Loose packing body destruction process simulation method and system combined with multiple physical fields

Abstract

The embodiment of the application provides a loose accumulation body destruction process simulation method and system combined with multiple physical fields, relates to the technical field of geological engineering and rock and soil mechanics, and first captures the traces of stress transmission field, pore medium seepage field and heat exchange field on the loose accumulation body, obtains a multiple physical field dynamic trace set; reversely models the multiple physical field coupling relationship based on the multiple physical field dynamic trace set, and obtains a reverse coupling model; then, imports physical parameters such as particle composition, pore distribution and mechanical response of the accumulation body into the reverse coupling model to obtain coupling evolution data; then, constructs a destruction evolution layered path according to the coupling evolution data; and finally, generates destruction process simulation data containing multiple-dimensional evolution information based on the destruction evolution layered path. The application can accurately simulate the destruction process of the loose accumulation body.

Description

Technical Field

[0001] This application relates to the fields of geological engineering and geotechnical mechanics, and more specifically, to a method and system for simulating the failure process of loose aggregates by combining multi-physics fields. Background Technology

[0002] In the fields of geological engineering and geotechnical mechanics, the stability study and failure process simulation of loose deposits have always been key and challenging topics. Loose deposits are widely found in nature, such as slope deposits, freeze-thawed loose bodies, and snow-deposited loose bodies in the high-altitude and cold mountainous areas of southeastern Tibet, my country. Their stability is affected by a variety of factors and is of great significance in engineering construction and geological disaster prevention.

[0003] Currently, simulation methods for the failure process of loose aggregates have many limitations. Traditional methods often focus on the influence of single physical fields, such as considering only the effect of stress transfer fields on the aggregate, or studying the effects of pore medium seepage fields or heat exchange fields in isolation. However, real-world loose aggregates exist in a complex multi-physics coupled environment, where stress transfer, pore medium seepage, and heat exchange fields are intertwined and interact, making single-physics field analysis unable to accurately reflect the actual failure process of the aggregate.

[0004] Furthermore, existing technologies often rely on pre-set assumptions when constructing simulation models, lacking accurate capture and reverse modeling of actual multiphysics dynamic interaction traces. This makes it difficult to obtain multiphysics coupling relationships that conform to reality, resulting in significant deviations between simulation results and actual damage processes. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a method and system for simulating the failure process of loosely packed bodies by combining multiphysics.

[0006] In conjunction with the first aspect of this application, a method for simulating the failure process of loosely packed bodies using multiphysics is provided, applied to a system for simulating the failure process of loosely packed bodies using multiphysics, the method comprising: The dynamic capture of the effects of multiple physical fields on loose aggregates, including stress transfer field, pore medium seepage field and heat exchange field, is used to obtain a set of dynamic effects of multiple physical fields based on the real-time state data of the loose aggregates. Based on the reverse modeling of the multiphysics coupling relationship using the set of dynamic action traces of multiphysics, a multiphysics reverse coupling model is obtained. The physical parameters of the loosely packed body are imported, including particle composition parameters, pore distribution parameters and mechanical response parameters. The physical parameters are then integrated into a multiphysics inverse coupling model to obtain coupling evolution data. Constructing a stratified path of failure evolution in loosely packed bodies based on coupling effect evolution data; Simulation data of the failure process of loosely packed bodies is generated based on the hierarchical failure evolution path, and the failure process simulation data contains multi-dimensional evolution information.

[0007] In conjunction with a second aspect of this application, a multiphysics-based simulation system for the failure process of loosely packed materials is provided. The multiphysics-based simulation system includes a machine-readable storage medium and a processor. The machine-readable storage medium stores machine-executable instructions. When the processor executes the machine-executable instructions, the multiphysics-based simulation system implements the aforementioned multiphysics-based simulation method for the failure process of loosely packed materials.

[0008] In conjunction with a third aspect of this application, a computer-readable storage medium is provided, wherein computer-executable instructions are stored therein, and when the computer-executable instructions are executed, the aforementioned method for simulating the failure process of loosely packed bodies incorporating multiphysics is implemented.

[0009] Combining any of the above aspects, by dynamically capturing the traces of multiphysics interactions on loosely packed bodies and obtaining a set of dynamic multiphysics interaction traces based on real-time state data, and then reverse-modeling the multiphysics coupling relationships based on this set, a multiphysics reverse coupling model is obtained. This model accurately reflects the actual coupling mechanism of multiphysics in loosely packed bodies, making the model closer to reality. By importing the physical parameters of the loosely packed bodies and integrating them into the reverse coupling model, coupling evolution data is obtained, further refining the interaction process between multiphysics and the characteristics of the packing bodies themselves. Based on the coupling evolution data, a hierarchical path for the failure evolution of loosely packed bodies is constructed, and failure process simulation data containing multi-dimensional evolution information is generated based on this path. This allows for a comprehensive simulation of the failure process of loosely packed bodies from multiple perspectives, accurately predicting its failure morphology and development trend. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained in conjunction with these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating the method for simulating the failure process of loosely packed bodies by combining multiphysics fields, as provided in this application embodiment. Detailed Implementation

[0012] Figure 1 This paper illustrates a flowchart of a multiphysics-based simulation method for the failure process of loosely packed bodies, as provided in an embodiment of this application. The detailed steps include: Step S110: Dynamically capture the traces of multi-physics fields formed on the loose aggregate. The multi-physics fields include stress transmission field, porous medium seepage field and heat exchange field. Based on the real-time state data of the loose aggregate, a set of dynamic traces of multi-physics fields is obtained.

[0013] In this embodiment, a loose deposit on a high-altitude mountain slope is selected as the unified application scenario. This loose deposit consists of rock particles, soil particles, and porous media of different sizes. Real-time status data is collected through a sensor network deployed at different spatial locations within the deposit, with a fixed time interval. The collected data includes particle displacement data, pore water pressure data, and temperature data. During the collection process, privacy-sensitive data such as particle displacement trajectories and pore water pressure distribution (if involving sensitive information related to mountain engineering construction or production safety) are processed using differential privacy technology. Noise data conforming to a preset noise distribution is added to the data to ensure that the data retains its analytical value without disclosing the original sensitive information. Based on the above real-time status data, traces of stress transfer field, pore media seepage field, and heat exchange field acting on the deposit are extracted. All traces are integrated into a multiphysics dynamic action trace set, which is stored in the form of a time series list. Each element in the list corresponds to multiphysics action trace data at a specific time point, and each trace data includes information such as the physical field type, the spatial range of action, and intensity characteristics.

[0014] Step S111: Based on the real-time state data of the loose aggregate, which records the physical state of the loose aggregate under natural conditions, extract the deformation traces formed by the stress transmission field acting on the surface and interior of the loose aggregate from the real-time state data. The deformation traces record the trajectory of particle displacement and structural deformation during stress transmission.

[0015] In this embodiment, real-time status data of loose deposits on high-altitude mountain slopes are used to filter out data related to particle displacement and structural deformation, including particle displacement, displacement direction, and structural strain values ​​at different spatial coordinate points. Spatiotemporal correlation analysis is performed on this data to extract deformation traces formed by the stress transfer field. These deformation traces are stored in a three-dimensional spatial region description format, containing information such as region boundary coordinates, mean and variance of particle displacement within the region, and distribution characteristics of structural strain. Each deformation trace corresponds to the result of a stress transfer event.

[0016] Step S112: Extract the seepage traces formed by the seepage field of the porous medium from the real-time status data. The seepage traces record the flow trajectory of the seepage medium inside the loose aggregate and the information on the changes in the pore structure.

[0017] In this embodiment, pore water pressure data, pore medium flow velocity data, and pore structure parameter variation data are extracted from real-time status data of loose deposits on high-altitude mountain slopes. Time series analysis and spatial distribution analysis are performed on the above data to extract seepage traces formed by the seepage field of the pore medium. These seepage traces are stored in the form of a combination of a three-dimensional flow path and a pore structure variation region, including the coordinates of the starting and ending points of the flow path, the velocity distribution characteristics along the path, and the range and degree of variation of pore structure parameters (such as porosity and pore connectivity).

[0018] Step S113: Extract temperature distribution traces formed by the heat exchange field from real-time status data. The temperature distribution traces record the temperature change trajectories of different regions of the loose aggregate during the heat transfer process.

[0019] In this embodiment, temperature data and temperature change rate data at different spatial coordinate points are selected from the real-time status data of loose deposits on slopes in high-altitude mountainous areas. By performing spatiotemporal clustering analysis on the above data, temperature distribution traces formed by the action of heat exchange fields are extracted. These temperature distribution traces are stored in the form of a three-dimensional temperature anomaly region description, including information such as the region boundary coordinates, the mean and variance of the temperature within the region, and the distribution characteristics of the temperature change rate.

[0020] Step S114: Perform time-series alignment processing on deformation traces, penetration traces, and temperature distribution traces, arrange the appearance order of each physical field action trace according to the time sequence, and generate a time-series action trace sequence.

[0021] In this embodiment, for deformation traces, seepage traces, and temperature distribution traces of loose deposits on slopes in high-altitude mountainous areas, the timestamp information corresponding to each trace is first extracted. Then, based on the timestamp, all traces are sorted in chronological order. During the sorting process, if multiple traces correspond to the same timestamp, they are arranged in the order of stress transfer field, pore medium seepage field, and heat exchange field. Finally, a time-series action trace sequence is generated. This sequence is stored in the form of a linear list, and each element in the list corresponds to a physical field action trace at a certain time point.

[0022] Step S115: Perform spatial location matching on each trace in the time-series action trace sequence to determine the overlapping area and associated position of traces of different physical fields in the space of the loosely packed body, and generate a spatially associated trace group.

[0023] In this embodiment, for the time-series of action traces on loose deposits on high-altitude mountain slopes, a three-dimensional spatial coordinate system is first established, and the spatial range of each trace is converted into a spatial region representation under this coordinate system. Then, the intersection of different trace spatial regions is calculated. If the volume of the intersection occupies a proportion of the volume of one of the trace spatial regions that reaches a preset threshold, then the two traces are considered to overlap in space and are grouped together. If the distance between two trace spatial regions (calculated with the coordinates of the region center) is less than a preset threshold, then the two traces are considered to be related in space and are grouped together. Finally, a spatially related trace group is generated. Each spatially related trace group contains traces from at least two different physical fields, as well as spatial overlap or related information between the traces.

[0024] Step S1151: Establish a three-dimensional spatial coordinate system for the loosely packed body, and convert each trace in the time-series action trace sequence into three-dimensional coordinate data according to its actual spatial location, generating a set of three-dimensional coordinates of the traces.

[0025] In this embodiment, for loose deposits on high-altitude mountain slopes, a three-dimensional rectangular coordinate system is established with a fixed reference point of the deposit as the origin. The X-axis is parallel to the horizontal extension direction of the deposit, the Y-axis is perpendicular to the X-axis and parallel to the horizontal direction, and the Z-axis is perpendicular to the horizontal plane. Then, the spatial location information (such as region boundary and center position) of each trace in the time-series of action traces is converted into three-dimensional coordinate data in this coordinate system. For example, the center position of a trace is converted into the form of (x-coordinate value, y-coordinate value, z-coordinate value). All converted three-dimensional coordinate data are integrated into a trace three-dimensional coordinate set, which is stored in a list format, where each element corresponds to the three-dimensional coordinate data of one trace.

[0026] Step S1152: Extract the three-dimensional coordinate boundary range of each trace, determine the minimum enclosing region of each trace in three-dimensional space, and generate a trace space range descriptor, which contains the vertex coordinates and volume information of the enclosing region.

[0027] In this embodiment, for the three-dimensional coordinate set of traces of loose deposits on a slope in a high-altitude mountainous area, for each trace, the minimum x-coordinate, maximum x-coordinate, minimum y-coordinate, maximum y-coordinate, minimum z-coordinate, and maximum z-coordinate of all its spatial coordinate points are extracted. Using these extreme values ​​as boundaries, the minimum enclosing cuboid region of the trace in three-dimensional space is determined. The vertex coordinates of the cuboid region (a total of eight vertices, the coordinates of each vertex are composed of the corresponding x, y, and z extreme values) and the volume (volume equals (maximum x-minimum x) multiplied by (maximum y-minimum y) multiplied by (maximum z-minimum z)) are calculated. The above information is integrated into a trace spatial range descriptor, with each trace corresponding to one descriptor, and the descriptor is stored in the form of structured data.

[0028] Step S1153: Traverse all traces in the temporalized action trace sequence, select one trace as the reference trace, and extract the spatial range descriptor and three-dimensional coordinate data of the reference trace.

[0029] In this embodiment, for the time-series of action traces of loose deposits on slopes in high-altitude mountainous areas, each trace is selected as a reference trace in the order of the sequence. When a trace is selected as a reference trace, the three-dimensional coordinate data corresponding to the reference trace is extracted from the three-dimensional coordinate set of the traces, and the spatial range descriptor corresponding to the reference trace is extracted from the spatial range descriptor set of the traces. The above information is temporarily stored for subsequent spatial matching analysis.

[0030] Step S1154: Compare the spatial range of the reference trace with the traces of other physical fields, calculate the intersection volume of the minimum enclosing region of the reference trace and the minimum enclosing region of other traces, and generate spatial overlap volume data.

[0031] In this embodiment, for the reference trace of loose deposits on a high-altitude mountain slope, the spatial intersection of its minimum enclosing region with the minimum enclosing region of other physical field traces is calculated. First, the overlap intervals of the two enclosing regions in the x, y, and z directions are determined. The overlap interval in the x direction is the maximum of the minimum x value of the reference trace and the minimum x value of the other traces, extending to the minimum of the maximum x value of the reference trace and the maximum x value of the other traces. The calculation method for the overlap intervals in the y and z directions is similar. If the starting value of the overlap interval in a certain direction is greater than the ending value, it is considered that the two enclosing regions do not overlap in that direction, and the intersection volume is zero; otherwise, the intersection volume is equal to (x overlap interval length) multiplied by (y overlap interval length) multiplied by (z overlap interval length). The calculated intersection volume data is recorded to generate spatial overlap volume data.

[0032] Step S1155: Set spatial overlap judgment criteria. When the spatial overlap volume data of two traces reaches the set overlap threshold, it is determined that there is a spatial overlap relationship between the two and they are recorded as candidate associated trace pairs.

[0033] In this embodiment, for the spatial overlap volume data of loose deposits on high-altitude mountain slopes, an overlap threshold is set as a certain proportion of the minimum enclosed area volume of the reference trace (this proportion is preset according to the structural characteristics of the deposits and the analysis requirements). For each pair of reference traces and other physical field traces, if their spatial overlap volume data is greater than or equal to the overlap threshold, it is determined that there is a spatial overlap relationship between the two. The pair of these two traces is recorded as a candidate associated trace pair, which is stored in a structured data format containing two trace identifiers and spatial overlap volume data.

[0034] Step S1156: Calculate the spatial distance between candidate associated trace pairs, measure the straight-line distance between the center coordinates of the two traces, and generate center distance data.

[0035] In this embodiment, for candidate associated trace pairs of loose deposits on high-altitude mountain slopes, the center coordinate data of each trace is extracted (the center coordinates are the geometric center coordinates of the smallest enclosed area of ​​the trace, i.e., ((maximum x + minimum x) / 2, (maximum y + minimum y) / 2, (maximum z + minimum z) / 2)). Then, the straight-line distance between the two center coordinates is calculated by taking the square root of (square of the difference between x coordinates + square of the difference between y coordinates + square of the difference between z coordinates). The calculated distance data is recorded to generate center distance data.

[0036] Step S1157: Analyze the temporal consistency of candidate associated trace pairs, confirm whether the occurrence time of the two traces is within the preset temporal association window, and eliminate false associated trace pairs with spatiotemporal misalignment.

[0037] In this embodiment, for candidate associated trace pairs of loose deposits on high-altitude mountain slopes, the occurrence timestamp information of each trace is extracted, and the difference (absolute value) between the two timestamps is calculated. If the difference is less than or equal to the preset time association window length, the two traces are considered to be temporally consistent; otherwise, they are considered not temporally consistent. Candidate associated trace pairs that are not temporally consistent are excluded, and candidate associated trace pairs that are temporally consistent are retained.

[0038] Step S1158: The trace pairs that pass the spatial overlap determination, center distance verification, and time sequence consistency verification are identified as valid associated trace pairs, and the physical field type and spatial coordinate information of the valid associated trace pairs are recorded.

[0039] In this embodiment, for candidate associated trace pairs on loose deposits on high-altitude mountain slopes, after spatial overlap determination (intersection volume reaches a threshold), center distance verification (center distance less than a preset threshold), and time sequence consistency verification (timestamp difference less than the time association window length), the trace pairs that meet the conditions are determined as valid associated trace pairs. Information such as the physical field type (e.g., stress transfer field, porous media seepage field), center coordinate data, and spatial overlap volume data of the two traces in each valid associated trace pair is recorded to generate a list of valid associated trace pairs.

[0040] Step S1159: Perform cluster analysis on all valid associated trace pairs, merge valid associated trace pairs containing common traces into a set, and identify clusters of multiple traces that are spatially related.

[0041] In this embodiment, a hierarchical clustering method is used to perform cluster analysis on the list of valid associated trace pairs in loose deposits on high-altitude mountain slopes. First, each valid associated trace pair is treated as an initial cluster. Then, the similarity between different clusters is calculated (using indicators such as the mean spatial distance of traces within the cluster and the matching degree of physical field types). The two clusters with the highest similarity are merged. This process is repeated until the similarity between clusters is below a preset threshold. Through cluster analysis, clusters containing multiple traces (at least two traces from different physical fields) are identified, and the traces in each cluster are spatially associated.

[0042] Step S11510: Integrate the traces in each cluster into a spatially associated trace group. Each spatially associated trace group contains traces from at least two different physical fields, and there is a definite spatial overlap or proximity relationship between the traces, generating a complete set of spatially associated trace groups.

[0043] In this embodiment, based on the clustering results of loose deposits on slopes in high-altitude mountainous areas, the traces in each cluster are integrated into a spatially associated trace group. Each spatially associated trace group includes the identifiers of all traces within the cluster, physical field types, spatial location information (center coordinates, minimum enclosing region), and descriptions of spatial overlap or proximity relationships between traces. All spatially associated trace groups are integrated into a spatially associated trace group set, which is stored in list form, with each element in the list corresponding to a spatially associated trace group.

[0044] Step S116: Extract the morphological information and change trajectory of each trace in each spatially associated trace group. The morphological information includes the shape, range and distribution density of the trace, and the change trajectory includes the expansion and strengthening process of the trace over time.

[0045] In this embodiment, for a spatially correlated trace set of loose deposits on a high-altitude mountain slope, the morphological information of each trace in each spatially correlated trace set is extracted, including the shape description of the trace (e.g., approximately cuboid, irregular shape), spatial range (volume of the smallest enclosed area), and distribution density (ratio of the number of data collection points within the trace area to the volume of the area). Simultaneously, the trajectory information of the trace is extracted, including the spatial range expansion of the trace over time (e.g., rate of change of volume) and the enhancement of intensity characteristics (e.g., the mean rate of change of particle displacement). This information is integrated into morphological and trajectory data, with each spatially correlated trace set corresponding to a set of morphological and trajectory data.

[0046] Step S117: Associate the morphological information and change trajectory with the typical action information of each physical field, label the physical field type and action mode corresponding to each trace, and generate a set of physical field-identified traces.

[0047] In this embodiment, for the morphology and change trajectory data of loose deposits on high-altitude mountain slopes, a typical action information database of each physical field is pre-established. This database includes typical morphological characteristics of stress transfer fields, porous media seepage fields, and heat exchange fields (e.g., stress transfer field traces are mostly strip-shaped, and porous media seepage field traces are mostly dendritic), and typical change trajectory characteristics (e.g., stress transfer field traces expand rapidly, and heat exchange field traces expand slowly). The morphological information and change trajectory of each trace are matched with the typical action information in the database. If the matching degree reaches a preset threshold, the physical field type and action mode corresponding to the trace are labeled (e.g., compressive stress in stress transfer fields, and positive seepage in porous media seepage fields), generating a set of physical field-identified traces. This set contains all labeled trace data.

[0048] Step S118: Filter out abnormal traces in the physical field-identified trace set, remove non-physical field action traces caused by environmental interference, and retain valid traces that reflect the multi-physical field action.

[0049] In this embodiment, for the set of physical field-identified traces of loose deposits on high-altitude mountain slopes, pre-defined criteria for identifying abnormal traces are established. These criteria include: the spatial range of the trace is too small (less than a preset minimum value); the trajectory of change does not conform to the laws of physical field action (e.g., the rate of change exceeds the reasonable range of physical field action); and there is no connection with other traces. Each trace is evaluated, and if it meets the criteria for an abnormal trace, it is removed from the set. Traces that meet the criteria for valid traces are retained, generating a set of valid traces.

[0050] Step S119: Classify and integrate the effective traces according to the physical field type and the timing of their action to form a subset of field-sequence traces.

[0051] In this embodiment, for the effective trace set of loose deposits on slopes in high-altitude mountainous areas, the traces are first divided into a stress transfer field trace subset, a porous medium seepage field trace subset, and a heat exchange field trace subset according to the physical field type. Then, the traces in each subset are sorted according to the action time sequence (time stamp) to form a sub-field time sequence trace subset. Each sub-field time sequence trace subset is stored in the form of a time sequence list, and each element in the list corresponds to the effective trace data of the physical field at a certain time point.

[0052] Step S1110: Summarize the time sequence trace subsets of each subfield to obtain a set of multi-physics field dynamic action traces that includes stress transmission field action traces, porous medium seepage field action traces, and heat exchange field action traces.

[0053] In this embodiment, for the sub-field time-series trace subsets of loose deposits on high-altitude mountain slopes, the stress transfer field trace subset, the porous medium seepage field trace subset, and the heat exchange field trace subset are summarized. The traces of different physical fields are integrated into the same time series table according to the timestamp to form a multi-physical field dynamic action trace set. Each time point element of this set contains the effective trace data of all physical fields at that time point.

[0054] Step S120: Based on the set of dynamic action traces of multiphysics fields, reverse model the coupling relationship of multiphysics fields to obtain the multiphysics field reverse coupling model.

[0055] In this embodiment, a reverse modeling method is used to construct the multiphysics coupling relationship for a set of multiphysics dynamic action traces of loose deposits on a high-altitude mountain slope. First, feature extraction is performed on the trace data in the set to obtain the action characteristic parameters of each physical field. Then, the correlation between the action characteristic parameters of different physical fields is analyzed, including correlation strength, correlation direction, and correlation timing. Finally, a multiphysics reverse coupling model is constructed based on these correlations. This model is represented in the form of a directed graph, where nodes represent the action state of physical fields, edges represent the coupling relationship between physical fields, and the attributes of the edges include information such as coupling strength, coupling direction, and action delay.

[0056] Step S121: Perform feature analysis on the stress transfer field action traces in the multiphysics dynamic action trace set, and extract the evolution rate and influence range information of the stress transfer field action traces. The evolution rate records the process of the traces expanding over time, and the influence range information records the spatial range covered by the traces.

[0057] In this embodiment, for stress transfer field action traces in a multiphysics dynamic action trace set of loose deposits on high-altitude mountain slopes, the timestamp information and spatial range information (volume of the minimum enclosing region) of each trace are extracted. The evolution rate of the trace is obtained by calculating the ratio of the change in the spatial range of the trace at adjacent time points to the time interval; the influence range information of the trace is obtained by statistically analyzing the volume and spatial coordinate range of the minimum enclosing region of the trace. The evolution rate and influence range information are integrated into stress transfer field action feature data, with each stress transfer field trace corresponding to a set of feature data.

[0058] Step S122: Perform feature analysis on the porosity medium seepage field action traces in the multiphysics dynamic action trace set, and extract the seepage depth and diffusion distribution information of the porosity medium seepage field action traces. The seepage depth records the extension process of the traces inside the loose accumulation body, and the diffusion distribution information records the spatial state of the trace distribution.

[0059] In this embodiment, for the porous media seepage field action traces in the multiphysics dynamic action trace set of loose deposits on high-altitude mountain slopes, the spatial coordinate information (such as coordinate values ​​along the depth direction of the deposit) and spatial distribution characteristics (such as the variance of seepage velocity distribution within the trace area) of each trace are extracted. The seepage depth is obtained by calculating the maximum extension distance of the trace along the depth direction; the diffusion distribution information (such as uniform distribution or concentrated distribution) is obtained by analyzing the uniformity of the seepage velocity distribution within the trace area. The seepage depth and diffusion distribution information are integrated into porous media seepage field action characteristic data, with each porous media seepage field trace corresponding to a set of characteristic data.

[0060] Step S123: Perform feature analysis on the heat exchange field action traces in the multiphysics dynamic action trace set, and extract the temperature gradient and transmission attenuation information of the heat exchange field action traces. The temperature gradient records the gradient difference of temperature change inside the trace, and the transmission attenuation information records the attenuation process of heat as the transmission distance increases.

[0061] In this embodiment, for the heat exchange field action traces in the multiphysics dynamic action trace set of loose deposits on high-altitude mountain slopes, temperature distribution data (such as temperature values ​​at different coordinate points within the region) and spatial distance data (such as the distance from the coordinate point to the center of the trace) are extracted for each trace. The temperature gradient is obtained by calculating the ratio of the difference in temperature values ​​between adjacent coordinate points to the difference in distance; the transmission attenuation information is obtained by analyzing the trend of temperature value changes with distance (such as linear decay or exponential decay). The temperature gradient and transmission attenuation information are integrated into heat exchange field action feature data, with each heat exchange field trace corresponding to a set of feature data.

[0062] Step S124: Based on the characteristic parameters of the three physical field action traces, locate the correlation points between the different physical field action traces. The correlation points record the overlapping nodes of one physical field action trace and another physical field action trace in spatial location and time sequence.

[0063] In this embodiment, for the three physical field characteristics data of loose deposits on high-altitude mountain slopes, the spatial center coordinates and timestamp information of each trace are first extracted. For any two traces of different physical fields, if the distance between their spatial center coordinates is less than a preset threshold and the difference in their timestamps is less than a preset time window length, then the two traces are considered to overlap in spatial location and time sequence, and the spatial center coordinates and timestamps are used as association points. All association points are integrated into an association point set, and each association point contains information such as the type of the two physical fields, spatial coordinates, and timestamps.

[0064] Step S125: Calculate the correlation of characteristic parameters of each physical field action trace at the correlation point, generate mutual influence data between different physical field action intensities, and obtain quantitative data of correlation intensity.

[0065] In this embodiment, for the set of associated points of loose deposits on a high-altitude mountain slope, for each associated point, the intensity characteristic parameters of the effects of two corresponding physical field traces are extracted (such as the strain value of the stress transfer field and the seepage velocity of the porous medium seepage field). The correlation coefficient between the two characteristic parameters (such as the Pearson correlation coefficient) is calculated to obtain the correlation of the characteristic parameters; the larger the absolute value of the correlation coefficient, the stronger the correlation. The correlation coefficient is used as the quantitative data of the correlation intensity, and each associated point corresponds to one quantitative data point of correlation intensity.

[0066] Step S126: Generate interaction direction data between physical fields based on the correlation intensity quantification data, record the promoting or inhibiting relationship between the action of one physical field and the action of another physical field, and obtain interaction direction determination data.

[0067] In this embodiment, for the correlation intensity quantification data of loose deposits on high-altitude mountain slopes, if the correlation intensity quantification data (correlation coefficient) is positive, it is considered that the effect of one physical field has a promoting relationship with the effect of another physical field; if the correlation intensity quantification data is negative, it is considered that there is an inhibitory relationship. The above relationship is recorded as action direction data, with each correlation point corresponding to one action direction data, and the action direction data is stored in the form of a binary identifier (such as "promoting" or "inhibiting").

[0068] Step S127: Based on the distribution of associated points, quantitative data of associated strength, and data on the direction of action, reconstruct the coupling path between multiple physical fields. The coupling path records the transmission route of different physical fields interacting through associated points.

[0069] In this embodiment, a path reconstruction algorithm is used to construct coupling paths between multiple physics fields based on the distribution of associated points, quantified data of associated strength, and determination data of the direction of action of loose deposits on slopes in high-altitude mountainous areas. First, associated points are used as path nodes; then, the connection weights between nodes are determined based on the quantified data of associated strength (the greater the associated strength, the higher the weight), and the connection directions are determined based on the determination data of the direction of action (e.g., from the stress transfer field node to the pore medium seepage field node, it indicates the effect of the stress transfer field on the pore medium seepage field); finally, the coupling path is reconstructed by finding connection sequences with higher weights. This path is represented in the form of a directed weighted graph, where nodes are associated points, edges are the transmission routes between physics fields, the weight of the edges is the quantified data of associated strength, and the direction of the edges is the direction of action.

[0070] Step S128: Perform time consistency processing on the reconstructed coupling path to keep the evolution of the coupling path synchronized with the time changes of the multiphysics field action traces, and obtain the time-calibrated coupling path.

[0071] In this embodiment, for the reconstruction coupling path of loose deposits on a high-altitude mountain slope, the timestamp information of each node in the path and the temporal change information of the multiphysics field interaction traces are extracted. By adjusting the temporal order of the path nodes, the evolution order of the coupling path is made consistent with the temporal change order of the multiphysics field interaction traces; at the same time, the weights of the path edges are adjusted over time so that the trend of weight change is consistent with the trend of intensity change of the multiphysics field interaction traces, thus obtaining the temporally calibrated coupling path.

[0072] Step S129: Based on the timing-calibrated coupling path, set the core parameters of the coupling model, including the transmission efficiency parameter and the action delay parameter of the coupling path, and construct the initial reverse coupling model.

[0073] In this embodiment, core parameters of the coupling model are defined for the time-calibrated coupling path of loose deposits on a high-altitude mountain slope. The transfer efficiency parameter is determined based on the weights of the edges of the coupling path (the higher the weight, the larger the transfer efficiency parameter); the action delay parameter is determined based on the difference in timestamps between the nodes of the coupling path (the larger the difference in timestamps, the larger the action delay parameter). Based on these core parameters, an initial reverse coupling model is constructed. This model is represented by a combination of mathematical equations, where each equation corresponds to a coupling relationship between physical fields. The equations include the transfer efficiency parameter, the action delay parameter, etc.

[0074] Step S1210: Iteratively optimize the initial inverse coupling model using subsequent trace data from the multiphysics dynamic action trace set, adjust the core parameters to match the coupling relationship output by the model with the actual trace information, and obtain the final multiphysics inverse coupling model.

[0075] In this embodiment, for the initial reverse coupling model of loose deposits on a high-altitude mountain slope, subsequent trace data from the multiphysics dynamic action trace set are input into the model to obtain the coupling relationship prediction result output by the model. The prediction result is compared with the actual coupling relationship information in the subsequent trace data to calculate the prediction error (such as the difference between the predicted coupling strength and the actual coupling strength). The core parameters of the model (such as the transmission efficiency parameter and the action delay parameter) are adjusted according to the prediction error, and the above process is repeated until the prediction error is less than a preset threshold to obtain the final multiphysics reverse coupling model.

[0076] Step S12101: From the multiphysics dynamic action trace set, separate the early trace data that has been used for modeling and the unused subsequent trace data, and determine the time range of the subsequent trace data and the corresponding physical field action characteristics.

[0077] In this embodiment, for the multiphysics dynamic action trace set of loose deposits on a high-altitude mountain slope, the set is divided into early trace data and later trace data based on timestamp information. The early trace data is used to construct the initial inverse coupling model, and the later trace data is used for model optimization. The time range of the later trace data is determined (e.g., from a certain timestamp to the last timestamp of the set), and the physical field action characteristics corresponding to the later trace data are extracted, including physical field type, action intensity, and action spatial range.

[0078] Step S12102: Classify the subsequent trace data according to the physical field type to generate subsequent stress transfer field trace subsets, subsequent porous medium seepage field trace subsets, and subsequent heat exchange field trace subsets.

[0079] In this embodiment, the subsequent trace data of loose deposits on slopes in high-altitude mountainous areas are divided into a subset of subsequent stress transfer field traces, a subset of subsequent porous media seepage field traces, and a subset of subsequent heat exchange field traces according to the type of physical field. Each subset contains subsequent trace data of the corresponding physical field.

[0080] Step S12103: Import the subsequent stress transfer field trace subset, the subsequent porous medium seepage field trace subset, and the subsequent heat exchange field trace subset into the initial reverse coupling model in sequence, start the model operation to generate the corresponding simulated coupling relationship data, and the simulated coupling relationship data records the interaction relationship between the various physical fields predicted by the model.

[0081] In this embodiment, the subsets of subsequent stress transfer field traces, subsequent porous media seepage field traces, and subsequent heat exchange field traces of loose deposits on high-altitude mountain slopes are sequentially input into the initial reverse coupling model. Based on the input trace data, the model generates simulated coupling relationship data through internal mathematical equations. This data includes information such as the predicted correlation points, correlation strength, and direction of action between physical fields.

[0082] Step S12104: Extract key coupling parameters from the simulated coupling relationship data. The key coupling parameters include the transmission efficiency parameter and the action delay parameter of the coupling path, and generate a simulated coupling parameter set.

[0083] In this embodiment, for the simulated coupling relationship data of loose deposits on slopes in high-altitude mountainous areas, key coupling parameters are extracted, including the transmission efficiency parameters of the coupling path (such as the action transmission efficiency value predicted by the model) and the action delay parameters (such as the action delay time value predicted by the model). The above parameters are integrated into a set of simulated coupling parameters.

[0084] Step S12105: Analyze the actual physical field coupling characteristics from the subsequent trace data, and calculate the actual coupling parameter set based on the spatiotemporal correlation and intensity change of the actual traces. The parameter type of the actual coupling parameter set is consistent with that of the simulated coupling parameter set.

[0085] In this embodiment, for the subsequent trace data of loose deposits on high-altitude mountain slopes, the spatiotemporal correlation of the actual traces (such as the spatial overlap and temporal synchronization of traces from different physical fields) and intensity changes (such as the trend of trace intensity changes) are analyzed. The actual coupling parameters are calculated, including the transmission efficiency parameter (such as the actual action transmission efficiency value, which is calculated by the intensity change of the actual trace and the action time) and the action delay parameter (such as the actual action delay time value, which is calculated by the difference of the timestamps of the actual traces). The actual coupling parameter set is generated, and the parameter types of this set are consistent with the simulated coupling parameter set.

[0086] Step S12106: Compare the simulated coupling parameter set with the actual coupling parameter set, calculate the difference value of each parameter, and generate a parameter difference matrix. The parameter difference matrix records the deviation process between the simulated value and the actual value.

[0087] In this embodiment, for the simulated coupling parameter set and the actual coupling parameter set of the loose deposits on the slope of a high-altitude mountainous area, for each parameter, the difference between the simulated value and the actual value is calculated (difference value). The difference values ​​of all parameters are integrated into a parameter difference matrix, which is stored in the form of a two-dimensional table. The rows represent the parameter type, the columns represent the time points, and the table elements are the difference values ​​of the corresponding parameters at the corresponding time points.

[0088] Step S12107: Based on the parameter difference matrix, identify the core parameters that need to be adjusted and the direction of adjustment, set the adjustment range for the parameters corresponding to the difference values, and generate a parameter adjustment plan.

[0089] In this embodiment, for the parameter difference matrix of loose deposits on a high-altitude mountain slope, if the absolute value of the difference of a certain parameter is greater than a preset threshold, then the parameter is considered to need adjustment. The adjustment direction is determined by the sign of the difference value (a positive difference value indicates that the simulated value is greater than the actual value, and the parameter needs to be decreased; a negative difference value indicates that the simulated value is less than the actual value, and the parameter needs to be increased). The adjustment range is determined by the magnitude of the absolute value of the difference value (the larger the absolute value, the larger the adjustment range). A parameter adjustment plan is generated, which includes information such as the name of the parameter to be adjusted, the adjustment direction, and the adjustment range.

[0090] Step S12108: Modify the core parameters of the initial reverse coupling model according to the parameter adjustment scheme to generate the adjusted intermediate coupling model.

[0091] In this embodiment, for the initial reverse coupling model of loose deposits on a high-altitude mountain slope, the core parameters of the model (such as the transmission efficiency parameter and the action delay parameter) are modified according to the parameter adjustment scheme to generate the adjusted intermediate coupling model.

[0092] Step S12109: Import the subsequent trace data into the intermediate coupling model again, and repeat the simulation, analysis, comparison and adjustment process until the difference between the simulated coupling parameter set and the actual coupling parameter set is within the preset reasonable range.

[0093] In this embodiment, for the intermediate coupling model of loose deposits on a high-altitude mountain slope, subsequent trace data is input into the model again to generate new simulated coupling relationship data. The actual coupling parameter set is analyzed, and the simulated and actual parameter sets are compared to calculate the difference value. If the difference value is still greater than a preset reasonable range, the parameters are adjusted according to the new difference value, and the above process is repeated; if the difference value is within a reasonable range, the adjustment is stopped.

[0094] Step S121010: Record the final core parameter configuration, solidify the coupling path and action relationship description of the model, and obtain the multiphysics reverse coupling model that reflects the actual coupling state of the multiphysics field.

[0095] In this embodiment, for the adjusted intermediate coupling model of loose deposits on a high-altitude mountain slope, when the difference value is within a preset reasonable range, the final core parameter configuration of the model (including the specific values ​​of transmission efficiency parameters, action delay parameters, etc.) is recorded, and the coupling path and action relationship description of the model are solidified (such as the structure of the coupling path, the direction and intensity of the action between physical fields), thus obtaining the final multiphysics reverse coupling model.

[0096] Step S130: Import the physical parameters of the loose aggregate, which include particle composition parameters, pore distribution parameters and mechanical response parameters. Integrate the physical parameters into the multiphysics reverse coupling model to obtain coupling evolution data.

[0097] In this embodiment, for loose deposits on high-altitude mountain slopes, physical parameters are collected, including particle composition parameters (such as particle size distribution and particle shape characteristics), pore distribution parameters (such as porosity and pore connectivity), and mechanical response parameters (such as particle compressive strength and internal friction angle of the deposit). These physical parameters are input into a multiphysics inverse coupling model. The model adjusts its internal coupling relationship calculation logic based on the physical parameters to generate coupling evolution data. This data contains information such as the intensity, range, and direction of multiphysics coupling at different time points, and is stored in the form of a time-series table.

[0098] Step S140: Construct a stratified path for the failure evolution of loose aggregates based on the coupling effect evolution data.

[0099] In this embodiment, the coupling evolution data of loose deposits on high-altitude mountain slopes are analyzed to assess the impact of coupling effects at different time points on the deposit structure. This includes changes at the micro-particle level (such as particle displacement and collision) and changes at the macro-structural level (such as crack propagation and overall displacement). A hierarchical analysis method is used to construct sub-paths for failure evolution at the micro-particle level and the macro-structural level. These two sub-paths are then integrated into a hierarchical failure evolution path, stored in a tree structure. The root node represents the initial stable state, the child nodes represent different stages of failure evolution, and the leaf nodes represent the final failure state.

[0100] Step S141: Extract the action response data at the micro-particle level of the loosely packed body from the coupling evolution data. The action response data at the micro-particle level records the motion and interaction state of a single particle or particle cluster under the coupling of multiple physics fields.

[0101] In this embodiment, data related to the motion and interaction of individual particles or particle clusters are selected from the coupling evolution data of loose deposits on high-altitude mountain slopes. This includes particle displacement, displacement direction, velocity, acceleration, collision force between particles, and changes in cohesion. This data is then integrated into microscopic particle-level interaction response data, stored in a time-series table where each element corresponds to a specific time point in the microscopic particle interaction response information.

[0102] Step S142: Based on the action response data at the micro-particle level, track the displacement trajectory and force changes of each particle, record the changes in contact state and adhesion relationship between particles, and generate micro-particle evolution trajectory data.

[0103] In this embodiment, for the action response data of the micro-particle level of loose deposits on high-altitude mountain slopes, for each particle, its displacement, displacement direction, and stress conditions (such as stress, seepage force, and thermal force) at different time points are extracted. By performing time-series analysis on the above data, the displacement trajectory of the particles (such as the time-series curve of displacement) and the stress changes (such as the time-series curve of stress) are tracked; at the same time, changes in the contact state between particles (such as from contact to separation, from separation to contact) and changes in the bonding relationship (such as the increase or decrease of bonding force, and the breakage of bonding) are recorded, generating micro-particle evolution trajectory data. This data is stored in the form of a trajectory list for each particle, and each element in the list corresponds to the particle state information at a time point.

[0104] Step S143: Extract the action response data at the macroscopic structural level of the loose aggregate from the coupling evolution data. The action response data at the macroscopic structural level records the overall morphological changes and structural integrity changes of the loose aggregate.

[0105] In this embodiment, data related to the overall morphology and structural integrity of loose deposits on high-altitude mountain slopes are selected from the coupling effect evolution data. These data include the overall displacement, overall strain value, number and length of cracks, and structural stability coefficient of the deposits. This data is then integrated into macroscopic structural action response data, stored in a time-series list where each element corresponds to a specific time point in the macroscopic structural action response information.

[0106] Step S144: Based on the action response data at the macroscopic structural level, track the overall deformation trajectory of the loose aggregate and the formation process of structurally weak areas to generate macroscopic structural evolution characteristic data.

[0107] In this embodiment, for the action response data of the macroscopic structural level of loose deposits on high-altitude mountain slopes, data such as the displacement and strain values ​​of the entire deposit are extracted, and the overall deformation trajectory is tracked (such as the time-series variation curve of the overall displacement and the time-series variation curve of the overall strain). At the same time, data such as the number and length of cracks and stability coefficients are extracted, and the formation process of weak areas in the structure is analyzed (such as the starting position, extension direction, and length change of cracks). Macroscopic structural evolution characteristic data is generated, which is stored in the form of a time series list, where each element corresponds to the macroscopic structural evolution characteristic information at a certain time point.

[0108] Step S145: Perform correlation analysis between microscopic particle evolution trajectory data and macroscopic structural evolution characteristic data to generate data on the driving relationship between microscopic particle motion and macroscopic structural changes, thus obtaining microscopic-macroscopic correlation data.

[0109] In this embodiment, a correlation analysis algorithm is used to analyze the relationship between the microscopic particle evolution trajectory data and the macroscopic structural evolution characteristic data of loose deposits on high-altitude mountain slopes. For example, the temporal synchronicity between large-scale microscopic particle displacement and macroscopic structural crack propagation is analyzed. If the large-scale microscopic particle displacement occurs earlier than the macroscopic structural crack propagation, and their spatial locations are consistent, then the microscopic particle movement is considered to have a driving relationship with macroscopic structural changes. This driving relationship is integrated into microscopic-macroscopic correlation data, which is stored in the form of a list of correlation rules. Each rule contains information such as microscopic particle movement characteristics, macroscopic structural change characteristics, and correlation strength.

[0110] Step S146: Based on the correlation data between micro and macro, construct a sub-path of destruction evolution at the micro-particle level. The sub-path of destruction evolution at the micro-particle level records the evolution process from a stable state to an unstable state at the particle level.

[0111] In this embodiment, for the micro- and macro-level correlation data of loose deposits on high-altitude mountain slopes, association rules related to micro-particle instability are extracted. The evolution process of particles from a stable state (e.g., zero displacement, force equilibrium) to an unstable state (e.g., displacement exceeding a preset threshold, force imbalance) is analyzed, including the initiation time of instability, the triggering factors of instability (e.g., the intensity of multi-physics coupling reaching a threshold), and the propagation process of instability (e.g., the instability of a single particle triggers the instability of surrounding particles). Based on these analyses, a sub-path of failure evolution at the micro-particle level is constructed. This sub-path is stored in the form of a time-series node list, with each node corresponding to a particle instability event.

[0112] Step S1461: Extract the initial stable state parameters of micro particles from the micro-macro correlation data. The initial stable state parameters record the position, force and contact state of the particles when they are not significantly affected by multi-physics fields.

[0113] In this embodiment, for the micro- and macro-correlation data of loose deposits on high-altitude mountain slopes, micro-particle data with multiphysics field intensity less than a preset threshold are selected. Initial stability state parameters of the particles are extracted, including the spatial coordinates of the particle, the magnitude and direction of the forces acting on it (such as gravity and interparticle adhesion), and the contact state with surrounding particles (such as the number of contacting particles and their locations). These parameters are integrated into an initial stability state parameter set, with each particle corresponding to a set of initial stability state parameters.

[0114] Step S1462: Based on the initial steady state parameters, define the stable equilibrium interval for each particle. The stable equilibrium interval records the force range and position fluctuation range of the particle when it maintains a stable state.

[0115] In this embodiment, for the initial stability state parameter set of loose deposits on a high-altitude mountain slope, for each particle, a stable equilibrium interval is defined based on its initial stress condition (e.g., the magnitude of the stress is within ± a preset ratio of the initial stress value); and a stable equilibrium interval for position fluctuation is defined based on its initial position condition (e.g., the position coordinates are within ± a preset value of the initial position coordinates). These intervals are then integrated into a stable equilibrium interval for each particle.

[0116] Step S1463: Extract dynamic force data of multi-physics coupling applied to particles from micro-macro correlation data. The dynamic force data records the magnitude and direction changes of the forces acting on particles by the stress transmission field, the pore medium seepage field and the heat exchange field.

[0117] In this embodiment, for the micro- and macro-correlation data of loose deposits on high-altitude mountain slopes, micro-particle data with multi-physics field intensity greater than or equal to a preset threshold are selected. Dynamic force data is extracted, including the magnitude and direction changes of forces applied by the stress transmission field (such as compressive stress and tensile stress), forces applied by the pore medium seepage field (such as seepage thrust), forces applied by the heat exchange field (such as thermal expansion force), and the magnitude and direction changes of the resultant force of these forces. The above data is integrated into a dynamic force dataset, with each particle corresponding to a set of dynamic force data.

[0118] Step S1464: Track the changes in dynamic force data over time, mark the time points when the force on each particle exceeds the stable equilibrium range, and take the time points as the starting points of particle instability.

[0119] In this embodiment, for the dynamic force dataset of loose deposits on a high-altitude mountain slope, the dynamic force of each particle is tracked over time. If the force on a particle exceeds the stable equilibrium range at a certain moment, that moment is marked as the instability initiation point of the particle. The instability initiation points of all particles are then integrated into an instability initiation point list.

[0120] Step S1465: Record the displacement change and force adjustment process of the particle from the instability initiation point, and mark the motion mode change of the particle during the instability process. The motion mode change records the change of the particle from a stationary state to a sliding or rolling state.

[0121] In this embodiment, for the instability initiation point of loose aggregate particles on a high-altitude mountain slope, the displacement amount, displacement direction changes, and force magnitude and direction adjustment process of the particles from the instability initiation point are recorded. By analyzing the characteristics of displacement changes (such as the continuity of displacement and changes in direction), the change in particle motion mode is marked, such as from a static state (zero displacement) to a sliding state (displacement direction unchanged, displacement continuously increasing) or a rolling state (displacement direction changes periodically, displacement continuously increasing). The above information is integrated into particle instability process data.

[0122] Step S1466: Monitor the interaction between unstable particles and surrounding particles, record the occurrence process of collisions, compression and bonding fracture between particles, and generate a sequence of particle interaction events.

[0123] In this embodiment, for unstable particles in loose deposits on high-altitude mountain slopes, the interaction between these particles and surrounding particles is monitored, including collision events (such as spatial overlap of particles), compression events (such as increased force exerted by particles on surrounding particles), and bond fracture events (such as reduced bonding force between particles to zero). Information such as the occurrence time of these events, the particle identifiers involved, and the type of event is recorded to generate a particle interaction event sequence.

[0124] Step S1467: Based on the particle interaction event sequence, define the influence range and process of each particle after instability on surrounding particles, and generate particle influence diffusion data.

[0125] In this embodiment, for the particle interaction event sequence of loose deposits on a high-altitude mountain slope, the spatial distribution of the interaction events triggered by each unstable particle is analyzed, and the influence range of the unstable particle on the surrounding particles is delineated (e.g., the radius of the influence range is the maximum distance at which the interaction events occur between the surrounding particles). At the same time, the process of the influence is analyzed (e.g., the propagation speed of the influence, the attenuation of the influence intensity), and particle influence diffusion data is generated, with each unstable particle corresponding to a set of influence diffusion data.

[0126] Step S1468: Track the state changes of surrounding particles affected by the diffusion data of the particles, mark the chain reaction process of the affected particles from a stable state to an unstable state, and generate particle instability chain data.

[0127] In this embodiment, for the particle impact diffusion data of loose deposits on high-altitude mountain slopes, the state changes of the affected surrounding particles are tracked. If the force on the surrounding particles exceeds the stable equilibrium range or the displacement exceeds the position fluctuation range, the particle is marked to change from a stable state to an unstable state. The process of the above-mentioned chain reaction is recorded (such as unstable particle A causing particle B to become unstable, and particle B causing particle C to become unstable), and particle instability chain data is generated. This data is stored in the form of a chain event list.

[0128] Step S1469: Integrate the instability process of a single particle, the sequence of particle interaction events, the data on particle influence diffusion, and the data on particle instability chain in chronological order to form an evolution event chain at the particle level.

[0129] In this embodiment, for the single particle instability process, particle interaction event sequence, particle influence diffusion data and particle instability chain data of loose deposits on high-altitude mountain slopes, the above data are integrated into a particle-level evolution event chain according to the timestamp information. The event chain is stored in the form of a time-series event list, and each element in the list corresponds to a particle evolution event at a certain time point.

[0130] Step S14610: Based on the evolutionary event chain, construct a microscopic destruction evolution sub-path from the initial stable state through the instability initiation point, motion mode transformation, interaction events to the final unstable state.

[0131] In this embodiment, for the particle-level evolution event chain of loose deposits on high-altitude mountain slopes, key events are extracted from the event chain, including initial stable state events, instability initiation point events, motion mode transition events, interaction events, and final instability state events (such as particle displacement reaching a maximum threshold). These key events are then connected in chronological order to construct a microscopic failure evolution sub-path, which is stored in the form of a directed event chain.

[0132] Step S147: Based on the micro-macro correlation data, construct the failure evolution sub-path at the macro-structural level. The failure evolution sub-path at the macro-structural level records the evolution process of the loose aggregate from an intact state to a fractured or collapsed state.

[0133] In this embodiment, for the micro- and macro-level correlation data of loose deposits on high-altitude mountain slopes, association rules related to macro-structural failure are extracted. The evolution process of the deposits from an intact state (e.g., no cracks, overall stability coefficient greater than a preset threshold) to a fractured or collapsed state (e.g., with penetrating cracks, overall stability coefficient less than a preset threshold) is analyzed, including the onset time of failure, the triggering factors of failure (e.g., the scale of micro-particle instability reaching a threshold), and the expansion process of failure (e.g., cracks expanding from local to overall). Based on these analyses, a sub-path of failure evolution at the macro-structural level is constructed. This sub-path is stored in the form of a time-series node list, with each node corresponding to a macro-structural failure event.

[0134] Step S1471: Extract the initial macroscopic integrity state parameters of the loose aggregate from the micro-macro correlation data. The initial macroscopic integrity state parameters record the overall morphological structure, volume and structural integrity characteristics of the loose aggregate.

[0135] In this embodiment, for the micro- and macro-correlation data of loose deposits on high-altitude mountain slopes, macro-structural data when the multiphysics field intensity is less than a preset threshold are selected, and initial macro-intact state parameters are extracted, including the overall morphological structure description of the deposit (e.g., an approximate cone shape, overall height and base area), volume, and structural integrity characteristics (e.g., zero cracks, overall stability coefficient greater than a preset threshold). These parameters are then integrated into an initial macro-intact state parameter set.

[0136] Step S1472: Based on the initial macroscopic complete state parameters, divide the loosely packed body into macroscopic structural regions. Each region corresponds to a set of macroscopic units with similar structural characteristics, generating macroscopic structural partition data.

[0137] In this embodiment, based on the initial macroscopic complete state parameter set of loose deposits on high-altitude mountain slopes, the deposits are divided into multiple macroscopic structural regions according to their morphological and structural characteristics. Each macroscopic unit within a region has similar structural features (such as similar height and slope). For example, the deposits are divided into a top region, a middle region, and a bottom region. This regional information is then integrated into macroscopic structural partitioning data, with each region corresponding to a set of structural feature descriptions.

[0138] Step S1473: Extract dynamic response data of each macroscopic structural region from the micro-macro correlation data. The dynamic response data records the deformation, displacement and structural compactness changes of each macroscopic region under the coupling effect of multiple physics fields.

[0139] In this embodiment, for the micro- and macro-level correlation data of loose deposits on slopes in high-altitude mountainous areas, data on each macro-structural region under the coupling effect of multiple physics fields are selected, and dynamic response data are extracted, including the deformation of the region (such as the strain value of the region), displacement (such as the overall displacement amount and direction of the region), and changes in structural compactness (such as changes in porosity of the region). The above data are integrated into a dynamic response dataset for each macro-structural region.

[0140] Step S1474: Analyze the dynamic response data of each macroscopic structural region, mark the region with the most significant structural changes as the initial weak region, and record the location and structural change characteristics of the initial weak region.

[0141] In this embodiment, for the dynamic response dataset of each macroscopic structural region of the loose deposits on the slope of a high-altitude mountainous area, the degree of structural change in each region (such as the magnitude of deformation, the magnitude of displacement, and the rate of change of porosity) is analyzed. The region with the greatest degree of structural change is marked as the initial weak region, and information such as the spatial location of the region (such as the boundary coordinates of the region) and structural change characteristics (such as the direction of deformation and the rate of displacement) are recorded.

[0142] Step S1475: Track the evolution of the initial weak region, monitor its expansion direction, expansion process and the deepening of structural damage, and generate weak region evolution data.

[0143] In this embodiment, for the initial weak area of ​​the loose deposits on the slope of a high-altitude mountainous area, the changes in its range (such as the increase in area), expansion direction (such as expansion to the top or to the bottom), and structural damage degree (such as the increase in crack length and further increase in porosity) at different time points are tracked to generate weak area evolution data, which is stored in the form of a time-series regional feature list.

[0144] Step S1476: Analyze the impact of the initial weak region evolution data on the surrounding macroscopic structural regions, record the process of the decline in structural stability of the surrounding regions, mark the formation and development process of new weak regions, and generate multi-weak region co-evolution data.

[0145] In this embodiment, the evolution data of weak areas in loose deposits on high-altitude mountain slopes are analyzed to assess the impact of the expansion of initial weak areas on the surrounding macroscopic structural regions, including increased structural changes and decreased stability coefficients in the surrounding areas. The process of decreased structural stability in the surrounding areas is recorded. If the degree of structural change in the surrounding areas reaches a preset threshold, it is marked as a new weak area. The formation time and development process (such as range expansion and increased damage) of the new weak area are recorded to generate multi-weak-area co-evolution data.

[0146] Step S1477: Monitor the changes in the connection status between each weak area, mark the time nodes and connection paths of the weak areas connecting with each other, and record the structural fracture channels formed between different weak areas through the connection paths.

[0147] In this embodiment, for the collaborative evolution data of multiple weak areas of loose deposits on high-altitude mountain slopes, the spatial position changes of each weak area are monitored. If the spatial range of two weak areas overlaps or the distance is less than a preset threshold, the two weak areas are considered to be connected. The time node is marked as the connection time, and the connection path (such as the spatial coordinates of the connecting channel between two weak areas) is recorded to generate connection path data.

[0148] Step S1478: Analyze the changes in the mechanical bearing capacity of the overall structure of the loose accumulation after the formation of the through path, record the process of the gradual decrease in bearing capacity and the key decrease nodes, and generate bearing capacity evolution data.

[0149] In this embodiment, the changes in the overall mechanical bearing capacity of loose deposits on high-altitude mountain slopes are analyzed based on the data of the penetration path. This includes the magnitude of the bearing capacity (such as the maximum load the deposit can withstand) and the rate of decrease in bearing capacity. The process of the gradual decrease in bearing capacity is recorded, key nodes where the bearing capacity decreases to a preset threshold are marked, and bearing capacity evolution data is generated.

[0150] Step S1479: Track the overall structural changes corresponding to the bearing capacity evolution data, mark the starting point where the loose aggregate shows a clear trend of fracture or collapse, record the evolution process from the starting point to the state of complete fracture or collapse, and generate overall failure evolution data.

[0151] In this embodiment, for the bearing capacity evolution data of loose deposits on high-altitude mountain slopes, when the bearing capacity drops to a preset threshold, it is marked as the starting point where the loose deposits show a clear trend of fracture or collapse. The evolution process from this starting point to the state of complete fracture or collapse (such as a significant change in the overall shape of the deposits, making it unable to withstand any load) is recorded, including the direction of fracture or collapse expansion, expansion rate, final shape, etc., to generate overall failure evolution data.

[0152] Step S14710: Based on the initial macroscopic integrity state parameters, macroscopic structural partition data, weak area evolution data, multi-weak area collaborative evolution data, penetration path data, bearing capacity evolution data, and overall failure evolution data, construct a macroscopic failure evolution sub-path from the initial integrity state through the formation, expansion, and penetration of weak areas to the final fracture or collapse state.

[0153] In this embodiment, key events are extracted from the initial macroscopic integrity parameters, macroscopic structural zoning data, weak area evolution data, multi-weak area co-evolution data, penetration path data, bearing capacity evolution data, and overall failure evolution data of loose deposits on high-altitude mountain slopes. These key events include initial integrity events, weak area formation events, weak area expansion events, weak area penetration events, bearing capacity decline events, and final fracture or collapse events. These key events are then connected in chronological order to construct a macroscopic failure evolution sub-path, which is stored as a directed event chain.

[0154] Step S148: Analyze the time synchronization data of the micro-destruction evolution sub-path and the macro-destruction evolution sub-path, adjust the evolution nodes of the two sub-paths to keep them coordinated in the time dimension, and obtain the time-calibrated sub-path.

[0155] In this embodiment, for the microscopic and macroscopic failure evolution sub-paths of loose deposits on high-altitude mountain slopes, timestamp information of each node in the two sub-paths is extracted, and the time synchronization is analyzed. If the difference between the timestamp of a node in the microscopic failure evolution sub-path and the timestamp of a node in the macroscopic failure evolution sub-path is greater than a preset threshold, the timestamp of a node in one of the sub-paths is adjusted so that the time difference between the two is less than the threshold, thus obtaining a time-calibrated sub-path.

[0156] Step S149: Extract key evolution nodes from the time-calibrated sub-paths. The key evolution nodes record important state transition points during the destruction process. Connect the key evolution nodes in chronological order to generate a node association chain.

[0157] In this embodiment, for the time-calibrated subpath of loose deposits on a high-altitude mountain slope, key evolution nodes are extracted, including the initiation node of microparticle instability, the formation node of macroscopic weak regions, the key node of the chain reaction of microparticle instability, the node of the connection of macroscopic weak regions, and the node of the final failure state. These key evolution nodes are then connected in chronological order to generate a node association chain, which is stored as a directed node sequence.

[0158] Step S1410: Integrate the microscopic destruction evolution sub-path and the macroscopic destruction evolution sub-path based on the node association chain to obtain a hierarchical destruction evolution path that includes the effects of micro-particles and the macroscopic structural response.

[0159] In this embodiment, for the node association chain of loose deposits on slopes in high-altitude mountainous areas, the micro-destruction evolution sub-path and the macro-destruction evolution sub-path are integrated according to the order of the node association chain, so that the evolution nodes at the micro level correspond one-to-one with the evolution nodes at the macro level, resulting in a hierarchical destruction evolution path. This path is stored in the form of a hierarchical node list, and each node contains information on the action of micro particles and information on the macro-structure response.

[0160] Step S150: Generate simulation data of the failure process of loose aggregate based on the hierarchical failure evolution path, wherein the failure process simulation data contains multi-dimensional evolution information.

[0161] In this embodiment, the microscopic particle interaction information and macroscopic structural response information of each node in the layered failure evolution path of loose deposits on high-altitude mountain slopes are extracted, including particle displacement and collision at different time points, crack propagation and overall displacement of the deposits. This information is integrated into failure process simulation data, which contains evolutionary information in the time dimension (state at different time points), the microscopic dimension (changes at the particle level), and the macroscopic dimension (changes at the structural level). This data is stored in the form of a three-dimensional dynamic model and can be used to visualize the failure process.

[0162] Step S151: Extract the microscopic destruction evolution sub-path data in the hierarchical destruction evolution path, analyze the evolution nodes at the microscopic particle level and the particle state parameters of each node, and generate a microscopic evolution feature dataset.

[0163] In this embodiment, the microscopic failure evolution sub-path data is extracted from the layered failure evolution path of loose deposits on high-altitude mountain slopes. The particle state parameters of each evolution node are analyzed, including the spatial location, displacement, velocity, stress state, and contact state with surrounding particles. These parameters are integrated into a microscopic evolution feature dataset, which is stored as a time-series parameter list, with each element corresponding to the microscopic particle state parameters at a specific time point.

[0164] Step S152: Extract macroscopic destruction evolution sub-path data from the hierarchical destruction evolution path, analyze the evolution nodes at the macroscopic structural level and the structural state parameters of each node, and generate a macroscopic evolution feature dataset.

[0165] In this embodiment, the macroscopic failure evolution sub-path data of the loose deposits on a high-altitude mountain slope are extracted, and the structural state parameters of each evolution node are analyzed, including the overall displacement of the deposits, the number and length of cracks, stability coefficient, bearing capacity, etc. These parameters are integrated into a macroscopic evolution feature dataset, which is stored in the form of a time-series parameter list, with each element corresponding to a macroscopic structural state parameter at a specific time point.

[0166] Step S153: Align the micro-evolutionary feature dataset with the macro-evolutionary feature dataset according to time nodes to generate a time-aligned feature dataset.

[0167] In this embodiment, for the micro-evolutionary feature dataset and the macro-evolutionary feature dataset of loose deposits on slopes in high-altitude mountainous areas, the timestamp information of each dataset element is extracted, and the micro-evolutionary feature data with the same timestamp are associated with the macro-evolutionary feature data to generate a time-aligned feature dataset. This dataset is stored in the form of a time-aligned parameter list, and each element corresponds to the micro- and macro-feature data of a time point.

[0168] Step S154: Extract key feature indicators of damage evolution from the time-aligned feature dataset. The key feature indicators include the instability density of microparticles, the area of ​​weak regions in macrostructures, the rate of damage penetration, and the overall bearing capacity decay process.

[0169] In this embodiment, key feature indicators are extracted from the time-aligned feature dataset of loose deposits on high-altitude mountain slopes. The instability density of micro-particles is calculated as the ratio of the number of instable particles to the total number of particles in the deposit; the area of ​​weak regions in the macrostructure is calculated as the spatial extent of the weak regions; the rate of failure penetration is calculated as the ratio of the change in the length of the penetration path to the time interval; and the overall bearing capacity attenuation process is calculated as the ratio of the change in bearing capacity to the time interval. These indicators are integrated into a set of key feature indicators.

[0170] Step S155: Perform time-series evolution analysis on key feature indicators to generate a trend curve of each key feature indicator over time. The trend curve records the changes of the indicator during the destruction process.

[0171] In this embodiment, for the key feature index set of loose deposits on slopes in high-altitude mountainous areas, for each key feature index, the index value at different time points is extracted, and the trend curve of the index changing with time is generated by curve fitting method. The curve is stored in the form of a coordinate point sequence, with the horizontal axis representing time and the vertical axis representing the index value.

[0172] Step S156: Based on key characteristic indicators and trend curves, establish a stage division standard for the damage process, divide the damage process into the initial stable stage, the weak area formation stage, the damage expansion stage, and the final instability stage, and generate stage division results.

[0173] In this embodiment, the key characteristic indicators and trend curves of loose deposits on high-altitude mountain slopes are analyzed to determine their changing characteristics and establish stage division criteria. The initial stabilization stage is defined as stable key characteristic indicator values ​​(e.g., instability density less than a preset threshold, weak area area zero); the weak area formation stage is defined as the weak area area beginning to increase and the instability density beginning to rise; the failure propagation stage is defined as an increased failure penetration rate, rapid expansion of the weak area area, and an increased rate of bearing capacity attenuation; the final instability stage is defined as the instability density reaching its maximum value, the weak area reaching its maximum value, and the bearing capacity attenuating to its minimum value. Based on these criteria, the failure process is divided into four stages, generating stage division results that include the start time, end time, and stage characteristics of each stage.

[0174] For example, step S1561: smooth the trend curves of key feature indicators, analyze the smoothed trend curves, mark the inflection points of change for each key feature indicator, and record the time nodes when the rate of change of the indicator changes significantly, thereby generating a set of indicator inflection points.

[0175] In this embodiment, for the trend curves of key characteristic indicators of loose deposits on slopes in high-altitude mountainous areas, a smoothing algorithm (such as the moving average method) is used to smooth the curves and reduce noise interference. The smoothed trend curves are analyzed, and time points where the rate of change of the indicators changes significantly (such as the time point when the change shifts from slow to rapid) are marked. These points are then integrated into a set of indicator inflection points, with each key characteristic indicator corresponding to a set of inflection points.

[0176] Step S1562: Arrange the inflection points of all key feature indicators in chronological order, count the number of inflection points at different time points, and determine the key time intervals in which inflection points occur in clusters.

[0177] In this embodiment, for the set of inflection points of the index of loose deposits on the slope of high-altitude mountainous areas, the timestamp information of all inflection points is extracted, these timestamps are arranged in chronological order, the number of inflection points at each time node is counted, and the time interval with the number of inflection points greater than a preset threshold is determined as the key time interval.

[0178] Step S1563: Based on the key time interval, initially divide the candidate stage boundaries of the destruction process, with each candidate stage boundary corresponding to a time node where an inflection point occurs.

[0179] In this embodiment, for the key time intervals of loose deposits on slopes in high-altitude mountainous areas, the starting time node of each key time interval is used as the candidate stage boundary to preliminarily divide the stages of the failure process.

[0180] Step S1564: Extract the mean and rate of change of key feature indicators in each candidate stage, and determine that the characteristics of the initial stable stage are that the rate of change of indicators is slow and the values ​​are stable.

[0181] In this embodiment, for candidate stages of loose deposits on high-altitude mountain slopes, key characteristic index values ​​are extracted within each candidate stage, and the mean and rate of change of the index are calculated (the rate of change is the ratio of the change in the index value to the time interval). The initial stable stage is characterized by the absolute value of the rate of change of the index being less than a preset threshold and the fluctuation range of the mean index being less than a preset threshold.

[0182] Step S1565: Determine the characteristics of the weak region formation stage as follows: the area index of the macroscopic structural weak region begins to increase, and the density index of microscopic particle instability gradually rises.

[0183] In this embodiment, for the candidate stage of loose deposits on high-altitude mountain slopes, the characteristics of the weak area formation stage are that the rate of change of the macroscopic structural weak area area index is greater than zero (starting to grow) and the rate of change of the microscopic particle instability density index is greater than zero (gradually increasing).

[0184] Step S1566: Determine the characteristics of the damage propagation stage as a rapid increase in the damage penetration rate index, a continuous expansion of the weak area, and an accelerated process of bearing capacity decay.

[0185] In this embodiment, for the candidate stage of loose deposits on high-altitude mountain slopes, the characteristics of the failure and expansion stage are that the rate of change of the failure penetration rate index is greater than a preset threshold (rapid increase), the rate of change of the weak area area index is greater than a preset threshold (continuous expansion), and the rate of change of the bearing capacity decay rate index is greater than a preset threshold (acceleration).

[0186] Step S1567: Determine the characteristics of the final instability stage as follows: all key characteristic indicators reach extreme values, the macrostructure shows obvious fractures or collapses, and the microparticle instability density reaches its peak.

[0187] In this embodiment, for the candidate stage of loose deposits on high-altitude mountain slopes, the characteristics of the final instability stage are that all key characteristic indicators reach extreme values ​​(such as the instability density reaching the maximum value, the area of ​​the weak area reaching the maximum value, and the bearing capacity reaching the minimum value), the macrostructure shows obvious fractures or collapses (such as the presence of through cracks, the overall displacement exceeding the preset threshold), and the microparticle instability density reaches the peak value.

[0188] Step S1568: Based on the feature definitions of each stage, adjust the boundaries of the initially divided candidate stages to generate the final stage boundaries.

[0189] In this embodiment, for the preliminary division of candidate stage boundaries for loose deposits on slopes in high-altitude mountainous areas, the time nodes of the boundaries are adjusted according to the characteristic definitions of each stage so that the characteristics within each stage conform to the stage definition, thus generating the final stage boundaries.

[0190] Step S1569: Divide the failure process into the initial stabilization stage, the weak area formation stage, the failure propagation stage, and the final instability stage according to the time sequence, and determine the start time and end time of each stage.

[0191] In this embodiment, for the final stage boundary of the loose deposits on the slope of a high-altitude mountainous area, the failure process is divided into four stages according to the time sequence, and the start time (the time node before the stage boundary) and end time (the time node after the stage boundary) of each stage are determined.

[0192] Step S15610: Organize the boundary information, feature performance and key feature index data of each stage to generate a complete stage division result.

[0193] In this embodiment, for the four stages of loose deposits on slopes in high-altitude mountainous areas, the boundary information (start time, end time), characteristic performance (such as the characteristic of the initial stable stage being stable indicators), and key characteristic indicator data (such as indicator values ​​at different time points) of each stage are organized to generate a complete stage division result.

[0194] Step S157: Extract typical feature data for each stage. The typical feature data records the most representative micro-particle state and macro-structure state of the stage, generating a typical feature dataset for each stage.

[0195] In this embodiment, based on the stage division results of loose deposits on slopes in high-altitude mountainous areas, for each stage, the most representative microscopic particle state data (such as the distribution characteristics of unstable particles) and macroscopic structural state data (such as the morphological characteristics of weak areas) are extracted to generate a typical feature dataset for each stage.

[0196] Step S158: Based on the stage division results and the typical characteristic dataset of each stage, construct a dynamic evolution model of the destruction process. The dynamic evolution model is used to reproduce the destruction characteristics and evolution process of different stages.

[0197] In this embodiment, a dynamic evolution model of the failure process is constructed using a dynamic modeling method based on the stage division results and typical characteristic dataset of loose deposits on slopes in high-altitude mountainous areas. This model includes the state transition logic of different stages, the change law of characteristic parameters, etc., and can reproduce the failure characteristics and evolution process of the corresponding stage according to the input time parameters.

[0198] Step S159: Generate a multi-dimensional simulation data sequence containing time, micro, and macro dimensions through a dynamic evolution model. The multi-dimensional simulation data sequence records the continuous changes of each dimension's characteristics during the destruction process.

[0199] In this embodiment, for the dynamic evolution model of loose deposits on slopes in high-altitude mountainous areas, time series parameters are input, and the model generates a multi-dimensional simulation data sequence containing time dimension (different time points), micro dimension (particle state), and macro dimension (structural state) according to its internal logic. This sequence is stored in the form of a time-series multi-dimensional data list.

[0200] Step S1510: Integrate multi-dimensional simulation data sequences to obtain simulation data of the failure process of loose aggregates, which includes details of micro-particle evolution, macro-structural failure process, changes in key characteristic indicators, and stage division results.

[0201] In this embodiment, for the multi-dimensional simulation data sequence of loose deposits on high-altitude mountain slopes, the micro-particle evolution details, macro-structural failure process, key characteristic index changes and stage division results in the sequence are integrated, redundant data are removed, and simulation data of the failure process of loose deposits is generated. This data is stored in the form of a structured document and contains relevant information of all failure processes.

[0202] In the above embodiments, the multiphysics-integrated loose aggregate failure process simulation system for performing the above method embodiments has at least one processor, a control module (chipset) coupled to at least one of the processors, a memory coupled to the control module, a non-volatile memory (NVM) / storage device coupled to the control module, at least one load to / output device coupled to the control module, and a network interface coupled to the control module.

[0203] The processor may include at least one single-core or multi-core processor, and may include any combination of general-purpose processors or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). For some alternative implementations, a multiphysics-based simulation system for the destruction process of loosely packed bodies can serve as an electronic device such as the gateway described in the embodiments of this application.

[0204] In some alternative implementations, a multiphysics simulation system for the failure process of loosely packed bodies may include at least one computer-readable medium (e.g., a memory or NVM / storage device) having instructions and at least one processor fused with the at least one computer-readable medium and configured to execute the instructions to implement the module thereby performing the actions described in this disclosure.

[0205] In one embodiment, the control module may include any suitable interface controller to provide any suitable interface to at least one of the processors and / or any suitable device or component communicating with the control module.

[0206] The control module may include a memory controller module to provide an interface to the memory. The memory controller module may be a hardware module, a software module, and / or a firmware module.

[0207] The memory can be used, for example, to load and store data and / or instructions for a simulation system of the failure process of loosely packed bodies incorporating multiphysics. In one embodiment, the memory may include any suitable volatile memory, such as suitable DRAM.

[0208] In one embodiment, the control module may include at least one load-to-output controller to provide an interface to the NVM / storage device and (at least one) load-to-output device.

[0209] For example, an NVM / storage device can be used to store data and / or instructions. An NVM / storage device may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable (at least one) non-volatile storage device (e.g., at least one hard disk drive (HDD), at least one optical disc (CD) drive, and / or at least one digital universal optical disc (DVD) drive).

[0210] NVM / storage devices may include storage resources that are physically mounted on a device as part of a multiphysics-integrated loose aggregate destruction process simulation system, or that can be accessed by the device without being part of it. For example, an NVM / storage device may be accessed over a network via at least one load-to-output device.

[0211] At least one load-to-output device may provide an interface for the multiphysics-integrated loose aggregate failure process simulation system to communicate with any other suitable device. The load-to-output device may include communication components, input components, sensor components, etc. A network interface may provide an interface for the multiphysics-integrated loose aggregate failure process simulation system to communicate via at least one network. The multiphysics-integrated loose aggregate failure process simulation system may wirelessly communicate with at least one component of a wireless network based on at least one wireless network prior and / or protocol, such as accessing a communication prior-based wireless network.

[0212] In one embodiment, at least one of the processors may be integrated with the logic of at least one controller of the control module (e.g., a memory controller module). In one embodiment, at least one of the processors may be integrated with the logic of at least one controller of the control module to form a system-level integration. In one embodiment, at least one of the processors may be fused with the logic of at least one controller of the control module on the same die. In one embodiment, at least one of the processors may be fused with the logic of at least one controller of the control module on the same die to form a system-on-a-chip (SoC).

[0213] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0214] This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps in the multiphysics-based simulation method for the destruction process of loosely packed bodies described in the foregoing embodiments.

[0215] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the multiphysics-integrated loose aggregate destruction process simulation method described in the foregoing embodiments.

[0216] Finally, it should be noted that the above-disclosed embodiments are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for simulating the failure process of loosely packed bodies by combining multiphysics, characterized in that, The method includes: The dynamic capture of the effects of multiple physical fields on loose aggregates, including stress transfer field, pore medium seepage field and heat exchange field, is used to obtain a set of dynamic effects of multiple physical fields based on the real-time state data of the loose aggregates. Based on the reverse modeling of the multiphysics coupling relationship using the set of dynamic action traces of multiphysics, a multiphysics reverse coupling model is obtained. The physical parameters of the loosely packed body are imported, including particle composition parameters, pore distribution parameters and mechanical response parameters. The physical parameters are then integrated into a multiphysics inverse coupling model to obtain coupling evolution data. Constructing a stratified path of failure evolution in loosely packed bodies based on coupling effect evolution data; Simulation data of the failure process of loosely packed bodies is generated based on the hierarchical failure evolution path, and the failure process simulation data contains multi-dimensional evolution information.

2. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 1, characterized in that, The dynamic capture of multi-physics field effects on loose aggregates, including stress transfer fields, porous media seepage fields, and heat exchange fields, yields a set of dynamic multi-physics field effects based on real-time state data of the loose aggregates. This set includes: Based on the real-time state data of the loose aggregate, the real-time state data records the physical state of the loose aggregate in the natural environment. The deformation traces formed by the stress transmission field acting on the surface and interior of the loose aggregate are extracted from the real-time state data. The deformation traces record the trajectory of particle displacement and structural deformation during the stress transmission process. The seepage traces formed by the seepage field of the porous medium are extracted from real-time status data. The seepage traces record the flow trajectory of the seepage medium in the loose aggregate and the information on the changes in the pore structure. Extract temperature distribution traces formed by the heat exchange field from real-time status data. These temperature distribution traces record the temperature change trajectories of different regions of the loose aggregate during the heat transfer process. The deformation traces, penetration traces, and temperature distribution traces are time-aligned, and the occurrence order of each physical field action trace is arranged according to the time sequence to generate a time-sequential action trace sequence. Spatial location matching is performed on each trace in the temporally sequenced trace sequence to determine the overlapping areas and associated positions of traces of different physical fields in the space of the loosely packed body, and to generate a spatially associated trace group. Extract the morphological information and change trajectory of each trace in each spatially associated trace group. The morphological information includes the shape, range and distribution density of the trace, and the change trajectory includes the expansion and strengthening process of the trace over time. The morphological information and change trajectory are associated with the typical action information of each physical field, and the physical field type and action mode corresponding to each trace are labeled to generate a set of physical field-identified traces. Filter out abnormal traces in the set of physical field-identified traces, remove non-physical field interaction traces caused by environmental interference, and retain effective traces that reflect the multi-physical field interaction. The effective traces are classified and integrated according to the physical field type and the timing of their action to form a subset of field-sequence traces; By summarizing the time-series trace subsets of each subfield, a set of multi-physics field dynamic action traces is obtained, which includes traces of stress transmission field action, traces of pore medium seepage field action, and traces of heat exchange field action.

3. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 1, characterized in that, The multiphysics coupling relationship is modeled inversely based on the set of dynamic action traces of multiphysics, resulting in a multiphysics inverse coupling model, including: Feature analysis is performed on stress transfer field traces in a multiphysics dynamic action trace set to extract the evolution rate and influence range information of the stress transfer field traces. The evolution rate records the process of trace expansion over time, and the influence range information records the spatial range covered by the trace. Feature analysis is performed on the seepage field action traces in the porous medium in the set of dynamic action traces of multiphysics fields to extract the penetration depth and diffusion distribution information of the seepage field action traces in the porous medium. The penetration depth records the extension process of the traces in the loose accumulation body, and the diffusion distribution information records the spatial state of the trace distribution. Feature analysis is performed on the heat exchange field action traces in the multiphysics dynamic action trace set to extract the temperature gradient and transmission attenuation information of the heat exchange field action traces. The temperature gradient records the gradient difference of temperature change inside the trace, and the transmission attenuation information records the attenuation process of heat as the transmission distance increases. Based on the characteristic parameters of three physical field action traces, the correlation points between different physical field action traces are located. The correlation points record the overlapping nodes of one physical field action trace and another physical field action trace in spatial location and temporal sequence. Calculate the correlation of characteristic parameters of each physical field action trace at the correlation point, generate mutual influence data between different physical field action intensities, and obtain quantitative data of correlation intensity. Based on the correlation strength quantification data, the interaction direction data between various physical fields is generated, and the promoting or inhibiting relationship of the interaction of one physical field on the interaction of another physical field is recorded to obtain the interaction direction determination data. Based on the distribution of associated points, quantitative data of associated strength, and data on the direction of action, the coupling path between multiple physical fields is reconstructed. The coupling path records the transmission route of different physical fields interacting through associated points. The reconstructed coupling path is subjected to time consistency processing to keep the evolution of the coupling path synchronized with the time changes of the multiphysics field action traces, thus obtaining the time-calibrated coupling path. Based on the time-calibrated coupling path, the core parameters of the coupling model are set, including the transmission efficiency parameter and the action delay parameter of the coupling path, and an initial reverse coupling model is constructed. The initial inverse coupling model is iteratively optimized by using subsequent trace data from the multiphysics dynamic interaction trace set. The core parameters are adjusted to match the coupling relationship output by the model with the actual trace information, thus obtaining the final multiphysics inverse coupling model.

4. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 1, characterized in that, The construction of a hierarchical path for the failure evolution of loosely packed bodies based on coupling evolution data includes: The interaction response data at the micro-particle level of loosely packed bodies is extracted from the coupling evolution data. The interaction response data at the micro-particle level records the motion and interaction state of a single particle or particle cluster under the coupling of multiple physics fields. Based on the action response data at the micro-particle level, the displacement trajectory and force changes of each particle are tracked, and the changes in contact state and adhesion between particles are recorded to generate micro-particle evolution trajectory data. The interaction response data at the macroscopic structural level of the loose aggregate is extracted from the coupling evolution data. The interaction response data at the macroscopic structural level records the overall morphological changes and structural integrity changes of the loose aggregate. Based on macroscopic structural response data, we track the overall deformation trajectory of loose aggregates and the formation process of structurally weak areas to generate macroscopic structural evolution characteristic data. By performing correlation analysis between microscopic particle evolution trajectory data and macroscopic structural evolution characteristic data, data on the driving relationship between microscopic particle motion and macroscopic structural changes are generated, resulting in microscopic-macroscopic correlation data. Based on micro-macro correlation data, a sub-pathway for destruction evolution at the micro-particle level is constructed. This sub-pathway records the evolution process from a stable state to an unstable state at the particle level. Based on micro-macro correlation data, a sub-path of destruction evolution at the macro-structural level is constructed. The sub-path of destruction evolution at the macro-structural level records the evolution process of the loose aggregate from an intact state to a fractured or collapsed state. By analyzing the time synchronization data of the micro-destruction evolution sub-path and the macro-destruction evolution sub-path, the evolution nodes of the two sub-paths are adjusted to maintain coordination in the time dimension, resulting in time-calibrated sub-paths. Key evolution nodes are extracted from the time-calibrated sub-paths. These key evolution nodes record important state transition points during the destruction process. The key evolution nodes are then connected in chronological order to generate a node association chain. By integrating the microscopic and macroscopic damage evolution sub-paths based on the node association chain, a hierarchical damage evolution path that includes the effects of micro-particles and the macroscopic structural response is obtained.

5. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 4, characterized in that, The aforementioned method constructs a sub-pathway for the destruction evolution at the micro-particle level based on micro-macro correlation data. This sub-pathway records the evolutionary process from a stable state to an unstable state at the particle level, including: The initial stable state parameters of micro particles are extracted from micro-macro correlation data. These initial stable state parameters record the position, force, and contact state of the particles when they are not significantly affected by multiphysics fields. Based on the initial steady-state parameters, a stable equilibrium interval is defined for each particle. The stable equilibrium interval records the force range and position fluctuation range of the particle when it maintains a stable state. Dynamic force data of multiphysics coupling applied to particles are extracted from micro-macro correlation data. The dynamic force data records the magnitude and direction changes of the forces acting on the particles by the stress transmission field, the pore medium seepage field and the heat exchange field. Track the changes in dynamic force data over time, mark the time points when the force on each particle exceeds the stable equilibrium range, and take the time points as the starting points of particle instability; Record the displacement changes and force adjustment process of the particle from the instability initiation point, and mark the motion mode change of the particle during the instability process. The motion mode change records the change of the particle from a stationary state to a sliding or rolling state. Monitor the interaction between unstable particles and surrounding particles, record the occurrence process of collisions, compression and bonding fracture between particles, and generate a sequence of particle interaction events; Based on the sequence of particle interaction events, the influence range and process of each particle after instability on surrounding particles are defined, and particle influence diffusion data are generated. Track the state changes of surrounding particles affected by the diffusion data of particles, mark the chain reaction process of affected particles from a stable state to an unstable state, and generate particle instability chain data. The instability process of individual particles, the sequence of particle interaction events, the data on particle influence diffusion, and the data on particle instability chain reaction are integrated in chronological order to form an evolution event chain at the particle level. Based on the evolutionary event chain, a micro-destruction evolutionary sub-path is constructed from the initial stable state through the instability initiation point, motion mode transformation, interaction events to the final unstable state.

6. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 4, characterized in that, Based on micro-macro correlation data, a macroscopic structural level failure evolution sub-path is constructed. This macroscopic failure evolution sub-path records the evolution process of the loose aggregate from an intact state to a fractured or collapsed state, including: Initial macroscopic integrity parameters of loosely packed bodies are extracted from micro-macroscopic correlation data. These initial macroscopic integrity parameters record the overall morphological structure, volume, and structural integrity characteristics of the loosely packed bodies. Based on the initial macroscopic complete state parameters, the macroscopic structural regions of the loosely packed body are divided. Each region corresponds to a set of macroscopic units with similar structural characteristics, generating macroscopic structural partition data. Dynamic response data of each macroscopic structural region is extracted from micro-macro correlation data. The dynamic response data records the deformation, displacement and structural compaction changes of each macroscopic region under the coupling of multiple physics fields. Analyze the dynamic response data of each macroscopic structural region, mark the region with the most significant structural changes as the initial weak region, and record the location and structural change characteristics of the initial weak region. Track the evolution of the initial weak region, monitor its expansion direction, expansion process and the deepening of structural damage, and generate weak region evolution data; The study analyzes the impact of initial weak region evolution data on surrounding macroscopic structural regions, records the process of declining structural stability in surrounding regions, marks the formation and development of new weak regions, and generates multi-weak region co-evolution data. Monitor the changes in the connection status between weak areas, mark the time nodes and connection paths of the weak areas connecting with each other, and record the structural fracture channels formed between different weak areas through the connection paths. The mechanical bearing capacity of the loose aggregate changes after the formation of the through path is analyzed, the process of gradual decrease in bearing capacity and key decrease nodes are recorded, and bearing capacity evolution data is generated. Track the overall structural changes corresponding to the load-bearing capacity evolution data, mark the starting point of the loose accumulation body showing obvious fracture or collapse trend, record the evolution process from the starting point to the state of complete fracture or collapse, and generate overall failure evolution data. Based on initial macroscopic integrity parameters, macroscopic structural partition data, weak area evolution data, multi-weak area collaborative evolution data, penetration path data, bearing capacity evolution data, and overall failure evolution data, a macroscopic failure evolution sub-path is constructed from the initial integrity state through the formation, expansion, and penetration of weak areas to the final fracture or collapse state.

7. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 1, characterized in that, The simulation data of the failure process of loosely packed bodies generated based on the hierarchical failure evolution path includes multi-dimensional evolution information, including: Extract microscopic destruction evolution sub-path data from the hierarchical destruction evolution path, analyze the evolution nodes at the microscopic particle level and the particle state parameters of each node, and generate a microscopic evolution feature dataset. Extract macroscopic destruction evolution sub-path data from the hierarchical destruction evolution path, analyze the evolution nodes at the macroscopic structural level and the structural state parameters of each node, and generate a macroscopic evolution feature dataset. Align the micro-evolutionary feature dataset with the macro-evolutionary feature dataset according to time nodes to generate a time-aligned feature dataset; Key feature indicators of damage evolution are extracted from the time-aligned feature dataset. These key feature indicators include the instability density of microparticles, the area of ​​weak regions in macrostructures, the rate of damage penetration, and the overall bearing capacity decay process. A time-series evolution analysis is performed on key characteristic indicators to generate a trend curve for each key characteristic indicator over time. The trend curve records the changes of the indicator during the destruction process. Based on key characteristic indicators and trend curves, a stage division standard for the damage process is established, dividing the damage process into an initial stable stage, a weak area formation stage, a damage expansion stage, and a final instability stage, generating stage division results. Extract typical feature data for each stage. The typical feature data records the most representative micro-particle state and macro-structure state of the stage, and generate a typical feature dataset for each stage. Based on the stage division results and the typical characteristic dataset of each stage, a dynamic evolution model of the destruction process is constructed. The dynamic evolution model is used to reproduce the destruction characteristics and evolution process of different stages. A multi-dimensional simulated data sequence containing time, micro, and macro dimensions is generated through a dynamic evolution model. The multi-dimensional simulated data sequence records the continuous changes of each dimension's characteristics during the destruction process. By integrating multi-dimensional simulation data sequences, simulation data of the failure process of loose aggregates are obtained, which includes details of micro-particle evolution, macro-structural failure process, changes in key characteristic indicators, and stage division results.

8. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 3, characterized in that, The process involves iteratively optimizing the initial inverse coupling model using subsequent trace data from the multiphysics dynamic interaction trace set, adjusting core parameters to match the coupling relationship output by the model with the actual trace information, and obtaining the final multiphysics inverse coupling model, including: From the multiphysics dynamic interaction trace set, we can distinguish between the early trace data that has been used for modeling and the unused subsequent trace data, and determine the time range of the subsequent trace data and the corresponding physical field interaction characteristics. Subsequent trace data are classified according to physical field type to generate subsequent stress transfer field trace subsets, subsequent porous media seepage field trace subsets, and subsequent heat exchange field trace subsets. Subsequent stress transfer field trace subsets, subsequent porous medium seepage field trace subsets, and subsequent heat exchange field trace subsets are sequentially imported into the initial reverse coupling model. The model operation is then started to generate corresponding simulated coupling relationship data. The simulated coupling relationship data records the interaction relationships between the various physical fields predicted by the model. Extract key coupling parameters from the simulated coupling relationship data. These key coupling parameters include the transmission efficiency parameter and the effect delay parameter of the coupling path, and generate a simulated coupling parameter set. The actual physical field coupling characteristics are analyzed from subsequent trace data. Based on the spatiotemporal correlation and intensity changes of the actual traces, the actual coupling parameter set is calculated. The parameter type of the actual coupling parameter set is consistent with that of the simulated coupling parameter set. By comparing the simulated coupling parameter set with the actual coupling parameter set, the difference value of each parameter is calculated, and a parameter difference matrix is ​​generated. The parameter difference matrix records the deviation process between the simulated value and the actual value. Based on the parameter difference matrix, the core parameters that need to be adjusted and the direction of adjustment are identified, the adjustment range is set for the parameters corresponding to the difference values, and a parameter adjustment plan is generated. Modify the core parameters of the initial reverse coupling model according to the parameter adjustment scheme to generate the adjusted intermediate coupling model; Subsequent trace data are then imported back into the intermediate coupling model, and the simulation, analysis, comparison, and adjustment processes are repeated until the difference between the simulated coupling parameter set and the actual coupling parameter set is within a preset reasonable range. Record the final core parameter configuration, solidify the coupling path and interaction relationship description of the model, and obtain a multi-physics reverse coupling model that reflects the actual coupling state of the multi-physics field.

9. The method for simulating the failure process of loosely packed bodies by combining multiphysics as described in claim 2, characterized in that, The step involves spatially matching each trace in the time-series of action traces to determine the overlapping areas and associated positions of traces from different physical fields in the loosely packed body space, generating a spatially associated trace group, including: Establish a three-dimensional spatial coordinate system for the loosely packed body, and convert each trace in the time-series of action traces into three-dimensional coordinate data according to its actual spatial location, thereby generating a set of three-dimensional coordinates of the traces. Extract the three-dimensional coordinate boundary range of each trace, determine the minimum enclosing region of each trace in three-dimensional space, and generate a trace space range descriptor, which contains the vertex coordinates and volume information of the enclosing region; Traverse all traces in the temporalized trace sequence, select one trace as the reference trace, and extract the spatial range descriptor and three-dimensional coordinate data of the reference trace; The spatial range of the reference trace is compared with that of other physical field traces. The intersection volume of the minimum bounding region of the reference trace and the minimum bounding region of other traces is calculated to generate spatial overlap volume data. Set spatial overlap judgment criteria. When the spatial overlap volume data of two traces reaches the set overlap threshold, it is determined that there is a spatial overlap relationship between the two and they are recorded as candidate associated trace pairs. Spatial distance calculation is performed on candidate associated trace pairs, the straight-line distance between the center coordinates of the two traces is measured, and center distance data is generated; Analyze the temporal consistency of candidate associated trace pairs to confirm whether the occurrence time of the two traces is within the preset temporal association window, and eliminate false associated trace pairs with spatiotemporal misalignment; Trace pairs that pass spatial overlap determination, center distance verification, and time sequence consistency verification are identified as valid associated trace pairs, and their physical field type and spatial coordinate information are recorded. Cluster analysis was performed on all valid associated trace pairs to merge valid associated trace pairs containing common traces into a set, and clusters of multiple traces that are spatially related were identified. The traces in each cluster are integrated into a spatially correlated trace group. Each spatially correlated trace group contains traces from at least two different physical fields, and there is a definite spatial overlap or proximity relationship between the traces, thus generating a complete set of spatially correlated trace groups.

10. A simulation system for the failure process of loosely packed bodies combining multiphysics, characterized in that, The invention includes a processor and a computer-readable storage medium storing machine-executable instructions that, when executed by a computer, implement the multiphysics-integrated simulation method for the failure process of loosely packed bodies as described in any one of claims 1-9.

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