Engineering surveying and mapping system

Abstract

The invention relates to the technical field of geographic information measurement, in particular to an engineering surveying and mapping system, which comprises a foundation settlement offset analysis module, which is used for carrying out data division according to a subarea range of a foundation based on three-dimensional point cloud data acquired in engineering foundation monitoring, analyzing offset distribution of foundation settlement, and calculating the offset distribution of the foundation settlement by matching junction points of adjacent subareas; the settlement relation is analyzed, and foundation partition deviation distribution characteristics are generated. According to the method, the subarea processing is carried out on the foundation, the coordinate distribution, the density, the settlement difference value and other multi-dimensional information among the points are extracted based on the three-dimensional point cloud data, and the settlement characteristics of the interior of the subarea and the adjacent area can be revealed more meticulously. The settlement deviation value distribution is comprehensively analyzed, the settlement direction and the accumulated deviation change are accurately positioned, and a quantitative basis is provided for the overall stability evaluation of the foundation. And performing normalization processing and weight distribution adjustment, performing optimization correction on an error concentration area, and realizing dynamic smooth compensation of inter-partition errors.

Description

Technical Field

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

[0002] The technical field of geographic information measurement involves using 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, etc. Precise measurement and modeling of spatial data are achieved through devices such as total stations, unmanned aerial vehicles, laser scanners, and software platforms.

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

[0004] When dealing with foundation settlement problems, the existing technology mainly relies on traditional surveying means and single data analysis methods, lacking refined processing of partition data and dynamic analysis of the interrelationships between partitions, resulting in the difficulty of accurately reflecting the settlement characteristics at the intersection points of multiple regions. Due to insufficient centralized optimization of error distribution, high-density error regions within a partition may not be effectively identified, thus introducing large deviations in the overall calculation of the partition and affecting the accuracy of subsequent analysis. The existing technology's curvature analysis of terrain features only stays at the static level and cannot dynamically respond to the complexity of terrain changes, making it difficult to deeply explore the dynamic change trends of extreme points in the classification of complex terrains and the extraction of feature points. In addition, for dynamic deformation monitoring, the existing methods lack comprehensive analysis of the multi-period deformation characteristics of observation points and can only provide local displacement information, unable to reveal the overall deformation trend within the region and its impact on terrain features. This limitation may lead to insufficient terrain deformation prediction ability during engineering construction, ultimately having an adverse impact on construction efficiency and project quality. Summary of the Invention

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

[0006] To achieve the above 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 partition range of the foundation based on the three-dimensional point cloud data collected in the engineering foundation monitoring, 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;

[0008] Based on the foundation partition offset distribution characteristics, the error collaborative optimization module determines the error concentration area according to 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] Based on the engineering foundation error optimization compensation data, the partition terrain curvature analysis module performs curvature distribution analysis on the terrain data of the survey area of the engineering foundation, and combines the change trend of the point density in the flat area to obtain partition terrain curvature characteristic data;

[0010] The dynamic deformation data analysis module extracts the deformation direction change and rate fluctuation of the observation points in each partition within multiple time periods from the dynamic displacement data in the foundation monitoring, analyzes the deformation characteristics in multiple directions within the area, and adjusts the overall shape of the partition terrain curvature characteristic data to generate the deformation monitoring result of the engineering survey area.

[0011] As a further solution of the present invention, the obtaining step of analyzing the offset amount distribution of the foundation settlement is specifically as follows:

[0012] Based on the foundation point cloud data collected by the three-dimensional laser scanning device, data classification is performed, the coordinate distribution and point cloud density of each foundation partition are analyzed, the point cloud curvature of each area is measured and evaluated, and partition terrain and structure characteristic data are generated;

[0013] Based on the partition terrain and structure characteristic data, the height difference and point density difference between adjacent points within each partition are measured, and the settlement and density change conditions of the foundation are drawn according to the difference conditions to generate an offset amount distribution map of the foundation settlement.

[0014] As a further solution of the present invention, the obtaining step of the foundation partition offset distribution characteristics is specifically as follows:

[0015] Obtain the junction point data of the partition boundary from the offset amount distribution map of the foundation settlement, including the settlement direction and offset amount difference of the structure boundary and the pile cap joint, extract the three-dimensional coordinates of the junction point and the monitored settlement data, compare the settlement direction and offset amount of the junction points in adjacent partitions, and generate the cumulative settlement offset data between partitions by accumulating the settlement differences of the junction points;

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

[0017] ;

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

[0019] Among them, is the partition The average settlement value within Indicates the settlement difference at the junction point between the partition and the adjacent partition, and

[0020] As a further solution of the present invention, the steps for obtaining the error concentration area within the adjusted partition range are specifically as follows:

[0021] Based on the foundation partition offset distribution characteristics, obtain the standard deviation of each partition, and compare it with a preset error concentration area threshold. Mark the partitions that exceed the area threshold as the error concentration area to obtain a preliminary error concentration area marking result;

[0022] Based on the preliminary error concentration area marking result, use the formula:

[0023] ;

[0024] Calculate the settlement weight of the partition to obtain the error concentration area data combined with weight analysis;

[0025] Among them, is the settlement offset value of the partition , is the area of the partition , is the total number of partitions, is used to traverse all partitions, is the settlement offset value of all partitions , is the area of all partitions ;

[0026] Based on the error concentration area data combined with weight analysis, combine the offset characteristics of the partition junction points and the neighborhood trend to optimize and adjust the error concentration range within and between partitions to obtain an optimized result of the error concentration area.

[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] Extract the settlement offset direction and amplitude characteristics of the junction points, analyze the spatial distribution and settlement trend of the junction points, select neighborhood monitoring points to fit the settlement offset curve, judge the regional settlement change and re - allocate the weight of the settlement offset value, adjust the settlement relationship across partitions at the junction points, update the optimized result of the error concentration area to obtain 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 junction point, combining the partition area and neighborhood data, evaluate the error distribution characteristics, summarize the settlement compensation requirements within the partition, and generate engineering foundation error optimization compensation data.

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

[0031] Based on the engineering foundation error optimization compensation data, perform curvature distribution analysis on the terrain data of the engineering foundation survey area, extract the elevation values of the terrain point cloud data, fit the local curves of the measurement points to determine the radius of curvature, divide the area according to the curvature range, and generate the regional curvature distribution result;

[0032] Based on the regional curvature distribution result, use the formula:

[0033] ;

[0034] Calculate the curvature changing with time rate of change , and obtain the curvature distribution change rate analysis result;

[0035] Among them, , is the curvature value at the extreme point, , is the corresponding curvature value and time points, is the adjustment factor, is the local curvature change amplitude, is the local time difference;

[0036] Based on the curvature distribution change rate analysis result, extract the point density distribution within the flat area, count the number of points per unit area, analyze the range of point density change in the flat area, adjust the distribution defects of the flat area in combination with the point density change trend, and integrate the regional curvature and point density information to obtain the partition terrain curvature characteristic data.

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

[0038] Based on the dynamic displacement data in the foundation monitoring, through time - series processing of the multi - period displacement data of the observation points within the partition, extract the three - dimensional displacement direction and its change amplitude of each observation point, analyze the rate - gradient distribution in combination with the time interval, and classify the rate - gradient distribution characteristics within the partition to generate the internal dynamic trend classification result of the partition;

[0039] Based on the classification results of the internal dynamic trends of the partitions, by analyzing the displacement data in multiple directions at the observation points within each partition, statistically analyzing the displacement amplitudes and variation rules, and combining with the rate gradient characteristics to mark the regions with local direction changes, the analysis results of the multi-directional deformation characteristics within the partitions are generated.

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

[0041] Based on the analysis results of the multi-directional deformation characteristics within the partitions, according to the dynamic change trends of the extreme points on the slopes and steep areas, judging the association between the morphological change regions and the settlement trends, and marking the regions with density changes, the analysis results of the local morphological change characteristics are generated;

[0042] Based on the analysis results of the local morphological change characteristics, adjusting the terrain curvature characteristic data of the partitions, and generating the deformation monitoring results of the engineering surveying area by smoothing the curvature change values in the abnormal regions and correcting the curvature characteristics in the regions with point density changes.

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

[0044] In the present invention, through the partition processing of the foundation, based on the three-dimensional point cloud data, multi-dimensional information such as the coordinate distribution, density, and settlement difference between points is extracted, which can more meticulously reveal the settlement characteristics inside the partitions and adjacent regions. The comprehensive analysis of the settlement offset value distribution accurately locates the settlement direction and the cumulative offset change, providing a quantitative basis for the overall stability assessment of the foundation. The normalization processing and the adjustment of the weight distribution optimize and correct the error-concentrated regions, realizing the dynamic smooth compensation of the errors between the partitions, and significantly improving the accuracy of the data and the coordination between the partitions. Through the in-depth analysis of the terrain curvature distribution and the dynamic change rate of the extreme points, the positions and curvature parameters of the terrain feature points are optimized, laying a more accurate foundation for the terrain classification and characteristic analysis of the foundation. Combining the multi-period deformation characteristics and trend analysis in the dynamic displacement observation, correlating the multi-directional deformation characteristics with the curvature change trend, and dynamically adjusting the overall morphology of the complex terrain, ensuring the accuracy of the foundation settlement and deformation monitoring results. The multi-level optimization and overall logical design of the innovative solution in the participating items make each step of the processing from the settlement data acquisition to the partition analysis and then to the dynamic optimization more refined, significantly enhancing the adaptability of the engineering surveying to the complex terrain and dynamic settlement changes, and providing data guarantee for the precise construction of the engineering project. Description of the Drawings

[0045] Figure 1 is the system flowchart of the present invention;

[0046] Figure 2 is the flowchart for analyzing the offset distribution of the foundation settlement of the present invention;

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

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

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

[0050] Figure 6 Flow chart for obtaining the partition terrain curvature characteristic data of the present invention;

[0051] Figure 7 Flow chart for analyzing the deformation characteristics in multiple directions within the area of the present invention;

[0052] Figure 8 Flow chart for obtaining the deformation monitoring results of the engineering surveying area of the present invention. Specific embodiments

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

[0054] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, in the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0055] Please refer to Figure 1 , an engineering surveying system includes:

[0056] Based on the three-dimensional point cloud data collected in the engineering foundation monitoring, the foundation settlement offset analysis module divides the data according to the partition range of the foundation, extracts the coordinate distribution, neighborhood point density and settlement curvature change characteristics of the point cloud within the partition, obtains the settlement difference and density difference between adjacent points within the partition from them, analyzes the offset distribution of the foundation settlement according to the difference information, and obtains the cumulative settlement offset value between partitions by matching the settlement direction and offset difference of the junction points of adjacent partitions, including the structural boundary or the cap joint, analyzes the settlement relationship, and generates the foundation partition offset distribution characteristics;

[0057] Based on the foundation partition offset distribution characteristics, the error collaborative optimization module normalizes the settlement offset value, determines the error concentration area according to the standard deviation of the settlement offset value, and adjusts the error concentration area within the partition range according to the weight distribution of the settlement offset value within the partition. For the junction points between partitions, combined with the offset characteristics of the junction points and the neighborhood trend data, the cross-partition error relationship is corrected to generate the engineering foundation error optimization compensation data;

[0058] Based on the engineering foundation error optimization compensation data, the partition terrain curvature analysis module conducts a curvature distribution analysis on the terrain data of the surveyed area of the engineering foundation, divides the terrain into slope surfaces, steep areas, and flat areas according to the distribution of curvature values, extracts the extreme points of the slope surfaces and steep areas, calculates the curvature change rate of the extreme points, optimizes the positions and curvature parameters of the extreme points, and combines the change trend of the point density within the flat area to obtain the partition terrain curvature characteristic data;

[0059] The dynamic deformation data analysis module extracts the deformation direction changes and rate fluctuations of the observation points in each partition within multiple time periods from the dynamic displacement data in the foundation monitoring. According to the deformation rate gradient of adjacent observation points, the dynamic trends within the partition are classified, and the deformation characteristics in multiple directions within the area are analyzed. Combining with the dynamic change trends of the extreme points of the slope surfaces and steep areas, the local morphological change characteristics of the slope surfaces and steep areas are analyzed, and the change trend of the point density and its impact on the settlement smoothness are analyzed to adjust the overall morphology of the partition terrain curvature characteristic data and generate the deformation monitoring results of the engineering survey area;

[0060] The foundation partition offset distribution characteristics include the differences in settlement amounts within each partition, the relative offset values between partitions, and the structural interaction characteristics at the partition boundaries. The engineering foundation error optimization compensation data includes the normalization index of the settlement offset value, the error correction value for each partition, and the compensation coefficient for the global error. The partition terrain curvature characteristic data includes the curvature statistics of different terrain types such as slope surfaces, steep areas, and flat areas, the optimized positions of key terrain points, and the terrain contour line after curvature adjustment. The deformation monitoring results of the engineering survey area include the main deformation directions during the monitoring period, the statistical distribution of the deformation rate, the dynamic response characteristics of key observation points, and the impact assessment of local terrain changes.

[0061] Please refer to Figure 2 , and the specific steps for obtaining the offset distribution of the foundation settlement analysis are as follows:

[0062] Based on the foundation point cloud data collected by the three-dimensional laser scanning device, the data is classified 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, and the partition terrain and structure characteristic data are generated;

[0063] In engineering foundation monitoring, first, 3D laser scanners are used to collect point cloud data. Subsequently, the data is imported into AutoCAD Civil 3D, and the data is classified automatically according to the preset foundation partition range. The point cloud data of each partition will be further analyzed. Open-source geographic information systems such as QGIS are used to process the coordinate distribution in the point cloud, and the neighborhood point density is obtained by measuring the number of points within a specific volume. At the same time, MeshLab 3D mesh processing is used to analyze the curvature change of the point cloud, and the curvature analysis helps to evaluate the deformation trend of the foundation materials.

[0064] Based on the partition terrain and structural characteristic data, the height difference and point density difference between adjacent points within each partition are measured. According to the difference situation, the settlement and density change of the foundation are plotted, and an offset distribution map of the foundation settlement is generated.

[0065] In the processed point cloud data, the exact position and relative height of each point are further obtained through MeshLab, so that the settlement difference between adjacent points within the partition can be calculated. The settlement difference is obtained by measuring the vertical distance difference between adjacent points. For the density difference, the change in the point cloud density between adjacent points is analyzed through QGIS, that is, the difference in the number of points within the unit volume around each point. These difference data are used to plot the offset distribution map of the foundation settlement. This chart reveals potential structural problems and uneven settlement areas by showing the settlement and density changes in different partitions.

[0066] Please refer to Figure 3 , and the steps for obtaining the offset distribution characteristics of the foundation partition are specifically as follows:

[0067] Obtain the intersection point data of the partition boundary from the offset distribution map of the foundation settlement, including the settlement direction and offset difference of the structural boundary and the pile cap joint. Extract the three-dimensional coordinates of the intersection point and the monitored settlement data, compare the settlement direction and offset of the intersection points of adjacent partitions, and generate the cumulative settlement offset data between partitions by accumulating the settlement differences of the intersection points.

[0068] By matching the intersection points of adjacent partitions, including the differences in settlement direction and offset at the structural boundaries or pile cap joints, the 3D point cloud data of the intersection points is first obtained, and the change characteristics are extracted by measuring the settlement direction and displacement of each intersection point. The specific operation process includes: using a laser scanner to perform high-precision scanning on the intersection points, and importing the scanned data into point cloud processing software (such as CloudCompare) to separate the point cloud data on the boundaries of each partition. The offset difference of the intersection points is calculated through the coordinate change of the point cloud, and the settlement direction of the intersection points of adjacent partitions is obtained by using the comparative analysis method. Subsequently, combined with historical monitoring data, GIS tools such as QGIS are used to perform spatial analysis on the settlement data of the intersection points, and the settlement direction and offset of each intersection point are mapped 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 statistically obtained.

[0069] Based on the cumulative settlement offset data between partitions, the formula:

[0070] ;

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

[0072] Among them, is the average settlement value within partition , and this value is obtained by taking the average of the settlement amplitudes of all points in the point cloud data of the partition. For example, the settlement values of all points in the partition are collected through laser scanning, and the average settlement value of the partition is calculated through statistical analysis. is the settlement offset difference at the boundary between partitions, representing the difference in settlement amounts between the partition and the adjacent partition at the intersection point, which is obtained by measuring the settlement amplitudes of the intersection points and calculating the difference. is the total area of the foundation, representing the cumulative value of the areas of all partitions. The partition area is obtained by directly measuring the area range using a geographic information system (such as QGIS) and summing them up.

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

[0074] ;

[0075] ;

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

[0077] Based on the calculated average settlement offset relationship value , comparing with the historical reference value (such as 0.25), it is found that the current value is higher than the reference value, indicating that the overall foundation settlement offset has increased compared to the reference level, and there may be areas with excessive local settlement. Combining with the partition settlement values ' distribution, if the of a certain partition is significantly higher than that of other partitions, the bearing conditions or foundation treatment measures in this area need to be inspected keyly.

[0078] Please refer to Figure 4 , the specific steps to adjust the error concentration area within the partition range are as follows:

[0079] Based on the foundation partition offset distribution characteristics, obtain the standard deviation of each partition, and compare it with the preset error concentration area threshold. Mark the partitions exceeding the area threshold as the preliminary error concentration areas to obtain the preliminary error concentration area marking results;

[0080] Based on the obtained settlement offset values of each partition, first normalize these values. The normalization operation is performed by calculating the maximum and minimum values of the partition settlement offset values, and scaling the offset value of each partition to the range of 0 to 1. The specific steps are as follows: find the maximum and minimum values from the settlement offset data of all partitions, and ensure the comparability of the settlement offset values of each partition by normalizing the offset value of each partition. After normalization, calculate the standard deviation of the normalized values to judge the error concentration area. By calculating the standard deviation of the settlement offset value of each partition and comparing the standard deviation value with the preset error concentration area threshold, mark the partitions with larger standard deviation as the preliminary error concentration areas. For example, assume that the standard deviations of the settlement offset values of partition 1, partition 2, and partition 3 are 3.5, 2.8, and 1.2 respectively, and the preset error concentration area threshold is 3.0. By comparison, it is found that the standard deviation value of partition 1 (3.5) is greater than the threshold 3.0, and mark partition 1 as the error concentration area; the standard deviation values of partition 2 and partition 3 are lower than the threshold and are not marked as the error concentration area. After marking, identify the settlement distribution characteristics within partition 1 through further analysis.

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

[0082] Calculate the settlement weight of the calculation partition , and obtain the error concentration region data combined with weight analysis;

[0083] Among them, is the settlement offset value of the partition , which represents the average settlement amplitude within the partition. It is determined by obtaining the settlement values of each measuring point within each partition from the point cloud data and taking the average value, is the area of the partition , which represents the geographical scope size of the partition. It is measured through a Geographic Information System (GIS). For example, by measuring the regional boundary on the engineering drawing or directly obtaining the area value of each partition from the GIS system, is the total number of partitions, representing the total number of foundation areas to be calculated, is used to traverse all partitions, is the settlement offset value of all partitions , is the area of all partitions .

[0084] For example, there are three effective partitions. The settlement offset value of partition 1 , area , the settlement offset value of partition 2 , area , the settlement offset value of partition 3 , area .

[0085] Calculate the weighted value of each partition:

[0086] ;

[0087] ;

[0088] ;

[0089] Calculate the weighted sum:

[0090] ;

[0091] Calculate the weight of each partition:

[0092] ;

[0093] ;

[0094] ;

[0095] Result description:

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

[0097] Based on the data of the error concentration area combined with weight analysis, and combining the offset characteristics of the partition boundary points and the neighborhood trend, optimize and adjust the error concentration range within and between partitions to obtain the optimized result of the error concentration area.

[0098] First, from the calculated weight results, it can be seen that the weight of Zone 1 is the highest, accounting for 40.1%, Zone 2 follows, accounting for 39.0%, and Zone 3 is the lowest, accounting for 20.8%. Combining this weight distribution, identify Zones 1 and 2 as high-weight areas and prioritize them as the key areas for adjusting the error concentration area. Second, within Zones 1 and 2, use the offset characteristics of the boundary points to analyze the settlement relationship between these partitions and their adjacent partitions (such as the settlement offset difference at the boundary point between Zones 1 and 2). By measuring the settlement offset value of the partition boundary point, determine the error concentration at the boundary and adjust the boundary area division. Third, within Zones 1 and 2, analyze the error distribution according to the neighborhood trend data. For example, within Zone 1, classify the measurement points with high offset values and their nearby areas into the local key adjustment areas. Finally, integrate the low-weight area of Zone 3 with the adjustment results of the adjacent partitions, re-optimize the partition boundary, and evaluate the settlement characteristics after adjustment, and finally generate the optimized error concentration area data including Zones 1, 2, and 3.

[0099] Please refer to Figure 5 , and the specific steps for obtaining the optimized compensation data for the engineering foundation error are as follows:

[0100] Extract the settlement offset direction and amplitude characteristics of the boundary points, analyze the spatial distribution and settlement trend of the boundary points, select the neighborhood monitoring points to fit the settlement offset curve, judge the regional settlement change, reassign the weight of the settlement offset value, adjust the settlement relationship across partitions at the boundary points, update the optimized result of the error concentration area, and obtain the cross-partition error correction data.

[0101] For the intersection points of sub - intervals, analyze them by combining the offset characteristics of the intersection points and the neighborhood trend data. First, extract the settlement offset direction and amplitude characteristics from the three - dimensional point cloud data of each intersection point, and use three - dimensional model software to analyze the spatial distribution of the offset data of the intersection points to obtain the specific settlement direction changes and offset values of the intersection points in different partitions; then, through the neighborhood data trend analysis method, select the monitoring points adjacent to the intersection points, and obtain the settlement data around each intersection point in a step - by - step manner, and calculate its change trend. For example, by fitting the settlement offset curve of the neighborhood points, judge the settlement increment or decrement in this area; then, according to the characteristics of the settlement difference at the intersection points, adjust the settlement relationship across partitions at the intersection points, re - distribute the weights of the settlement offset values, evenly map the settlement difference distribution to the adjacent partitions, and update the correction results of the cross - partition error, and finally form optimized cross - partition error correction data.

[0102] Based on the cross - partition error correction data, by analyzing the cumulative amount of the partition settlement offset and the correction value of the intersection points, combining the partition area and neighborhood data, evaluate the error distribution characteristics, summarize the settlement compensation requirements within the partitions, and generate engineering foundation error optimization compensation data;

[0103] Extract the adjusted partition settlement offset data, re - delimit the error adjustment range for each partition, and re - count the settlement offset amount of each partition and the correction value of the partition intersection points by accumulating the settlement offset data within the partition and the error adjustment results of the intersection points; then, according to the correlation between the partition area and the settlement offset weight, optimize the compensation value of each area point - by - point. For example, adjust the compensation amplitude of the high - error area within the partition through the partition weight mapping method, and correct the overall distribution in combination with the neighborhood trend data; finally, integrate the compensated data into a standardized format and output a report including the internal error distribution characteristics of each partition, the settlement value after correction of the intersection points, and the overall optimized compensation data.

[0104] Please refer to Figure 6 , the specific steps for obtaining the partition terrain curvature characteristic data are as follows:

[0105] Based on the engineering foundation error optimization compensation data, conduct a curvature distribution analysis on the terrain data of the engineering foundation survey area, extract the elevation values of the terrain point cloud data, fit the local curves of the measurement points to determine the radius of curvature, divide the area according to the curvature range, and generate the regional curvature distribution results;

[0106] Conduct a curvature distribution analysis on the terrain data of the engineering foundation survey area. First, extract the elevation values of the terrain point cloud data, and calculate the curvature value of each point through the curvature formula where the radius of curvature Determined by fitting the local curves of the measurement points. After calculating the curvature values, the terrain is divided into slope surfaces, steep areas, and flat areas according to the curvature distribution. For example, set the curvature threshold range. Points with curvature values less than 0.01 are classified as flat areas, points between 0.01 and 0.05 are classified as slope surface areas, and points greater than 0.05 are classified as steep areas. Subsequently, extract the extreme points within the slope surfaces and steep areas. These points are obtained by detecting the local maximum or minimum values of the curvature. For example, by comparing the curvature values of each point with those of its neighboring points, mark the points with significant changes as extreme points.

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

[0108] ;

[0109] Calculate the curvature over time change rate , and obtain the analysis result of the curvature distribution change rate;

[0110] Among them, , is the curvature value of the extreme point, representing the curvature magnitudes measured at time points and respectively, , is the time point corresponding to the curvature values and , directly obtained through measurement records and used to calculate the time range of the curvature change, is the adjustment factor, describing the influence degree of the local curvature change on the overall curvature change rate. The value of the adjustment factor can be determined through the following steps: According to the statistical distribution of the curvature, analyze the fluctuation of the curvature values within the surveyed area. If the curvature values in a certain area fluctuate greatly, it indicates that the terrain in this area is complex, and a larger adjustment factor needs to be set to increase the contribution of the local curvature change to the overall change; otherwise, set a smaller one. Through the fitting analysis of the historical survey data of the same type of terrain, determine the reference range of the adjustment factor. For example, by statistically analyzing the curvature change rates of multiple similar terrains, find out the typical contribution ratio of the local curvature to the overall change as the basic value of the adjustment factor. is the local curvature change amplitude, representing the difference between the maximum and minimum curvature values near the extreme point and used to reflect the local terrain change range. For example: , is the local time difference, representing the time difference corresponding to the maximum and minimum curvatures and used to dynamically analyze the local change rate. For example: .

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

[0112] Basic part of calculating the curvature change rate:

[0113] ;

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

[0115] ;

[0116] ;

[0117] Assume the adjustment factor , calculate the local change contribution:

[0118] ;

[0119] Combine to obtain the total curvature change rate:

[0120] ;

[0121] The result shows that the curvature change rate is . This value represents the degree of change of curvature per unit time. Compared with the assumed historical reference value , the current result is slightly higher, indicating that the terrain change near the extreme points of the slope is relatively significant. Combining 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 the dynamic characteristics of the terrain and the optimization of construction design in engineering surveying. According to the calculation results, optimize the position of the extreme points and the curvature parameters. The specific operations include: comparing the curvature value of the extreme point with the curvature change trend of its neighborhood points, and adjusting the extreme points whose curvature values deviate significantly from the local trend. For example, refit the position and curvature value of the points with large deviations to ensure the continuity of the curvature distribution; combining the overall curvature distribution of the slope and the steep area, adjust the position of the extreme points to make them more accurately reflect the terrain characteristics in the areas with prominent curvature changes. For example, increase the position accuracy record of the high-curvature points in the steep area; after optimization, regenerate the optimized curvature parameter distribution to provide higher-precision input data for subsequent analysis.

[0122] Based on the analysis results of the curvature distribution change rate, extract the point density distribution in the flat area, count the number of points per unit area, analyze the range of point density change in the flat area, adjust the distribution defects of the flat area in combination with the point density change trend, and integrate the regional curvature and point density information to obtain the zonal terrain curvature characteristic data;

[0123] Combined with the changing trend of point density in the flat area, first extract the point coordinates and density distribution in the flat area from the topographic point cloud data. By counting the number of point clouds within a unit area, obtain the distribution range of point density in the flat area. For example, count the number of points in the grid and mark the areas with too high or too low point density; secondly, according to the changing trend of point density distribution, analyze whether there are potential topographic anomalies in the areas with high density. For example, through the superposition analysis of density and curvature values, identify the slopes or steep areas that may be missed; then, supplement or interpolate the points in the low-density area. For example, supplement the topographic data by fitting the neighborhood points to fill the distribution defects in the low-density area; finally, combine the point density analysis results with the curvature distribution results to uniformly generate the zonal topographic curvature characteristic data. The data includes the curvature classification results (flat, slope, steep areas), the spatial distribution of curvature values, and the spatial variation characteristics of point density, ensuring that the curvature characteristic data can comprehensively reflect the topographic characteristics of the surveyed area.

[0124] Please refer to Figure 7 for the specific steps to obtain the deformation characteristics in multiple directions within the analysis area:

[0125] Based on the dynamic displacement data in the ground-based monitoring, through time series processing of the multi-period displacement data of the observation points within the partition, extract the three-dimensional displacement direction and its change amplitude of each observation point, analyze the rate gradient distribution in combination with the time interval, and classify the distribution characteristics of the rate gradient within the partition to generate the zonal internal dynamic trend classification results;

[0126] Extract the change in deformation direction and rate fluctuation of the observation points in each partition from the dynamic displacement data in the ground-based monitoring. Through time series processing of the displacement data of each observation point, extract the three-dimensional displacement direction at each moment and calculate its change amplitude, analyze the direction change law and rate fluctuation range of different observation points. For example, for the observation points within the same partition, record their displacement paths during multiple periods and identify the periods with significant direction changes or abnormal rate fluctuations; subsequently, according to the displacement data of adjacent observation points, calculate their rate gradients. For example, normalize the displacement difference between two points and calculate the rate difference between adjacent points in combination with the time interval to identify the areas with large rate gradient changes; finally, classify the dynamic trends by analyzing the distribution of the rate gradient within the partition. For example, classify the areas with large gradient values and consistent directions as the overall deformation trend areas, and classify the areas with large rate gradient fluctuation ranges but inconsistent directions as the local dynamic fluctuation areas to provide data support for subsequent partition characteristic analysis

[0127] Based on the classification results of the internal dynamic trends in each partition, by analyzing the displacement data in multiple directions of the observation points within each partition, statistically analyzing the displacement amplitudes and variation patterns, and combining with the rate gradient characteristics to mark the regions with local direction changes, the analysis results of the multi-directional deformation characteristics within the partition are generated;

[0128] Analyze the deformation characteristics in multiple directions within the region. By decomposing the extracted deformation direction data into the dominant direction and the secondary direction according to the partition, identify the influence of different direction characteristics on the regional deformation. For example, statistically analyze the deformation direction data within a certain partition, identify that the dominant direction is the downward slope direction and the secondary direction is the horizontal expansion direction, and analyze the displacement amplitudes and time variation characteristics of these two directions; Subsequently, analyze the variation trend of the dominant direction over multiple time periods. For example, calculate the included angle change of the direction vectors in different time periods to determine whether there is a significant deviation in the direction change; Combining with the rate fluctuation characteristics, analyze whether there are significant differences in the deformation rates in different directions. For example, identify that the rate change in the downward slope direction 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 regions with significant local direction changes, providing a basis for subsequent dynamic morphology analysis and foundation optimization.

[0129] Please refer to Figure 8 , the specific steps for obtaining the deformation monitoring results of the engineering surveying area are as follows:

[0130] Based on the analysis results of the multi-directional deformation characteristics within the partition, according to the dynamic change trends of the extreme points on the slope surface and in the steep areas, judge the correlation between the morphological change area and the settlement trend, and mark the areas with density changes to generate the analysis results of the local morphological change characteristics;

[0131] Combined with the dynamic change trends of the extreme points on the slope surface and in the steep areas, analyze the local morphological change characteristics of the slope surface and the steep areas. By extracting the dynamic displacement data of these areas, identify the change trajectories of the curvature extreme points over multiple time periods, and analyze the change characteristics of the displacement direction and amplitude around the extreme points. For example, calculate the displacement direction consistency and the velocity change range of the observation points around a certain extreme point to determine whether there is a morphological change trend in the local area; Subsequently, analyze the change rules of these areas through point density. For example, extract the point density data within the steep area, judge the range of the interval change between points, and identify whether the areas with significant point density changes correspond to the rapid settlement points on the slope surface or in the steep areas; Combining with the analysis of settlement smoothness, judge the correlation between density change and settlement trend. For example, mark the areas with reduced point density and regard them as potential unstable areas of local morphological changes.

[0132] Based on the analysis results of the local morphological change characteristics, adjust the curvature characteristic data of the partition terrain, and generate the deformation monitoring results of the engineering surveying area by smoothing the curvature change values in the abnormal areas and correcting the curvature characteristics in the areas with point density changes;

[0133] Adjust the overall shape of the partitioned terrain curvature feature data to generate the deformation monitoring results of the engineering surveying and mapping area. Combine the key areas marked in the curvature feature data, and by adjusting the influence range of local abnormal points in the dynamic displacement data, for example, normalize the abnormal settlement points near the extreme points to smooth the locally excessive curvature change values to ensure the coherence of the terrain data in the overall shape; through the results of dynamic trend classification, correct the characteristic areas in different partitions, for example, adjust the areas with significant changes in local point density to make their curvature characteristics gradually transition to the surrounding areas; subsequently, combine the results of local shape analysis to generate the overall deformation monitoring result file, mark the dynamic change trends, local characteristic areas and potential unstable points of different partitions, and provide detailed surveying and mapping data support for the optimal design of the engineering foundation.

[0134] The above is only the preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. An engineering surveying and mapping system, characterized in that, The system includes: Based on the three-dimensional point cloud data collected in the engineering foundation monitoring, the foundation settlement offset analysis module 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; Based on the foundation partition offset distribution characteristics, the error collaborative optimization module judges the error concentration area according to 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 the engineering foundation error optimization compensation data; Based on the engineering foundation error optimization compensation data, the partition terrain curvature analysis module analyzes the curvature distribution of the terrain data in the surveyed area of the engineering foundation, and combines the change trend of the point density in the flat area to obtain the partition terrain curvature characteristic data; The dynamic deformation data analysis module extracts the deformation direction change and rate fluctuation of the observation points in each partition within multiple time periods from the dynamic displacement data in the foundation monitoring, analyzes the deformation characteristics in multiple directions in the area, and adjusts the overall shape of the partition terrain curvature characteristic data to generate the deformation monitoring results of the engineering survey area.

2. The engineering surveying and mapping system according to claim 1, characterized in that, The specific acquisition steps for analyzing the offset distribution of the foundation settlement are as follows: Based on the foundation point cloud data collected by the three-dimensional laser scanning device, data classification is carried out, 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 the partition terrain and structure characteristic data; Based on the partition terrain and structure characteristic data, the height difference and point density difference between adjacent points in each partition are measured, and the settlement and density change of the foundation are drawn according to the difference situation to generate the offset distribution map of the foundation settlement.

3. The engineering surveying and mapping system according to claim 2, wherein, The specific acquisition steps for the foundation partition offset distribution characteristics are as follows: Obtain the intersection point data of the partition boundary from the offset distribution map of the foundation settlement, including the settlement direction and offset difference of the structural boundary and the pile cap joint, extract the three-dimensional coordinates of the intersection point and the monitored settlement data, compare the settlement direction and offset of the adjacent partition intersection points, and generate the cumulative settlement offset data between partitions by accumulating the settlement difference of the intersection points; Based on the cumulative settlement offset data between partitions, use the formula: ； Calculate the average settlement offset relationship value , analyze the settlement relationship, and generate the characteristic of the foundation partition offset distribution Among them, is the average settlement value within the partition, represents the difference in settlement between the partition and the adjacent partition at the junction point, and is the total area of the foundation.

4. The engineering surveying and mapping system according to claim 3, characterized in that, The specific acquisition steps for adjusting the error concentration area within the partition range are as follows: Based on the foundation partition offset distribution characteristics, obtain the standard deviation value of each partition, and compare it with the preset error concentration area threshold. Mark the partition that exceeds the area threshold as the error concentration area to obtain the preliminary error concentration area marking result; Based on the preliminary error concentration area marking result, use the formula: ； Computing partition settlement weight to obtain the data of the error concentration region combined with weight analysis; Among them, is the settlement offset value of the partition , is the area of the partition , is the total number of partitions, used to traverse all partitions, is the settlement offset value of all partitions , is the area of all partitions ; Based on the error concentration area data combined with weight analysis, combine the offset characteristics of the partition intersection points and the neighborhood trend to optimize and adjust the error concentration range within the partition and between partitions to obtain the error concentration area optimization result.

5. The engineering surveying and mapping system according to claim 4, characterized in that, The specific acquisition steps for the engineering foundation error optimization compensation data are as follows: Extract the settlement offset direction and amplitude characteristics of the intersection points, analyze the spatial distribution and settlement trend of the intersection points, select neighborhood monitoring points to fit the settlement offset curve, judge the regional settlement change and redistribute the weight of the settlement offset value, adjust the settlement relationship across partitions at the intersection points, update the optimization result of the error concentration area, and obtain 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 points, combining the partition area and neighborhood data, evaluate the error distribution characteristics, summarize the settlement compensation requirements within the partition, and generate engineering foundation error optimization compensation data.

6. The engineering surveying and mapping system according to claim 5, characterized in that, The specific steps for obtaining the partition terrain curvature characteristic data are as follows: Based on the engineering foundation error optimization compensation data, conduct a curvature distribution analysis on the terrain data of the engineering foundation survey area, extract the elevation values of the terrain point cloud data, fit the local curves of the measurement points to determine the radius of curvature, divide the area according to the curvature range, and generate the regional curvature distribution result; Based on the regional curvature distribution result, use the formula: ； Calculate curvature Over time Rate of change to obtain the analysis result of the rate of change of the curvature distribution; Among them, , is the curvature value of the extreme point, , is the time point corresponding to the curvature value and , is the adjustment factor, is the local curvature change amplitude, is the local time difference; Based on the analysis result of the curvature distribution change rate, extract the point density distribution in the flat area, count the number of points per unit area, analyze the range of point density change in the flat area, combine the point density change trend to adjust the distribution defect of the flat area, and integrate the regional curvature and point density information to obtain the partition terrain curvature characteristic data.

7. The engineering surveying and mapping system according to claim 6, characterized in that, The specific steps for obtaining the deformation characteristics in multiple directions within the analysis area are as follows: Based on the dynamic displacement data in the foundation monitoring, through time series processing of the multi-period displacement data of the observation points within the partition, extract the three-dimensional displacement direction and its change amplitude of each observation point, analyze the rate gradient distribution in combination with the time interval, and classify the rate gradient distribution characteristics within the partition to generate the classification result of the internal dynamic trend within the partition; Based on the classification result of the internal dynamic trend within the partition, by analyzing the multi-directional displacement data of the observation points within each partition, statistically analyze the displacement amplitude and change law, and mark the areas with local direction changes in combination with the rate gradient characteristics to generate the analysis result of the multi-directional deformation characteristics within the partition.

8. The engineering surveying and mapping system according to claim 7, wherein, The specific steps for obtaining the deformation monitoring result of the engineering survey area are as follows: Based on the analysis result of the multi-directional deformation characteristics within the partition, according to the dynamic change trend of the extreme points on the slope and steep areas, judge the association between the morphological change area and the settlement trend, and mark the areas with density changes to generate the analysis result of the local morphological change characteristics; Based on the analysis result of the local morphological change characteristics, adjust the partition terrain curvature characteristic data, and generate the deformation monitoring result of the engineering survey area by smoothing the curvature change value of the abnormal area and correcting the curvature characteristics of the point density change area.

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