Land engineering monitoring and early warning method based on multi-source data fusion

Abstract

The invention relates to the technical field of state monitoring, in particular to a land engineering monitoring and early warning method based on multi-source data fusion, which comprises the following steps of: collecting spatial position records, generating a multi-source monitoring sequence set, identifying displacement records, associating the displacement records with a sight line observation direction, and forming an earth surface visual direction offset diffusion layer; and constructing a candidate three-dimensional displacement vector and generating a three-dimensional deformation fusion map, forming a hydraulic hysteresis coupling sequence group based on the deformation record and the water level record, and generating a land engineering monitoring and early warning grade group. According to the method, an association chain between monitoring data is constructed through combined processing logic of multi-source observation information, so that deformation records and sight observation form unified expression in direction association and space mapping, and a continuous deformation field is constructed in adjacent region difference arrangement of a candidate structure of three-dimensional displacement; the identification process of the engineering body state is expanded from single-source measurement to multi-dimensional information-oriented fusion judgment, and the integrity and coherence of a monitoring and early warning result are improved.

Description

Technical Field

[0001] This invention relates to the field of condition monitoring technology, and in particular to a land engineering monitoring and early warning method that integrates multi-source data. Background Technology

[0002] The field of condition monitoring technology mainly studies how to continuously collect and process the structural parameters, physical quantity changes, and environmental factors of target objects to determine their operational status, structural stability, and potential risks. This field covers core aspects such as sensor measurement, data acquisition, state quantity calculation, feature extraction, trend identification, and anomaly judgment. It relies on the systematic observation and analysis of multi-dimensional information to achieve comprehensive characterization and continuous tracking of the state changes of engineering or natural bodies. Traditional land engineering monitoring and early warning methods target land engineering objects such as surface deformation, foundation settlement, slope stability, and underground structure safety. These methods rely on the deployment of single sensors for measurement, such as using fixed inclinometers to record lateral displacement, single-point settlement plates to read settlement, and single rain gauges to reflect external conditions. The obtained single-source data is manually recorded and compared in chronological order, and then combined with preset thresholds to determine whether anomalies exist based on the increase or decrease of monitored quantities or changes in the time series. This approach relies solely on single-type measurement factors for analysis and lacks the processing and utilization of the correlation between data from different sources.

[0003] Existing technologies rely on single sensors to acquire surface deformation or external environmental quantities and record and compare them manually. The data sources are isolated in both spatial and typological dimensions, failing to reflect the coupling relationship between multiple types of measurements. Single-source measurements result in a lack of distinguishing criteria for the directionality and distribution of deformation causes. Structural state changes are judged solely by the increase or decrease of values, which is prone to misjudgment or omission of risk points. The manual comparison mode cannot maintain continuous and stable characteristics in terms of time steps, making it difficult to form a unified reference basis between different measurement types. The lack of description of the attribution relationship of measurement data in the spatial grid leads to the inability to characterize the correlation chain between surface deformation, groundwater changes, and slope structural state, resulting in problems such as scattered monitoring results and limited understanding of structural state. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a land engineering monitoring and early warning method based on multi-source data fusion, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a land engineering monitoring and early warning method based on multi-source data fusion, comprising the following steps: S1: Based on the spatial location records of the digital elevation grid within the land engineering monitoring area, add time stamps to the spatial location records according to the unified time synchronization, and map the monitoring equipment to the digital elevation grid units according to the spatial location mapping relationship to generate a multi-source monitoring sequence set; S2: Based on the multi-source monitoring sequence set, identify the directional correlation between the line-of-sight geometric vector parameters associated with the three-dimensional displacement records of the ground monitoring station and the line-of-sight observation records of the satellite, and generate a surface line-of-sight offset diffusion layer; S3: Using the aforementioned surface line-of-sight offset diffusion layer, calculate the spatial projection relationship between the line-of-sight geometric vector parameters associated with the satellite line-of-sight observation records of the digital elevation grid unit and the displacement direction of the monitoring station, construct multiple candidate three-dimensional displacement vectors, and generate a three-dimensional deformation fusion map; S4: Based on the three-dimensional deformation fusion map, analyze the deformation records of the slope grid near the groundwater level well or pore water pressure device, and perform interpolation pairing with the groundwater level or pore water pressure records at the same location at a uniform time step to generate a hydraulic hysteresis coupling sequence group.

[0005] As a further embodiment of the present invention, the multi-source monitoring sequence set includes time series units, spatial grid sequence units, and equipment corresponding sequence units; the surface line-of-sight offset diffusion layer includes a line-of-sight offset distribution field, a slope diffusion structure layer, and an attenuation diffusion zone; the three-dimensional deformation fusion map includes a three-dimensional deformation vector distribution volume, a grid difference correlation volume, and a deformation integration block; and the hydraulic hysteresis coupling sequence group includes an energy accumulation sequence, a hysteresis enclosure sequence, and a hydraulic coupling relationship sequence.

[0006] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Based on the spatial location records of the digital elevation grid within the land engineering monitoring area, perform time-series registration of the coordinate parameters and time synchronization parameters carried by the spatial location records, perform unified encoding processing on the time field of the location records, and generate spatial time-series encoded frames. S102: Call the spatial temporal coding frame to perform mapping retrieval of spatial coordinate parameters and monitoring device identification parameters, perform coordinate assignment calculation on the coordinate parameters corresponding to each device identification parameter and digital elevation grid unit parameters, and perform index sorting on the grid unit index and record time field obtained by the assignment calculation to obtain the device grid corresponding chain; S103: Based on the device grid corresponding chain, perform the acquisition interval verification on the grid cell index and the corresponding time field, perform difference operation on the time field and the acquisition interval parameter, and rearrange them in order according to the difference size to establish a multi-source monitoring sequence set.

[0007] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the multi-source monitoring sequence set, calculate the direction difference of the direction vector parameters of the line-of-sight geometric vector parameters associated with the three-dimensional displacement records of the ground monitoring station and the line-of-sight observation records, and perform vector angle calculation between the output value of the direction difference calculation and the direction vector parameters of the three-dimensional displacement records. Determine the direction association of the records based on the angle calculation results, and generate a set of direction association factors. S202: Call the aforementioned direction correlation factor set, perform difference calculation on the line-of-sight direction displacement component parameters and satellite line-of-sight observation component parameters, and compare the component difference values ​​obtained from the difference calculation with the line-of-sight direction displacement component parameters one by one to obtain the line-of-sight offset sequence; S203: Based on the line-of-sight offset sequence, perform distance weight distribution calculation on the offset component corresponding to the grid and the line connection parameter of the center point of the digital elevation grid, identify the diffusion distribution of the offset on the slope, and establish a surface line-of-sight offset diffusion layer.

[0008] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the surface line offset diffusion layer, perform vector projection calculation on the line offset component parameters of the grid cell and the line geometric vector parameters associated with the satellite line observation records of the digital elevation grid cell, and compare the projection component obtained by the vector projection calculation with the displacement direction component parameters of the monitoring station. By comparing the angle difference output by comparison, generate a spatial projection correlation matrix. S302: Call the spatial projection correlation matrix, perform vector recombination operation on each projection component parameter and the displacement direction component parameter of the monitoring station, and perform amplitude comparison on the components in the recombination operation so that each group of recombination results forms a three-dimensional vector set that can be used as displacement candidates. Then, perform amplitude sorting on the vector amplitude parameters of the three-dimensional vector set to obtain a three-dimensional displacement candidate sequence. S303: Based on the three-dimensional displacement candidate sequence, perform differential calculation on the component parameters of the three-dimensional displacement vector of the grid and the three-dimensional displacement vector of the neighboring grid, and perform serialization rearrangement so that the displacement distribution between grids presents a continuous differential distribution feature, and establish a three-dimensional deformation fusion map.

[0009] As a further aspect of the present invention, the vector projection calculation process specifically involves limiting a minimum projection amplitude threshold when performing projection decomposition on the view offset component parameters, so that projection components below the minimum projection amplitude threshold are eliminated before entering the spatial projection correlation matrix. The component comparison process specifically involves setting an upper limit parameter for the angle difference when outputting the angle difference between the projection component and the displacement direction component parameter of the monitoring station, so that the projection component exceeding the upper limit parameter for the angle difference is marked as a low correlation component before the recombination operation. The vector recombination operation specifically involves introducing an amplitude scaling factor when performing amplitude comparison between each projection component parameter and the displacement direction component parameter of the monitoring station, so that the amplitude parameter of any vector in the recombined three-dimensional vector set satisfies the amplitude range defined by the amplitude scaling factor. The process of performing differential calculation on the component parameters of the three-dimensional displacement vector of the grid and the three-dimensional displacement vector of the neighboring grid is specifically to limit the peak differential threshold of the differential result between any three-dimensional displacement vector of the grid and the corresponding three-dimensional displacement vector of the adjacent four-dimensional digital elevation grid cell, so that the differential result exceeding the peak differential threshold is preferentially adjusted to the differential continuous interval during serialization rearrangement.

[0010] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the three-dimensional deformation fusion map, target area screening is performed on the deformation component parameters of the slope grid near the location of the groundwater level well or the location of the pore water pressure device, and the time step is aligned between the screened deformation component parameters and the time field of the corresponding groundwater level record or pore water pressure record to generate a time series pairing result. S402: Call the time-series pairing result, perform a two-dimensional plane connection operation on the water level component parameters and displacement component parameters, and perform polyline reconstruction on the data points obtained from the connection operation, so that the polyline reconstruction result forms several closed segments in the plane, and then perform surface accumulation operation on the contour point coordinates of the closed segments to obtain the enclosed area sequence set; S403: Based on the enclosed area sequence set, perform sequence sorting on the area component parameters and corresponding time fields, and perform segment division operation on the sorted area component parameters to form a continuous segment structure in the time dimension of the enclosed area. Perform sequence convergence on the continuous segment structure to establish a hydraulic hysteresis coupling sequence group.

[0011] As a further aspect of the present invention, the target area screening process specifically involves limiting the peak allowable horizontal distance between the slope grid and the location of the groundwater well or the location of the pore water pressure device based on a distance threshold parameter, so that the slope grid deformation component parameters exceeding the distance threshold parameter are removed before time step alignment. The process of aligning the time field of the selected deformation component parameters with the time field of the corresponding groundwater level record or pore water pressure record is specifically to limit a unified time step parameter during the alignment process, so that the time field of any deformation component parameter strictly matches the unified time step parameter after interpolation correction. The process of performing a two-dimensional planar connection operation on the water level component parameters and the displacement component parameters specifically involves introducing a minimum connection interval threshold before forming data point connections, so that data point intervals below the minimum connection interval threshold do not participate in the polyline reconstruction. The process of performing surface accumulation and addition on the coordinates of the contour points of the closed section specifically involves performing a sequence consistency check on the coordinates of the contour points before surface accumulation and addition, so that the sequence arrangement of any contour point coordinates satisfies the closure requirement for area calculation. The process of performing segmentation operation on the area component parameters specifically involves limiting the variation range of the area component parameters based on the area fluctuation threshold parameter, so that the continuous segment structure after segmentation satisfies the condition of continuous area change.

[0012] As a further aspect of the present invention, the method further includes step S5: S5: Using the hydraulic hysteresis coupling sequence group, determine the joint performance between the energy change trend and the grid deformation amplitude change, perform a level classification on the combined performance of deformation amplitude and hydraulic energy change, assign risk level labels to grid units, and generate a land engineering monitoring and early warning level group. The land engineering monitoring and early warning level group includes a risk level identifier set, a level zoning unit set, and an early warning level rule set.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the hydraulic hysteresis coupling sequence group, perform trend comparison operation on the energy component parameters and the grid deformation amplitude component parameters, and synchronously verify the change difference output by the trend comparison with the time series field of the energy component. Through the synchronous verification result, perform difference aggregation on the joint relationship between energy change and deformation amplitude change to generate an energy-deformation joint matrix. S502: Call the energy-deformation joint matrix, perform a combined partitioning operation on the energy component parameters and the deformation amplitude component parameters, and perform interval comparison on the components in the combined partition to form a grade basis with amplitude segment characteristics in the combined interval. Perform discrete rearrangement on the amplitude segments in the grade basis to obtain the deformation-energy segment sequence. S503: Based on the deformation-energy segment sequence, perform a level mapping operation on the segment parameters and grid unit identifier parameters, and re-index the level index formed during the mapping operation with the grid unit identifier parameters so that each grid unit corresponds to a risk level identifier, and establish a land engineering monitoring and early warning level group.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a chain of correlations between monitoring data is constructed through the joint processing logic of multi-source observation information. This enables deformation records and line-of-sight observations to form a unified expression in directional correlation and spatial mapping. It also enables candidate structures of three-dimensional displacement to construct a continuous deformation field in the differential arrangement of adjacent areas. Furthermore, it enables water level changes and displacement changes to form a quantifiable energy expression in the alignment of time steps and the accumulation of enclosed area. Finally, it enables grid cells to obtain risk level identification under the combination of energy trends and deformation amplitudes. This systematically presents the intrinsic correlation between surface deformation and groundwater dynamics, and expands the identification process of engineering body status from single-source measurement to fusion judgment oriented towards multi-dimensional information, thereby improving the completeness and consistency of monitoring and early warning results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a land engineering monitoring and early warning method based on multi-source data fusion, comprising the following steps: S1: Based on the spatial location records of the digital elevation grid within the land engineering monitoring area, add time stamps to the spatial location records according to the unified time synchronization, map the monitoring equipment to the digital elevation grid units according to the spatial location mapping relationship, and arrange the spatial location records in chronological order according to the acquisition interval to generate a multi-source monitoring sequence set; S2: Based on the multi-source monitoring sequence set, identify the directional correlation between the line-of-sight geometric vector parameters associated with the three-dimensional displacement records of ground monitoring stations and the line-of-sight observation records of satellites. According to the connection of the center points of the digital elevation grid and the surface undulation characteristics, the line-of-sight offset records of the monitoring stations are gradually attenuated and diffused along the slope grid to generate a surface line-of-sight offset diffusion layer. S3: Using a surface line-of-sight offset diffusion layer, calculate the spatial projection relationship between the line-of-sight geometric vector parameters associated with satellite line-of-sight observation records of digital elevation grid units and the displacement direction of monitoring stations, construct multiple candidate three-dimensional displacement vectors and arrange them in sequence with the three-dimensional displacement vectors of neighboring grids to generate a three-dimensional deformation fusion map. S4: Based on the three-dimensional deformation fusion map, analyze the deformation record of the slope grid near the groundwater well or pore water pressure device, perform interpolation pairing with the groundwater level or pore water pressure record at the same location at a uniform time step, generate multiple closed areas by connecting the broken lines on the water level displacement plane, and calculate the cumulative area in the closed area as the energy reflection under hydraulic action to generate a hydraulic hysteresis coupling sequence group; S5: Using hydraulic hysteresis coupling sequence groups, determine the joint performance between energy change trend and grid deformation amplitude change, perform level classification on the combined performance of deformation amplitude and hydraulic energy change, assign risk level labels to grid units, and generate land engineering monitoring and early warning level groups. The multi-source monitoring sequence set includes time series units, spatial grid sequence units, and equipment-corresponding sequence units. The surface line-of-sight migration diffusion layer includes the line-of-sight migration distribution field, slope diffusion structure layer, and attenuation diffusion zone. The three-dimensional deformation fusion map includes the three-dimensional deformation vector distribution volume, grid difference correlation volume, and deformation integrated block. The hydraulic hysteresis coupling sequence group includes the energy accumulation sequence, hysteresis enclosure sequence, and hydraulic coupling relationship sequence. The land engineering monitoring and early warning level group includes the risk level identifier set, level zoning unit set, and early warning level rule set.

[0023] Please see Figure 2 The specific steps of S1 are as follows: S101: Based on the spatial location records of the digital elevation grid within the land engineering monitoring area, perform time-series registration of the coordinate parameters and time synchronization parameters carried by the spatial location records, perform unified encoding processing on the time field of the location records, and generate spatial time-series encoded frames. The data is stored as a CSV file containing heterogeneous data from multiple sources. The file details geospatial information collected throughout the day on December 10, 2024, by various types of terminals, including drones, mobile monitoring vehicles, and fixed monitoring posts. Data columns include device ID, etc. coordinate, coordinate, Coordinates and time of transmission, for example, the first line of the file records data collected by the drone. The fifth line records the data collected from the fixed monitoring piles. When time parameters exist in both string and Unix timestamp formats, the process involves reading the spatial location records, performing time-series registration between the coordinate parameters and the time synchronization parameters carried in the spatial location records (establishing various correspondences between coordinate points and time points), comparing the values ​​of the time fields in the coordinate parameters and the time synchronization parameters, and using a regular expression matching algorithm to identify the format type of the time fields. Recognize as a standard date and time string, The timestamp is identified as a double-precision floating-point Unix timestamp. Based on the comparison results, i.e., the different format types identified, a unified encoding process is performed on the time field of the location record. In this embodiment, the unified encoding format is set to a millisecond-level offset relative to the task's baseline time, and a baseline time parameter is defined. To monitor the time of midnight on the day of the task, i.e., "2024-12-1000:00:00.000", the corresponding Unix timestamp value is: The unified coding calculation formula is set as follows: ,in This represents the encoded time value. This represents the Unix timestamp value corresponding to the record to be converted. For time records in string format, they are first converted to Unix timestamps, such as the time recorded by a drone. Convert to Substituting into the formula, we get For the time recorded for fixed piles Substituting into the formula, we get The process iterates through each record in the file, performing the above conversion calculations one by one, replacing all the original heterogeneous time fields with uniform millisecond-level integer codes, and generating a spatial time-series encoded frame. S102: The spatial time-series encoded frame is called to perform mapping retrieval between spatial coordinate parameters and monitoring device identification parameters. The coordinate parameters corresponding to each device identification parameter are used to perform coordinate assignment calculations with digital elevation grid cell parameters. The grid cell index obtained from the assignment calculation is indexed and sorted with the record time field to obtain the device grid corresponding chain. The process involves mapping and retrieving spatial coordinate parameters to monitoring device identification parameters. Specifically, this involves building a hash map in memory, using the device identification parameters... As a key, the device contains , , Coordinates and The list of tuples is used as the value, for example, to retrieve the constructed tuples. , The data structure calculates the coordinate assignment of each device identifier parameter to the corresponding coordinate parameters and digital elevation grid cell parameters. Pre-defined grid parameters include the grid origin coordinates. for and the spatial span of a single grid cell The value here is set to 10 meters. Taking into account the distortion caused by latitude and longitude projection, the step size in the longitude direction is set accordingly. for degrees, step size in the latitude direction for The degree, the formula for calculating the affiliation is as follows: as well as ,in This indicates the floor function, which is used for rounding down. coordinates Substitute into the formula to calculate the column index. row index The point is determined to belong to the index identifier. After calculating the data points using the grid cells, the grid cell indexes obtained from the calculations are sorted with the record time field to reorganize the data. The triplet form is used to perform a multi-level sorting algorithm, according to... Sort in ascending order according to lexicographical order. Under the same circumstances, according to Sort the values ​​in ascending order, for example, in a grid. The data below is arranged as follows This yields the corresponding chain for the device mesh.

[0024] S103: Based on the equipment grid corresponding chain, the collection interval is checked between the grid cell index and the corresponding time field. The difference operation is performed on the time field and the collection interval parameter and the order is rearranged according to the difference value to establish a multi-source monitoring sequence set. Extract the monitoring records sorted by time contained within the data, verify the collection interval between the grid cell index and the corresponding time field, and define the collection interval parameter. This parameter is set according to the sampling frequency specifications for land engineering monitoring. The millisecond verification process involves performing a difference operation between adjacent time fields and the acquisition interval parameter. Specifically, within the same grid cell, the time code difference between two adjacent records is calculated. Assuming a grid There are three records in memory, with the timestamps as follows: (source ), (source ), (source First calculate and The difference milliseconds, calculation and The difference Milliseconds, introducing a deviation evaluation formula Calculate the deviation of each interval from the standard interval, and substitute the values ​​to obtain the deviation of the first pair of records. The deviation of the second pair of records After the calculation is completed, the values ​​are rearranged in order of magnitude of the difference, based on the deviation value. Sort the records in ascending order of priority, because ,determination The combined acquisition interval better meets the standard requirements and is superior. The combination of processes enables quantitative evaluation and screening of the temporal continuity of multi-source data within the same spatial grid. The sorted preferred record pairs and their corresponding spatiotemporal attribute parameters are encapsulated to establish a multi-source monitoring sequence set.

[0025] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the multi-source monitoring sequence set, the direction difference of the direction vector parameters of the line-of-sight geometric vector parameters associated with the three-dimensional displacement records of the ground monitoring station and the satellite line-of-sight observation records is calculated. The output value of the direction difference calculation is then used to perform vector angle calculation with the direction vector parameters of the three-dimensional displacement records. The direction association of the records is determined by the angle calculation result, and a set of direction association factors is generated. The dataset merges high-precision 3D point information from fixed ground observation stations with large-scale area observation data from satellite radar. It extracts 3D displacement records from ground monitoring stations within the same time window, recording specific displacement values ​​of ground points in the east, north, and vertical directions. For example, the records show a monitoring point moving 25 mm eastward, -15 mm northward, and dropping 8 mm vertically. Simultaneously, it reads the line-of-sight geometric vector parameters associated with the corresponding satellite line-of-sight observation records. Based on the incident angle and azimuth angle parameters of the satellite orbit, it synthesizes a unit vector representing the satellite observation direction. The direction difference is calculated between the direction vector parameters of the line-of-sight geometric vector parameters associated with the 3D displacement records of the ground monitoring stations and the satellite line-of-sight observation records. This process involves subtracting the satellite line-of-sight unit vector from each component of the normalized ground displacement direction vector. The corresponding components of the quantity are used to construct a difference vector describing the difference in geometrical line of sight. The output value of the direction difference calculation is used to perform a vector angle operation with the direction vector parameter of the three-dimensional displacement record. The inverse cosine value is obtained by calculating the dot product of the difference vector and the original ground displacement vector and dividing it by the product of their magnitudes. The actual angle between the two vectors in space is obtained. For example, if the angle is calculated to be 30 degrees, the angle operation result is used to determine the direction association of the record. The calculated angle value is compared with the preset judgment threshold range. If the angle is in the high association range between 0 degrees and 45 degrees, it is determined that the ground measured data and the satellite observation data are consistent in geometric trend. The record is assigned a corresponding association weight according to the cosine value of the angle. The record that passes the consistency verification and is accompanied by weight information is encapsulated to generate a direction association factor set.

[0026] S202: Call the direction association factor set, perform difference calculation on the line-of-sight direction displacement component parameters and the satellite line-of-sight observation component parameters, and compare the component difference values ​​obtained from the difference calculation with the line-of-sight direction displacement component parameters to obtain the line-of-sight offset sequence; For monitoring record pairs determined to have consistent geometric trends, a projection operation is performed. The difference between the displacement component parameters along the line of sight and the satellite line of sight observation component parameters is calculated. The process involves projecting the three-dimensional displacement vector recorded by the ground monitoring station according to the direction cosine of the satellite line of sight. This is done by multiplying the displacement components in the east, north, and sky directions by the axial coefficients corresponding to the satellite line of sight vector and summing the results. The theoretical projection length of the ground displacement along the satellite line of sight is calculated; for example, the calculated theoretical projection value is 12.05 mm. Simultaneously, the actual deformation observation value obtained from satellite interferometry at the same location point is extracted, for example, 10.50 mm. The difference between the two values ​​is calculated to be 1.55 mm. The absolute deviation in meters is calculated, and the component difference obtained from the difference calculation is compared with the displacement component parameter in the line of sight direction component. The calculated absolute deviation is divided by the theoretical projection value of the ground displacement to obtain a percentage value describing the degree of relative deviation, which in this example is 1.55 / 12.05≈12.86%. The comparison result is compared with a preset error stability threshold, for example, with 15% as the dividing line. If the calculated deviation rate is less than the threshold, the deviation is considered to be within a reasonable random error range and is retained as a correction parameter. If it exceeds the threshold, it is marked as an anomaly. The deviation values ​​verified by quantitative comparison and the corresponding spatial grid index are arranged in order to obtain the line of sight offset sequence.

[0027] S203: Based on the line-of-sight offset sequence, perform distance weight distribution calculation on the offset components corresponding to the grid and the line connection parameters of the center point of the digital elevation grid, identify the diffusion distribution of the offset on the slope, and establish a surface line-of-sight offset diffusion layer. This method provides line-of-sight direction error correction values ​​at the center points of discrete grids within the monitoring area. The processing aims to extend the influence of deviations at discrete points to the entire continuous surface monitoring area through spatial interpolation. It reads the geometric center coordinates of each grid cell in the digital elevation model (DEM), calculates the distance weight distribution for the offset components of the grid and the line parameters connecting the center points of the DEM grids, and for any target grid to be corrected within the area, calculates the Euclidean straight-line distance between it and the center points of every source grid with known offsets. For example, if the target grid is measured to be 50 meters away from a source grid, an inverse distance weight model is constructed based on the distance values. The closer the source grid is, the greater its influence on the target grid. The influence decreases exponentially with the distance. The weighted average of the offset values ​​of the surrounding source grids is calculated using the weighted coefficients to simulate the physical diffusion process of error or deformation characteristics on the continuous terrain slope. The estimated offset value at the target grid is calculated, and the diffusion distribution of the offset on the slope is identified. The effective influence radius of each source offset data is determined by setting a weight truncation threshold to prevent invalid long-distance data from interfering with the local calculation results. The smoothed correction value obtained from the grid calculation of the whole area is mapped back to the geospatial coordinate system to establish a surface view offset diffusion layer.

[0028] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the surface line offset diffusion layer, perform vector projection calculation on the line offset component parameters of the grid cells and the line geometry vector parameters associated with the satellite line observation records of the digital elevation grid cells, and compare the projection components obtained by the vector projection calculation with the displacement direction component parameters of the monitoring station. By comparing the angle difference output by comparison, generate a spatial projection correlation matrix. The line-of-sight offset component parameter of the grid cell is extracted. This parameter is a scalar value; for example, the offset value of grid "R63C51" is 1.55 mm. Simultaneously, the line-of-sight geometric vector parameter associated with the satellite line-of-sight observation record of the digital elevation grid cell is read. The record is a unit vector of the satellite line-of-sight direction after initial geometric correction. Vector projection calculation is performed on the line-of-sight offset component parameter of the grid cell and the line-of-sight geometric vector parameter associated with the satellite line-of-sight observation record of the digital elevation grid cell. Specifically, the line-of-sight offset scalar value is multiplied by the corrected line-of-sight direction unit vector to obtain a three-dimensional vector representation of the offset in space. For example, projecting the 1.55 mm offset onto the corrected line-of-sight direction yields a small three-dimensional spatial offset vector. Ground monitoring data is then used. The station records the original three-dimensional displacement direction component parameters, which are a unit vector representing the actual direction of ground motion. The projected components obtained by vector projection calculation are compared with the displacement direction component parameters of the monitoring station. The comparison process is to obtain the cosine value of the included angle by calculating the dot product of the two spatial vectors and dividing by their respective magnitudes, and then calculate the angle difference. For example, if the angle between the two direction vectors is calculated to be five degrees, the angle difference output by comparison is used to generate a quantitative index for each grid describing the consistency between the line of sight offset and the actual direction of ground motion. This index is stored as the correlation strength in a two-dimensional matrix structure. The rows and columns of this matrix correspond to the geographic index of the grid, and each element value in the matrix is ​​the angle difference of the corresponding grid or the correlation degree converted from it, thus generating a spatial projection correlation matrix.

[0029] S302: Call the spatial projection correlation matrix, perform vector recombination operation on each projection component parameter and the displacement direction component parameter of the monitoring station, and perform amplitude comparison on the components in the recombination operation so that each group of recombination results forms a three-dimensional vector set that can be used as displacement candidates. Then, perform amplitude sorting on the vector amplitude parameters of the three-dimensional vector set to obtain the three-dimensional displacement candidate sequence. For each grid cell with high correlation strength in the matrix, the projected component parameters and the corresponding monitoring station displacement direction component parameters are extracted. For example, a three-dimensional spatial offset vector obtained by projecting the line-of-sight offset scalar, and a unit vector representing the true direction of ground motion. A vector recombination operation is performed on each projected component parameter and the monitoring station displacement direction component parameter. Using these two vectors as basis vectors, a new set of spatial vectors is generated through linear combination. For example, the two vectors are weighted summed or orthogonally decomposed and then recombinated to explore the true displacement direction. In the recombination operation, the amplitude of the components is compared, referencing the total displacement recorded in the original ground displacement record. The recombined vectors are then compared. The magnitude is compared with the observed value to select vectors that are reasonable in terms of physical amplitude and eliminate unrealistic calculation results. This makes each group of recombined results form a set of three-dimensional vectors that can be used as displacement candidates. For example, for a mesh, after recombining and screening, three displacement candidate vectors that are reasonable in both direction and magnitude are obtained. Then, the magnitude parameters of the vectors in the three-dimensional vector set are sorted by amplitude. The magnitude of each candidate vector is calculated, which is the total displacement in three-dimensional space. The vectors in the set are arranged in descending or ascending order of magnitude value. The sorted displacement candidate vector list of each mesh is organized according to the mesh index order to obtain the three-dimensional displacement candidate sequence.

[0030] S303: Based on the candidate sequence of three-dimensional displacements, perform differential calculation on the component parameters of the three-dimensional displacement vector of the mesh and the three-dimensional displacement vector of the neighboring mesh, and perform serialization rearrangement so that the displacement distribution between meshes presents a continuous differential distribution feature, and establish a three-dimensional deformation fusion map. The optimal displacement vector is selected from the candidate sequence of each grid. The vector ranked first is chosen as the initial 3D displacement solution for the grid. Difference calculations are performed on the component parameters of the grid's 3D displacement vector and those of neighboring grids. This process, based on the physical assumption of spatial continuity in surface deformation, traverses each grid, calculating the differences between the eastward, northward, and celestial components of the initial displacement solution and their corresponding components in directly adjacent grids. For example, the difference between the eastward displacement component of grid A and its eastern neighbor, grid B, is calculated to obtain a difference value describing the local deformation gradient. This difference value is then serialized and rearranged. If the calculated difference value... If the displacement exceeds a reasonable threshold set based on geological conditions, it indicates that the initial displacement solution of the grid has a jump or error. The candidate sequence will be returned, and the differential calculation will be re-performed using the second-best sorted displacement vector until a candidate solution that makes the displacement change of the grid and its neighborhood the smoothest is found. Through this iterative adjustment, the displacement distribution between the grids will show a gradually changing continuous differential distribution characteristic throughout the monitoring area. The three-dimensional displacement vectors determined after the grid has been optimized by neighborhood coordination will be summarized and rendered as a color cloud map or vector field map covering the entire monitoring area to establish a three-dimensional deformation fusion map.

[0031] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the three-dimensional deformation fusion map, target area screening is performed on the deformation component parameters of the slope grid near the location of the groundwater level well or the location of the pore water pressure device. The time step is aligned between the screened deformation component parameters and the time field of the corresponding groundwater level record or pore water pressure record to generate time series pairing results. The process meticulously characterizes the precise deformation values ​​of each grid cell within the monitoring area in three-dimensional space. It then initiates a screening procedure targeting specific hydrogeologically affected areas, retrieving the geographic coordinates of groundwater level monitoring wells or pore water pressure gauges stored in the database. Target area screening is performed on the deformation component parameters of slope grids near groundwater level wells or pore water pressure gauges. This process involves traversing spatial distances, calculating the Euclidean distance between the geometric center coordinates of each slope grid and the coordinates of the nearest water level monitoring point, and comparing this distance value with a preset influence radius threshold. The influence radius is set at fifty meters. If the distance between a grid and water level well number three is thirty-five meters, then... The grid is identified as the controlled target grid. Deformation time series data containing eastward, northward, and vertical directions are extracted from the grid. At the same time, the groundwater level depth or pore water pressure readings of the corresponding monitoring wells within the same time span are read. The time step of the filtered deformation component parameters is aligned with the time field of the groundwater level record or pore water pressure record at the corresponding location. Since deformation monitoring (such as satellite overpass) is low-frequency discrete sampling, while water level monitoring is high-frequency continuous sampling, a linear interpolation algorithm is used. Taking the moment of deformation record as the reference, two adjacent moment points are found on the time axis of water level record. The instantaneous water level value at the moment of deformation is calculated using the linear proportional relationship, and the time series pairing result is generated.

[0032] S402: Call the timing pairing result, perform a two-dimensional plane connection operation on the water level component parameters and displacement component parameters, and perform polyline reconstruction on the data points obtained from the connection operation, so that the polyline reconstruction result forms several closed segments in the plane, and then perform surface accumulation operation on the contour point coordinates of the closed segments to obtain the enclosed area sequence set; The results include a series of corresponding data pairs of groundwater level values ​​and surface three-dimensional displacement values ​​at the same time. The processing aims to analyze the nonlinear response relationship between the two. A two-dimensional plane connection operation is performed on the water level component parameters and the displacement component parameters to construct a two-dimensional rectangular coordinate system. The groundwater level or pore water pressure values ​​are mapped to the horizontal axis, and the surface displacement values ​​(selecting the vertical or slope displacement most sensitive to hydrological changes) are mapped to the vertical axis. According to the chronological order of occurrence, the data of each discrete time point is projected onto the coordinate plane, and the data points obtained from the connection operation are reconstructed using polyline. Straight line segments are used to connect two adjacent projection points in time to simulate the dynamic trajectory of displacement with water level changes. Due to the viscoelastic or plastic characteristics of geological materials, the same... The displacement corresponds to different water level values. This phenomenon causes the trajectory lines to not completely overlap, but to form a loop structure similar to a hysteresis loop. This results in several closed segments in the plane from the reconstructed polyline. The closed polygons formed in each complete hydrological cycle are identified. Then, the coordinates of the contour points of the closed segments are accumulated and summed. Using the polygon coordinate analytical geometry algorithm, each vertex on the closed segment is traversed in a counterclockwise or clockwise direction. The product of the x-coordinate of the current vertex and the y-coordinate of the next vertex is calculated, and the product of the y-coordinate of the current vertex and the x-coordinate of the next vertex is subtracted. The calculation results of the vertices are accumulated and summed. Half of the absolute value is taken as the geometric area of ​​the closed segment. The area physically represents the energy dissipated by the strata under hydraulic action or the degree of irreversible deformation, thus obtaining the area sequence set of the enclosed area.

[0033] S403: Based on the enclosed area sequence set, perform sequence sorting on the area component parameters and corresponding time fields, and perform segment division operation on the sorted area component parameters to make the enclosed area form a continuous segment structure in the time dimension. Perform sequence convergence on the continuous segment structure to establish a hydraulic hysteresis coupling sequence group. The study recorded the hysteresis loop area values ​​triggered by each independent hydrological event within the monitoring period. Based on the start and end times of each closed segment, the area component parameters and corresponding time fields were sequenced. The calculated area values ​​were arranged chronologically along the time axis, forming a time-varying area change sequence. Segmentation operations were then performed on the sorted area component parameters. A baseline value for area stability based on statistical data was set, with the baseline area defined as five units. The area values ​​appearing consecutively in the sequence were compared with the baseline value. If the hysteresis loop area significantly increased within a certain period, it indicated that the strata... When the hydraulic coupling effect is enhanced or the stability is reduced within a time period, the entire time series is divided into different state intervals such as stable period, fluctuating period or accelerated deformation period according to the change characteristics of area value. This makes the area of ​​the enclosed area form a continuous segment structure in the time dimension. Feature aggregation is performed on the divided segments, and sequence convergence is performed on the continuous segment structure. Statistical features such as average area, peak area and duration within the same state interval are encapsulated to construct a complete logical chain describing the evolution of the formation hydraulic response characteristics over time. The chain can reflect the cumulative damage effect of slope or surface under repeated rise and fall of groundwater and establish a hydraulic hysteresis coupling sequence group.

[0034] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the hydraulic hysteresis coupling sequence group, a trend comparison operation is performed on the energy component parameters and the mesh deformation amplitude component parameters. The change difference output by the trend comparison is synchronously checked with the time series field of the energy component. The difference aggregation is performed on the joint relationship between energy change and deformation amplitude change through the synchronous check result to generate the energy-deformation joint matrix. The energy component parameters, i.e., the hysteresis loop area value in each hydrological cycle, and the corresponding grid deformation amplitude component parameters, i.e., the maximum cumulative displacement in the cycle, are extracted from the sequence. Trend comparison calculation is performed on the energy component parameters and the grid deformation amplitude component parameters to calculate the slope of change of energy component and deformation amplitude in continuous time window. For example, if the hysteresis energy of a certain grid increases by 30% in the last two cycles, while the deformation amplitude only increases by 5% in the same period, the difference between these two growth rate values ​​is compared. The change difference output by the trend comparison is synchronously checked with the time series field of the energy component to ensure that the two physical quantities involved in the comparison strictly correspond to the same hydrogeological period. The difference aggregation is performed on the joint relationship between energy change and deformation amplitude change through the synchronous check results. The calculated growth rate difference is used as a key indicator to measure the evolution of formation damage. If the energy growth is significantly faster than the deformation growth, it suggests that invisible structural damage or erosion is occurring inside the soil, generating an energy-deformation joint matrix.

[0035] S502: Call the energy-deformation joint matrix, perform a combined partitioning operation on the energy component parameters and the deformation amplitude component parameters, and perform interval comparison on the components in the combined partition to form a grade basis with amplitude segment characteristics. Perform discrete rearrangement on the amplitude segments in the grade basis to obtain the deformation-energy segment sequence. A two-dimensional threshold standard is set for classification. A combined partitioning operation is performed on the energy component parameter and the deformation amplitude component parameter to construct a two-dimensional evaluation plane with the energy change rate on the horizontal axis and the deformation change rate on the vertical axis. The plane is divided into four or more feature quadrants. For example, a region with high energy growth and high deformation growth is defined as a severe instability zone, and a region with high energy growth but low deformation growth is defined as a latent damage zone. Interval comparison is performed on the components in the combined partitioning to map each specific data point in the matrix to the above-defined region. For example, if a data point of a certain grid falls into the latent damage zone with an energy change rate greater than 20% and a deformation change rate less than 10%, the combined interval forms a grade basis with amplitude segment characteristics. A corresponding risk weight value is assigned to each region, such as five for the severe instability zone, four for the latent damage zone, and one for the stable zone. Discrete rearrangement is performed on the amplitude segments in the grade basis. The grids are reordered from high to low according to the risk weight value of the region they fall into. The grid data belonging to the same risk feature segment are aggregated together to obtain the deformation-energy segment sequence.

[0036] S503: Based on the deformation-energy segment sequence, perform a level mapping operation on the segment parameters and grid unit identifier parameters, and re-index the level index formed during the mapping operation with the grid unit identifier parameters so that each grid unit corresponds to a risk level identifier, and establish a land engineering monitoring and early warning level group. The process aims to transform mathematical risk ranking into intuitive engineering early warning labels. It performs a level mapping operation between section parameters and grid cell identifier parameters, pre-setting a standard engineering early warning color code or numerical level standard. For example, data in the highest-risk section is mapped to a red Level 1 warning, the second-highest-risk section to an orange Level 2 warning, and so on. It iterates through each data item in the sequence, binding the grid's unique identifier to the calculated warning level. The level index formed during the mapping operation is then re-indexed with the grid cell identifier parameters, creating an index table with the warning level as the key and the grid list as the value. Grid IDs marked as red Level 1 warnings are grouped into the same set, enabling rapid retrieval of high-risk areas requiring immediate intervention. This ensures that each grid cell corresponds to a risk level identifier, completing the transformation from multi-source data analysis to decision support and establishing a land engineering monitoring and early warning level group.

[0037] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A land engineering monitoring and early warning method based on multi-source data fusion, characterized in that, Includes the following steps: S1: Based on the spatial location records of the digital elevation grid within the land engineering monitoring area, add time stamps to the spatial location records according to the unified time synchronization, and map the monitoring equipment to the digital elevation grid units according to the spatial location mapping relationship to generate a multi-source monitoring sequence set; S2: Based on the multi-source monitoring sequence set, identify the directional correlation between the line-of-sight geometric vector parameters associated with the three-dimensional displacement records of the ground monitoring station and the line-of-sight observation records of the satellite, and generate a surface line-of-sight offset diffusion layer; S3: Using the aforementioned surface line-of-sight offset diffusion layer, calculate the spatial projection relationship between the line-of-sight geometric vector parameters associated with the satellite line-of-sight observation records of the digital elevation grid unit and the displacement direction of the monitoring station, construct multiple candidate three-dimensional displacement vectors, and generate a three-dimensional deformation fusion map; S4: Based on the three-dimensional deformation fusion map, analyze the deformation records of the slope grid near the groundwater level well or pore water pressure device, and perform interpolation pairing with the groundwater level or pore water pressure records at the same location at a uniform time step to generate a hydraulic hysteresis coupling sequence group.

2. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 1, characterized in that, The multi-source monitoring sequence set includes time series units, spatial grid sequence units, and equipment corresponding sequence units. The surface line-of-sight offset diffusion layer includes a line-of-sight offset distribution field, a slope diffusion structure layer, and an attenuation diffusion zone. The three-dimensional deformation fusion map includes a three-dimensional deformation vector distribution volume, a grid difference correlation volume, and a deformation integration block. The hydraulic hysteresis coupling sequence group includes an energy accumulation sequence, a hysteresis enclosure sequence, and a hydraulic coupling relationship sequence.

3. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Based on the spatial location records of the digital elevation grid within the land engineering monitoring area, perform time-series registration of the coordinate parameters and time synchronization parameters carried by the spatial location records, perform unified encoding processing on the time field of the location records, and generate spatial time-series encoded frames. S102: Call the spatial temporal coding frame to perform mapping retrieval of spatial coordinate parameters and monitoring device identification parameters, perform coordinate assignment calculation on the coordinate parameters corresponding to each device identification parameter and digital elevation grid unit parameters, and perform index sorting on the grid unit index and record time field obtained by the assignment calculation to obtain the device grid corresponding chain; S103: Based on the device grid corresponding chain, perform the acquisition interval verification on the grid cell index and the corresponding time field, perform difference operation on the time field and the acquisition interval parameter, and rearrange them in order according to the difference size to establish a multi-source monitoring sequence set.

4. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Based on the multi-source monitoring sequence set, calculate the direction difference of the direction vector parameters of the line-of-sight geometric vector parameters associated with the three-dimensional displacement records of the ground monitoring station and the line-of-sight observation records, and perform vector angle calculation between the output value of the direction difference calculation and the direction vector parameters of the three-dimensional displacement records. Determine the direction association of the records based on the angle calculation results, and generate a set of direction association factors. S202: Call the aforementioned direction correlation factor set, perform difference calculation on the line-of-sight direction displacement component parameters and satellite line-of-sight observation component parameters, and compare the component difference values ​​obtained from the difference calculation with the line-of-sight direction displacement component parameters one by one to obtain the line-of-sight offset sequence; S203: Based on the line-of-sight offset sequence, perform distance weight distribution calculation on the offset component corresponding to the grid and the line connection parameter of the center point of the digital elevation grid, identify the diffusion distribution of the offset on the slope, and establish a surface line-of-sight offset diffusion layer.

5. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 4, characterized in that, The specific steps for S3 are as follows: S301: Based on the surface line offset diffusion layer, perform vector projection calculation on the line offset component parameters of the grid cell and the line geometric vector parameters associated with the satellite line observation records of the digital elevation grid cell, and compare the projection component obtained by the vector projection calculation with the displacement direction component parameters of the monitoring station. By comparing the angle difference output by comparison, generate a spatial projection correlation matrix. S302: Call the spatial projection correlation matrix, perform vector recombination operation on each projection component parameter and the displacement direction component parameter of the monitoring station, and perform amplitude comparison on the components in the recombination operation so that each group of recombination results forms a three-dimensional vector set that can be used as displacement candidates. Then, perform amplitude sorting on the vector amplitude parameters of the three-dimensional vector set to obtain a three-dimensional displacement candidate sequence. S303: Based on the three-dimensional displacement candidate sequence, perform differential calculation on the component parameters of the three-dimensional displacement vector of the grid and the three-dimensional displacement vector of the neighboring grid, and perform serialization rearrangement so that the displacement distribution between grids presents a continuous differential distribution feature, and establish a three-dimensional deformation fusion map.

6. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 5, characterized in that, The vector projection calculation process specifically involves limiting a minimum projection amplitude threshold when performing projection decomposition on the view offset component parameters, so that projection components below the minimum projection amplitude threshold are eliminated before entering the spatial projection correlation matrix. The component comparison process specifically involves setting an upper limit parameter for the angle difference when outputting the angle difference between the projection component and the displacement direction component parameter of the monitoring station, so that the projection component exceeding the upper limit parameter for the angle difference is marked as a low correlation component before the recombination operation. The vector recombination operation specifically involves introducing an amplitude scaling factor when performing amplitude comparison between each projection component parameter and the displacement direction component parameter of the monitoring station, so that the amplitude parameter of any vector in the recombined three-dimensional vector set satisfies the amplitude range defined by the amplitude scaling factor. The process of performing differential calculation on the component parameters of the three-dimensional displacement vector of the grid and the three-dimensional displacement vector of the neighboring grid is specifically to limit the peak differential threshold of the differential result between any three-dimensional displacement vector of the grid and the corresponding three-dimensional displacement vector of the adjacent four-dimensional digital elevation grid cell, so that the differential result exceeding the peak differential threshold is preferentially adjusted to the differential continuous interval during serialization rearrangement.

7. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 6, characterized in that, The specific steps of S4 are as follows: S401: Based on the three-dimensional deformation fusion map, target area screening is performed on the deformation component parameters of the slope grid near the location of the groundwater level well or the location of the pore water pressure device, and the time step is aligned between the screened deformation component parameters and the time field of the corresponding groundwater level record or pore water pressure record to generate a time series pairing result. S402: Call the time-series pairing result, perform a two-dimensional plane connection operation on the water level component parameters and displacement component parameters, and perform polyline reconstruction on the data points obtained from the connection operation, so that the polyline reconstruction result forms several closed segments in the plane, and then perform surface accumulation operation on the contour point coordinates of the closed segments to obtain the enclosed area sequence set; S403: Based on the enclosed area sequence set, perform sequence sorting on the area component parameters and corresponding time fields, and perform segment division operation on the sorted area component parameters to form a continuous segment structure in the time dimension of the enclosed area. Perform sequence convergence on the continuous segment structure to establish a hydraulic hysteresis coupling sequence group.

8. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 7, characterized in that, The target area screening process specifically involves limiting the peak allowable horizontal distance between the slope grid and the location of the groundwater well or the pore water pressure device based on a distance threshold parameter, so that slope grid deformation component parameters exceeding the distance threshold parameter are removed before time step alignment. The process of aligning the time field of the selected deformation component parameters with the time field of the corresponding groundwater level record or pore water pressure record is specifically to limit a unified time step parameter during the alignment process, so that the time field of any deformation component parameter strictly matches the unified time step parameter after interpolation correction. The process of performing a two-dimensional planar connection operation on the water level component parameters and the displacement component parameters specifically involves introducing a minimum connection interval threshold before forming data point connections, so that data point intervals below the minimum connection interval threshold do not participate in the polyline reconstruction. The process of performing surface accumulation and addition on the coordinates of the contour points of the closed section specifically involves performing a sequence consistency check on the coordinates of the contour points before surface accumulation and addition, so that the sequence arrangement of any contour point coordinates satisfies the closure requirement for area calculation. The process of performing segmentation operation on the area component parameters specifically involves limiting the variation range of the area component parameters based on the area fluctuation threshold parameter, so that the continuous segment structure after segmentation satisfies the condition of continuous area change.

9. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 1, characterized in that, The method further includes step S5: S5: Using the hydraulic hysteresis coupling sequence group, determine the joint performance between the energy change trend and the grid deformation amplitude change, perform a level classification on the combined performance of deformation amplitude and hydraulic energy change, assign risk level labels to grid units, and generate a land engineering monitoring and early warning level group. The land engineering monitoring and early warning level group includes a risk level identifier set, a level zoning unit set, and an early warning level rule set.

10. The land engineering monitoring and early warning method based on multi-source data fusion according to claim 9, characterized in that, The specific steps of S5 are as follows: S501: Based on the hydraulic hysteresis coupling sequence group, perform trend comparison operation on the energy component parameters and the grid deformation amplitude component parameters, and synchronously verify the change difference output by the trend comparison with the time series field of the energy component. Through the synchronous verification result, perform difference aggregation on the joint relationship between energy change and deformation amplitude change to generate an energy-deformation joint matrix. S502: Call the energy-deformation joint matrix, perform a combined partitioning operation on the energy component parameters and the deformation amplitude component parameters, and perform interval comparison on the components in the combined partition to form a grade basis with amplitude segment characteristics in the combined interval. Perform discrete rearrangement on the amplitude segments in the grade basis to obtain the deformation-energy segment sequence. S503: Based on the deformation-energy segment sequence, perform a level mapping operation on the segment parameters and grid unit identifier parameters, and re-index the level index formed during the mapping operation with the grid unit identifier parameters so that each grid unit corresponds to a risk level identifier, and establish a land engineering monitoring and early warning level group.

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