Road foundation pit slope collapse detection method, system and equipment and storage medium

Abstract

The invention relates to the technical field of engineering monitoring data analysis, in particular to a road foundation pit slope collapse detection method, system and equipment and a storage medium, and the method comprises the following steps: analyzing resistivity data, constructing a boundary zone, combining fracture density with rock stratum crushing degree, adjusting modeling granularity, and analyzing construction load and stress response; evaluating disturbance intensity, extracting displacement data, screening point groups with consistent directions, predicting a displacement extension direction, detecting a saturation sign by combining rainfall and permeation data, and establishing a saturation diffusion surface. According to the method, resistivity changes are captured in real time through an electrode array, continuous boundary recognition is achieved, structural unit differences are determined, modeling precision is optimized by means of the rock stratum crushing degree and fracture dense distribution, construction equipment load characteristics are captured, the disturbance stress change trend is evaluated synchronously, and continuous displacement and boundary extension directions are determined. And the saturation accumulation area is identified by combining rainfall evaporation data and rock-soil permeability characteristics, so that the reliability of potential collapse risk positioning of the side slope is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering monitoring data analysis, and particularly to a highway foundation pit slope collapse detection method, system, device, and storage medium. Background Art

[0002] The technical field of engineering monitoring data analysis includes the whole process of collecting, processing, modeling, and analyzing the monitoring data of the operating states of various engineering structures, aiming to achieve the dynamic grasp of the engineering state and risk warning, including the sensing acquisition of monitoring information, data transmission, information fusion, anomaly identification, and data mining, etc. Through the comprehensive calculation and analysis of spatio-temporal data, the potential diseases or instability trends of structures are identified and applied to geological disaster prevention, urban infrastructure safety assessment, transportation engineering safety guarantee, etc. The systematic analysis of engineering monitoring data depends on the integration of multiple technologies, such as a sensor network for measuring physical parameters, a monitoring model based on time series analysis, a structural deformation trend judgment algorithm, and a risk index calculation system, which is an important part of ensuring the safety of major projects. Among them, the highway foundation pit slope collapse detection method refers to a technical method for carrying out real-time monitoring and anomaly identification for the possible unstable phenomena such as sliding and collapse of the foundation pit and its slope body formed by excavation along the highway during natural or construction processes, involving surface displacement monitoring, slope deformation trend extraction, risk factor identification, data modeling for collapse precursor identification, etc. Specifically, by deploying surface inclinometers, three-dimensional laser scanning systems, and image monitoring devices, etc., the geometric changes of the structure and the evolution information of cracks are collected, and based on the continuously changing data, the potential collapse positions and time windows are identified through threshold calculation methods, acceleration evolution methods, and curvature change analysis methods. The method uses quantitative analysis means to construct a multi-source heterogeneous data fusion model to realize the slope stability analysis and collapse pre-identification process based on physical field quantities.

[0003] The traditional highway foundation pit slope collapse detection technology directly measures the surface phenomena of structural deformation and crack changes by equipment, which is limited to the responsive tracking of existing deformations, and it is difficult to detect the abnormal changes and penetration accumulation states of internal microstructures in a timely manner, lacking in-depth correlation analysis of the deformation inducing factors. Under complex working conditions such as continuous rainfall or construction disturbances, it is difficult to identify the collapse trend in advance only by monitoring apparent data, resulting in early warning lag and delay of preventive measures, increasing the risk of engineering accidents. Summary of the Invention

[0004] The purpose of the present invention is to solve the defects existing in the prior art, and to propose a highway foundation pit slope collapse detection method, system, device, and storage medium.

[0005] In order to achieve the above purpose, the present invention adopts the following technical scheme: A highway foundation pit slope collapse detection method, including the following steps: S1: Analyze the resistivity data collected in the vertical and horizontal electrode arrays using the electrode layout array. According to the differences between adjacent measurement points, screen the candidate structural boundary nodes, combine with the slope direction of the terrain surface, identify the nodes with the same direction, construct a continuous boundary zone, and generate a boundary difference distribution map. S2: Call the boundary difference distribution map, analyze the cross-density degree of multiple unit fracture lines in the area, combine with the rock fragmentation degree of the area, adjust the modeling granularity levels of multiple units, construct a slope area model, and map the data collected by the stress sensors into the model. S3: Use the slope area model to analyze the construction behaviors in the target area, identify the load parameters and spatial action coordinates generated by the construction equipment, evaluate the change trend of the stress response at each monitoring point, evaluate the disturbance intensity of multiple construction behaviors, and establish disturbance change trend information. S4: Call the disturbance change trend information, extract the continuous displacement direction data recorded by the displacement monitoring points, calculate the displacement direction angle between adjacent monitoring points, screen the point groups with the same displacement direction, and compare with the trend information of the continuous boundary zone to predict the displacement extension direction and establish a slope deformation extension line.

[0006] As a further solution of the present invention, the boundary difference distribution map includes the structural boundary position, the resistivity change direction, and the resistivity difference amplitude. The slope area model includes the structural partition range, the modeling granularity level, and the sensor mapping unit. The disturbance change trend information includes the construction load application position, the stress change trend of the monitoring points, and the disturbance influence spatial range. The slope deformation extension line includes the displacement direction path, the fracture direction matching section, and the deformation trend continuous area.

[0007] As a further solution of the present invention, the specific steps for obtaining the boundary difference distribution map are as follows: S111: Obtain the electrode layout array, collect the resistivity measurement data of each node with vertically and horizontally arranged electrodes, number and mark the spatial positions of the measurement points to form a basic sequence of the resistivity of the measurement points, and obtain the archived data of the resistivity measurement points. S112: According to the archived data of the resistivity measurement points, extract the resistivity difference and spatial distance between adjacent measurement points, calculate the difference significance score for each pair of measurement points, screen the candidate structural boundary nodes, and output the candidate set of the structural interface. S113: According to the candidate set of the structural interface, compare the included angle between the resistivity change direction of each candidate node and the corresponding terrain slope direction, identify the nodes with the same direction, construct a continuous boundary zone, and obtain the boundary difference distribution map.

[0008] As a further solution of the present invention, the specific steps for obtaining the slope area model are as follows: S211: Invoke the boundary difference distribution map, divide the target area into multiple structural units, extract the spatial distribution of fracture lines within each unit, analyze the intersection angles and the number of intersection points between adjacent fracture lines, evaluate the structural interference degree within the unit according to the fracture line distribution density, and obtain the fracture structure intersection degree distribution map; S212: According to the fracture structure intersection degree distribution map, obtain the fracture intersection degree, rock formation fragmentation degree, and structural feature offset degree data corresponding to each unit, calculate the modeling granularity level score for each unit, and obtain the modeling granularity level distribution map; S213: According to the modeling granularity level distribution map, establish a three-dimensional structure model of the target slope area, invoke the coordinates of the structure model, and combine with the layout position information of the stress sensors to align and map the real-time data of the corresponding sensors to the three-dimensional structure grid nodes to obtain the slope area model.

[0009] As a further solution of the present invention, the steps for obtaining the disturbance change trend information are specifically as follows: S311: Invoke the slope area model, collect the equipment load parameters and spatial action coordinates of the construction behavior, record the construction behavior time series, and combine with the stress monitoring point distribution to establish a load action information group; S312: According to the load action information group, analyze the response change sequence of each stress monitoring point during continuous construction, calculate the stress change amplitude, change rate, and spatio-temporal correlation, and establish a stress response change trend group; S313: Based on the load action information group and the stress response change trend group, use the influence range, average load amplitude, stress change rate, and spatial dispersion of monitoring points of each type of construction behavior to calculate the disturbance intensity score, evaluate the disturbance intensity of various construction behaviors, and establish the disturbance change trend information.

[0010] As a further solution of the present invention, the steps for obtaining the slope body deformation extension line are specifically as follows: S411: Invoke the disturbance change trend information, collect the continuous displacement direction data recorded by multiple displacement monitoring points in the area, calculate the coordinate difference vector between consecutive moments of each monitoring point, extract the continuous displacement direction, and generate a displacement direction angle sequence; S412: According to the displacement direction angle sequence, obtain the displacement direction angle between each adjacent monitoring point, screen the point groups with consistent displacement directions, and obtain the point group screening record; S413: According to the point group screening record, obtain the extension vector of the point group, and compare it with the trend information of the continuity boundary band to predict the displacement extension direction of the slope area and establish the slope body deformation extension line.

[0011] As a further solution of the present invention, the method further includes: S5: Based on the deformation extension line of the slope body, obtain the rainfall and evaporation monitoring data in the construction area for multiple consecutive periods. Combine the surface infiltration characteristics and the water-holding capacity data of the rock and soil, analyze the time difference and cumulative gap between rainfall input and the change of water retention in the stratum, identify the mismatched area between the continuous infiltration accumulation state and the slope body absorption rate, detect the signs of saturation process accumulation, and establish an environmentally induced saturation diffusion surface; The environmentally induced saturation diffusion surface includes rainfall infiltration distribution, the range of water retention change in the rock and soil, and the saturation accumulation response area.

[0012] As a further solution of the present invention, the steps for obtaining the environmentally induced saturation diffusion surface are specifically as follows: S511: Based on the deformation extension line of the slope body, obtain the rainfall and evaporation monitoring data in the construction area for multiple consecutive periods, calculate the difference between rainfall and evaporation in multiple time periods, and generate a water difference sequence; S512: Combine the water difference sequence, and by analyzing the surface infiltration characteristics and the water-holding capacity of the rock and soil of the target slope, evaluate the cumulative gap between rainfall input and the change of water retention in the stratum per unit time to obtain the analysis result of seepage characteristics; S513: Based on the analysis result of the seepage characteristics, identify the mismatched area between the continuous infiltration accumulation state and the slope body absorption rate, detect the signs of saturation process accumulation, and establish an environmentally induced saturation diffusion surface.

[0013] A highway foundation pit slope collapse detection system, which is used to execute the above-mentioned highway foundation pit slope collapse detection method. The system includes: The boundary recognition module uses an electrode layout array to collect vertical and horizontal electrode data, analyzes the resistivity difference of each monitoring point, screens the structural boundary nodes whose spatial coordinates are consistent with the slope direction, constructs a continuous boundary band, and generates a boundary difference distribution map; The structure modeling module performs spatial density analysis on the intersection density of fracture lines in each unit based on the boundary difference distribution map, adjusts the spatial division granularity of multiple units in combination with the degree of rock stratum fragmentation, performs spatial mapping on the monitoring data continuously collected by the stress sensors, and obtains the slope area model according to the three-dimensional structure model of the slope area; The disturbance evaluation module, based on the slope area model, collects the operation data of construction equipment in the target area, identifies the load parameters and acting coordinates, performs time-series correlation on the construction load and the response data of the stress monitoring points, analyzes the disturbance effects of various types of construction behaviors within the spatial range, evaluates the disturbance intensity of the construction behaviors, and establishes disturbance change trend information; Based on the information on the trend of disturbance changes, the deformation tracking module extracts the continuous direction change records of multiple displacement monitoring points in the area, calculates the spatial direction angles between adjacent monitoring points, screens out the point groups with consistent directions, combines with the trend of the continuous boundary zone, predicts the displacement extension direction, and establishes the slope deformation extension line. Based on the slope deformation extension line, the infiltration and diffusion module collects the rainfall and evaporation amounts at each monitoring point within consecutive periods, combines with the surface infiltration characteristics and the water-holding capacity parameters of the rock and soil, analyzes the cumulative difference between the rainfall input and the water-holding change within the time period, locates the mismatch area between the infiltration accumulation rate and the water-holding rate, detects the signs of accumulation during the saturation process, and establishes the environmentally induced saturation diffusion surface.

[0014] On the other hand, a highway foundation pit slope collapse detection device is provided. The highway foundation pit slope collapse detection device includes: a processor; a memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, any one of the methods in the above-mentioned highway foundation pit slope collapse detection method is implemented.

[0015] On the other hand, a computer-readable storage medium is provided. At least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by the processor to implement any one of the methods in the above-mentioned highway foundation pit slope collapse detection method.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by using the electrode array to capture the resistivity changes in real time, continuous boundary recognition is achieved and the differences between structural units are clarified. The modeling accuracy is optimized by means of the degree of rock formation fragmentation and the dense distribution of fractures. The load characteristics of construction equipment are accurately captured and the trend of disturbance stress changes is evaluated synchronously. The continuous displacement and the boundary extension direction are clarified. By combining the rainfall and evaporation data and the rock and soil infiltration characteristics, the saturation accumulation area is accurately identified, effectively improving the reliability of locating the potential collapse risk of the slope. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the working process of the present invention; Figure 2 It is a flowchart for obtaining the boundary difference distribution diagram of the present invention; Figure 3 It is a flowchart for obtaining the slope area model of the present invention; Figure 4 It is a flowchart for obtaining the information on the trend of disturbance changes of the present invention; Figure 5 It is a flowchart for obtaining the slope deformation extension line of the present invention; Figure 6 It is a flowchart for obtaining the environmentally induced saturation diffusion surface of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0020] Embodiment 1: Please refer to Figure 1 , the present invention provides a technical solution: a method for detecting the collapse of a highway foundation pit slope, including the following steps: S1: Using an electrode layout array, analyze the resistivity data collected in the vertical and horizontal electrode arrays. According to the differences between adjacent measurement points, screen candidate structural boundary nodes, combine the terrain surface slope direction, identify nodes with the same direction, construct a continuous boundary band, and generate a boundary difference distribution map; S2: Call the boundary difference distribution map, analyze the cross-density of multiple unit fracture lines in the area, combine the rock fracture degree of the area, adjust the modeling granularity level of multiple units, construct a slope area model, and map the data collected by the stress sensor into the model; S3: Using the slope area model, analyze the construction behavior of the target area, identify the load parameters and spatial action coordinates generated by the construction equipment, evaluate the change trend of the stress response of each monitoring point, evaluate the disturbance intensity of multiple construction behaviors, and establish disturbance change trend information; S4: Call the disturbance change trend information, extract the continuous displacement direction data recorded by the displacement monitoring points, calculate the displacement direction angle between adjacent monitoring points, screen the point groups with the same displacement direction, and compare them with the trend information of the continuous boundary band to predict the displacement extension direction and establish a slope deformation extension line; S5: According to the slope deformation extension line, obtain the rainfall and evaporation monitoring data for multiple consecutive periods in the construction area, combine the surface infiltration characteristics and the rock and soil water holding capacity data, analyze the time difference and cumulative gap between rainfall input and formation water holding change, identify the mismatch area between the continuous infiltration accumulation state and the slope absorption rate, detect the signs of saturated process accumulation, and establish an environment-induced saturated diffusion surface.

[0021] The boundary difference distribution map includes the structural boundary position, the resistivity change direction, and the resistivity difference amplitude. The slope area model includes the structural partition range, the modeling granularity level, and the sensor mapping unit. The disturbance change trend information includes the construction load application position, the stress change trend of the monitoring points, and the disturbance influence spatial range. The slope deformation extension line includes the displacement direction path, the fracture direction matching section, and the deformation trend continuous area. The environment-induced saturation diffusion surface includes the rainfall infiltration distribution, the range of soil and rock water retention change, and the saturation accumulation response area.

[0022] Please refer to Figure 2 , the steps for obtaining the boundary difference distribution map are specifically as follows: S111: Obtain the electrode layout array, collect the resistivity measurement data of each node of the vertically and horizontally arranged electrodes, number the measurement points and mark their spatial positions to form the basic sequence of the resistivity of the measurement points, and obtain the archived data of the resistivity measurement points; When obtaining the electrode layout array, first arrange the electrode array in a grid pattern with a vertical and horizontal spacing of 1.5 m in the target slope area. Set 8 groups of vertical electrode nodes in the depth direction and 12 groups of nodes in the horizontal direction, forming a total of 96 cross-measurement points. Assign a number to each measurement point and record its three-dimensional spatial coordinates. The numbering sequence extends horizontally from the left side of the top of the slope to the right end of the slope bottom in turn, forming a data structure matrix with a numbering rule. At the same time, use a resistivity imaging instrument to perform a complete resistivity acquisition on all electrode measurement points. The current injection interval is set to 5 seconds, and the duration of each injection is 0.5 seconds. Record the measured resistivity values of each group of measurement points in turn. After the acquisition is completed, correspond the original resistivity data with the measurement point numbers, and combine the coordinate values recorded by the GNSS positioning device to establish the basic data sequence of the resistivity measurement points. Subsequently, archive the resistivity values in the form of a two-dimensional matrix according to the measurement point numbers. Each measurement point records a set of data including the number, three-dimensional coordinates, original resistivity value, and acquisition timestamp dataset, and finally forms the complete archived data of the resistivity measurement points for subsequent analysis and call.

[0023] S112: According to the archived data of the resistivity measurement points, extract the resistivity difference and spatial distance between adjacent measurement points, and use the formula: ; Calculate the difference significance score for each pair of measurement points, screen the candidate structural boundary nodes, and output the candidate set of the structural interface; Among them, is the difference significance score for the th pair of measurement points, is the normalized value of the resistivity difference between the th pair of measurement points, which is obtained by separately extracting the original resistivity values of this pair of measurement points, calculating their difference, and then dividing by the range of the resistivity differences of all pairs of measurement points. [[ID=XX]] is the The normalized value of the spatial distance between measurement points is obtained by calculating the Euclidean distance from the three-dimensional spatial coordinates of the pair of measurement points and then normalizing all distance data. is the The moisture content of the area between the measurement points is the average moisture content value recorded by the humidity sensor deployed at the grid position where the pair of measurement points is located. is the The slope value of the area between the measurement points is calculated based on the terrain elevation data on the projection line segment between the pair of measurement points, dimensionless. is the total number of measurement point pairs, and k represents the corresponding measurement point pair number; According to the resistivity measurement point archived data, extract any two adjacent measurement point pairs resistivity difference , the calculation method is: for the measurement points extract their resistivity values respectively , the difference between the two is , and then perform normalization processing to obtain , the distance calculation part extracts the corresponding measurement point three-dimensional coordinates , the Euclidean distance is , and then normalize to get , the humidity value is collected by the humidity sensor embedded in the grid where the pair of measurement points is located, and recorded as the average humidity value per unit time. The slope value is obtained by obtaining the elevation change on the projection line segment between the two measurement points and the horizontal distance calculated, , after dimensionless, it is the input value. Subsequently, substitute the above parameters into the following formula: ; Taking a specific example, the number of a certain measurement point pair is (k = 5), and the resistivity of the corresponding measurement points is , , then: ; In the sample dataset , we get: ; coordinates , we get the distance: ; During the normalization process, if , we get: ; The average value of the regional moisture content sensor is , and the slope value is , substituting them into the above formula, we get: ; When the cumulative sum of the known denominator terms is 134.2, there is: ; Among them, the difference significance score refers to the multi-factor normalized response intensity calculated for any two adjacent electrode measurement points, which is used to characterize the probabilistic quantification index of whether the measurement point is located in the potential structure boundary region, used to represent the relative concentration of the mutation trend in the resistivity spatial field, and further used to construct a continuous path recognition mechanism for the boundary line. In practical engineering applications, a measurement point pair with a higher score means that it exhibits heterogeneous properties in the three-dimensional structures of structure, topography, and hydrology, so it is preferentially retained as a boundary candidate node. Finally, the spatial distribution of this score sequence forms the basis of the entire boundary difference distribution map, which is used to determine the subsequent modeling granularity, perturbation direction, and structure evolution path. Through the above calculations, the same processing is performed on all measurement point pairs to extract the significance scores of the node pairs that form the candidate boundary set.

[0024] Table 1 Resistivity Measurement Point Normalization and Significance Scoring Table: ; Table 1 lists the main physical parameters of different measurement point pairs and the calculated significance scores, and the candidate nodes of the structural interface can be screened according to the scores.

[0025] S113: According to the candidate set of the structural interface, compare the included angle between the resistivity change direction of each candidate node and the corresponding topographic slope direction, identify the nodes with the same direction, construct a continuous boundary band, and obtain the boundary difference distribution map; Based on the candidate set of the structural interface, extract the resistivity change direction where each node is located one by one, that is, the resistivity change vector direction from the previous measurement point to the current measurement point, and calculate the included angle between this vector and the topographic slope orientation vector , and the method is to use the spatial vector included angle formula , where is the resistivity change direction vector, is the slope direction vector. After calculating the included angle, extract the nodes that satisfy , that is, the node group with the same direction is regarded as the node group with the same direction, construct a boundary path with direction continuity, connect this node group to form a boundary band, and draw a two-dimensional boundary difference map in combination with its spatial distribution relationship. Taking the candidate nodes 5 and 6 after the above screening as an example, the resistivity vector direction is 15 degrees east of south, and the slope direction calculated from the DEM elevation data is 10 degrees east of south, then , which meets the continuity judgment condition and is included in the construction of the continuous boundary path. Finally, all nodes that meet the direction consistency requirements form a boundary band to support subsequent modeling. ​

[0026] Please refer to Figure 3 , and the steps for obtaining the slope area model are specifically as follows: S211: Call the boundary difference distribution map, divide the target area into multiple structural units, extract the spatial distribution of fracture lines within each unit, analyze the intersection angles and the number of intersection points between adjacent fracture lines, evaluate the structural interference degree within the unit according to the fracture line distribution density, and obtain the fracture structure intersection degree distribution map; When calling the boundary difference distribution map, first divide the target slope area into several structural units according to the equivalent area. Each unit is a square area of 10m×10m, and a total of 64 structural units are divided. The numbering method is marked as S1 to S64 in the order from the upper left to the lower right. Through the boundary belt trend and intensity distribution data identified in the boundary difference distribution map, overlay the geological structure layer, and extract the spatial distribution data of all fracture lines within each unit, including the starting and ending point coordinates, length, trend, and dip angle information of the fracture lines. Number the fracture lines and count the intersection angles between each pair of fracture lines. The intersection angle is calculated as the absolute value of the included angle between two fracture line vectors, and the number of intersection points is realized by calculating the projection intersection points of the fracture lines. For example, three fracture lines f1, f2, and f3 are identified in the structural unit S17, where the included angle between f1 and f2 is 24°, the included angle between f2 and f3 is 18°, and there are two intersection points, so the number of intersection points is 2. After recording the intersection angles and the number of intersection points of all units, calculate the fracture density as the ratio of the total length of the fracture lines to the unit area, and perform normalization processing on the density values of all units. Finally, integrate and output the average fracture intersection angle, the number of intersection points, and the fracture density of each unit to form the fracture structure intersection degree distribution map as the input for the subsequent grain size level evaluation.

[0027] S212: According to the fracture structure intersection degree distribution map, obtain the fracture intersection degree, the degree of rock layer fragmentation, and the degree of structural feature offset data corresponding to each unit, and use the formula: ; Calculate the modeling grain size level score of each unit to obtain the modeling grain size level distribution map; Among them, is the modeling grain size level score of the i-th unit, is the normalized fracture line density of the i-th unit, which is obtained by normalizing the ratio of the number of fracture lines in the unit to the unit area, is the average normalized fracture line density of all units in the slope area, which is obtained by averaging all ; is the normalized longitudinal structure offset value of the i-th unit, which is obtained by calculating and normalizing the angle offset between the fracture lines in the unit and the longitudinal main structure direction, is the normalized lateral structural offset value of the $i$-th unit, obtained by calculating the offset of the fracture line within the unit from the lateral main structural direction and normalizing it. is the normalized degree of rock stratum fragmentation of the $i$-th unit, obtained by evaluating the integrity index from the core sampling results and performing normalization processing. is the index number of the structural unit within the modeling area; According to the fracture structure intersection degree distribution map, extract the fracture line density of the $j$-th structural unit , and for each unit, obtain each parameter according to the following steps: First, calculate the fracture line density , where is the total length of all fracture lines within the unit, is the unit area. Normalize all from the minimum value to the maximum value to , and then calculate the average value of all to obtain . Then, extract the angle between the trend direction of all fracture lines in this unit and the regional longitudinal main structural direction (assumed to be 20° east of north). After taking the absolute value and averaging, normalize it to obtain . Calculate the normalization result of the angle between the fracture line and the lateral main structural direction (assumed to be 15° south of east) in the same way as , and the degree of rock stratum fragmentation According to the core samples obtained from the geological drilling of this unit, conduct an analysis of the rock mass integrity index . If $RQD = 55\%$, then after normalization (assuming the normalization range 0–1 corresponds to $RQD$ 100–0%). Substitute the above parameters into the following modeling granularity level calculation formula: ; Taking the structural unit S17 as an example, , , , , , substituting into the formula gives: ; Among them, the modeling granularity level score is a quantitative expression of the modeling accuracy requirements for each unit within the slope area, comprehensively considering the fracture line intersection complexity (local structural disturbance risk), structural feature offset intensity (possible geometric anomaly trend), and rock stratum integrity (medium continuity reliability). The higher the value, the higher density of grid modeling strategy needs to be adopted for the unit area to enhance the ability to capture the response of micro-stresses and micro-displacements in this area; a lower value indicates that a coarser grid strategy can be appropriately adopted for this area to improve the operation efficiency. This scoring mechanism can be directly used as the basis for determining the grid division density, allocating the regional accuracy during the modeling stage, and inputting parameters for subsequent deformation trend and disturbance response prediction. Calculate the granularity scores of all structural units by the same method , and divide the modeling granularity levels according to the value range, such as being low granularity, being medium granularity, being high granularity.

[0028] Table 2 Calculation parameter table for modeling granularity levels: ; As shown in Table 2, the modeling granularity level scores of each unit are calculated through unified normalization and vector angle analysis, providing the basis for modeling level division.

[0029] S213: According to the modeling granularity level distribution map, establish a three-dimensional structure model of the target slope area, call the coordinates of the structure model, and combine with the layout position information of the stress sensors to align and map the real-time data of the corresponding sensors to the three-dimensional structure grid nodes to obtain the slope area model; According to the modeling granularity level distribution map, divide the space levels of the target slope area into three types of units: high granularity, medium granularity, and low granularity. Use a three-dimensional modeling tool to construct a structure model, set the minimum unit side length of the grid to 0.5m for the high granularity area, 1m for the medium granularity area, and 2m for the low granularity area. Uniformly generate a three-dimensional grid body, establish a unique spatial index at each grid node, match the coordinates with the stress sensor layout table, and interpolate and match the stress sensors arranged inside the structural unit to the structural grid nodes according to their spatial coordinates. If the matching accuracy error is less than 0.15m, then bind this grid node, and push and update the stress data collected by the sensor to the corresponding grid point attributes of the structure model in real time. The completed three-dimensional structure model contains the spatial distribution information of the fracture structure and the dynamic change information of the real-time stress data, constituting a complete slope area model.

[0030] Please refer to Figure 4 , and the specific steps for obtaining the disturbance change trend information are as follows: S311: Call the slope area model, collect the equipment load parameters and spatial action coordinates of the construction behavior, record the construction behavior time series, and combine with the stress monitoring point distribution to establish a load action information group; When calling the slope area model, first locate each construction area unit in the model, extract the construction log and the operation record of the construction machinery, identify the current construction behavior category (such as piling, excavation, concrete pouring), retrieve the real-time positioning coordinate information of the construction equipment recorded by the construction monitoring system, record its spatial action point set, and then obtain the vertical pressure data applied to the slope area by this type of construction equipment (such as a crawler pile driver) per unit time during actual operation. After taking the average, it is used as the load parameter value of this type of equipment. For example, during a certain piling construction, the average value of the single vertical load applied by the equipment is 27.5 kN. The spatial action coordinates of the equipment are matched to specific grid nodes through the GNSS positioning system. The start and end times of the construction are marked as 08:30 to 09:10 respectively. A time stamp sequence is constructed for each construction period, and data such as the construction equipment number, type, load parameter, action coordinates, and time series are combined into a structured data table. According to the monitoring point layout diagram, all stress sensors within the construction action area are spatially associated to establish a construction load action information group.

[0031] S312: According to the construction load action information group, analyze the response change sequence of each stress monitoring point during continuous construction, calculate the stress change amplitude, change rate, and spatio-temporal correlation, and establish a stress response change trend group; Based on the established construction load action information group, extract the stress monitoring point numbers and their time series stress data within the action area during construction one by one, analyze the stress change curves of each monitoring point in the three stages before, during, and after construction, and calculate the change amplitude of the stress response , change rate , where is the time interval between the stress peak and the starting point. The spatio-temporal correlation is evaluated by the spatial distribution consistency of the change amplitudes between measurement points within different time windows. For example, within the piling construction action area, the stress of sensor S23 rises from 42.3 kPa to 71.2 kPa during 08:30 to 09:10, with an amplitude of 28.9 kPa, a duration of 40 minutes, and a change rate of 0.7225 kPa / min. The change amplitudes of adjacent points S24 and S25 are 27.5 and 30.2 kPa respectively. A correlation coefficient analysis is carried out on the change consistency among the three points. If the Pearson coefficients are all greater than 0.75, it is recorded as high spatio-temporal correlation. Finally, the change amplitude, rate, and correlation index of each monitoring point are classified and recorded according to the construction behavior to establish a stress response change trend group.

[0032] S313: Based on the construction load action information group and the stress response change trend group, using the influence range, average load amplitude, stress change rate, and spatial dispersion of monitoring points of each type of construction behavior, adopt the formula: ; Calculate the disturbance intensity score, evaluate the disturbance intensity of various construction behaviors, and establish the disturbance change trend information; Among them, is the disturbance intensity score of the th type of construction behavior, is the normalized value of the influence range area corresponding to the th type of construction behavior, which is obtained by constructing a convex hull area through the extension boundary of the spatial action point of the construction load, calculating the actual area, and then performing maximum-minimum normalization processing, is the normalized value of the average load amplitude corresponding to the th type of construction behavior. The single-unit load amplitude value sequence generated by the construction equipment of this type during the specified construction stage is collected, and the average value is taken and then normalized, is the normalized value of the stress change rate corresponding to the th type of construction behavior. For the stress monitoring points in the affected area, the slope of its continuous response curve is calculated and averaged, and then normalized, is the normalized value of the spatial dispersion of the stress response points under the influence of the th type of construction behavior. The spatial dispersion of the points is calculated through the variance of the nearest neighbor distances between the monitoring points and then normalized, represents the category number of the construction behavior; Based on the load action information group and the stress response change trend group, for each type of construction behavior (numbered ), its influence area range is statistically analyzed, a convex hull area of the construction space action coordinates is constructed and the area is calculated. For example, the convex hull area constructed by the coordinate distribution of the action points of the first type of construction (concrete pouring) is 168 m². If the maximum-minimum normalization range is 50 m² to 300 m², the normalization result is: ; The same applies to the normalization of the average load amplitude. Assuming that the average unit load of this type of equipment is 12.8 kN and the normalization range is 5 - 35 kN, we get: ; For the normalization of the stress change rate, assuming that its average change rate is 0.51 kPa / min and the maximum-minimum value range is 0.1 - 1.2, then: ; The spatial dispersion is calculated by the variance of the nearest neighbor distances of 5 sensors in the affected area, which is set to 0.042 m², and the maximum-minimum normalization range is 0.01 - 0.12, we get: ; Substitute the above normalization results into the disturbance intensity formula: ; During the calculation , so: ; Among them, the disturbance intensity score is a numerical index that quantitatively describes the ability of each type of construction behavior to cause stress field disturbance in a specific area. It belongs to a multi-factor fusion type evaluation parameter. The higher its value, the more significant the disturbance effect is produced by this type of construction behavior under the conditions of a wide spatial range, strong load, strong response of monitoring points, and high local concentration. It is used to assist in distinguishing the interference strength of various construction methods on the slope structure stability risk, and provides quantitative support for construction zoning management, interference impact warning, and stability control. In engineering applications, the disturbance intensity score will be used as the main driving quantity in the trend chart to mark the disturbed sections and provide input basis for subsequent tasks such as time series prediction and dynamic regulation.

[0033] Table 3 Construction Behavior Disturbance Intensity Parameters and Calculation Results Table: ; As shown in Table 3, the disturbance intensity scores corresponding to different construction behaviors can be used to compare the disturbance capabilities of various behaviors on the slope area. The results show that pile driving operations have a stronger disturbance influence, and the calculated score is the highest, providing a reference for the disturbance level for the next deformation prediction.

[0034] Please refer to Figure 5 , and the specific steps for obtaining the slope deformation extension line are as follows: S411: Call the disturbance change trend information, collect the continuous displacement direction data recorded by multiple displacement monitoring points in the area, calculate the coordinate difference vector between consecutive moments for each monitoring point, extract the continuous displacement direction, and generate a displacement direction angle sequence; When calling the disturbance change trend information, first extract the real-time displacement data of multiple displacement monitoring points arranged in the slope area. In the specific implementation process, displacement sensors such as GNSS devices perform continuous coordinate measurements every 5 minutes on site. Taking four adjacent monitoring points numbered P1, P2, P3, and P4 in the area as an example, the continuous records at a certain construction period are 3 time nodes: , , , and the example of the coordinate change of the monitoring points is as follows: The coordinate of monitoring point P1 changes from (20.0m, 40.0m) to (20.3m, 40.1m), and then to (20.5m, 40.2m), and so on for other points. For each monitoring point, calculate the coordinate difference vector between adjacent moments. For example, for monitoring point P1 at time node to The coordinate difference vector between them is (0.3 m, 0.1 m). Then, the continuous displacement direction of this monitoring point is extracted, and the direction angle is calculated based on the due north direction in the coordinate system. For example, the direction angle corresponding to the above vector is 18.4° east of north. Similarly, the continuous displacement directions of the coordinate difference vectors of all monitoring points at each moment are calculated to form a complete displacement direction data set. Then, the included angle differences between each pair of displacement direction vectors between adjacent time nodes are calculated and sorted into a continuous displacement direction included angle sequence for subsequent analysis.

[0035] S412: According to the displacement direction included angle sequence, obtain the displacement direction included angles between each adjacent monitoring point, screen out the point groups with consistent displacement directions, and obtain the point group screening records; Use the displacement direction included angle sequence for further analysis. Obtain the included angles between the displacement directions of adjacent monitoring points one by one, using the spatial vector included angle calculation method. For example, taking monitoring points P1 and P2 as an example, the displacement directions of P1 in adjacent time periods are 18.4° east of north and 19.0° respectively, and those of P2 are 18.7° east of north and 19.3° respectively. Then, the included angle differences between the displacement directions of adjacent points are |18.4° - 18.7°| = 0.3° and |19.0° - 19.3°| = 0.3°, indicating a high consistency in the displacement directions between the two points. Apply this calculation method to the entire monitoring point network and use an included angle threshold for consistency judgment. The threshold is set to 5°. When the included angle difference is less than the threshold, it is considered that the displacement directions are consistent. For example, among the monitoring points in the entire area, the included angles between P1, P2, and P3 are 0.3° and 2.5° respectively, then they are included in the same group to form a monitoring point group with the same movement trend; if the direction included angle of monitoring point P4 is 6.5°, then this point is processed separately and not included in this point group. Finally, all monitoring points are classified, and a complete displacement direction consistency point group screening record is output for subsequent displacement direction trend analysis.

[0036] S413: According to the point group screening records, obtain the extension vectors of the point groups, compare them with the trend information of the continuous boundary band, predict the displacement extension direction of the slope area, and establish the slope deformation extension line; Based on the point group records with consistent displacement directions obtained through screening, further determine the displacement extension direction of the slope area. First, calculate the overall extension vector of each point group. The calculation method is to perform vector averaging on the continuous displacement vectors of each monitoring point in each point group. For example, the displacement vectors of the point group with consistent displacement directions (P1, P2, P3) are averaged to form an extension vector (0.32 m, 0.12 m), and then convert this extension vector into a direction angle. Taking the example data calculation, assuming the average extension vector is (0.32 m, 0.12 m), the direction angle is 20.6° east of north. Next, call the previously constructed continuity boundary belt trend information. If the continuity boundary belt trend is 22° east of north, compare the extension vector with the boundary trend information to judge the consistency of the displacement extension trend. Calculate the included angle difference as |20.6° - 22°| = 1.4°, indicating that the displacement extension direction is highly consistent with the boundary belt trend. Therefore, confirm that the displacement extension direction of this group is along the boundary belt direction, and accordingly establish a slope deformation extension line diagram to complete the regional displacement trend analysis and subsequent deformation warning.

[0037] Table 4 Calculation table of continuous displacement directions and included angles of monitoring points: ; As shown in Table 4, the calculation results of the displacement difference vectors, displacement direction angles, and included angle differences of the monitoring points clearly reflect the spatial consistency of the regional displacement direction, facilitating further judgment of the extension direction and drawing of the slope deformation line.

[0038] Please refer to Figure 6 , the steps for obtaining the environmentally induced saturated diffusion surface are specifically as follows: S511: According to the slope deformation extension line, obtain the rainfall and evaporation monitoring data for multiple consecutive periods in the construction area, calculate the difference between rainfall and evaporation in multiple time periods, and generate a moisture difference sequence; According to the deformation extension line of the slope body, extract the rainfall and evaporation data recorded by meteorological monitoring stations located in the key deformation areas for multiple consecutive periods (each period is 7 days). The periods are selected for a total of four weeks (28 days) during the critical construction period. The monitoring data includes the daily rainfall (mm) and evaporation (mm). For example, within the first period, the rainfall amounts for 7 consecutive days are 6.2 mm, 3.4 mm, 7.1 mm, 5.0 mm, 2.3 mm, 0 mm, and 4.8 mm respectively, and the evaporation amounts are 3.5 mm, 2.1 mm, 3.2 mm, 4.0 mm, 5.0 mm, 6.1 mm, and 4.5 mm respectively. Calculate the difference between the daily rainfall and evaporation within each period. For example, the difference between rainfall and evaporation on the first day of the first period is 6.2 mm - 3.5 mm = 2.7 mm. After calculating day by day, obtain the daily water balance value, and then summarize it into the cumulative water difference value for each period. For example, the cumulative water difference within the first period is 4.5 mm. Calculate the cumulative value for each period in turn to form a complete 28-day periodic water difference sequence, and use this sequence as an important basis for subsequent infiltration analysis.

[0039] S512: Combine the water difference sequence, and by analyzing the surface infiltration characteristics and rock and soil water-holding capacity of the target slope, evaluate the cumulative gap between rainfall input and formation water-holding change per unit time to obtain the analysis result of infiltration characteristics; Combine the generated water difference sequence above to further analyze the surface infiltration characteristics and rock and soil water-holding capacity of the target slope area. First, use the in-situ infiltration test on site and the indoor soil test data to determine the infiltration coefficient of the target area. For example, the in-situ infiltration test results show that the infiltration coefficient of the sandy clay on the surface layer of the slope is , and the infiltration coefficient of the deep rock and soil is approximately . At the same time, analyze the rock and soil water-holding capacity based on the core test obtained by drilling. For example, the porosity measurement results show that the porosity of the surface soil is 34% and the porosity of the deep rock mass is 12%. Combine the monitoring data of the pore water pressure sensor to evaluate the gap between the rainfall infiltration input per unit time of the formation and the actual water-holding change. Take each period as the analysis unit. For example, the cumulative water difference in the first period is 4.5 mm. Considering the surface runoff loss coefficient of 0.3, the actual rainfall entering the soil layer is about 3.15 mm. According to the comparison of the infiltration coefficient and water-holding capacity, calculate the actual formation storage capacity to be about 2.5 mm, and obtain the cumulative gap of 0.65 mm, which is recorded as the core parameter of the infiltration analysis for this period. After 4 periods, form continuous cumulative gap data and output it as the analysis result of the slope infiltration characteristics.

[0040] S513: According to the analysis result of infiltration characteristics, identify the mismatched area between the continuous infiltration accumulation state and the slope body absorption rate, detect the signs of saturation process accumulation, and establish an environmentally induced saturation diffusion surface; Based on the analysis results of the slope seepage characteristics, the continuous cumulative gap data is regionally evaluated, and the matching degree between the cumulative seepage state and the actual absorption rate of the slope body is further analyzed. Taking the data of 0.65 mm in the first cycle, 1.1 mm in the second cycle, 1.8 mm in the third cycle, and 2.2 mm in the fourth cycle as an example, it is determined that the regional seepage accumulation trend gradually increases, while the actual absorption rate of the slope shows an obvious downward trend with the increase of depth, indicating that there are obvious mismatches in some areas. By evaluating the pore water pressure data at different depths of the slope, such as the pore water pressures monitored at depths of 3 m, 5 m, and 7 m are 15 kPa, 24 kPa, and 33 kPa respectively, the continuous increase in pressure means that the signs of regional seepage saturation accumulation are relatively obvious. Especially in the deep areas with relatively low local permeability coefficients, the water cannot diffuse in time, and the rising speed of the pore water pressure is significantly greater than the formation absorption capacity, which is determined as the potential saturation accumulation area. Combining the above information, the obvious mismatch areas are marked, their spatial distribution contours are clarified, and a complete environmental-induced saturation diffusion surface is established as an important basis for slope stability analysis and safety monitoring.

[0041] Table 5 Monitoring data table of slope seepage accumulation and pore water pressure: ; As shown in Table 5, the rising trends of the cumulative gap and pore water pressure in each cycle of the slope clearly reflect the gradual intensification of regional seepage saturation, support the process of establishing the environmental-induced saturation diffusion surface, and clearly show obvious mismatches and water accumulation phenomena in the slope area.

[0042] The highway foundation pit slope collapse detection system is used to execute the above-mentioned highway foundation pit slope collapse detection method. The system includes: The boundary recognition module uses the electrode layout array to collect vertical and horizontal electrode data, analyzes the resistivity differences of each monitoring point, screens the structural boundary nodes whose spatial coordinates are consistent with the slope direction, constructs a continuous boundary band, and generates a boundary difference distribution map; The structural modeling module conducts spatial density analysis on the cross-density of fracture lines in each unit based on the boundary difference distribution map, adjusts the spatial division granularity of multiple units in combination with the rock fracture degree, performs spatial mapping on the monitoring data continuously collected by the stress sensors, and obtains the slope area model according to the three-dimensional structure model of the slope area; The disturbance evaluation module, based on the slope area model, collects the operation data of construction equipment in the target area, identifies the load parameters and action coordinates, correlates the construction load with the response data of the stress monitoring points in time series, analyzes the disturbance effects of various types of construction behaviors within the spatial range, evaluates the disturbance intensity of the construction behaviors, and establishes the disturbance change trend information; Based on the disturbance change trend information, the deformation tracking module extracts the continuous direction change records of multiple displacement monitoring points in the area, calculates the spatial direction angles for adjacent monitoring points, screens out the point groups with consistent directions, combines with the trend of the continuous boundary zone, predicts the displacement extension direction, and establishes the slope deformation extension line. Based on the slope deformation extension line, the infiltration and diffusion module collects the rainfall and evaporation at each monitoring point within consecutive periods, combines with the surface infiltration characteristics and the water holding capacity parameters of the rock and soil, analyzes the cumulative difference in rainfall input and water holding change within the time period, locates the mismatch area between the infiltration accumulation rate and the water holding rate, detects the signs of saturation process accumulation, and establishes the environmentally induced saturation diffusion surface.

[0043] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A highway foundation pit slope collapse detection method, characterized in that Including the following steps: S1: Using the electrode layout array, analyze the resistivity data collected in the vertical and horizontal electrode arrays. According to the differences between adjacent measurement points, screen the candidate structural boundary nodes, combine with the slope direction of the terrain surface, identify the nodes with the same direction, construct a continuous boundary zone, and generate a boundary difference distribution map; S2: Call the boundary difference distribution map, analyze the cross-density of multiple unit fracture lines in the area, combine with the rock fragmentation degree of the area, adjust the modeling granularity level of multiple units, construct a slope area model, and map the data collected by the stress sensors into the model; S3: Using the slope area model, analyze the construction behavior of the target area, identify the load parameters and spatial action coordinates generated by the construction equipment, evaluate the change trend of the stress response at each monitoring point, evaluate the disturbance intensity of multiple construction behaviors, and establish disturbance change trend information; S4: Call the disturbance change trend information, extract the continuous displacement direction data recorded by the displacement monitoring points, calculate the displacement direction angle between adjacent monitoring points, screen the point groups with the same displacement direction, and compare with the trend information of the continuous boundary zone to predict the displacement extension direction and establish the slope deformation extension line.

2. The highway foundation pit slope collapse detection method according to claim 1, characterized in that, The specific steps for obtaining the boundary difference distribution map are as follows: S111: Obtain the electrode layout array, collect the resistivity measurement data of each node with vertical and horizontal electrodes, number and mark the spatial positions of the measurement points to form a basic sequence of resistivity of the measurement points, and obtain the archived data of the resistivity measurement points; S112: According to the archived data of the resistivity measurement points, extract the resistivity difference and spatial distance between adjacent measurement points, calculate the difference significance score of each pair of measurement points, screen the candidate structural boundary nodes, and output the candidate set of the structural interface; S113: According to the candidate set of the structural interface, compare the included angle between the resistivity change direction of each candidate node and the corresponding terrain slope direction, identify the nodes with the same direction, construct a continuous boundary zone, and obtain the boundary difference distribution map.

3. The highway foundation pit slope collapse detection method according to claim 2, characterized in that, The specific steps for obtaining the slope area model are as follows: S211: Call the boundary difference distribution map, divide the target area into multiple structural units, extract the spatial distribution of the fracture lines in each unit, analyze the intersection angle and the number of intersection points between adjacent fracture lines, and evaluate the structural interference degree in the unit according to the fracture line distribution density to obtain the fracture structure cross-degree distribution map; S212: According to the fracture structure cross-degree distribution map, obtain the data of the fracture cross-degree, rock fragmentation degree, and structural feature offset degree corresponding to each unit, calculate the modeling granularity level score of each unit, and obtain the modeling granularity level distribution map; S213: According to the modeling granularity level distribution map, establish a three-dimensional structure model of the target slope area, call the coordinates of the structure model, and combine with the layout position information of the stress sensors to align and map the real-time data of the corresponding sensors to the three-dimensional structure grid nodes to obtain the slope area model.

4. The highway foundation pit slope collapse detection method according to claim 3, characterized in that The specific steps for obtaining the disturbance change trend information are as follows: S311: Invoke the slope area model, collect the equipment load parameters and spatial action coordinates of the construction behavior, record the construction behavior time series, and combine with the stress monitoring point distribution to establish a load action information group; S312: According to the load action information group, analyze the response change sequence of each stress monitoring point during continuous construction, calculate the stress change amplitude, change rate and spatio-temporal correlation, and establish a stress response change trend group; S313: Based on the load action information group and the stress response change trend group, use the influence range, average load amplitude, stress change rate, and spatial dispersion of monitoring points of each type of construction behavior to calculate the disturbance intensity score, evaluate the disturbance intensity of multiple construction behaviors, and establish disturbance change trend information.

5. The highway foundation pit slope collapse detection method according to claim 4, characterized in that The specific steps for obtaining the slope deformation extension line are as follows: S411: Invoke the disturbance change trend information, collect the continuous displacement direction data recorded by multiple displacement monitoring points in the area, calculate the coordinate difference vector between consecutive moments of each monitoring point, extract the continuous displacement direction, and generate a displacement direction angle sequence; S412: According to the displacement direction angle sequence, obtain the displacement direction angle between each adjacent monitoring point, screen the point groups with consistent displacement directions, and obtain the point group screening record; S413: According to the point group screening record, obtain the extension vector of the point group, compare it with the trend information of the continuity boundary band, predict the displacement extension direction of the slope area, and establish a slope deformation extension line.

6. The highway foundation pit slope collapse detection method according to claim 1, characterized in that The method further includes: S5: According to the slope deformation extension line, obtain the rainfall and evaporation monitoring data for multiple consecutive periods in the construction area, combine the surface infiltration characteristics and soil and rock water holding capacity data, analyze the time difference and cumulative gap between rainfall input and formation water holding change, identify the mismatch area between continuous infiltration accumulation state and slope absorption rate, detect the signs of saturation process accumulation, and establish an environment-induced saturation diffusion surface.

7. The highway foundation pit slope collapse detection method according to claim 6, characterized in that The specific steps for obtaining the environment-induced saturation diffusion surface are as follows: S511: According to the slope deformation extension line, obtain the rainfall and evaporation monitoring data for multiple consecutive periods in the construction area, calculate the difference between rainfall and evaporation in multiple time periods, and generate a water difference sequence; S512: Combine the water difference sequence, and by analyzing the surface infiltration characteristics and soil and rock water holding capacity of the target slope, evaluate the cumulative gap between rainfall input and formation water holding change per unit time to obtain the analysis result of infiltration characteristics; S513: According to the analysis result of infiltration characteristics, identify the mismatch area between continuous infiltration accumulation state and slope absorption rate, detect the signs of saturation process accumulation, and establish an environment-induced saturation diffusion surface.

8. Highway foundation pit slope collapse detection system, characterized in that, The system is used to implement the highway foundation pit slope collapse detection method according to any one of claims 1-7. The system includes: The boundary recognition module uses the electrode layout array to collect the vertical and horizontal electrode data, analyzes the resistivity difference of each monitoring point, screens the structural boundary nodes with consistent spatial coordinates and slope directions, constructs a continuity boundary band, and generates a boundary difference distribution map; Based on the boundary difference distribution map, the structure modeling module conducts spatial density analysis on the crossing density of fracture lines within each unit, adjusts the spatial division granularity of multiple units in combination with the rock stratum fragmentation degree, performs spatial mapping on the monitoring data continuously collected by the stress sensors, and obtains the slope area model according to the three-dimensional structure model of the slope area; Based on the slope area model, the disturbance evaluation module collects the operation data of construction equipment in the target area, identifies the load parameters and acting coordinates, conducts time-series correlation between the construction load and the response data of the stress monitoring points, analyzes the disturbance effects of various types of construction behaviors within the spatial range, evaluates the disturbance intensity of the construction behaviors, and establishes the disturbance change trend information; Based on the disturbance change trend information, the deformation tracking module extracts the continuous direction change records of multiple displacement monitoring points in the area, calculates the spatial direction angles for adjacent monitoring points, filters out the point groups with consistent directions, and predicts the displacement extension direction in combination with the trend of the continuous boundary zone to establish the slope body deformation extension line; Based on the slope body deformation extension line, the infiltration and diffusion module collects the rainfall and evaporation amounts at each monitoring point within a continuous period, analyzes the cumulative difference between the rainfall input and the water retention change within the time period in combination with the surface infiltration characteristics and the rock and soil water retention capacity parameters, locates the mismatch area between the infiltration accumulation rate and the water retention rate, detects the signs of saturation process accumulation, and establishes the environment-induced saturation diffusion surface.

9. Highway foundation pit slope collapse detection equipment, characterized in that, The highway foundation pit slope collapse detection device includes: A processor; A memory, on which computer-readable instructions are stored. When the computer-readable instructions are executed by the processor, the method described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, Program code is stored in the computer-readable storage medium, and the program code can be called by the processor to execute the method described in any one of claims 1 to 7.

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