Intelligent geological structure stability detection method and system based on multi-source data fusion

Abstract

The invention provides an intelligent geologic structure stability detection method and system based on multi-source data fusion, and relates to the technical field of geologic structure detection.The method comprises the steps that firstly, multi-source geologic spatio-temporal data with spatio-temporal coordinate marks is collected, and a geologic spatio-temporal association field is constructed to achieve linkage representation of the multi-source data in the spatio-temporal dimension; and mining an interaction effect of multi-source data based on the geological space-time correlation field, and generating a geological structure coupling effect chain. And injecting an evolution intervention factor into the geologic structure coupling effect chain to correct the evolution direction, tracing geologic structure evolution traceability information, and generating a stability pre-judgment result and an evolution intervention scheme. According to the method, deep fusion and dynamic analysis of multi-source data are realized, the stability of the geological structure can be accurately pre-judged, an intervention scheme can be formulated, and the reliability of stability detection of the geological structure is improved.

Description

Technical Field

[0001] This invention relates to the field of geological structure detection technology, and more specifically, to an intelligent geological structure stability detection method and system based on multi-source data fusion. Background Technology

[0002] In the field of geological structure stability assessment, traditional methods often rely on a single type of data source, such as spatial distribution data of geological bodies or temporal data of geological stress, to evaluate the stability of geological structures. However, geological structures are complex and dynamically changing systems, and their stability is influenced by a combination of factors. A single data source cannot comprehensively and accurately reflect the true state and evolutionary trends of geological structures.

[0003] Existing multi-data fusion methods, while attempting to integrate some geological data, mostly involve simple data stacking without deeply exploring the spatiotemporal relationships between different data sources. This fails to achieve a coordinated representation of multi-source data across time and space. Consequently, when analyzing the stability of geological structures, it is difficult to accurately grasp the interaction effects between various components of the geological structure, hindering the formation of a comprehensive and systematic analysis of geological structure coupling effects. Furthermore, traditional methods lack effective intervention mechanisms and source-tracing analysis capabilities for the evolution of geological structures, making it difficult to predict stability changes in advance and formulate targeted intervention plans. This makes it difficult to meet the needs of precise detection and effective control of geological structure stability in practical geological engineering. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide an intelligent geological structure stability detection method based on multi-source data fusion, the method comprising: Collect multi-source geological spatiotemporal data, which includes spatial distribution data of geological bodies with spatiotemporal coordinates, time series data of geological stress, geological environment correlation data, and geological media interaction data; A geological spatiotemporal correlation field is constructed. This field integrates the correlation relationships of multi-source geological spatiotemporal data under different spatiotemporal coordinates to achieve the linkage representation of multi-source geological spatiotemporal data in the spatiotemporal dimension, forming a dynamically updated geological spatiotemporal correlation field. Based on the dynamic updating geological spatiotemporal correlation field, the interaction effects of multi-source geological spatiotemporal data are mined to generate a geological structure coupling effect chain that runs through all aspects of the geological structure. Evolutionary intervention factors are injected into the geological structure coupling effect chain. The evolutionary direction of the geological structure coupling effect chain is corrected through the dynamic interaction between the evolutionary intervention factors and the geological structure coupling effect chain, thus forming a corrected geological structure coupling effect chain. Based on the revised geological structure coupling effect chain, the evolution and origin information of geological structures are traced, generating prediction results of geological structure stability and targeted evolution intervention schemes.

[0005] Furthermore, embodiments of the present invention also provide an intelligent geological structure stability detection system based on multi-source data fusion, comprising: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the above-described intelligent geological structure stability detection method based on multi-source data fusion by executing the machine-executable instructions.

[0006] Based on the above, by collecting multi-source geological spatiotemporal data containing various types and attached spatiotemporal coordinates, the constructed geological spatiotemporal correlation field can integrate the correlation relationships of multi-source data under different spatiotemporal coordinates, realize the linkage representation of multi-source data in the spatiotemporal dimension, and form a dynamically updated geological spatiotemporal correlation field. The geological structure coupling effect chain mined based on this geological spatiotemporal correlation field runs through all aspects of the geological structure. Evolutionary intervention factors are injected into the geological structure coupling effect chain, and its evolutionary direction is corrected through dynamic interaction. This can actively intervene in the evolutionary process of the geological structure, enhance the control capability of geological structure changes, and trace the evolutionary source information of the geological structure based on the corrected coupling effect chain. The generated stability prediction results and targeted evolutionary intervention schemes can detect potential geological structure stability problems in advance and take effective measures for prevention and control, thereby improving the accuracy, timeliness and effectiveness of geological structure stability detection. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of the execution flow of the intelligent geological structure stability detection method based on multi-source data fusion provided in the embodiments of the present invention.

[0008] Figure 2 This is a schematic diagram of exemplary hardware and software components of an intelligent geological structure stability detection system based on multi-source data fusion provided in an embodiment of the present invention. Detailed Implementation

[0009] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an intelligent geological structure stability detection method based on multi-source data fusion, provided in one embodiment of the present invention. The following is a detailed description of this intelligent geological structure stability detection method based on multi-source data fusion.

[0010] Step S110: Collect multi-source geological spatiotemporal data, which includes spatial distribution data of geological bodies with spatiotemporal coordinates, time series data of geological stress, geological environment correlation data, and geological medium interaction data.

[0011] This embodiment uses the stability detection of a mountain's geological structure in a certain area as an application scenario. When collecting spatial distribution data of geological bodies, a three-dimensional laser scanning technique is used to perform a comprehensive scan of the mountain, acquiring morphological data of geological bodies at different depths on the mountain's surface and inside. This data includes three-dimensional spatial coordinates, using the nationally unified geodetic coordinate system. Simultaneously, geological drilling technology is used to obtain core samples from different locations, and the material composition of the samples is analyzed. Combined with the scanning data, the spatial distribution of geological bodies is determined, forming spatial distribution data. For geological stress time series data, stress sensors are deployed at different heights and lithologies on the mountain. The sensors collect stress data at preset time intervals (e.g., once per hour) and record the time coordinates of each collection. The time coordinates use an international standard time format. Data collection continues for at least one year to obtain a complete stress change time series, forming geological stress time series data. The collection of geological environmental correlation data includes data on environmental factors such as rainfall, temperature, and humidity in the area. This is achieved by deploying environmental monitoring stations. The monitoring stations record the values ​​of each environmental factor and their corresponding spatiotemporal coordinates. For example, rainfall data includes the geographical location and time information of the rainfall, forming geological environmental correlation data. Geological media interaction data is obtained by monitoring the interactions between different geological media (such as rocks, soil, and groundwater) inside the mountain. For example, by monitoring the flow path and flow rate changes of groundwater, the interaction between groundwater and surrounding rocks is analyzed, and the spatial and temporal location, duration, and diffusion of the interaction effects are recorded to form geological media interaction data.

[0012] During the collection of the aforementioned multi-source geological spatiotemporal data, data anonymization technology was used to convert specific location information into regional codes for privacy-sensitive data (such as the specific locations of some monitoring stations). At the same time, the data was encrypted during transmission and storage to ensure data privacy and security and prevent leakage.

[0013] Step S120: Construct a geological spatiotemporal correlation field. The geological spatiotemporal correlation field integrates the correlation relationships of multi-source geological spatiotemporal data under different spatiotemporal coordinates to realize the linkage representation of multi-source geological spatiotemporal data in the spatiotemporal dimension, forming a dynamically updated geological spatiotemporal correlation field.

[0014] Step S121: Analyze the spatial coordinate attributes of the geological body spatial distribution data, and extract spatial feature information that can characterize the three-dimensional morphology, spatial position relationship and spatial distribution density of the geological body. All spatial feature information is bound to the original spatial coordinates of the geological body spatial distribution data.

[0015] In this step, the spatial coordinate attributes of the geological bodies collected in step S110 are first analyzed. For the three-dimensional morphology of the geological bodies, the length, width, height parameters, and surface curvature variations are extracted by analyzing the point cloud data of the geological body surface in the scanned data. These features reflect the overall shape and local unevenness of the geological body. The extraction of spatial positional relationships involves comparing the spatial coordinates of different geological body units to determine their relative positions, such as vertical, horizontal, front-back relationships, and distances. The calculation of spatial distribution density involves counting the number of geological body units within a unit volume of space to characterize the density of geological bodies in space. For example, for a rock layer in a mountain, its specific location within the mountain is determined by its three-dimensional coordinates. The positional relationship between this rock layer and other surrounding rock layers is analyzed, and the distribution density of this rock layer within a specific spatial range is calculated. These extracted three-dimensional morphology, spatial positional relationships, and spatial distribution density information together constitute spatial feature information, and each spatial feature information is bound to the original spatial coordinates corresponding to that rock layer.

[0016] Step S122: Analyze the time series attributes of the geological stress time series data, extract time feature information that can characterize the stress change cycle, the occurrence time of stress peak and the stress decay time series, and bind all time feature information to the original time coordinates of the geological stress time series data.

[0017] For geological stress time-series data, the focus is on analyzing its time series attributes. Extracting the stress change cycle involves analyzing long-term collected stress data, observing the cyclical process of stress values ​​increasing, decreasing, and then increasing again, and determining the duration of each cycle, i.e., the stress change cycle. The stress peak occurrence time series records the time coordinates corresponding to each time the stress reaches its maximum value, analyzing the temporal patterns of these peaks, such as whether they occur at a specific time of day or in a specific season. The stress decay time series analyzes the process of stress decreasing from its peak value to a stable value, recording the duration of the decay process and the stress value changes at different time points. Taking stress sensor data at a certain location on a mountain as an example, processing one year's worth of stress data reveals that the stress changes at this location exhibit a certain seasonal cycle, with stress peaks occurring every summer, and the timing of these peaks showing a certain regularity. Simultaneously, the process of stress decaying from its peak value to a stable value takes a certain amount of time. This information about the stress change cycle, peak occurrence time series, and decay time series forms temporal characteristic information and is linked to the corresponding original time coordinates.

[0018] Step S123: Analyze the spatiotemporal cross attributes of the geological environment association data, and extract cross feature information that can characterize the spatial influence range, temporal duration effect, and spatiotemporal synergistic changes of environmental factors. All cross feature information is bound to the spatiotemporal coordinates corresponding to the geological environment association data.

[0019] Geological environmental data possesses spatiotemporal cross-attributes. When analyzing these attributes, for example, regarding the spatial impact range of environmental factors, rainfall data is analyzed based on rainfall records from monitoring stations at different locations to determine the geographical extent of rainfall impact and its coverage across different areas of the mountain. The temporal duration effect analyzes the duration of environmental factors' influence on geological structures; for instance, the impact of a heavy rainfall event on mountain stability may last for days or even weeks. Spatiotemporal synergistic changes study the interplay and changes between different environmental factors in space and time. For example, rising temperatures may be accompanied by decreasing humidity, and these changes exhibit different synergistic relationships under different spatiotemporal coordinates. Through the analysis of these aspects, cross-feature information is extracted. For instance, if the spatial impact range of a rainfall event covers the upper and middle parts of a mountain, and the temporal duration effect is five days, during which time it shows a negatively correlated spatiotemporal synergistic change with temperature, these cross-feature information are linked to the spatiotemporal coordinates corresponding to the rainfall event.

[0020] Step S124: Analyze the spatiotemporal attributes of the geological medium interaction data, and extract interaction feature information that can characterize the spatiotemporal location of the medium interaction, the duration of the interaction, and the spatiotemporal trajectory of the diffusion of the interaction effect. All interaction feature information is bound to the spatiotemporal coordinates of the geological medium interaction data interaction process.

[0021] For geological media interaction data, the spatiotemporal attributes of the interaction are analyzed. The spatiotemporal location of the media interaction is determined through monitoring data. For example, the interaction between groundwater and rock may occur at a specific fault location in a mountain, with a specific start time. The duration of the interaction refers to the time elapsed from the start to the end of the interaction process, such as a groundwater-rock interaction lasting for a month. The spatiotemporal trajectory of the interaction's diffusion effect tracks the process of the interaction's impact spreading from its location to the surrounding area. For example, minerals carried by groundwater undergo chemical reactions with surrounding rocks, and the area of ​​influence gradually expands. By monitoring the changes in the concentration of chemical reaction products at different spatiotemporal locations, the trajectory of the interaction's diffusion effect is determined. The information representing the spatiotemporal location of the interaction, its duration, and the spatiotemporal trajectory of its diffusion effect is extracted to form interaction feature information, which is then bound to the spatiotemporal coordinates of the interaction process.

[0022] Step S125: Based on the principle of consistency of spatiotemporal coordinates, establish coordinate mapping rules for spatial feature information, temporal feature information, cross feature information and interactive feature information, and define the association logic of different types of feature information under the same spatiotemporal coordinates.

[0023] Step S1251: Integrate the spatiotemporal coordinate types corresponding to spatial feature information, temporal feature information, cross feature information, and interactive feature information, unify the coordinate representation standard, so that spatial coordinates adopt the same three-dimensional coordinate system and temporal coordinates adopt the same time measurement standard.

[0024] In this sub-step, the spatiotemporal coordinate types corresponding to the various feature information extracted in steps S121 to S124 are first collected. The spatial coordinates of the spatial feature information may come from different measuring devices and exist in different coordinate systems; they need to be uniformly converted to the nationally unified three-dimensional geodetic coordinate system. Regarding time coordinates, different monitoring devices may use different time measurement standards, such as Beijing time or Greenwich Mean Time; all time coordinates need to be uniformly converted to Beijing time. For example, the spatial coordinates of a geological unit are converted from a local coordinate system to three-dimensional geodetic coordinates, and the time recorded by a stress sensor is converted from Greenwich Mean Time to Beijing time, ensuring that the spatiotemporal coordinate representation standards of various feature information are consistent.

[0025] Step S1252: Analyze the correlation between spatiotemporal coordinates during the evolution of geological structures, determine the correspondence between spatial coordinates and time coordinates, and define the evolutionary correlation of different spatial locations at different time points.

[0026] The evolution of geological structures is a process that changes over time, with different spatial locations exhibiting different states at different points in time. By analyzing the historical evolution data of a mountain's geological structure, we can understand the geological changes at different spatial locations (such as the summit, mid-slope, and foot of the mountain) at different points in time (such as the past ten or twenty years), and determine the correspondence between spatial and temporal coordinates. For example, a specific spatial location on the mountain may have experienced different stress changes and environmental influences over the past five years; these changes correspond to different points in time, thus defining the evolutionary correlation of that spatial location at different points in time.

[0027] Step S1253: Based on the association between spatial feature information and temporal feature information, establish a point-to-point mapping rule for coordinates so that the spatial coordinates of each spatial feature information can correspond to the specific temporal coordinates of the temporal feature information, forming a spatiotemporal coordinate pair.

[0028] Spatial feature information describes the spatial characteristics of a geological body, while temporal feature information describes the changes in factors such as stress over time. To establish the relationship between the two, a point-to-point mapping rule is adopted. For example, a specific rock unit in a mountain (spatial feature information, with specific spatial coordinates) will be subjected to different stresses at different times (temporal feature information, with specific temporal coordinates). The spatial coordinates of the rock unit are mapped one-to-one with the time coordinates of the corresponding stress, forming a spatiotemporal coordinate pair, such as (spatial coordinates of rock unit A, time coordinates of the stress peak).

[0029] Step S1254: Establish coordinate range mapping rules for the association between cross feature information and spatial and temporal feature information, so that the spatiotemporal coordinate range of cross feature information can cover the spatial coordinates of the corresponding spatial feature information and the temporal coordinates of the temporal feature information.

[0030] Cross-feature information involves the spatiotemporal cross-influence of environmental factors, and its influence has a certain range. The coordinate range mapping rule ensures that the spatiotemporal coordinate range of the cross-feature information includes the spatial coordinates of the corresponding spatial feature information and the temporal coordinates of the temporal feature information. For example, the spatiotemporal coordinate range of a rainfall event (cross-feature information) covers the upper and middle parts of a mountain within a certain time period. The spatial coordinates of the geological features (such as rock strata distribution) within this region and the temporal coordinates of the stress temporal features within that time period should both fall within the spatiotemporal coordinate range of this rainfall event, thus achieving the correlation between the cross-feature information and the spatial and temporal feature information.

[0031] Step S1255: Establish coordinate trajectory mapping rules for the association between interactive feature information and spatial feature information, temporal feature information, and cross feature information, so that the spatiotemporal trajectory coordinates of interactive feature information can be dynamically associated with the spatiotemporal coordinates of the other three types of feature information, reflecting the spatiotemporal continuity of the interaction process.

[0032] Interactive feature information describes the interaction process between geological media, exhibiting a spatiotemporal trajectory. Coordinate trajectory mapping rules dynamically link the spatiotemporal trajectory coordinates of the interactive process with the spatiotemporal coordinates of spatial, temporal, and cross-feature information. For example, the spatiotemporal trajectory of groundwater flow (interactive feature information) is from high to low on a mountain. During this flow, its spatial location (spatial coordinates of spatial feature information), its flow state at different points in time (temporal coordinates of temporal feature information), and the environmental factors it is affected by (spatiotemporal coordinates of cross-feature information) are all associated with this flow trajectory. This dynamic association demonstrates the spatiotemporal continuity of the interactive process.

[0033] Step S1256: Define the association conditions for different types of feature information under each mapping rule. The association conditions are determined based on the physical characteristics of the geological structure and the inherent logic of the data.

[0034] For each mapping rule established in steps S1253 to S1255, the association conditions need to be defined. Taking the point-to-point mapping rule as an example, the association condition may be that the geological unit described by the spatial feature information is indeed subjected to corresponding stress at the time point corresponding to the temporal feature information. This is determined based on the physical characteristic that stress in the geological structure will act on a specific geological body. For the range mapping rule, the association condition may be that the influence range of environmental factors of the cross feature information actually includes the spatial coordinates of the spatial feature information and the temporal coordinates of the temporal feature information. This is determined based on the inherent logic that the influence of environmental factors has spatial and temporal ranges. The association condition for the coordinate trajectory mapping rule is that the spatiotemporal trajectory of the interactive feature information overlaps and interacts with the spatiotemporal coordinates of the other three types of feature information in time and space. This is determined based on the physical characteristics of the geological medium interaction process.

[0035] Step S1257: Define the priority order of mapping rules. When there are multiple association logics under the same spatiotemporal coordinates, select the dominant association logic according to the priority order.

[0036] In practice, multiple mapping rules may exist for the same spatiotemporal coordinates. To avoid confusion, a priority order needs to be defined. For example, at a specific spatiotemporal coordinate, point-to-point mapping rules can be applied to associate spatial and temporal feature information, while range mapping rules can be applied to associate cross-feature information with spatial and temporal feature information. In this case, based on the focus of geological structure stability detection, a priority order is defined. Assuming that stress factors have a significant impact on geological structure stability, the point-to-point mapping rule (associating spatial and temporal feature information, involving stress data) has a higher priority than the range mapping rule, thus selecting the point-to-point mapping rule as the dominant association logic.

[0037] Step S1258: Construct a dynamic adjustment method for mapping rules. When the coordinate characteristics of newly acquired multi-source geological spatiotemporal data change, automatically adjust the specific parameters of the mapping rules and verify the compatibility of the adjusted mapping rules with existing coordinate data.

[0038] Because geological structures are dynamic, the coordinate characteristics of newly acquired multi-source geological spatiotemporal data may change. Therefore, it is necessary to construct a dynamic adjustment method for mapping rules. For example, as a mountain erodes, the spatial coordinates of a geological body may change slightly. In this case, the correspondence between the spatial coordinates of the geological body and the temporal coordinates of the temporal feature information in the point-to-point mapping rules needs to be adjusted. The system will automatically detect the changes in the coordinate characteristics of the new data and adjust the relevant parameters in the mapping rules, such as the specific values ​​in the coordinate correspondence. After adjustment, the system verifies its compatibility by comparing the logical consistency between the feature information associated under the new mapping rules and the existing coordinate data, ensuring that the adjusted mapping rules can still accurately associate various feature information.

[0039] Step S1259: Integrate the point-to-point mapping rules, range mapping rules, trajectory mapping rules, association conditions, priority order and dynamic adjustment methods to generate coordinate mapping rules, and define the association logic of spatial feature information, temporal feature information, intersection feature information and interaction feature information under the same spatiotemporal coordinates.

[0040] The point-to-point mapping rules, range mapping rules, and trajectory mapping rules determined in steps S1253 to S1258, along with the associated conditions, priority order, and dynamic adjustment methods corresponding to each type of rule, are integrated. Through this integration, a complete coordinate mapping rule system is formed. This system can clearly define the association logic between spatial feature information, temporal feature information, cross-feature information, and interactive feature information under the same spatiotemporal coordinates. For example, under a certain spatiotemporal coordinate, based on the priority order and association conditions, it is determined that cross-feature information is associated with other feature information through range mapping rules, and the specific association methods and conditions are clearly defined.

[0041] Step S126: According to the coordinate mapping rules, spatial feature information, temporal feature information, cross feature information and interactive feature information are associated with coordinates in the preset spatiotemporal coordinate system to generate an initial spatiotemporal association matrix. Each element in the initial spatiotemporal association matrix corresponds to a multi-source feature association combination under a specific spatiotemporal coordinate.

[0042] A pre-defined spatiotemporal coordinate system is established, comprising three-dimensional spatial coordinates and one-dimensional temporal coordinates, capable of covering all spatiotemporal ranges of the multi-source geological spatiotemporal data collected in step S110. Then, according to the coordinate mapping rules generated in step S125, spatial feature information, temporal feature information, cross-feature information, and interactive feature information are associated with coordinates in the pre-defined spatiotemporal coordinate system. For example, the original spatial coordinates bound to a certain spatial feature information correspond to a certain spatial coordinate point in the pre-defined spatiotemporal coordinate system, and the original temporal coordinates bound to the temporal feature information correspond to a certain temporal coordinate point in the pre-defined spatiotemporal coordinate system. Through the association logic in the coordinate mapping rules, the above-mentioned different types of feature information are associated and combined in the pre-defined spatiotemporal coordinate system. Each specific spatiotemporal coordinate point (or point on a range or trajectory) corresponds to a multi-source feature association combination, and these combinations are arranged in the order of spatiotemporal coordinates to form an initial spatiotemporal association matrix. For example, at the coordinate point (x1, y1, z1, t1) in the preset spatiotemporal coordinate system, the corresponding multi-source feature association combination may include the three-dimensional morphological features of the geological body at that coordinate point, the peak stress features at that time, the rainfall environment features at that time, and the interaction features between groundwater and rocks, etc.

[0043] Step S127: Based on the principle of continuity of the spatiotemporal evolution of geological structure, assign spatiotemporal transmission weights to each multi-source feature association combination in the initial spatiotemporal correlation matrix. The spatiotemporal transmission weights are used to reflect the transmission capability and influence range of the multi-source feature association combination in the spatiotemporal dimension.

[0044] The evolution of geological structures is continuous; that is, the geological state at a certain moment and location is influenced by the geological state at the previous moment and the surrounding locations, and also affects the geological state at subsequent moments and the surrounding locations. Based on this principle, a spatiotemporal transmission weight is assigned to each multi-source feature association combination in the initial spatiotemporal correlation matrix. The determination of the spatiotemporal transmission weight considers multiple factors, such as the material composition of the geological body (the hardness of the rock affects the stress transmission capacity) and the integrity of the geological structure (the presence of faults affects the diffusion of the influence range). Multi-source feature association combinations with strong transmission capacity and wide influence range are assigned higher spatiotemporal transmission weights; conversely, those with weak transmission capacity are assigned lower weights. For example, the multi-source feature association combination corresponding to intact and hard rock strata in a mountain has a strong stress transmission capacity and a wide influence range, therefore, this combination has a higher spatiotemporal transmission weight; while the combination corresponding to rock strata with fissures has a lower spatiotemporal transmission weight.

[0045] Step S128: Utilize the spatiotemporal transmission weights to dynamically iterate and update the initial spatiotemporal correlation matrix, incorporating the feature changes of newly acquired multi-source geological spatiotemporal data, supplementing the multi-source feature association combinations under the newly added spatiotemporal coordinates, and forming the updated spatiotemporal correlation matrix.

[0046] Step S1281: Establish a dynamic update triggering method for the initial spatiotemporal correlation matrix. When the newly acquired multi-source geological spatiotemporal data reaches the preset update threshold, the matrix update process is automatically started.

[0047] The preset update threshold can be determined based on the frequency of data acquisition and the rate of change in geological structure. For example, the matrix update process can be triggered when the amount of newly acquired multi-source geological spatiotemporal data reaches 10% of the initial data volume. The system monitors the acquisition of new data in real time, and automatically starts the update process when the preset threshold condition is met, ensuring that the matrix can reflect the new geological data characteristics in a timely manner.

[0048] Step S1282: Analyze the spatiotemporal coordinates and feature information of the newly acquired multi-source geological spatiotemporal data, and determine the spatiotemporal coordinate position of the newly acquired multi-source geological spatiotemporal data and its correlation with the existing feature information according to the established coordinate mapping rules.

[0049] For newly acquired multi-source geological spatiotemporal data, its spatiotemporal coordinates are first analyzed to determine their positions in a pre-defined spatiotemporal coordinate system. Then, feature information of the new data is extracted, such as the spatial distribution characteristics of new geological bodies and stress temporal characteristics. Following the coordinate mapping rules established in step S125, the correlation between the new feature information and existing feature information in the initial spatiotemporal correlation matrix is ​​analyzed to determine the spatiotemporal coordinate positions corresponding to the new data and how to associate and combine them with existing feature information.

[0050] Step S1283: Based on the calculation rules of spatiotemporal transmission weight, calculate the spatiotemporal transmission weight of the association combination of newly collected data feature information and existing feature information, inject the new association combination and its corresponding spatiotemporal transmission weight into the initial spatiotemporal association matrix, supplement the matrix elements under the newly added spatiotemporal coordinates, and expand the spatiotemporal coverage of the initial spatiotemporal association matrix.

[0051] The calculation rule for spatiotemporal transmission weights is based on an algorithm formulated according to the influencing factors (such as material composition and structural integrity) determined in step S127. For new association combinations formed by the feature information of newly acquired data and existing feature information, their spatiotemporal transmission weights are calculated according to this rule. For example, if new geological environment association data for a certain region is acquired, it is associated with and combined with the existing spatial and temporal feature information of that region. The spatiotemporal transmission weight of this new association combination is calculated based on the geological structural characteristics of the region. Then, the new association combination and its weights are added to the initial spatiotemporal association matrix. For newly added spatiotemporal coordinates (i.e., spatiotemporal coordinates not included in the initial matrix), corresponding elements are added to the matrix, thereby expanding the spatiotemporal coverage of the matrix.

[0052] Step S1284: Analyze the impact of the new correlation combination on the original correlation combination in the initial spatiotemporal correlation matrix. Based on the interaction of spatiotemporal transmission weights, adjust the spatiotemporal transmission weight values ​​of the original correlation combination so that the overall weight distribution of the matrix conforms to the evolution law of geological structure.

[0053] The addition of new correlation combinations affects existing ones because the various parts of a geological structure are interconnected. By analyzing the interaction between the spatiotemporal transmission weights of new correlation combinations and the weights of existing ones, it can be determined that a high weight of a new correlation combination might weaken the weights of surrounding existing combinations, or specific characteristics of a new correlation combination might enhance the weights of certain existing combinations. Based on these interactions, the spatiotemporal transmission weights of the existing correlation combinations are adjusted. For example, if newly acquired geological media interaction data from a fault activity results in a new correlation combination with a high spatiotemporal transmission weight, and fault activity affects the distribution of surrounding stress, it is necessary to reduce the stress transmission-related weights of existing correlation combinations around the fault so that the overall weight distribution of the matrix accurately reflects the evolution of the geological structure.

[0054] Step S1285: Drive the initial spatiotemporal correlation matrix to dynamically iterate and update it through spatiotemporal transmission weights to form a candidate spatiotemporal correlation matrix. Based on the continuity and consistency of the spatiotemporal evolution of geological structures, check the spatiotemporal coordinate connection relationship of each correlation combination in the candidate spatiotemporal correlation matrix, and predict the new correlation combinations that may appear in the future according to the characteristic change trend of newly collected data. Reserve matrix expansion space in advance, and record the detailed information of each matrix update, including update time, number of newly added correlation combinations, weight adjustment, etc., to form a matrix update log.

[0055] Based on steps S1283 and S1284, the initial spatiotemporal correlation matrix is ​​iteratively updated using the dynamic changes in spatiotemporal transmission weights, forming a candidate spatiotemporal correlation matrix after each update. Then, the spatiotemporal coordinate connections of each correlation combination in the candidate matrix are checked to ensure that, in terms of time series, correlation combinations from previous moments can reasonably transition to those from later moments; and in terms of spatial location, correlation combinations at adjacent locations can connect with each other, conforming to the continuity and consistency of the spatiotemporal evolution of geological structures. Simultaneously, based on the characteristic change trends of newly acquired data, such as a continuous increase in stress values ​​in a certain area, it is predicted that new stress peak characteristics and spatial characteristics of that area may appear in the future, and corresponding spatial locations are reserved in advance in the matrix. During each update, detailed information such as the update time, the number of newly added correlation combinations, and the adjusted weights of existing correlation combinations are recorded to form a matrix update log for subsequent traceability and analysis.

[0056] Step S1286: Through continuous dynamic iterative updates, supplement the multi-source feature association combination under the new spatiotemporal coordinates, and adjust the spatiotemporal transmission weight of the original combination to form the updated spatiotemporal association matrix.

[0057] Following steps S1281 to S1285, the initial spatiotemporal correlation matrix is ​​continuously and dynamically updated. As new data is continuously collected and injected, the multi-source feature correlation combinations under newly added spatiotemporal coordinates in the matrix gradually become richer, and the spatiotemporal transmission weights of the original correlation combinations are continuously adjusted according to the interaction relationships. After multiple iterations, an updated spatiotemporal correlation matrix is ​​finally formed that accurately reflects the current multi-source geological spatiotemporal data correlation relationships.

[0058] Step S129: Integrate the updated spatiotemporal correlation matrix with the physical interaction law of geological structure to construct a multi-dimensional geological spatiotemporal correlation field framework, which includes spatial correlation dimension, temporal correlation dimension and spatiotemporal cross-correlation dimension.

[0059] The physical laws governing geological structures include the gravitational interactions between geological bodies, stress transmission patterns, and the influence of the mechanical properties of geological media (such as elastic modulus and Poisson's ratio) on the stability of geological structures. By integrating the updated spatiotemporal correlation matrix with these physical laws, a geological spatiotemporal correlation field framework is constructed. The spatial correlation dimension primarily reflects the correlation between geological feature information at different spatial locations, such as the positional relationship and interaction between adjacent rock layers. The temporal correlation dimension reflects the correlation of geological feature information at different points in time, such as the impact of stress changes over time on subsequent geological states. The spatiotemporal cross-correlation dimension comprehensively considers both temporal and spatial factors, reflecting the correlation of geological feature information under spatiotemporal cross-correlation conditions, such as the impact of a certain environmental factor on the geological structure of a specific spatial region within a specific time period. Through the setting of these three dimensions, a multi-dimensional geological spatiotemporal correlation field framework is constructed.

[0060] Step S1210: Incorporate the multi-source feature association combination and spatiotemporal transmission weight in the updated spatiotemporal correlation matrix into the geological spatiotemporal correlation field framework to form a dynamically updated geological spatiotemporal correlation field.

[0061] Each multi-source feature association combination in the updated spatiotemporal correlation matrix obtained in step S128 is mapped to the spatial correlation dimension, temporal correlation dimension, and spatiotemporal cross-correlation dimension of the geological spatiotemporal correlation field framework according to its spatiotemporal coordinates. Simultaneously, the spatiotemporal transmission weight of each association combination is also incorporated into the framework as an indicator of the correlation strength of the combination in each dimension. Since the spatiotemporal correlation matrix is ​​dynamically updated, the multi-source feature association combinations and spatiotemporal transmission weights incorporated into the framework will also change dynamically, thereby enabling the entire geological spatiotemporal correlation field to be dynamically updated and reflect the real-time linkage representation of multi-source geological spatiotemporal data in the spatiotemporal dimensions.

[0062] Step S130: Based on the dynamically updated geological spatiotemporal correlation field, mine the interaction effects of multi-source geological spatiotemporal data to generate a geological structure coupling effect chain that runs through all aspects of the geological structure.

[0063] Step S131: Extract the correlation combination of spatial distribution characteristics of geological bodies and interaction characteristics of geological media from the spatial correlation dimension of the dynamically updated geological spatiotemporal correlation field, analyze the interaction forms of the two in space, and define the triggering conditions and manifestations of spatial interaction.

[0064] Within the spatial correlation dimension of the dynamically updated geological spatiotemporal correlation field, we screen for correlation combinations between the spatial distribution characteristics of geological bodies and the interaction characteristics of geological media. For example, the spatial distribution characteristics of a rock stratum within a mountain (such as its strike, dip angle, and thickness) and the interaction characteristics of surrounding groundwater (such as its infiltration pathways and chemical reactions with the rock stratum). Analyzing these correlation combinations determines the spatial interaction forms between the two. For instance, groundwater infiltration may lead to rock softening, which is achieved through chemical reactions between water and rock minerals. Spatial triggering conditions include groundwater flow reaching a certain threshold and the presence of fissures in the rock stratum. When these conditions are met, the interaction begins. The manifestations of this interaction are observable changes such as a decrease in the mechanical strength of the rock stratum and volume expansion.

[0065] Step S132: Extract the correlation combination of geological stress temporal characteristics and geological environment correlation characteristics from the temporal correlation dimension of the dynamically updated geological spatiotemporal correlation field, analyze the interaction law between the two in time, and divide the time-effect evolution stage and stage characteristics.

[0066] In the temporal correlation dimension, the correlation combination between the temporal characteristics of geological stress and the correlation characteristics of the geological environment is extracted. For example, the temporal characteristics of stress changes over time in a certain area of ​​a mountain (such as periodic fluctuations in stress) are combined with the environmental correlation characteristics of rainfall changes over time in that area. The interaction law between the two over time is analyzed. For example, an increase in rainfall may lead to an increase in the self-weight of the mountain, thereby causing an increase in stress, and the two show a certain positive correlation. Based on the above interaction law, the temporal evolution stages are divided, such as the initial stage (when rainfall is low, stress increases slowly), the development stage (when rainfall increases, stress rises rapidly), the stable stage (when rainfall is stable, stress remains at a high level), and the decay stage (when rainfall decreases, stress gradually decreases). Each stage has different characteristics. For example, the characteristic of the development stage is that both rainfall and stress values ​​show a rapid growth trend.

[0067] Step S133: Extract cross-dimensional association combinations of different types of feature information from the spatiotemporal cross-association dimensions of the dynamically updated geological spatiotemporal correlation field, analyze the transmission path of cross-dimensional interaction, and define the transmission start point, transmission node and transmission end point.

[0068] In the spatiotemporal cross-correlation dimension, cross-dimensional correlation combinations of different types of feature information are extracted, such as combinations of spatial distribution characteristics of geological bodies, temporal characteristics of geological stress, correlation characteristics of geological environment, and interaction characteristics of geological media. The transmission paths of cross-dimensional interactions within these combinations are analyzed, i.e., how changes in one type of feature information affect another through intermediate links. For example, changes in rainfall environmental characteristics (increased rainfall) serve as the starting point of transmission, leading to increased groundwater flow (changes in geological media interaction characteristics), which is a transmission node; increased groundwater flow, in turn, softens the rock strata (changes in spatial distribution characteristics of geological bodies), which is another transmission node; rock softening ultimately leads to changes in stress distribution (changes in temporal characteristics of geological stress), which is the transmission endpoint. Through the above analysis, the transmission starting point, each transmission node, and the final transmission endpoint of cross-dimensional interactions are clearly identified.

[0069] Step S134: Based on spatial interaction patterns, temporal interaction rules, and cross-dimensional transmission paths, construct a classification framework for the interaction effects of multi-source data. The classification framework includes spatial coupling effects, temporal synergistic effects, and cross-dimensional transmission effects.

[0070] By combining the spatial interaction patterns determined in step S131, the temporal interaction patterns derived in step S132, and the cross-dimensional transmission paths obtained in step S133, a classification framework for multi-source data interaction effects is constructed. Spatial coupling effects refer to the effects produced by the spatial interaction of different geological bodies, such as the spatial coupling of rock strata and groundwater leading to changes in rock strata strength. Temporal synergistic effects refer to the effects produced by the synergistic changes in geological stress and environmental factors over time, such as the synergistic increase in rainfall and stress over time leading to a decrease in geological structural stability. Cross-dimensional transmission effects are the effects produced by the mutual transmission of different types of feature information across spatiotemporal dimensions, such as the effect of environmental factors influencing the spatial distribution and stress state of geological bodies by affecting the interaction of geological media. These three effects together constitute the classification framework for multi-source data interaction effects.

[0071] Step S135: In response to the spatial coupling effect, integrate the effects of different spatial association combinations to form a spatial coupling effect sequence, which reflects the progressive relationship of spatial effects and the process of expanding the scope of influence.

[0072] For spatial coupling effects within the classification framework, the manifestations of all spatially related combinations (such as combinations of interactions between geological bodies and geological media at different locations) are collected. These manifestations are then integrated according to spatial location (e.g., from the mountain surface to the core) or the order of action. For example, the interaction between surface soil and rainwater results in increased soil moisture content, which in turn affects the stability of the underlying rock strata. The interaction between the underlying rock strata and groundwater results in rock softening. This transmission of effects from the surface to the interior forms a progressive spatial relationship. Simultaneously, the extent of influence for each manifestation is recorded, such as how the influence of increased soil moisture content gradually expands to a larger surface area, and how the extent of rock softening expands with groundwater infiltration. These integrated findings form a sequence of spatial coupling effects.

[0073] Step S136: For time synergy effects, integrate the evolutionary stage characteristics of different time-related combinations to form a time synergy effect sequence, which reflects the continuity and intensity change trend of the time effect.

[0074] To address the temporal synergistic effect, the evolutionary stage characteristics of different temporal combinations (such as stress-environmental factor combinations at different time periods) were collected. These evolutionary stage characteristics were then integrated chronologically. For example, in the initial stage of rainfall, stress begins to increase slowly (initial stage characteristics); as rainfall continues, stress rises rapidly (development stage characteristics); after rainfall stops, stress remains stable (stable stage characteristics); and after a period of time, stress gradually decreases (decline stage characteristics). These stage characteristics, arranged chronologically, reflect the continuity of the effects of time. Simultaneously, the changes in stress intensity at each stage were analyzed, such as from low intensity in the initial stage to high intensity in the development stage, and then to decreased intensity in the decline stage, forming an intensity change trend. This integrated content constitutes a temporal synergistic effect sequence.

[0075] Step S137: For cross-dimensional transmission effects, integrate the transmission path information of different cross-dimensional association combinations to form a cross-dimensional transmission effect sequence. The cross-dimensional transmission effect sequence reflects the transmission efficiency of cross-dimensional effects and the superposition effect of influence.

[0076] For cross-dimensional transmission effects, information on transmission paths of different cross-dimensional combinations is collected, such as transmission paths from environmental factors to geological media interaction, then to the spatial distribution of geological bodies, and finally to stress states. The transmission efficiency of each path is analyzed, i.e., the speed and extent to which changes in one stage are transmitted to the next. For example, increased rainfall leads to a rapid increase in groundwater flow, indicating high transmission efficiency; however, increased groundwater flow may result in a slower rock softening process, with relatively lower transmission efficiency. The cumulative effect refers to the combined impact of multiple cross-dimensional transmission paths acting on the same geological structure. For instance, in addition to rainfall, temperature changes also affect the interaction between groundwater and rock layers by influencing the physical properties of groundwater (such as viscosity). The combined effects of these two transmission paths make the degree of rock softening more significant. Integrating this transmission path information, transmission efficiency, and cumulative effect results forms a sequence of cross-dimensional transmission effects.

[0077] Step S138: Sort the spatial coupling effect sequence, the temporal synergistic effect sequence, and the cross-dimensional transmission effect sequence according to the actual components of the geological structure, so that each effect sequence can correspond to each link of the geological structure from the surface to the core.

[0078] Step S1381: Obtain the actual composition of the geological structure, define the complete geological structure segments from the surface to the core, and each geological structure segment corresponds to a specific structural morphology, material composition and functional characteristics.

[0079] By surveying and analyzing the geological structure of the mountain, its actual composition was obtained. From the surface to the core, the complete geological structure may be divided into layers including the topsoil layer, weathered rock layer, and intact bedrock layer. The topsoil layer has a loose granular structure, mainly composed of soil particles and organic matter, and its function is to withstand surface vegetation and some rainwater infiltration. The weathered rock layer has a fractured rock structure, composed of weathering products formed from the original rock, and its function is to serve as a transition layer between the topsoil layer and the underlying intact bedrock layer, providing a certain buffer for the transmission of moisture and stress. The intact bedrock layer has a dense, massive rock structure, composed of hard rock minerals, and its function is the main load-bearing structure of the mountain, determining its overall stability.

[0080] Step S1382: Analyze the spatial location characteristics of each geological structural segment, determine the specific range of each geological structural segment in the spatial distribution of geological structures, and the spatial connection relationship between adjacent geological structural segments.

[0081] For each geological structural element, its spatial location characteristics are analyzed. For example, the topsoil layer is distributed across the mountain surface, covering it from the summit to the foot, with varying thickness at different locations. Its specific range can be defined using three-dimensional coordinates, such as its altitude within a certain range. The weathered rock layer lies beneath the topsoil layer and also has its specific three-dimensional coordinate range. The spatial connection between adjacent geological structural elements refers to their contact patterns. For instance, the topsoil layer and the weathered rock layer are in direct contact, and the contact surface may have some unevenness; there may be a gradual transition zone between the weathered rock layer and the intact bedrock layer, rather than a completely separate interface.

[0082] Step S1383: Analyze the spatial range of each effect in the spatial coupling effect sequence, and match each spatial coupling effect with the corresponding geological structural link so that the spatial range of the effect is consistent with the spatial range of the geological structural link.

[0083] Each effect in the spatial coupling effect sequence has its specific spatial range. For example, a spatial coupling effect might be the softening effect of groundwater on weathered rock layers, and its spatial range is mainly the area where the weathered rock layers are located. Comparing the spatial range of this effect with the spatial range of the geological structural segments defined in step S1381, we find that they are consistent. Therefore, this spatial coupling effect is matched to the geological structural segment of weathered rock layers. In this way, all effects in the spatial coupling effect sequence are matched to their corresponding geological structural segments.

[0084] Step S1384: Analyze the temporal evolution characteristics of each geological structural segment, determine the temporal response law of each geological structural segment in the geological evolution process and the temporal connection sequence with adjacent geological structural segments.

[0085] Each geological structural element exhibits different temporal response patterns during geological evolution. For example, the topsoil layer responds relatively quickly to environmental factors (such as rainfall), showing changes in moisture content shortly after rainfall; weathered rock layers respond relatively slowly, requiring a certain amount of time for softening; and intact bedrock layers respond even more slowly, with a longer timescale for stress accumulation and release. The temporal sequence of adjacent geological structural elements refers to how changes in one element can cause changes in adjacent elements at subsequent times. For instance, after rainfall, the moisture content of the topsoil layer increases, and after a period of time, water seeps into the weathered rock layer, causing changes in the weathered rock layer, demonstrating the temporal sequence between the two.

[0086] Step S1385: Analyze the temporal action phase of each effect in the temporal synergistic effect sequence, and match each temporal synergistic effect with the corresponding geological structural link so that the temporal action phase of the effect matches the temporal response law of the geological structural link.

[0087] Each effect in the temporal synergistic effect sequence has a specific temporal phase. For example, a certain temporal synergistic effect is the synergistic effect of increased rainfall and increased stress, and its temporal phase is during and for a period of time after rainfall. Analyzing the temporal response patterns of this temporal phase and various geological structural segments reveals that this effect mainly affects the stress state of weathered rock layers and intact bedrock layers. The temporal response patterns of weathered rock layers and intact bedrock layers to stress changes coincide with the temporal phase of this effect. Therefore, this temporal synergistic effect is matched to these two geological structural segments: weathered rock layers and intact bedrock layers.

[0088] Step S1386: Analyze the cross-dimensional interaction characteristics of each geological structural segment, and determine the interaction mode and sequence of each geological structural segment with other geological structural segments under the spatiotemporal cross dimension.

[0089] Cross-dimensional interaction characteristics refer to the interactions between geological structural elements and other elements within a spatiotemporal cross-dimensional framework. For example, weathered rock layers interact with both the surface soil layer and the intact bedrock layer within this spatiotemporal cross-dimensional framework. The interaction with the surface soil layer occurs through the infiltration of water and solutes, with water first penetrating into the weathered rock layer. The interaction with the intact bedrock layer occurs through stress transmission, with stress changes in the weathered rock layer being transmitted to the intact bedrock layer. By analyzing these interaction methods and sequences, the cross-dimensional interaction characteristics of each geological structural element can be clarified.

[0090] Step S1387: Analyze the transmission path of each effect in the cross-dimensional transmission effect sequence, match each cross-dimensional transmission effect with the corresponding geological structural link, so that the transmission start point, transmission node and transmission end point of the effect correspond to the interaction relationship of the geological structural link.

[0091] Each effect in a cross-dimensional transmission effect sequence has its own transmission path, including a transmission start point, a transmission node, and a transmission end point. For example, the transmission path of a certain cross-dimensional transmission effect is: rainfall (environmental factor) → increased surface soil moisture content (transmission start point, surface soil layer) → water infiltration into weathered rock layer (transmission node, weathered rock layer) → softening of weathered rock layer → stress transmission to intact bedrock layer (transmission end point, intact bedrock layer). Mapping the transmission start point, node, and end point in this transmission path to the interaction relationships of geological structural elements reveals consistency with the interaction sequence between the surface soil layer, weathered rock layer, and intact bedrock layer. Therefore, this cross-dimensional transmission effect is matched to these three geological structural elements.

[0092] Step S1388: Based on the arrangement order of geological structural segments from the surface to the core, and the matching relationship between the spatial coupling effect sequence and the geological structural segments, sort the spatial coupling effect sequence so that the order of the spatial coupling effect sequence is consistent with the arrangement order of the geological structural segments.

[0093] The geological structural elements are arranged in the following order from the surface to the core: surface soil layer → weathered rock layer → intact bedrock layer. Based on the matching relationship between the spatial coupling effect sequence and the geological structural elements in step S1383, the spatial coupling effects acting on the surface soil layer are placed at the beginning of the sequence, those acting on the weathered rock layer are placed in the middle, and those acting on the intact bedrock layer are placed at the end, ensuring that the order of the spatial coupling effect sequence matches the order of the geological structural elements.

[0094] Step S1389: Using the same sorting method, sort the time synergy effect sequence and the cross-dimensional transmission effect sequence respectively, so that the order of the time synergy effect sequence and the cross-dimensional transmission effect sequence corresponds to the arrangement order of geological structural links from the surface to the core.

[0095] For the temporal synergistic effect sequence, based on the matching relationship with geological structural elements in step S1385, the temporal synergistic effects acting on the surface soil layer are ranked first, followed by those acting on the weathered rock layer and the intact bedrock layer, so that their order corresponds to the arrangement order of geological structural elements from the surface to the core. The cross-dimensional transmission effect sequence is also processed in the same way, and the sequence is sorted according to the arrangement order of geological structural elements based on the matching relationship in step S1387.

[0096] Step S13810: Based on the interaction process of each link of the geological structure, check whether the sorted spatial coupling effect sequence, temporal synergistic effect sequence and cross-dimensional transmission effect sequence cover all links of the geological structure, and supplement the corresponding effect sequences for the uncovered links.

[0097] The three sorted effect sequences are examined to see if each geological structural element has a corresponding effect. For example, if the intact bedrock layer has only one effect in the spatial coupling effect sequence, while other elements have multiple effects, there may be incomplete coverage. In this case, the dynamically updated geological spatiotemporal correlation field is re-analyzed to uncover other possible spatial coupling effects of the intact bedrock layer and supplement them into the spatial coupling effect sequence to ensure that all geological structural elements are covered by the effect sequence.

[0098] Step S139: Based on the physical connection relationship and mechanical transmission characteristics of each link of the geological structure, the sorted spatial coupling effect sequence, temporal synergistic effect sequence and cross-dimensional transmission effect sequence are connected in series to form an initial geological structure coupling effect chain. Each geological structure link in the initial geological structure coupling effect chain corresponds to one or more interaction effects.

[0099] There are physical connections between the various components of a geological structure, such as the topsoil layer covering the weathered rock layer, which is in close contact with the intact bedrock layer. Simultaneously, each component possesses mechanical transmission characteristics, such as stress being transmitted downwards through the contact surface. Utilizing these physical connections and mechanical transmission characteristics, the ordered sequences of spatial coupling effects, temporal synergistic effects, and cross-dimensional transmission effects are linked in series. For example, the spatial coupling effects of the topsoil layer (such as rainwater infiltration leading to soil softening) influence the spatial coupling effects of the weathered rock layer (such as groundwater softening of the weathered rock layer) through physical connections. Simultaneously, temporal synergistic effects and cross-dimensional transmission effects are transmitted sequentially according to the connections and mechanical transmission order between the components. The linked effect sequence forms the initial chain of geological structural coupling effects, where each geological structural component (such as the topsoil layer) corresponds to one or more interaction effects (spatial coupling effects, temporal synergistic effects, cross-dimensional transmission effects, etc.).

[0100] Step S1310: Strengthen the effect correlation of the initial geological structure coupling effect chain to generate a geological structure coupling effect chain.

[0101] For example, step S13101: extract the effect characteristic parameters of adjacent geological structural links in the initial geological structural coupling effect chain, analyze the correlation between the two in terms of action mode, action intensity, influence range and time characteristics, and determine the correlation and difference points.

[0102] In the initial chain of coupled geological structures, adjacent geological structural elements, such as the topsoil layer and weathered rock layer, each possess their own characteristic parameters. The characteristic parameters of the topsoil layer may include soil moisture content and shear strength, with the action mechanism being rainwater infiltration. The intensity of this effect is moderate, affecting the entire topsoil layer, and its duration is short-term. The characteristic parameters of the weathered rock layer include rock moisture content and mechanical strength, with the action mechanism being groundwater softening. The intensity of this effect is relatively strong, affecting the entire weathered rock layer, and its duration is medium-term. Analysis of these parameters reveals that both are related to moisture in their action mechanism and affect the entirety of their respective segments—this is the point of convergence. The difference lies in the intensity and duration of the effect.

[0103] Step S13102: Based on the correlation fit point, strengthen the positive correlation between the effects of adjacent geological structural links, and establish the mapping relationship between the effect parameters of the previous geological structural link and the effect parameters of the next geological structural link by supplementing intermediate transition effect parameters.

[0104] For points of convergence (where the mode of action is related to water and the scope of influence is global), the positive correlation between adjacent effects is strengthened. For example, there is a close relationship between rainwater infiltration in the topsoil layer (effect parameter) and groundwater recharge in the weathered rock layer (effect parameter). By supplementing intermediate transitional effect parameters (such as soil permeability coefficient), a mapping relationship between the two is established, i.e., groundwater recharge in the weathered rock layer equals rainwater infiltration in the topsoil layer multiplied by the soil permeability coefficient. In this way, the effect parameter of the preceding stage can influence the effect parameter of the following stage through intermediate transitional parameters, thus strengthening the positive correlation.

[0105] Step S13103: For the associated difference points, trace their corresponding multi-source geological spatiotemporal data; if the difference is due to data acquisition bias, regenerate the effect characteristic parameters using the corrected data; if the difference is due to the natural evolution characteristics of the geological structure, retain the difference and record the difference attributes.

[0106] For points of correlation with discrepancies (differences in intensity and duration), trace their corresponding multi-source geological spatiotemporal data. If the difference in intensity is found to be due to sensor malfunction during the collection of mechanical strength data of weathered rock layers, leading to data acquisition bias, then correct the data and recalculate the effect characteristic parameters of the weathered rock layers using the corrected data. If the difference is due to the natural evolution characteristics of the geological structure, such as the difference in mineral composition between the weathered rock layers and the surface soil, resulting in different sensitivity to moisture and response times, then retain the above differences and record the difference attribute, such as "the difference stems from the difference in duration characteristics caused by the difference in mineral composition of the rock layers."

[0107] Step S13104: Establish a mutual verification method for the effects of adjacent geological structural links. Verify the triggering conditions of the effect of the next geological structural link by the results of the previous geological structural link effect, and infer the effect of the previous geological structural link effect by the performance of the next geological structural link effect, thus forming a two-way verification.

[0108] The effects of the previous geological structural stage (topsoil layer) (such as soil moisture content reaching a certain threshold) serve as one of the triggering conditions for the effects of the subsequent geological structural stage (weathered rock layer) (groundwater softening). This verifies whether the weathered rock layer does indeed begin to soften when the topsoil moisture content reaches this threshold, thus confirming the triggering effect of the previous stage on the subsequent stage. Simultaneously, by observing the manifestation of the effect of the subsequent stage (such as the degree of reduction in the mechanical strength of the wind-eroded rock layer), the effect of the previous stage (such as whether the amount of soil infiltration is sufficient to cause that degree of strength reduction) can be inferred, forming a two-way verification mechanism to ensure the accuracy of the correlation between adjacent stage effects.

[0109] Step S13105: Perform the above-mentioned correlation enhancement operation on all adjacent geological structural links in the initial geological structural coupling effect chain; then, check whether the effect parameters of adjacent geological structural links in the geological structural coupling effect chain are continuous in the spatiotemporal coordinates, and supplement or adjust the effect parameters for discontinuous links.

[0110] Following steps S13101 to S13104, correlation enhancement operations are performed on all adjacent geological structural links (such as weathered rock layers and intact bedrock layers) in the initial geological structural coupling effect chain. After completion, the continuity of effect parameters of adjacent links in spatiotemporal coordinates is checked. For example, at a certain time coordinate point, the groundwater flow effect parameter of the weathered rock layer is a certain value, while the stress change effect parameter of the intact bedrock layer has not changed before that time point, resulting in a spatiotemporal discontinuity. In this case, transitional effect parameters are added, such as the time parameter required for groundwater to infiltrate into the intact bedrock layer, or the start time of the stress change effect parameter of the intact bedrock layer is adjusted to maintain continuity with the effect parameters of the weathered rock layer in spatiotemporal coordinates.

[0111] Step S13106: Analyze the overall effect distribution of the initial geological structure coupling effect chain, identify geological structure links with effect parameters below the preset threshold, and supplement the correlation effect information of the link based on the interaction law of multi-source geological spatiotemporal data.

[0112] A threshold effect parameter is preset to measure the significance of the effect. Analysis of the overall effect distribution of the initial geological structure coupling effect chain reveals that the stress transmission effect parameter of a certain geological structural link (such as a local area of ​​a complete bedrock layer) is below the threshold, indicating that the effect of that link is not significant enough or lacks sufficient information. Based on the interaction patterns of multi-source geological spatiotemporal data, such as the influence of joint distribution within a complete bedrock layer on stress transmission, information on the correlation between joint characteristics and stress transmission in that local area is supplemented to ensure that the effect parameter of that link reaches above the preset threshold, thus enriching the content of the effect chain.

[0113] Step S13107: Based on the physical evolution law of geological structure, check whether the effect transmission path of the enhanced geological structure coupling effect chain is continuous.

[0114] The physical evolution of geological structures includes the continuity of stress transmission and the conservation of material migration. Examining the coupled effect chain of the intensified geological structure is crucial to determine whether the effect transmission path conforms to these principles. For example, is the stress transmission path from weathered rock layers to intact bedrock layers continuous, and are there any unwarranted interruptions? Does the groundwater flow path conform to hydraulic laws, and are there any contradictory flow directions? If discontinuities in the transmission path are found, such as unexplained stress disappearance during stress transmission, then the multi-source geological spatiotemporal data should be re-analyzed to identify the cause and correct the transmission path to ensure it conforms to the physical evolution of the geological structure.

[0115] Step S13108: Reorganize the arrangement of effects in the effect chain according to the order of geological structural links from the surface to the core, and label each effect with its corresponding geological structural link identifier and effect type identifier.

[0116] After completing the above reinforcement and inspection, the arrangement of effects in the effect chain was re-examined according to the geological structural links from the surface to the core (surface soil layer → weathered rock layer → intact bedrock layer). Each effect was labeled with an identifier, such as "surface soil layer - spatial coupling effect - 001", where "surface soil layer" is the geological structural link identifier, "spatial coupling effect" is the effect type identifier, and "001" is the sequence number of that type of effect in that link. This labeling makes the effect chain structure clearer, facilitating subsequent analysis and application.

[0117] Step S13109: Integrate all adjustment information during the strengthening process to generate a geological structure coupling effect chain that has been strengthened by correlation.

[0118] All adjustment information, including effect parameter corrections, intermediate transition parameter supplements, differential attribute records, and two-way verification results, conducted during the correlation enhancement process, will be integrated to form a detailed adjustment report. This report will then be combined with the reorganized and annotated effect chain to generate the final correlation-enhanced geological structure coupling effect chain.

[0119] Step S140: Inject evolutionary intervention factors into the geological structure coupling effect chain, and correct the evolutionary direction of the geological structure coupling effect chain through the dynamic interaction between the evolutionary intervention factors and the geological structure coupling effect chain, thereby forming a corrected geological structure coupling effect chain.

[0120] Step S141: Based on the need for geological structure stability regulation, key factors that can affect the interaction effect of multi-source geological spatiotemporal data are screened. The key factors include geological medium improvement factors, stress regulation factors, environmental intervention factors, and spatial structure optimization factors.

[0121] The need for geological structure stability regulation is to ensure the stability of the mountain's geological structure over a future period, preventing geological disasters such as landslides and collapses. Based on this need, this study analyzes the interaction effects of multi-source geological spatiotemporal data to identify key factors that can influence these effects. Geological medium improvement factors refer to those that can improve the properties of the geological medium, such as increasing the strength of soil or rock by adding solidifying agents. Stress regulation factors refer to those that can adjust the stress state of the geological body, such as reducing stress concentration in localized areas through unloading engineering. Environmental intervention factors refer to those that can alter geological environmental conditions, such as constructing drainage systems to reduce the impact of groundwater on the geological body. Spatial structure optimization factors refer to those that can optimize the spatial distribution of the geological body, such as filling fissures in the mountain through backfilling. All of these factors can directly or indirectly affect the interaction effects of multi-source geological spatiotemporal data, thereby regulating the stability of the geological structure.

[0122] Step S142: For each key factor, extract its core attributes that can act on the coupling effect. The core attributes include the mode of action, the scope of action, the intensity of action, and the time-effect characteristics, forming an attribute description set corresponding to each key factor.

[0123] For geological media improvement factors, their mechanism of action involves altering the composition and structure of the geological media through physical or chemical methods, such as adding cement or other solidifying agents to the soil. The scope of action is the specific geological area requiring improvement, such as an unstable region in the topsoil layer. The intensity of action is determined by parameters such as the amount and concentration of the solidifying agent, which dictate the strength of the improvement effect. The duration of the improvement effect is also considered; for example, the effective period of a solidifying agent may range from several years to decades. These core attributes are extracted to form a set of attribute descriptions for geological media improvement factors. Similarly, for pressure regulation factors, environmental intervention factors, and spatial structure optimization factors, their mechanisms of action (e.g., excavation methods in unloading projects), scope of action (e.g., stress concentration areas), intensity of action (e.g., excavation depth and range), and duration of effect (e.g., the duration of the unloading effect) are extracted to form their respective attribute description sets.

[0124] Step S143: Transform the attribute description set of each key factor into a standardized evolutionary intervention factor. The evolutionary intervention factor has a unified representation form and can directly interact with the effect links in the geological structure coupling effect chain.

[0125] Establish a unified representation format for evolutionary intervention factors, such as a structured data format including fields such as factor type, action mode code, action range coordinates, action intensity level, and time-dependent characteristic parameters. Transform the information in the attribute description set of geological medium improvement factors according to this representation format. For example, the factor type is "geological medium improvement," the action mode code corresponds to "adding solidifying agent," the action range coordinates are the three-dimensional coordinate range of the unstable area, the action intensity level is determined based on the amount and concentration of solidifying agent added, and the time-dependent characteristic parameter is the effective period. Through this transformation, the attribute description set of each key factor is converted into a standardized evolutionary intervention factor, enabling data-level interaction with the effect links in the geological structure coupling effect chain.

[0126] Step S144: Analyze the effect characteristics of each effect link in the geological structure coupling effect chain, define the sensitive evolutionary intervention factor type and sensitivity threshold range corresponding to each effect link, and form a matching relationship table between effect links and evolutionary intervention factors.

[0127] Each link in the geological structure coupling effect chain has its specific effect characteristics. For example, a certain effect link is the softening effect of groundwater on weathered rock layers, and its effect characteristics include softening rate and softening degree. Analyzing these effect characteristics helps determine which types of evolutionary intervention factors can affect it, i.e., sensitive evolutionary intervention factor types. For this softening effect link, the construction of a drainage system among environmental intervention factors may be a sensitive evolutionary intervention factor type, because drainage can reduce the amount of groundwater, thereby mitigating the softening effect. Further defining the sensitivity threshold range, such as the drainage volume of the drainage system needing to reach a certain threshold to significantly slow down the softening rate, this threshold range is the sensitivity threshold range. Compiling the sensitive evolutionary intervention factor types and sensitivity threshold ranges for each effect link forms a matching relationship table between effect links and evolutionary intervention factors, for example, "Groundwater softening effect link - Environmental intervention factor (drainage system) - Drainage volume ≥ a certain value".

[0128] Step S145: Based on the matching table of effect links and evolutionary intervention factors, the corresponding evolutionary intervention factors are precisely injected into each sensitive effect link of the geological structure coupling effect chain, so that the evolutionary intervention factors can directly act on the target effect.

[0129] Based on the matching table formed in step S144, the evolutionary intervention factor corresponding to each effect stage is found. For example, for the groundwater softening effect stage of weathered rock layers, the matched evolutionary intervention factor is the drainage system construction factor among the environmental intervention factors. This evolutionary intervention factor is precisely injected into the location of the softening effect stage in the geological structure coupling effect chain according to its range of action coordinates, so that the evolutionary intervention factor can directly act on the target effect, that is, by simulating the construction and operation of the drainage system, the groundwater flow in this stage is reduced, thereby affecting the softening effect.

[0130] Step S146: Establish a dynamic interaction process for the coupling effect chain between evolutionary intervention factors and geological structures. The dynamic interaction process is used to simulate the interaction process between evolutionary intervention factors and coupling effects, and to reflect the way evolutionary intervention factors regulate effects.

[0131] Step S1461: Analyze the core attributes and characteristics of evolutionary intervention factors, extract the parameters of their mode of action, intensity of action, duration of effect, and scope of influence, and form a parameter set for evolutionary intervention factors.

[0132] In this step, the core attributes and characteristics of the standardized evolutionary intervention factors generated in step S143 are further analyzed. Taking the geological medium improvement factor as an example, its action mode parameters include the solidifying agent type code and injection method code (such as borehole injection or surface spraying); the action intensity parameters include the solidifying agent concentration gradient value and injection pressure level; the aging parameters include the initial setting reaction time, intensity growth cycle, and effective action years; and the influence range parameters include the horizontal diffusion radius threshold and the vertical penetration depth threshold. Through the systematic extraction of the above parameters, a structured set of evolutionary intervention factor parameters is formed, in which each parameter includes a numerical range, unit of measurement, and physical meaning description to ensure the accuracy and interpretability of subsequent interactive calculations.

[0133] Step S1462: Analyze the effect characteristic parameters of each effect link in the geological structure coupling effect chain, including effect intensity parameters, effect duration parameters, effect influence range parameters, and effect evolution rate parameters, to form a set of effect parameters.

[0134] For the pre-corrected geological structure coupling effect chain, all effect links (such as groundwater softening effect and stress concentration effect) are traversed, and their effect characteristic parameters are analyzed. Taking the groundwater softening effect as an example, the effect intensity parameters include the rate of change of the rock softening coefficient and the amplitude of pore water pressure increase; the effect duration parameters include the initial response delay time and the duration of stabilization; the effect influence range parameters include the area of ​​the horizontal softening zone and the vertical softening depth; and the effect evolution rate parameters include the daily change rate of the softening coefficient and the pore water pressure conduction velocity. The above parameters are classified and organized according to the effect links to form an effect parameter set, and each parameter is associated with a corresponding spatiotemporal coordinate range and dimension conversion rules.

[0135] Step S1463: Based on the principles of geological structure mechanics and data interaction theory, construct the basic architecture of the dynamic interaction process. The basic architecture includes an evolutionary intervention factor input module, an effect receiving module, an interactive calculation module, and a result output module.

[0136] Based on the principles of effective stress in rock mechanics and Darcy's law in permeability mechanics, and combined with the coupling effect model in multi-source data interaction theory, a basic framework for a dynamic interactive process is constructed. The evolutionary intervention factor input module receives standardized intervention factor parameters, the effect receiving module collects current parameters of the effect stage in real time, the interactive calculation module performs parameter coupling calculations as the core processing unit, and the result output module generates a report on changes in effect parameters. All modules communicate bidirectionally through a standardized data interface. The interface protocol includes a data verification mechanism, anomaly handling procedures, and a synchronization clock signal to ensure the real-time performance and integrity of the data stream.

[0137] Step S1464: In the evolutionary intervention factor input module, establish an input interface for the set of evolutionary intervention factor parameters and set data validation rules to validate the format and range of the input evolutionary intervention factor parameters.

[0138] The input interface adopts a distributed data receiving architecture, supporting multi-channel parallel input. Data validation rules include a three-layer validation mechanism: the first layer is format validation, verifying whether the parameter data type (e.g., integer, floating-point) and encoding format (e.g., ASCII, binary) conform to preset standards; the second layer is range validation, comparing the input parameter value with a predefined safety threshold range, for example, the curing agent concentration must not exceed the soil adsorption saturation; the third layer is logic validation, verifying the inherent logical relationship between parameters, such as the injection pressure and penetration depth needing to satisfy a nonlinear coupling equation. A tiered alarm mechanism is triggered when validation anomalies occur; minor anomalies return correction suggestions, while severe anomalies terminate input and record an error log.

[0139] Step S1465: In the effect receiving module, establish a receiving interface for the effect parameter set to receive real-time data of the effect parameters of each effect link in the geological structure coupling effect chain.

[0140] The receiving interface adopts a sensing layer data acquisition architecture based on IoT protocols, deploying edge computing nodes to achieve local data processing. Dedicated data acquisition channels are configured for different types of effect parameters (such as stress, displacement, and moisture content): stress parameters utilize a distributed fiber optic sensor network with a sampling frequency of 1kHz; moisture content parameters employ a time domain reflectometer (TDR) with a sampling interval of 5 minutes. The receiving interface has a built-in data preprocessing unit that performs filtering and denoising (using a wavelet threshold denoising algorithm), outlier removal (based on the 3σ criterion), and spatiotemporal registration operations to ensure the signal-to-noise ratio and spatiotemporal consistency of the received data.

[0141] Step S1466: In the interactive calculation module, design the interactive calculation logic of evolutionary intervention factor parameters and effect parameters. The interactive calculation logic is determined based on the intrinsic relationship between the two and includes calculation methods for superposition of effect intensity and correction of effect evolution rate.

[0142] The interactive computation logic adopts a layered computational architecture: the bottom layer is the physical mechanism layer, which establishes a theoretical relationship model between intervention factors and effect parameters based on the constitutive equations of soil and rock (such as the Drucker-Prager yield criterion); the middle layer is the data-driven layer, which trains a BP neural network using historical intervention case datasets to optimize the correction coefficients of the theoretical model; the top layer is the fusion decision layer, which uses DS evidence theory to fuse the calculation results of the physical model and the data model. The superposition calculation of effect intensity adopts the vector synthesis method, which performs a dot product operation on the spatial distribution intensity vector of the intervention factor and the initial intensity vector of the effect parameter; the effect evolution rate correction adopts the proportional adjustment method, which dynamically adjusts the rate coefficient according to the duration of the intervention factor's effect, such as a correction function that decays exponentially over time.

[0143] Step S1467: Set dynamic adjustment parameters for the interactive computing logic. The dynamic adjustment parameters are adjusted according to the effect parameter change data under the action of evolutionary intervention factors.

[0144] The dynamically adjusted parameters include a coupling coefficient matrix, a time-varying weight vector, and boundary condition thresholds. The coupling coefficient matrix represents the influence weight of different intervention factor parameters on the effect parameters and is updated in real-time using a sliding window algorithm (window size of 100 sets of sample data). The time-varying weight vector reflects the changes in dominant factors at different interaction stages and is optimized using an adaptive genetic algorithm. The boundary condition thresholds set the safe fluctuation range of the effect parameters, with upper and lower limits of the thresholds based on a 95% confidence interval generated by Monte Carlo simulation. The adjustment mechanism employs closed-loop feedback control, performing parameter optimization once every preset calculation cycle (e.g., 1 hour) to ensure dynamic adaptation between the interactive calculation logic and the actual geological response.

[0145] Step S1468: In the result output module, construct the output format of the effect parameter change results, and define the output change parameter type, change magnitude and change trend description.

[0146] The output format uses Extensible Markup Language (XML) for structured description, including basic information sections (interaction timestamp, intervention factor ID, effect stage ID), change parameter sections (parameter name, original value, new value, absolute change, relative rate of change), trend analysis sections (rate of change, acceleration, predicted stationary time), and confidence assessment sections (model calculation error, data confidence level). The trend description uses the third derivative sign method: the first derivative sign indicates the direction of increase or decrease, the second derivative sign indicates the rate of change, and the third derivative sign indicates trend reversal characteristics. The output results also support a visualization interface, generating dynamic change curves (fitted with B-spline curves) and spatiotemporal distribution heatmaps.

[0147] Step S1469: Using historical multi-source geological data and corresponding intervention case data, calibrate the interactive calculation logic and dynamic adjustment parameters in the dynamic interactive process.

[0148] Geological monitoring data (such as borehole stress monitoring data and slope displacement monitoring data) and artificial intervention cases (such as anchor reinforcement projects and drainage system renovations) of the mountain area over the past ten years were collected to construct a calibration dataset with over 100,000 samples. Cross-validation was used to divide the dataset into a training set (70%), a validation set (15%), and a test set (15%). The neural network weights in the interactive computation logic were optimized using the training set, the initial values ​​of the dynamically adjusted parameters were adjusted using the validation set, and finally, the calibration effect was evaluated using the test set. Evaluation metrics included root mean square error (RMSE), mean absolute percentage error (MAPE), and decision accuracy. Calibration was completed when all metrics met preset thresholds (e.g., RMSE < 5%).

[0149] Step S147: Drive the interaction between evolutionary intervention factors and the geological structure coupling effect chain through a dynamic interactive process, and record the parameter change data of the corresponding effect link under the action of each evolutionary intervention factor.

[0150] A dynamic interactive process is initiated, enabling evolutionary intervention factors to interact with the geological structure coupling effect chain according to predefined interaction rules. During the interaction, the parameter changes of the corresponding effect stage under the action of each evolutionary intervention factor are monitored and recorded in real time. For example, the drainage system construction factor acts on the groundwater softening effect stage, recording the specific numerical changes of parameters such as groundwater flow, rock softening rate, and mechanical strength at different time points. The above data includes the initial value of the parameter, the changed value, and the intermediate value during the change process, forming a complete parameter change record.

[0151] Step S148: Based on the recorded data on changes in parameters of the effect link, adjust the intensity and timeliness characteristics of the evolutionary intervention factor according to the preset relationship between intervention intensity and effect change.

[0152] A pre-defined relationship between intervention intensity and effect change is established, such as the relationship curve between the effect intensity (drainage volume) of the drainage system and the degree of reduction in the softening rate of the groundwater softening effect. Based on the effect parameter change data recorded in step S147, such as a decrease in the softening rate at the current drainage volume, a comparison is made with the pre-defined relationship. If the current softening rate reduction does not meet the expected target, the intervention intensity is insufficient, and the drainage volume needs to be increased (increasing the effect intensity). If the softening rate reduction exceeds expectations and may adversely affect other effect stages, the drainage volume needs to be reduced (reducing the effect intensity). Simultaneously, the duration of parameter changes is compared with the pre-defined time-effect characteristics, and the time-effect characteristics of the evolutionary intervention factors are adjusted, such as extending or shortening the operation time of the drainage system.

[0153] Step S149: Record the evolution and change data of each effect link in the geological structure coupling effect chain during the interaction process, and perform correlation analysis between the evolution and change data and the parameters of the evolution intervention factor.

[0154] Throughout the interaction between evolutionary intervention factors and the coupled effect chain of geological structures, not only are the parameter changes of the target effect link recorded, but also the evolutionary changes of other related effect links. For example, the construction of drainage systems, in addition to affecting the groundwater softening effect link, may also affect the moisture content effect link of the topsoil layer. Correlation analysis is performed on all these evolutionary change data with the parameters of the evolutionary intervention factors (such as drainage volume, operating time, etc.) to determine the specific degree and direction of the influence of different parameters of the evolutionary intervention factors on each effect link.

[0155] Step S1410: Based on the results of the correlation analysis, update the effect parameters of each effect link in the geological structure coupling effect chain to form a corrected geological structure coupling effect chain.

[0156] Based on the correlation analysis, the influence of the parameters of the evolutionary intervention factors on the effect parameters of each effect stage is clarified. For example, the increase in drainage volume leads to a decrease in the softening rate parameter of the groundwater softening effect stage, and simultaneously a decrease in the water content parameter of the surface soil layer water content effect stage. Based on these influences, the effect parameters of the corresponding effect stages in the geological structure coupling effect chain are updated, and the updated effect parameters are integrated into the effect chain to form a corrected geological structure coupling effect chain. This geological structure coupling effect chain can reflect the geological structure coupling effect after the action of the evolutionary intervention factors.

[0157] Step S150: Based on the corrected geological structure coupling effect chain, trace the evolution and origin information of the geological structure, generate the geological structure stability prediction results and targeted evolution intervention schemes.

[0158] Step S151: Extract the effect parameter change trajectory of each effect link from the corrected geological structure coupling effect chain. The effect parameter change trajectory includes the specific values ​​and change trends of the effect parameters at different spatiotemporal nodes, forming a set of effect evolution trajectories.

[0159] In the revised geological structure coupling effect chain, each effect link has a trajectory of effect parameters changing over time and space. For example, the groundwater softening effect of weathered rock layers has effect parameters such as softening rate and softening degree, which have specific values ​​at different spatiotemporal nodes (e.g., different times and different locations), and these values ​​exhibit certain trends (e.g., gradually increasing or decreasing over time). Extracting the specific values ​​and trends of these effect parameters at different spatiotemporal nodes forms the effect parameter change trajectory, and the change trajectories of all effect links together constitute the set of effect evolution trajectories.

[0160] Step S152: Based on the set of effect evolution trajectories, trace the initial triggering factors and key influencing factors in the evolution process of each effect link, define the origin and development path details of the effect, and form effect source tracing information.

[0161] For each trajectory in the set of effect evolution trajectories, the changes in effect parameters in its initial stage are analyzed to identify the initial triggering factors that cause changes in that effect stage. For example, the initial triggering factor for the groundwater softening effect in weathered rock layers might be an increase in groundwater flow due to a period of sustained heavy rainfall. During the evolution process, key influencing factors may include changes in rainfall, alterations in groundwater flow paths, and changes in the composition of the geological medium. By analyzing the correspondence between these factors and the effect parameter change trajectories, the details of the development path of the effect from its origin to its current state are defined, such as: initial stage increased rainfall → increased groundwater flow → softening begins → intermediate stage drainage system intervention → softening rate slows down → current stage stable softening level, etc., forming the effect source information for each effect stage.

[0162] Step S153: Integrate the effect source information of all effect links, analyze the overall evolution source information of the geological structure, and define the evolution path, key evolution nodes and core factors driving the evolution of the geological structure from the initial state to the current state.

[0163] The source information of all effect links obtained in step S152 is integrated to analyze the evolution process of the geological structure as a whole. The initial state of the geological structure refers to its state at the start of monitoring or at a certain initial moment, while the current state refers to its current state after a series of evolutions. By integrating the source information of each effect link, the evolutionary path of the geological structure from the initial state to the current state is traced. For example, in the initial state, the parameters of each effect link are stable → a certain triggering factor (such as heavy rainfall) causes some effect links to begin to change → other effect links respond accordingly → evolutionary intervention factors take effect → effect parameters are adjusted → reaching the current state. Key evolutionary nodes are important time points or events in the evolutionary path, such as the time of occurrence of heavy rainfall or the time when the drainage system starts operating. The core factors driving the evolution are the factors that play a dominant role in the entire evolution process, such as continuous groundwater action and stress accumulation and release.

[0164] Step S154: Based on the core evaluation indicators of geological structure stability, correlate evolutionary tracing information with the core evaluation indicators, and analyze the degree and direction of influence of core factors on each core evaluation indicator.

[0165] The core evaluation indicators for geological structure stability include the mechanical strength, deformation, stress state, and groundwater permeability coefficient of the geological body. The core driving factors in the evolutionary source information determined in step S153 are correlated with these core evaluation indicators. For example, groundwater action, one of the core driving factors, can affect the mechanical strength (reducing strength), deformation (increasing deformation), and groundwater permeability coefficient (which may change due to porosity variations) of the geological body. The degree of influence of groundwater action on each core evaluation indicator (e.g., by what percentage does it reduce mechanical strength) and the direction of influence (whether it reduces or increases it) are analyzed to clarify the relationship between the core factors and the evaluation indicators.

[0166] Step S155: Based on the degree and direction of impact, predict the trend of stability changes of the geological structure in different time periods in the future, and identify the types of stability risks that may occur and the spatiotemporal range of the risks.

[0167] Based on the degree and direction of influence of core factors on core evaluation indicators, and combined with the current stability state of the geological structure, a geological evolution prediction model is used to predict the trend of stability changes in different time periods (e.g., short-term, medium-term, and long-term). For example, in the short term (next month), due to the continued influence of groundwater but the effective functioning of the drainage system, the stability of the geological structure may slowly decline; in the medium term (next six months), with the long-term operation of the drainage system and the implementation of other intervention measures, stability may gradually stabilize; in the long term (next few years), if the core driving factors change (e.g., a significant increase in rainfall), stability may decline again. During the prediction process, potential stability risk types, such as landslides, collapses, and debris flows, are identified, along with the spatiotemporal range where these risks may occur, such as a region having a higher probability of experiencing a landslide in the future.

[0168] Step S156: Based on the predicted trend of stability changes and risk information, determine the current stability level of each region of the geological structure according to the preset stability level classification standard, and output the distribution information of stable regions, risk warning regions and high-risk regions.

[0169] A pre-defined stability level classification standard is established, classifying stability into four levels: stable, relatively stable, risk warning, and high risk, based on the numerical range of core evaluation indicators such as deformation and mechanical strength of the geological body. Based on the predicted trend of stability changes and risk information, combined with the current values ​​of each core evaluation indicator, the current stability level of each region of the geological structure is determined. For example, a region with small deformation and high mechanical strength is considered a stable region; a region with slowly increasing deformation and decreasing mechanical strength is considered a risk warning region; and a region with deformation exceeding a critical value and significantly decreased mechanical strength is considered a high-risk region. The distribution information of these regions is output in the form of a three-dimensional geological model, clearly showing the specific location and extent of stable, risk warning, and high-risk regions within the mountain.

[0170] Step S157: For risk warning areas and high-risk areas, combine the corresponding evolutionary tracing information and risk types to determine the core driving factors and target stability status that need to be adjusted, and generate evolutionary intervention targets.

[0171] For risk warning areas and high-risk areas, based on their corresponding evolutionary source information (such as the area's evolutionary path, key nodes, and core driving factors) and risk type (such as landslide risk), the core driving factors that need adjustment are determined. For example, if the risk type of a high-risk area is landslide, its core driving factors are high groundwater permeability and stress concentration. The core driving factors that need adjustment are reducing groundwater permeability and stress concentration in the area. The target stability state is to improve the stability level of the area from high risk to stable or relatively stable, specifically manifested in the core evaluation indicators (such as deformation and mechanical strength) reaching the corresponding stability standards. By clarifying the core driving factors that need adjustment and the target stability state, evolutionary intervention targets are generated.

[0172] Step S158: Based on the evolutionary intervention objectives, screen intervention methods that can act on the core driving factors. The intervention methods include media improvement methods, stress regulation methods, environmental optimization methods, and structural reinforcement methods.

[0173] Based on the evolutionary intervention objectives generated in step S157, suitable intervention methods are selected. Media improvement methods are suitable for adjusting the properties of geological media, such as solidifying soil or rock in high-risk areas to improve their mechanical strength and address the core driving factor of groundwater softening. Stress control methods can be used to reduce stress concentration, such as lowering local stress levels through engineering measures like excavating unloading trenches. Environmental optimization methods are used to improve geological environmental conditions, such as strengthening drainage systems to reduce groundwater infiltration. Structural reinforcement methods, such as constructing retaining walls and anchor bolt support, directly enhance the stability of geological structures and are suitable for addressing high-risk areas with large deformation. One or more intervention methods are selected and combined according to the type of core driving factor and the requirements of the intervention objectives.

[0174] Step S159: Combine the screened intervention methods, define the timing, scope, intensity and sequence of implementation of each intervention method, and generate an intervention strategy.

[0175] Multiple intervention methods selected from the screening process are combined to achieve the best intervention effect. For example, for a specific risk warning area, a combination of drainage systems (environmental optimization) and anchor bolt support (structural reinforcement) can be selected. The timing of implementation for each intervention method is defined, such as first implementing the drainage system to reduce the impact of groundwater, and then implementing anchor bolt support after the groundwater parameters stabilize. The scope of action should cover the entire risk area to ensure that all risk points are addressed. The intensity of action is determined based on the intervention objectives and geological conditions; for example, the drainage volume of the drainage system must meet design requirements, and the anchoring force of the anchor bolts must meet strength standards. The implementation sequence is determined according to the principle of starting with the easy and then moving to the difficult, and controlling before reinforcing; for example, surface drainage is carried out first, followed by groundwater drainage, and finally anchor bolt support. The above content is integrated to generate a detailed intervention strategy.

[0176] Step S1510: Integrate stability prediction results, regional distribution information, evolutionary intervention targets and intervention strategies to generate complete geological structure stability prediction results and targeted evolutionary intervention plans. The geological structure stability prediction results are used to present the stability status and risk trends.

[0177] The stability trend prediction from step S155, the regional distribution information from step S156, the evolutionary intervention objectives from step S157, and the intervention strategies from step S159 are integrated and summarized. The geological structure stability prediction results section presents the current stability status of the geological structure (stability level of each region) and future risk trends (stability changes and possible risk types, spatiotemporal ranges at different time periods). The targeted evolutionary intervention plan section details the evolutionary intervention objectives, the combination of intervention methods used, the timing, scope, intensity, and sequence of implementation for each intervention method.

[0178] Based on the same inventive concept, please refer to Figure 2 The diagram shows a schematic block diagram of an intelligent geological structure stability detection system 100 based on multi-source data fusion, provided in an embodiment of this application, for executing the above-described intelligent geological structure stability detection method based on multi-source data fusion. The intelligent geological structure stability detection system 100 based on multi-source data fusion may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0179] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located in the intelligent geological structure stability detection system 100 based on multi-source data fusion and are separately configured. Alternatively, the machine-readable storage medium 120 can also be integrated into the processor 130 and can communicate and interact with external systems through the communication unit 110. Optionally, the processor 130 may include one or more processing cores; for example, the processor 130 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the intelligent geological structure stability detection method based on multi-source data fusion provided in the aforementioned method embodiments.

[0180] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A method for detecting the stability of geological structures based on multi-source data fusion, characterized in that, The method includes: Collect multi-source geological spatiotemporal data, which includes spatial distribution data of geological bodies with spatiotemporal coordinates, time series data of geological stress, geological environment correlation data, and geological media interaction data; A geological spatiotemporal correlation field is constructed. This field integrates the correlation relationships of multi-source geological spatiotemporal data under different spatiotemporal coordinates to achieve the linkage representation of multi-source geological spatiotemporal data in the spatiotemporal dimension, forming a dynamically updated geological spatiotemporal correlation field. Based on the dynamic updating geological spatiotemporal correlation field, the interaction effects of multi-source geological spatiotemporal data are mined to generate a geological structure coupling effect chain that runs through all aspects of the geological structure. Evolutionary intervention factors are injected into the geological structure coupling effect chain. The evolutionary direction of the geological structure coupling effect chain is corrected through the dynamic interaction between the evolutionary intervention factors and the geological structure coupling effect chain, thus forming a corrected geological structure coupling effect chain. Based on the revised geological structure coupling effect chain, the evolutionary origin information of geological structures is traced, generating geological structure stability prediction results and targeted evolutionary intervention schemes.

2. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 1, characterized in that, The construction of the geological spatiotemporal correlation field, which integrates the correlation relationships of multi-source geological spatiotemporal data under different spatiotemporal coordinates, realizes the linkage representation of multi-source geological spatiotemporal data in the spatiotemporal dimension, and forms a dynamically updated geological spatiotemporal correlation field, includes: The spatial coordinate attributes of the spatial distribution data of geological bodies are analyzed, and spatial feature information that can characterize the three-dimensional morphology, spatial positional relationship and spatial distribution density of geological bodies is extracted. All spatial feature information is bound to the original spatial coordinates of the spatial distribution data of geological bodies. The time series attributes of geological stress time series data are analyzed to extract time feature information that can characterize the stress change cycle, the occurrence time of stress peak and stress decay time. All time feature information is bound to the original time coordinates of geological stress time series data. The spatiotemporal cross attributes of geological environment associated data are analyzed, and cross feature information that can characterize the spatial influence range, temporal duration effect and spatiotemporal synergistic change of environmental factors is extracted. All cross feature information is bound to the spatiotemporal coordinates corresponding to the geological environment associated data. The spatiotemporal attributes of geological media interaction data are analyzed, and interaction feature information that can characterize the spatiotemporal location of media interaction, duration of interaction, and spatiotemporal trajectory of interaction impact diffusion is extracted. All interaction feature information is bound to the spatiotemporal coordinates of the geological media interaction data interaction process. Based on the principle of spatiotemporal coordinate consistency, coordinate mapping rules are established for spatial feature information, temporal feature information, cross feature information and interactive feature information, and the association logic of different types of feature information under the same spatiotemporal coordinates is defined. According to the coordinate mapping rules, spatial feature information, temporal feature information, cross feature information and interactive feature information are associated with coordinates in the preset spatiotemporal coordinate system to generate an initial spatiotemporal association matrix. Each element in the initial spatiotemporal association matrix corresponds to a multi-source feature association combination under a specific spatiotemporal coordinate. Based on the principle of continuity of the spatiotemporal evolution of geological structure, each multi-source feature association combination in the initial spatiotemporal correlation matrix is ​​assigned a spatiotemporal transmission weight. The spatiotemporal transmission weight is used to reflect the transmission capability and influence range of the multi-source feature association combination in the spatiotemporal dimension. Using spatiotemporal transmission weights, the initial spatiotemporal correlation matrix is ​​dynamically iteratively updated to incorporate the feature changes of newly acquired multi-source geological spatiotemporal data, supplement the multi-source feature association combination under the newly added spatiotemporal coordinates, and form an updated spatiotemporal correlation matrix. By integrating the updated spatiotemporal correlation matrix with the physical interaction laws of geological structures, a multi-dimensional geological spatiotemporal correlation field framework is constructed, which includes spatial correlation dimension, temporal correlation dimension, and spatiotemporal cross-correlation dimension. The multi-source feature association combination and spatiotemporal transmission weight in the updated spatiotemporal correlation matrix are incorporated into the geological spatiotemporal correlation field framework to form a dynamically updated geological spatiotemporal correlation field.

3. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 1, characterized in that, The method of mining the interaction effects of multi-source geological spatiotemporal data based on dynamically updated geological spatiotemporal correlation fields generates a chain of geological structural coupling effects that runs through all aspects of the geological structure, including: From the spatial correlation dimension of the dynamically updated geological spatiotemporal correlation field, the correlation combination of the spatial distribution characteristics of geological bodies and the interaction characteristics of geological media is extracted, the interaction forms of the two in space are analyzed, and the triggering conditions and manifestations of spatial interaction are defined. The correlation combination of geological stress temporal characteristics and geological environment correlation characteristics is extracted from the temporal correlation dimension of the dynamically updated geological spatiotemporal correlation field. The interaction law between the two in time is analyzed, and the evolution stages of time action and the characteristics of each stage are divided. We extract cross-dimensional association combinations of different types of feature information from the spatiotemporal cross-association dimensions of dynamically updated geological spatiotemporal correlation fields, analyze the transmission paths of cross-dimensional interactions, and define the transmission start point, transmission node, and transmission end point. Based on spatial interaction patterns, temporal interaction rules, and cross-dimensional transmission paths, a classification framework for the interaction effects of multi-source data is constructed. This classification framework includes spatial coupling effects, temporal synergistic effects, and cross-dimensional transmission effects. To address the spatial coupling effect, the effects of different spatial association combinations are integrated to form a spatial coupling effect sequence, which reflects the progressive relationship of spatial effects and the process of expanding the scope of influence. To address the temporal synergy effect, the evolutionary stage characteristics of different temporal association combinations are integrated to form a temporal synergy effect sequence, which reflects the continuity and intensity change trend of the time effect. For cross-dimensional transmission effects, transmission path information of different cross-dimensional association combinations is integrated to form a cross-dimensional transmission effect sequence. The cross-dimensional transmission effect sequence reflects the transmission efficiency and superposition effect of cross-dimensional action. The spatial coupling effect sequence, the temporal synergistic effect sequence, and the cross-dimensional transmission effect sequence are sorted according to the actual components of the geological structure, so that each effect sequence can correspond to each link of the geological structure from the surface to the core. Based on the physical connection relationship and mechanical transmission characteristics of each link of the geological structure, the sorted spatial coupling effect sequence, temporal synergistic effect sequence and cross-dimensional transmission effect sequence are connected in series to form an initial geological structure coupling effect chain. Each geological structure link in the initial geological structure coupling effect chain corresponds to one or more interaction effects. The effect correlation of the initial geological structure coupling effect chain is strengthened to generate a geological structure coupling effect chain.

4. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 1, characterized in that, The process of injecting evolutionary intervention factors into the geological structure coupling effect chain, and correcting the evolutionary direction of the geological structure coupling effect chain through the dynamic interaction between the evolutionary intervention factors and the geological structure coupling effect chain, to form a corrected geological structure coupling effect chain, includes: Based on the need for geological structure stability regulation, key factors that can affect the interaction effect of multi-source geological spatiotemporal data are screened. These key factors include geological medium improvement factors, stress regulation factors, environmental intervention factors, and spatial structure optimization factors. For each key factor, extract its core attributes that can affect the coupling effect. The core attributes include the mode of action, the scope of action, the intensity of action, and the time-effect characteristics, forming a set of attribute descriptions corresponding to each key factor. The attribute description set of each key factor is transformed into a standardized evolutionary intervention factor. The evolutionary intervention factor has a unified representation form and can directly interact with the effect links in the geological structure coupling effect chain. The effect characteristics of each effect link in the geological structure coupling effect chain are analyzed, the sensitive evolutionary intervention factor types and sensitivity threshold ranges corresponding to each effect link are defined, and a matching relationship table between effect links and evolutionary intervention factors is formed. Based on the matching table of effect links and evolutionary intervention factors, the corresponding evolutionary intervention factors are precisely injected into each sensitive effect link of the geological structure coupling effect chain, so that the evolutionary intervention factors can directly act on the target effect. A dynamic interaction process is established for the coupling effect chain between evolutionary intervention factors and geological structures. This dynamic interaction process is used to simulate the interaction process between evolutionary intervention factors and coupling effects, reflecting the way evolutionary intervention factors regulate effects. The dynamic interactive process drives the interaction between evolutionary intervention factors and the geological structure coupling effect chain, and records the parameter change data of the corresponding effect link under the action of each evolutionary intervention factor. Based on the recorded data on changes in parameters of the effect links, the intensity and timeliness of the evolutionary intervention factors are adjusted according to the pre-defined relationship between intervention intensity and effect change. Record the evolution and change data of each effect link in the geological structure coupling effect chain during the interaction process, and conduct correlation analysis between the evolution and change data and the parameters of evolution intervention factors; Based on the results of correlation analysis, the effect parameters of each effect link in the geological structure coupling effect chain are updated to form a corrected geological structure coupling effect chain.

5. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 2, characterized in that, Based on the principle of spatiotemporal coordinate consistency, coordinate mapping rules are established for spatial feature information, temporal feature information, cross-feature information, and interactive feature information. These rules define the association logic of different types of feature information under the same spatiotemporal coordinates, including: Integrate the spatiotemporal coordinate types corresponding to spatial feature information, temporal feature information, cross feature information and interactive feature information, unify the coordinate representation standard, so that spatial coordinates adopt the same three-dimensional coordinate system and temporal coordinates adopt the same time measurement standard. Analyze the correlation between spatiotemporal coordinates during the evolution of geological structures, determine the correspondence between spatial and temporal coordinates, and define the evolutionary correlation of different spatial locations at different points in time; To establish a point-to-point mapping rule for the relationship between spatial and temporal feature information, the spatial coordinates of each spatial feature information can be mapped to specific temporal coordinates of the temporal feature information, forming a spatiotemporal coordinate pair. To address the association between cross-feature information and spatial and temporal feature information, coordinate range mapping rules are established so that the spatiotemporal coordinate range of cross-feature information can cover the spatial coordinates of the corresponding spatial feature information and the temporal coordinates of the temporal feature information. To establish coordinate trajectory mapping rules for the association between interactive feature information and spatial feature information, temporal feature information, and cross feature information, the spatiotemporal trajectory coordinates of interactive feature information can be dynamically associated with the spatiotemporal coordinates of the other three types of feature information, thus reflecting the spatiotemporal continuity of the interaction process. Define the association conditions for different types of feature information under each mapping rule. The association conditions are determined based on the physical characteristics of the geological structure and the inherent logic of the data. Define the priority order of mapping rules. When there are multiple association logics under the same spatiotemporal coordinates, select the dominant association logic according to the priority order. A dynamic adjustment method for mapping rules is constructed. When the coordinate characteristics of newly acquired multi-source geological spatiotemporal data change, the specific parameters of the mapping rules are automatically adjusted, and the compatibility of the adjusted mapping rules with existing coordinate data is verified. By integrating the point-to-point mapping rules, range mapping rules, trajectory mapping rules, association conditions, priority order, and dynamic adjustment methods, coordinate mapping rules are generated, and the association logic of spatial feature information, temporal feature information, intersection feature information, and interaction feature information under the same spatiotemporal coordinates is defined.

6. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 3, characterized in that, The process of sorting the spatial coupling effect sequence, temporal synergistic effect sequence, and cross-dimensional transmission effect sequence according to the actual components of the geological structure, so that each effect sequence corresponds to each stage of the geological structure from the surface to the core, includes: To obtain the actual composition of the geological structure, define the complete geological structure segments from the surface to the core, and each geological structure segment corresponds to a specific structural morphology, material composition and functional characteristics. Analyze the spatial location characteristics of each geological structural segment to determine the specific range of each geological structural segment in the spatial distribution of geological structures and the spatial connection relationship between adjacent geological structural segments. Analyze the spatial range of each effect in the spatial coupling effect sequence, and match each spatial coupling effect with the corresponding geological structural segment to make the spatial range of the effect consistent with the spatial range of the geological structural segment; Analyze the temporal evolution characteristics of each geological structural segment, determine the temporal response patterns of each geological structural segment during the geological evolution process, and the temporal connection sequence with adjacent geological structural segments; Analyze the temporal action phase of each effect in the temporal synergistic effect sequence, and match each temporal synergistic effect with the corresponding geological structural segment to make the temporal action phase of the effect match the temporal response law of the geological structural segment. Analyze the cross-dimensional interaction characteristics of each geological structural segment, and determine the interaction mode and sequence of each geological structural segment with other geological structural segments under the spatiotemporal intersection dimension; The transmission path of each effect in the cross-dimensional transmission effect sequence is analyzed, and each cross-dimensional transmission effect is matched with the corresponding geological structural link, so that the transmission start point, transmission node and transmission end point of the effect correspond to the interaction relationship of the geological structural link; Based on the arrangement order of geological structural segments from the surface to the core, and the matching relationship between the spatial coupling effect sequence and the geological structural segments, the spatial coupling effect sequence is sorted so that the order of the spatial coupling effect sequence is consistent with the arrangement order of the geological structural segments. Using the same sorting method, the temporal synergistic effect sequence and the cross-dimensional transmission effect sequence were sorted respectively, so that the order of the temporal synergistic effect sequence and the cross-dimensional transmission effect sequence corresponded to the arrangement order of geological structural links from the surface to the core. Based on the interaction process of various geological structures, we check whether the sorted spatial coupling effect sequence, temporal synergistic effect sequence, and cross-dimensional transmission effect sequence cover all geological structure elements, and supplement the corresponding effect sequences for the uncovered elements.

7. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 4, characterized in that, The dynamic interaction process for establishing the coupling effect chain between evolutionary intervention factors and geological structures is used to simulate the interaction process between evolutionary intervention factors and coupling effects, reflecting the way evolutionary intervention factors regulate effects, including: The core attributes and characteristics of evolutionary intervention factors are analyzed, and parameters of their mode of action, intensity of action, duration of effect, and scope of influence are extracted to form a set of parameters for evolutionary intervention factors. The effect characteristic parameters of each effect link in the geological structure coupling effect chain are analyzed, including effect intensity parameters, effect duration parameters, effect influence range parameters, and effect evolution rate parameters, forming a set of effect parameters; Based on the principles of geological structure mechanics and data interaction theory, a basic framework for dynamic interaction processes is constructed. The basic framework includes an evolutionary intervention factor input module, an effect receiving module, an interactive calculation module, and a result output module. In the evolutionary intervention factor input module, an input interface for the set of evolutionary intervention factor parameters is established, and data validation rules are set to validate the format and range of the input evolutionary intervention factor parameters. In the effect receiving module, an interface for receiving the effect parameter set is established to receive real-time data of the effect parameters of each effect link in the geological structure coupling effect chain. In the interactive calculation module, an interactive calculation logic for evolutionary intervention factor parameters and effect parameters is designed. The interactive calculation logic is determined based on the intrinsic relationship between the two and includes calculation methods for superposition of effect intensity and correction of effect evolution rate. Dynamic adjustment parameters are set for the interactive computing logic, and these dynamic adjustment parameters are adjusted based on the change data of effect parameters under the influence of evolutionary intervention factors; In the results output module, construct the output format of the effect parameter change results, and define the output change parameter type, change magnitude and change trend description; By utilizing historical multi-source geological data and corresponding intervention case data, the interactive calculation logic and dynamic adjustment parameters in the dynamic interactive process are calibrated.

8. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 1, characterized in that, The method of tracing the evolution and origin information of geological structures based on the modified geological structure coupling effect chain generates geological structure stability prediction results and targeted evolution intervention schemes, including: The effect parameter change trajectory of each effect link is extracted from the modified geological structure coupling effect chain. The effect parameter change trajectory includes the specific values ​​and change trends of the effect parameters at different spatiotemporal nodes, forming a set of effect evolution trajectories. Based on the set of effect evolution trajectories, the initial triggering factors and key influencing factors in the evolution process of each effect are traced, the origin and development path details of the effect are defined, and effect source tracing information is formed; Integrate the effect source information of all effect links, analyze the overall evolution source information of the geological structure, and define the evolution path, key evolution nodes and core factors driving the evolution of the geological structure from the initial state to the current state. Based on the core evaluation index of geological structure stability, we correlate evolutionary tracing information with the core evaluation index to analyze the degree and direction of influence of core factors on each core evaluation index. Based on the degree and direction of impact, predict the trend of geological structure stability changes in different time periods in the future, and identify the types of potential stability risks and the spatiotemporal range of risk occurrence. Based on the predicted trend of stability changes and risk information, and according to the preset stability level classification standard, the current stability level of each region of the geological structure is determined, and the distribution information of stable regions, risk warning regions and high-risk regions is output. For risk warning areas and high-risk areas, based on the corresponding evolutionary tracing information and risk types, the core driving factors and target stability states that need to be adjusted are determined, and evolutionary intervention targets are generated. Based on the evolutionary intervention objectives, intervention methods that can act on the core driving factors are selected. These intervention methods include media improvement methods, stress regulation methods, environmental optimization methods, and structural reinforcement methods. The selected intervention methods are combined, and the timing, scope, intensity, and order of implementation of each intervention method are defined to generate an intervention strategy. By integrating stability prediction results, regional distribution information, evolutionary intervention targets and intervention strategies, a complete geological structure stability prediction result and a targeted evolutionary intervention plan are generated. The geological structure stability prediction result is used to present the stability status and risk trend.

9. The intelligent geological structure stability detection method based on multi-source data fusion according to claim 2, characterized in that, The method of dynamically iteratively updating the initial spatiotemporal correlation matrix using spatiotemporal transmission weights incorporates feature changes from newly acquired multi-source geological spatiotemporal data, supplements the multi-source feature association combinations under newly added spatiotemporal coordinates, and forms an updated spatiotemporal correlation matrix, including: A dynamic update triggering method for the initial spatiotemporal correlation matrix is ​​established, which automatically starts the matrix update process when the newly acquired multi-source geological spatiotemporal data reaches the preset update threshold. The spatiotemporal coordinates and feature information of the newly acquired multi-source geological spatiotemporal data are analyzed. According to the established coordinate mapping rules, the spatiotemporal coordinate positions of the newly acquired multi-source geological spatiotemporal data and their correlation with existing feature information are determined. Based on the calculation rules of spatiotemporal transmission weight, the spatiotemporal transmission weight of the newly collected data feature information and the existing feature information is calculated. The new association combination and its corresponding spatiotemporal transmission weight are injected into the initial spatiotemporal association matrix, the matrix elements under the newly added spatiotemporal coordinates are supplemented, and the spatiotemporal coverage of the initial spatiotemporal association matrix is ​​expanded. The influence of new correlation combinations on the original correlation combinations in the initial spatiotemporal correlation matrix is ​​analyzed. Based on the interaction of spatiotemporal transmission weights, the spatiotemporal transmission weight values ​​of the original correlation combinations are adjusted so that the overall weight distribution of the matrix conforms to the evolution law of geological structure. The initial spatiotemporal correlation matrix is ​​dynamically iterated and updated by driving the spatiotemporal transmission weights to form candidate spatiotemporal correlation matrices. Based on the continuity and consistency of the spatiotemporal evolution of geological structures, the spatiotemporal coordinate connection relationship of each correlation combination in the candidate spatiotemporal correlation matrix is ​​checked. According to the characteristic change trend of newly collected data, new correlation combinations that may appear in the future are predicted, matrix expansion space is reserved in advance, and detailed information of each matrix update is recorded, including update time, number of new correlation combinations, weight adjustment, etc., to form a matrix update log. Through continuous dynamic iterative updates, the multi-source feature association combinations under new spatiotemporal coordinates are added, and the spatiotemporal transmission weights of the original combinations are adjusted to form an updated spatiotemporal association matrix.

10. An intelligent geological structure stability detection system based on multi-source data fusion, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the intelligent geological structure stability detection method based on multi-source data fusion as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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