An engineering surveying and mapping system

Abstract

The present invention relates to the field of geographic information measurement technology, specifically an engineering surveying and mapping system, the system comprising: a foundation settlement offset analysis module, based on the three-dimensional point cloud data collected during engineering foundation monitoring, divides the data according to the partition range of the foundation, analyzes the offset distribution of the foundation settlement, analyzes the settlement relationship by matching the intersection points of adjacent partitions, and generates the foundation partition offset distribution characteristics. In the present invention, by partitioning the foundation, based on the three-dimensional point cloud data, extracting multi-dimensional information such as coordinate distribution, density and settlement difference between points, it is possible to reveal the settlement characteristics of the internal partition and the adjacent areas in more detail. The comprehensive analysis of the distribution of settlement offset values ​​accurately locates the settlement direction and the cumulative offset changes, providing a quantitative basis for the overall stability assessment of the foundation. Normalization processing and weight distribution adjustment optimize and correct the error concentration area, and realize dynamic smooth compensation of errors between partitions.

Description

Technical Field

[0001] The present invention relates to the field of geographic information measurement technology, and in particular to an engineering surveying and mapping system. Background Art

[0002] The field of geographic information measurement technology involves the use of modern surveying and mapping technologies and tools to collect, process, analyze and manage geospatial data, including global positioning systems, remote sensing, geographic information systems and traditional surveying and mapping technologies, and the use of equipment and software platforms such as total stations, drones, laser scanners to achieve accurate measurement and modeling of spatial data.

[0003] The engineering surveying and mapping system is used for tasks such as topographic mapping, construction layout, deformation monitoring, and final acceptance within engineering projects. By integrating multiple surveying and mapping technologies, the system provides precise spatial data and graphical displays for engineering construction, ensuring construction accuracy, improving construction efficiency, guaranteeing project quality, and optimizing project management.

[0004] When dealing with foundation settlement issues, existing technologies mainly rely on traditional surveying and mapping methods and single data analysis methods. They lack the detailed processing of partition data and dynamic analysis of the relationship between partitions, resulting in difficulty in accurately reflecting the settlement characteristics at the intersection of multiple regions. Due to insufficient centralized optimization of error distribution, high-density error areas within the partition may not be effectively identified, thereby introducing large deviations in the overall calculation of the partition, affecting the accuracy of subsequent analysis. Existing technologies only analyze the curvature of terrain features at a static level and are unable to dynamically respond to the complexity of terrain changes. In the classification and feature point extraction of complex terrain, it is difficult to reflect the in-depth exploration of the dynamic change trends of extreme points. In addition, for dynamic deformation monitoring, existing methods lack a comprehensive analysis of the multi-period deformation characteristics of the observation points, and can only provide local displacement information, but cannot reveal the overall deformation trend in the region and its impact on terrain characteristics. This limitation may lead to insufficient ability to predict terrain deformation during engineering construction, ultimately adversely affecting construction efficiency and project quality. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an engineering surveying and mapping system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an engineering surveying and mapping system includes:

[0007] The foundation settlement offset analysis module divides the data according to the foundation partition range based on the 3D point cloud data collected during engineering foundation monitoring, analyzes the offset distribution of foundation settlement, analyzes the settlement relationship by matching the intersection points of adjacent partitions, and generates the foundation partition offset distribution characteristics;

[0008] The error collaborative optimization module determines the error concentration area based on the foundation partition offset distribution characteristics and the standard deviation of the settlement offset value, adjusts the error concentration area within the partition range, and corrects the cross-partition error relationship to generate engineering foundation error optimization compensation data;

[0009] The partitioned terrain curvature analysis module performs curvature distribution analysis on the terrain data of the surveying and mapping area of ​​the engineering foundation based on the engineering foundation error optimization compensation data, and obtains the partitioned terrain curvature characteristic data in combination with the change trend of the point density in the flat area;

[0010] The dynamic deformation data analysis module extracts the deformation direction changes and rate fluctuations of the observation points in each partition over multiple time periods from the dynamic displacement data in foundation monitoring, analyzes the deformation characteristics in multiple directions in the area, adjusts the overall form of the partitioned terrain curvature characteristic data, and generates deformation monitoring results for the engineering surveying and mapping area.

[0011] As a further solution of the present invention, the steps of obtaining the offset distribution for analyzing foundation settlement are specifically as follows:

[0012] Based on the foundation point cloud data collected by 3D laser scanning equipment, data classification is performed and the coordinate distribution and point cloud density of each foundation partition are analyzed. The point cloud curvature of each area is measured and evaluated to generate partition terrain and structural characteristic data.

[0013] Based on the partitioned terrain and structural characteristic data, the height differences and point density differences between adjacent points in each partition are measured, and the settlement and density changes of the foundation are plotted according to the differences to generate an offset distribution map of the foundation settlement.

[0014] As a further solution of the present invention, the steps for obtaining the ground partition offset distribution characteristics are specifically as follows:

[0015] Obtaining the intersection point data of the partition boundaries from the offset distribution map of the foundation settlement, including the settlement direction and offset difference between the structure boundary and the cap joint, extracting the three-dimensional coordinates of the intersection points and the monitored settlement data, comparing the settlement direction and offset of the intersection points of adjacent partitions, and generating the cumulative settlement offset data between the partitions by accumulating the settlement differences of the intersection points;

[0016] Based on the cumulative settlement offset data between the partitions, the formula is used:

[0017] ;

[0018] Calculate the average settlement offset relationship value , analyze the settlement relationship and generate the distribution characteristics of foundation partition offset;

[0019] in, It is a partition The average settlement value within Indicates the The difference in settlement between the partition and the adjacent partition at the junction point, is the total area of ​​the foundation.

[0020] As a further solution of the present invention, the step of obtaining the error concentration area within the adjustment partition range is specifically as follows:

[0021] Based on the ground partition offset distribution characteristics, the standard deviation value of each partition is obtained, and the standard deviation value is compared with a preset error concentration area threshold, and the partitions exceeding the regional threshold are marked as error concentration areas, thereby obtaining a preliminary error concentration area marking result;

[0022] Based on the preliminary error concentration area marking results, the formula is used:

[0023] ;

[0024] Compute partitions Sedimentation weight , get the error concentration area data combined with weight analysis;

[0025] in, It is a partition The settlement offset value, It is a partition The area, is the total number of partitions, Used to traverse all partitions, All partitions The settlement offset value, All partitions area;

[0026] Based on the error concentration area data combined with the weight analysis, combined with the offset characteristics of the partition intersection points and the neighborhood trend, the error concentration range within and between partitions is optimized and adjusted to obtain the error concentration area optimization result.

[0027] As a further solution of the present invention, the steps for obtaining the engineering foundation error optimization compensation data are specifically as follows:

[0028] Extracting the settlement offset direction and amplitude characteristics of the intersection point, analyzing the spatial distribution and settlement trend of the intersection point, selecting neighboring monitoring points to fit the settlement offset curve, judging the regional settlement change and redistributing the weight of the settlement offset value, adjusting the settlement relationship across partitions at the intersection point, updating the optimization result of the error concentration area, and obtaining cross-partition error correction data;

[0029] Based on the cross-partition error correction data, by analyzing the cumulative amount of partition settlement offset and the correction value of the intersection point, combined with the partition area and neighborhood data, the error distribution characteristics are evaluated, the settlement compensation needs within the partition are summarized, and the engineering foundation error optimization compensation data is generated.

[0030] As a further solution of the present invention, the steps for obtaining the curvature characteristic data of the partitioned terrain are specifically as follows:

[0031] Based on the engineering foundation error optimization compensation data, a curvature distribution analysis is performed on the terrain data of the engineering foundation surveying area, the elevation value of the terrain point cloud data is extracted, the local curve of the measurement point is fitted to determine the curvature radius, the area is divided according to the curvature range, and the regional curvature distribution result is generated;

[0032] Based on the regional curvature distribution results, the formula is adopted:

[0033] ;

[0034] Calculating curvature Over time The rate of change , and obtain the analysis results of the curvature distribution change rate;

[0035] in, , is the curvature value of the extreme point, , is the corresponding curvature value and time point, is the adjustment factor, is the local curvature variation, is the local time difference;

[0036] Based on the results of the curvature distribution change rate analysis, the point density distribution in the flat area is extracted, the number of points per unit area is counted, and the point density change range in the flat area is analyzed. The distribution defects in the flat area are adjusted based on the point density change trend, and the regional curvature and point density information are integrated to obtain the regional terrain curvature characteristic data.

[0037] As a further solution of the present invention, the steps of obtaining deformation characteristics in multiple directions within the analysis area are specifically as follows:

[0038] Based on the dynamic displacement data from foundation monitoring, the multi-period displacement data of observation points within the partition are processed in time series to extract the three-dimensional displacement direction and change amplitude of each observation point. The rate gradient distribution is analyzed in combination with the time interval, and the rate gradient distribution characteristics within the partition are classified to generate the dynamic trend classification results within the partition.

[0039] Based on the dynamic trend classification results within the partition, by analyzing the multi-directional displacement data of the observation points in each partition, statistically analyzing the displacement amplitude and change pattern, and combining the rate gradient characteristics to mark the areas with local direction changes, the multi-directional deformation characteristic analysis results within the partition are generated.

[0040] As a further solution of the present invention, the steps for obtaining deformation monitoring results of the engineering surveying and mapping area are specifically as follows:

[0041] Based on the results of the multi-directional deformation characteristics analysis within the partition, the correlation between the morphological change area and the settlement trend is determined according to the dynamic change trend of the extreme points of the slope surface and steep area, and the areas of density change are marked to generate the local morphological change characteristic analysis results;

[0042] Based on the analysis results of the local morphological change characteristics, the partitioned terrain curvature characteristic data is adjusted, and the deformation monitoring results of the engineering surveying area are generated by smoothing the curvature change values ​​of the abnormal area and correcting the curvature characteristics of the point density change area.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are:

[0044] In the present invention, by zoning the foundation, the coordinate distribution, density, settlement difference and other multi-dimensional information between points are extracted based on three-dimensional point cloud data, which can reveal the settlement characteristics of the internal and adjacent areas of the partition in more detail. The comprehensive analysis of the distribution of settlement offset values ​​accurately locates the settlement direction and cumulative offset changes, providing a quantitative basis for the overall stability assessment of the foundation. Normalization processing and weight distribution adjustment optimize and correct the error concentration area, realize dynamic smooth compensation of errors between partitions, and significantly improve the accuracy of the data and the coordination between partitions. Through in-depth analysis of the terrain curvature distribution and the dynamic change rate of extreme points, the position and curvature parameters of the terrain feature points are optimized, laying a more accurate foundation for the classification and characteristic analysis of the foundation. Combined with the multi-period deformation characteristics and trend analysis in dynamic displacement observation, the multi-directional deformation characteristics are associated with the curvature change trend, and the overall morphology of the complex terrain is dynamically adjusted to ensure the accuracy of the foundation settlement and deformation monitoring results. The innovative solution's multi-level optimization of participating items and overall logical design make each step of processing, from settlement data acquisition to zoning analysis to dynamic optimization, more refined, significantly enhancing the adaptability of engineering surveying and mapping to complex terrain and dynamic settlement changes, and providing data support for the precise construction of engineering projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a system flow chart of the present invention;

[0046] Figure 2 A flow chart for analyzing the offset distribution of foundation settlement according to the present invention;

[0047] Figure 3 A flow chart for obtaining the foundation partition offset distribution characteristics of the present invention;

[0048] Figure 4 A flow chart of adjusting the error concentration area within the partition range of the present invention;

[0049] Figure 5 A flow chart of the present invention for obtaining engineering foundation error optimization compensation data;

[0050] Figure 6 A flow chart for obtaining curvature characteristic data of a partitioned terrain according to the present invention;

[0051] Figure 7 A flowchart of the present invention for analyzing deformation characteristics in multiple directions within a region;

[0052] Figure 8 This is a flow chart of the present invention for obtaining deformation monitoring results in engineering surveying and mapping areas. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0055] See also Figure 1 , an engineering surveying and mapping system comprises:

[0056] The foundation settlement offset analysis module is based on the three-dimensional point cloud data collected during engineering foundation monitoring. It divides the data according to the partition range of the foundation, extracts the coordinate distribution of the point cloud within the partition, the neighborhood point density and the settlement curvature change characteristics, and obtains the settlement difference and density difference between adjacent points within the partition. Based on the difference information, the offset distribution of the foundation settlement is analyzed. By matching the intersection points of adjacent partitions, including the settlement direction and offset differences of the structural boundaries or the pedestal joints, the cumulative settlement offset values ​​between the partitions are obtained, the settlement relationship is analyzed, and the foundation partition offset distribution characteristics are generated;

[0057] The error collaborative optimization module normalizes the settlement offset values ​​based on the distribution characteristics of foundation partition offsets, determines the error concentration area based on the standard deviation of the settlement offset values, and adjusts the error concentration area within the partition based on the weight distribution of the settlement offset values ​​within the partition. For the intersection points between partitions, the intersection point offset characteristics and neighborhood trend data are combined to correct the cross-partition error relationship and generate engineering foundation error optimization compensation data;

[0058] The zoning terrain curvature analysis module analyzes the curvature distribution of the terrain data in the surveyed area of ​​the engineering foundation based on the engineering foundation error optimization compensation data. It divides the terrain into slopes, steep areas, and flat areas according to the distribution of curvature values. It extracts the extreme points of the slopes and steep areas, calculates the curvature change rate of the extreme points, optimizes the location and curvature parameters of the extreme points, and combines the change trend of the point density in the flat area to obtain the curvature characteristic data of the zoning terrain.

[0059] The dynamic deformation data analysis module extracts the deformation direction changes and rate fluctuations of each partition's observation points over multiple time periods from the dynamic displacement data in foundation monitoring. It then classifies the dynamic trends within the partition based on the deformation rate gradients of adjacent observation points, analyzes the deformation characteristics in multiple directions within the region, and analyzes the local morphological change characteristics of the slope and steep areas in combination with the dynamic change trends of the extreme points on the slope and steep areas. It also analyzes the changing trends of the point density and its impact on settlement smoothness, adjusts the overall morphology of the partitioned terrain curvature characteristic data, and generates deformation monitoring results for the engineering surveying and mapping area.

[0060] The distribution characteristics of foundation zoning offsets include the differences in settlement within each zone, the relative values ​​of offsets between zones, and the structural interaction characteristics at the zone boundaries. The engineering foundation error optimization compensation data include the normalized exponent of the settlement offset value, the error correction value of each zone, and the compensation coefficient of the global error. The zoning terrain curvature characteristic data include the curvature statistics of different terrain types such as slopes, steep and flat areas, the optimized positions of key terrain points, and the terrain contour lines after curvature adjustment. The deformation monitoring results of the engineering surveying and mapping area include the main deformation directions during the monitoring period, the statistical distribution of deformation rates, the dynamic response characteristics of key observation points, and the impact assessment of local terrain changes.

[0061] See also Figure 2 , the specific steps for obtaining the offset distribution for analyzing foundation settlement are as follows:

[0062] Based on the foundation point cloud data collected by 3D laser scanning equipment, data classification is performed and the coordinate distribution and point cloud density of each foundation partition are analyzed. The point cloud curvature of each area is measured and evaluated to generate partition terrain and structural characteristic data.

[0063] During engineering foundation monitoring, point cloud data is first collected using a 3D laser scanner. This data is then imported into AutoCAD Civil 3D, where it is automatically categorized according to pre-set foundation partitions. The point cloud data for each partition is further analyzed using open-source geographic information systems such as QGIS to process the coordinate distribution within the point cloud. Neighborhood point density is determined by measuring the number of points within a specific volume. Furthermore, curvature changes in the point cloud are analyzed using MeshLab 3D mesh processing. This curvature analysis helps assess deformation trends in foundation materials.

[0064] Based on the terrain and structural characteristics data of the subarea, the height difference and point density difference between adjacent points in each subarea are measured. According to the differences, the settlement and density changes of the foundation are plotted to generate the offset distribution map of the foundation settlement.

[0065] MeshLab further derives the precise location and relative height of each point from the processed point cloud data, enabling calculation of differential settlement between adjacent points within a subarea. This differential settlement is determined by measuring the difference in vertical distance between adjacent points. For density differences, QGIS analyzes the variation in point cloud density between adjacent points—the difference in the number of points per unit volume around each point. This differential data is used to create an offset distribution map of foundation settlement. This chart, by displaying the variations in settlement and density across different subareas, reveals potential structural issues and areas of uneven settlement.

[0066] See also Figure 3 ,The specific steps for obtaining the distribution characteristics of foundation partition offset are:

[0067] Obtain the intersection point data of the partition boundaries from the offset distribution map of foundation settlement, including the settlement direction and offset differences between the structure boundary and the cap joint. Extract the three-dimensional coordinates of the intersection points and the monitored settlement data. Compare the settlement directions and offsets of the intersection points of adjacent partitions. By accumulating the settlement differences of the intersection points, generate the cumulative settlement offset data between the partitions.

[0068] By matching the intersection points of adjacent partitions, including differences in settlement direction and offset at structural boundaries or abutment joints, three-dimensional point cloud data of the intersection points is first acquired. The settlement direction and displacement of each intersection point are then measured to extract the variation characteristics. The specific process involves using a laser scanner to perform high-precision scanning of the intersection points. The scanned data is then imported into point cloud processing software (such as CloudCompare) to separate the point cloud data at each partition boundary. The offset differences at the intersection points are calculated based on the coordinate changes of the point cloud, and a comparative analysis method is used to determine the settlement direction of the intersection points of adjacent partitions. Subsequently, combined with historical monitoring data, GIS tools such as QGIS are used to perform spatial analysis of the settlement data at the intersection points, mapping the settlement direction and offset of each intersection point to the corresponding partition boundary. By accumulating the offset values ​​and direction differences of the intersection points, the cumulative settlement offset of each partition is calculated.

[0069] Based on the cumulative settlement offset data between zones, the formula is used:

[0070] ;

[0071] Calculate the average settlement offset relationship value , analyze the settlement relationship and generate the distribution characteristics of foundation partition offset;

[0072] in, It is a partition The average sedimentation value within the The settlement amplitude of all points in the partition point cloud data is averaged. For example, the settlement values ​​of all points in the partition are collected by laser scanning, and the average settlement value of the partition is calculated through statistical analysis. is the settlement offset difference between the partition boundaries, indicating the The difference in settlement between a partition and its adjacent partitions at the junction is obtained by measuring the settlement amplitude at the junction and calculating the difference. It is the total land area, which represents the cumulative value of all partition areas. The partition areas are obtained by directly measuring the regional range and summing them up through a geographic information system (such as QGIS).

[0073] For example, the settlement point data for zone 1 is , then the average settlement value of zone 1 is , the point data of partition 2 is ,but , the settlement values ​​at the intersection of partition 1 and partition 2 are 10 and 8, then For example, if the area of ​​partition 1 is 50 and the area of ​​partition 2 is 60, then the total area of ​​the foundation is , enter the partition settlement value :The settlement values ​​of partition 1 and partition 2 are 12.33 and 14.00 respectively, taking into account the difference in boundary settlement offset : The boundary settlement offsets of the intersection points are 2 and 3 respectively.

[0074] ;

[0075] ;

[0076] Calculate the average settlement offset relationship value : ;

[0077] According to the calculated average settlement offset relationship value , compared with the historical benchmark value (for example, 0.25), it is found that the current value is higher than the benchmark value, indicating that the overall foundation settlement deviation has increased compared with the benchmark level, and there may be areas where local settlement exceeds the standard. Combined with the zoning settlement value The distribution of It is significantly higher than other zones, and the bearing conditions or foundation treatment measures in this area need to be checked in detail.

[0078] See also Figure 4 ,The specific steps for obtaining the error concentration area within the adjustment partition range are:

[0079] Based on the distribution characteristics of the ground-based partition offset, the standard deviation value of each partition is obtained and compared with the preset error concentration area threshold. The partitions exceeding the regional threshold are marked as preliminary error concentration areas, and the preliminary error concentration area marking results are obtained;

[0080] Based on the acquired settlement offset values ​​for each subarea, these values ​​are first normalized. This normalization process scales each subarea's offset value to a range of 0 to 1 by calculating its maximum and minimum values. Specifically, the maximum and minimum values ​​are found from the settlement offset data for all subareas. Each subarea's offset values ​​are normalized to ensure comparability. After normalization, the standard deviation of the normalized values ​​is calculated to identify error concentration areas. The standard deviation of each subarea's settlement offset value is calculated and compared with a preset error concentration threshold. Subareas with larger standard deviations are marked as preliminary error concentration areas. For example, suppose the settlement offset standard deviations for subareas 1, 2, and 3 are 3.5, 2.8, and 1.2, respectively, and the preset error concentration threshold is 3.0. Comparison reveals that the standard deviation of subarea 1 (3.5) is greater than the threshold, so subarea 1 is marked as an error concentration area. The standard deviations of subareas 2 and 3 are below the threshold, so they are not marked as error concentration areas. After marking, further analysis was performed to identify the characteristics of the settlement distribution within zone 1.

[0081] Based on the preliminary error concentration area marking results, the formula is used: ;

[0082] Compute partitions Sedimentation weight , obtain the error concentration area data combined with weight analysis;

[0083] in, It is a partition The settlement offset value represents the average settlement amplitude in the partition. The settlement values ​​of each measuring point in each partition are obtained through point cloud data and the average value is taken to determine the settlement. It is a partition The area represents the geographical extent of the partition, which is measured by a geographic information system (GIS), for example, by measuring the regional boundaries on an engineering drawing or directly obtaining the area value of each partition from a GIS system. is the total number of partitions, indicating the total number of foundation areas being calculated, Used to traverse all partitions, All partitions The settlement offset value, All partitions area.

[0084] For example, there are three valid partitions, and the settlement offset value of partition 1 is ,area , the settlement offset value of partition 2 ,area , the settlement offset value of zone 3 ,area .

[0085] Calculate the weighted value for each partition:

[0086] ;

[0087] ;

[0088] ;

[0089] Compute the weighted sum:

[0090] ;

[0091] Calculate the weight of each partition:

[0092] ;

[0093] ;

[0094] ;

[0095] Result description:

[0096] The calculation results show that zone 1 has the highest weight, accounting for 40.1% of the overall foundation settlement offset, followed by zone 2, accounting for 39%, and zone 3 has the lowest weight, accounting for 20.8%.

[0097] Based on the error concentration area data combined with weight analysis, combined with the offset characteristics of the partition intersection points and the neighborhood trend, the error concentration range within and between partitions is optimized and adjusted to obtain the error concentration area optimization result;

[0098] First, the weight calculation results show that Partition 1 has the highest weight, accounting for 40.1%, followed by Partition 2, accounting for 39.0%, and Partition 3 has the lowest weight, accounting for 20.8%. Based on this weight distribution, Partitions 1 and 2 are identified as high-weight areas and are prioritized as adjustment priorities for error concentration areas. Second, within Partitions 1 and 2, the offset characteristics of the intersection points are used to analyze the settlement relationship between these parts and their adjacent parts (for example, the settlement offset difference at the intersection point between Partitions 1 and 2). By measuring the settlement offset values ​​of the partition intersection points, the error concentration on the boundary is determined, and the boundary area division is adjusted. Third, within Partitions 1 and 2, the error distribution is analyzed based on the neighborhood trend data. For example, within Partition 1, the measurement points with high offset values ​​and their surrounding areas are designated as local key adjustment areas. Finally, the low-weight areas of Partition 3 are integrated with the adjustment results of the adjacent parts, the partition boundaries are re-optimized, and the adjusted settlement characteristics are evaluated. Ultimately, the optimized error concentration area data including Partitions 1, 2, and 3 are generated.

[0099] See also Figure 5 ,The specific steps for obtaining engineering foundation error optimization compensation data are as follows:

[0100] Extract the settlement offset direction and amplitude characteristics of the intersection point, analyze the spatial distribution and settlement trend of the intersection point, select neighboring monitoring points to fit the settlement offset curve, determine the regional settlement changes and redistribute the weights of the settlement offset values, adjust the settlement relationship across zones at the intersection point, update the optimization results of the error concentration area, and obtain cross-zone error correction data;

[0101] The intersection points between partitions are analyzed in combination with the intersection point offset characteristics and neighborhood trend data. First, the settlement offset direction and amplitude characteristics are extracted from the three-dimensional point cloud data of each intersection point. The offset data of the intersection points are spatially analyzed using three-dimensional model software to obtain the specific settlement direction changes and offset values ​​of the intersection points of different partitions; then, through the neighborhood data trend analysis method, the monitoring points adjacent to the intersection points are selected, and the settlement data around each intersection point are obtained in a step-by-step manner, and its change trend is calculated. For example, by fitting the settlement offset curve of the neighborhood points, the settlement increase or decrease in the area is judged; then, according to the characteristics of the settlement difference at the intersection point, the settlement relationship across partitions at the intersection point is adjusted, the weights of the settlement offset values ​​are redistributed, the settlement difference distribution is evenly mapped to the adjacent partitions, and the correction results of the cross-partition errors are updated, finally forming optimized cross-partition error correction data.

[0102] Based on the cross-zone error correction data, by analyzing the cumulative amount of zone settlement offset and the correction value of the intersection point, combined with the zone area and neighborhood data, the error distribution characteristics are evaluated, the settlement compensation needs within the zone are summarized, and the engineering foundation error optimization compensation data is generated;

[0103] The adjusted partition settlement offset data is extracted, and the error adjustment range of each partition is redefined. By accumulating the settlement offset data within the partition and the error adjustment results of the intersection points, the settlement offset of each partition and the correction value of the partition intersection point are recalculated; then, according to the correlation between the partition area and the settlement offset weight, the compensation value of each area is optimized point by point. For example, the compensation amplitude of the high error area within the partition is adjusted through the partition weight mapping method, and the overall distribution is corrected in combination with the neighborhood trend data; finally, the compensated data is integrated into a standardized format, and a report including the internal error distribution characteristics of each partition, the corrected settlement value of the intersection point, and the overall optimized compensation data is output.

[0104] See also Figure 6 , the specific steps for obtaining the curvature characteristic data of the partitioned terrain are:

[0105] Based on the engineering foundation error optimization compensation data, the curvature distribution analysis is performed on the terrain data of the engineering foundation surveying area, the elevation value of the terrain point cloud data is extracted, the local curve of the measurement point is fitted to determine the curvature radius, and the area is divided according to the curvature range to generate the regional curvature distribution results;

[0106] The curvature distribution analysis of the terrain data in the engineering foundation survey area is carried out. First, the elevation value of the terrain point cloud data is extracted, and the curvature formula is used to calculate the curvature distribution of the terrain data. Calculate the curvature value of each point, where the curvature radius After calculating the curvature value by fitting the local curve of the measurement points, the terrain is divided into slopes, steep areas, and flat areas based on the curvature distribution. For example, a curvature threshold range is set, and points with a curvature value less than 0.01 are classified as flat areas, points between 0.01 and 0.05 are classified as slopes, and points greater than 0.05 are classified as steep areas. Subsequently, extreme points are extracted within the slopes and steep areas. These points are obtained by detecting local maxima or minima of the curvature, for example, by comparing the curvature value of each point with its neighboring points, marking points with significant changes as extreme points.

[0107] Based on the regional curvature distribution results, the formula is used:

[0108] ;

[0109] Calculating curvature Over time The rate of change , and obtain the analysis results of the curvature distribution change rate;

[0110] in, , is the curvature value of the extreme point, representing the time point and The measured curvature, , is the corresponding curvature value and The time point is directly obtained through measurement records and used to calculate the time range of curvature change. It is an adjustment factor that describes the degree of influence of local curvature changes on the rate of change of overall curvature. The value of the adjustment factor can be determined by the following steps: According to the statistical distribution of curvature, analyze the fluctuation of curvature values ​​in the surveying and mapping area. If the curvature value of a certain area fluctuates greatly, it means that the terrain of the area is complex and the adjustment factor needs to be set larger to increase the contribution of local curvature changes to the overall change; otherwise, it should be set smaller. The reference range of the adjustment factor can be determined by fitting and analyzing the historical surveying and mapping data of similar terrains. For example, by statistically analyzing the curvature change rates of multiple similar terrains, the typical contribution ratio of local curvature to the overall change can be found as the basic value of the adjustment factor. It is the amplitude of local curvature change, which represents the difference between the maximum and minimum curvature near the extreme point. It is used to reflect the range of local terrain changes. For example: , It is the local time difference, which represents the time difference between the maximum and minimum curvature values. It is used to dynamically analyze the local change rate. For example: .

[0111] For example, if the curvature value of a slope extreme point is (time )and (time ), local maximum , local minimum .

[0112] The basic part of calculating the rate of change of curvature:

[0113] ;

[0114] Calculate the local curvature change amplitude and time difference:

[0115] ;

[0116] ;

[0117] Assumption Adjustment Factor , calculate the local variation contribution:

[0118] ;

[0119] Combined to get the total curvature change rate:

[0120] ;

[0121] The results show that the curvature change rate is This value represents the degree of change in curvature per unit time, compared to an assumed historical baseline value. , the current result is slightly higher, indicating that the terrain changes near the extreme points of the slope are more significant. Combined with the results of the curvature change rate, the dynamic changes of the slope or steep area can be identified, providing a basis for the analysis of terrain dynamic characteristics and the optimization of construction design in engineering surveying and mapping. According to the calculation results, the position and curvature parameters of the extreme points are optimized. The specific operations include: comparing the curvature value of the extreme point with the curvature change trend of its neighboring points, adjusting the extreme points whose curvature values ​​deviate significantly from the local trend, such as refitting the position and curvature value of the points with large offsets to ensure the continuity of the curvature distribution; combining the overall curvature distribution of the slope and steep area, adjusting the position of the extreme point so that it more accurately reflects the terrain characteristics in the area with prominent curvature changes, for example, increasing the position accuracy record of high curvature points in steep areas; after the optimization is completed, the optimized curvature parameter distribution is regenerated to provide more accurate input data for subsequent analysis.

[0122] Based on the results of the curvature distribution change rate analysis, the point density distribution in the flat area is extracted, the number of points per unit area is counted, the point density change range in the flat area is analyzed, and the distribution defects in the flat area are adjusted based on the point density change trend. The regional curvature and point density information are integrated to obtain the curvature characteristic data of the partitioned terrain.

[0123] Combined with the changing trend of point density in the flat area, the point coordinates and density distribution in the flat area are first extracted from the terrain point cloud data. By counting the number of point clouds per unit area, the distribution range of point density in the flat area is obtained. For example, The number of points in the grid is counted, and areas with too high or too low point density are marked; secondly, based on the changing trend of the point density distribution, whether there are potential terrain anomalies in the area with too high density is analyzed, for example, by superimposing the density and curvature values, the slopes or steep areas that may be missed are identified; then, the points in the low-density area are supplemented or interpolated, for example, the terrain data is supplemented by fitting the neighborhood points to fill the distribution defects in the low-density area; finally, the point density analysis results are combined with the curvature distribution results to uniformly generate the partitioned terrain curvature feature data, which includes the curvature classification results (flat, slope, steep area), the spatial distribution of the curvature values ​​and the spatial variation characteristics of the point density, to ensure that the curvature feature data can fully reflect the terrain characteristics of the surveying and mapping area.

[0124] See also Figure 7 , the steps for obtaining the deformation characteristics in multiple directions in the analysis area are as follows:

[0125] Based on the dynamic displacement data from foundation monitoring, the multi-period displacement data of observation points within the partition are processed in time series to extract the three-dimensional displacement direction and change amplitude of each observation point. The rate gradient distribution is analyzed in combination with the time interval, and the rate gradient distribution characteristics within the partition are classified to generate the dynamic trend classification results within the partition.

[0126] From the dynamic displacement data in foundation monitoring, the deformation direction changes and rate fluctuations of the observation points in each partition over multiple time periods are extracted. By performing time series processing on the displacement data of each observation point, the three-dimensional displacement direction at each moment is extracted and its change amplitude is calculated. The direction change rules and rate fluctuation ranges of different observation points are analyzed. For example, for the observation points in the same partition, their displacement paths in multiple time periods are recorded to identify the time periods with significant direction changes or abnormal rate fluctuations. Subsequently, the rate gradient is calculated based on the displacement data of adjacent observation points. For example, the displacement difference between two points is normalized, and the rate difference of adjacent points is calculated based on the time interval to identify areas with large rate gradient changes. Finally, the dynamic trends are classified by analyzing the distribution of rate gradients within the partition. For example, areas with large gradient values ​​and consistent directions are classified as overall deformation trend areas, and areas with large rate gradient fluctuation ranges but inconsistent directions are classified as local dynamic fluctuation areas, providing data support for subsequent partition characteristic analysis.

[0127] Based on the dynamic trend classification results within the partition, by analyzing the multi-directional displacement data of the observation points in each partition, statistically analyzing the displacement amplitude and change pattern, and combining the rate gradient characteristics to mark the area of ​​local direction change, the multi-directional deformation characteristic analysis results within the partition are generated;

[0128] Analyze the deformation characteristics of multiple directions in the area, and decompose the extracted deformation direction data into dominant and secondary directions according to the partitions to identify the impact of different directional characteristics on regional deformation. For example, the deformation direction data in a certain partition is statistically analyzed to identify the dominant direction as the downward direction of the slope and the secondary direction as the horizontal expansion direction, and analyze the displacement amplitude and time change characteristics of these two directions; then, analyze the change trend of the dominant direction in multiple time periods, such as calculating the angle change of the direction vector in different time periods to determine whether there is a significant offset in the direction change; combined with the rate fluctuation characteristics, analyze whether there is a large difference in the deformation rate in different directions, for example, identify that the rate change in the downward direction of the slope is significantly greater than the rate change in the horizontal expansion direction; finally, based on the multi-directional characteristics and rate change results, mark the areas with significant local directional changes, providing a basis for subsequent dynamic morphological analysis and foundation optimization.

[0129] See also Figure 8 ,The specific steps for obtaining deformation monitoring results in engineering surveying and mapping areas are as follows:

[0130] Based on the results of multi-directional deformation characteristics analysis within the partition, the dynamic change trend of the extreme points on the slope and steep areas is used to determine the relationship between the morphological change area and the settlement trend, and the areas with density changes are marked to generate the local morphological change characteristics analysis results;

[0131] Combined with the dynamic change trends of extreme points on slopes and steep areas, the local morphological change characteristics of slopes and steep areas are analyzed. By extracting the dynamic displacement data of these areas, the change trajectories of the curvature extreme points in multiple time periods are identified, and the changing characteristics of the displacement direction and amplitude around the extreme points are analyzed. For example, the displacement direction consistency and velocity change range of the observation points around a certain extreme point are calculated to determine whether there is a morphological change trend in the local area. Subsequently, the change patterns of these areas are analyzed through point density. For example, the point density data in the steep area is extracted to determine the range of interval changes between points and to identify whether the areas with significant point density changes correspond to rapid settlement points on the slope or steep area. Combined with the analysis of settlement smoothness, the relationship between density changes and settlement trends is determined. For example, areas with reduced point density are marked as potential unstable areas for local morphological changes.

[0132] Based on the results of local morphological change feature analysis, the curvature feature data of the partitioned terrain is adjusted. By smoothing the curvature change values ​​of the abnormal area and correcting the curvature features of the point density change area, the deformation monitoring results of the engineering surveying area are generated;

[0133] The overall morphology of the partitioned terrain curvature characteristic data is adjusted to generate deformation monitoring results for the engineering surveying and mapping area. Combined with the key areas marked in the curvature characteristic data, the influence range of local abnormal points in the dynamic displacement data is adjusted, such as normalizing abnormal settlement points near extreme points, smoothing local excessive curvature changes, and ensuring the consistency of the terrain data in the overall morphology. Based on the results of dynamic trend classification, the characteristic areas in different partitions are corrected, such as adjusting areas with significant changes in local point density so that their curvature characteristics gradually transition with the surrounding areas. Subsequently, combined with the results of local morphological analysis, an overall deformation monitoring result file is generated, marking the dynamic change trends, local characteristic areas and potential unstable points of different partitions, providing detailed surveying and mapping data support for the optimization design of engineering foundations.

[0134] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An engineering surveying and mapping system, characterized in that: The system comprises: The foundation settlement offset analysis module divides the data according to the foundation partition range based on the 3D point cloud data collected during engineering foundation monitoring, analyzes the offset distribution of foundation settlement, analyzes the settlement relationship by matching the intersection points of adjacent partitions, and generates the foundation partition offset distribution characteristics; The error collaborative optimization module determines the error concentration area based on the foundation partition offset distribution characteristics and the standard deviation of the settlement offset value, adjusts the error concentration area within the partition range, and corrects the cross-partition error relationship to generate engineering foundation error optimization compensation data; The partitioned terrain curvature analysis module performs curvature distribution analysis on the terrain data of the surveying and mapping area of ​​the engineering foundation based on the engineering foundation error optimization compensation data, and obtains the partitioned terrain curvature characteristic data in combination with the change trend of the point density in the flat area; The steps for obtaining the curvature characteristic data of the partitioned terrain are specifically as follows: Based on the engineering foundation error optimization compensation data, a curvature distribution analysis is performed on the terrain data of the engineering foundation surveying area, the elevation value of the terrain point cloud data is extracted, the local curve of the measurement point is fitted to determine the curvature radius, the area is divided according to the curvature range, and the regional curvature distribution result is generated; Based on the regional curvature distribution results, the formula is adopted: ； Calculating curvature Over time The rate of change , and obtain the analysis results of the curvature distribution change rate; in, , is the curvature value of the extreme point, , is the corresponding curvature value and time point, is the adjustment factor, is the local curvature variation, is the local time difference; Based on the results of the curvature distribution change rate analysis, the point density distribution in the flat area is extracted, the number of points per unit area is counted, the point density change range in the flat area is analyzed, and the distribution defects in the flat area are adjusted based on the point density change trend. The regional curvature and point density information are integrated to obtain the curvature characteristic data of the partitioned terrain. The dynamic deformation data analysis module extracts the deformation direction changes and rate fluctuations of the observation points in each partition over multiple time periods from the dynamic displacement data in foundation monitoring, analyzes the deformation characteristics in multiple directions in the area, adjusts the overall form of the partitioned terrain curvature characteristic data, and generates deformation monitoring results for the engineering surveying and mapping area.

2. The engineering surveying and mapping system according to claim 1, characterized in that: The steps for obtaining the offset distribution for analyzing foundation settlement are specifically as follows: Based on the foundation point cloud data collected by 3D laser scanning equipment, data classification is performed and the coordinate distribution and point cloud density of each foundation partition are analyzed. The point cloud curvature of each area is measured and evaluated to generate partition terrain and structural characteristic data. Based on the partitioned terrain and structural characteristic data, the height differences and point density differences between adjacent points in each partition are measured, and the settlement and density changes of the foundation are plotted according to the differences to generate an offset distribution map of the foundation settlement.

3. The engineering surveying and mapping system according to claim 2, characterized in that: The steps for obtaining the foundation partition offset distribution characteristics are specifically as follows: Obtaining the intersection point data of the partition boundaries from the offset distribution map of the foundation settlement, including the settlement direction and offset difference between the structure boundary and the cap joint, extracting the three-dimensional coordinates of the intersection points and the monitored settlement data, comparing the settlement direction and offset of the intersection points of adjacent partitions, and generating the cumulative settlement offset data between the partitions by accumulating the settlement differences of the intersection points; Based on the cumulative settlement offset data between the partitions, the formula is used: ； Calculate the average settlement offset relationship value , analyze the settlement relationship and generate the distribution characteristics of foundation partition offset; in, It is a partition The average settlement value within Indicates the The difference in settlement between the partition and the adjacent partition at the junction point, is the total area of ​​the foundation.

4. The engineering surveying and mapping system according to claim 3, characterized in that: The specific steps for obtaining the error concentration area within the adjustment partition range are: Based on the ground partition offset distribution characteristics, the standard deviation value of each partition is obtained, and the standard deviation value is compared with a preset error concentration area threshold, and the partitions exceeding the regional threshold are marked as error concentration areas, thereby obtaining a preliminary error concentration area marking result; Based on the preliminary error concentration area marking results, the formula is used: ； Compute partitions Sedimentation weight , get the error concentration area data combined with weight analysis; in, It is a partition The settlement offset value, It is a partition The area, is the total number of partitions, Used to traverse all partitions, All partitions The settlement offset value, All partitions area; Based on the error concentration area data combined with the weight analysis, combined with the offset characteristics of the partition intersection points and the neighborhood trend, the error concentration range within and between partitions is optimized and adjusted to obtain the error concentration area optimization result.

5. The engineering surveying and mapping system according to claim 4, characterized in that: The steps for obtaining the engineering foundation error optimization compensation data are specifically as follows: Extracting the settlement offset direction and amplitude characteristics of the intersection point, analyzing the spatial distribution and settlement trend of the intersection point, selecting neighboring monitoring points to fit the settlement offset curve, judging the regional settlement change and redistributing the weight of the settlement offset value, adjusting the settlement relationship across partitions at the intersection point, updating the optimization result of the error concentration area, and obtaining cross-partition error correction data; Based on the cross-partition error correction data, by analyzing the cumulative amount of partition settlement offset and the correction value of the intersection point, combined with the partition area and neighborhood data, the error distribution characteristics are evaluated, the settlement compensation needs within the partition are summarized, and the engineering foundation error optimization compensation data is generated.

6. The engineering surveying and mapping system according to claim 1, characterized in that: The steps for obtaining deformation characteristics in multiple directions within the analysis area are specifically as follows: Based on the dynamic displacement data from foundation monitoring, the multi-period displacement data of observation points within the partition are processed in time series to extract the three-dimensional displacement direction and change amplitude of each observation point. The rate gradient distribution is analyzed in combination with the time interval, and the rate gradient distribution characteristics within the partition are classified to generate the dynamic trend classification results within the partition. Based on the dynamic trend classification results within the partition, by analyzing the multi-directional displacement data of the observation points in each partition, statistically analyzing the displacement amplitude and change pattern, and combining the rate gradient characteristics to mark the areas with local direction changes, the multi-directional deformation characteristic analysis results within the partition are generated.

7. The engineering surveying and mapping system according to claim 6, characterized in that: The specific steps for obtaining the deformation monitoring results of the engineering surveying area are as follows: Based on the results of the multi-directional deformation characteristics analysis within the partition, the correlation between the morphological change area and the settlement trend is determined according to the dynamic change trend of the extreme points of the slope surface and steep area, and the areas of density change are marked to generate the local morphological change characteristic analysis results; Based on the analysis results of the local morphological change characteristics, the partitioned terrain curvature characteristic data is adjusted, and the deformation monitoring results of the engineering surveying area are generated by smoothing the curvature change values ​​of the abnormal area and correcting the curvature characteristics of the point density change area.

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