difference between the numerical value 1 and the static exposure benchmark value, and superimposed with the congestion smoothing constant, a congestion reduction coefficient characterizing the macroscopic traffic flow congestion pattern is constructed.

[0042] Multiply the free-state human walking speed, the crowding reduction coefficient, and the effective early warning response time together to obtain the characteristic value of effective evacuation and escape capability;

[0043] Divide the average evacuation path length from the evaluation unit to the safe zone by the effective evacuation escape capability characteristic value to obtain the base number of the obstructed evacuation time characterizing the actual evacuation time under the crowd congestion reduction.

[0044] Multiply the base time for obstructed evacuation by the panic amplification factor to obtain the panic obstruction timeliness index;

[0045] Using the panic blocking timeliness index, the static exposure benchmark value is non-linearly amplified to obtain the vulnerability index of the disaster-bearing body in the perceived space blockage.

[0046] Further technical solution: In step S50, the calculation logic for the comprehensive risk assessment value of the sudden geological environmental problem is as follows:

[0047] Multiply the vulnerability index of the disaster-bearing body due to spatial obstruction by the ecological and economic linkage loss conversion coefficient to obtain the socio-economic linkage loss amplification index.

[0048] The nonlinear exponential gain is calculated based on the socio-economic loss amplification index, and the result of the gain calculation is directly multiplied by the sudden dynamic instability probability index to obtain the comprehensive risk assessment value of the sudden geological environmental problem.

[0049] Further technical solution: In step S60, the comprehensive risk assessment value of the sudden geological environmental problem is compared with the preset multi-level risk thresholds, and corresponding control instructions are generated, specifically including:

[0050] Comprehensive risk assessment value of sudden geological environmental problems Compared with the first risk warning threshold and the first risk warning threshold Perform a comparison;

[0051] when The system issues a command to maintain the status quo, and then performs the next feature parameter collection according to the normal cycle.

[0052] when This triggers an evacuation order;

[0053] when Generate a forced evacuation order

[0054] This invention provides a risk assessment method for sudden geological environmental problems in mining areas, which has the following advantages compared with the prior art:

[0055] 1. This invention constructs a material source potential energy index, which nonlinearly amplifies the relative ratio of the thickness of shallow loose deposits to the terrain shear strength (the ratio of the tangent of the slope angle to the internal friction angle), thereby realizing the quantitative characterization of the potential sliding energy of slope material.

[0056] 2. This invention constructs a mining-induced stress disturbance index containing time-dependent damage, and couples the benchmark value of mining geometric disturbance with the residual characteristic value of time damage (the difference between the mining time-space advance ratio and the strength stress ratio) index, which can dynamically reflect the time-varying amplification effect of factors such as the mining engineering advance speed, active mining time, and rock mechanical properties on mining-induced stress.

[0057] 3. This invention introduces the rock mass rheological property index and the fault zone penalty coefficient to perform interactive calculations on the material source potential energy index and the mining stress disturbance index, thereby obtaining a sudden dynamic instability probability index that converges to the 0 to 1 range, which improves the scientific nature of disaster probability prediction.

[0058] 4. This invention introduces macroscopic traffic flow congestion patterns, quantifies the hindering effect of crowd density on evacuation speed through a congestion reduction coefficient, and constructs a disaster-bearing vulnerability index based on perceived spatial congestion by combining average evacuation path length, effective early warning response time, and panic amplification factor, thereby realizing a dynamic assessment of the vulnerability of personnel evacuation.

[0059] 5. This invention multiplies the probability index of sudden dynamic instability and the vulnerability index of the disaster-bearing body by the index gain to obtain a comprehensive risk assessment value, and compares it with multi-level risk thresholds to generate graded control instructions (maintain status quo, evacuation, forced evacuation), providing a quantitative decision-making basis for early warning and emergency management of geological disasters in mining areas. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the process of the present invention.

[0061] Figure 2 The logic diagram for calculating the probability index of sudden dynamic instability.

[0062] Figure 3 This is a data flow diagram for integrating risk assessment logic and early warning and control. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0064] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0065] Please see Figure 1 The present invention provides a method for risk assessment of sudden geological environmental problems in mining areas, comprising:

[0066] S10. Obtain the source potential energy index based on the relative ratio of the source characteristic parameters and the terrain shear strength characterized by the terrain evolution characteristic parameters;

[0067] S20. By coupling and nesting the strength-stress ratio obtained based on rock mechanics characteristic parameters with the mining time-space advance ratio obtained based on mining engineering characteristic parameters, the mining-induced stress disturbance index containing time-dependent damage is obtained.

[0068] S30. Perform interactive calculations on the source potential energy index and the mining stress disturbance index to obtain the probability index of sudden dynamic instability.

[0069] S40. Obtain the dynamic evacuation characteristic parameters and geological early warning parameters within the evaluation unit. By introducing the macro traffic flow congestion law (congestion reduction coefficient), quantify the game relationship between the actual evacuation time and the effective early warning response time under crowd congestion reduction, and obtain the vulnerability index of the disaster-bearing body for sensing spatial congestion.

[0070] S50. Interact with the probability index of sudden dynamic instability and the vulnerability index of the disaster-bearing body to obtain a comprehensive risk assessment value for sudden geological environmental problems;

[0071] S60. Compare the comprehensive risk assessment value of sudden geological environmental problems with the preset multi-level risk thresholds and generate corresponding control instructions;

[0072] The terrain evolution characteristic parameters include the surface terrain slope angle;

[0073] The source characteristics parameters include the actual observed thickness of the shallow loose deposits and the internal friction angle of the loose deposits;

[0074] The mining engineering characteristic parameters include the actual observed values ​​of mining thickness, actual observed values ​​of mining depth, ore layer dip angle, working face advance speed, active mining duration, and working face strike length;

[0075] The rock mass mechanical characteristic parameters include the uniaxial compressive strength of the overlying strata and the average unit weight of the overlying strata;

[0076] The dynamic evacuation characteristic parameters include the actual personnel density within the evaluation unit, the extreme crowding personnel density threshold, the average evacuation path length, and the free-state human walking speed.

[0077] The geological early warning parameters include the effective early warning response time.

[0078] The above technical solution integrates geological disaster-causing factors with the dynamic characteristics of the vulnerability of the disaster-bearing body, thus addressing the shortcomings of existing risk assessment methods for sudden geological environmental problems in mining areas. Existing methods often focus on a single disaster type or a single influencing factor, such as evaluating landslide risk solely based on topographic slope and loose deposit thickness, or evaluating mining-induced stress disturbance solely based on mining depth, mining thickness, and overlying strata mechanical parameters. This invention, through steps S10 and S20, systematically considers the coupled effects of multiple factors, including material supply, topographic shear strength, mining-induced damage over time, and rock rheological properties. Furthermore, in step S30, these factors are interactively calculated to obtain a probability index of sudden dynamic instability, thereby overcoming the deficiency of existing methods in lacking multi-factor coupling consideration.

[0079] Furthermore, existing methods generally neglect the spatial congestion effect of disaster-bearing bodies (personnel) during emergency evacuation, i.e., they fail to incorporate dynamic social factors such as actual population density, congestion reduction speed, and effective early warning response time into the risk assessment system. This invention, through step S40, explicitly obtains dynamic evacuation characteristic parameters and geological early warning parameters, and introduces macroscopic traffic flow congestion patterns to quantify the game relationship between actual evacuation time under congestion reduction speed and effective early warning response time, thereby obtaining a vulnerability index of disaster-bearing bodies that perceives spatial congestion. This innovative consideration makes the assessment of disaster-bearing body vulnerability more realistic and solves the problem of existing methods neglecting the dynamic vulnerability of disaster-bearing bodies.

[0080] Finally, in step S50, the probability index of sudden dynamic instability and the vulnerability index of the disaster-bearing body are interacted to obtain a comprehensive risk assessment value for sudden geological environmental problems. In step S60, this value is compared with preset multi-level risk thresholds to generate corresponding control instructions. This overall technical concept makes the risk assessment results more accurate, effectively supporting tiered control and emergency decision-making, and overcoming the shortcomings of existing methods that are inaccurate in assessment and unable to support decision-making. Through the comprehensive assessment of the technical solution of this invention, the system can issue evacuation instructions in a timely manner, avoiding potential risks caused by insufficient assessment of a single factor or inaccurate assessment of personnel evacuation capacity, thereby improving the level of safety assurance in mining areas.

[0081] Preferably, in step S10, the calculation logic for the source potential energy index is as follows:

[0082] The ratio of the actual observed thickness of the shallow loose deposit to the reference critical loose deposit thickness constant is calculated to obtain the relative thickness characteristic value that characterizes the relative ratio of the source characteristic parameters;

[0083] Divide the tangent of the slope angle of the land surface by the sum of the tangent of the internal friction angle of the loose deposit and the shear stability offset constant to prevent division by zero anomaly, and obtain the shear instability ratio that characterizes the shear strength of the land surface.

[0084] Using the shear instability ratio, the thickness relative characteristic value is nonlinearly amplified exponentially to obtain the material source potential energy index.

[0085] Specifically, in step S10, the formula for calculating the source potential energy index is:

[0086]

[0087] in, The potential energy index of the source material. The actual observed thickness of the shallow loose deposits (unit: ), For reference critical loose material thickness constant (unit: ), The slope angle of the land surface (unit: ), The internal friction angle of loose deposits (unit: ). ), To prevent the division-by-zero anomaly of the shear stability bias constant, This is the shear instability ratio. This represents the relative characteristic value of thickness.

[0088] Among them, the source potential energy index This index, used to quantify the potential energy or instability trend of geological hazard sources, is based on the concept of comprehensively considering the physical characteristics of the source and the stability of the surrounding terrain to provide a unified and comparable risk measure. This index can serve as an important input parameter for subsequent risk assessment models, reflecting the likelihood of sudden instability of the source under specific conditions. Actual observed thickness of shallow loose deposits. This refers to the actual measured thickness of the loose soil or rock debris layer covering the surface of a mining area, which is prone to landslides, debris flows, and other disasters. This thickness directly reflects the source volume and potential energy, and is typically obtained through geological exploration, borehole sampling, ground-penetrating radar (GPR), or remote sensing image interpretation. For example, the thickness of loose deposits at different locations can be determined by combining borehole sampling with stratigraphic profile analysis, or estimated using high-precision lidar (LiDAR) data combined with geological models. A critical loose deposit thickness constant is referenced. This refers to the minimum or reference thickness required for loose deposits to remain stable under specific geological and topographical conditions. When the actual observed thickness exceeds this critical value, the risk of instability increases significantly. This constant is usually determined based on regional geological experience, historical disaster data analysis, or theoretical mechanical calculations. For example, it can be determined by statistical analysis of the minimum thickness of landslides that have occurred in the region (taking the minimum thickness), or by simulating the stability coefficient under different thicknesses using slope stability analysis software. (Surface topographic slope angle) This refers to the tilt angle of a surface unit in a mining area, a key topographic factor affecting the stability of loose deposits. A steeper slope results in a stronger gravitational component acting along the slope, making instability more likely. This angle can be calculated from digital elevation model (DEM) data, for example, by performing slope analysis on DEM data using GIS software, or directly obtaining it using tools such as inclinometers. (Internal friction angle of loose deposits) The internal friction angle refers to the ability of loosely packed particles to resist shear failure. It is an important parameter of the shear strength of soil or rock debris. The larger the internal friction angle, the better the stability of the material source. This parameter is usually obtained through laboratory geotechnical tests (such as direct shear tests and triaxial compression tests) or field shear tests. For example, soil samples can be collected from the field, and a direct shear test can be conducted in the laboratory to determine the internal friction angle, or it can be estimated using empirical formulas combined with soil type. A shear stability offset constant is used to prevent division-by-zero anomalies. This is a small positive value used to avoid mathematical anomalies caused by a zero or excessively small denominator when calculating the shear instability ratio, especially when the internal friction angle of the loose aggregate is zero or extremely small. Its function is to ensure the numerical stability of the calculation and provide a reasonable lower limit for extreme instability conditions. This constant is usually set based on empirical values ​​from numerical simulations or safety margins in engineering practice; for example, it can be set to a very small positive number, such as 0.001. Shear instability ratio This ratio quantifies the relationship between the shear stress on the material source caused by the terrain slope and the shear strength of the material source itself. When the shear force caused by the slope angle approaches or exceeds the shear capacity of the material source, this ratio increases, indicating an increased risk of instability. Thickness relative eigenvalue This ratio reflects the multiple relationship between the actual thickness of the loose deposit and the reference critical thickness. When the actual thickness is much greater than the reference critical thickness, it indicates that the potential volume and energy of the material source are larger, and its contribution to risk is also greater.

[0089] This application solves the problem of accurately characterizing the potential instability risk of material sources in the risk assessment of sudden geological environmental problems in mining areas by introducing a comprehensive calculation formula to accurately quantify the source potential energy index. This formula organically combines the thickness characteristics of the material source with the shear strength of the terrain. Specifically, the source potential energy index... The calculation first uses the thickness relative eigenvalues. To characterize the size and potential energy of the source, the actual observed thickness of the shallow, loose deposits is used. It directly reflects the material basis of the source, while referencing the critical loose material thickness constant. This provides a stability benchmark, when Much larger When the time is greater, it indicates that the potential destructive power of the source is greater; secondly, the formula uses an exponential function. To introduce the influence of topographic shear strength, the slope angle of the ground surface is considered. This represents the gravity-driven shear stress, while the internal friction angle of loose aggregates... This represents the material source's ability to resist shear failure, and is the shear stability bias constant to prevent division-to-zero anomalies. The numerical stability of the calculation was ensured, and the shear instability ratio was [value missing]. It intuitively reflects the stability of the material source under the current terrain conditions, when the slope angle... Increase or internal friction angle When the value decreases, the shear instability ratio increases, and the exponential term increases accordingly, thus increasing the source potential energy index. The increase is significant, accurately reflecting the nonlinear growth characteristics of instability risk. Through this product-like coupling, the scheme considers not only the "quantity" (thickness) of the source but also its "quality" (stability), making the calculation of the source potential energy index more comprehensive and accurate. This provides a more reliable input for the subsequent calculation of the sudden dynamic instability probability index, thereby improving the accuracy and reliability of the entire risk assessment method.

[0090] The following is a specific example to illustrate this. When conducting risk assessments in mining areas, the source potential energy index can be calculated as follows: First, geological drilling or ground-penetrating radar is used to survey the shallow, loose sediments in a specific area to obtain actual observed values ​​of their thickness. For example, above a certain goaf, the thickness of the loose soil layer was measured to be 5 meters through drilling. Simultaneously, based on the geological conditions and historical disaster data of the mining area, a reference critical loose material thickness constant was established. The slope is 2 meters. Next, using high-precision digital elevation model (DEM) data, the surface slope angle of the area is calculated. For example, the slope angle is calculated to be 25 degrees (converted to radians) (or obtained directly using tools such as an inclinometer). Then, samples of loose deposits are collected from the site, and direct shear tests are conducted in the laboratory to determine their internal friction angle. For example, the measured internal friction angle is 30 degrees (converted to radians). To avoid calculation anomalies, a shear stability offset constant is set to prevent division-to-zero anomalies. Let it be a very small positive number, such as 0.001. Substitute these parameters into the formula. From this, the source potential energy index of the region can be calculated. For example, if , , , , ,but In this way, the potential energy of material sources in different regions can be quantified, providing data support for risk assessment.

[0091] Through the above technical solution, this application provides a method for accurately quantifying the potential energy index of mineral resources in mining areas. This method couples the relative characteristic value of the mineral resource thickness with the shear instability ratio of the terrain, comprehensively and accurately reflecting the potential instability risk of the mineral resource. Compared to assessments based solely on experience or a single parameter, this method comprehensively considers both the material basis of the mineral resource (thickness) and the stability of its surrounding environment (terrain slope and internal friction angle), making the calculated potential energy index more scientific and reliable. This precise quantification helps improve the accuracy of risk assessment for sudden geological environmental problems in mining areas, providing a more solid data foundation for subsequent risk warnings and control instructions, thereby effectively guiding safe production and emergency management in mining areas.

[0092] Preferably, in step S20, the calculation logic for the mining stress disturbance index containing time-related damage is as follows:

[0093] Multiply the actual observed mining thickness by the cosine of the ore dip angle, and then divide by the actual observed mining depth superimposed with the burial depth offset constant to obtain the benchmark value of mining geometric disturbance.

[0094] The mining time-space advance ratio is obtained by dividing the product of the working face advance speed and the active mining duration by the working face strike length.

[0095] The strength-stress ratio is obtained by dividing the uniaxial compressive strength of the overlying strata by the product of the average unit weight of the overlying strata and the actual observed value of the mining depth.

[0096] The difference between the time-space advance ratio and the strength stress ratio is calculated to obtain the time-space damage residual characteristic value;

[0097] Using the spatiotemporal damage residual characteristic value, the mining geometric disturbance benchmark value is subjected to nonlinear exponential product amplification processing to obtain the mining stress disturbance index containing time-dependent damage;

[0098] Specifically, in step S20, the formula for calculating the mining stress disturbance index containing time-related damage is as follows:

[0099]

[0100] in, The stress disturbance index is the mining stress index. The actual observed thickness (unit: ) ), Dip angle of the ore layer (unit: ), The actual observed depth (unit: ), Burial depth offset constant (unit: ), The working face advance speed (unit: ), Duration of active mining (in units) ), The length of the working face (unit: ), Uniaxial compressive strength of the overlying rock strata (unit: ), The average unit weight of the overlying strata (in units of 1000 g / cm³) ), Characterizing the strength-stress ratio, Characterizing the time-space advance ratio in mining, As the benchmark value for geometric disturbance in mining, These are the residual characteristics of spatiotemporal damage.

[0101] Among them, the mining stress disturbance index This method is used to quantify the degree of disturbance caused by mining activities to the stress field of the surrounding rock mass, and specifically considers the characteristics of this disturbance's accumulation and evolution over time, i.e., "time-dependent damage." Its magnitude directly reflects the potential risk level of geological disasters induced by mining. Actual observed values ​​for mining thickness. This refers to the actual thickness of the ore layer mined during the mining process. This parameter is one of the key geometric factors affecting the formation of goafs and the collapse of overlying strata. Its value can be obtained through field measurements, geological exploration data, or mining design drawings. Ore layer dip angle. The dip angle refers to the angle between the ore layer plane and the horizontal plane. The ore layer dip angle has a significant impact on the stability of the goaf roof, stress distribution, and the range of mining-induced influence. It can be obtained through geological exploration or mine surveying. Actual observed mining depth. The depth of the mining face relative to the ground surface or a reference datum is a crucial parameter affecting the self-weight stress of the overlying strata, the degree of stress concentration caused by mining, and surface subsidence. It can be obtained through borehole data or mine surveying. (Deep offset constant) It is an introduced numerical constant whose purpose is to avoid [problems] at mining depth. When the value is extremely small or zero, a mathematical division-by-zero anomaly occurs. This value can also be used as a model calibration parameter to fine-tune the impact on mining depth, thereby better fitting actual engineering data. Working face advance speed. This refers to the distance the mining face advances per unit time. This parameter reflects the dynamic intensity and rate of mining activities and is an important factor affecting the time-dependent changes in the mining stress field. It can be obtained from mining production records. Active mining duration. This refers to the total duration of continuous mining activities in a specific area or working face. This parameter, together with the working face advance speed, determines the cumulative effect of mining disturbance and can be obtained through mining production plans or historical data. Working face strike length. This refers to the length of the mining face along the strike of the ore layer. This parameter affects the planar dimensions of the goaf and the lateral range of mining-induced stress, and can be obtained through mining design or on-site measurements. Uniaxial compressive strength of the overlying strata. The overlying strata refer to the maximum stress that the overlying rock strata can withstand under uniaxial compression. This parameter is an important indicator of the rock mass's resistance to deformation and failure, and can be obtained through rock mechanics tests. The average unit weight of the overlying strata... The average weight per unit volume of the overlying rock strata is used to calculate the self-weight stress exerted by the overlying rock strata on the underlying ore layer. This parameter can be obtained through rock sample density measurements and geological data. Strength-stress ratio. This characterizes the relationship between the strength of the rock mass itself and the self-weight stress of the overlying strata. It reflects the rock mass's ability to resist external loads relative to its initial stress level and is an important indicator for judging rock mass stability. Mining time-space-to-cut ratio It comprehensively reflects the dynamic progress of mining activities in time and space. It quantifies the cumulative effect of mining disturbance along the working face within a specific time period, reflecting the dynamic characteristics of the mining process. Mining geometric disturbance baseline value. This primarily reflects the fundamental contribution of mining geometry parameters (mining thickness, ore dip angle, and mining depth) to rock mass stress disturbance, providing an initial disturbance quantity based on the mining spatial morphology. Spatiotemporal damage residual characteristic values. The cumulative damage effect of mining activities on the rock mass in time and space was quantified by the difference between the time-space advance ratio and the strength-stress ratio. A relatively large time-space advance ratio or a relatively small strength-stress ratio indicates that the degree of rock mass damage may be higher.

[0102] The proposed solution introduces a specific mathematical model to calculate the mining stress disturbance index containing time-related damage. The model is This model combines the geometric characteristics of mining activities with dynamic, time-dependent damage effects. Among these, the benchmark value for mining geometric disturbance is... As a fundamental element, it primarily captures actual observed thickness values. Dip angle of ore layer and actual observed values ​​of mining depth The initial contribution of isostatic geometric parameters to rock mass stress disturbance is considered. This term provides a physically meaningful benchmark for mining-induced disturbance by taking into account the influence of the seam dip angle on the effective mining thickness and the adjustment of the overlying load by the mining depth. (Burial depth offset constant) The introduction of this not only ensures the numerical stability of the calculation but also provides the model with a flexible calibration space. Based on this, the model uses an exponential term... To characterize the time-dependent cumulative damage effect of mining-induced stress disturbance, this index term includes the mining time-space advance ratio. Strength-stress ratio Mining time-space advance ratio This comprehensively reflects the working face advance speed. Duration of active mining and working face length The dynamic intensity and spatial range of the mining activity are determined by this value; the larger the value, the more intense or longer the mining disturbance. Strength-stress ratio This represents the uniaxial compressive strength of the overlying rock strata. Average unit weight of overlying strata and mining depth The ability of the rock mass itself to resist deformation and failure is determined by this. The residual characteristic value of spatiotemporal damage is formed by calculating the difference between the mining advance ratio and the strength-stress ratio. When the dynamic impact of mining activities (mining time-space ratio) exceeds the resistance capacity of the rock mass itself (strength-stress ratio), the difference becomes positive and is amplified by an exponential function, thus accurately simulating the nonlinear process of mining-induced stress disturbance accumulating over time and rock mass damage intensifying. Finally, multiplying the mining geometric disturbance baseline value by this exponential term achieves deep coupling between static geometric disturbance and dynamic time-dependent damage, making the mining-induced stress disturbance index... It can comprehensively and dynamically reflect the true stress state and potential instability risk of the rock mass in the mining area. This calculation method overcomes the shortcomings of traditional methods in quantifying time-sensitive damage, and provides a more refined and accurate input for risk assessment of sudden geological environmental problems in mining areas.

[0103] The following is a specific example to illustrate this. When assessing mining-induced stress disturbance at an underground mine working face, it is first necessary to collect relevant geological and mining parameters. For example, through on-site measurements and geological exploration, the actual observed values ​​of the mining thickness are obtained. The depth is 2.8 meters, and the dip angle of the ore layer is... The depth is 20 degrees (approximately 0.349 radians), based on actual observations. The depth is 650 meters. Meanwhile, based on the mining plan and production records, the working face advance speed is determined. The rate is 4.5 meters per day, and the duration of active mining is [missing information]. The working face length is 120 days. The overlying strata are 250 meters high. The uniaxial compressive strength was determined using rock mechanics tests and geological data. The pressure is 25 MPa, and the average unit weight of the overlying strata is... The burial depth offset constant is 0.026 MN / m³. The distance can be set to 1.5 meters based on experience or model calibration. Substitute the above parameters into the mining stress disturbance index, which includes time-related damage. The calculation formula is as follows: This allows us to obtain the mining-induced stress disturbance index of the working face at the current moment. In this way, the dynamic impact of mining activities on the rock mass stress field can be quantified, providing crucial data for mine risk assessment.

[0104] Through the above technical solution, this application provides a method for accurately quantifying the mining-induced stress disturbance index containing time-related damage. This method, by introducing a specific mathematical model, effectively solves the problem in traditional risk assessment methods of accurately capturing the dynamic impact of mining activities on the rock mass stress field and the accumulation of time-related damage. Specifically, the model incorporates the geometric characteristics of mining (such as actual observed mining thickness)... Dip angle of ore layer Actual observed values ​​of mining depth ) and the dynamic process of mining (such as the working face advance speed) Duration of active mining Working face length And the mechanical properties of the rock mass itself (such as the uniaxial compressive strength of the overlying strata) and the rock mass itself. Average unit weight of overlying strata This method organically combines mining and dynamic factors. It reflects the static impact through a benchmark value of mining geometric disturbance and accurately simulates the nonlinear, time-dependent cumulative effect of mining activities on rock mass stress disturbance using exponential spatiotemporal damage residual characteristic values. This coupled calculation method, which comprehensively considers both static and dynamic factors, results in a highly accurate mining-induced stress disturbance index. It can more comprehensively and realistically reflect the actual stress state and potential instability risk of the rock mass in the mining area, thereby significantly improving the accuracy and reliability of risk assessment for sudden geological environmental problems in the mining area, and providing more solid data support for the formulation of risk warning and control instructions.

[0105] Preferably, in step S30, the calculation logic for the sudden dynamic instability probability index is as follows:

[0106] The mining stress disturbance index containing time-dependent damage is calculated by power-law based on the time-dependent index characterizing the rheological properties of the rock mass, to obtain the rheological time-dependent disturbance amplification value.

[0107] The cumulative value of the comprehensive disaster intensity is obtained by multiplying the rheological time-effect disturbance amplification value, the material source potential energy index coupled with the terrain, and the penalty coefficient of the geological structure fault zone of the mining area based on the fault density.

[0108] The negative of the cumulative comprehensive disaster intensity value is used for nonlinear exponential decay calculation, and the convergence is limited by subtracting the decay calculation result from the numerical value 1. Within the range of the interval, the probability index of sudden dynamic instability is obtained;

[0109] Specifically, in step S30, the formula for calculating the probability index of sudden dynamic instability is:

[0110]

[0111] in, This is a probability index for sudden dynamic instability. This represents the penalty coefficient for geological fault zones in the mining area. The sudden dynamic instability probability index is a time-dependent index characterizing the rheological properties of rock masses. The output value interval converges and is limited to . Within the range, This represents the amplified value of the rheological aging disturbance. This is the cumulative value of the overall disaster intensity. The potential energy index of the source material. This is the stress disturbance index during mining.

[0112] Sudden Dynamic Instability Probability Index This index is used to quantify the probability of sudden dynamic instability events occurring in a mining area. Its value ranges from 0 to 1, with higher values ​​indicating a higher probability of instability. The introduction of this index shifts risk assessment from qualitative or semi-quantitative indicators to a more statistically significant probability measure, providing a more intuitive and quantitative basis for subsequent risk management and decision-making. (Mining area geological structural fault zone penalty coefficient) This coefficient reflects the presence of geological fault zones within a mining area and their amplifying effect on the risk of geological instability. Geological fault zones are typically weak points in the geological system, easily inducing or exacerbating geological disasters. The coefficient can be determined in various ways, such as by factors like the distance between the mining area and known fault zones, the activity of the fault zones, their size, and their density. For example, when the mining area is near an active fault zone, the coefficient can be larger; when the mining area is far from a fault zone or the fault zone is inactive, the coefficient can be smaller. Alternatively, it can be calibrated by dimensionless calculation of the spatial potential energy influence range based on the absolute geometric features of the faults extracted from the three-dimensional geological exploration model of the mining area. The specific calculation formula is as follows: ,in, This represents the strike length of the nearest fault obtained through geological exploration, and the unit is 1. , This represents the actual elevation difference of the fault as determined by geological survey, with units of 1. , This represents the shortest spatial straight-line distance from the centroid of the evaluation unit to the fault plane, calculated in the 3D model, with units of 1. This formula calculates the penalty coefficient by ratioing the damage scale (representing the area affected by the fault slip) to the square of the spatial distance, achieving a completely objective determination of the penalty coefficient and avoiding the uncertainty in the technical solution caused by artificially introducing an attenuation coefficient. Timeliness index characterizing the rheological properties of rock mass. This index describes the time-dependent deformation and strength decay characteristics of rock materials under long-term stress, i.e., rheological properties. The rheological properties of rock mass mean that its response to mining disturbances is not instantaneous but gradually develops over time, thus affecting its long-term stability. This index can be derived and calculated based on objective strain observation data from indoor nonlinear creep tests in rock mechanics. The specific calculation formula is as follows: ,in, This indicates that the test measured the loading time to reach... The cumulative creep strain observation value at time, This represents the instantaneous elastic strain observed in the specimen at the moment of loading, as measured in the experiment. This indicates the duration of active mining operations in the mining project plan, in units of... , Represents a standard unit time reference value and is strictly taken as a constant 1. This formula completely abandons empirical assignment and directly utilizes the objective physical ratio of strain development over time to accurately and definitively quantify the rheological time-dependent damage characteristics of rock masses under long-term stress. Sudden dynamic instability probability index. The output value interval converges and is limited to . Within this scope, this limitation ensures As a probability value, the accuracy of its physical meaning is crucial. Essentially, the probability value should lie between 0 (impossible) and 1 (certain). Through the selection of mathematical functions and the adjustment of parameters, the calculation result is ensured to consistently conform to this range, avoiding physically meaningless negative values ​​or values ​​greater than 1. Rheological aging disturbance amplification value. Indicates the stress disturbance index during mining. Considering the time-dependent index of rock mass rheological properties The amplification effect after the influence. When A value greater than 1 indicates that the rheological properties of the rock mass amplify the impact of mining disturbances, causing the risk of instability to increase cumulatively over time; when... A value less than 1 may indicate that the rock mass's response to disturbance exhibits a certain degree of hysteresis or attenuation. (Comprehensive cumulative disaster intensity value) Taking into account multiple key factors such as source potential energy, mining-induced stress disturbance, the influence of geological structural fault zones, and the time-dependent properties of rock mass rheology, the comprehensive intensity or severity leading to sudden dynamic instability was quantified. The larger this cumulative value, the higher the potential intensity of sudden dynamic instability in the mining area.

[0113] In the aforementioned risk assessment methods, to more accurately quantify the probability of sudden dynamic instability in the mining area, this application introduces a sudden dynamic instability probability index. The calculation formula for the source potential energy index is expressed as a non-linear exponential function. and mining stress disturbance index Coupled with the geological structure of the mining area, a penalty coefficient for fault zones was further introduced. and time-dependent indices characterizing the rheological properties of rock masses Specifically, the source potential energy index It reflects the energy reserves of potentially unstable materials, while the mining-induced stress disturbance index This quantifies the impact of mining activities on rock mass stability. In the calculation process, the mining-induced stress disturbance index... Firstly, through the timeliness index Perform exponential operations to generate the rheological time-dependent perturbation amplification value. This allows the impact of mining disturbances to be dynamically adjusted based on the rheological properties of the rock mass, thus more accurately reflecting the rock mass response under long-term mining. Subsequently, this rheological time-dependent disturbance amplification value... With source potential energy index and the penalty coefficient of geological structural fault zones in the mining area Multiplying these values ​​together constitutes the cumulative value of the overall disaster intensity. This cumulative value not only considers the basic impacts of material sources and extraction activities, but also... The coefficients were weighted for the key disaster-causing factor of geological structural fault zones, and through... The index corrected for the time-dependent behavior of rock mass materials. Finally, this comprehensive cumulative disaster intensity value was substituted into... In the form of a probability function, the cumulative catastrophic intensity is transformed into a sudden dynamic instability probability index between 0 and 1. This nonlinear transformation mechanism ensures that as the intensity of the disaster increases, the probability of instability gradually approaches 1 in a manner consistent with actual geological laws, thus providing a more scientific and comprehensive quantitative indicator of instability risk.

[0114] As a specific implementation method, in calculating the probability index of sudden dynamic instability In this case, the penalty coefficient of the geological structural fault zone in the mining area can be determined first based on the geological exploration report and historical geological disaster data of the mining area. For example, if there is an active fault zone within the mining area that runs through the mining zone, then It can be set to 1.5; if the fault zone is inactive and far from the mining area, then... It can be set to 1.1. Meanwhile, by conducting indoor rock mechanics tests, especially creep tests, on the main rock mass types in the mining area, the rheological parameters of the rock mass can be obtained, thereby determining the timeliness index characterizing the rheological properties of the rock mass. For example, for soft rocks with significant creep characteristics, It can be set to 1.2; for hard rocks with insignificant creep characteristics, It can be set to 0.9. After obtaining the source potential energy index... and mining stress disturbance index Then, substitute these parameters into the formula. In, for example, if the calculation yields It is 0.5. It is 0.8, and Take 1.5, If we take 1.2, then we can calculate... for Then, by substituting all the values ​​into the formula, the final probability index of sudden dynamic instability is obtained. In this way, the fragility of geological structures and the time-dependent behavior of rock materials can be incorporated into the quantitative assessment of instability probability.

[0115] Through the above technical solution, this application effectively solves the problem of how to transform the potential energy of the material source and the disturbance of mining-induced stress into an instability probability with practical physical meaning in the risk assessment of sudden geological environmental problems in mining areas, while fully considering the influence of geological structural fault zones and the time-dependent properties of rock mass rheology. This is achieved by introducing a penalty coefficient for geological structural fault zones in the mining area. This allows for targeted amplification of risks in geologically vulnerable areas, avoiding underestimation of risks due to neglecting the influence of fault zones. Simultaneously, it introduces a timeliness index characterizing the rheological properties of the rock mass. This allows the impact of mining-induced stress disturbances to be dynamically corrected based on the long-term deformation characteristics of the rock mass, thus more accurately reflecting the stability evolution of the rock mass under long-term mining. Finally, through a nonlinear probability function, these complex factors are comprehensively transformed into a probability index for sudden dynamic instability ranging from 0 to 1. This provides a more accurate, reliable, and statistically significant quantitative basis for subsequent risk level classification and control instruction generation, significantly improving the scientific nature and practicality of risk assessment.

[0116] Preferably, in step S40, the calculation logic for the vulnerability index of the disaster-bearing body to spatial congestion is as follows:

[0117] The static exposure baseline value is obtained by calculating the ratio of the actual population density within the evaluation unit to the extreme crowding population density threshold.

[0118] Based on the difference between the numerical value 1 and the static exposure benchmark value, and superimposed with the congestion smoothing constant, a congestion reduction coefficient characterizing the macroscopic traffic flow congestion pattern is constructed.

[0119] Multiply the free-state human walking speed, the crowding reduction coefficient, and the effective early warning response time together to obtain the characteristic value of effective evacuation and escape capability;

[0120] Divide the average evacuation path length from the evaluation unit to the safe zone by the effective evacuation escape capability characteristic value to obtain the base number of the obstructed evacuation time characterizing the actual evacuation time under the crowd congestion reduction.

[0121] Multiply the base time for obstructed evacuation by the panic amplification factor to obtain the panic obstruction timeliness index;

[0122] Using the panic blocking timeliness index, the static exposure benchmark value is non-linearly amplified to obtain the vulnerability index of the disaster-bearing body in the perceived space blockage.

[0123] Specifically, in step S40, the formula for calculating the vulnerability index of the disaster-bearing body to perceived spatial blockage is as follows:

[0124]

[0125] in, As a vulnerability index for disaster-bearing bodies, To evaluate the actual population density within the unit (unit: ), The maximum crowding density threshold (unit: ), As a factor amplifying panic, The average evacuation path length from the evaluation unit to the safe zone (in units of...) ), Human walking speed in free state (unit: ), It is the crowding smoothing constant. Effective early warning response time (unit: ), The congestion reduction coefficient (characterizing the macroscopic traffic flow congestion pattern). This is the static exposure baseline value. To determine the characteristic value of effective evacuation and escape capabilities, As a base for delaying evacuation, The panic-induced blockade timeliness index.

[0126] Vulnerability Index of Disaster-bearing Body Used to quantify the likelihood and extent of damage suffered by a vulnerable body due to its own characteristics and environment in the event of a sudden geological disaster, the concept encompasses multiple dimensions such as exposure, sensitivity, and adaptability. In practical applications, the vulnerability index of the disaster-bearing body is... It can serve as a key input in risk assessment models to comprehensively assess potential socioeconomic losses. For example, it can be combined with the intensity of disaster-causing factors to predict the risk of casualties, property damage, or disruption of social functioning. Actual population density within the assessment unit. This refers to the actual number of people per unit area within a specific evaluation region. This parameter is a fundamental indicator for measuring the degree of population exposure and directly reflects the potential number of affected people and the actual population density. This information can be obtained in various ways, such as using video surveillance systems for real-time people counting and density estimation; or using wireless sensor networks, Wi-Fi / Bluetooth detection, and other technologies combined with mobile device signal strength to locate people and calculate density within a given area. (Ultimate Crowdedness Density Threshold) This refers to the maximum crowd density that occurs in a specific space when it reaches a state of extreme overcrowding. Exceeding this threshold may lead to severe immobility or even stampedes. This threshold is a key reference point for assessing evacuation efficiency and safety. (Extreme Crowding Crowd Density Threshold) The determination of the maximum density for safe evacuation can be based on national or industry safety standards, such as referring to the personnel density regulations for safe evacuation in building design codes; or by analyzing data from historical overcrowding events and combining them with ergonomic studies to determine the maximum density at which people can move safely in different types of spaces. Panic amplification factor. This is used to quantify the negative impact of panic on evacuation behavior and efficiency during emergencies. Panic can lead to disorderly running, pushing and shoving, and incorrect route selection, thus significantly increasing evacuation time and spatial congestion. The panic amplification factor is also mentioned. The value can be calibrated based on statistical data of population behavior in historical disaster events. For example, it can be determined by analyzing the rate of decline in population evacuation speed in different types of disasters; or by modeling behavioral patterns under different levels of panic through psychological and sociological research combined with expert experience, thereby setting corresponding amplification coefficients. Specifically, the panic amplification factor... It is calibrated based on the statistical velocity dispersion of the actual movement trajectories of people in unannounced emergency drills, and its specific calculation formula is as follows: ,in, This represents the sample standard deviation of the evacuation speed of the drill crowd, calculated using machine vision tracking, and the unit is 1. , This represents the actual measured average evacuation speed of the drill participants, expressed in units of... This formula, using the ratio of the overall standard deviation to the average velocity of the crowd, directly characterizes the disorder and chaos caused by panic using purely objective kinematic statistical parameters, greatly enhancing the engineering feasibility of the vulnerability assessment model. The average evacuation path length from the assessment unit to the safe zone... This refers to the average distance from any point within the evaluation unit to the nearest safe zone. This parameter is the basis for calculating the theoretical evacuation time and reflects the physical difficulty of the evacuation, as well as the average evacuation path length. Precise calculations can be performed using Geographic Information Systems (GIS). For example, by using shortest path algorithms combined with building layout maps and terrain data, the average distance from each location to a pre-set safe point can be calculated; or by conducting on-site surveys and measurements, combined with evacuation drill data, the actual evacuation route length can be obtained. (Free-state human walking speed) This refers to the average walking speed of a normal human being under ideal conditions of no obstacles, no crowding, and no panic. This parameter is the benchmark value for calculating theoretical evacuation speed. It represents the walking speed of a human being in a free state. It can be set based on ergonomic studies and standard data, for example, referencing the average walking speed of adults on flat ground (typically 1.2-1.5 m / s); or appropriately adjusted according to the characteristics of specific groups (such as the elderly or children). Crowding smoothing constant Used to smooth the calculation of congestion reduction coefficients in mathematical models, avoiding calculation instability or unreasonableness at specific densities, and ensuring stable model operation under various density conditions. Congestion smoothing constant. The value of is usually a small positive number, which can be determined through model calibration and sensitivity analysis, for example, by simulating the evacuation process under different levels of congestion. To make the model output more consistent with actual observation data; or, based on rules of thumb, to set a small value that effectively avoids division by zero errors without significantly affecting the calculation results. Effective early warning response time Effective early warning response time refers to the time interval between receiving an early warning message and the commencement of effective evacuation actions by the public. This parameter reflects the efficiency of the early warning system and the response speed of the public, and is a key factor in assessing the evacuation window. Evaluations can be made using data from early warning system tests and drills. For example, the average time from the issuance of an alarm to the start of movement can be recorded. Alternatively, questionnaires and interviews can be conducted to understand the public's comprehension and reaction speed to early warning information, thereby providing an estimate.

[0127] The proposed solution introduces a vulnerability index for disaster-bearing bodies based on the perception of spatial congestion. The calculation formula aims to more comprehensively and accurately assess the risks faced by personnel in the event of sudden geological environmental problems in mining areas. This formula combines static exposure risk with dynamic evacuation capacity and considers the actual impact of crowding and panic on evacuation efficiency. Specifically, the static exposure baseline value in the formula... This directly reflects the actual personnel density within the evaluation unit. relative to the extreme crowding population density threshold The proportion directly quantifies the degree of exposure of people in the current area. Based on this, an exponential term is introduced to incorporate complex factors in the dynamic evacuation process, including the congestion reduction coefficient. This coefficient characterizes the macroscopic traffic flow congestion pattern, namely, that as population density increases, the actual walking speed of the crowd decreases significantly, thus prolonging evacuation time. This coefficient is related to the free-state human walking speed. and effective early warning response time Together, they constitute the characteristic value of effective evacuation and escape capability. This characteristic value reflects the actual evacuation capacity of the crowd after considering congestion reduction within a given warning time. Further, this is achieved by adjusting the average evacuation path length. The base value for hindered evacuation time was obtained by comparing it with the characteristic value of effective evacuation and escape capability. This base quantifies the relative time required to complete an evacuation, taking into account the crowding effect. Finally, the panic amplification factor... Introduced, forming a panic-blocking timeliness index This index further amplifies the decline in evacuation efficiency caused by panic, thus increasing the vulnerability index of disaster-bearing bodies. This approach more realistically reflects the actual risks faced by populations due to spatial congestion and psychological factors during emergency situations. Through this coupled calculation, the proposed solution overcomes the oversimplification of vulnerability assessments of disaster-bearing structures in traditional methods, making the risk assessment results more instructive.

[0128] The following is a specific example to illustrate this. In a mining area risk assessment system, in order to calculate the vulnerability index of disaster-bearing bodies due to spatial obstruction... The process can be carried out in the following steps. First, by deploying an intelligent video surveillance system in the underground or surface work areas of the mine, the actual personnel density within the evaluation unit can be obtained in real time. This system utilizes image recognition and deep learning algorithms to count and locate people in a video stream, and calculates real-time density based on the area. Simultaneously, it sets an extreme crowding density threshold based on mining area safety management regulations and historical data. For example, it can be set at 4 people per square meter. Panic amplification factor. Calibration can be performed based on experience data from past emergency drills or similar disasters in the mining area; for example, a value of 1.5 could be set to reflect the decrease in evacuation efficiency under panic conditions. The average evacuation path length from the evaluation unit to the safe zone is also considered. The calculation can be performed using a 3D model in a mining area Geographic Information System (GIS). This model includes a detailed layout of underground tunnels, exits, and refuge chambers, and the path is determined using a shortest path algorithm. (Free-state human walking speed) It can be uniformly set to 1.2 m / s. (Crowding smoothing constant) It can be set to a small positive number, such as 0.01, to ensure the stability of the calculation. Effective early warning response time. The threshold is determined based on the actual test results of the mine area early warning system and the response speed of personnel; for example, it can be set to 60 seconds. Substituting these parameters into the formula above, the vulnerability index of the disaster-bearing body in the current evaluation unit can be calculated in real time. .

[0129] Through the aforementioned technical solution, this application can more accurately quantify the vulnerability of disaster-bearing bodies in sudden geological environmental problems in mining areas. This solution not only considers the static degree of personnel exposure but also innovatively introduces the congestion reduction coefficient characterized by macroscopic traffic flow congestion patterns and the panic amplification factor. This allows for the dynamic assessment of the decrease in actual evacuation efficiency caused by spatial congestion and psychological factors during emergency evacuation. This enhances the vulnerability index of disaster-bearing bodies. The calculation results more accurately reflect the actual risks faced by personnel in mining areas during emergencies, avoiding the potential underestimation of risk that may occur with traditional assessment methods. Therefore, it provides more reliable input for subsequent comprehensive risk assessments, leading to more targeted and effective control instructions, thereby improving the scientific rigor and effectiveness of emergency response in mining areas and maximizing the protection of personnel safety.

[0130] Preferably, in step S50, the calculation logic for the comprehensive risk assessment value of the sudden geological environmental problem is as follows:

[0131] The obtained vulnerability index of the disaster-bearing body is multiplied by the ecological and economic linkage loss conversion coefficient to obtain the socio-economic linkage loss amplification index.

[0132] The nonlinear exponential gain is calculated based on the socio-economic loss amplification index, and the result of this gain calculation is directly multiplied by the probability index of sudden dynamic instability to obtain the comprehensive risk assessment value of sudden geological environmental problems.

[0133] Specifically, in step S50, the formula for calculating the comprehensive risk assessment value of sudden geological environmental problems is as follows:

[0134]

[0135] in, This is the comprehensive risk assessment value for sudden geological environmental problems. The conversion coefficient between ecological and economic losses. This is a probability index for sudden dynamic instability. It is an index that amplifies the associated losses to the socio-economic system.

[0136] Among them, the comprehensive risk assessment value of sudden geological environmental problems This is the final assessment result, representing the overall risk level of sudden geological environmental problems in the mining area. This assessment value can be presented numerically to quantify risk, thus providing a basis for decision-makers to manage risk and formulate response strategies. It can be implemented by displaying this assessment value as the final output in a risk assessment report or monitoring interface, or as input for subsequent risk level classification and control instruction generation. Ecological and economic collateral loss conversion coefficient. Used to measure the vulnerability index of disaster-bearing bodies Transformed into a comprehensive risk assessment value The coefficient reflects the severity of a series of indirect and derivative losses that may be triggered by a geological disaster, in addition to direct losses, such as ecological damage, economic stagnation, and supply chain disruptions. The coefficient can be quantified based on various factors, including historical disaster data analysis, expert experience assessment, regional economic structure, and ecological sensitivity. For example, different coefficients can be set based on the ecological importance, economic output, and population density of the mining area. The value can be calculated, or by establishing a multi-factor weighting model; specifically, the ecological and economic linkage loss conversion coefficient. It is derived from the dimensionless conversion of absolute economic value based on financial audit data and statutory recovery costs. The specific calculation formula is as follows: ,in, This represents the actual projected ecological area of ​​the land surface affected by underground mining subsidence or landslides, and the unit is 1. , This refers to the legally mandated benchmark quota for land reclamation and ecological restoration per unit area issued by the local natural resources management department, with the unit being... , This represents the actual floor area of ​​the unit to be evaluated, in units of 1. , This represents the value of fixed assets and equipment per unit area within the evaluation unit output by the mining area financial audit system, with the unit being... This formula resolutely excludes the practice of subjective scoring and weighting by experts in multi-objective evaluations. Based on the fixed asset value of the mining area itself, it calculates the proportion of additional associated ecological restoration costs, forming an absolutely certain risk amplification factor with legal and financial support. Sudden Dynamic Instability Probability Index This index characterizes the likelihood or probability of sudden geological environmental problems occurring in the mining area. It is obtained through the interactive calculation of the material source potential energy index and the mining-induced stress disturbance index. This index serves as one of the basic inputs for calculating the comprehensive risk assessment value. Its value typically ranges from 0 to 1, with higher values ​​indicating a higher probability of instability. Socioeconomic Collateral Loss Amplification Index It is the conversion coefficient between ecological and economic losses. Vulnerability Index of Disaster-Bearing Body The product of these two factors is used to amplify or adjust the probability index of sudden dynamic instability. The impact on the comprehensive risk assessment value comprehensively considers the potential socio-economic and ecological losses after a disaster, quantifying them as an amplification effect on risk. This index is implemented as an exponential function. The power term affects the final comprehensive risk assessment value in a non-linear manner, thus more realistically reflecting the complexity and severity of the risk.

[0137] The solution in this application uses a sudden dynamic instability probability index. Vulnerability index of disaster-bearing bodies with obstructed perception space Nonlinear interaction is performed to obtain the comprehensive risk assessment value of sudden geological environmental problems. Specifically, this method uses a sudden dynamic instability probability index. As a fundamental risk factor, this index fully considers the impact of source characteristics, topographic evolution characteristics, rock mechanics characteristics, and mining engineering characteristics on the possibility of geological instability. Based on this, an ecological and economic loss conversion coefficient is introduced. Vulnerability Index of Disaster-Bearing Body The product of these two factors is the socioeconomic collateral loss amplification index. This is the power term of the exponential function. This exponential coupling mechanism causes the contribution of the vulnerability of the disaster-bearing body to the overall risk to grow non-linearly; that is, when the vulnerability of the disaster-bearing body is high, even if the probability of instability increases... Unchanged, overall risk assessment value This will also be significantly amplified. This reflects a complex multiplier effect between the probability of disaster events and the vulnerability of the socio-economic and ecological environment in sudden geological environmental problems in mining areas, rather than a simple linear superposition. In this way, the scheme can more comprehensively and accurately assess the overall risk of sudden geological environmental problems in mining areas, effectively solving the limitations of assessment caused by considering only a single risk dimension or a simple linear combination, thus providing a more reliable basis for the generation of subsequent risk management instructions.

[0138] The following is a specific example to illustrate this. When conducting a risk assessment of a mining area, we first assume that the probability index of sudden dynamic instability is calculated according to steps S10 to S40 above. The value is 0.3, and the vulnerability index of disaster-bearing bodies to spatial obstruction is also mentioned. The value is 0.6. To quantify the impact of ecological and economic collateral losses, a conversion coefficient between ecological and economic collateral losses can be set based on the sensitivity of the surrounding ecological environment and the intensity of economic activities in the mining area. The value is 2.0. Substitute these values ​​into the formula. In, that is The calculated result of 0.996 is the comprehensive risk assessment value for the current sudden geological environmental problems in the mining area. This value can be further compared with the preset risk threshold to trigger corresponding risk control measures. For example, if the preset highest risk threshold is 0.8, then the assessment value of 0.996 will indicate that the mining area is in an extremely high-risk state and requires immediate emergency response measures.

[0139] Through the aforementioned technical solution, this application enables an effective and non-linear interaction between the probability index of sudden dynamic instability and the vulnerability index of the disaster-bearing body, thereby obtaining a more comprehensive and accurate integrated risk assessment value for sudden geological environmental problems. This combined approach overcomes the limitations of traditional risk assessment methods, which only focus on the probability of disaster occurrence or only on the vulnerability of the disaster-bearing body, failing to fully consider the complex coupling relationship between the two. In particular, by introducing a conversion coefficient for ecological and economic collateral losses and amplifying the impact of vulnerability on comprehensive risk in an exponential form, the assessment results can more realistically reflect the collateral losses to the socio-economic and ecological environment that may be triggered after a disaster event, thus avoiding underestimating the actual risks in highly vulnerable areas. This provides mine managers with a more guiding basis for risk assessment, helping to formulate more accurate and effective risk warning and emergency response strategies, thereby minimizing casualties and property losses.

[0140] Preferably, in step S60, the comprehensive risk assessment value of the sudden geological environmental problem is compared with the preset multi-level risk thresholds, and corresponding control instructions are generated, specifically including:

[0141] Comprehensive risk assessment value of sudden geological environmental problems Compared with the first risk warning threshold and the first risk warning threshold Perform a comparison;

[0142] when The system issues a command to maintain the status quo, and then performs the next feature parameter collection according to the normal cycle.

[0143] when This triggers an evacuation order;

[0144] when Generate a forced evacuation command.

[0145] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for risk assessment of sudden geological environmental problems in mining areas, characterized in that, include: S10. Obtain the source potential energy index based on the relative ratio of the source characteristic parameters and the terrain shear strength characterized by the terrain evolution characteristic parameters; S20. By coupling and nesting the strength-stress ratio obtained based on rock mechanics characteristic parameters with the mining time-space advance ratio obtained based on mining engineering characteristic parameters, the mining-induced stress disturbance index containing time-dependent damage is obtained. S30. Perform interactive calculations on the source potential energy index and the mining stress disturbance index to obtain the probability index of sudden dynamic instability. S40. Obtain the dynamic evacuation characteristic parameters and geological early warning parameters within the evaluation unit. By introducing the macroscopic traffic flow congestion law, quantify the game relationship between the actual evacuation time and the effective early warning response time under the congestion and speed reduction of the crowd, and obtain the vulnerability index of the disaster-bearing body that perceives spatial congestion. S50. Interact with the probability index of sudden dynamic instability and the vulnerability index of the disaster-bearing body to obtain a comprehensive risk assessment value for sudden geological environmental problems; S60. Compare the comprehensive risk assessment value of sudden geological environmental problems with the preset multi-level risk thresholds and generate corresponding control instructions.

2. The risk assessment method for sudden geological environmental problems in mining areas according to claim 1, characterized in that, The terrain evolution characteristic parameters include the surface terrain slope angle; The source characteristics parameters include the actual observed thickness of the shallow loose deposits and the internal friction angle of the loose deposits; The mining engineering characteristic parameters include the actual observed values ​​of mining thickness, actual observed values ​​of mining depth, ore layer dip angle, working face advance speed, active mining duration, and working face strike length; The rock mass mechanical characteristic parameters include the uniaxial compressive strength of the overlying strata and the average unit weight of the overlying strata; The dynamic evacuation characteristic parameters include the actual personnel density within the evaluation unit, the extreme crowding personnel density threshold, the average evacuation path length, and the free-state human walking speed. The geological early warning parameters include the effective early warning response time.

3. The risk assessment method for sudden geological environmental problems in mining areas according to claim 2, characterized in that, In step S10, the calculation logic for the source potential energy index is as follows: The ratio of the actual observed thickness of the shallow loose deposit to the reference critical loose deposit thickness constant is calculated to obtain the relative thickness characteristic value that characterizes the relative ratio of the source characteristic parameters; Divide the tangent of the slope angle of the land surface by the sum of the tangent of the internal friction angle of the loose deposit and the shear stability offset constant to prevent division by zero anomaly, and obtain the shear instability ratio that characterizes the shear strength of the land surface. Using the shear instability ratio, the thickness relative characteristic value is nonlinearly amplified exponentially to construct the material source potential energy index of the terrain coupling that introduces the terrain tangential stress.

4. The risk assessment method for sudden geological environmental problems in mining areas according to claim 2, characterized in that, In step S20, the calculation logic for the mining stress disturbance index containing time-related damage is as follows: Multiply the actual observed mining thickness by the cosine of the ore dip angle, and then divide by the actual observed mining depth superimposed with the burial depth offset constant to obtain the benchmark value of mining geometric disturbance. The mining time-space advance ratio is obtained by dividing the product of the working face advance speed and the active mining duration by the working face strike length. The strength-stress ratio is obtained by dividing the uniaxial compressive strength of the overlying strata by the product of the average unit weight of the overlying strata and the actual observed value of the mining depth. The difference between the time-space advance ratio and the strength stress ratio is calculated to obtain the time-space damage residual characteristic value; Using the spatiotemporal damage residual characteristic value, the mining geometric disturbance benchmark value is subjected to nonlinear exponential product amplification to obtain the mining stress disturbance index containing time-related damage.

5. The risk assessment method for sudden geological environmental problems in mining areas according to claim 2, characterized in that, In step S30, the calculation logic for the sudden dynamic instability probability index is as follows: The mining stress disturbance index containing time-dependent damage is calculated by power-law based on the time-dependent index characterizing the rheological properties of the rock mass, to obtain the rheological time-dependent disturbance amplification value. The cumulative value of the comprehensive disaster intensity is obtained by multiplying the rheological time-effect disturbance amplification value, the material source potential energy index coupled with the terrain, and the penalty coefficient of the geological structure fault zone of the mining area based on the fault density. As the aforementioned nonlinear dynamic model, the negative of the cumulative comprehensive disaster intensity value is used for nonlinear exponential decay calculation, and the convergence is limited by subtracting the decay calculation result from the numerical value 1. Within the range of , the probability index of sudden dynamic instability is obtained.

6. The risk assessment method for sudden geological environmental problems in mining areas according to claim 2, characterized in that, In step S40, the calculation logic for the vulnerability index of the disaster-bearing body to spatial congestion is as follows: The static exposure baseline value is obtained by calculating the ratio of the actual population density within the evaluation unit to the extreme crowding population density threshold. Based on the difference between the numerical value 1 and the static exposure benchmark value, and superimposed with the congestion smoothing constant, a congestion reduction coefficient characterizing the macroscopic traffic flow congestion pattern is constructed. Multiply the free-state human walking speed, the crowding reduction coefficient, and the effective early warning response time together to obtain the characteristic value of effective evacuation and escape capability; Divide the average evacuation path length from the evaluation unit to the safe zone by the effective evacuation escape capability characteristic value to obtain the base number of the obstructed evacuation time characterizing the actual evacuation time under the crowd congestion reduction. Multiply the base time for obstructed evacuation by the panic amplification factor to obtain the panic obstruction timeliness index; Using the panic blocking timeliness index, the static exposure benchmark value is non-linearly amplified to obtain the vulnerability index of the disaster-bearing body in the perceived space blockage.

7. The risk assessment method for sudden geological environmental problems in mining areas according to claim 1 or 2, characterized in that, In step S50, the calculation logic for the comprehensive risk assessment value of the sudden geological environmental problem is as follows: Multiply the vulnerability index of the disaster-bearing body due to spatial obstruction by the ecological and economic linkage loss conversion coefficient to obtain the socio-economic linkage loss amplification index. The nonlinear exponential gain is calculated based on the socio-economic loss amplification index, and the result of the gain calculation is directly multiplied by the sudden dynamic instability probability index to obtain the comprehensive risk assessment value of the sudden geological environmental problem.

8. The risk assessment method for sudden geological environmental problems in mining areas according to claim 2, characterized in that, In step S60, the comprehensive risk assessment value of the sudden geological environmental problem is compared with the preset multi-level risk thresholds, and corresponding control instructions are generated, specifically including: Comprehensive risk assessment value of sudden geological environmental problems Compared with the first risk warning threshold and the first risk warning threshold Perform a comparison; when The system issues a command to maintain the status quo, and then performs the next feature parameter collection according to the normal cycle. when This triggers an evacuation order; when Generate a forced evacuation command.

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