Mountain stability evaluation method and device, computer equipment and storage medium

Abstract

The invention relates to a mountain stability evaluation method and device, computer equipment and a storage medium, and relates to the technical field of mountain stability. The evaluation method comprises the following steps: dividing a mountain into a plurality of voxels; an adjacent structure diagram and density field posterior probability distribution of the mountain are constructed, the adjacent structure diagram is constructed through muon data recorded by muon detectors arranged in the grotto, and the density field posterior probability distribution is probability distribution of all density models of the mountain under the muon data and the adjacent structure diagram; sampling from the density field posterior probability distribution according to the adjacent structure diagram to obtain a plurality of density field samples of the mountain; obtaining a density value of each voxel according to the plurality of density field samples; and according to the density values of the voxels, obtaining a stability abnormal region in the mountain. According to the mountain stability assessment method, the stability abnormal area in the mountain can be accurately determined, and the accuracy of mountain stability assessment is improved.

Description

Technical Field

[0001] This invention relates to the technical field of mountain stability, specifically to a method, apparatus, computer equipment, and storage medium for assessing mountain stability. Background Technology

[0002] Grottoes are built into mountains, and the stability of the mountains affects the safety of these cultural relics. Currently, methods such as ground-penetrating radar (GPR), seismic exploration, drilling, and numerical simulation are mainly used to assess the stability of the mountains to which grottoes are attached. However, these methods have limitations, including limited detection depth, insufficient resolution, localized detection capabilities, destructive nature, reliance on ideal parameters and prior assumptions, and difficulty in reflecting the actual structure, making it difficult to accurately assess the stability of the mountains to which grottoes are attached. Summary of the Invention

[0003] To overcome the problems existing in related technologies, the present invention provides a method, apparatus, computer equipment and storage medium for evaluating mountain stability.

[0004] According to a first aspect of the present invention, a method for assessing mountain stability is provided, the method being used to assess the stability of a mountain to which a grotto is attached, the method comprising: The mountain is divided into multiple voxels; The adjacency structure diagram and density field posterior probability distribution of the mountain are constructed. The adjacency structure diagram is constructed using muon data recorded by muon detectors installed in the cave. The density field posterior probability distribution is the probability distribution of all density models of the mountain under the muon data and the adjacency structure diagram. Based on the adjacency structure diagram, multiple density field samples of the mountain are obtained by sampling from the posterior probability distribution of the density field; The density value of each voxel is obtained based on multiple density field samples. Based on the density values ​​of multiple voxels, the regions of instability in the mountain are obtained.

[0005] In some exemplary embodiments of the present invention, constructing the adjacency structure map and density field posterior probability distribution of the mountain includes: The muon detector is used to record the muon data within a preset time period; Based on the muon data, determine the muon transmittance; The adjacent structure diagram is derived based on the point cloud data of the mountain and the muon transmittance.

[0006] In some exemplary embodiments of the present invention, the step of deriving the adjacency structure diagram based on the point cloud data of the mountain and the muon transmittance includes: Based on the point cloud data and the muon transmittance, a voxel map model of the mountain is obtained, wherein the voxel map model includes the initial density value of each voxel; Each voxel in the voxel graph model is treated as a node in the adjacency structure graph, wherein the attributes of the node include the initial density value; The connection between each two adjacent voxels in the voxel graph model is used as an edge connecting the two corresponding nodes in the adjacency structure graph. The two corresponding nodes refer to the nodes corresponding to the two adjacent voxels. Each edge corresponds to a weight, which is used to reflect the structural similarity between the two nodes connected by the edge. The greater the structural similarity, the greater the weight.

[0007] In some exemplary embodiments of the present invention, the weight is the product of the geological structure main direction similarity weight, joint density weight, and slope aspect weight, or the weight is the weighted product of the geological structure main direction similarity weight, joint density weight, and slope aspect weight.

[0008] In some exemplary embodiments of the present invention, the construction of the adjacency structure map and density field posterior probability distribution of the mountain further includes: Construct the prior probability distribution of the density field of the mountain, wherein the prior probability distribution of the density field is the initial probability distribution of all the density models constructed based on the adjacency structure graph; Based on the muon transmittance and muon statistical laws, a likelihood function is constructed, wherein the likelihood function represents the probability of observing the muon transmittance under all the density models; Based on the prior probability distribution of the density field and the likelihood function, the posterior probability distribution of the density field is constructed, wherein the posterior probability distribution of the density field is proportional to the product of the prior probability distribution of the density field and the likelihood function.

[0009] In some exemplary embodiments of the present invention, the step of sampling from the posterior probability distribution of the density field according to the adjacency structure graph to obtain multiple density field samples of the mountain includes: For each node, repeat the following operation until convergence to the posterior probability distribution of the density field or the maximum number of sampling rounds is reached: Sample from all the density models of the posterior probability distribution of the density field, and keep the density of the remaining nodes in the sampled density models unchanged except for the current node; In response to the target edge connected to the current node having a weight less than a preset weight, the target edge is deleted, and / or, in response to the absolute difference in density between the current node and the target node in the target density model being greater than a preset density difference, the edge connecting the target node and the current node is deleted, where the target node is a node adjacent to the current node, and the target density model is the density model used as a sample.

[0010] In some exemplary embodiments of the present invention, obtaining the density value of each voxel based on a plurality of density field samples includes: The average density of the node corresponding to each voxel in the multiple density field samples is used as the density value of the corresponding voxel; and / or, The step of determining the stability anomaly region in the mountain based on the density values ​​of multiple voxels includes: All voxels whose density values ​​are less than a preset density value are taken as a voxel set; The region formed by connecting multiple voxels in the voxel set is defined as the stability anomalous region.

[0011] According to a second aspect of the present invention, a mountain stability assessment device is provided, the device being used to assess the stability of a mountain to which a grotto is attached, the device comprising: The segmentation module is configured to divide the mountain into multiple voxels; The construction module is configured to construct the adjacency structure graph and density field posterior probability distribution of the mountain, wherein the adjacency structure graph is constructed using muon data recorded by muon detectors installed in the cave, and the density field posterior probability distribution is the probability distribution of all density models of the mountain under the muon data and the adjacency structure graph; The sampling module is configured to sample from the posterior probability distribution of the density field according to the adjacency structure graph to obtain multiple density field samples of the mountain. The first determining module is configured to obtain the density value of each voxel based on a plurality of density field samples; The second determining module is configured to determine the unstable region in the mountain based on the density values ​​of the plurality of voxels.

[0012] According to a third aspect of the present invention, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0013] According to a fourth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the method described in the first aspect.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: The method for assessing mountain stability of the present invention constructs the posterior probability distribution of the density field of the mountain by combining the adjacency structure diagram of the mountain, and samples are performed under the constraints of the adjacency structure diagram of the mountain. The density inversion results of the mountain obtained have structural consistency and physical rationality, which can accurately determine the stability anomaly areas in the mountain and improve the accuracy of mountain stability assessment.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] Figure 1 This is a flowchart illustrating a method for assessing mountain stability according to a first exemplary embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for assessing mountain stability according to a second exemplary embodiment of the present invention; Figure 3 This is a flowchart illustrating a method for assessing mountain stability according to a third exemplary embodiment of the present invention; Figure 4 This is a flowchart illustrating a method for assessing mountain stability according to a fourth exemplary embodiment of the present invention; Figure 5 This is a flowchart illustrating a method for assessing mountain stability according to a fifth exemplary embodiment of the present invention; Figure 6 This is a flowchart illustrating a method for assessing mountain stability according to a sixth exemplary embodiment of the present invention; Figure 7 This is a flowchart illustrating a method for assessing mountain stability according to a seventh exemplary embodiment of the present invention; Figure 8 This is a block diagram of a mountain stability assessment device according to an exemplary embodiment of the present invention; Figure 9 This is a block diagram of a computer device according to an exemplary embodiment of the present invention.

[0018] In the picture: 81-Division module; 82-Construction module; 83-Sampling module; 84-First determination module; 85-Second determination module; 900-Computer device; 901-Computation unit; 902-Read-only memory (ROM); 903-Random access memory (RAM); 904-Bus; 905-Input / output (I / O); 906-Input unit; 907-Output unit; 908-Storage unit; 909-Communication unit. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0020] Grottoes are built into mountains, and the stability of the mountains affects the safety of these cultural relics. Currently, methods such as ground-penetrating radar (GPR), seismic exploration, drilling, and numerical simulation are mainly used to assess the stability of the mountains to which grottoes are attached. However, these methods have limitations, including limited detection depth, insufficient resolution, localized detection capabilities, destructive nature, reliance on ideal parameters and prior assumptions, and difficulty in reflecting the actual structure, making it difficult to accurately assess the stability of the mountains to which grottoes are attached.

[0021] To address the aforementioned technical problems, this invention provides a method for assessing the stability of mountains attached to grottoes. The method involves dividing the mountain into multiple voxels. An adjacency structure graph and a posterior probability distribution of the density field are constructed. The adjacency structure graph is constructed using muon data recorded by muon detectors installed within the grottoes. The posterior probability distribution of the density field is the probability distribution of all density models of the mountain under the conditions of the muon data and the adjacency structure graph. Based on the adjacency structure graph, samples are taken from the posterior probability distribution of the density field to obtain multiple density field samples of the mountain. Based on these multiple density field samples, the density value of each voxel is obtained. Based on the density values ​​of the multiple voxels, stability anomaly regions within the mountain are identified. By combining the adjacency structure graph with the constructed density field posterior probability distribution of the mountain, and sampling is performed under the constraints of the adjacency structure graph, the obtained density inversion results of the mountain exhibit structural consistency and physical rationality, accurately identifying stability anomaly regions within the mountain and improving the accuracy of mountain stability assessment.

[0022] An exemplary embodiment of this invention provides a method for assessing the stability of a mountain to which a grotto is attached. A grotto is a cave built into a natural mountain or carved by humans. Grotto artifacts include not only the grotto itself, but also the historical remains and cultural information within it, such as murals and stone carvings. Mountain stability refers to the ability of a mountain to maintain its original shape and balance after resisting forms of damage such as landslides, collapses, and toppling.

[0023] like Figure 1 As shown in the exemplary embodiment of the present invention, the method for assessing mountain stability includes: S101. Divide the mountain into multiple voxels.

[0024] The mountain is divided into multiple cubic regions, each of which is a voxel.

[0025] In some examples, the mountain can be divided into multiple voxels based on point cloud data acquired at historical moments. The point cloud data is a collection of 3D coordinates of each point on the mountain surface, obtained through high-speed, high-precision measurements using 3D laser scanning technology. A digital surface model of the mountain is generated based on this point cloud data; the area enclosed by this model is the mountain's interior. A voxel resolution is set, ranging from 0.5m × 0.5m × 0.5m to 2m × 2m × 2m, for example, 1m × 1m × 1m. The digital surface model of the mountain and the enclosed interior region are then divided into multiple cubic regions according to the voxel resolution; each cubic region represents a voxel of the mountain.

[0026] S102. Construct the adjacency structure diagram and density field posterior probability distribution of the mountain.

[0027] The adjacency structure graph is constructed using muon data recorded by muon detectors installed inside the grotto. The adjacency structure graph of the mountain represents the spatial connections between multiple voxels of the mountain. The mountain's adjacency structure graph consists of multiple nodes and multiple edges; each node represents a voxel, and each edge represents the connection between two nodes.

[0028] The posterior probability distribution of the density field is the probability distribution of all density models of the mountain under the muon data and adjacency structure diagram.

[0029] In an exemplary embodiment of the present invention, such as Figure 2 As shown, the construction of the adjacency structure diagram and density field posterior probability distribution of the mountain in step S102 includes: S102-1. Use a muon detector to record muon data within a preset time period.

[0030] In some examples, one or more muon detectors may be placed within the grotto. The position and orientation of the muon detectors are aligned with the coordinate system of the point cloud data, ensuring that the muon data and point cloud data are in the same three-dimensional spatial reference frame. The preset duration can be from 1 month to 12 months, for example, 6 months. The one or more muon detectors within the grotto record the muon flux after muons pass through the mountainside attached to the grotto within the preset duration, which is then used as muon data.

[0031] S102-2. Determine the muon transmittance based on the muon data.

[0032] In some examples, the muon transmittance is determined based on the muon flux recorded by the muon detector within a preset time period in step S102-1 after muons have passed through the mountain to which the cave is attached, and the historically acquired muon flux of the area where the mountain to which the cave is attached is located under open-air conditions and within the preset time period. The muon transmittance is the ratio of the muon flux recorded by the muon detector within the preset time period after muons have passed through the mountain to which the cave is attached to to the muon flux acquired at a historical moment under open-air conditions and within the preset time period. The muon transmittance may include sub-munion transmittance in multiple observation directions, defined by the zenith angle and azimuth angle; different observation directions have different zenith angles and / or azimuth angles.

[0033] S102-3. Based on the point cloud data of the mountain and the muon transmittance, the adjacent structure diagram is obtained.

[0034] In an exemplary embodiment of the present invention, such as Figure 3 As shown, step S102-3, based on the point cloud data and muon transmittance of the mountain, derives the adjacent structure diagram, including: S102-3-1. Based on point cloud data and muon transmittance, obtain the voxel model of the mountain.

[0035] In some examples, each muon passes through multiple voxels, and the transmissivity of the muon is constructed using a variant of the Beer-Lambert law: ; in, It is muon transmittance. It is the number of any voxel that the muon passed through. It is numbered The density of voxels, It was Muzi who passed through the numbered The path length of the voxel. .

[0036] By reversing the above formula, taking the natural logarithm of the muon transmittance obtained in step S102-2, and then multiplying it by -1, we can obtain... , It is the muon coarse imaging data of the mountain, which includes the average density-thickness product of each muon path.

[0037] Point cloud data and muon coarse imaging data of the mountain are input into a graph neural network (Graph Neural Network). The Graph Neural Network is used to identify the internal geological structure of the mountain and output a voxel map model of the mountain. A Graph Neural Network is a deep learning model used to process graph-structured data. The voxel map model of the mountain divides the mountain into multiple voxels and assigns one or more physical attribute values ​​to each voxel, such as density or lithology. In this exemplary embodiment, the voxel map model includes an initial density value for each voxel.

[0038] S102-3-2, Treat each voxel in the voxel graph model as a node in the adjacency structure graph.

[0039] In this exemplary embodiment, the node's attributes include an initial density value.

[0040] Each voxel in the voxel graph model generated in step S102-3-1 is used as a node in the adjacency structure graph, and the initial density value of each voxel in the voxel graph model is used as the attribute of the corresponding node in the adjacency structure graph.

[0041] S102-3-3. The connection between every two adjacent voxels in the voxel graph model is taken as an edge connecting the two corresponding nodes in the adjacency structure graph. The two corresponding nodes refer to the nodes corresponding to the two adjacent voxels.

[0042] In this exemplary embodiment, each edge corresponds to a weight, which reflects the structural similarity between the two nodes connected by the edge. The greater the structural similarity, the greater the weight.

[0043] In some examples, the connection between every two adjacent voxels in the voxel graph model generated in step S102-3-1 is used as an edge connecting the corresponding two nodes in the adjacency structure graph, and a weight is assigned to each edge.

[0044] In an exemplary embodiment of the present invention, the weight is the product of the similarity weight of the main direction of the geological structure, the joint density weight, and the aspect weight, or the weight is the weighted product of the similarity weight of the main direction of the geological structure, the joint density weight, and the aspect weight.

[0045] The similarity weight of the main direction of geological structure is used to reflect whether the dominant directions of the internal structural surfaces of two adjacent nodes are consistent. The higher the degree of consistency, the greater the similarity weight of the main direction of geological structure.

[0046] Joint density weight is used to reflect the difference in the degree of rock fragmentation between two adjacent nodes. The smaller the difference, the greater the joint density weight.

[0047] The aspect weight is used to reflect whether the strata dip and slope aspect of the geological structure of two adjacent nodes are consistent. The higher the consistency, the greater the aspect weight.

[0048] In some examples, point cloud data and muon coarse imaging data of the mountain are input into a graph neural network (Graph NNN) to identify the internal geological structure of the mountain. While outputting the voxel map model of the mountain, the spatial and attribute correlations between voxels can also be derived. Based on the voxel map model and the spatial and attribute correlations between voxels, a graph convolutional network is used to automatically identify the structural weaknesses of the mountain, namely, adjacent voxels with low consistency in the dominant direction of internal structural surfaces, adjacent voxels with large differences in the degree of rock fragmentation, and adjacent voxels with low consistency in the dip and aspect of the geological strata. These weaknesses are used to calculate the similarity weights of the principal geological direction, joint density weights, and aspect weights of the edges between adjacent nodes corresponding to adjacent voxels.

[0049] In some examples, the weight of an edge is the product of the geological structure similarity weight, joint density weight, and slope aspect weight of the edge between two adjacent nodes.

[0050] In other examples, the weight of an edge between two adjacent nodes is the weighted product of the similarity weight of the main geological structure direction, the joint density weight, and the aspect weight. For example, the weighting factor for the similarity weight of the main geological structure direction is 0.5, the weighting factor for the joint density weight is 0.3, and the weighting factor for the aspect weight is 0.2.

[0051] The weights of the edges are not fixed constants, but are assigned based on the similarity of the main geological structure of adjacent nodes, the difference in joint density, and the consistency of slope aspect. This can reflect the degree of structural similarity between the two nodes connected by the edge, ensuring that the density inversion of the mountain is smoothly constrained in areas with consistent structure, while retaining the necessary density mutation capability in areas with weak structure, thereby improving the accuracy and geological rationality of the density inversion of the mountain.

[0052] In an exemplary embodiment of the present invention, the prior probability distribution and likelihood function of the mountain's density field are constructed using Bayesian probability theory to obtain the posterior probability distribution of the mountain's density field. Bayesian probability theory, with Bayes' theorem at its core, is used to update probability judgments based on prior knowledge and new evidence in situations of uncertainty. Figure 4 As shown, the construction of the adjacency structure diagram and density field posterior probability distribution of the mountain in step S102 also includes: S102-4. Construct the prior probability distribution of the density field of the mountain.

[0053] In this exemplary embodiment, the prior probability distribution of the density field is the initial probability distribution of all density models constructed based on the adjacency structure graph.

[0054] In some examples, the prior probability distribution of the density field of a mountain is constructed using the following formula.

[0055] ; in, It is the prior probability distribution of the density field. It is all density models, This is a diagram of the adjacent structure of the mountain; It is a regularization factor used to control the strength of density field smoothness. The larger the value, the stronger the constraint of the prior probability distribution of the density field on the density continuity. It can be set to a fixed value or can be adaptively learned through Bayesian probability theory to adapt to the density inversion needs under different geological structures. and It is the number of two adjacent nodes in the adjacency diagram; It refers to all nodes in the adjacency graph; In the adjacency diagram, the adjacent nodes are numbered as follows: The nodes and their numbers are The weight of the edges of the node; It is numbered in the adjacency structure diagram. The initial density value of the nodes; It is numbered in the adjacency structure diagram. The initial density value of the nodes.

[0056] The prior probability distribution of the density field constructed based on the adjacency structure graph has directionality and structural control capabilities compared to the Gaussian smooth density field prior probability distribution, and can highlight the influence of weak surfaces of the structure on density perturbations.

[0057] S102-5. Construct the likelihood function based on muon transmittance and muon statistical laws.

[0058] In this exemplary embodiment, the likelihood function represents the probability of observing muon transmittance under all density models.

[0059] In some examples, the likelihood function is... The statistical regularity of muons can be represented by a Poisson distribution. A Poisson distribution is the probability distribution of the number of occurrences of a random event within a fixed time interval or a fixed spatial region. The process of muons reaching a muon detector fits the application scenario of the Poisson distribution. Therefore, a likelihood function is constructed based on muon transmittance and the Poisson distribution, representing the probability of observing muon transmittance under all density models.

[0060] S102-6. Construct the posterior probability distribution of the density field based on the prior probability distribution and likelihood function of the density field.

[0061] In this exemplary embodiment, the posterior probability distribution of the density field is proportional to the product of the prior probability distribution of the density field and the likelihood function.

[0062] In some examples, the posterior probability distribution of the density field of a mountain is constructed using the following formula.

[0063] ; in, It is the posterior probability distribution of the density field, which is the probability distribution of all density models of the mountain under the conditions of muon transmittance and adjacency structure diagram.

[0064] S103. Based on the adjacency structure diagram, sample from the posterior probability distribution of the density field to obtain multiple density field samples of the mountain.

[0065] In some examples, the Graph Structure Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior probability distribution of the density field, obtaining multiple density field samples of the mountain. The Markov Chain Monte Carlo algorithm is a statistical algorithm that estimates model parameters by sampling from a probability distribution. It constructs a Markov chain conforming to a specific probability distribution for sampling, gradually approximating the stable state of the target distribution using a stochastic process. For example, after modeling using Bayesian probability theory, samples are generated from a complex posterior probability distribution of the density field. The Graph Structure Markov Chain Monte Carlo algorithm, building upon the standard Markov Chain Monte Carlo algorithm, guides sampling based on the adjacency structure graph. It allows for larger movements in areas with higher edge weights and smaller movements in areas with lower edge weights, thus exploring high-probability regions more efficiently and accurately.

[0066] In an exemplary embodiment of the present invention, such as Figure 5 As shown, in step S103, based on the adjacency structure graph, multiple density field samples of the mountain are obtained by sampling from the posterior probability distribution of the density field, including: For each node, repeat the following operation until convergence to the posterior probability distribution of the density field or the maximum number of sampling rounds is reached: S103-1. Sample from all density models of the posterior probability distribution of the density field, and keep the density of all nodes in the sampled density models unchanged except for the current node.

[0067] In some examples, the Gibbs sampling algorithm can be used to sample from all density models of the posterior probability distribution of the density field, while keeping the densities of all nodes in the sampled density models unchanged except for the current node. The Gibbs sampling algorithm is a specific algorithm of the Markov chain Monte Carlo algorithm, which samples only one variable at a time, fixing the current values ​​of other variables.

[0068] The formula for sampling from all density models of the posterior probability distribution of the density field using the Gibbs sampling algorithm is as follows: ; in, In the adjacency structure graph, except for the one numbered... The density of nodes other than the node, It is the probability distribution that keeps the density of all nodes in the density model used as samples constant, except for the current node. Is with number The set of adjacent nodes of a given node.

[0069] S103-2. In response to the target edge connected to the current node having a weight less than a preset weight, delete the target edge, and / or, in response to the absolute difference between the density of the current node and the target node in the target density model being greater than a preset density difference, delete the edge connecting the target node and the current node.

[0070] The target node is the node adjacent to the current node, and the target density model is the density model used as the sample.

[0071] The preset weight and preset density difference can be set according to the actual conditions of the mountain. In some examples, the preset weight can be 0.5. In other examples, the preset density difference can be 0.8 g / cm³.

[0072] If the weight of the target edge connected to the current node is less than the preset weight, or if the absolute difference in density between the current node and the target node in the target density model is greater than the preset density difference, it indicates a significant structural difference between the target node and the current node. Deleting the edge between the target node and the current node removes the target node from the set of nodes adjacent to the current node, thus eliminating the smoothing constraint on the target node and improving the responsiveness to structural abrupt changes.

[0073] The maximum number of sampling rounds can be set according to the actual situation. In some examples, the maximum number of sampling rounds can be 100,000.

[0074] S104. Based on multiple density field samples, obtain the density value of each voxel.

[0075] In an exemplary embodiment of the present invention, the average density of the node corresponding to each voxel in multiple density field samples is calculated as the density value of the corresponding voxel.

[0076] In some examples, in addition to obtaining the density value of each voxel, the density confidence interval of each voxel can also be obtained. The densities of the nodes corresponding to each voxel in multiple density field samples are sorted from smallest to largest, and the densities falling between a first preset percentage and a second preset percentage are used as the density confidence interval for that voxel. The first preset percentage is less than the second preset percentage; the first preset percentage can be 20%, and the second preset percentage can be 80%.

[0077] S105. Based on the density values ​​of multiple voxels, the areas of instability in the mountain are obtained.

[0078] In an exemplary embodiment of the present invention, such as Figure 6 As shown, step S105 involves obtaining the stability anomaly regions within the mountain based on the density values ​​of multiple voxels, including: S105-1. Collect all voxels whose density values ​​are less than the preset density value as a voxel set.

[0079] The preset density value can be set according to the specific density of the mountain's rock mass. In some examples, the rock mass density of the mountain can be between 2.3 g / cm³ and 2.6 g / cm³, and the preset density value can be 1.5 g / cm³.

[0080] S105-2. The region formed by connecting multiple voxels in the voxel set is regarded as the stability anomalous region.

[0081] The density value of a single voxel is less than the preset density value, and the area with low density is smaller in size, so it will not affect the stability of the mountain.

[0082] The area formed by multiple voxels with density values ​​less than the preset density value is relatively large and may be a structure such as cracks, which may cause disasters such as collapse and landslides that endanger the cultural relics in the grotto. Therefore, the area formed by multiple voxels connected in the voxel set is regarded as an unstable region.

[0083] An exemplary embodiment of the present invention provides a method for assessing the stability of a mountain, used to evaluate the stability of a mountain to which a grotto is attached.

[0084] like Figure 7 As shown in the exemplary embodiment, the method for assessing mountain stability includes: S701, Divide the mountain into multiple voxels.

[0085] S702. Use a muon detector to record muon data within a preset time period.

[0086] S703. Determine the muon transmittance based on the muon data.

[0087] S704. Based on point cloud data and muon transmittance, a voxel model of the mountain is obtained.

[0088] S705. Treat each voxel in the voxel graph model as a node in the adjacency structure graph.

[0089] S706. Treat the connection between every two adjacent voxels in the voxel graph model as an edge connecting the two corresponding nodes in the adjacency structure graph. The two corresponding nodes refer to the nodes corresponding to the two adjacent voxels.

[0090] S707, Construct the prior probability distribution of the density field of the mountain.

[0091] S708. Construct the likelihood function based on muon transmittance and muon statistical laws.

[0092] S709. Construct the posterior probability distribution of the density field based on the prior probability distribution and likelihood function of the density field.

[0093] S710. For each node, repeat the following operation until convergence to the posterior probability distribution of the density field or the maximum number of sampling rounds is reached: sample from all density models of the posterior probability distribution of the density field, and keep the density of the remaining nodes in the sampled density models unchanged except for the current node; delete the target edge in response to the target edge connected to the current node having a weight less than the preset weight.

[0094] S711. Calculate the average density of the node corresponding to each voxel in multiple density field samples, and use it as the density value of the corresponding voxel.

[0095] S712. Collect all voxels whose density values ​​are less than the preset density value as a voxel set.

[0096] S713. The region formed by connecting multiple voxels in a voxel set is considered as a region of instability anomaly.

[0097] In this exemplary embodiment, the posterior probability distribution of the mountain's density field is constructed by combining the adjacency structure graph of the mountain, and sampling is performed under the constraints of the adjacency structure graph. The obtained density inversion results of the mountain have structural consistency and physical rationality. Regions formed by connecting multiple voxels with density values ​​less than a preset density value are designated as stability anomaly regions, which can accurately identify stability anomaly regions in the mountain and improve the accuracy of mountain stability assessment.

[0098] An exemplary embodiment of the present invention provides a device for assessing the stability of a mountain, used to evaluate the stability of a mountain to which a grotto is attached. For example... Figure 8As shown in the exemplary embodiment, the hill stability assessment device includes a division module 81, a construction module 82, a sampling module 83, a first determination module 84, and a second determination module 85.

[0099] The partitioning module 81 is configured to divide the mountain into multiple voxels.

[0100] The construction module 82 is configured to construct the adjacency structure graph and the posterior probability distribution of the density field of the mountain. The adjacency structure graph is constructed using muon data recorded by muon detectors installed inside the cave. The posterior probability distribution of the density field is the probability distribution of all density models of the mountain under the muon data and the adjacency structure graph.

[0101] The sampling module 83 is configured to sample from the posterior probability distribution of the density field based on the adjacency structure graph to obtain multiple density field samples of the mountain.

[0102] The first determining module 84 is configured to obtain the density value of each voxel based on multiple density field samples.

[0103] The second determining module 85 is configured to determine the stability anomaly region in the mountain based on the density values ​​of multiple voxels.

[0104] In this exemplary embodiment, the posterior probability distribution of the density field of the mountain is constructed by combining the adjacency structure diagram of the mountain, and sampling is performed under the constraint of the adjacency structure diagram of the mountain. The density inversion result of the mountain obtained has structural consistency and physical rationality, which can accurately determine the stability anomaly area in the mountain and improve the accuracy of mountain stability assessment.

[0105] In an exemplary embodiment of the present invention, the construction module 82 is further configured to: Use a muon detector to record muon data within a preset time period; Determine muon transmittance based on muon data; Based on the point cloud data of the mountain and the muon transmittance, the adjacent structure diagram is obtained.

[0106] In an exemplary embodiment of the present invention, the construction module 82 is further configured to: Based on point cloud data and muon transmittance, a voxel map model of the mountain is obtained, wherein the voxel map model includes the initial density value of each voxel; Each voxel in the voxel graph model is treated as a node in the adjacency structure graph, where the node's attributes include an initial density value. The connection between each two adjacent voxels in the voxel graph model is used as an edge connecting the two corresponding nodes in the adjacency structure graph. The two corresponding nodes refer to the nodes corresponding to the two adjacent voxels. Each edge corresponds to a weight, which is used to reflect the structural similarity between the two nodes connected by the edge. The greater the structural similarity, the greater the weight.

[0107] In an exemplary embodiment of the present invention, the weight is the product of the similarity weight of the main direction of geological structure, the joint density weight, and the aspect weight, or the weight is the weighted product of the similarity weight of the main direction of geological structure, the joint density weight, and the aspect weight.

[0108] In an exemplary embodiment of the present invention, the construction module 82 is further configured to: Construct the prior probability distribution of the density field of the mountain, where the prior probability distribution of the density field is the initial probability distribution of all density models constructed based on the adjacency structure graph; Based on muon transmissivity and muon statistics, a likelihood function is constructed, where the likelihood function represents the probability of observing muon transmissivity under all density models. Based on the prior probability distribution and likelihood function of the density field, the posterior probability distribution of the density field is constructed, where the posterior probability distribution of the density field is proportional to the product of the prior probability distribution of the density field and the likelihood function.

[0109] In an exemplary embodiment of the present invention, the sampling module 83 is further configured to: For each node, repeat the following operation until convergence to the posterior probability distribution of the density field or the maximum number of sampling rounds is reached: Sample from all density models of the posterior probability distribution of the density field, and keep the density of all nodes in the sampled density models unchanged except for the current node; In response to the target edge connected to the current node having a weight less than a preset weight, delete the target edge, and / or, in response to the absolute difference between the densities of the current node and the target node in the target density model being greater than a preset density difference, delete the edge connecting the target node and the current node. The target node is the node adjacent to the current node, and the target density model is the density model used as a sample.

[0110] In an exemplary embodiment of the present invention, the first determining module 84 is further configured to: The average density of the node corresponding to each voxel in multiple density field samples is used as the density value of the corresponding voxel.

[0111] In an exemplary embodiment of the present invention, the second determining module 85 is further configured to: All voxels with a density value less than a preset density value are treated as a voxel set; The region formed by connecting multiple voxels in a voxel set is considered a region of instability anomaly.

[0112] Each module in the aforementioned mountain stability assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0113] An exemplary embodiment of the present invention provides a computer device, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-described methods for assessing mountain stability.

[0114] An exemplary embodiment of the present invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of any of the above-described methods for assessing mountain stability. The non-transitory computer-readable storage medium may be a ROM, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0115] An exemplary embodiment of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described methods for assessing mountain stability.

[0116] refer to Figure 9 The following is a structural block diagram of a computer device that can be used as a reference for the present invention. The computer device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. The RAM 903 may also store various programs and data required for the operation of the computer device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0117] Multiple components in computer device 900 are connected to I / O interface 905, including: input unit 906, output unit 907, storage unit 908, and communication unit 909. Input unit 906 can be any type of device capable of inputting information to computer device 900. Input unit 906 can receive input numerical or character information and generate key signal inputs related to user settings and / or function control of computer device 900, and may include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 907 can be any type of device capable of presenting information, and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 908 may include, but is not limited to, hard disk and optical disk. Communication unit 909 allows computer device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0118] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as methods for assessing mountain stability. For example, in some embodiments, the method for assessing mountain stability can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the computer device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the method for assessing mountain stability described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform methods for assessing mountain stability by any other suitable means (e.g., by means of firmware).

[0119] The computer device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described mountain stability assessment method.

[0120] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered illustrative only, and the true scope and spirit of the invention are indicated by the claims.

[0121] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for assessing mountain stability, characterized in that, The assessment method is used to assess the stability of the mountain to which the grotto is attached, and the assessment method includes: The mountain is divided into multiple voxels; The adjacency structure diagram and density field posterior probability distribution of the mountain are constructed. The adjacency structure diagram is constructed using muon data recorded by muon detectors installed in the cave. The density field posterior probability distribution is the probability distribution of all density models of the mountain under the muon data and the adjacency structure diagram. Based on the adjacency structure diagram, multiple density field samples of the mountain are obtained by sampling from the posterior probability distribution of the density field; The density value of each voxel is obtained based on multiple density field samples. Based on the density values ​​of multiple voxels, the regions of instability in the mountain are obtained.

2. The method for assessing mountain stability according to claim 1, characterized in that, The construction of the adjacency structure map and density field posterior probability distribution of the mountain includes: The muon detector is used to record the muon data within a preset time period; Based on the muon data, determine the muon transmittance; The adjacent structure diagram is derived based on the point cloud data of the mountain and the muon transmittance.

3. The method for assessing mountain stability according to claim 2, characterized in that, The process of deriving the adjacency structure diagram based on the point cloud data of the mountain and the muon transmittance includes: Based on the point cloud data and the muon transmittance, a voxel map model of the mountain is obtained, wherein the voxel map model includes the initial density value of each voxel; Each voxel in the voxel graph model is treated as a node in the adjacency structure graph, wherein the attributes of the node include the initial density value; The connection between each two adjacent voxels in the voxel graph model is used as an edge connecting the two corresponding nodes in the adjacency structure graph. The two corresponding nodes refer to the nodes corresponding to the two adjacent voxels. Each edge corresponds to a weight, which is used to reflect the structural similarity between the two nodes connected by the edge. The greater the structural similarity, the greater the weight.

4. The method for assessing mountain stability according to claim 3, characterized in that, The weight is the product of the similarity weight of the main direction of geological structure, the joint density weight, and the aspect weight, or the weight is the weighted product of the similarity weight of the main direction of geological structure, the joint density weight, and the aspect weight.

5. The method for assessing mountain stability according to claim 2, characterized in that, The construction of the adjacency structure diagram and density field posterior probability distribution of the mountain also includes: Construct the prior probability distribution of the density field of the mountain, wherein the prior probability distribution of the density field is the initial probability distribution of all the density models constructed based on the adjacency structure graph; Based on the muon transmittance and muon statistical laws, a likelihood function is constructed, wherein the likelihood function represents the probability of observing the muon transmittance under all the density models; Based on the prior probability distribution of the density field and the likelihood function, the posterior probability distribution of the density field is constructed, wherein the posterior probability distribution of the density field is proportional to the product of the prior probability distribution of the density field and the likelihood function.

6. The method for assessing mountain stability according to claim 3, characterized in that, The step of sampling from the posterior probability distribution of the density field according to the adjacency structure graph to obtain multiple density field samples of the mountain includes: For each node, repeat the following operation until convergence to the posterior probability distribution of the density field or the maximum number of sampling rounds is reached: Sample from all the density models of the posterior probability distribution of the density field, and keep the density of the remaining nodes in the sampled density models unchanged except for the current node; In response to the target edge connected to the current node having a weight less than a preset weight, the target edge is deleted, and / or, in response to the absolute difference in density between the current node and the target node in the target density model being greater than a preset density difference, the edge connecting the target node and the current node is deleted, where the target node is a node adjacent to the current node, and the target density model is the density model used as a sample.

7. The method for assessing mountain stability according to claim 6, characterized in that, The step of obtaining the density value of each voxel based on multiple density field samples includes: The average density of the node corresponding to each voxel in the multiple density field samples is used as the density value of the corresponding voxel; and / or, The step of determining the stability anomaly region in the mountain based on the density values ​​of multiple voxels includes: All voxels whose density values ​​are less than a preset density value are taken as a voxel set; The region formed by connecting multiple voxels in the voxel set is defined as the stability anomalous region.

8. A device for assessing mountain stability, characterized in that, The assessment device is used to assess the stability of the mountain to which the grotto is attached, and the assessment device includes: The segmentation module is configured to divide the mountain into multiple voxels; The construction module is configured to construct the adjacency structure graph and density field posterior probability distribution of the mountain, wherein the adjacency structure graph is constructed using muon data recorded by muon detectors installed in the cave, and the density field posterior probability distribution is the probability distribution of all density models of the mountain under the muon data and the adjacency structure graph; The sampling module is configured to sample from the posterior probability distribution of the density field according to the adjacency structure graph to obtain multiple density field samples of the mountain. The first determining module is configured to obtain the density value of each voxel based on a plurality of density field samples; The second determining module is configured to determine the unstable region in the mountain based on the density values ​​of the plurality of voxels.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for evaluating mountain stability 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 the processor, it implements the steps of the method for evaluating mountain stability according to any one of claims 1 to 7.

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