Static pre-evaluation and dynamic correction-based stope impact risk quantitative dynamic evaluation method and system

Abstract

The invention relates to the technical field of mining, and provides a stope impact risk quantitative dynamic evaluation method and system based on static pre-evaluation and dynamic correction. After a static risk index of stope impact risk is determined through a static pre-evaluation method, multi-source dynamic information of the current state of a stope is obtained; and dynamically correcting the static danger index of the stope impact danger, and finally determining a dynamic danger coefficient of the stope impact danger so as to dynamically evaluate the mine stope impact danger. Therefore, a complete evaluation closed loop of the stope impact danger is formed in the stope impact danger evaluation; the dynamic danger coefficient is dynamically updated along with changes of monitoring data and production activities of the stope, automatic lifting of the stope impact danger level is achieved, and conversion of stope impact danger evaluation from static division to dynamic diagnosis and from qualitative judgment to quantitative calculation is achieved.

Description

Technical Field

[0001] This application relates to the field of mining technology, and in particular to a method and system for quantitative dynamic evaluation of mining site impact risk based on static pre-evaluation and dynamic correction. Background Technology

[0002] Rockburst is a typical dynamic disaster in coal mining, characterized by the sudden and violent release of elastic energy accumulated in the coal and rock mass, leading to mine roadway damage, equipment destruction, and casualties. With the increasing depth of coal mining, the high ground stress, intense mining disturbances, and complex geological conditions faced in deep mining have made rockburst disasters more frequent and severe, becoming one of the most significant threats to the safe and efficient mining of deep coal resources. Therefore, accurate and reliable rockburst hazard assessment of mining areas and roadways is a prerequisite and key to achieving early warning and effective prevention of the disaster.

[0003] Currently, the commonly used shock hazard rating methods in the industry mainly include static evaluation methods based on geological conditions and dynamic early warning methods based on real-time monitoring. Static evaluation methods (such as the comprehensive index method and multi-factor coupling analysis method) primarily rely on relatively fixed geological factors such as coal seam shock tendency, mining depth, geological structure, and roof lithology to classify the regional, a priori hazard levels of the mining area or working face. Dynamic early warning methods depend on microseismic monitoring systems, ground acoustic monitoring systems, online stress monitoring systems, drill cuttings analysis, and other means to monitor the dynamic response of the coal and rock mass in real time. Summary of the Invention

[0004] The purpose of this application is to provide a quantitative dynamic evaluation method and system for mining area impact hazard based on static pre-evaluation and dynamic correction, so as to solve or alleviate the problems existing in the prior art.

[0005] To achieve the above objectives, this application provides the following technical solution: This application provides a quantitative dynamic evaluation method for the rockfall hazard of a mining area based on static pre-evaluation and dynamic correction, including: determining the static hazard index of the rockfall hazard of the mining area through a static pre-evaluation method. Based on the multi-source dynamic information of the current state of the mining area, a static hazard index for the mining area's impact risk is determined. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

[0006] Preferably, the static hazard index of the mining area's impact risk is determined using a multi-factor coupling evaluation method or a comprehensive index method. .

[0007] Preferably, the stress relief effect index of the mine is determined based on the multi-source dynamic information acquired from the mining area. Support effectiveness index Other influence coefficients Mining disturbance coefficient Among them, multi-source dynamic information includes at least real-time monitoring data, production activity parameters, and engineering measure effect parameters, and the engineering measures include at least one of the following: roof pre-fracture and pressure relief, drilling pressure relief, blasting pre-fracture, hydraulic fracturing, and support. Furthermore, a dynamic risk factor model was constructed: Static hazard index of mining area impact risk Dynamic correction is performed; where, The dynamic risk coefficient for the impact hazard in the mining area; The static hazard index for the risk of rock bursts in the mining area; The pressure relief effect index for the risk of mining impact is as follows: Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient The dynamic weighting coefficients.

[0008] Preferably, in response to the mine adopting roof pre-fracture and pressure relief, based on the obtained roof pre-fracture and pressure relief measures' implementation effect parameters, including the microseismic energy release before and after roof pre-fracture and pressure relief, the frequency of microseismic events, the spatial concentration of microseismic events, and the number of high-energy microseismic events, the formula is used: Determine the pressure relief effect index of pre-cracking pressure relief in the roof slab ; In the formula, This is to release energy through micro-vibrations after the pre-cracking and decompression of the roof slab. To release energy through micro-vibrations before pre-cracking and depressurization of the roof slab; The attenuation rate of microseismic energy release before and after pre-cracking and decompression of the roof slab; The frequency of microseismic events before and after the pre-cracking and decompression of the roof slab. The frequency of microseismic events before the pre-cracking and depressurization of the roof slab; The percentage decrease in the frequency of microseismic events before and after pre-cracking and depressurization of the roof slab; These are the concentrated distribution radii of microseismic events before and after the pre-cracking and depressurization of the roof, used to characterize the spatial concentration of microseismic events before and after the pre-cracking and depressurization of the roof. The effective radius expansion coefficient for pressure relief during pre-cracking and pressure relief of the roof slab; The rate of change of high-energy microseismic events before and after pre-cracking and depressurization of the roof slab; These represent the number of high-energy microseismic events before and after the pre-cracking and depressurization of the roof slab; These are the micro-seismic energy release attenuation rates. The proportion of microseismic events decreased Effective radius expansion coefficient Rate of change of high-energy microseismic events The weighting coefficients.

[0009] Preferably, in response to the coal seam borehole pressure relief in the mine, based on the obtained parameters of the borehole pressure relief measures, including the coal stress and drill cuttings volume before and after borehole pressure relief, the following formula is used: Determine the pressure relief effect index of coal seam borehole pressure relief ; In the formula, These are the coal body stress and drill cuttings volume before the coal seam borehole pressure relief. These represent the stress reduction in the coal body and the reduction in drill cuttings volume after pressure relief from coal seam drilling. The stress reduction rate before and after pressure relief in coal seam drilling are respectively Drill cuttings reduction rate Importance weights, among which, .

[0010] Preferably, based on the average working resistance, rated working resistance, and dynamic load coefficient of the support structure in the obtained support measure implementation effect parameters, according to the constructed mine support effect model: Determine the support effectiveness index after supporting the mine. ; In the formula, The average working resistance and rated working resistance of the support structure used to support the mine. The dynamic load factor of the support structure for supporting the mine; The static load support capacity of the support structure for supporting the mine is respectively. Dynamic load stability The contribution weight; among which, .

[0011] Preferably, a mining disturbance model is constructed: In the formula, The mining disturbance coefficient represents the risk of impact in the mining area. This represents the average daily mining speed of the mine. The preset mining speed of the mine; The distance between the working face of the mine and its nearest critical structure; This is a preset reference distance for the influence of key structures on the stress state of the surrounding coal and rock mass; Mining disturbance factors Construction proximity influence factor The factor weights; where, .

[0012] Preferably, the pressure relief effect index is determined using the analytic hierarchy process (AHP), principal component analysis (PCA), or machine learning methods. Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient The dynamic weighting coefficients.

[0013] Preferred dynamic hazard coefficient in response to the risk of impact in the mining area If so, it is determined that there is no risk of impact in the mine's workings; Dynamic risk coefficient in response to mining site impact hazard If so, the mine's working area is determined to be at low risk of impact. Dynamic risk coefficient in response to mining site impact hazard If so, the mine is classified as having a medium risk of impact. Dynamic risk coefficient in response to mining site impact hazard If so, the mine is determined to be at high risk of impact.

[0014] This embodiment also provides a quantitative dynamic evaluation system for mining area rockfall hazard based on static pre-evaluation and dynamic correction. This evaluation system is deployed with any of the aforementioned quantitative dynamic evaluation methods for mining area rockfall hazard based on static pre-evaluation and dynamic correction. The evaluation system includes: The static index unit is configured to determine the static hazard index of the mining area's impact risk using a static pre-evaluation method. ; The dynamic evaluation unit is configured to evaluate the static hazard index of the mining area's impact risk based on multi-source dynamic information about the current state of the mining area. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

[0015] Beneficial effects: The embodiments of this application provide a quantitative dynamic evaluation method and system for the hazard assessment of mining area impact based on static pre-evaluation and dynamic correction. The static hazard index of mining area impact risk is determined through a static pre-evaluation method. Subsequently, based on the multi-source dynamic information on the current state of the mining area, a static hazard index for the mining area's shock risk was determined. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

[0016] Therefore, in the assessment of mining rock hazard, factors such as static geology, dynamic monitoring, pressure relief, support, and mining disturbance are considered within a unified mathematical quantitative framework, forming a complete closed loop for the assessment of mining rock hazard. By quantifying the influencing factors of mining rock hazard into specific numerical values, compared with traditional qualitative or semi-quantitative methods, the assessment process of mining rock hazard is made more objective and refined; dynamic hazard coefficient It is dynamically updated according to changes in monitoring data and mining operations, realizing the "automatic rise and fall" of mining impact hazard level. It can reflect the current risk status of the mining area in real time, realizing the transformation of mining impact hazard assessment from "static classification" to "dynamic diagnosis" and from "qualitative judgment" to "quantitative calculation". Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. Wherein: Figure 1 This is a flowchart illustrating a method for quantitative dynamic evaluation of mining area impact hazard based on static pre-evaluation and dynamic correction, according to some embodiments of this application. Figure 2 This is a logical diagram illustrating the process of quantitative dynamic evaluation of mining site impact risk according to some embodiments of this application; Figure 3 This is a system diagram of a stope impact hazard assessment provided according to some embodiments of this application; Figure 4 A logic diagram of dynamic risk factors provided according to some embodiments of this application; Figure 5 A logic diagram of the borehole pressure relief effect index provided according to some embodiments of this application; Figure 6 This is a framework diagram of dynamic assessment of impact hazard provided according to some embodiments of this application; Figure 7 This is a schematic diagram of the structure of a dynamic evaluation system for quantitative mining impact risk based on static pre-evaluation and dynamic correction, provided according to some embodiments of this application. Detailed Implementation

[0018] The present application will now be described in detail with reference to the accompanying drawings and embodiments. Various examples are provided by way of explanation and not by way of limitation. In fact, those skilled in the art will understand that modifications and variations can be made to the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0019] Current static assessment methods for rockburst risk treat the hazard zone as static, completely ignoring the fundamental impact of dynamic factors such as stress relief measures (e.g., roof pre-fracturing, coal seam drilling), support conditions, and mining activity intensity on the hazard state. For example, a statically defined "high-risk zone" may have its actual risk significantly reduced after effective stress relief; conversely, a "low-risk zone" may experience a sharp increase in risk when encountering strong mining disturbances or crossing faults. Static assessment methods cannot capture this dynamic evolution, leading to inaccurate and outdated assessment results. Furthermore, static classifications typically cover a large area, failing to pinpoint the specific location and time within the working face where the risk is highest, thus hindering the management needs of refined and intelligent mining.

[0020] Existing dynamic monitoring and early warning methods for rockburst hazards mostly rely on the absolute values ​​of monitoring parameters (such as microseismic energy exceeding a certain number of joules or stress exceeding a certain number of megapascals). This model lacks the fusion analysis of multi-source information, making it prone to false alarms and missed alarms. For example, a high-energy microseismic event may originate from an effective pressure relief blast, which is actually a safe energy release; while a slow stress accumulation process may pose a serious threat before reaching the threshold. The "single-parameter, threshold-based" method in dynamic monitoring and early warning cannot distinguish between these fundamentally different situations, resulting in highly biased alarms. On the other hand, current evaluations of the effectiveness of engineering measures such as pressure relief and support heavily depend on the field experience and qualitative judgments of technicians (such as "the amount of drill cuttings seems less after pressure relief" or "the sound is muffled"). There is a lack of an objective and unified set of quantitative indicators to scientifically evaluate the effectiveness of pressure relief. This leads to a certain degree of blindness in the implementation of rockburst prevention measures, making it impossible to accurately determine whether the measures are sufficient and in place, and when reinforcement is needed, resulting in both resource waste and safety risks.

[0021] Furthermore, traditional dynamic monitoring and early warning methods suffer from the logical flaw of a "only escalating, never decreasing" danger level. Once a traditional dynamic monitoring and early warning system issues an alarm, the danger level is often continuously marked. Even if effective pressure relief measures are subsequently taken to substantially reduce the risk, the system cannot dynamically lower the danger level. This rigid logic of "only escalating, never decreasing" does not conform to the objective law of dynamic risk evolution, easily leading to "early warning fatigue" and reducing the authority and guidance of the early warning.

[0022] Therefore, there is a fundamental technical gap in the existing analytical methods for rockburst hazard: there is a lack of a quantitatively correlated and integrated bridge between "static geological assessment" and "dynamic production activities and prevention measures." This keeps rockburst hazard assessment at the rudimentary stage of "static classification" or "simple alarm," unable to achieve true "dynamic diagnosis."

[0023] Based on this, this embodiment proposes a quantitative dynamic evaluation method for mining area impact risk based on static pre-evaluation and dynamic correction. It systematically integrates multiple factors such as static geological conditions, dynamic monitoring information, pressure relief support effect, and mining disturbance intensity, and outputs a dynamic risk index in real time through a unified and quantitative model that can truly reflect the current actual risk status. This realizes the transformation from "static classification" to "dynamic diagnosis" and from "qualitative judgment" to "quantitative calculation".

[0024] like Figures 1 to 6 As shown, this quantitative dynamic evaluation method for mining area impact hazard based on static pre-evaluation and dynamic correction includes: Step S101: Determine the static hazard index of the mining area's rockfall risk using a static pre-evaluation method. .

[0025] In this embodiment, the evaluation area is initially divided into danger zones based on the relatively stable geological and mining conditions of the mining area, thereby obtaining the static hazard index of the mining area's rockburst risk. Specifically, multi-source static data from the mining area is collected, mainly including: geological conditions (coal seam thickness, dip angle, hardness, and roof and floor lithology), mining parameters (mining depth, mining height, and working face layout), and stress field characteristics (original rock stress distribution and tectonic stress field characteristics). Then, using a multi-factor coupling evaluation method or a comprehensive index method, the multi-source static data is integrated based on factors such as the mining area's geological structure, mining depth, and coal and rock physical and mechanical properties to obtain the initial hazard zone division for the entire evaluation area. A normalized static hazard index is then assigned to each evaluation unit (i.e., each divided zone). .

[0026] Among them, the static hazard index for determining the risk of impact in the mining area First, evaluation indicators are selected to determine the main static geological factors affecting rockburst, such as mining depth, roof lithology, geological structure, and coal seam rockburst tendency. Next, the weights of each evaluation indicator are determined: the analytic hierarchy process (AHP) can be used to compare the main static geological factors affecting rockburst pairwise and construct a judgment matrix to calculate the weight of each factor. Then, a scoring standard is established: a graded scoring standard is developed for each factor, and a static hazard index model is constructed. Calculate the static hazard index for each evaluation unit. In the formula, For the first Scoring of static geological factors The weights are determined using the analytic hierarchy process (AHP). .

[0027] Finally, the static hazard index of the evaluation unit was determined using the maximum score normalization method. To process it, specifically, according to the formula: Static hazard index for determining the risk of rock bursts in a mining area Among them, the normalized static risk index In this way, static geological evaluation can be used to achieve static classification and qualitative judgment of the impact risk of the mining area.

[0028] Step S102: Based on the multi-source dynamic information of the current state of the stope, determine the static hazard index of the stope's impact risk. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

[0029] In this embodiment, the static risk baseline is corrected in real time using multi-source dynamic information from the mine. This multi-source dynamic information mainly includes: real-time monitoring data (microseismic parameters (energy, frequency, magnitude, and spatiotemporal evolution characteristics of microseismic events), stress monitoring data (peak stress in the coal seam, stress gradient), and other monitoring data), production activity parameters (mining speed, relative spatial position of the working face and key structures), engineering measures (roof pre-fracturing and pressure relief, borehole pressure relief, blasting pre-fracturing, hydraulic fracturing, support, etc.), and effect parameters (drill cuttings parameters (drill cuttings change rate, whether it exceeds the critical value) and related manual detection information). Here, the pressure relief effect index of the mine is mainly determined by the collected multi-source dynamic information. Support effectiveness index Other influence coefficients Mining disturbance coefficient Quantify it.

[0030] In this embodiment, the pressure relief effect index is used. Quantitatively evaluate the effectiveness of various pressure relief measures, specifically by utilizing microseismic energy before and after pressure relief, event frequency, effective radius, and high-energy events (energy exceeding...). Based on the changes in the roof pre-fracturing pressure relief, a quantitative calculation model for the pressure relief effect index is constructed. Specifically, when the mine adopts roof pre-fracturing pressure relief, the model is based on the obtained roof pre-fracturing pressure relief effect parameters, including the microseismic energy release before and after roof pre-fracturing pressure relief, the frequency of microseismic events, the spatial concentration of microseismic events, and the number of high-energy microseismic events, calculated according to the formula: Determine the pressure relief effect index of pre-cracking pressure relief in the roof slab .

[0031] In the formula, This is to release energy through micro-vibrations after the pre-cracking and decompression of the roof slab. To release energy through micro-vibrations before pre-cracking and depressurization of the roof slab; The attenuation rate of microseismic energy release before and after pre-cracking and decompression of the roof slab; This refers to the frequency (i.e., the number of microseismic events) of the roof before and after pre-cracking and depressurization. The frequency of microseismic events before the pre-cracking and depressurization of the roof slab; The percentage decrease in the frequency of microseismic events before and after pre-cracking and depressurization of the roof slab; These are the concentrated distribution radii of microseismic events before and after the pre-cracking and depressurization of the roof, used to characterize the spatial concentration of microseismic events before and after the pre-cracking and depressurization of the roof. The effective radius expansion coefficient for pressure relief during pre-cracking and pressure relief of the roof slab; The rate of change of high-energy microseismic events before and after pre-cracking and depressurization of the roof slab; These represent the number of high-energy microseismic events before and after the pre-cracking and depressurization of the roof slab; These are the micro-seismic energy release attenuation rates. The proportion of microseismic events decreased Effective radius expansion coefficient Rate of change of high-energy microseismic events The weighting coefficients can be obtained by analyzing historical depressurization case data using the analytic hierarchy process.

[0032] When a coal seam is subjected to borehole pressure relief in a mine, based on the obtained parameters of the borehole pressure relief measures, including the coal stress and drill cuttings volume before and after pressure relief, the following formula is used: Determine the pressure relief effect index of coal seam borehole pressure relief In the formula, These are the coal body stress and drill cuttings volume before the coal seam borehole pressure relief. These represent the stress reduction in the coal body and the reduction in drill cuttings volume after pressure relief from coal seam drilling. The stress reduction rate before and after pressure relief in coal seam drilling are respectively Drill cuttings reduction rate The importance weights can be determined using the analytic hierarchy process (AHP) or statistical regression calculations on historical data. .

[0033] In this embodiment, the support effect index is used. The study quantitatively evaluates the control effects of active support (such as bolts and cables) and passive support (such as hydraulic support structures) on surrounding rock stability. Based on online monitoring data such as support structure resistance and support density, a mine support effect model is constructed to determine the support effect index. Among them, based on the average working resistance, rated working resistance, and dynamic load coefficient of the support structure in the obtained support measure implementation effect parameters, according to the constructed mine support effect model: Determine the support effectiveness index after supporting the mine. In the formula, The average working resistance and rated working resistance of the support structure used to support the mine. The dynamic load factor of the support structure for supporting the mine; The static load support capacity of the support structure for supporting the mine is respectively. Dynamic load stability The contribution weights can be determined through the analytic hierarchy process or statistical regression calculations on historical data; among them, .

[0034] In this embodiment, the mining disturbance coefficient is used. The disturbance to the surrounding rock caused by production activities such as mining speed and intensity is quantified. Specifically, based on parameters such as mining speed and the distance between the working face and key structures, the mining disturbance coefficient for the mine's impact risk is determined through a constructed mine mining disturbance model. The mine mining disturbance model is as follows: In the formula, The mining disturbance coefficient represents the risk of impact in the mining area. This represents the average daily mining speed of the mine. The preset mining speed of the mine; The distance between the working face of the mine and its nearest critical structure; This is a preset reference distance for the influence of key structures on the stress state of the surrounding coal and rock mass; Mining disturbance factors Construction proximity influence factor The factor weights; where, Factor weights The factor weights represent the importance of the mining activity itself to the overall mining disturbance risk per unit time. This demonstrates the importance of the amplification effect of geological structures on mining disturbances in the overall disturbance risk. The relative proportions of the two risk sources, "human-controlled activities (mining speed)" and "objective geological structure (structural proximity)," in the total mining disturbance risk were jointly quantified. This proportion can be determined through the analytic hierarchy process or statistical regression calculations on historical data.

[0035] Here, key structures refer to geological structures that, under specific geological and mining conditions, significantly affect the stress distribution in the mining area, are prone to energy accumulation, or induce dynamic disasters. These include: faults (especially reverse faults and normal faults), folds (synclinal axis and anticline axis), zones of rapid changes in coal seam thickness (bifurcations, thinning, pinch-outs), boundaries of hard roof strata (such as thick sandstone), boundaries of igneous intrusive bodies, collapse columns, etc.

[0036] In this embodiment, although major dynamic factors such as pressure relief, support, and mining disturbance have been integrated, some factors that are difficult to measure directly or are not yet included in the actual mining environment may still exist. These include subtle changes in on-site rockfall prevention management (such as the degree of emphasis in pre-shift meetings and the immediate effect of temporary measures), and slight adaptation deviations in core parameters (such as weighting coefficients) under specific geological conditions for certain regional or sporadic geological anomalies (which experienced technicians can perceive, but the instruments have not yet captured clear signals). To address this, this embodiment reserves influencing index coefficients. It characterizes the degree of influence of dynamic indicators that cannot be precisely quantified at present (such as shock management factors) on shock hazard assessment.

[0037] Reserved influence index coefficient It is a quantitative adjustment parameter used to characterize and incorporate indicators not included in the core dynamic indicators ( Dynamic factors that are directly modeled in the data but have potential or indirect impacts on the impact risk of the mining area. These are identified through reserved influence index coefficients. This provides a quantifiable and adjustable entry point for dynamic indicators that cannot be precisely quantified at the moment. This is achieved by reserving coefficients that influence these indicators. Based on calculations using core data, and according to comprehensive judgments, a small, controlled adjustment can be made to the final risk index, thereby making the results closer to complex realities and enhancing the system's practicality and human-machine collaborative decision-making capabilities.

[0038] In other words, reserve the coefficient of influence index. These are adjustment parameters designed to enhance the model's adaptability and engineering practicality. They are mainly used to absorb the following two types of influences: (1) minor dynamic factors: such as changes in local erosion control intensity and unmodeled microclimate effects; (2) model system bias correction: in the initial stage of application in a specific mining area, the model can be finely adjusted based on a small amount of historical data. In regular applications, if there is no clear additional information, the coefficients of the influencing indicators are reserved. Its dynamic weighting coefficient The values ​​are usually small, reflecting their auxiliary role in the evaluation system.

[0039] Furthermore, through the constructed dynamic hazard coefficient model: In the formula, The dynamic risk coefficient for the impact hazard in the mining area; The static hazard index for the risk of rock bursts in the mining area; The pressure relief effect index for the risk of mining impact is as follows: Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient The dynamic weighting coefficients.

[0040] In this embodiment, the risk of mine impact was effectively reduced through pressure relief and support measures, while mining disturbance increased the risk of mine impact. Pressure relief effect index. Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient All values ​​are normalized values, and their range is [range missing]. In other words, the stress relief effect index for determining the risk of mining site impact. Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient After normalization, the dynamic hazard coefficient of the mining area's impact risk is determined using the constructed dynamic hazard coefficient model. .

[0041] In this embodiment, the pressure relief effect index can be determined using the analytic hierarchy process, principal component analysis, or machine learning methods. Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient Dynamic weighting coefficients Among them, the analytic hierarchy process (AHP) calculates the dynamic weight coefficients by constructing a judgment matrix and performs consistency verification; the principal component analysis (PCA) extracts the main influencing factors based on historical data and determines the weights of the main influencing factors; and the machine learning method uses historical case data, with each index as input and whether an impact or degree of danger has occurred as a label, to train logistic regression models, random forest models, etc., and the feature importance of the model is the dynamic weight coefficient.

[0042] In a specific example, the dynamic weight coefficients are determined using the Analytic Hierarchy Process (AHP). First, a hierarchical model is established, including: a target layer (dynamic weight coefficients) and a criterion layer (pressure relief effect index). Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient Then, the importance between any two influencing factors is determined using the 1-9 scaling method to construct a judgment matrix. , where the judgment matrix elements in Indicates the first The influencing factor is relative to the first The importance of each influencing factor is then determined. Next, based on the constructed judgment matrix, the relative weight of each influencing factor is calculated using the sum-product method.

[0043] In a specific application scenario, the 1-9 scale method is used to compare the importance of any two influencing factors. The specific meanings are shown in Table 1 below: Table 1. Comparison of influencing factors using the 1-9 scaling method. Then, construct the judgment matrix. As shown in Table 2: Table 2 Constructing the judgment matrix Next, the weight vectors are calculated. Specifically, first, the elements of each column of the judgment matrix are summed, as shown in Table 3 below: Table 3. Calculation of column summation in the judgment matrix Then, divide each element by the sum of its column to normalize the judgment matrix column by column. The normalized matrix is ​​shown in Table 4. Table 4 is as follows: Table 4. Normalization of the judgment matrix Then, the average value of each row of the normalized matrix is ​​calculated to obtain the weight vector of each influencing factor, i.e., the dynamic weight coefficients.

[0044] That is, the pressure relief effect index Dynamic weighting coefficients Support effectiveness index Dynamic weighting coefficients Mining disturbance coefficient Dynamic weighting coefficients Reserved coefficients for influencing indicators Dynamic weighting coefficients It should be noted here that the dynamic weighting coefficient... The sum of is 1 (rounded to the nearest whole number).

[0045] When performing consistency checks on dynamic weight coefficients, firstly, according to the formula: Calculate the largest eigenvalue ;in, This represents the number of dynamic weighting coefficients.

[0046] Then, according to the formula: Calculate the consistency index And determine the average random consistency index. In a specific example, multiple judgment matrices of the same order are constructed using a random method, and their consistency indices are calculated for each. Then, for all consistency indicators The average random consistency index is obtained by taking the average. In addition, the average random consistency index can be determined by looking up a table (Table 1). Table 1 is as follows: Table 1 Standard values ​​of the average random consistency index (RI) Consistency Indicators The closer a value is to 0, the better the consistency of the judgment matrix. When completely consistent, Then the consistency index .

[0047] Next, according to the formula: Calculate the consistency ratio When the consistency ratio If the consistency of the matrix is ​​acceptable, the calculated dynamic weight coefficients are valid after passing the consistency check; if the consistency ratio is... Then the judgment matrix is ​​reconstructed until the consistency ratio is reached. .

[0048] In another specific example, principal component analysis is used to determine the dynamic weight coefficients. Specifically, the collected data is first analyzed... In a historical sample The candidate evaluation indicators (such as daily advance, distance from fault, total microseismic energy, average stress value, drill cuttings volume, etc.) are standardized using the Z-score method, and the covariance matrix (or correlation matrix) of the standardized data is calculated. The eigenvalues ​​of the covariance matrix are sorted from largest to smallest, and the eigenvectors corresponding to each eigenvalue are the directions of each principal component.

[0049] Then, the dynamic weight coefficients can be determined based on the variance contribution rate of the feature values. Specifically, this is done by selecting the first principal component. Eigenvectors (representing the pairs of original variables) (importance), and according to the formula: The absolute value of each index in this feature vector Normalization is performed to obtain the corresponding dynamic weight coefficients.

[0050] In another specific example, machine learning methods are used to inversely determine the dynamic weight coefficients. In the logistic regression model, the model is trained using collected training samples, and the absolute values ​​of the obtained regression coefficients are taken and then normalized to obtain the dynamic weight coefficients. In the random forest model, after training the model using collected training data, the model can directly output the Gini importance of each feature (measuring the average contribution of that feature to reducing the uncertainty of the target variable when used as a splitting node across all model decision trees). Normalizing the Gini importance of the features yields the corresponding dynamic weight coefficients.

[0051] In this embodiment, the dynamic risk factor of the mining area impact risk was determined. Subsequently, based on the dynamic risk factor A dynamic evaluation of the rockfall hazard in mine workings is conducted. This includes assessing the dynamic hazard coefficient of the rockfall hazard in the workings. If the dynamic risk coefficient of the mine's stope is [value missing], then the mine stope is determined to have no rockburst risk; when the dynamic risk coefficient of the stope's rockburst risk is [value missing], the mine stope is determined to have no rockburst risk. If the dynamic risk coefficient of the mine's stope is low, then the mine stope is determined to be at a low risk of impact; If the dynamic risk coefficient of the mine's rockfall risk is [value missing], then the mine's stope is classified as having a medium rockfall risk. If so, the mine is determined to be at high risk of impact.

[0052] Specifically, the classification of the impact hazard levels in the mining area is shown in Table 5, as follows: Table 5 Classification of Dynamic Impact Hazard Assessment Levels In this embodiment, the sole basis for classifying the mining area impact hazard level is the calculated dynamic hazard coefficient. This effectively ensures the objectivity and consistency of the evaluation. Furthermore, the evaluation of the mining area is dynamic; that is, the hazard level of the evaluation area is not fixed but changes with the dynamic hazard coefficient. The dynamic changes in the dynamic risk coefficient allow for real-time switching between four different evaluation levels: blue, yellow, orange, and red, enabling dynamic evaluation of the mining area. Simultaneously, this is achieved by... The numerical values ​​are converted into intuitive colors and clear instructions, utilizing dynamic risk factors. Precisely identifying the main factors leading to increased risk (such as whether it is insufficient pressure relief or excessively rapid mining) provides a direct basis for taking targeted measures, provides clear and effective decision support for on-site management, and realizes a closed loop from "data" to "action".

[0053] like Figure 7 As shown, this embodiment also provides a quantitative dynamic evaluation system for mining area impact hazard based on static pre-evaluation and dynamic correction. This system is deployed with any of the aforementioned quantitative dynamic evaluation methods for mining area impact hazard based on static pre-evaluation and dynamic correction. The evaluation system includes: The static index unit is configured to determine the static hazard index of the mining area's impact risk using a static pre-evaluation method. ; The dynamic evaluation unit is configured to evaluate the static hazard index of the mining area's impact risk based on multi-source dynamic information about the current state of the mining area. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

[0054] The quantitative dynamic evaluation system for mining area impact hazard based on static pre-evaluation and dynamic correction provided in this embodiment can realize the steps and processes of the quantitative dynamic evaluation method for mining area impact hazard based on static pre-evaluation and dynamic correction in any of the above embodiments, and achieve the same technical effect, which will not be described in detail here.

[0055] In the description of this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0056] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A quantitative dynamic evaluation method for mining area impact hazard based on static pre-evaluation and dynamic correction, characterized in that, include: Static hazard index of mining area impact risk is determined using static pre-evaluation method. ; Based on the multi-source dynamic information on the current state of the mining area, a static hazard index for the mining area's shock risk is determined. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

2. The method according to claim 1, characterized in that, The static hazard index of the mining area's impact risk is determined by the multi-factor coupling evaluation method or the comprehensive index method. .

3. The method according to claim 1, characterized in that, Based on the acquired multi-source dynamic information of the mining area, the pressure relief effect index of the mine is determined. Support effectiveness index Other influence coefficients Mining disturbance coefficient Among them, multi-source dynamic information includes at least real-time monitoring data, production activity parameters, and engineering measure effect parameters, and the engineering measures include at least one of the following: roof pre-fracture and pressure relief, drilling pressure relief, blasting pre-fracture, hydraulic fracturing, and support. Furthermore, a dynamic risk factor model was constructed: Static hazard index of mining area impact risk Perform dynamic correction; In the formula, The dynamic risk coefficient for the impact hazard in the mining area; The static hazard index for the risk of rock bursts in the mining area; The pressure relief effect index for the risk of mining impact is as follows: Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient The dynamic weighting coefficients.

4. The method according to claim 3, characterized in that, In response to the mine's roof pre-fracture and pressure relief measures, based on the obtained parameters of the roof pre-fracture and pressure relief measures, including the microseismic energy release before and after the pre-fracture and pressure relief, the frequency of microseismic events, the spatial concentration of microseismic events, and the number of high-energy microseismic events, the formula is as follows: Determine the pressure relief effect index of pre-cracking pressure relief in the roof slab ; In the formula, This is to release energy through micro-vibrations after the pre-cracking and decompression of the roof slab. To release energy through micro-vibrations before pre-cracking and depressurization of the roof slab; The attenuation rate of microseismic energy release before and after pre-cracking and decompression of the roof slab; The frequency of microseismic events before and after the pre-cracking and decompression of the roof slab. The frequency of microseismic events before the pre-cracking and depressurization of the roof slab; The percentage decrease in the frequency of microseismic events before and after pre-cracking and depressurization of the roof slab; These are the concentrated distribution radii of microseismic events before and after the pre-cracking and depressurization of the roof, used to characterize the spatial concentration of microseismic events before and after the pre-cracking and depressurization of the roof. The effective radius expansion coefficient for pressure relief during pre-cracking and pressure relief of the roof slab; The rate of change of high-energy microseismic events before and after pre-cracking and depressurization of the roof slab; These represent the number of high-energy microseismic events before and after the pre-cracking and depressurization of the roof slab; These are the micro-vibration energy release attenuation rates. The proportion of microseismic events decreased Effective radius expansion coefficient Rate of change of high-energy microseismic events The weighting coefficients.

5. The method according to claim 3, characterized in that, In response to the coal seam borehole pressure relief adopted in the mine, based on the obtained parameters of the borehole pressure relief measures, including the coal stress and drill cuttings volume before and after borehole pressure relief, the following formula is used: Determine the pressure relief effect index of coal seam borehole pressure relief ; In the formula, These are the coal body stress and drill cuttings volume before the coal seam borehole pressure relief. These represent the stress reduction in the coal body and the reduction in drill cuttings volume after pressure relief from coal seam drilling. The stress reduction rate before and after pressure relief in coal seam drilling are respectively Drill cuttings reduction rate Importance weights, among which, .

6. The method according to claim 3, characterized in that, Based on the average working resistance, rated working resistance, and dynamic load coefficient of the support structure from the obtained parameters of the support measures implementation effect, according to the constructed mine support effect model: Determine the support effectiveness index after supporting the mine. ; In the formula, The average working resistance and rated working resistance of the support structure used to support the mine. The dynamic load factor of the support structure for supporting the mine; The static load support capacity of the support structure for supporting the mine is respectively. Dynamic load stability The contribution weight; among which, .

7. The method according to claim 3, characterized in that, Constructing a mining disturbance model: In the formula, The mining disturbance coefficient represents the risk of impact in the mining area. This represents the average daily mining speed of the mine. The preset mining speed of the mine; The distance between the working face of the mine and its nearest critical structure; This is a preset reference distance for the influence of key structures on the stress state of the surrounding coal and rock mass; Mining disturbance factors Construction proximity influence factor The factor weights; where, .

8. The method according to claim 3, characterized in that, Determine the pressure relief effect index using analytic hierarchy process, principal component analysis, or machine learning methods. Support effectiveness index Reserved influence index coefficient Mining disturbance coefficient The dynamic weighting coefficients.

9. The method according to claim 1, characterized in that, Dynamic risk coefficient in response to mining site impact hazard If so, it is determined that there is no risk of impact in the mine's workings; Dynamic risk coefficient in response to mining site impact hazard If so, the mine's working area is determined to be at low risk of impact. Dynamic risk coefficient in response to mining site impact hazard If so, the mine is classified as having a medium risk of impact. Dynamic risk coefficient in response to mining site impact hazard If so, the mine is determined to be at high risk of impact.

10. A quantitative dynamic evaluation system for mining area impact hazard based on static pre-evaluation and dynamic correction, characterized in that, The evaluation system is equipped with the quantitative dynamic evaluation method for mining area impact hazard based on static pre-evaluation and dynamic correction as described in any one of claims 1-9. The evaluation system includes: The static index unit is configured to determine the static hazard index of the mining area's impact risk using a static pre-evaluation method. ; The dynamic evaluation unit is configured to evaluate the static hazard index of the mining area's impact risk based on multi-source dynamic information about the current state of the mining area. Dynamic corrections are performed to determine the dynamic risk coefficient of the mining area's impact hazard. To dynamically evaluate the impact risk of mines and mining areas.

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