Land surveying method and system based on dynamic remote sensing monitoring

Abstract

The application provides a land surveying and mapping method and system based on dynamic remote sensing monitoring, and belongs to the technical field of land surveying and mapping, and comprises the following steps: S1. Obtain existing control point data and topographic map data of a to-be-measured area, and extract the permanent measurement marker points arranged from the existing control point data as static calibration objects. The application can improve the intelligent level and operation efficiency of surveying and mapping path planning, reduce manual intervention and field arrangement workload, improve the splicing accuracy and fusion robustness of multi-station measurement data, and realize high-precision spatial registration without relying on manual targets. Through air-ground cooperation and dynamic encryption triggering mechanism, the detail capturing capability for complex terrain areas is effectively enhanced, and data redundancy is avoided. Finally, a complete automatic closed loop from data acquisition to result output is formed, and a digital elevation model and a digital line graph with change marks are directly output, thereby providing efficient and reliable technical support for dynamic monitoring and rapid updating of land resources.

Description

Technical Field

[0001] This invention relates to the field of land surveying technology, specifically to land surveying methods and systems based on dynamic remote sensing monitoring. Background Technology

[0002] Traditional surveying methods primarily rely on ground surveying equipment such as total stations, GNSS receivers, and levels. This involves manually setting up control points, conducting field observations, and processing data to ultimately generate surveying results such as digital line maps and digital elevation models. With technological advancements, low-altitude unmanned aerial vehicle (UAV) surveying and ground mobile surveying systems are increasingly being used in surveying operations, improving data acquisition efficiency to some extent.

[0003] However, existing technologies still have shortcomings. First, measurement route planning and station deployment rely heavily on manual experience, lacking systematic intelligent decision support. This leads to uneven control network deployment and low operational efficiency, especially in large areas or areas with complex terrain, where the rationality of route selection directly affects surveying accuracy and cost. Second, when stitching and fusing multi-station measurement data, there is a lack of a unified, dynamically identifiable spatial benchmark. It usually requires manually deployed targets or post-hoc feature point matching, which not only increases the workload in the field but also limits the stitching accuracy to the accuracy of target deployment and the identifiability of image features. Third, there is poor coordination between ground surveying and low-altitude or mobile surveying. The two often operate independently, failing to achieve efficient complementary surveying, resulting in insufficient capture of details in areas with drastic terrain changes. Therefore, a land surveying method and system based on dynamic remote sensing monitoring are proposed. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a land surveying method and system based on dynamic remote sensing monitoring. This method can improve the intelligence level and operational efficiency of surveying route planning, reduce manual intervention and field deployment workload, and form a complete automated closed loop from data acquisition to output. It directly outputs digital elevation models and digital line maps with change markers, providing efficient and reliable technical support for the dynamic monitoring and rapid updating of land resources.

[0005] The technical solution adopted in this invention is as follows: The land surveying method based on dynamic remote sensing monitoring includes the following steps: S1. Obtain existing control point data and topographic map data for the area to be measured, and extract the established permanent measurement markers from the existing control point data as static calibration objects; at the same time, identify natural features with unique spatial location and long-term stability from the topographic map data as auxiliary calibration objects; based on the spatial distribution dispersion and visibility conditions of each calibration object in the area to be measured, calculate the comprehensive priority score of each calibration object, and generate a calibration object operation ranking table according to the comprehensive priority score; S2. Based on the calibration object operation sorting table, select the calibration object with the highest comprehensive priority score as the primary reference point; starting from the primary reference point, use the minimum span coverage algorithm to plan a ground measurement travel path that passes through multiple high-priority calibration objects in sequence; at the same time, based on the ground measurement travel path, determine the deployment positions of multiple ground stations, so that each ground station and its adjacent stations share at least one high-priority calibration object; S3. Generate an observation task instruction package for each ground station. The observation task instruction package includes the current station's position coordinates, a list of shared calibration objects to be observed, and preset thresholds for observation angles and distances. Arrange all observation task instruction packages in sequence according to the ground measurement travel path to generate a sequence of operation instructions containing continuous multi-station observation tasks. S4. Control the ground surveying robot to stop at each station sequentially according to the operation instruction sequence. At each station, the ground surveying robot automatically aims at the shared calibration object to be observed at the current station and measures the horizontal angle, vertical angle, and slant distance to generate measurement metadata for the current station. At the same time, during the execution of the measurement task by the ground surveying robot, control the auxiliary scanning equipment mounted on the UAV or mobile surveying vehicle to continuously scan the surface morphology of the route area between the current station and the next station to generate auxiliary point cloud data. S5. Using the high-priority calibration object shared between two adjacent stations as the spatial registration reference, the coordinate data of the current calibration object measured by the previous station and the coordinate data of the same calibration object measured by the next station are jointly adjusted to obtain the optimized coordinate value of the current shared calibration object; based on the optimized coordinate value, the measurement metadata of the two adjacent stations is converted to the same spatial coordinate system to form a measurement data chain of continuous stations; at the same time, the spatial feature points in the auxiliary point cloud data are matched with the shared calibration object, and the auxiliary point cloud data is attached to the measurement data chain to generate encrypted three-dimensional mapping data of the survey area; S6. Compare the encrypted 3D mapping data of the survey area with the existing topographic map data obtained in S1 to identify the location and range of changes in topographic features; based on the encrypted 3D mapping data of the survey area, use the triangulation algorithm to generate a digital elevation model of the survey area, and output a digital line map of the survey area based on the digital elevation model.

[0006] Preferably, in step S1, the method for calculating the comprehensive priority score of the calibration object is as follows: obtaining the spatial distribution dispersion index of each calibration object, wherein the spatial distribution dispersion index is positively correlated with the average distance between the calibration object and its neighboring calibration objects; obtaining the visibility condition score of each calibration object, wherein the visibility condition score is calculated based on the terrain shading angle within a preset radius around the current calibration object; and performing a weighted summation of the spatial distribution dispersion index and the visibility condition score to obtain the comprehensive priority score.

[0007] Preferably, in step S2, the planning of the ground measurement travel path further includes: when there is no natural shared calibration object between two adjacent stations, a temporary measurement target is manually deployed within the visual range of the two adjacent stations, and the current temporary measurement target is added as a new auxiliary calibration object to the calibration object operation sorting table, and the comprehensive priority score of the current temporary measurement target is updated.

[0008] Preferably, in step S4, the specific steps for the ground measurement robot to perform measurements at each station include: first, calculating and executing the initial rotation angle of the aiming unit based on the current station's position coordinates and the estimated coordinates of the shared calibration object to be observed; then, activating the automatic target recognition function to accurately aim at the shared calibration object; finally, after the aiming stabilizes, continuously collecting horizontal angle, vertical angle, and slant distance data for multiple measurement cycles, and taking their arithmetic mean as the final measurement metadata of the current station for the current shared calibration object.

[0009] Preferably, in step S4, when controlling the auxiliary scanning device to perform surface morphology scanning, a dynamic triggering mechanism is also included: when the change in elevation difference between two adjacent stations measured by the ground surveying robot exceeds a preset threshold, the auxiliary scanning device is triggered to perform a gridded encrypted scan of the area between the two current stations, and the point cloud data obtained from the encrypted scan is marked as data of key areas of interest.

[0010] Preferably, in step S5, the method for joint adjustment calculation includes: using the station coordinates of two adjacent stations as known values ​​and the spatial coordinates of the shared calibration object as unknowns, establishing observation equations for observing the current shared calibration object from the two stations respectively; using the least squares method to jointly calculate the observation equations to obtain the optimized coordinate values ​​of the shared calibration object and its positional error; if the positional error exceeds the preset accuracy requirement, then marking the current shared calibration object as a calibration object to be verified, and inserting a repeated observation task for the current calibration object to be verified into the operation instruction sequence.

[0011] Preferably, the surveying method further includes a path simulation verification step before executing step S2: based on the calibration object operation sequence table, the ground surveying robot is simulated to move along the planned ground surveying path, and the aiming process of each shared calibration object to be observed is simulated at each preset station; if it is found during the simulation that any station cannot see its shared calibration object, the path planning in step S2 is triggered to perform iterative correction, adjusting the current station's deployment position or replacing the shared calibration object, until the visibility conditions of all stations are verified.

[0012] Preferably, in step S6, the method for comparing the differences with existing topographic map data is as follows: unify the encrypted 3D mapping data of the survey area and the existing topographic map data into the same coordinate system; discretize the contour lines in the existing topographic map data to obtain a reference point cloud; calculate the spatial distance difference between each data point in the encrypted 3D mapping data of the survey area and the nearest neighbor point in the reference point cloud; determine the continuous area formed by data points whose spatial distance difference exceeds the preset tolerance range as the area where the topographic features have changed, and output the change marker layer of the current area.

[0013] This invention also proposes a land surveying system based on dynamic remote sensing monitoring, comprising: The data parsing and calibration management unit is used to acquire existing control point data and topographic map data of the area to be measured, extract static and auxiliary calibration objects, calculate the comprehensive priority score, and generate a calibration object operation sorting table. The path planning and station deployment unit is used to plan the ground measurement travel path and determine the deployment position of each station based on the calibration object operation sequence table. The path planning and station deployment unit is communicatively connected to the data parsing and calibration object management unit. The operation instruction generation unit is used to generate an observation task instruction package for each station and arrange all observation task instruction packages into an operation instruction sequence according to the time sequence. The operation instruction generation unit is communicatively connected to the path planning and station deployment unit. A collaborative measurement control unit is used to control a ground measurement robot to perform automatic measurement according to the operation instruction sequence, and simultaneously control an auxiliary scanning device to perform surface scanning. The collaborative measurement control unit is communicatively connected to the operation instruction generation unit. The data fusion and adjustment unit is used to receive measurement metadata from the ground measurement robot and auxiliary point cloud data from the auxiliary scanning equipment, and to perform joint adjustment and data fusion with the shared calibration object as the spatial registration reference to generate encrypted 3D mapping data of the survey area. The data fusion and adjustment unit is communicatively connected to the collaborative measurement control unit. The surveying results output unit is used to compare the encrypted 3D surveying data of the survey area with the existing topographic map data, and generate a digital elevation model and a digital line map as the final surveying results. The surveying results output unit is communicatively connected to the data fusion and adjustment unit and the data analysis and calibration object management unit.

[0014] Preferably, the collaborative measurement control unit includes: a ground measurement robot control module for controlling the rotation of the aiming head of the total station or measurement robot, automatic target recognition, distance measurement, and data recording; an auxiliary scanning equipment control module for controlling the flight / driving path, scanning frequency, and scanning range of the UAV or mobile measurement vehicle; and a dynamic triggering module for receiving real-time measurement data from the ground measurement robot and determining whether to trigger the auxiliary scanning equipment to perform encrypted scanning based on whether the change in elevation difference between adjacent stations exceeds a preset threshold; wherein, a real-time data link is established between the ground measurement robot control module and the dynamic triggering module, and the dynamic triggering module is communicatively connected to the auxiliary scanning equipment control module.

[0015] The beneficial effects of this invention are: This invention can improve the intelligence level and operational efficiency of surveying route planning, reduce manual intervention and field deployment workload; enhance the splicing accuracy and fusion robustness of multi-station measurement data, and achieve high-precision spatial registration without relying on manual targets; effectively enhance the ability to capture details in complex terrain areas through air-ground collaboration and dynamic encryption triggering mechanisms, and avoid data redundancy; and finally form a complete automated closed loop from data acquisition to output, directly outputting digital elevation models and digital line maps with change markers, providing efficient and reliable technical support for the dynamic monitoring and rapid updating of land resources. Attached Figure Description

[0016] Figure 1 A flowchart of a land surveying method based on dynamic remote sensing monitoring, according to an embodiment of the present invention; Figure 2 This is a flowchart of the calibration item comprehensive priority scoring sub-flowchart of one embodiment of the present invention; Figure 3 This is a flowchart of a joint adjustment calculation sub-process according to an embodiment of the present invention; Figure 4 This is a flowchart of a dynamic encryption scan triggering sub-process according to an embodiment of the present invention; Figure 5 This is a block diagram of a land surveying system based on dynamic remote sensing monitoring, according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figures 1-4 As shown, the land surveying method based on dynamic remote sensing monitoring according to an embodiment of the present invention includes the following steps: S1. Obtain existing control point data and topographic map data for the area to be measured, and extract the established permanent measurement markers from the existing control point data as static markers; at the same time, identify natural features with unique spatial location and long-term stability from the topographic map data as auxiliary markers; based on the spatial distribution dispersion and visibility conditions of each marker in the area to be measured, calculate the comprehensive priority score of each marker, and generate a marker operation ranking table based on the comprehensive priority score; S2. Based on the calibration object operation sorting table, select the calibration object with the highest comprehensive priority score as the primary benchmark point; starting from the primary benchmark point, use the minimum span coverage algorithm to plan a ground measurement travel path that passes through multiple high-priority calibration objects in sequence; at the same time, based on the ground measurement travel path, determine the deployment positions of multiple ground stations, so that each ground station and its adjacent stations share at least one high-priority calibration object; S3. Generate an observation task instruction package for each ground station. The observation task instruction package contains the current station's position coordinates, a list of shared calibration objects to be observed, and preset thresholds for observation angles and distances. Arrange all observation task instruction packages in sequence according to the ground measurement travel path to generate a sequence of operation instructions containing continuous multi-station observation tasks. S4. Control the ground surveying robot to stop at each survey station in sequence according to the operation instructions. At each survey station, the ground surveying robot automatically aims at the shared calibration object to be observed at the current survey station and measures the horizontal angle, vertical angle and slant distance to generate the measurement metadata of the current survey station. At the same time, during the execution of the measurement task by the ground surveying robot, control the auxiliary scanning equipment mounted on the UAV or mobile surveying vehicle to continuously scan the surface morphology of the route area between the current survey station and the next survey station to generate auxiliary point cloud data. S5. Using the high-priority calibration object shared between two adjacent stations as the spatial registration reference, the coordinate data of the current calibration object measured by the previous station and the coordinate data of the same calibration object measured by the next station are jointly adjusted to obtain the optimized coordinate value of the current shared calibration object; based on the optimized coordinate value, the measurement metadata of the two adjacent stations is converted to the same spatial coordinate system to form a measurement data chain of continuous stations; at the same time, the spatial feature points in the auxiliary point cloud data are matched with the shared calibration object, and the auxiliary point cloud data is attached to the measurement data chain to generate encrypted 3D mapping data of the survey area; S6. Compare the encrypted 3D mapping data of the survey area with the existing topographic map data obtained in S1 to identify the location and range of changes in topographic features; based on the encrypted 3D mapping data of the survey area, use the triangulation algorithm to generate a digital elevation model of the survey area, and output a digital line map of the survey area based on the digital elevation model.

[0019] Further, in step S1, the method for calculating the comprehensive priority score of the calibration object is as follows: obtain the spatial distribution dispersion index of each calibration object, which is positively correlated with the average distance between the calibration object and its neighboring calibration objects; obtain the visibility condition score of each calibration object, which is calculated based on the terrain shading angle within a preset radius around the current calibration object; and perform a weighted summation of the spatial distribution dispersion index and the visibility condition score to obtain the comprehensive priority score.

[0020] Furthermore, in step S2, the planning of the ground measurement travel path also includes: when there is no natural shared calibration object between two adjacent stations, temporary measurement targets are manually deployed within the visual range of the two adjacent stations, and the current temporary measurement targets are added as new auxiliary calibration objects to the calibration object operation sorting table, and the comprehensive priority score of the current temporary measurement targets is updated.

[0021] Furthermore, in step S4, the specific steps for the ground measurement robot to perform measurements at each station include: First, based on the current station's position coordinates and the estimated coordinates of the shared calibration object to be observed, the initial rotation angle of the aiming unit is calculated and executed; then, the automatic target recognition function is activated to accurately aim at the shared calibration object; finally, after the aiming stabilizes, the horizontal angle, vertical angle, and slant distance data of multiple measurement cycles are continuously collected, and their arithmetic mean is taken as the final measurement metadata of the current station for the current shared calibration object.

[0022] Furthermore, in step S4, when controlling the auxiliary scanning device to perform surface morphology scanning, a dynamic triggering mechanism is also included: when the change in elevation between two adjacent stations measured by the ground surveying robot exceeds a preset threshold, the auxiliary scanning device is triggered to perform a gridded and encrypted scan of the area between the two stations, and the point cloud data obtained from the encrypted scan is marked as data of key areas of interest.

[0023] Furthermore, in step S5, the method for joint adjustment calculation includes: using the station coordinates of two adjacent stations as known values ​​and the spatial coordinates of the shared calibration object as unknowns, establishing observation equations for observing the current shared calibration object from the two stations respectively; using the least squares method to jointly calculate the observation equations to obtain the optimized coordinate values ​​of the shared calibration object and its positional error; if the positional error exceeds the preset accuracy requirement, then marking the current shared calibration object as a calibration object to be verified, and inserting a repeated observation task for the current calibration object to be verified into the work instruction sequence.

[0024] Furthermore, the surveying method also includes a path simulation verification step before executing step S2: based on the calibration object operation sequence table, the ground surveying robot is simulated to move along the planned ground surveying path, and the aiming process of each shared calibration object to be observed is simulated at each preset station; if it is found during the simulation that any station cannot see its shared calibration object, the path planning in step S2 is triggered to perform iterative correction, adjusting the current station's deployment position or replacing the shared calibration object, until the visibility conditions of all stations are verified.

[0025] Furthermore, in step S6, the method for comparing the differences with existing topographic map data is as follows: unify the encrypted 3D mapping data of the survey area with the existing topographic map data under the same coordinate system; discretize the contour lines in the existing topographic map data to obtain a reference point cloud; calculate the spatial distance difference between each data point in the encrypted 3D mapping data of the survey area and the nearest neighbor point in the reference point cloud; determine the continuous area formed by data points whose spatial distance difference exceeds the preset tolerance range as the area where the topographic features have changed, and output the change marker layer of the current area.

[0026] When implementing this invention, it is first necessary to obtain existing control point data and topographic map data of the area to be measured, and extract two types of markers from them: permanent measurement markers that have been set up (static markers) and natural features with unique spatial location and long-term stability, such as independent house corners, road intersections, etc. (auxiliary markers).

[0027] To quantify the suitability of each calibrator in subsequent measurements, a comprehensive priority score is defined. It is composed of two weighted factors.

[0028] The first factor is the spatial distribution dispersion index. , defined as the Euclidean plane distance from the current calibrator to its nearest neighbor calibrator, i.e. The physical meaning is A higher value indicates that the current benchmark is more isolated within its neighborhood and has stronger coverage representativeness, thus reducing path detours when used as a measurement benchmark. The second factor is the line-of-sight score. By statistically analyzing the preset radius around the current calibration object Calculation of terrain shielding angles in all directions: ,in The value of 1 or 0 depends on whether the maximum occlusion angle in the current direction is less than a preset threshold. , A higher value indicates better visibility.

[0029] The dispersion index, after being normalized, is then weighted and summed with the visibility score. ,in These are the maximum and minimum values ​​of the dispersion index for all calibration materials, respectively. The weighting coefficients and .according to The calibration work sorting table is generated by sorting from highest to lowest. The one with the highest rating It was selected as the primary benchmark point. This step transforms the original topographic map and control point data into a sequence of benchmarks with a priority order, providing a quantitative basis for route planning.

[0030] Based on the calibration work sorting table ,from Starting from this point, a minimum span coverage algorithm is used to plan the ground survey travel path. The specific strategy is as follows: Let the current point be... The set of unvisited markers is Then the next target point is... The process involves selecting the nearest unvisited calibrator as the next station each time.

[0031] Repeat the current process until all required functions are covered. High-priority calibrators Connecting them sequentially will result in a polyline path. The sections between adjacent markers on the current path are the segments that the ground measurement robot needs to traverse sequentially.

[0032] Based on this, the deployment locations of ground stations were further determined: for each pair of adjacent calibration objects and Two stations were set up along the line connecting them or in the vicinity of the line. (near )and (near ), and demand At the same time can be and It was observed that adjacent stations share the same high-priority calibration object. When there is no naturally shared calibration object between two adjacent calibration objects, a temporary measurement target is manually deployed within the visible range and added to the ranking table as a new auxiliary calibration object, with its priority set to [priority value missing]. ( The value is a small positive number (e.g., 0.05) to ensure that natural calibration objects are used preferentially. Through this step, the abstract calibration object sorting sequence is transformed into specific ground measurement paths and station location coordinates, constructing an executable measurement operation framework.

[0033] After the location of the observation station is determined, an observation task instruction package is generated for each observation station. ,in For the station The three-dimensional coordinates This is a list of shared calibration objects that the current station needs to observe. and These are the angle observation threshold and the distance observation threshold, respectively. All command packets are sequentially arranged according to the ground measurement travel path to obtain the operation command sequence. ( (Total number of stations).

[0034] Ground surveying robot according to the sequence of work instructions The robot stops sequentially at each station to perform measurements. At each station, the robot first calculates the initial rotation angle based on its own coordinates and the estimated coordinates of the target object, drives the aiming unit to turn in the current direction, and then activates the automatic target recognition function for precise aiming. After aiming stabilizes, continuous data acquisition is performed. One test (usually) horizontal angle vertical angle and slant distance Take their arithmetic mean , , This serves as the final measurement metadata for the current calibration object from the current station. Simultaneously, auxiliary scanning equipment mounted on a UAV or mobile survey vehicle continuously scans the terrain along the route between the current and next stations, generating auxiliary point cloud data. To adaptively enhance measurement density in areas with drastic terrain changes, this method employs a dynamic triggering mechanism: defining the change in elevation between adjacent stations. and preset threshold (For example, 1m); if This triggers the auxiliary scanning device to perform a gridded, encrypted scan of the current area, with the encrypted point density being the same as the regular scan density. times ,like .

[0035] Adjacent stations and Shared benchmarks For example, let's set up a survey station. The coordinates are , measured The polar coordinate data is ; measuring station The coordinates are The measured data is Based on the transformation relationship between spatial rectangular coordinates and polar coordinates, two sets of observation equations can be listed respectively. For example, from the station... Observations show that:

[0036] in For calibration The coordinates to be optimized This is the residual. Similarly, from the station... Three similar equations can be obtained.

[0037] Solve the system of two sets of six equations simultaneously using the least squares method, that is, find the equation that minimizes the sum of squared residuals. In practical calculations, the nonlinear equations are linearized at approximate values ​​to obtain the normal equations. The correction number was calculated. Thus, optimized coordinates are obtained. And calculate the positional error. ,in For the residual vector, For the weight matrix, The total number of observed equations, The number of unknowns. If If the accuracy requirement is exceeded, the current calibration object will be marked as a calibration object to be verified, and a repeat observation task will be inserted into the job instruction sequence.

[0038] Obtain shared benchmarks After optimizing the coordinates, they are used as the spatial registration benchmark. Measurement data from both stations are then converted to a unified coordinate system referenced by the current optimized coordinates, thus achieving seamless connection of measurement data chains between adjacent stations. For point cloud data acquired by auxiliary scanning equipment, spatial feature points (such as room corners and edges) are extracted, and the spatial distances between these feature points and the optimized calibration coordinates are calculated. ;like Then determine the shared calibration object corresponding to the current feature point. Then, the entire point cloud block is registered to the coordinate system of the measurement data chain through rigid body transformation, forming encrypted 3D mapping data of the survey area. .

[0039] Finally, the encrypted 3D mapping data will be integrated. Compare the differences with the existing topographic map data obtained in step S1 to identify the location and extent of changes in topographic features.

[0040] The specific method is to discretize existing topographic map data and sample it as a reference point cloud. .for Each point in ,exist Finding the spatial nearest neighbor Calculate elevation difference like ( If a preset tolerance (e.g., 0.3m) is used, then the mark is made. This is the point of change.

[0041] Clustering the set of all continuously changing points into several changing regions using connected component analysis. It also outputs a change marker map. Based on this, it... A digital elevation model was constructed, and the Delaunay triangulation algorithm was used to divide the plane coordinates. Constructing an irregular triangular network, for any triangle Known vertex elevation Then any point inside the triangle barycentric coordinates (satisfy The corresponding elevation is .

[0042] The entire survey area is divided into regular grid spacing. Divide the grid and perform the above linear interpolation on the center point of each grid to obtain a regular grid digital elevation model.

[0043] Furthermore, contour lines are generated in the digital line map based on the digital elevation model, for a given elevation value. Retrieve the elevation span of adjacent grid points in the raster. Line segments are used to linearly interpolate the coordinates of contour points, and then these coordinates are traced and connected to form contour lines; simultaneously, from... Alternatively, the boundaries of ground features (such as building outlines and road edges) can be extracted from the measurement data chain, regularized, and then superimposed to finally output an updated digital line map.

[0044] Example 2 The method corresponding to the above embodiments, such as Figure 5 As shown, this invention also proposes a land surveying system based on dynamic remote sensing monitoring, comprising: The data parsing and calibration management unit is used to acquire existing control point data and topographic map data of the area to be measured, extract static and auxiliary calibration objects, calculate the comprehensive priority score, and generate a calibration object operation sorting table. The path planning and station deployment unit is used to plan the ground measurement travel path and determine the deployment position of each station based on the calibration object operation sequence table. The path planning and station deployment unit is communicatively connected to the data analysis and calibration object management unit. The operation instruction generation unit is used to generate an observation task instruction package for each station and arrange all observation task instruction packages into an operation instruction sequence according to the time sequence. The operation instruction generation unit is communicatively connected to the path planning and station deployment unit. The collaborative measurement control unit is used to control the ground measurement robot to perform automatic measurement according to the sequence of operation instructions, and at the same time control the auxiliary scanning equipment to perform surface scanning. The collaborative measurement control unit is communicatively connected to the operation instruction generation unit. The data fusion and adjustment unit is used to receive measurement metadata from the ground measurement robot and auxiliary point cloud data from the auxiliary scanning equipment. It performs joint adjustment and data fusion with the shared calibration object as the spatial registration reference to generate encrypted 3D mapping data of the survey area. The data fusion and adjustment unit is connected to the collaborative measurement control unit. The surveying results output unit is used to compare the encrypted 3D surveying data of the survey area with the existing topographic map data, and generate a digital elevation model and a digital line map as the final surveying results. The surveying results output unit is connected to the data fusion and adjustment unit and the data analysis and calibration object management unit.

[0045] Furthermore, the collaborative measurement control unit includes: a ground measurement robot control module, used to control the rotation of the aiming head of the total station or measurement robot, automatic target recognition, distance measurement, and data recording; an auxiliary scanning equipment control module, used to control the flight / driving path, scanning frequency, and scanning range of the UAV or mobile measurement vehicle; and a dynamic triggering module, used to receive real-time measurement data from the ground measurement robot and determine whether to trigger the auxiliary scanning equipment to perform encrypted scanning based on whether the change in elevation difference between adjacent stations exceeds a preset threshold; wherein, a real-time data link is established between the ground measurement robot control module and the dynamic triggering module, and the dynamic triggering module is communicatively connected to the auxiliary scanning equipment control module.

[0046] Step S5 aims to unify the measurement data and auxiliary point cloud data from adjacent stations into the same spatial coordinate system and improve the coordinate accuracy of the shared calibration object through joint adjustment. Taking a calibration object shared by two adjacent stations as an example, assuming the coordinates of the previous and subsequent stations are known, each station performs polar coordinate measurements on the current shared calibration object, obtaining the slope distance and two angle values. Based on the conversion relationship between spatial rectangular coordinates and polar coordinates, a set of observation equations containing three equations (corresponding to X, Y, and Z coordinates respectively) can be listed from the observation data of each station. Due to the existence of measurement errors, these two sets of equations cannot be strictly true simultaneously. Therefore, the two sets of six equations are combined, with the three-dimensional coordinates of the shared calibration object to be determined as the unknowns, and the least squares method is used for joint calculation. During the calculation process, different weights can be assigned according to the accuracy differences of each observation value, and finally the optimized coordinates of the current calibration object and its positional error are obtained. If the positional error exceeds the preset accuracy requirement (e.g., 5mm), the current calibration object is marked as needing verification and a repeated observation task is inserted in subsequent operations. After obtaining the optimized coordinates of the shared calibration object, these coordinates are used as the spatial registration reference. Measurement data from both stations are then converted to a unified coordinate system with the current optimized coordinates as the reference, thus achieving seamless connection of measurement data chains between adjacent stations. For point cloud data acquired by auxiliary scanning equipment, spatial feature points (such as building corners and edge points) are extracted. The spatial distance between these feature points and the coordinates of the optimized calibration object is calculated. If the distance is less than a matching threshold (e.g., 0.05 meters), the current feature point is determined to correspond to the shared calibration object. The entire point cloud block is then registered to the coordinate system of the measurement data chain through rigid body transformation, forming encrypted 3D mapping data for the survey area. Through these steps, the original multi-source observation data, after adjustment optimization and spatial registration, is transformed into high-precision, unified spatial reference 3D point cloud data, with errors controlled within a preset threshold.

[0047] Step S1 transforms historical data into a priority ranking of calibration objects using a weighted scoring model; Step S2 transforms the ranking into ground measurement paths and station locations based on minimum distance decisions; Step S3 arranges the station locations into an executable sequence of instructions; Step S4 performs collaborative data acquisition based on the instructions and uses an elevation difference trigger function to achieve adaptive encryption; Step S5 calculates the optimized coordinates of shared calibration objects using a least squares adjustment model to complete the spatial registration and fusion of multi-source data; Step S6 generates the final digital elevation model and digital line map through nearest neighbor difference detection and triangulation interpolation.

[0048] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with the current embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0049] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0050] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0051] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0052] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0053] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0054] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A land surveying method based on dynamic remote sensing monitoring, characterized in that, Includes the following steps: S1. Obtain existing control point data and topographic map data for the area to be measured, and extract the established permanent measurement markers from the existing control point data as static calibration objects; at the same time, identify natural features with unique spatial location and long-term stability from the topographic map data as auxiliary calibration objects; based on the spatial distribution dispersion and visibility conditions of each calibration object in the area to be measured, calculate the comprehensive priority score of each calibration object, and generate a calibration object operation ranking table according to the comprehensive priority score; S2. Based on the calibration task sorting table, select the calibration task with the highest comprehensive priority score as the primary benchmark point; Starting from the primary reference point, a ground measurement path is planned by using the minimum span coverage algorithm, passing through multiple high-priority calibration objects in sequence. At the same time, based on the ground measurement path, the deployment positions of multiple ground stations are determined, so that each ground station and its adjacent stations share at least one high-priority calibration object. S3. Generate an observation task instruction package for each ground station. The observation task instruction package includes the current station's position coordinates, a list of shared calibration objects to be observed, and preset thresholds for observation angles and distances. Arrange all observation task instruction packages in sequence according to the ground measurement travel path to generate a sequence of operation instructions containing continuous multi-station observation tasks. S4. Control the ground surveying robot to stop at each station sequentially according to the operation instruction sequence. At each station, the ground surveying robot automatically aims at the shared calibration object to be observed at the current station and measures the horizontal angle, vertical angle, and slant distance to generate measurement metadata for the current station. At the same time, during the execution of the measurement task by the ground surveying robot, control the auxiliary scanning equipment mounted on the UAV or mobile surveying vehicle to continuously scan the surface morphology of the route area between the current station and the next station to generate auxiliary point cloud data. S5. Using the high-priority calibration object shared between two adjacent stations as the spatial registration reference, the coordinate data of the current calibration object measured by the previous station and the coordinate data of the same calibration object measured by the next station are jointly adjusted to obtain the optimized coordinate value of the current shared calibration object. Based on the optimized coordinate values, the measurement metadata of two adjacent stations is converted to the same spatial coordinate system to form a measurement data chain of continuous stations; at the same time, the spatial feature points in the auxiliary point cloud data are matched with the shared calibration object, and the auxiliary point cloud data is attached to the measurement data chain to generate encrypted 3D mapping data of the survey area. S6. Compare the encrypted 3D mapping data of the survey area with the existing topographic map data obtained in S1 to identify the location and range of changes in topographic features; based on the encrypted 3D mapping data of the survey area, use the triangulation algorithm to generate a digital elevation model of the survey area, and output a digital line map of the survey area based on the digital elevation model.

2. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, In step S1, the method for calculating the comprehensive priority score of the calibration object is as follows: obtain the spatial distribution dispersion index of each calibration object, which is positively correlated with the average distance between the calibration object and its neighboring calibration objects; obtain the visibility condition score of each calibration object, which is calculated based on the terrain shading angle within a preset radius around the current calibration object; and perform a weighted summation of the spatial distribution dispersion index and the visibility condition score to obtain the comprehensive priority score.

3. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, In step S2, the planning of the ground measurement travel path also includes: when there is no natural shared calibration object between two adjacent stations, a temporary measurement target is manually set up within the visual range of the two adjacent stations, and the current temporary measurement target is added as a new auxiliary calibration object to the calibration object operation sorting table, and the comprehensive priority score of the current temporary measurement target is updated.

4. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, In step S4, the specific steps for the ground measurement robot to perform measurements at each station include: First, based on the current station's position coordinates and the estimated coordinates of the shared calibration object to be observed, the initial rotation angle of the aiming unit is calculated and executed; then, the automatic target recognition function is activated to accurately aim at the shared calibration object; finally, after the aiming stabilizes, the horizontal angle, vertical angle, and slant distance data of multiple measurement cycles are continuously collected, and their arithmetic mean is taken as the final measurement metadata of the current station for the current shared calibration object.

5. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, In step S4, when controlling the auxiliary scanning device to perform surface morphology scanning, a dynamic triggering mechanism is also included: when the change in elevation difference between two adjacent stations measured by the ground surveying robot exceeds a preset threshold, the auxiliary scanning device is triggered to perform a gridded encrypted scan of the area between the two current stations, and the point cloud data obtained from the encrypted scan is marked as data of key areas of interest.

6. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, In step S5, the method for joint adjustment calculation includes: using the station coordinates of two adjacent stations as known values ​​and the spatial coordinates of the shared calibration object as unknowns, establishing observation equations for observing the current shared calibration object from the two stations respectively; using the least squares method to jointly calculate the observation equations to obtain the optimized coordinate values ​​of the shared calibration object and its positional error; if the positional error exceeds the preset accuracy requirement, the current shared calibration object is marked as a calibration object to be verified, and a repeated observation task for the current calibration object to be verified is inserted into the operation instruction sequence.

7. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, The surveying method also includes a path simulation verification step before executing step S2: based on the calibration object operation sequence table, the ground surveying robot is simulated to move along the planned ground surveying path, and the aiming process of each shared calibration object to be observed is simulated at each preset station; if it is found during the simulation that any station cannot see its shared calibration object, the path planning in step S2 is triggered to perform iterative correction, adjusting the current station's deployment position or replacing the shared calibration object, until the visibility conditions of all stations are verified.

8. The land surveying method based on dynamic remote sensing monitoring according to claim 1, characterized in that, In step S6, the method for comparing the differences with existing topographic map data is as follows: unify the encrypted three-dimensional mapping data of the survey area with the existing topographic map data into the same coordinate system; discretize the contour lines in the existing topographic map data to obtain a reference point cloud; Calculate the spatial distance difference between each data point in the encrypted 3D mapping data of the survey area and the nearest neighbor point in the reference point cloud; The continuous area formed by data points whose spatial distance difference exceeds the preset tolerance range is identified as the area where the terrain features have changed, and the change marker layer of the current area is output.

9. A land surveying system based on dynamic remote sensing monitoring, characterized in that, include: The data parsing and calibration management unit is used to acquire existing control point data and topographic map data of the area to be measured, extract static and auxiliary calibration objects, calculate the comprehensive priority score, and generate a calibration object operation sorting table. The path planning and station deployment unit is used to plan the ground measurement travel path and determine the deployment position of each station based on the calibration object operation sequence table. The path planning and station deployment unit is communicatively connected to the data parsing and calibration object management unit. The operation instruction generation unit is used to generate an observation task instruction package for each station and arrange all observation task instruction packages into an operation instruction sequence according to the time sequence. The operation instruction generation unit is communicatively connected to the path planning and station deployment unit. A collaborative measurement control unit is used to control a ground measurement robot to perform automatic measurement according to the operation instruction sequence, and simultaneously control an auxiliary scanning device to perform surface scanning. The collaborative measurement control unit is communicatively connected to the operation instruction generation unit. The data fusion and adjustment unit is used to receive measurement metadata from the ground measurement robot and auxiliary point cloud data from the auxiliary scanning equipment, and to perform joint adjustment and data fusion with the shared calibration object as the spatial registration reference to generate encrypted 3D mapping data of the survey area. The data fusion and adjustment unit is communicatively connected to the collaborative measurement control unit. The surveying results output unit is used to compare the encrypted 3D surveying data of the survey area with the existing topographic map data, and generate a digital elevation model and a digital line map as the final surveying results. The surveying results output unit is communicatively connected to the data fusion and adjustment unit and the data analysis and calibration object management unit.

10. The land surveying system based on dynamic remote sensing monitoring according to claim 9, characterized in that, The collaborative measurement control unit includes: a ground measurement robot control module for controlling the rotation of the aiming head of the total station or measurement robot, automatic target recognition, distance measurement, and data recording; an auxiliary scanning equipment control module for controlling the flight / driving path, scanning frequency, and scanning range of the UAV or mobile measurement vehicle; and a dynamic triggering module for receiving real-time measurement data from the ground measurement robot and determining whether to trigger the auxiliary scanning equipment to perform encrypted scanning based on whether the change in elevation difference between adjacent stations exceeds a preset threshold. A real-time data link is established between the ground measurement robot control module and the dynamic triggering module, and the dynamic triggering module is communicatively connected to the auxiliary scanning equipment control module.

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