Dynamic mining path planning method and system based on three-dimensional geologic model

Abstract

The invention discloses a dynamic mining path planning method and system based on a three-dimensional geologic model, and relates to the technical field of intelligent mining. The method comprises the following steps: acquiring geological parameters of a mining working face in real time to establish a geological risk assessment matrix; a geological risk change matrix is obtained through differential operation, and a risk change coefficient is calculated; when the risk change coefficient exceeds a preset threshold value, updating the three-dimensional geologic model; calculating the updating amplitude of the three-dimensional geologic model; and when the updating amplitude is greater than a preset updating threshold value, performing global path re-planning, otherwise, determining an influence section and performing local path updating. According to the invention, real-time monitoring and intelligent response to geological condition changes are realized, the problems of large calculation amount caused by frequent global path re-planning and influence on production efficiency caused by frequent adjustment of mining equipment in related technologies are effectively solved, and the high efficiency of mining path planning and the continuity of mining operation are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent mining technology, and in particular to a method and system for dynamic mining path planning based on a three-dimensional geological model. Background Technology

[0002] With the continuous development of mining technology, intelligent mining has become an important trend in the industry. In underground mining, the geological conditions of the mining area are complex and variable, with uneven distribution of ground stress and risks such as groundwater seepage and rock fracturing. This places higher demands on the safety and efficiency of mining operations. Therefore, accurately grasping the changes in geological conditions during the mining process and rationally planning mining routes accordingly has become a key issue that urgently needs to be addressed in the field of intelligent mining.

[0003] To address the aforementioned issues, the relevant technology employs a path planning method based on a three-dimensional geological model. This method collects real-time geological information, including borehole data, geological exploration data, and equipment sensor data, dynamically updates the three-dimensional geological model using an incremental modeling algorithm, and recalculates the optimal mining path based on the updated model using a pre-defined path planning algorithm. This method can promptly reflect changes in geological conditions during the mining process, significantly improving the accuracy and reliability of path planning.

[0004] However, as the scale and depth of mining expand, geological conditions become more drastic and unpredictable. Current technological solutions require recalculating the entire mining path each time geological conditions change, which not only involves a large computational load but also necessitates frequent adjustments to mining equipment, impacting production efficiency. Summary of the Invention

[0005] This application provides a dynamic mining path planning method and system based on a three-dimensional geological model, which can accurately respond to changes in geological conditions while improving the efficiency of mining path planning.

[0006] Firstly, this application provides a dynamic mining path planning method based on a three-dimensional geological model, applied to a mining path planning system. The method includes: acquiring geological parameters of the mining face in real time and establishing a geological risk assessment matrix based on these parameters, which include rock stress, groundwater level, and rock mass fissure; acquiring the geological risk assessment matrices of the current and previous moments and performing a difference operation to obtain a geological risk change matrix; calculating the geological risk change coefficient of the current moment based on the element values ​​of the geological risk change matrix and preset risk weight coefficients; when the geological risk change coefficient is greater than a preset risk threshold, acquiring real-time point cloud data of the mining face and updating the three-dimensional geological model based on the real-time point cloud data and the geological risk change coefficient; calculating the update magnitude of the updated three-dimensional geological model based on the updated model, which includes changes in model geometry and physical parameters; when the update magnitude is greater than or equal to a preset update threshold, replanning the mining path based on the updated three-dimensional geological model; when the update magnitude is less than the preset update threshold, determining the affected section of the current mining path and performing a local path update on the affected section.

[0007] By adopting the above technical solution, the mining path planning system establishes a geological risk assessment matrix based on real-time acquired geological parameters, obtains a geological risk change matrix through differential operations, and calculates the geological risk change coefficient, thus achieving real-time monitoring of dynamic changes in geological conditions. When the geological risk change coefficient exceeds a preset threshold, the mining path planning system acquires real-time point cloud data to update the 3D geological model, and selects a strategy of global path replanning or local path update based on the magnitude of the update. By closely integrating model updates with path planning, the system ensures the rapid response capability of the mining path to changes in geological conditions while avoiding unnecessary global path replanning, significantly improving the safety and efficiency of mining operations.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, real-time point cloud data of the mining face is acquired, and the three-dimensional geological model is updated based on the real-time point cloud data and the geological risk change coefficient. Specifically, this includes: acquiring real-time point cloud data of the mining face; comparing the real-time point cloud data with the geometric data of the current three-dimensional geological model at the corresponding location to obtain geometric deviation; updating the geometric shape of the current three-dimensional geological model based on the geometric deviation to generate a corrected three-dimensional geological model; and locally adjusting the physical and mechanical parameters contained in the corrected three-dimensional geological model according to the geological risk change coefficient to obtain an updated three-dimensional geological model.

[0009] By employing the aforementioned technical solution, the mining path planning system performs a geometric comparison between real-time point cloud data and the current 3D geological model to obtain geometric deviations. Based on these deviations, the model morphology is updated, and physical and mechanical parameters are locally adjusted according to the geological risk variation coefficient. This step-by-step model update method not only ensures the accuracy of the model's geometric morphology but also reflects changes in the rock mass's mechanical properties through dynamic adjustments to physical parameters. This update strategy enables the updated 3D geological model to more realistically reflect the actual state of the mining face, providing a reliable basis for subsequent path planning.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, calculating the update magnitude of the three-dimensional geological model based on the updated three-dimensional geological model specifically includes: establishing a sequence of scanning planes along the current mining path based on the updated three-dimensional geological model, wherein each scanning plane in the sequence is perpendicular to the mining path direction; calculating the cross-sectional curves of the geometric deviation on each scanning plane, and determining whether the maximum deviation value of each cross-sectional curve exceeds a preset reference value; marking the cross-sections exceeding the preset reference value as changed cross-sections, and calculating the ratio of the number of changed cross-sections to the total number of scanning planes to determine the geometric change of the model; extracting the physical and mechanical parameters in the corrected three-dimensional geological model at the spatial location corresponding to the changed cross-section, and calculating the corresponding parameter change rate; weighting the parameter change rate with the deviation value of each changed cross-section as a weight to obtain the physical parameter change; and weighted summing the geometric change of the model and the physical parameter change to obtain the update magnitude of the three-dimensional geological model.

[0011] By employing the above technical solution, the mining path planning system establishes a sequence of scanning planes along the mining path. By analyzing the geometric deviations and physical parameter changes on each scanning plane, it achieves precise quantification of the model update magnitude. The mining path planning system marks sections exceeding preset benchmark values ​​as changed sections, calculates the geometric changes, and uses the deviation values ​​of the changed sections as weights to calculate the physical parameter changes. This update magnitude calculation method based on section analysis not only considers the correlation of spatial location but also introduces a weighting mechanism, making the update magnitude calculation results more objective and accurate, and providing a scientific basis for the selection of path update strategies.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the update magnitude of the three-dimensional geological model is obtained by weighted summation of the geometric changes and physical parameter changes of the model. Specifically, this includes: acquiring the spatial location information of the change section and calculating the risk assessment coefficient of each change section, which is used to describe the degree of influence of the change section on the structural stability of the three-dimensional geological model; if the risk assessment coefficient is greater than a preset risk assessment threshold, the corresponding change section is marked as a high-risk area, otherwise it is marked as a low-risk area; multiplying the geometric changes and physical parameter changes of the model in the high-risk area by a first weighting coefficient to obtain the high-risk update magnitude; multiplying the geometric changes and physical parameter changes of the model in the low-risk area by a second weighting coefficient to obtain the low-risk update magnitude, where the second weighting coefficient is less than the first weighting coefficient; and weighted summing the high-risk update magnitude and the low-risk update magnitude to determine the update magnitude of the three-dimensional geological model.

[0013] By employing the aforementioned technical solution, the mining path planning system calculates the risk assessment coefficient for each changing section, dividing the changing sections into high-risk and low-risk areas, and using different weighting coefficients to calculate the update magnitude. By setting the first weighting coefficient to be greater than the second weighting coefficient, the mining path planning system gives greater emphasis to changes in high-risk areas. This risk-level-based weighted calculation method not only improves the rationality of the update magnitude calculation but also highlights the impact of key areas, enabling the mining path planning system to pay more attention to the safety of high-risk areas during path planning.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after determining the affected section of the current mining path when the update magnitude is less than a preset update threshold, the method further includes: obtaining the cumulative adjustment amount of the current mining path, the cumulative adjustment amount being the cumulative adjustment magnitude of the current mining path updated through the local path; when the cumulative adjustment amount is greater than or equal to the preset cumulative adjustment threshold, replanning the mining path based on the updated three-dimensional geological model, and clearing the cumulative adjustment amount to zero.

[0015] By adopting the above technical solution, the mining path planning system introduces the concept of cumulative adjustment, recording the magnitude of adjustments accumulated through local updates. When the cumulative adjustment exceeds a preset threshold, the system triggers a global path replanning and resets the cumulative adjustment to zero. This path update mechanism based on cumulative effects avoids frequent global replanning and can promptly detect potential risks arising from local update accumulation, achieving dynamic balance in mining path planning and improving the system's operational efficiency and reliability.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, the affected area is locally updated, specifically including: when the cumulative adjustment amount is less than a preset cumulative adjustment threshold, extracting the three-dimensional geological model corresponding to the affected area to obtain a local three-dimensional geological model; calculating and generating a candidate path set based on the geological parameters and the geological risk change coefficient of the local three-dimensional geological model, the candidate path set including multiple candidate local paths; prioritizing the candidate path set according to the path length, mining equipment load and the geological risk change coefficient; and determining the candidate local path with the highest priority as the updated mining path.

[0017] By adopting the above technical solution, the mining path planning system extracts a local 3D geological model and generates a set of candidate paths during local path updates. These paths are then prioritized based on path length, equipment load, and geological risk variation coefficients. This local path optimization method under multi-objective constraints considers not only mining efficiency but also equipment performance and geological safety. By selecting the highest-priority candidate path as the update path, the mining path planning system achieves optimal local path updates, ensuring both the safety and economy of mining operations.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after performing a local path update to obtain an updated mining path, the method further includes: uniformly sampling a sequence of reference points along the current mining path within the affected area, the sequence of reference points including multiple reference points; calculating the vertical distance between each reference point and the updated mining path, and determining the maximum value of the vertical distance as the adjustment range of the local path update; updating the cumulative adjustment amount according to the adjustment range to obtain a new cumulative adjustment amount.

[0019] By adopting the above technical solution, after completing a local path update, the mining path planning system obtains a sequence of reference points through uniform sampling within the affected section, calculates the maximum vertical distance from the reference points to the updated path as the adjustment range, and updates the cumulative adjustment amount accordingly. This path deviation evaluation method based on reference points achieves accurate quantification of the local path update effect. Simultaneously, the dynamic updating of the cumulative adjustment amount provides an important basis for subsequent path planning strategy selection, improving the adaptability and controllability of the mining path planning system.

[0020] In a second aspect, this application provides a mining path planning system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the mining path planning system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer-readable storage medium including instructions that, when executed on a mining path planning system, cause the mining path planning system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer program product that, when run on a mining path planning system, causes the mining path planning system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the mining path planning system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By adopting a dynamic path planning scheme that establishes a geological risk assessment matrix based on geological parameters, calculates the geological risk change coefficient, updates the three-dimensional geological model according to the risk change coefficient, and selects global or local path updates based on the update magnitude, real-time monitoring and intelligent response to changes in geological conditions are achieved. Moreover, it can adaptively select update strategies according to the degree of model update, effectively solving the problems of large computational load and frequent adjustment of mining equipment caused by frequent global path replanning in related technologies, thereby achieving high efficiency in mining path planning and continuity of mining operations.

[0025] 2. By adopting a step-by-step model update scheme that compares real-time point cloud data with the current model geometrically, updates the model shape based on geometric deviation, and adjusts physical and mechanical parameters according to the geological risk change coefficient, the geometric accuracy of the model and the dynamic adaptability of physical parameters can be guaranteed at the same time. This effectively solves the problems of insufficient model update and inability to reflect the dynamic changes of rock mass mechanical properties in related technologies, and thus realizes the accurate expression of the actual state of the mining face by the three-dimensional geological model.

[0026] 3. By adopting a path update mechanism that introduces cumulative adjustment and triggers global path replanning based on the cumulative adjustment, it is possible to monitor the cumulative effect of local path updates, promptly identify potential risks, and effectively solve the problem in related technologies that it is impossible to assess the cumulative impact of multiple local updates and that the path may easily deviate from the optimal solution. This achieves dynamic balance and long-term stability in mining path planning. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a dynamic mining path planning method based on a three-dimensional geological model, as described in an embodiment of this application. Figure 2 This is another flowchart illustrating a dynamic mining path planning method based on a three-dimensional geological model in an embodiment of this application; Figure 3 This is a schematic diagram of the physical device structure of a mining path planning system in the embodiments of this application. Detailed Implementation

[0028] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0030] To facilitate understanding, the application scenarios of the embodiments of this application are described below.

[0031] In related technologies, mining paths can adapt to geological changes by dynamically updating a three-dimensional geological model and performing global path replanning based on this model. One such dynamic mining path planning method based on a three-dimensional geological model can update the model according to real-time geological information and recalculate the optimal path, thereby improving the accuracy of path planning to a certain extent.

[0032] The method described in this application, which dynamically assesses changes in geological risk and adaptively selects path planning strategies based on the magnitude of model updates, achieves intelligent decision-making for global path replanning and local path updates by quantitatively analyzing changes in geological risk and distinguishing the magnitude of model updates. This not only ensures the rapid response of the path to key geological changes but also avoids unnecessary global calculations caused by minor changes.

[0033] As can be seen, the dynamic path planning scheme in this application can not only achieve accurate response to changes in geological conditions, but also effectively solve the problems of large computational load and low production efficiency caused by frequent global replanning in related technologies, thereby achieving a balance between safety, efficiency and continuity in mining operations.

[0034] The following describes the process of the method provided in this implementation, based on the above scenario. Please refer to... Figure 1 This is a flowchart illustrating a dynamic mining path planning method based on a three-dimensional geological model, as described in an embodiment of this application.

[0035] S101. Obtain the geological parameters of the mining face in real time, and establish a geological risk assessment matrix based on the geological parameters, which include rock stress, groundwater level and rock mass fissure degree. Among them, the mining face refers to the working area in an underground mine where mining operations are underway; geological parameters are physical quantities used to characterize the geological conditions of the mining face; rock stress represents the magnitude and direction of the force borne by the rock strata; groundwater level refers to the elevation of the groundwater; rock mass porosity is used to represent the degree of development and distribution density of fractures in the rock mass; and the geological risk assessment matrix represents a two-dimensional array used to assess geological risks, with its row elements corresponding to different geological parameters and column elements corresponding to different times.

[0036] The mining path planning system continuously performs this step before and during mining operations. Specifically, the system collects real-time rock stress data using stress sensors deployed at the mining face, monitors groundwater level changes in real time using water level sensors, and obtains rock mass fracture information using acoustic detection equipment. The system maps the collected geological parameters to risk values ​​between 0 and 1 according to preset evaluation rules, constructing an m×n dimensional geological risk assessment matrix, where m represents the number of geological parameter types and n represents different time points. Each matrix element represents the risk value of a specific geological parameter at different times.

[0037] S102. Obtain the geological risk assessment matrix at the current time and the previous time, and perform difference operation to obtain the geological risk change matrix; Here, the current moment represents the time point when the system performs this calculation; the previous moment refers to the time point when the calculation was performed last time; the difference operation represents the mathematical operation of subtracting corresponding elements from two matrices; the geological risk change matrix is ​​used to represent the change of geological risk between two adjacent moments.

[0038] The mining path planning system performs this step after constructing a geological risk assessment matrix. Specifically, the system retrieves the geological risk assessment matrix constructed at the previous moment from the database, subtracts it element-wise from the newly constructed geological risk assessment matrix at the current moment, and obtains a geological risk change matrix with the same dimensions. Each element in the geological risk assessment matrix represents the increase or decrease in geological risk at the corresponding location between two moments; a positive value indicates an increase in risk, and a negative value indicates a decrease in risk.

[0039] S103. Based on the element values ​​of the geological risk change matrix and the preset risk weight coefficients, calculate the geological risk change coefficient at the current moment. Among them, the risk weight coefficient represents the importance of different geological parameters and assessment indicators in the overall risk assessment; the geological risk change coefficient refers to the degree of change in comprehensive geological risk expressed by a single value.

[0040] The mining route planning system executes this step immediately after obtaining the geological risk change matrix. Specifically, the system first assigns corresponding risk weight coefficients to each element in the geological risk change matrix based on historical data analysis and expert experience. A larger weight value indicates a greater impact of that parameter on mining safety. Then, the system multiplies each element value in the geological risk change matrix by its corresponding risk weight coefficient and sums the results to obtain a scalar value, namely the geological risk change coefficient. This coefficient comprehensively reflects the degree of change in overall geological risk compared to the previous time point.

[0041] S104. When the geological risk change coefficient is greater than the preset risk threshold, real-time point cloud data of the mining face is obtained, and the three-dimensional geological model is updated based on the real-time point cloud data and the geological risk change coefficient. Among them, the preset risk threshold represents the critical value of geological risk change that the system determines requires model updates; point cloud data refers to the set of discrete three-dimensional coordinate points of the mining face, used to represent the geometric shape of the face; the three-dimensional geological model represents the digital expression of the geological structure of the mining area, including geometric shape and physical parameter information; the geometric shape represents the spatial outline and surface features of the face; the physical parameter information refers to various parameters describing the mechanical properties of the rock mass, such as elastic modulus and Poisson's ratio.

[0042] The mining path planning system executes this step when it detects that the geological risk change coefficient exceeds a preset threshold. Specifically, the system first activates point cloud data acquisition equipment deployed at the mining face to perform high-density scanning sampling of the current working face, acquiring a point cloud dataset containing information such as spatial location and reflection intensity. The system then registers the acquired point cloud data with the existing 3D geological model, identifying areas where the working face morphology has changed. Simultaneously, the system determines the weight and scope of model updates based on the magnitude of the geological risk change coefficient. For areas with significant risk changes, the system performs a larger-scale and higher-weight model update; for areas with minor risk changes, it performs local fine-tuning. The update process includes both geometric adjustments and physical parameter corrections to the model.

[0043] Optionally, in some embodiments, a mobile laser scanning scheme can be used to acquire point cloud data of the working face and update the model: multiple reference stations are set up at the mining working face; a track-mounted laser scanner is used to perform mobile scanning along a preset path; the acquired multi-station point cloud data is registered and stitched together; noise is filtered and data is simplified based on the stitched point cloud data; the processed point cloud data is compared with the existing model to update the model geometry.

[0044] It is understandable that other methods can be used to acquire point cloud data of the working face and update the model, such as using optical measurement, sonar detection and other technologies, or using deep learning and other methods for model updating, which are not limited here.

[0045] S105. Based on the updated three-dimensional geological model, calculate the update range of the three-dimensional geological model, which includes the changes in the model's geometric shape and physical parameters. Among them, update magnitude indicates the degree of change of the 3D geological model compared to before the update; change in model geometry refers to the degree of change in the model's outline, which can be measured by indicators such as volume change rate and surface displacement; change in physical parameters is used to represent the magnitude of change in the model's internal physical and mechanical properties, including changes in parameters such as stress field and permeability; change calculation refers to the process of quantifying the degree of change of the model through mathematical methods; mesh partitioning represents the process of discretizing a continuous model into a finite number of mesh units.

[0046] The mining path planning system executes this step immediately after updating the 3D geological model. Specifically, the system first establishes a standard mesh in the updated 3D geological model, discretizing it into several unit cells. The system then calculates the changes in both geometry and physical parameters for each unit cell. Geometric changes are obtained by calculating the displacement and volume change rate at the unit cell's vertices; physical parameter changes are obtained by calculating the change rates of parameters such as stress field and permeability within the unit cell. The system uses a weighted average to synthesize the changes in all unit cells to obtain the overall update magnitude of the 3D geological model, where the weighting coefficients are related to the importance of the unit cell's location.

[0047] S106. When the update magnitude is greater than or equal to the preset update threshold, the mining path is replanned based on the updated three-dimensional geological model. Among them, the preset update threshold refers to the critical value of the model update that triggers global path replanning. It is determined by analyzing historical mining engineering data and combining the requirements of mining safety regulations. This preset update threshold can be adjusted according to the actual mining area conditions and safety management requirements. The mining path represents the movement trajectory of mining equipment in underground space.

[0048] The mining path planning system executes this step when it detects that the model update exceeds a preset update threshold. Specifically, the system first constructs a path planning space based on the updated 3D geological model, establishing a set of constraints that includes geological conditions, equipment parameters, and safety requirements. In this constraint set, geological conditions include rock mass strength and groundwater distribution; equipment parameters include equipment size, turning radius, and climbing ability; and safety requirements include minimum distance from fault zones and roof support strength. The system transforms these constraints into a cost function for path planning, imposing a larger cost on hazardous areas. Subsequently, while ensuring all constraints are met, the system calculates a new mining path with the objective of minimizing the overall cost.

[0049] Optionally, in some embodiments, a partitioning optimization scheme can be used to achieve global replanning of the mining path: the mining area is divided into multiple relatively independent blocks according to the geological structure; path segments that meet the constraints are generated within each block; the connection paths between blocks are optimized; the safety margin of each block path is evaluated; blocks that do not meet the requirements are locally adjusted; and the complete path is obtained by integrating and smoothing.

[0050] It is understandable that other methods can be used to achieve global replanning of mining paths, such as using artificial intelligence algorithms to optimize paths or using hybrid strategies for path generation; no specific method is specified here.

[0051] S107. When the update magnitude is less than the preset update threshold, determine the affected section of the current mining path and perform local path update on the affected section.

[0052] Among them, the affected section represents the spatial range in which the update of the three-dimensional geological model affects the current mining path; local path update refers to the process of replanning the mining path only within the affected section.

[0053] The mining path planning system executes this step when it detects that the model update magnitude is less than a preset update threshold. Specifically, the system first identifies the affected sections that spatially intersect with the current mining path based on the spatial distribution of parameter change areas in the updated 3D geological model. The system calculates the start and end coordinates of the affected sections and extends a certain safety distance at each end as a transition section. For each identified affected section, the system extracts the corresponding local 3D geological model data, ensuring that the endpoint positions and tangent vectors remain consistent with the original path to guarantee the continuity of the updated path. Based on the local model data, the system optimizes the path, generating new path segments that meet all constraints, and finally smoothly connects the new path segments with the rest of the original path.

[0054] Optionally, in some embodiments, the determination of the affected segment and the local path update can be achieved through a parameter gradient analysis scheme: calculating the parameter change gradient field of the model update region; extracting connected regions whose gradient values ​​exceed a threshold; determining the intersection segments between the connected regions and the current path; setting transition buffers at both ends of the intersection segments; extracting the local model data of the expanded segment; and performing local path optimization and smooth connection.

[0055] It is understandable that other methods can be used to determine the affected area and update the local path, such as using topology analysis to determine the affected area or using other local optimization algorithms for path updating, which are not limited here.

[0056] In this embodiment, an intelligent path planning scheme is adopted, which involves establishing a geological risk assessment matrix in real time based on geological parameters, obtaining the geological risk change coefficient through differential calculation, updating the three-dimensional geological model according to the risk change coefficient, and adaptively selecting global or local path updates based on the update magnitude. This achieves real-time monitoring and intelligent response to changes in geological conditions and allows for flexible selection of update strategies according to actual needs. It effectively solves the problems in related technologies where the mining path of the entire area needs to be recalculated every time geological conditions change, resulting in a large amount of computation and frequent adjustments to mining equipment that affect production efficiency. This achieves high efficiency in mining path planning, continuity in mining operations, and stability in system operation.

[0057] In light of the above scenarios, the method provided in this implementation will now be described in more detail. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating a dynamic mining path planning method based on a three-dimensional geological model in an embodiment of this application.

[0058] S201. Obtain the geological parameters of the mining face in real time, and establish a geological risk assessment matrix based on the geological parameters, which include rock stress, groundwater level and rock mass fissure degree. S202. Obtain the geological risk assessment matrix at the current time and the previous time, and perform difference operation to obtain the geological risk change matrix; S203. Based on the element values ​​of the geological risk change matrix and the preset risk weight coefficients, the geological risk change coefficient at the current moment is calculated. Steps S201 to S203 and Figure 1 The descriptions of steps S101 to S103 in the embodiments are similar and will not be repeated here. Please refer to the descriptions of the corresponding steps.

[0059] S204. When the geological risk change coefficient exceeds a preset risk threshold, acquire real-time point cloud data of the mining face, and update the three-dimensional geological model based on the real-time point cloud data and the geological risk change coefficient, including: Acquire real-time point cloud data of the mining face, compare the real-time point cloud data with the geometric data of the current three-dimensional geological model at the corresponding location, and obtain the geometric deviation. Based on this geometric deviation, the current three-dimensional geological model is updated geometrically to generate a corrected three-dimensional geological model. Based on the geological risk variation coefficient, the physical and mechanical parameters contained in the corrected three-dimensional geological model are locally adjusted to obtain the updated three-dimensional geological model.

[0060] Among them, geometric data represents the numerical information describing the spatial shape and location in the three-dimensional geological model; geometric deviation refers to the spatial positional difference between point cloud data and model geometric data; geometric morphology update represents the process of correcting the model shape based on geometric deviation; physical and mechanical parameters refer to various indicators describing the mechanical properties of rock mass, such as elastic modulus, Poisson's ratio, compressive strength, etc.; local adjustment represents targeted modification of parameters only in a specific area.

[0061] The mining path planning system performs this step after acquiring real-time point cloud data. Specifically, the system first preprocesses the acquired point cloud data, including noise filtering, data downsampling, and spatial registration. Then, it compares the processed point cloud data with the current 3D geological model in the same coordinate system, calculating the shortest distance from each point cloud point to the model surface and generating a geometric deviation field. Based on this field, the system uses an adaptive mesh deformation algorithm to locally adjust the model's morphology, ensuring spatial consistency between the model surface and the point cloud data. Finally, based on the magnitude and spatial distribution of the geological risk variation coefficient, the system regionally adjusts the physical and mechanical parameters in the corrected model; the adjustment magnitude is positively correlated with the geological risk variation coefficient.

[0062] S205. Based on the updated three-dimensional geological model, calculate the update magnitude of the three-dimensional geological model. This update magnitude includes changes in the model's geometric shape and physical parameters, including: Based on the updated 3D geological model, a sequence of scanning planes is established along the current mining path, with each scanning plane in the sequence perpendicular to the direction of the mining path. Calculate the cross-sectional curves of the geometric deviation on each scanning plane, and determine whether the maximum deviation value of each cross-sectional curve exceeds the preset reference value; Cross sections exceeding the preset benchmark value are marked as variable cross sections, and the ratio of the number of variable cross sections to the total number of scanning planes is calculated and determined as the geometric change of the model. Extract the physical and mechanical parameters from the corrected three-dimensional geological model at the spatial location corresponding to the changed cross section, and calculate the corresponding parameter change rate; The change rate of the parameter is weighted by the deviation value of each changing section to obtain the change amount of the physical parameter; The update magnitude of the 3D geological model is obtained by weighted summing of the geometric changes and physical parameter changes, including: The spatial location information of the changing cross section is obtained, and the risk assessment coefficient of each changing cross section is calculated. The risk assessment coefficient is used to describe the degree of influence of the changing cross section on the structural stability of the three-dimensional geological model. If the risk assessment coefficient is greater than the preset risk assessment threshold, the corresponding change section will be marked as a high-risk area; otherwise, the corresponding change section will be marked as a low-risk area. Multiply the geometric and physical parameter changes of the model in the high-risk area by the first weighting coefficient to obtain the high-risk update magnitude. The change in model geometry and the change in physical parameters of the low-risk region are multiplied by the second weighting coefficient to obtain the low-risk update magnitude. The second weighting coefficient is smaller than the first weighting coefficient. The update magnitude of the three-dimensional geological model is determined by weighted summation of the high-risk update magnitude and the low-risk update magnitude.

[0063] Among them, the scanning plane sequence represents a set of vertical cross-sections uniformly set along the mining path; the cross-sectional curve refers to the intersection of the scanning plane and the model surface; the preset benchmark value represents the threshold standard used to determine the significance of cross-sectional changes; the changed cross section refers to the position of the scanning plane where the geometric change exceeds the benchmark value; the parameter change rate is used to represent the relative degree of change of physical and mechanical parameters; the risk assessment coefficient represents the intensity of the influence of the changed cross section on the stability of the model structure; the high-risk area and the low-risk area represent the areas where the risk assessment coefficient is higher or lower than the preset risk assessment threshold, respectively; the first weighting coefficient and the second weighting coefficient are used to adjust the contribution of the high-risk area and the low-risk area in the calculation of the overall update amplitude, respectively.

[0064] The mining path planning system performs this step after updating the 3D geological model to quantify the degree of update. Specifically, based on the updated 3D geological model, the system first establishes a series of scanning planes along the currently executing mining path, each perpendicular to the path's direction. Next, it calculates the cross-sectional curves formed by geometric deviations on each scanning plane and checks whether the maximum deviation value of each curve exceeds a preset benchmark value. Scanning planes with maximum deviations exceeding the benchmark value are marked as changed sections, and the ratio of the number of changed sections to the total number of scanning planes is calculated; this ratio represents the geometric change in the model. Then, the system extracts the physical and mechanical parameters from the corrected 3D geological model at the corresponding spatial locations of these changed sections and calculates the rate of change for these parameters. Using the deviation value of each changed section as a weight, a weighted average of the parameter change rates is calculated to obtain the physical parameter change. Finally, the system obtains the spatial location information of each changed section and calculates a risk assessment coefficient, which describes the impact of the changed sections on the model's structural stability. The risk assessment coefficient is calculated by comprehensively considering three main aspects: the spatial location characteristics of the changing cross section (including distance from the fault, rock fragmentation, and overlying stratum thickness), changes in geomechanical parameters (including stress concentration and strength parameter attenuation), and the rate of parameter change. Specifically, a weighted summation method is used to combine the location risk factor, mechanical risk factor, and rate of change risk factor. Each factor is normalized and assigned a corresponding weight, which can be determined based on engineering experience and expert evaluation. If the risk assessment coefficient is greater than a preset threshold, it is marked as a high-risk area; otherwise, it is a low-risk area. The geometric and physical parameter changes in high-risk areas are multiplied by a larger first weighting coefficient, while those in low-risk areas are multiplied by a smaller second weighting coefficient. The results are then summed to obtain the final update magnitude.

[0065] Optionally, in some embodiments, the update magnitude of the three-dimensional geological model can be calculated by partitioning and weighting based on stress field analysis: constructing the stress distribution field around the changing cross section; calculating the stress concentration factor and stress gradient; determining the risk assessment coefficient based on stress characteristics; dividing the high and low risk areas; establishing a partition weighting system; calculating the weighted update magnitude of each area; and integrating to obtain the final update magnitude.

[0066] It is understandable that other methods can be used to achieve the partitioned weighted calculation of the update range of the three-dimensional geological model, such as using a multi-criteria decision-making method for risk assessment, or using intelligent algorithms to dynamically adjust the weight coefficients; no limitation is made here.

[0067] S206. When the update magnitude is greater than or equal to the preset update threshold, the mining path is replanned based on the updated three-dimensional geological model. Step S206 and Figure 1 The description of step S106 in the above embodiments is similar and will not be repeated here. Please refer to the description of the corresponding step.

[0068] S207. When the update magnitude is less than the preset update threshold, determine the affected section of the current mining path; The mining path planning system executes this step when it detects that the model update magnitude is less than a preset update threshold. Specifically, the system first constructs a search buffer for the current mining path in 3D space, with the buffer width determined by the operating range of the mining equipment. The system then performs a spatial intersection operation between the buffer and the updated area of ​​the 3D geological model to obtain the intersection region. At the boundary of the intersection region, the system extends outward by a preset safety distance, defining the extended area as the affected section. Simultaneously, the system records the start and end coordinates of the affected section on the mining path, along with the corresponding path parameters, preparing for subsequent local path updates.

[0069] S208. Obtain the cumulative adjustment amount of the current mining path, which is the cumulative adjustment magnitude of the current mining path updated through the local path; Among them, the cumulative adjustment amount represents the overall degree of change of the mining path after multiple local updates; the adjustment range is used to represent the amount of path change caused by a single local update; and the number of local updates represents the cumulative number of times the path has undergone local adjustments.

[0070] The mining path planning system performs this step after identifying the affected sections. Specifically, the system reads historical adjustment records of the current mining path from the system database, including the time, location, and adjustment magnitude of each local update. The system accumulates all historical adjustment magnitudes, considering both the temporal order and spatial correlation of the adjustments, and applies appropriate weights to adjustments in adjacent or overlapping areas. The system determines the total weighted adjustment as the cumulative adjustment, which reflects the overall degree of change in the mining path after multiple local updates.

[0071] S209. When the cumulative adjustment amount is greater than or equal to the preset cumulative adjustment threshold, the mining path is replanned based on the updated three-dimensional geological model, and the cumulative adjustment amount is cleared to zero. The preset cumulative adjustment threshold represents the upper limit of the cumulative adjustment amount that triggers global path replanning. It is determined based on the motion characteristics and safety margin requirements of the mining equipment. This includes first calculating the maximum path deviation during safe operation of the equipment and setting a baseline value, then determining the maximum allowable cumulative turning angle by combining the equipment's turning performance and working radius, and finally considering the constraints of geological conditions on the equipment's motion to obtain the preset cumulative adjustment threshold. Global path replanning refers to a complete recalculation of the path for the entire mining area. The zeroing operation means resetting the cumulative adjustment amount to the initial value.

[0072] The mining path planning system performs this step after calculating the cumulative adjustment amount. Specifically, the system compares the cumulative adjustment amount with a preset cumulative adjustment threshold. When the cumulative adjustment amount exceeds the threshold, it indicates that the mining path has undergone multiple local updates, resulting in a significant cumulative deviation that may affect the overall optimization performance of the path. In this case, the system re-plans the global path based on the updated 3D geological model, generating a new optimal path. After completing the global replanning, the system sets the cumulative adjustment amount to zero and updates the path adjustment records. Simultaneously, the system sends the newly planned path parameters to the mining equipment execution system to ensure that the equipment operates according to the new path.

[0073] S210, and perform a local path update on the affected section, including: When the cumulative adjustment amount is less than the preset cumulative adjustment threshold, the three-dimensional geological model corresponding to the affected section is extracted to obtain a local three-dimensional geological model. Based on the geological parameters and geological risk variation coefficient of the local three-dimensional geological model, a set of candidate paths is calculated and generated, which includes multiple candidate local paths; The candidate path set is prioritized based on path length, mining equipment load, and geological risk variation coefficient. The highest priority candidate local path is determined as the updated mining path.

[0074] Among them, the local three-dimensional geological model represents the geological model data within the spatial range corresponding to the affected section, including information such as rock strata structure, physical parameters, and risk distribution; the candidate path set refers to the set of multiple optional local paths that meet safety and equipment constraints; the path length represents the spatial distance of the path; the mining equipment load is used to represent the power consumption, mechanical stress, and operating efficiency of the equipment when executing the path; the geological risk change coefficient is a comprehensive index that characterizes the degree of dynamic change in geological conditions; and the priority ranking refers to the process of comprehensively scoring and ranking the candidate paths based on multiple evaluation indicators.

[0075] The mining path planning system performs this step after identifying the affected area. Specifically, when the cumulative adjustment amount is less than a preset cumulative adjustment threshold, the system extracts the 3D geological model data corresponding to the affected area, including spatial geometric information, physical and mechanical parameters, and geological risk distribution. Based on the extracted local model, the system constructs a set of constraints for path planning, including: geological safety constraints (such as fault avoidance and stability requirements), equipment movement constraints (such as minimum turning radius and maximum climbing angle), and operational efficiency constraints (such as equipment power limits and mining efficiency requirements). The system uses an improved RRT (Fast Random Search Tree) algorithm to generate multiple candidate local paths while meeting the constraints. For each candidate path, the system calculates its comprehensive score: path length reflects operating time and energy consumption; equipment load considers mechanical wear and operational efficiency; and the geological risk variation coefficient characterizes safety and reliability. The system uses the analytic hierarchy process (AHP) to determine the weights of each indicator, calculates the weighted score of the candidate paths, and prioritizes them from highest to lowest score. Finally, the mining path planning system selects the highest priority path, i.e. the path with the highest score, as the updated local path, and ensures a smooth transition between the updated path and the original path at the connection point.

[0076] Optionally, in some embodiments, local path updates can be achieved by employing a dynamic programming-based update scheme: constructing a path search grid in the affected segment; designing a cost function that considers multiple objectives; using a dynamic programming algorithm to search for the optimal path; smoothing the optimal path; verifying the feasibility of the optimal path; and generating detailed path execution parameters.

[0077] It is understandable that other methods can be used to achieve local path updates, such as using artificial potential fields for path planning or using reinforcement learning methods to dynamically optimize the path; no limitation is made here.

[0078] S211. Within the affected section, a reference point sequence is obtained by uniformly sampling along the current mining path. The reference point sequence includes multiple reference points. Among them, the reference point sequence represents a set of spatial sampling points uniformly distributed along the mining path; the sampling interval refers to the distance between adjacent reference points, which is usually determined according to the path curvature and the evaluation accuracy requirements; the reference point contains spatial coordinates and path parameter information; uniform sampling refers to the process of setting sampling points at fixed intervals along the path.

[0079] The mining path planning system performs this step after completing the local path update. Specifically, the system first determines the start and end coordinates of the affected section. Then, according to a preset sampling interval, the system uniformly samples along the current mining path within the section. For each sampling location, the system records its three-dimensional spatial coordinates, tangent direction, curvature, and other path characteristic parameters, forming a reference point sequence. These reference points will be used to evaluate the path deviation caused by the local path update.

[0080] S212. Calculate the vertical distance between each reference point and the updated mining path, and determine the maximum value of the vertical distance as the adjustment range of the local path update. Wherein, the vertical distance represents the shortest distance from the reference point to the updated path; the path normal vector is used to represent the vertical direction of the path at a certain point; and the projection point represents the vertical projection position of the reference point on the updated path.

[0081] The mining path planning system performs this step after obtaining the reference point sequence. Specifically, for each reference point, the system calculates its perpendicular distance to the updated mining path. The calculation process includes: identifying the path point on the updated path closest to the reference point; calculating the tangent direction at that path point; constructing a plane perpendicular to the tangent direction; calculating the projection point of the reference point onto this plane; and calculating the distance between the projection point and the reference point. The system selects the maximum value from all calculated perpendicular distances and uses it as the adjustment range for this local path update.

[0082] S213. Update the cumulative adjustment amount according to the adjustment range to obtain the new cumulative adjustment amount.

[0083] The new cumulative adjustment represents the latest value obtained after updating the current cumulative adjustment.

[0084] The mining path planning system executes this step after determining the adjustment range for a local path update. Specifically, the system first obtains the currently stored cumulative adjustment amount. Considering the time factor, the system performs attenuation processing on earlier adjustments, with the attenuation coefficient decreasing as the time interval increases. For spatially adjacent or overlapping adjustment areas, the system calculates a correlation coefficient based on the degree of overlap to avoid repeatedly calculating the adjustment amount for the same area. The system then weights and sums the adjustment range of the current local update with the processed cumulative adjustment amount to obtain a new cumulative adjustment amount. This new cumulative adjustment amount is stored in the database for use as the basis for the next path update.

[0085] In this embodiment, a comprehensive solution is adopted, which involves real-time construction of a geological risk assessment matrix for risk monitoring, step-by-step updating of the three-dimensional geological model based on point cloud data and risk change coefficients, calculation of update magnitude through scanning plane sequence analysis and risk zoning weighted calculation, and introduction of a cumulative adjustment mechanism for local path optimization. This solution achieves accurate perception of geological condition changes, accurate quantification of model updates, and dynamic optimization of path planning. Furthermore, it establishes an update decision mechanism that considers cumulative effects, effectively solving problems such as insufficient model update accuracy, inability to accurately assess the impact of local updates, and single path planning strategies in related technologies. This results in the safe and reliable operation of the mining path planning system and improved path planning efficiency.

[0086] The mining path planning system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a mining path planning system in an embodiment of this application.

[0087] It should be noted that, Figure 3 The structure of the mining path planning system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0088] like Figure 3 As shown, the mining path planning system includes a CPU 301, which can perform various appropriate actions and processes based on a program stored in the read-only memory ROM 302 or a program loaded from the storage section 308 into the random access memory RAM 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0089] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0090] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0091] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0093] Specifically, the mining path planning system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the dynamic mining path planning method based on a three-dimensional geological model provided in the above embodiment.

[0094] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the mining path planning system described in the above embodiments; or it may exist independently and not incorporated into the mining path planning system. The storage medium carries one or more computer programs that, when executed by a processor of the mining path planning system, cause the mining path planning system to implement the dynamic mining path planning method based on a three-dimensional geological model provided in the above embodiments.

[0095] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0096] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0097] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for dynamic mining path planning based on a three-dimensional geological model, characterized in that, The method is applied to a mining path planning system, and comprises: Real-time acquisition of geological parameters of a mining working face, and establishment of a geological risk assessment matrix based on the geological parameters, the geological parameters including rock stratum stress, underground water level and rock mass fissure degree; Acquisition of geological risk assessment matrices of a current time and a previous time, and differential operation to obtain a geological risk change matrix; Based on element values of the geological risk change matrix and a preset risk weight coefficient, a geological risk change coefficient of the current time is calculated; When the geological risk change coefficient is greater than a preset risk threshold, real-time point cloud data of the mining working face is acquired, and a three-dimensional geological model is updated based on the real-time point cloud data and the geological risk change coefficient; Based on the updated three-dimensional geological model, an update amplitude of the three-dimensional geological model is calculated, the update amplitude including a model geometric shape change amount and a physical parameter change amount; When the update amplitude is greater than or equal to a preset update threshold, a mining path is re-planned based on the updated three-dimensional geological model; When the update amplitude is less than the preset update threshold, an influence section of a current mining path is determined, and the influence section is subjected to local path updating.

2. The method of claim 1, wherein, The real-time point cloud data of the mining working face is acquired, and the three-dimensional geological model is updated based on the real-time point cloud data and the geological risk change coefficient, specifically comprising: The real-time point cloud data of the mining working face is acquired, and the real-time point cloud data is compared with geometric data of a corresponding position of a current three-dimensional geological model to obtain a geometric deviation; Based on the geometric deviation, the current three-dimensional geological model is subjected to geometric shape updating to generate a corrected three-dimensional geological model; According to the geological risk change coefficient, physical and mechanical parameters contained in the corrected three-dimensional geological model are subjected to local adjustment to obtain an updated three-dimensional geological model.

3. The method of claim 2, wherein, Based on the updated three-dimensional geological model, an update amplitude of the three-dimensional geological model is calculated, specifically comprising: Based on the updated three-dimensional geological model, a scanning plane sequence is established along a current mining path, each scanning plane in the scanning plane sequence being perpendicular to a mining path direction; Cross-sectional curves of the geometric deviation on each scanning plane are calculated, and it is judged whether a maximum deviation value of each cross-sectional curve exceeds a preset reference value; Cross sections exceeding the preset reference value are marked as change cross sections, and a proportion of the change cross section quantity to a total number of scanning planes is calculated to determine a model geometric change amount; Physical and mechanical parameters in the corrected three-dimensional geological model at a corresponding spatial position of the change cross section are extracted, and a corresponding parameter change rate is calculated; The parameter change rates are weighted and averaged with the deviation values of the change cross sections as weights to obtain a physical parameter change amount; The model geometric change amount and the physical parameter change amount are weighted and summed to obtain the update amplitude of the three-dimensional geological model.

4. The method of claim 3, wherein, The model geometric change amount and the physical parameter change amount are weighted and summed to obtain the update amplitude of the three-dimensional geological model, specifically comprising: Obtaining spatial position information of the change section, and calculating a risk evaluation coefficient of each change section, the risk evaluation coefficient being used to express an influence degree of the change section on structural stability of the three-dimensional geological model; If the risk evaluation coefficient is greater than a preset risk evaluation threshold, the corresponding change section is marked as a high-risk area, otherwise the corresponding change section is marked as a low-risk area; Multiplying the model geometry change amount and the physical parameter change amount of the high-risk area by a first weight coefficient respectively to obtain a high-risk update amplitude; Multiplying the model geometry change amount and the physical parameter change amount of the low-risk area by a second weight coefficient respectively to obtain a low-risk update amplitude, the second weight coefficient being less than the first weight coefficient; Weighted sum of the high-risk update amplitude and the low-risk update amplitude to determine the update amplitude of the three-dimensional geological model.

5. The method of claim 1, wherein, After determining the influence section of the current mining path when the update amplitude is less than a preset update threshold, the method further comprises: Obtaining a cumulative adjustment amount of the current mining path, the cumulative adjustment amount being a cumulative adjustment amplitude of the current mining path through the local path update; When the cumulative adjustment amount is greater than or equal to a preset cumulative adjustment threshold, re-planning the mining path based on the updated three-dimensional geological model, and clearing the cumulative adjustment amount.

6. The method of claim 5, wherein, The local path update of the influence section comprises: When the cumulative adjustment amount is less than the preset cumulative adjustment threshold, extracting the three-dimensional geological model corresponding to the influence section to obtain a local three-dimensional geological model; Based on the geological parameters and the geological risk change coefficient of the local three-dimensional geological model, a candidate path set is calculated and generated, the candidate path set including a plurality of candidate local paths; According to the path length, the mining equipment load and the geological risk change coefficient, the candidate path set is prioritized; The candidate local path with the highest priority is determined as the updated mining path.

7. The method of claim 6, wherein, After the local path update, the method further comprises: In the influence section, a reference point sequence is uniformly sampled along the current mining path, the reference point sequence including a plurality of reference points; The perpendicular distance between each reference point and the updated mining path is calculated, and the maximum value of the perpendicular distance is determined as the adjustment amplitude of the local path update; According to the adjustment amplitude, the cumulative adjustment amount is updated to obtain a new cumulative adjustment amount.

8. A mining path planning system characterized by, The mining path planning system comprises one or more processors and a memory; the memory is coupled with the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors invoke the computer instructions to make the mining path planning system execute the method of any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions run on the mining path planning system, the mining path planning system executes the method of any one of claims 1-7.

10. A computer program product, characterised in that, When the computer program product is run on a mining path planning system, it causes the mining path planning system to perform the method of any one of claims 1-7.

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