Complex structure rock acoustic emission source positioning method, device and equipment based on sparse grid path tracking algorithm and medium

Abstract

The invention provides a complex structure rock acoustic emission source positioning method and device based on a sparse grid path tracking algorithm, equipment and a medium, and relates to the technical field of rock mechanics. The method comprises the following steps: carrying out three-dimensional space division on a rock with a complex structure by utilizing a self-adaptive octree structure, and dividing the rock into non-cubic sparse grids; based on an arbitrary angle path tracking algorithm and sight visibility detection, calculating a shortest P-wave propagation path between a sensor and a grid node, and calculating minimum propagation time; determining the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position in the grid through a trilinear interpolation algorithm; and constructing a target function based on the time difference of arrival, and determining the acoustic emission source position of the complex structure rock according to the minimum value of the target function. According to the embodiment of the invention, the calculation cost in the path tracking process is obviously reduced, and the determined position of the acoustic emission source of the rock with the complex structure is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of rock mechanics, and in particular to a complex structure rock acoustic emission source localization method, device, equipment and medium based on a sparse grid path tracking algorithm. Background Technique

[0002] The accurate localization of acoustic emission (AE) sources is crucial for identifying precursor information of rock failure. One of the most fundamental and classical problems in acoustic emission analysis is the localization of AE sources, which clarifies the location where microcracks occur. The earliest source localization method is the Geiger method, which uses sensor coordinates and the arrival times of received AE signals to transform the localization problem into solving a linear equation. Subsequently, various algorithms and methods for source localization have been developed, including relative localization algorithms, grid search algorithms, simplex localization algorithms, etc. Most of these algorithms assume that the elastic wave propagation path between the AE source and the sensor is a straight line. However, this assumption has a major drawback because it does not take into account the influence of geometric and material irregularities (such as caves and tunnels) on the wave propagation path. In complex structure rocks, there is usually a large difference between the shortest propagation path and the straight-line propagation path of elastic waves. Therefore, in complex structure rocks, the application of localization algorithms based on straight-line travel paths often leads to serious localization errors.

[0003] In recent years, in order to improve the positioning accuracy, researchers have developed various source localization algorithms applicable to complex structures. Baxter et al. (2007) proposed a ΔT source localization algorithm applicable to complex geometric structures, which improves source localization by utilizing the difference between artificial sources and arrival time signals. Gollob et al. (2017) introduced a source localization method called FastWay, which considered the influence of geometry on wave propagation and used the Dijkstra algorithm to search for the fastest wave propagation path between the source and the sensors, thereby estimating the source location. Dong et al. (2020) proposed a velocity-free AE source localization method based on the a* search algorithm, which is applicable to structures with holes. Compared with the Dijkstra algorithm, this method enhances the directionality of path search, significantly improves the pathfinding efficiency, and improves the smoothness of the path by increasing the number of node connections. However, the angle of path tracking is still limited by the dense grid, and the accuracy needs to be improved by increasing the grid density. Jiang et al. (2021) developed a source localization algorithm combining the fast marching method and the second-order difference method, which successfully achieved high-precision localization in complex structures with holes, and this method is applicable to complex velocity structures. It should be noted that this method needs to traverse all available grid nodes to solve the Eikonal equation and calculate the segments of the wave propagation path through high-order interpolation. The above algorithms have played an important role in improving the accuracy and reliability of AE source localization in complex structures, but due to relying on dense grid search, they generally have a high computational cost. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for locating acoustic emission sources of rocks in complex structures based on a sparse grid path tracking algorithm, which not only significantly reduces the computational cost in the path tracking process, but also enables the determined position of the acoustic emission source of rocks in complex structures to be more accurate.

[0005] In a first aspect, the present invention provides a method for locating acoustic emission sources of rocks in complex structures based on a sparse grid path tracking algorithm, including the following steps: Using an adaptive octree structure to divide the complex structure rock in three-dimensional space into non-cubic sparse grids, where the non-cubic sparse grids include multiple grids and corresponding grid nodes; Based on an arbitrary angle path tracking algorithm and line-of-sight visibility detection, calculating the shortest P-wave propagation path between the sensor and the grid node, and calculating the minimum propagation time between the sensor and the grid node; Based on the shortest P-wave propagation path and the minimum propagation time, through a trilinear interpolation algorithm, determining the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid; Construct an objective function based on the time difference of arrival, and determine the acoustic emission source location of the complex structure rock according to the minimum value of the objective function.

[0006] Preferably, according to the acoustic emission source location method for complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention, the arbitrary angle path tracking algorithm is as follows: Start from the starting node to expand the potential path until reaching the target node, and determine the shortest P-wave propagation path between the starting node and the target node; The specific steps of expanding the potential path include: Maintain the open set and the closed set, and expand the minimum cost node with the total cost first; the open set contains the explored nodes, and the closed set includes the node itself and all explored neighbor nodes; Perform line-of-sight visibility detection before node expansion. If the parent node is not visible, select the optimal alternative parent node from the closed set; Dynamically update the path cost and parent node relationship of adjacent nodes to ensure that the shortest P-wave propagation path satisfies the collision-free straight line constraint.

[0007] Preferably, according to the acoustic emission source location method for complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention, the function for calculating the total cost satisfies: f(s)=g(s)+h(s) Where f(s) is the total cost of each node s, g(s) is the cumulative cost from the starting node to the current node s, h(s) is the heuristic estimated cost from the current node s to the target node, and g(s) is recursively defined as: g(s)=g(parent(s))+c(parent(s),s) Where c(·) represents the geometric distance between nodes, parent(s) is the parent node for obtaining the current node s, and c(parent(s),s) is the distance between the current node s and the parent node; The line-of-sight visibility detection is to exclude the straight-line path intersecting with the cave node by judging whether the straight-line path between two nodes intersects with the cave node.

[0008] Preferably, according to the acoustic emission source location method for complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention, the steps of the trilinear interpolation algorithm specifically include: Based on the grid sizes lx, ly, lz and the known arrival times of the eight vertices, perform interpolation step by step along the x, z, and y axes, and calculate the intermediate travel time on the x-axis, the intermediate travel time on the z-axis and the travel time at a specific position in sequence; Based on the intermediate travel time of the x-axis, the intermediate travel time of the z-axis, and the travel time at a specific position, determine the theoretical arrival time at any specific position within the grid.

[0009] Preferably, according to the method for locating acoustic emission sources in complex-structured rocks based on the sparse grid path tracking algorithm provided by the present invention, the formula for interpolating and calculating the intermediate travel time of the x-axis is: (1) where T 12 , T 43 , T 56 , T 87 are the intermediate travel times of the x-axis respectively, T1, T2, T3, T4, T5, T6, T7, T8 are the vertex time values respectively, and the size of the octree grid on the x-axis is denoted as lx, is the length of the projection of any specific position within the grid on the x-axis; The formula for interpolating and calculating the intermediate travel time of the z-axis is: (2) where T 1256 , T 4387 are the intermediate travel times of the z-axis respectively, T 12 , T 43 , T 56 , T 87 are the intermediate travel times of the x-axis respectively, and the size of the octree grid on the z-axis is denoted as lz, z is the length of the projection of any specific position within the grid on the z-axis; The formula for interpolating and calculating the travel time at a specific position along the y-axis is: (3) where T 1256 , T 4387 are the intermediate travel times of the z-axis respectively, and the size of the octree grid on the y-axis is denoted as ly, y is the length of the projection of any specific position within the grid on the y-axis.

[0010] Preferably, according to the method for locating acoustic emission sources in complex-structured rocks based on the sparse grid path tracking algorithm provided by the present invention, define any specific position in the complex-structured rock as ξ ( x , y , z ), and use the travel time at a specific position interpolated and calculated along the y-axis as any characteristic position ξ ( x , y ,z The theoretical arrival time of T ( x , y , z ) The travel time gradient corresponding to the theoretical arrival time is expressed as: (4) (5) Wherein, is the travel time gradient, is the derivative of the theoretical arrival time T with respect to the middle travel time on the x-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time on the y-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time on the z-axis; By effectively backtracking along the negative gradient direction - to determine the path from any specific position ξ ( x , y , z ) to the acoustic emission source, and taking the path from any specific position ξ ( x , y , z ) to the acoustic emission source as the shortest time path.

[0011] Preferably, according to the acoustic emission source location method for complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention, the formula of the objective function is: (6) Wherein, n represents the total number of sensors, and by minimizing the objective function f( ξ 0) to determine the acoustic emission source location of the complex structure rocks, i and j represent different sensor numbers, is the position of the i-th sensor, the actual arrival time at the position of the i-th sensor is t i , and the theoretical arrival time at the position of the i-th sensor calculated using the path tracking algorithm is T( ), is the position of the j-th sensor, t j is the actual arrival time at the position of the j-th sensor, and T( ) is the theoretical arrival time corresponding to the position of the j-th sensor.

[0012] In a second aspect, the present invention also provides an acoustic emission source location device for complex structure rocks based on the sparse grid path tracking algorithm, including: A partitioning module, configured to partition a complex-structured rock in three-dimensional space by using an adaptive octree structure into non-cubic sparse grids, where the non-cubic sparse grids include a plurality of grids and corresponding grid nodes; A calculation module, configured to calculate the shortest P-wave propagation path between a sensor and a grid node, and calculate the minimum propagation time between the sensor and the grid node, based on an arbitrary-angle path tracking algorithm and line-of-sight visibility detection; An arrival time difference determination module, configured to determine the arrival time difference between the theoretical arrival time and the actual arrival time of a potential acoustic emission source signal to any specific position within a grid, based on the shortest P-wave propagation path and the minimum propagation time, by using a trilinear interpolation algorithm; An acoustic emission source location determination module, configured to construct an objective function based on the arrival time difference and determine the acoustic emission source location of the complex-structured rock according to the minimum value of the objective function.

[0013] In a third aspect, the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where when the processor executes the program, the method for locating an acoustic emission source of a complex-structured rock based on a sparse grid path tracking algorithm as described in any one of the above is implemented.

[0014] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for locating an acoustic emission source of a complex-structured rock based on a sparse grid path tracking algorithm as described in any one of the above is implemented.

[0015] In a fifth aspect, the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method for locating an acoustic emission source of a complex-structured rock based on a sparse grid path tracking algorithm as described in any one of the above is implemented.

[0016] A method, device, equipment and medium for locating acoustic emission sources in complex - structured rocks based on a sparse - grid path - tracking algorithm. By using an adaptive octree structure to divide the complex - structured rocks in three - dimensional space into non - cubic sparse grids, where the non - cubic sparse grids include multiple grids and corresponding grid nodes; based on an arbitrary - angle path - tracking algorithm and line - of - sight visibility detection, calculating the shortest P - wave propagation path between the sensor and the grid node, and calculating the minimum propagation time between the sensor and the grid node; based on the shortest P - wave propagation path and the minimum propagation time, through a trilinear interpolation algorithm, determining the arrival - time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid; constructing an objective function based on the arrival - time difference, and determining the acoustic emission source position of the complex - structured rocks according to the minimum value of the objective function. It not only significantly reduces the computational cost in the path - tracking process, but also enables more accurate location of acoustic emission sources in complex - structured rocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 is one of the schematic flowcharts of the method for locating acoustic emission sources in complex - structured rocks based on a sparse - grid path - tracking algorithm provided by the present invention.

[0019] Figure 2 is a schematic diagram of the conversion from an octree grid to a corner - edge diagram provided by the present invention.

[0020] Figure 3 is a schematic diagram of a source - leaf identification diagram containing a specified position in space provided by the present invention.

[0021] Figure 4 is a schematic diagram of an AE source adjacent - grid identification diagram provided by the present invention.

[0022] Figure 5 is a schematic diagram of converting an octree grid into a corner - edge diagram provided by the present invention.

[0023] Figure 6 is a schematic diagram of calculating travel time in an octree grid using trilinear interpolation provided by the present invention.

[0024] Figure 7 is a schematic diagram of a three - dimensional room - and - pillar mining model provided by the present invention.

[0025] Figure 8 It is a schematic diagram of the artificial AE source setting provided by the present invention.

[0026] Figure 9 It is a schematic diagram of the heat map of the arrival time of the acoustic emission P wave provided by the present invention.

[0027] Figure 10 It is a schematic diagram for comparing the positioning errors between the Geiger method and the acoustic emission source localization method for complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention.

[0028] Figure 11 It is a schematic diagram of the box plot of the positioning error provided by the present invention.

[0029] Figure 12 It is a schematic structural diagram of the acoustic emission source localization device for complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention.

[0030] Figure 13 It is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0031] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present invention fall within the protection scope of the present invention.

[0032] The following combines Figures 1 - 13 to describe a method, device, equipment and medium for acoustic emission source localization of complex structure rocks based on the sparse grid path tracking algorithm of the present invention, which not only significantly reduces the calculation cost in the path tracking process, but also can make the determined position of the acoustic emission source of complex structure rocks more accurate.

[0033] Figure 1 It is one of the flow schematic diagrams of a method for acoustic emission source localization of complex structure rocks based on the sparse grid path tracking algorithm provided by the present invention. As Figure 1 shown, the method may include, but is not limited to, steps S100 to S400: S100, using an adaptive octree structure to divide the complex structure rock in three-dimensional space into non-cubic sparse grids, where the non-cubic sparse grids include a plurality of grids and corresponding grid nodes; S200, based on the arbitrary angle path tracking algorithm and line-of-sight visibility detection, calculate the shortest P-wave propagation path between the sensor and the grid node, and calculate the minimum propagation time between the sensor and the grid node; S300. Based on the shortest P-wave propagation path and the minimum propagation time, determine the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid through a trilinear interpolation algorithm; S400. Construct an objective function based on the arrival time difference and determine the acoustic emission source position of the complex structure rock according to the minimum value of the objective function.

[0034] In step S100 of some embodiments, the complex structure rock is divided into a three-dimensional space using an adaptive octree structure, which is divided into non-cubic sparse grids. Among them, the non-cubic sparse grids include multiple grids and corresponding grid nodes.

[0035] Specifically, it may include but is not limited to the following steps: Step 1.1: Adaptive non-cubic octree construction.

[0036] Initial root grid setting: According to the geometric characteristics of the complex structure rock (such as the tunnel and cave orientation, shape, distribution), the root grid is adaptively adjusted to an elongated non-cube to avoid the generation of redundant units perpendicular to the tunnel axis. For example, for a tunnel extending along the y-axis, the size of the root grid in the x / z axis direction can be compressed to 1 / 2 - 1 / 3 of the original cube side length.

[0037] Recursive subdivision rule: If the current grid intersects with a tunnel or a cave, it is divided into 8 sub-grids (see Figure 2 ). This process continues recursively until one of the following termination conditions is met: (1) reaching the preset maximum octree level (usually set to 8 levels); (2) the sub-grid no longer intersects with the cave.

[0038] Leaf node generation: The grids that are not further subdivided are marked as leaf nodes, and there are no discontinuities (such as caves) inside them to ensure the continuity of the wave propagation time field.

[0039] Step 1.2: Corner graph conversion and temporary node insertion.

[0040] Corner graph generation: Convert the octree leaf nodes into a graph structure (see Figure 2 ), where the nodes in the graph represent the grid vertices and the edges represent the wave propagation paths. The grids inside the cave are excluded from the graph to reduce invalid calculations.

[0041] Temporary node insertion mechanism: (1) Source leaf node positioning: Quickly locate the leaf nodes containing the acoustic emission source or sensor through an octree O(log(n)) complexity search (see Figure 3 ).

[0042] The complexity of octree search is O(log(n)), which enables the quick identification of the container where a given position is located. As Figure 3 shown, the small white dot represents the given position (specific position), and the blue grid represents the identified source leaf node. Figure 3 The equivalent octree model is shown on the right.

[0043] (2) Cross-level neighborhood search: For the source leaf node, recursively search for leaf nodes of the same level or lower levels on adjacent faces (see Figure 4 ). If the adjacent grid is a non-leaf node, traverse its child nodes downward until a leaf node is found.

[0044] Finding adjacent leaves at higher octree levels is not easy. To solve this problem, we first identify all six adjacent grids or lower-level leaves at the same level, as Figure 4 shown. If the adjacent grid is a leaf node (i.e., has no sub-grids), it is directly regarded as the identified neighbor. Otherwise, we recursively search for all sub-leaves located on the adjacent face of the source leaf.

[0045] (3) Temporary edge construction: Connect the temporary node to the graph vertices of all adjacent leaf nodes (see Figure 5 shown), ensuring that the connecting edges do not intersect with the cave. After the path calculation is completed, the temporary edges are deleted to restore the original graph structure.

[0046] It should be noted that directly connecting the new node to the eight nodes of the source leaf may introduce additional path lengths, especially when the source leaf node is at a low octree level and the grid is large. This may result in sub-optimal paths during subsequent path tracking. To avoid this problem, the inserted node is connected to the graph nodes of all its neighbors. If some connections extend outside the grid, we ensure that these connections do not intersect with any cave leaf nodes. After determining the optimal path, the temporary nodes and edges are deleted to restore the original graph structure.

[0047] In step S200 of some embodiments, based on the arbitrary-angle path tracking algorithm and line-of-sight visibility detection, the shortest P-wave propagation path between the sensor and the grid node, and the minimum propagation time between the sensor and the grid node are calculated.

[0048] In some embodiments of the present invention, the arbitrary-angle path tracking algorithm is: Starting from the starting node, expand the potential path until the target node is reached, and determine the shortest P-wave propagation path between the starting node and the target node; The specific steps of expanding the potential path include: Maintain the open set and the closed set, and expand the node with the minimum cost preferentially through the total cost; the open set contains the explored nodes, and the closed set includes the current node itself and all explored neighbor nodes; Perform line-of-sight visibility detection before node expansion. If the parent node is not visible, select the optimal alternative parent node from the closed set; Dynamically update the path cost and parent node relationship of adjacent nodes to ensure that the shortest P-wave propagation path satisfies the collision-free straight-line constraint. The collision-free straight-line constraint is to determine whether two nodes can "see" each other, that is, whether there is a straight line connecting the two without intersecting any cave.

[0049] The arbitrary-angle path tracking algorithm specifically includes the following steps: Step 2.1: Algorithm initialization.

[0050] Initialize the open set (open) and the closed set (closed) as empty sets. Set the path cost g(s) of the starting node s to 0, the parent node points to itself, and insert it into the open set. The total cost f(s)=g(s)+h(s), where f(s) is the total cost of each node s, g(s) is the cumulative cost from the starting node to the current node s, and h(s) is the heuristic estimated cost from the current node s to the target node.

[0051] Step 2.2: Main loop processing.

[0052] Node extraction: Pop the node s with the minimum total cost f(s) from the open set.

[0053] Vertex optimization: Call the SetVertex(s) function: (1) Check the line-of-sight visibility (lineOfSight function) between s and its parent node parent(s).

[0054] (2) If it is not visible, select the neighbor node s' that minimizes g(s')+c(s',s) from the closed set as the new parent node and update g(s).

[0055] Target node determination: If s is the target node, return the optimal path.

[0056] Closed set update: Move s into the closed set.

[0057] Neighbor node processing: Traverse the neighbor nodes s' of s.

[0058] (1) If s' already exists in the closed set and not in the open set, reset its g(s')=∞ and remove the parent node association. (2) Call the UpdateVertex(s, s') function to update the path cost: Calculate the new cost g'(s') = g(parent(s)) + c(parent(s), s').

[0059] If g'(s') < g(s'), update the parent node and re-insert it into the open set.

[0060] The classical A* search algorithm was proposed by Hart et al. (1968) and is widely used in path planning problems. This method determines the shortest path between two points by starting from the starting node and gradually expanding potential paths until the target node is reached. However, the path obtained by the A* algorithm is limited by the grid structure and usually produces sharp and irregular turns on the path. This is because the path has to follow the edges of the graph. To overcome this problem, an arbitrary angle path planning algorithm is adopted to trace the wave propagation trajectory.

[0061] In the embodiment of the present invention, each graph node s in the domain has an associated parent node, which records the starting position where the wave arrives at s. In addition, each node also maintains a cost value f(s) = g(s) + h(s), where g(s) represents the cost from the starting node to s, and h(s) is the estimated cost from s to the target. The cost g(s) can also be expressed as g(s) = g(parent(s)) + c(parent(s), s), where c(·) represents the geometric distance between nodes, parent(s) is the parent node for obtaining the current node s, and c(parent(s), s) is the distance calculated between the current node s and its parent node.

[0062] The function for calculating the total cost satisfies: f(s)=g(s)+h(s) where f(s) is the total cost of each node, g(s) is the cumulative cost from the starting node to the current node s, h(s) is the heuristic estimated cost from the current node s to the target node, and g(s) is recursively defined as: g(s)=g(parent(s))+c(parent(s),s) where c(·) represents the geometric distance between nodes, parent(s) is the parent node for obtaining the current node s, and c(parent(s), s) is the distance calculated between the current node s and its parent node; The line-of-sight visibility detection excludes the straight-line paths that intersect with the cave nodes by determining whether the straight-line path between two nodes intersects with the cave nodes.

[0063] There are two sets of nodes in the path tracing algorithm, namely the open set (the open set) and the closed set (the closed set), which are used to manage the search process. The open set contains the explored nodes, but the neighbors of these nodes have not been fully examined; while the closed set contains the nodes whose neighbors have all been explored. The lineofsight function, that is, the line of sight visibility detection, is used to determine whether two nodes can "see" each other, that is, whether there is a straight line connecting the two without intersecting any cave. This function ensures that a connection is established only when there are no obstacles. The argmin function returns the node with the lowest cost in the intersection of the closed set and the neighbor nodes.

[0064] At the beginning of the algorithm, the source node is added to the open set. In each iteration of the main loop, the algorithm selects the node s' with the minimum cost from the open set and explores its neighbor nodes. The search strategy for neighbor nodes follows the strategy in the previous section. Before each node s is expanded, a line of sight check is performed. If there is a line of sight between s and parent(s'), then g(s') and parent(s') remain unchanged; otherwise, they are updated by selecting the shortest path from the starting node to each visible neighbor s'' and the shortest path from s'' to s'. This iterative process continues until the target node is successfully found. Then, the corresponding optimal path and travel time are determined.

[0065] Calculate the shortest P-wave propagation path between the sensor and the grid nodes, and calculate the minimum propagation time between the sensor and the grid nodes.

[0066] In step S300 of some embodiments, based on the shortest P-wave propagation path and the minimum propagation time, the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position in the grid is determined by the trilinear interpolation algorithm.

[0067] In some embodiments of the present invention, the octree in space ensures that each grid in the rock mass does not contain discontinuities, such as chambers. This can ensure the continuity of the arrival time field within each grid, making interpolation an effective method for estimating the arrival time at any position within the grid. The trilinear interpolation principle used in the present invention is as Figure 6 shown.

[0068] The steps of the trilinear interpolation algorithm specifically include: Based on the octree grid sizes lx, ly, lz and the known arrival times of the eight vertices, interpolate level by level along the x, z, and y axes, and calculate the travel time in the middle of the x-axis, the travel time in the middle of the z-axis, and the travel time at a specific position in turn; Determine the theoretical arrival time at any specific position within the grid based on the x-axis mid-travel time, the z-axis mid-travel time, and the specific position travel time.

[0069] In some embodiments of the present invention, the dimensions of the octree grid on the three axes are respectively denoted as lx, y, and lz. The travel times at the graph nodes (T1 to T8) are known values. First, perform interpolation along the x-axis to calculate the mid-travel times (T12, T43, T56, and T87). The formula for calculating the x-axis mid-travel time by interpolation along the x-axis is: (1) where T 12 , T 43 , T 56 , T 87 are the x-axis mid-travel times respectively, T1, T2, T3, T4, T5, T6, T7, T8 are the vertex time values respectively, and the dimension of the octree grid on the x-axis is denoted as lx, is the length of the projection of any specific position within the grid on the x-axis.

[0070] Calculate the mid-travel times T1256 and T4387 by interpolation along the z-axis. The formula for calculating the z-axis mid-travel time by interpolation along the z-axis is: (2) where T 1256 , T 4387 are the z-axis mid-travel times respectively, T 12 , T 43 , T 56 , T 87 are the x-axis mid-travel times respectively, and the dimension of the octree grid on the z-axis is denoted as lz, z is the length of the projection of any specific position within the grid on the z-axis.

[0071] The formula for calculating the travel time at a specific position by interpolation along the y-axis is: (3) where T 1256 , T 4387 are the z-axis mid-travel times respectively, the dimension of the grid on the y-axis is denoted as ly, y is the length of the projection of any specific position within the grid on the y-axis.

[0072] First, define any specific position in the complex-structured rock as ξ ( x , y , z), thus, equations (1)-(3) can be used to effectively determine any specific position in the rock with complex structure ξ ( x , y , z ) of the theoretical arrival time T(x, y, z).

[0073] Specifically, the travel time at a specific position obtained by interpolation along the y-axis is used as the theoretical arrival time ξ ( x , y , z ) at any characteristic position in the rock with complex structure T ( x , y , z ).

[0074] Among them, the travel time gradient corresponding to the theoretical arrival time is expressed as: (4) (5) Among them, is the travel time gradient, is the derivative of the theoretical arrival time T with respect to the middle travel time of the x-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time of the y-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time of the z-axis.

[0075] By effectively backtracking along the negative gradient direction - to determine the path from any specific position ξ ( x , y , z ) to the acoustic emission source, and taking the path from any specific position ξ ( x , y , z ) to the acoustic emission source as the shortest time path.

[0076] In step S400 of some embodiments, a target function is constructed based on the arrival time difference, and the acoustic emission source position of the rock with complex structure is determined according to the minimum value of the target function.

[0077] In some embodiments of the present invention, the formula of the target function is: (6) Among them, n represents the total number of sensors. By minimizing the target function f( ξ0) To determine the acoustic emission source location of the complex - structured rock, i and j represent different sensor numbers. is the location of the i - th sensor, and the actual arrival time at the location of the i - th sensor is t i , and the theoretical arrival time at the location of the i - th sensor calculated using the path - tracking algorithm is T( ). is the location of the j - th sensor, t j is the actual arrival time at the location of the j - th sensor, and T( ) is the theoretical arrival time corresponding to the location of the j - th sensor.

[0078] In practical applications, the actual arrival time t (where i represents the sensor number) at each sensor location can be determined through the recorded elastic - wave signals and the automatic arrival - time picking technique. i .

[0079] The corresponding theoretical travel time calculated using the path - tracking algorithm is T( ). Then, an objective function can be constructed to quantify the difference between the actual arrival time and the theoretical arrival time recorded at a given location ξ 0, and the specific expression can be seen in the above formula 6.

[0080] By minimizing the objective function f( ξ 0), the acoustic emission source location of the complex - structured rock can be determined.

[0081] In some embodiments of the present invention, a specific example is a three - dimensional room - and - pillar mining model containing four ore pillars, as Figure 7 shown. The size of this model is 200 mm×1200 mm×720 mm, and the thicknesses of the floor and the roof are both 240 mm. The ore pillars are cubes with a size of 240 mm × 240 mm×240 mm, and are evenly distributed on the floor. The physical properties of the rock are as follows: density ρ = 2700 kg / m³, Young's modulus E = 50 GPa, Poisson's ratio = 0.28, and P - wave velocity Vp = 4899 m / s. The small yellow cylinders on the outer boundary represent AE sensors. The layout of the sensors is specifically designed: four sensors are installed on four ore pillars, and the other two sensors are placed on the roof and the floor respectively. This layout ensures that the waves emitted from any acoustic emission source in the rock cannot reach the four sensors along a straight line simultaneously.

[0082] The specific coordinates of the AE sensors are shown in Table 1: Table 1:

[0083] To simulate an artificial AE source, a radial sine stress wave is applied at the source point, as shown Figure 8 on the left side. Figure 8 A total of 144 representative points are selected as AE source points on the right side. These source points are evenly distributed at intervals of 240 mm along all spatial dimensions to comprehensively verify the proposed AE source localization framework. The red dots in the figure represent these selected AE source points, and the remaining boundaries are kept free.

[0084] Figure 9 The heatmap in shows the arrival times of AE signals in 8 channels for 144 AE events. The heatmap uses a color gradient to represent the arrival time in microseconds, where the x-axis represents different channels and the y-axis represents the AE event number. The color intensity reflects the arrival time, with higher intensity (yellow) indicating a later arrival time and lower intensity (black) indicating an earlier arrival time. This heatmap comprehensively shows the spatial and temporal distribution of AE event arrivals.

[0085] Figure 10 Compares the Geiger method ( Figure 10 the left part in) and the AE source localization method proposed in the embodiment of the present invention in terms of the localization error within the 3D room-and-pillar mining model domain ( Figure 10 the right part in). The points in the figure represent the true source positions, and their colors and sizes represent the localization errors. Compared with the Geiger method, the error distribution is more uniform, and the errors of the vast majority of points are reduced (mainly shown as blue and green points). In the areas where the Geiger method has larger localization errors, the errors are significantly controlled. This shows that the present invention is more effective when considering complex wave propagation effects, thus improving the localization accuracy.

[0086] Figure 11 Displays the box plots of the localization errors of the Geiger method and the complex structure rock acoustic emission source localization method based on the sparse grid path tracking algorithm proposed in the present invention, including the overall error and the axis-specific errors. The box plot summarizes the statistical distribution of the errors, including the interquartile range (IQR, represented by the box), the range within 1.5 IQR (whiskers), the median (black horizontal line), the mean (blue dot), and the outliers (green dots). For the Geiger method, the localization errors show a wider distribution, especially in the X and Z directions, with higher medians and more outliers. This indicates greater variability and lower accuracy in its performance. In contrast, the method proposed in the present invention significantly reduces the localization errors, which can be reflected by the narrower box, lower median, and fewer outliers. The average localization error is reduced from 0.19 m to 0.07 m. Compared with the Geiger method, the proposed method has enhanced consistency and the error distribution also converges significantly, highlighting its superior accuracy and robustness in AE source localization.

[0087] A method, device, equipment and medium for locating acoustic emission sources of complex - structure rocks based on a sparse - grid path - tracking algorithm provided by the present invention divides a complex - structure rock into a three - dimensional space by using an adaptive octree structure, and divides it into non - cubic sparse grids. Among them, the non - cubic sparse grids include multiple grids and corresponding grid nodes; based on an arbitrary - angle path - tracking algorithm and line - of - sight visibility detection, it calculates the shortest P - wave propagation path between a sensor and a grid node, and calculates the minimum propagation time between the sensor and the grid node; based on the shortest P - wave propagation path and the minimum propagation time, through a trilinear interpolation algorithm, it determines the arrival - time difference between the theoretical arrival time and the actual arrival time of a potential acoustic emission source signal to any specific position within the grid; based on the arrival - time difference, it constructs an objective function, and determines the acoustic emission source position of the complex - structure rock according to the minimum value of the objective function. This not only significantly reduces the computational cost during the path - tracking process, but also enables the determined acoustic emission source position of the complex - structure rock to be more accurate.

[0088] The following describes the device for locating acoustic emission sources of complex - structure rocks based on a sparse - grid path - tracking algorithm provided by the present invention. The device for locating acoustic emission sources of complex - structure rocks based on a sparse - grid path - tracking algorithm described below can be correspondingly referred to the method for locating acoustic emission sources of complex - structure rocks based on a sparse - grid path - tracking algorithm described above.

[0089] As Figure 12 shown is a schematic structural diagram of the device for locating acoustic emission sources of complex - structure rocks based on a sparse - grid path - tracking algorithm provided by the present invention. A device for locating acoustic emission sources of complex - structure rocks based on a sparse - grid path - tracking algorithm includes the following modules: A division module 121, which is used to divide a complex - structure rock into a three - dimensional space by using an adaptive octree structure, and divides it into non - cubic sparse grids. Among them, the non - cubic sparse grids include multiple grids and corresponding grid nodes; A calculation module 122, which is used to calculate the shortest P - wave propagation path between a sensor and a grid node and calculate the minimum propagation time between the sensor and the grid node based on an arbitrary - angle path - tracking algorithm and line - of - sight visibility detection; An arrival - time - difference determination module 123, which is used to determine the arrival - time difference between the theoretical arrival time and the actual arrival time of a potential acoustic emission source signal to any specific position within the grid through a trilinear interpolation algorithm based on the shortest P - wave propagation path and the minimum propagation time; An acoustic - emission - source - position determination module 124, which is used to construct an objective function based on the arrival - time difference and determine the acoustic emission source position of the complex - structure rock according to the minimum value of the objective function.

[0090] Preferably, the complex-structure rock acoustic emission source positioning device based on the sparse grid path tracking algorithm provided by the present invention is specifically further configured to expand potential paths starting from the starting node until the target node is reached, and determine the shortest P-wave propagation path between the starting node and the target node; The specific steps for expanding the potential path include: Maintain an open set and a closed set, and expand the minimum-cost node with the lowest total cost first; the open set contains the explored nodes, and the closed set includes the current node itself and all explored neighbor nodes; Perform line-of-sight visibility detection before node expansion. If the parent node is not visible, select the optimal alternative parent node from the closed set; Dynamically update the path cost and parent node relationship of adjacent nodes to ensure that the shortest P-wave propagation path satisfies the collision-free straight-line constraint.

[0091] Preferably, the complex-structure rock acoustic emission source positioning device based on the sparse grid path tracking algorithm provided by the present invention is specifically further configured to satisfy the function for calculating the total cost: f(s)=g(s)+h(s) where f(s) is the total cost of each node s, g(s) is the cumulative cost from the starting node to the current node s, h(s) is the heuristic estimated cost from the current node s to the target node, and g(s) is recursively defined as: g(s)=g(parent(s))+c(parent(s),s) where c(·) represents the geometric distance between nodes, parent(s) is the parent node for obtaining the current node s, and c(parent(s),s) is the distance calculated between the current node s and the parent node; The method further includes: the line-of-sight visibility detection is to determine whether the straight-line path between two nodes intersects with the cave nodes to exclude the straight-line paths that intersect with the cave nodes.

[0092] Preferably, the complex-structure rock acoustic emission source positioning device based on the sparse grid path tracking algorithm provided by the present invention is specifically further configured to perform step-by-step interpolation along the x, z, and y axes based on the grid sizes lx, ly, lz and the known arrival times at the eight vertices, and calculate the x-axis intermediate travel time, the z-axis intermediate travel time, and the travel time at a specific position in sequence; Based on the x-axis intermediate travel time, the z-axis intermediate travel time, and the travel time at the specific position, determine the theoretical arrival time at any specific position within the grid.

[0093] Preferably, the formula for calculating the x-axis intermediate travel time by interpolation along the x-axis for the complex-structure rock acoustic emission source positioning device based on the sparse grid path tracking algorithm provided by the present invention is: (1) Among them, T 12 , T 43 , T 56 , T 87 are respectively the intermediate travel time on the x-axis. T1, T2, T3, T4, T5, T6, T7, T8 are respectively the vertex time values. The size of the octree grid on the x-axis is denoted as lx, and l is the length of the projection of any specific position within the grid on the x-axis; The formula for interpolating along the z-axis to calculate the intermediate travel time on the z-axis is: (2) Among them, T 1256 , T 4387 are respectively the intermediate travel time on the z-axis. T 12 , T 43 , T 56 , T 87 are respectively the intermediate travel time on the x-axis. The size of the octree grid on the z-axis is denoted as lz, and z is the length of the projection of any specific position within the grid on the z-axis; The formula for interpolating along the y-axis to calculate the travel time at a specific position is: (3) Among them, T 1256 , T 4387 are respectively the intermediate travel time on the z-axis. The size of the octree grid on the y-axis is denoted as ly, and y is the length of the projection of any specific position within the grid on the y-axis.

[0094] Preferably, the complex structure rock acoustic emission source location device based on the sparse grid path tracking algorithm provided by the present invention is specifically further used to define any specific position in the complex structure rock as ξ ( x , y , z ), and use the travel time obtained by interpolating along the y-axis at a specific position as the ξ ( x , y , z ) of the theoretical arrival time T ( x , y , z ) at any characteristic position in the complex structure rock; The travel time gradient corresponding to the theoretical arrival time is denoted as: (4) (5) Among them, is the travel time gradient, is the derivative of the theoretical arrival time T with respect to the middle travel time on the x-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time on the y-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time on the z-axis; By effectively backtracking along the negative gradient direction - to determine the path from any specific position ξ ( x , y , z ) to the acoustic emission source, and taking the path from any specific position ξ ( x , y , z ) to the acoustic emission source as the shortest time path.

[0095] Preferably, for the acoustic emission source location device of complex structure rock based on the sparse grid path tracking algorithm provided by the present invention, it is specifically further used that the formula of the objective function is: (6) Among them, n represents the total number of sensors. By minimizing the objective function f( ξ 0) to determine the acoustic emission source location of the complex structure rock, i and j represent different sensor numbers, is the position of the i-th sensor, and the actual arrival time at the position of the i-th sensor is t i , and the theoretical arrival time at the position of the i-th sensor calculated using the path tracking algorithm is T( ), is the position of the j-th sensor, t j is the actual arrival time at the position of the j-th sensor, and T( ) is the theoretical arrival time corresponding to the position of the j-th sensor.

[0096] A method, device, equipment and medium for locating acoustic emission sources in complex-structured rocks based on a sparse grid path tracking algorithm. By using an adaptive octree structure to divide the complex-structured rocks in three-dimensional space into non-cubic sparse grids, where the non-cubic sparse grids include multiple grids and corresponding grid nodes; based on an arbitrary-angle path tracking algorithm and line-of-sight visibility detection, calculating the shortest P-wave propagation path between the sensor and the grid node and the minimum propagation time between the sensor and the grid node; based on the shortest P-wave propagation path and the minimum propagation time, determining the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid through a trilinear interpolation algorithm; constructing an objective function based on the arrival time difference and determining the acoustic emission source position of the complex-structured rocks according to the minimum value of the objective function. This not only significantly reduces the computational cost in the path tracking process but also enables the determined acoustic emission source position of the complex-structured rocks to be more accurate.

[0097] Figure 13 An example of the physical structure diagram of an electronic device is shown as Figure 13 shown. The electronic device may include: a processor 1310, a communication interface 1320, a memory 1330, and a communication bus 1340. Among them, the processor 1310, the communication interface 1320, and the memory 1330 complete mutual communication through the communication bus 1340. The processor 1310 can call the logical instructions in the memory 1330 to execute the method for locating acoustic emission sources in complex-structured rocks based on a sparse grid path tracking algorithm. The method includes: using an adaptive octree structure to divide the complex-structured rocks in three-dimensional space into non-cubic sparse grids, where the non-cubic sparse grids include multiple grids and corresponding grid nodes; based on an arbitrary-angle path tracking algorithm and line-of-sight visibility detection, calculating the shortest P-wave propagation path between the sensor and the grid node and the minimum propagation time between the sensor and the grid node; based on the shortest P-wave propagation path and the minimum propagation time, determining the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid through a trilinear interpolation algorithm; constructing an objective function based on the arrival time difference and determining the acoustic emission source position of the complex-structured rocks according to the minimum value of the objective function.

[0098] In addition, when the logical instructions in the aforementioned memory 1330 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0099] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the complex structure rock acoustic emission source location method based on the sparse grid path tracking algorithm provided by the above-mentioned various methods. The method includes: using an adaptive octree structure to perform three-dimensional space division on the complex structure rock, dividing it into non-cubic sparse grids, where the non-cubic sparse grids include multiple grids and corresponding grid nodes; based on an arbitrary angle path tracking algorithm and line-of-sight visibility detection, calculating the shortest P-wave propagation path between the sensor and the grid node, and calculating the minimum propagation time between the sensor and the grid node; based on the shortest P-wave propagation path and the minimum propagation time, through a trilinear interpolation algorithm, determining the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid; constructing an objective function based on the arrival time difference, and determining the acoustic emission source position of the complex structure rock according to the minimum value of the objective function.

[0100] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for locating the acoustic emission source of complex structure rocks based on the sparse grid path tracking algorithm provided by the above-mentioned various methods. The method includes: dividing the complex structure rocks in three-dimensional space by using an adaptive octree structure into non-cubic sparse grids, where the non-cubic sparse grids include multiple grids and corresponding grid nodes; calculating the shortest P-wave propagation path between the sensor and the grid nodes and the minimum propagation time between the sensor and the grid nodes based on the arbitrary angle path tracking algorithm and the line-of-sight visibility detection; determining the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid through a trilinear interpolation algorithm based on the shortest P-wave propagation path and the minimum propagation time; constructing an objective function based on the arrival time difference and determining the location of the acoustic emission source of the complex structure rocks according to the minimum value of the objective function.

[0101] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0102] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for locating acoustic emission sources of complex - structured rocks based on a sparse - grid path - tracking algorithm, characterized in that, Including: Using an adaptive octree structure to divide a complex - structured rock in three - dimensional space into non - cubic sparse grids, where the non - cubic sparse grids include multiple grids and corresponding grid nodes; Based on an arbitrary - angle path - tracking algorithm and line - of - sight visibility detection, calculating the shortest P - wave propagation path between the sensor and the grid nodes, and calculating the minimum propagation time between the sensor and the grid nodes; Based on the shortest P - wave propagation path and the minimum propagation time, through a trilinear interpolation algorithm, determining the arrival - time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid; Constructing an objective function based on the arrival - time difference and determining the acoustic emission source position of the complex - structured rock according to the minimum value of the objective function.

2. The method for locating acoustic emission sources of complex structure rocks based on the sparse grid path tracking algorithm according to claim 1, characterized in that The arbitrary - angle path - tracking algorithm includes: Starting from the starting node to expand potential paths until reaching the target node, and determining the shortest P - wave propagation path between the starting node and the target node; The specific steps of expanding the potential path include: Maintaining an open set and a closed set, and preferentially expanding the minimum - cost node through the total cost; the open set contains the explored nodes, and the closed set includes the current node and all explored neighbor nodes; Performing line - of - sight visibility detection before node expansion. If the parent node is not visible, select the optimal alternative parent node from the closed set; Dynamically updating the path cost and parent - node relationship of adjacent nodes to ensure that the shortest P - wave propagation path satisfies the collision - free straight - line constraint.

3. The method for locating acoustic emission sources of complex structure rocks based on the sparse grid path tracking algorithm according to claim 2, wherein, The function for calculating the total cost satisfies: f(s)=g(s)+h(s) where f(s) is the total cost of each node s, g(s) is the cumulative cost from the starting node to the current node s, h(s) is the heuristic estimated cost from the current node s to the target node, and g(s) is recursively defined as: g(s)=g(parent(s))+c(parent(s),s) where c(·) represents the geometric distance between nodes, parent(s) is the parent node for obtaining the current node s, and c(parent(s),s) is the distance calculated between the current node s and its parent node; The method further includes: The line - of - sight visibility detection is to exclude the straight - line paths that intersect with the cave nodes by determining whether the straight - line path between two nodes intersects with the cave nodes.

4. The method for locating acoustic emission sources of complex structure rocks based on the sparse grid path tracking algorithm according to claim 1, characterized in that, The steps of the trilinear interpolation algorithm specifically include: Based on the grid sizes lx, ly, lz and the known arrival times of the eight vertices, interpolating step - by - step along the x, z, and y axes to calculate the x - axis intermediate travel time, the z - axis intermediate travel time, and the specific - position travel time in sequence; Based on the x - axis intermediate travel time, the z - axis intermediate travel time, and the specific - position travel time, determining the theoretical arrival time at any specific position within the grid.

5. The method for locating the acoustic emission source of a complex - structured rock based on a sparse - grid path - tracking algorithm according to claim 4, characterized in that The formula for interpolating along the x - axis to calculate the x - axis intermediate travel time is: (1) Among them, T 12 , T 43 , T 56 , T 87 are respectively the middle travel time on the x-axis, and T1, T2, T3, T4, T5, T6, T7, T8 are respectively different vertex time values. The size of the octree grid on the x-axis is represented as lx , is the length of the projection of any specific position within the grid on the x-axis; The formula for interpolating along the z - axis to calculate the z - axis intermediate travel time is: (2) Among them, T 1256 , T 4387 are respectively the intermediate travel time of the z-axis, T 12 , T 43 , T 56 , T 87 are respectively the intermediate travel time of the x-axis. The size of the octree grid on the z-axis is expressed as lz , where z is the length of the projection of any specific position within the grid on the z-axis; Interpolate along the y-axis to calculate the travel time at a specific position The formula is as follows: (3) Among them, T 1256 and T 4387 are the intermediate travel time on the z-axis respectively. The size of the octree grid on the y-axis is expressed as ly , where y is the length of the projection of any specific position within the grid on the y-axis.

6. The method for locating acoustic emission sources of complex structure rocks based on the sparse grid path tracking algorithm according to claim 5, wherein The method further includes: Define any specific position in the complex-structured rock as ξ ( x , y , z ), and use the travel time at the specific position obtained by interpolation along the y-axis as the theoretical arrival time ξ ( x , y , z ) at any characteristic position T ( x , y , z ) in the complex-structured rock; The travel - time gradient corresponding to the theoretical arrival time is expressed as: (4) (5) Among them, is the travel time gradient, is the derivative of the theoretical arrival time T with respect to the middle travel time on the x-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time on the y-axis, is the derivative of the theoretical arrival time T with respect to the middle travel time on the z-axis; By backtracking effectively along the negative gradient direction - to determine the path from any specific position ξ ( x , y , z ) to the acoustic emission source, and taking the path from any specific position ξ ( x , y , z ) to the acoustic emission source as the shortest time path.

7. The method for locating acoustic emission sources of complex structure rocks based on the sparse grid path tracking algorithm according to claim 6, wherein The formula of the objective function is as follows: (6) where n represents the total number of sensors, and the acoustic emission source location of the complex structure rock is determined by minimizing the objective function f( ξ 0), i and j represent different sensor numbers, is the location of the i-th sensor, and the actual arrival time at the location of the i-th sensor is t i , and the theoretical arrival time at the location of the i-th sensor calculated using the path tracking algorithm is T( ), is the location of the j-th sensor, t j is the actual arrival time at the location of the j-th sensor, and T( ) is the theoretical arrival time corresponding to the location of the j-th sensor.

8. A complex structure rock acoustic emission source location device based on a sparse grid path tracking algorithm, characterized in that, including: a partitioning module, configured to partition the complex-structured rock in three-dimensional space by using an adaptive octree structure into non-cubic sparse grids, where the non-cubic sparse grids include a plurality of grids and corresponding grid nodes; a calculation module, configured to calculate the shortest P-wave propagation path between the sensor and the grid nodes and calculate the minimum propagation time between the sensor and the grid nodes based on an arbitrary-angle path tracking algorithm and line-of-sight visibility detection; an arrival time difference determination module, configured to determine the arrival time difference between the theoretical arrival time and the actual arrival time of the potential acoustic emission source signal to any specific position within the grid through a trilinear interpolation algorithm based on the shortest P-wave propagation path and the minimum propagation time; an acoustic emission source position determination module, configured to construct an objective function based on the arrival time difference and determine the acoustic emission source position of the complex-structured rock according to the minimum value of the objective function.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the program, it implements the acoustic emission source localization method for complex-structured rocks based on a sparse grid path tracking algorithm according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the acoustic emission source localization method for complex-structured rocks based on a sparse grid path tracking algorithm according to any one of claims 1 to 7.

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