Geographic information survey data management system and method applying GIS (Geographic Information System) technology

Abstract

The invention discloses a geographic information survey data management system and method applying a GIS technology, and relates to the field of geographic information survey management. Multi-source information data collected by geographic information survey equipment is preprocessed to obtain a time sequence data structure and a transition data structure which can be spliced; acquiring elevation data at the splicing position of the target time sequence data structure and the adjacent time sequence data structure, calculating and adjusting the relative position relation between the target time sequence data structure and the adjacent time sequence data structure by using the splicing transition model according to an elevation data difference; determining the width of a transition region based on the adjusted elevation data difference at the splicing position, and smoothing the transition region through the splicing transition model; splicing the transition region after smoothing processing with the time sequence data structure, and completing smooth transition at the splicing position of the target time sequence data structure and the adjacent time sequence data structure; through the transition data structure and the processing logic thereof, the accuracy of the spliced digital terrain three-dimensional model is ensured to the greatest extent.

Description

Technical Field

[0001] This invention relates to the field of geographic survey information processing technology, specifically a geographic information survey data management system and method that applies GIS technology. Background Technology

[0002] When stitching together time-series data of geographic survey information, topological breaks may occur due to the discontinuity of geographic entities or errors in the survey data source, resulting in elevation faults in the time-series data stitching.

[0003] Existing technologies typically employ dynamic encryption strategies based on irregular triangular networks, gradually increasing the triangle density at the splicing boundaries to achieve a gradual elevation transition. For example, Chinese invention patent (CN114862715A) discloses a "TIN progressive encryption denoising method that integrates terrain feature semantic information," which overcomes the problem of excessive smoothing of ridge features in traditional denoising and can effectively restore the terrain surface structure features while denoising the original data.

[0004] Alternatively, seamless fusion can be achieved through digital elevation models, that is, by eliminating geometric misalignments and elevation jumps at the splicing points using algorithms, forming a continuous and smooth terrain surface at the splicing points; for example, Chinese invention patent (CN119904592A) discloses a "method for 3D reconstruction and visualization of news scenes based on multi-source remote sensing data", which uses spatially weighted iterative nearest point algorithm and tensor projection algorithm to extract feature points of multimodal data, and after processing by bidirectional attention deep neural network and screening by local geometric consistency constraints, it generates registered multi-source remote sensing data, performs data augmentation through variational autoencoder network and generative adversarial network, and uses the augmented data for depth information generation and texture feature transfer, generates an initial 3D mesh model based on Poisson reconstruction algorithm, and performs topology optimization by combining multi-view projection consistency constraints and mesh optimization algorithm to obtain an optimized 3D scene model;

[0005] While the above technical solution can achieve a smooth transition at the splicing point in actual operation, there will still be a certain degree of deviation between the terrain structure at the splicing point and the actual terrain structure, which will affect the accuracy of the final digital terrain 3D model. Summary of the Invention

[0006] The purpose of this invention is to provide a geographic information survey data management system and method that applies GIS technology, so as to solve the problem in the prior art that there is a certain degree of deviation between the terrain structure at the splicing point and the actual terrain structure.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a geographic information survey data management method using GIS technology, which preprocesses multi-source information data collected by geographic information survey equipment to obtain splicable time-series data structures and transition data structures;

[0008] Obtain the elevation data at the junction of the target time series data structure and the adjacent time series data structure, calculate and adjust the relative positional relationship between the target time series data structure and the adjacent time series data structure based on the elevation data difference using the junction transition model;

[0009] The width of the transition area is determined based on the difference in elevation data at the adjusted splicing point, and the transition area is smoothed using a splicing transition model.

[0010] The smoothed transition region is then spliced ​​with the adjacent time-series data structure to achieve a smooth transition at the splicing point between the target time-series data structure and the adjacent time-series data structure.

[0011] Based on the above technical solution, the elevation data of all adjacent time-series data structures at the splicing point of the target time-series data structure are obtained, forming multiple sets of elevation data groups H1 to H2. s Each set of elevation data includes target elevation data. and adjacent elevation data Where k represents the k-th elevation data group;

[0012] Calculate the elevation difference Δh between the target elevation data and adjacent elevation data in each elevation data group. k This yields the set of elevation data differences, ΔH.

[0013] Based on the calculated maximum elevation data difference, the relative positional relationship between the target temporal data structure and adjacent temporal data structures is adjusted using a splicing transition model.

[0014] According to the above technical solution, for the adjustment of relative positional relationships, the target time series data structure is adjusted upward, downward, or unchanged based on the sign distribution of all calculated elevation data differences.

[0015] By adjusting the position, the absolute value of the maximum positive difference and the absolute value of the minimum negative difference are made equal in the set of elevation data differences at the junction of the adjusted target time series data structure and all its adjacent time series data structures.

[0016] According to the above technical solution, when all Δh exist in the set ΔH k When the value is greater than 0, the position of the target time series data structure is adjusted downwards to obtain set ΔH′, and the elevation data difference in set ΔH′ exists as Δh. k >0 and Δh k <0;

[0017] When all Δh exist in the set ΔH k When <0, the position of the target time series data structure is adjusted upwards to obtain set ΔH′, and the elevation data difference in set ΔH′ exists as Δh. k >0 and Δh k <0;

[0018] When Δh exists in set ΔH or set ΔH′ k >0 and Δh k When <0, extract the maximum elevation data difference Δh from set ΔH or set ΔH′. max Minimum difference between elevation data and Δh min And assigning an absolute value, we get |Δh max |and|Δh min |;

[0019] If |Δh max |=|Δh min If |Δh max |>|Δh min |or|Δh max |<|Δh min If |, then adjust the position of the target time-series data structure downwards or upwards until |Δh. max |=|Δh min |

[0020] By adjusting the position of the target time-series data structure, the elevation data difference between the target time-series data structure and the adjacent time-series data structures is adjusted to be equal to the absolute value of the maximum value and the absolute value of the minimum value. In this way, the adjustment range of the target time-series data structure is minimized, and the transition area between the target time-series data structure and the adjacent time-series data structures is narrowest after the adjustment. This can effectively reduce the amount of computation while ensuring the accuracy of the digital terrain 3D model.

[0021] According to the above technical solution, in step S3, the width of the transition area is determined based on the elevation data difference Δh at the splicing point after the positional relationship is adjusted. k And the assigned weight value α, determines the width L of the transition region between the target temporal data structure and each adjacent temporal data structure. k ;

[0022] Retrieve a transitional data structure from the transitional database that matches the target time series data structure and the corresponding adjacent time series data structures;

[0023] Based on the determined width L k The retrieved transition data structure is trimmed to obtain the transition region;

[0024] The transition region obtained by the cutting is smoothed using a splicing transition model.

[0025] According to the above technical solution, the smoothing process for the transition region includes the following steps:

[0026] A1. In the overlapping area of ​​the transition region and the target time-series data structures and adjacent time-series data structures on both sides, calculate the elevation data gradient fields on both sides respectively.

[0027] A2. Within the overlapping region, the gradient fields on both sides of the transition region are weighted and fused based on a distance-related weighting function to generate a fused gradient.

[0028] A3. Construct a seamless fusion equation, the goal of which is to make the gradient of the elevation field in the transition region approximate the fused gradient field;

[0029] A4. Set boundary conditions:

[0030] At the splicing boundary between the target time series data structure and its adjacent time series data structures and the transition region, the original elevation data remains unchanged;

[0031] In the transition region, towards the inner boundary of the splicing boundary, the normal derivative of the constrained elevation field approaches the normal derivative of the elevation field at the splicing point of the standard time series data structure and its adjacent time series data structures.

[0032] A5. Under the stated boundary conditions, the seamless fusion equation is numerically solved to obtain the smoothed transition region elevation field.

[0033] The above technical solution ensures that the boundary elevation data of the target time-series data structure and its adjacent time-series data structures remain unchanged, while the elevation data within the transition area changes. This results in a smooth transition of the target time-series data structure and its adjacent time-series data structures within the transition area. Furthermore, this change in elevation data occurs throughout the entire transition area, not just at the boundaries, leading to smaller changes in the smoothed elevation data and thus improving the accuracy of the digital terrain 3D model.

[0034] According to the above technical solution, after the numerical solution is completed, terrain curvature constraints are introduced or edge sharpening is performed to optimize the smoothed transition area.

[0035] According to the above technical solution, the multi-source information data is one or more combinations of three-dimensional coordinate data, optical effect data, video stream data, lidar point cloud data, positioning attitude data, and IoT sensor monitoring data, and the corresponding sources are one or more combinations of total station, GNSS, UAV aerial survey, lidar, and IoT sensors.

[0036] The preprocessing of the multi-source information data includes unifying the coordinate system, timestamp normalization, unifying the data format, and data cleaning.

[0037] For both raster and vector data, spatial alignment is also required.

[0038] A geographic information survey data management system, comprising:

[0039] Geographic information surveying equipment is used to collect multi-source information data;

[0040] The data preprocessing module is used to preprocess multi-source information data collected by geographic information surveying equipment;

[0041] Database: Used to store preprocessed time-series data structures and transitional data structures;

[0042] The 3D scene model building module is used to build a digital terrain 3D model based on the temporal data structure and the transition data structure by using a stitched transition model.

[0043] Data retrieval module: used to retrieve time-series data structures or transitional data structures from the database;

[0044] Elevation Data Difference Calculation Module: This module calculates the elevation data difference at the junction of retrieved temporal or transitional data structures based on the requirements of the 3D scene model construction module.

[0045] According to the above technical solution, the three-dimensional scene model construction module includes a data position adjustment module, a transition region determination module, and a smoothing processing module;

[0046] The position adjustment module is used to adjust the position of the target time-series data structure based on the elevation data difference obtained by the elevation data difference calculation module.

[0047] The transition region determination module is used to determine the width of the transition region based on the elevation data difference obtained by the elevation data difference calculation module.

[0048] The smoothing module is used to smooth the transition area to ensure a smooth transition at the splicing point.

[0049] Compared with the prior art, the beneficial effects of the present invention are: the multi-source geographic information time-series stitching method provided by the present invention effectively solves the problems of elevation change, transition distortion and efficiency bottleneck in multi-source and heterogeneous geographic information data stitching, and significantly improves the global consistency, geometric accuracy and generation efficiency of digital terrain 3D models;

[0050] By comprehensively analyzing the elevation differences between the target block and all adjacent blocks and adopting an intelligent location adjustment strategy, the terrain abrupt changes and seams caused by neglecting corner differences or insufficient local adjustments in traditional methods are fundamentally eliminated, ensuring a smooth and natural elevation transition at the splicing point.

[0051] The transition region is processed by an intelligent smoothing algorithm based on gradient fusion and physical constraints. The elevation of the original key terrain boundary is kept unchanged, while the internal terrain change trend is constrained to be consistent with the original landform. This preserves the real structure and detailed features of the terrain to the greatest extent and avoids excessive smoothing distortion.

[0052] An adaptive transition region generation mechanism is proposed, which dynamically and reasonably determines the width of the transition region based on the elevation difference and applies strict spatial constraints, effectively avoiding unnecessary computational redundancy.

[0053] By replacing direct modification of the original data boundaries with transition region processing, the frequent operations on the core time series data structure are reduced, the overall computational complexity is lowered, and the efficiency of massive data processing is improved.

[0054] In the process of retrieving and processing transitional data, the consistency of time information is strictly guaranteed to prevent splicing errors caused by time phase differences, thus ensuring the accuracy and reliability of the spatiotemporal dimensions of the final 3D model.

[0055] This invention breaks through the limitations of traditional geographic information stitching technology in terms of global consistency processing, intelligent smoothing and fidelity, and efficiency optimization, and provides a systematic, efficient, and high-precision time-series data stitching solution. Its core value lies in achieving seamless, natural, and highly faithful terrain transitions with minimal adjustment costs and computing resources, laying a solid technical foundation for building a high-precision and high-reliability digital twin geographic environment. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the steps of a geographic information survey data management method using GIS technology according to the present invention.

[0057] Figure 2 This is a schematic diagram of the multi-source information data preprocessing process in a geographic information survey data management method using GIS technology according to the present invention;

[0058] Figure 3 This is a schematic diagram illustrating the relationship between the target temporal data structure and adjacent temporal data structures in a geographic information survey data management method using GIS technology according to the present invention.

[0059] Figure 4 This is a schematic diagram illustrating the execution flow of the splicing transition model in a geographic information survey data management method using GIS technology according to the present invention. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Example 1: As Figure 1 As shown, the present invention provides a technical solution for a geographic information survey data management method using GIS technology, which processes geographic information survey data and establishes a digital model of geographic data based on the geographic information survey data, mainly used for the construction of digital models of cities, mountainous areas, rural areas and large-scale infrastructure.

[0062] In this method, S1, the multi-source information data collected by the geographic information surveying equipment is preprocessed to obtain a splicable time-series data structure and a transitional data structure.

[0063] Specifically: Multi-source information data mainly consists of one or more combinations of three-dimensional coordinate data, optical effect data, video stream data, lidar point cloud data, positioning attitude data, and IoT sensor monitoring data, with the corresponding sources being one or more combinations of total station, GNSS, UAV aerial survey, lidar, and IoT sensors;

[0064] Preprocessing for multi-source information data includes the following: unifying the coordinate system, normalizing timestamps, unifying data formats, and cleaning data. For raster and vector data, spatial alignment is also required. For example, for raster data, all raster data need to be resampled to the same spatial resolution and accurately registered to the same grid origin. For vector data, if the observation points are changing, the point data at different time points need to be associated with fixed locations, or the statistical results of point data, line data, or area data need to be aggregated onto a regular spatial grid.

[0065] In this embodiment, a unified coordinate system refers to converting multi-source information data to the same geographic coordinate system and projected coordinate system; timestamp normalization refers to adding accurate and consistent timestamps to each multi-source information data and unifying the time zone, converting it into a standard time format; a unified data format refers to defining consistent attribute field names, types, and units, converting information data from different sources into a unified storage format, such as point data, line data, polygon data, or raster data; and data cleaning refers to handling missing values, outliers, or duplicate data to ensure correct spatial topological relationships, such as ensuring that polygons do not overlap or have no gaps.

[0066] In this embodiment, as Figure 2As shown, the construction of time-series data structures and transitional data structures adopts a spatiotemporal cube model. The data preprocessing module organizes multi-source information data into a three-dimensional structure (X). i -X j ,Y i -Y j (,T,P), where X i -X j Y represents the longitude range of this three-dimensional structure. i -Y j The latitudinal range of the three-dimensional structure is represented by T, the time information is represented by P, and other attribute information sets of the three-dimensional structure are represented by P, such as elevation data, temperature data, attribute values, etc. The preprocessed time-series data structure and the transition data structure are stored in the database. For example, the time-series data structure is stored in the time-series database, and the transition data structure is stored in the transition database.

[0067] It should be noted that the transition data structure is a data structure with the same properties as the time-series data structure, used to smoothly transition at the splicing points of the time-series data structures, reduce the elevation data difference at the splicing points, avoid excessive abrupt changes at the splicing points, and improve the accuracy of the digital terrain 3D model.

[0068] Further: S2, obtain the elevation data at the junction of the target time series data structure and the adjacent time series data structure, calculate and adjust the relative positional relationship between the target time series data structure and the adjacent time series data structure based on the elevation data difference using the junction transition model.

[0069] Specifically: such as Figure 3 As shown, the target time-series data structure has eight adjacent time-series data structures, located at the four edges and four vertices, forming eight elevation data groups H1 to H8. Each elevation data group includes the target elevation data. and adjacent elevation data Where k = 1, 2, 3, ..., 8.

[0070] In the above technical solution, not only are the adjacent time-series data structures of the four sides of the target time-series data structure retrieved, but also the corresponding adjacent time-series data structures of the four vertices are retrieved. This is because when splicing time-series data structures, if there is a large difference in elevation data between the target time-series data structure and the adjacent time-series data structure corresponding to a certain vertex, the final digital terrain 3D model will have abrupt change points, affecting the accuracy of the digital terrain 3D model.

[0071] like Figure 4 As shown, the elevation data difference calculation module calculates the elevation data difference Δh at the junction of the target time series data structure and the adjacent time series data structure according to the following formula. k Perform the calculation:

[0072]

[0073] The set of elevation data differences at the junction of the target time series data structure and adjacent time series data structures is obtained as ΔH = {Δh}. k};k=1,2,3,…,8.

[0074] In this embodiment, for the elevation data difference Δh k The calculation is the maximum value of the elevation data difference at the junction of the target temporal data structure and the adjacent temporal data structure, because only for the maximum elevation data difference Δh k Only by performing calculations can the target time series data structure be adjusted more precisely based on the calculation results, so that the adjusted target time series data structure can achieve a smooth transition with adjacent time series data structures at the splicing point.

[0075] When all Δh exist in the set ΔH k When the value is greater than 0, the position of the target time series data structure is adjusted downwards to obtain set ΔH′, and the elevation data difference in set ΔH′ exists as Δh. k >0 and Δh k <0;

[0076] When all Δh exist in the set ΔH k When <0, the position of the target time series data structure is adjusted upwards to obtain set ΔH′, and the elevation data difference in set ΔH′ exists as Δh. k >0 and Δh k <0;

[0077] When Δh exists in set ΔH or set ΔH′ k >0 and Δh k When <0, extract the maximum elevation data difference Δh from set ΔH or set ΔH′. max Minimum difference between elevation data and Δh min And assigning an absolute value, we get |Δh max |and|Δh min |;

[0078] If |Δh max |=|Δh min If |Δh max |>|Δh min |or|Δh max |<|Δh min If |, then adjust the position of the target time-series data structure downwards or upwards until |Δh. max |=|Δh min |

[0079] In the above technical solution, by adjusting the position of the target time-series data structure, the elevation data difference between the target time-series data structure and the eight adjacent time-series data structures is adjusted to be equal to the absolute value of the maximum value and the absolute value of the minimum value. In this way, the adjustment range of the target time-series data structure is minimized, and after the adjustment, the transition area between the target time-series data structure and the eight adjacent time-series data structures is narrowest. This can effectively reduce the amount of computation while ensuring the accuracy of the digital terrain 3D model.

[0080] In this embodiment, the position of the target time-series data structure is adjusted by the position adjustment module in the splicing transition model.

[0081] In another embodiment of the present invention, the following scheme is adopted for the positional relationship adjustment strategy of the target time-series data structure:

[0082] When Δh exists in set ΔH k =0 and Δh k When the number of 0s exceeds a set threshold, the positional relationship of the target time-series data structure will not be adjusted;

[0083] It is possible that, after adjusting the positional relationships of the target time-series data structure, Δh exists in the set ΔH′. k When the number of zeros exceeds the set threshold, the adjustment scheme of this implementation shall prevail;

[0084] The purpose is to reduce the need for smooth transitions at the stitching points through transition areas. While transition areas can achieve smooth transitions at the stitching points, they still have some impact on the final digital terrain 3D model. However, compared to other methods, smooth transitions at the stitching points through transition areas can ensure the accuracy of the digital terrain 3D model as much as possible.

[0085] In the two embodiments described above, with the constraint of ensuring the highest accuracy of the final digital terrain 3D model, no two target time-series data structures are adjacent during the adjustment of the position of the target time-series data structure. The purpose is to reduce the number of adjustments to the position of the time-series data structure and reduce the overall amount of data processing.

[0086] Further: S3. Determine the width of the transition area based on the difference in elevation data at the adjusted splicing point, and smooth the transition area using a splicing transition model.

[0087] Specifically, the elevation data differences in set ΔH or set ΔH′ are retrieved to determine the elevation data differences at the splicing point between the target time series data structure and adjacent time series data structures. Based on the elevation data differences Δh at the splicing point... kAssign a weight value α to determine the width of the transition region, where the transition region is truncated from the transition data structure;

[0088] Transition region width data L k =α*Δh k ,in, or

[0089] The transition data structure is retrieved from the transition database using the data retrieval module, based on the transition region width data L. k The corresponding transitional data structure is truncated to ensure the accuracy of the region width, because changes in the region width directly affect the amount of computation and the accuracy of the subsequent digital terrain 3D model.

[0090] The time information T of the transition data structure is the same as that of the target temporal data structure and its adjacent temporal data structures; this avoids the situation where the time information is different, which would cause a large deviation between the transition data structure and the temporal data structure and affect the generation of the digital terrain 3D model;

[0091] In this embodiment, the smoothing process of the transition region using a splicing transition model includes the following steps:

[0092] A1. Calculate the gradient field in the overlapping area between the transition region and the target temporal data structure and its adjacent temporal data structures.

[0093] Specifically, determine the elevation datasets DEM1 and DEM2 on both sides of the transition region, near the junction of the target time series data structure and the adjacent time series data structures;

[0094] Calculate the gradient data V1 and V2 for DEM1 and DEM2 respectively, where, Where z1 and z2 represent the elevation data at the junction of the transition region with the target time series data structure and the adjacent time series data structure, respectively, and x and y represent the coordinate points within the transition region, respectively.

[0095] Generate a weighted fusion gradient field V in the overlapping region fuse ;

[0096] V fuse =ω(x,y)V1+[1-ω(x,y)]V;

[0097] Where ω(x,y) represents the weight function. d1 and d2 represent the distances from a point in the transition region to the two boundaries of the transition region, respectively, and V represents the gradient data at the junction of the target temporal data structure or the adjacent temporal data structure with the transition region.

[0098] A2. Construct the fusion equation based on the gradient field;

[0099] Specifically, the problem of seamlessly integrating the transition region with the target temporal data structure or adjacent temporal data structures is transformed into:

[0100]

[0101] Where φ is the elevation field. To represent the area of ​​elevation data to be processed, Represents the vector differential operator;

[0102] The seamless integration problem is equivalent to solving The above equation is a seamless fusion equation between the transition region and the target temporal data structure and adjacent temporal data structures.

[0103] A3. Set conditions for the boundary of the overlapping area;

[0104] Specifically, the boundary conditions for the concatenation of the target temporal data structure and its adjacent temporal data structures with the transition region are defined as follows: That is, the elevation data at the boundary where the target time series data structure and its adjacent time series data structures are spliced ​​with the transition region remains unchanged from the original elevation data;

[0105] Boundary conditions are defined for the boundary between the transition region and the target temporal data structure, as well as the junction of its adjacent temporal data structures. That is, the boundary of the transition region tends to be the elevation data of the target time series data structure and the boundary of the transition region where the adjacent time series data structures are spliced ​​together;

[0106] By setting the boundary conditions described above, the boundary elevation data of the target time-series data structure and its adjacent time-series data structures remain unchanged, while the elevation data within the transition area changes. This ensures a smooth transition of the target time-series data structure and its adjacent time-series data structures within the transition area. Furthermore, this change in elevation data occurs throughout the entire transition area, not just at the boundaries, resulting in smaller changes in the smoothed elevation data and thus improving the accuracy of the digital terrain 3D model.

[0107] A4. Solve the seamless fusion equation constructed in A2 numerically;

[0108] Specifically, the seamless fusion equations are discretized using the finite difference method to obtain a system of linear equations; then, the system of linear equations is solved iteratively using the conjugate gradient method to finally complete the smoothing of the transition region.

[0109] A5. Optimize the solution results;

[0110] Specifically, terrain curvature constraints or edge sharpening are introduced to optimize the smoothed transition area, improving the similarity between the transition area and the original terrain.

[0111] In this embodiment, the width of the transition region is designed to be 100m. Steps A1 to A5 are used to complete the smoothing process of the transition region, so that the elevation data of the two sides of the transition region are consistent with the elevation data of the target time series data structure and the splicing point of its adjacent time series data structures. In this method, the smoothing process of the transition region is used instead of the smoothing process of the splicing point of the target time series data structure and its adjacent time series data structures. This allows the width of the transition region to be set according to the actual terrain conditions, thereby reducing the transition smoothing that may occur during the smoothing process and causing changes in the terrain structure. Furthermore, since the transition region is continuous, additional constraints can be added during the smoothing process. For example, fine-tuning can be performed on areas where the elevation data remains unchanged, thereby replacing the adjustment of areas where the elevation data changes. This ensures that the terrain structure does not change significantly after the smoothing process, thus ensuring the accuracy of the final digital terrain 3D model.

[0112] Furthermore, S4, the smoothed transition region is spliced ​​with the adjacent time-series data structure to complete the smooth transition at the splicing point between the target time-series data structure and the adjacent time-series data structure.

[0113] In this embodiment, after the smoothing process of the transition region is completed, the elevation data of the two sides of the transition region is consistent with the elevation data of the splicing point of the target time series data structure and its adjacent time series data structures. At this time, the splicing of the transition region with the target time series data structure and its adjacent time series data structures can be completed directly.

[0114] Meanwhile, the time-series data structures that have already been stitched together will not undergo any positional adjustments or changes. In subsequent adjustments to the positions of the time-series data structures, the opposite direction to the previous adjustment should be used as much as possible. The purpose is to reduce the amount of adjustment to the overall terrain structure and ensure the consistency of the digital terrain 3D model.

[0115] Example 2: A geographic information survey data management system, comprising:

[0116] Geographic information surveying equipment is used to collect multi-source information data;

[0117] The data preprocessing module is used to preprocess multi-source information data collected by geographic information surveying equipment;

[0118] Database: Used to store preprocessed time-series data structures and transitional data structures;

[0119] The 3D scene model building module is used to build a digital terrain 3D model based on the temporal data structure and the transition data structure by using a stitched transition model.

[0120] Data retrieval module: used to retrieve time-series data structures or transitional data structures from the database;

[0121] Elevation data difference calculation module: Used to calculate the elevation data difference at the splicing point of the retrieved time-series data structure or transition data structure according to the requirements of the 3D scene model construction module;

[0122] The 3D scene model construction module includes a data position adjustment module, a transition region determination module, and a smoothing processing module.

[0123] The position adjustment module is used to adjust the position of the target time-series data structure based on the elevation data difference obtained by the elevation data difference calculation module.

[0124] The transition region determination module is used to determine the width of the transition region based on the elevation data difference obtained by the elevation data difference calculation module.

[0125] The smoothing module is used to smooth the transition area to ensure a smooth transition at the splicing point.

[0126] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for managing geographic information survey data using GIS technology, characterized in that: Multi-source information data collected by geographic information surveying equipment is preprocessed to obtain splicable time-series data structures and transitional data structures; Obtain the elevation data at the junction of the target time series data structure and the adjacent time series data structure, calculate and adjust the relative positional relationship between the target time series data structure and the adjacent time series data structure based on the elevation data difference using the junction transition model; The width of the transition area is determined based on the difference in elevation data at the adjusted splicing point, and the transition area is smoothed using a splicing transition model. The smoothed transition region is spliced ​​with the adjacent time-series data structure to achieve a smooth transition at the splicing point between the target time-series data structure and the adjacent time-series data structure.

2. The geographic information survey data management method using GIS technology according to claim 1, characterized in that: Obtain the elevation data at the splicing point of all adjacent time series data structures of the target time series data structure, forming multiple elevation data groups H1 to H2. s Each set of elevation data includes target elevation data. and adjacent elevation data Where k represents the k-th elevation data group; Calculate the elevation difference Δh between the target elevation data and adjacent elevation data in each elevation data group. k This yields the set of elevation data differences, ΔH. Based on the calculated maximum elevation data difference, the relative positional relationship between the target temporal data structure and adjacent temporal data structures is adjusted using a splicing transition model.

3. The geographic information survey data management method using GIS technology according to claim 2, characterized in that: For adjustments to relative positional relationships, the target time-series data structure is adjusted upwards, downwards, or left unchanged based on the sign distribution of all calculated elevation data differences. By adjusting the position, the absolute value of the maximum positive difference and the absolute value of the minimum negative difference are made equal in the set of elevation data differences at the junction of the adjusted target time series data structure and all its adjacent time series data structures.

4. A method for managing geographic information survey data using GIS technology according to claim 2 or 3, characterized in that: When all Δh exist in the set ΔH k When the value is greater than 0, the position of the target time series data structure is adjusted downwards to obtain set ΔH′, and the elevation data difference in set ΔH′ exists as Δh. k >0 and Δh k <0; When all Δh exist in the set ΔH k When <0, the position of the target time series data structure is adjusted upwards to obtain set ΔH′, and the elevation data difference in set ΔH′ exists as Δh. k >0 and Δh k <0; When Δh exists in set ΔH or set ΔH′ k >0 and Δh k When <0, extract the maximum elevation data difference Δh from set ΔH or set ΔH′. max Minimum difference between elevation data and Δh min And assigning an absolute value, we get |Δh max |and|Δh min |; If |Δh max |=|Δh min If |Δh max |>|Δh min |or|Δh max |<|Δh min If |, then adjust the position of the target time-series data structure downwards or upwards until |Δh. max |=|Δh min | 5. A method for managing geographic information survey data using GIS technology according to claim 4, characterized in that: In step S3, the width of the transition area is determined based on the elevation difference Δh at the splicing point after adjustment of the positional relationship. k And the assigned weight value α, determines the width L of the transition region between the target temporal data structure and each adjacent temporal data structure. k ; Retrieve a transitional data structure from the transitional database that matches the target time series data structure and the corresponding adjacent time series data structures; Based on the determined width L k The retrieved transition data structure is trimmed to obtain the transition region; The transition region obtained by the cutting is smoothed using a splicing transition model.

6. A method for managing geographic information survey data using GIS technology according to claim 5, characterized in that: The smoothing process for transition regions includes the following steps: A1. In the overlapping area of ​​the transition region and the target time-series data structures and adjacent time-series data structures on both sides, calculate the elevation data gradient fields on both sides respectively. A2. Within the overlapping region, the gradient fields on both sides of the transition region are weighted and fused based on a distance-related weighting function to generate a fused gradient. A3. Construct a seamless fusion equation, the goal of which is to make the gradient of the elevation field in the transition region approximate the fused gradient field; A4. Set boundary conditions: At the splicing boundary between the target time series data structure and its adjacent time series data structures and the transition region, the original elevation data remains unchanged; In the transition region, towards the inner boundary of the splicing boundary, the normal derivative of the constrained elevation field approaches the normal derivative of the elevation field at the splicing point of the standard time series data structure and its adjacent time series data structures. A5. Under the given boundary conditions, the seamless fusion equation is numerically solved to obtain the smoothed transition region elevation field.

7. A method for managing geographic information survey data using GIS technology according to claim 6, characterized in that: After completing the numerical solution, terrain curvature constraints are introduced or edge sharpening is performed to optimize the smoothed transition region.

8. A method for managing geographic information survey data using GIS technology according to claim 1, characterized in that: The multi-source information data is one or more combinations of three-dimensional coordinate data, optical effect data, video stream data, lidar point cloud data, positioning attitude data, and IoT sensor monitoring data, and the corresponding sources are one or more combinations of total station, GNSS, UAV aerial survey, lidar, and IoT sensors. The preprocessing of the multi-source information data includes unifying the coordinate system, timestamp normalization, unifying the data format, and data cleaning. For both raster and vector data, spatial alignment is also required.

9. A geographic information survey data management system that implements the geographic information survey data management method using GIS technology as described in any one of claims 1-8, characterized in that, include: Geographic information surveying equipment is used to collect multi-source information data; The data preprocessing module is used to preprocess multi-source information data collected by geographic information surveying equipment; Database: Used to store preprocessed time-series data structures and transitional data structures; The 3D scene model building module is used to build a digital terrain 3D model based on the temporal data structure and the transition data structure by using a stitched transition model. Data retrieval module: used to retrieve time-series data structures or transitional data structures from the database; Elevation Data Difference Calculation Module: This module calculates the elevation data difference at the junction of retrieved temporal or transitional data structures based on the requirements of the 3D scene model construction module.

10. The geographic information survey data management system according to claim 9, characterized in that: The 3D scene model construction module includes a data position adjustment module, a transition region determination module, and a smoothing processing module. The position adjustment module is used to adjust the position of the target time-series data structure based on the elevation data difference obtained by the elevation data difference calculation module. The transition region determination module is used to determine the width of the transition region based on the elevation data difference obtained by the elevation data difference calculation module. The smoothing module is used to smooth the transition area to ensure a smooth transition at the splicing point.

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