Data fusion method and electronic device
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CCCC SHANGHAI DREDGING CO LTD
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-12
Smart Images
Figure CN121600362B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of surveying and mapping, and in particular to a data fusion method and electronic device. Background Technology
[0002] 3D laser scanning technology is widely used due to its unique advantages such as non-contact measurement, high efficiency, and high density. It is typically combined with other sensors to acquire 3D point cloud data of the Earth's surface, and is mainly categorized into airborne, vehicle-mounted, handheld, and station-based methods. For example, UAV-based lidar measurement uses a lidar sensor mounted on a UAV, combined with a Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU), to acquire point cloud data. Due to the influence of instrumentation, environment, and personnel operation, its elevation measurement accuracy is approximately 10-15 cm. Similarly, UAV aerial photogrammetry quickly acquires ground terrain information using digital cameras, with elevation measurement accuracy similar to lidar. Compared to traditional total stations and real-time dynamic differential positioning (RTK) measurement technologies, it is more efficient, has wider coverage, and generates larger data volumes, but its accuracy is lower, failing to reach the 1-3 cm accuracy of traditional measurements. However, traditional technologies cannot quickly acquire large-area, high-density point cloud data. Therefore, in practical operations, multiple measurement technologies are often used simultaneously, but the accuracy and density of the acquired measurement data vary significantly, making it difficult to integrate and utilize them.
[0003] Regarding data fusion, we face several challenges: First, the quality of fusion cannot be guaranteed, as simply using averaging methods can lower the overall quality of the results due to low-precision data. Second, uncertainty propagation occurs, as the uncertainty of low-precision data can contaminate the fusion results. Third, there is the challenge of weight allocation: how to scientifically assign higher weights to high-precision data while not completely discarding the value of low-precision data.
[0004] Therefore, there is an urgent need for a data fusion method that can effectively overcome the above problems, fuse multi-source data (i.e., data of different precision and density collected by different sensors), improve the quality of low-precision data, and ensure the effectiveness of data fusion. Summary of the Invention
[0005] This application provides a data fusion method and electronic device, which solves the problem that multi-source data is difficult to fuse and the quality of the fused data cannot be guaranteed.
[0006] This application provides a data fusion method, comprising the following steps: obtaining a first dataset and a second dataset, wherein the first dataset is a collection of low-precision, high-density data, and the second dataset is a collection of high-precision, low-density data; generating a third dataset by applying linear interpolation to the first dataset; determining an elevation difference dataset based on the second dataset and the third dataset; determining the weight of each third data point in the third dataset based on the elevation difference dataset; determining the elevation difference value of each third data point based on the weight of each third data point and the elevation difference dataset; and determining a fourth dataset based on the elevation difference value of each third data point and the third dataset, wherein the fourth dataset is a collection of high-precision, high-density data.
[0007] In one embodiment, the low-precision, high-density data is obtained through UAV lidar or UAV aerial photogrammetry.
[0008] In one embodiment, the high-precision low-density data is obtained through real-time dynamic differential positioning or total station measurement.
[0009] In one embodiment, the step of generating a third dataset by applying linear interpolation to the first dataset includes: applying bilinear interpolation or kriging interpolation to the first dataset to generate a third dataset, wherein the third dataset is a regular grid dataset and the grid spacing is determined according to the measurement scale.
[0010] In one embodiment, determining the elevation difference dataset based on the second dataset and the third dataset includes: finding each third data point in the third dataset that corresponds to each second data point in the second dataset; for each pair of corresponding second and third data points, subtracting the elevation value of the third data point from the elevation value of the second data point to obtain the elevation difference value of the second data point; and determining the elevation difference dataset based on the elevation difference values of each second data point.
[0011] In one embodiment, determining the weight of each third data point in the third dataset based on the elevation difference dataset includes:
[0012] Based on the elevation difference dataset, a triangular network is constructed; based on the triangular network, a first polygon is constructed; each third data point is inserted into the first polygon to construct a second polygon; the weight of each third data point is determined based on the overlapping area of the first and second polygons.
[0013] In one embodiment, constructing a triangulation network based on the elevation difference dataset includes: constructing triangles using points in the elevation difference dataset, wherein the circumcircle of each triangle does not contain any points from the elevation difference dataset; and combining the triangles to construct the triangulation network.
[0014] In one embodiment, constructing a first polygon based on the triangulation includes: if an edge in the triangulation is shared by two triangles in the triangulation, then the line connecting the centers of the circumcircles of the two triangles is taken as an interior edge of the first polygon; if an edge in the triangulation is used only by one triangle in the triangulation, then the perpendicular bisector from the center of the circumcircle of the triangle to the edge is taken as a boundary edge of the first polygon; and constructing the first polygon based on the interior edge and the boundary edge.
[0015] In one embodiment, determining the elevation difference of each third data based on the weights of each third data and the elevation difference dataset includes: determining the elevation difference of each second data in the second dataset based on the elevation difference dataset; and for each third data, multiplying the weight of the third data by the elevation difference of the corresponding second data and summing the results to obtain the elevation difference of the third data.
[0016] This application also provides an electronic device, which includes: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-described data fusion method.
[0017] The solution provided in the above embodiments of this application improves the accuracy of low-precision point cloud data by fusing low-precision, high-density point cloud data with high-precision, low-density point cloud data, thereby forming high-density, high-precision point cloud data and achieving multi-source data fusion. Using the solution provided in the above embodiments of this application can improve the overall accuracy and reliability of the data, thus leveraging the value of low-precision, high-density data. Furthermore, it is easy to program in computers, allowing for rapid multi-source data fusion processing through software. Attached Figure Description
[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below.
[0019] Figure 1 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;
[0020] Figure 2 This is a flowchart illustrating the data fusion method provided in an embodiment of this application;
[0021] Figure 3 This is one of the schematic diagrams of the triangular mesh construction process provided in the embodiments of this application;
[0022] Figure 4 This is a second schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0023] Figure 5 This is the third schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0024] Figure 6 This is the fourth schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0025] Figure 7 This is the fifth schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0026] Figure 8 This is the sixth schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0027] Figure 9 This is the seventh schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0028] Figure 10 This is the eighth schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0029] Figure 11 This is the ninth schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0030] Figure 12 This is the tenth schematic diagram of the triangular mesh construction process provided in the embodiments of this application;
[0031] Figure 13 This is eleventh of the schematic diagrams illustrating the triangular mesh construction process provided in the embodiments of this application;
[0032] Figure 14 This is a schematic diagram of the constructed triangular network provided in the embodiments of this application;
[0033] Figure 15 This is a schematic diagram of the first quadrilateral construction process provided in the embodiments of this application;
[0034] Figure 16 This is one of the schematic diagrams illustrating the construction process of the second quadrilateral provided in the embodiments of this application;
[0035] Figure 17 This is the second schematic diagram of the second quadrilateral construction process provided in the embodiments of this application. Detailed Implementation
[0036] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.
[0037] Similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0038] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 100 can be used to execute the data fusion method provided in an embodiment of this application. Figure 1 As shown, the electronic device 100 includes: one or more processors 102 and one or more memories 104 storing processor-executable instructions. The processors 102 are configured to execute the data fusion method provided in the following embodiments of this application.
[0039] The processor 102 may be a gateway, a smart terminal, or a device that includes a central processing unit (CPU), a graphics processing unit (GPU), or other forms of processing units with data processing capabilities and / or instruction execution capabilities. It can process data from other components in the electronic device 100 and control other components in the electronic device 100 to perform desired functions.
[0040] The memory 104 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 102 may execute the program instructions to implement the data fusion method described below. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs.
[0041] In one embodiment, Figure 1 The illustrated electronic device 100 may further include an input device 106, an output device 108, and a data acquisition device 110, these components being interconnected via a bus system 112 and / or other forms of connection mechanisms (not shown). It should be noted that... Figure 1 The components and structure of the electronic device 100 shown are merely exemplary and not limiting; the electronic device 100 may also have other components and structures as needed.
[0042] The input device 106 can be a device used by a user to input commands, and may include one or more of a keyboard, mouse, microphone, and touchscreen. The output device 108 can output various information (e.g., images or sounds) to the outside (e.g., the user), and may include one or more of a display, speaker, etc. The data acquisition device 110 can acquire low-precision high-density data and high-precision low-density data, and store the acquired data in the memory 104 for use by other components. For example, the data acquisition device 110 can be a UAV lidar device, a UAV aerial photography device, a real-time dynamic differential positioning device, and a total station.
[0043] In one embodiment, the devices in the example electronic device 100 used to implement the data fusion method of the present application embodiment can be integrated or distributed. For example, the processor 102, memory 104, input device 106 and output device 108 can be integrated into one unit, while the data acquisition device 110 can be separated.
[0044] In one embodiment, the example electronic device 100 for implementing the data fusion method of the present application embodiment can be implemented as a smart terminal such as a smartphone, tablet computer, desktop computer, server, vehicle device, etc.
[0045] Figure 2 This is a flowchart illustrating a data fusion method according to an embodiment of this application. Figure 2 As shown, the method includes steps 210-260.
[0046] Step 210: Obtain the first dataset and the second dataset, wherein the first dataset is a collection of low-precision, high-density data, and the second dataset is a collection of high-precision, low-density data.
[0047] The data in the first and second datasets can be point cloud data. Point cloud data refers to a set of vectors in a three-dimensional coordinate system, where each point contains at least three coordinate information. It is used to accurately describe the geometry of an object's surface.
[0048] The low-precision, high-density data is obtained through UAV LiDAR or UAV aerial photogrammetry, while the high-precision, low-density data is obtained through real-time dynamic differential positioning or total station measurement. Specifically, UAV LiDAR measurement or UAV aerial photogrammetry is used to acquire a low-precision, high-density point cloud dataset within the survey area. , High-precision, low-density datasets within the same survey area are obtained using RTK or total station measurements. , .
[0049] Step 220: Apply linear interpolation to the first dataset to generate the third dataset.
[0050] A third dataset is generated by applying bilinear interpolation or kriging interpolation to the first dataset. This third dataset is a regular grid dataset, with the grid spacing determined based on the measurement scale. Specifically, this applies to low-precision, high-density point cloud datasets. Regular grid datasets are generated using bilinear interpolation or kriging interpolation. , The grid spacing is determined according to the measurement scale, and is generally no more than 0.1 mm on the map.
[0051] Step 230: Determine the elevation difference dataset based on the second dataset and the third dataset.
[0052] In the third dataset, find the third data corresponding to each second data in the second dataset. If a third data exists at the same position as a second data, then that third data corresponds to that second data. If no third data exists at the same position as a second data, then find the third data closest to that second data, and that third data corresponds to that second data. The positions of the second and third data can be planar positions, i.e. and For each corresponding set of second and third data points, the elevation value of the second data point is subtracted from the elevation value of the third data point to obtain the elevation difference value of the second data point. Based on the elevation differences of each set of second data points, the elevation difference dataset is determined. Specifically, this is based on a high-precision, low-density dataset. planar position In the corresponding rule grid dataset Find the same location or the closest point Then obtain the elevation value of that point. Calculate its elevation difference - Obtain the elevation difference dataset .
[0053] Step 240: Determine the weight of each third data point in the third dataset based on the elevation difference dataset.
[0054] Step 240 above includes steps 2401-2404, as follows:
[0055] Step 2401: Construct a triangulation network based on the elevation difference dataset.
[0056] Triangles are constructed using points from the elevation difference dataset, where the circumcircle of each triangle does not contain any points from the elevation difference dataset. These triangles are then combined to construct the triangulation network. Specifically, the elevation difference dataset... On a plane (only the plane coordinates of points are displayed, ignoring elevation differences), Construct a triangulation network where each triangle satisfies the following condition: the circumcircle of any triangle does not contain any elevation difference datasets other than the vertices of that triangle. Other points in the triangle. The specific steps for constructing a triangulation are as follows:
[0057] Step 24011: As Figure 3 As shown, first construct a large triangle to include all elevation difference datasets. Enclose the points in the middle, assuming There are a total of four points.
[0058] Step 24012: As Figure 4 As shown, the elevation difference dataset is extracted from the large triangle mentioned above. One point Connect the point to the three vertices of the large triangle containing it, dividing the large triangle into multiple smaller triangles to form an initial triangulation.
[0059] Step 24013: As Figure 5 As shown, in the initial triangulation above, the circumcircle of each small triangle is constructed.
[0060] Step 24014: As Figures 6-8 As shown, the elevation difference dataset is extracted. The next point In the initial triangulation, find all triangles whose circumcircle contains the given point. Figure 7 The triangle corresponding to the red circumcircle in the middle), and delete the common edge of the triangle containing that point ( Figure 7 (the side of the red triangle in the middle), at this time a point is formed. A polygonal hole centered on the center (such as...) Figure 8 ).
[0061] Step 24015: As Figure 9 As shown, from point Connect the vertices of the polygonal hole to form a new triangle.
[0062] Step 24016: Check the quadrilaterals formed by the newly formed triangle and its adjacent triangles. If there exists a quadrilateral whose diagonals, after being swapped, satisfy the criterion that the circumcircles of the two new triangles do not contain any other points, then swap that diagonal. Repeat this checking and swapping process until no more quadrilaterals can be optimized. Figures 10-12 The diagram shows a possible implementation of quadrilateral optimization. The constructed red triangle and the adjacent blue triangle form a quadrilateral that shares one side, namely one diagonal of the quadrilateral. Figure 10 (The red side of the triangle), at this point, the circumcircle of the red triangle includes the vertices of the non-shared sides of the blue triangle, and the circumcircle of the blue triangle also includes one vertex of the non-shared side of the red triangle (e.g., ...). Figure 10 As shown), this does not meet the above criteria, and the diagonal of the quadrilateral ( Figure 10 The red border of the Chinese-style border is adjusted to the other diagonal of the quadrilateral. Figure 11 (with purple border in the middle), at this time, the circumcircles of the two adjacent triangles do not contain the vertices of the triangles (e.g., purple border in the middle). Figure 12 As shown in the figure, it satisfies the above criteria.
[0063] Step 24017: Repeat steps 24012-24016 above until the elevation difference data point set is obtained. All points have been processed and obtained as follows: Figure 13 The result.
[0064] Step 24018: Delete all triangles related to the large triangle constructed in Step 1 to obtain the final triangulation, as shown below. Figure 14 As shown.
[0065] Step 2402: Construct the first polygon based on the triangular mesh.
[0066] If an edge in the triangulation is shared by two triangles in the triangulation, the line connecting the centers of the circumcircles of the two triangles is taken as an interior edge of the first polygon. If an edge in the triangulation is used by only one triangle in the triangulation, the perpendicular bisector from the center of the circumcircle of the triangle to the edge is taken as a boundary edge of the first polygon. The first polygon is constructed based on the interior edges and boundary edges.
[0067] like Figure 15 As shown, the specific process for constructing the first polygon is as follows:
[0068] Step 24021: Perform the following operations on each edge of the triangular mesh:
[0069] (1) Find two adjacent triangles that share the same side;
[0070] (2) Calculate the circumcenters of the two triangles respectively;
[0071] (3) Connect the centers of the two circumcircles to obtain a line segment that is one side of the first polygon;
[0072] (4) Repeat the above operation to obtain the internal edges of the first polygon.
[0073] Step 24022: Boundary Processing: For edges of a triangulated mesh located on the convex hull boundary (with only one adjacent triangle), perform the following operations:
[0074] (1) Only calculate the circumcenter of the triangle;
[0075] (2) Draw the perpendicular bisector from the center of the circle to the boundary edge, and extend it outwards far enough until it intersects with the boundary of the data range to form the boundary edge of the first polygon.
[0076] Step 24023: Forming polygonal regions: around the elevation difference dataset For each point in the data set, connect all the internal and boundary edges (i.e., the edges generated in steps 24021 and 24022) of its associated triangulation network. These edges, together with the data range boundary, naturally form a closed polygon, thus creating the elevation difference dataset. The corresponding first polygon.
[0077] Step 2403: Insert each of the third data points into the first polygon one by one to construct the second polygon.
[0078] Extract planar coordinates from the regular grid data generated in step 220 Then, insert it point by point into the first polygon formed in step 2402, and reconstruct a new polygon, namely the second polygon. Specifically, as follows: Figure 16 As shown, after inserting a third data point (the black dot inside the magenta polygon), a new polygon is reconstructed using the same method as described above, based on the inserted point and the elevation difference dataset. Since the third data point is inserted point by point, the number of the second polygons formed is the same as the number of the third data points.
[0079] Step 2404: Determine the weight of each third data point based on the overlapping area of the first polygon and the second polygon.
[0080] Taking the process of determining the weight of a third data point as an example, after inserting a third data point, a second polygon is reconstructed. The constructed second polygon and the first polygon will form multiple overlapping regions, and the overlap area of these overlapping regions is... ).like Figure 16As shown, the areas enclosed by the magenta lines are multiple overlapping regions. The weights are determined based on the size of the overlapping areas, specifically... In the formula As weight, The area of a certain overlapping region. That is, a third piece of data has multiple weights, and the magnitude of each weight depends on the size of the overlap area between the second polygon and the first polygon formed after the insertion of the third piece of data. For example... Figure 17 As shown, there are four overlapping regions in total, with overlapping areas of... , , , The weight is determined based on the size of the overlapping area, for example... The weight of the region is Similarly, it can be calculated , , .
[0081] Step 250: Determine the elevation difference value of each third data based on the weight of each third data and the elevation difference dataset.
[0082] Based on the elevation difference dataset, determine the elevation difference value of each second data point in the second dataset. For each third data point, multiply the weight of the third data point by the elevation difference value of the corresponding second data point and sum the results to obtain the elevation difference value of the third data point. Specifically, The formula for calculating the elevation difference of a point is:
[0083]
[0084] like Figure 17 As shown, taking the process of determining the elevation difference of a third data point as an example, the elevation difference of a newly inserted third data point... ,in, , , , These are the elevation differences within the corresponding polygons. Here, the corresponding polygons refer to the polygons that correspond to the weights of the third data, that is, the polygons corresponding to the overlapping areas of the weights of the third data.
[0085] Step 260: Determine the fourth dataset based on the elevation differences of each third dataset and the third dataset, wherein the fourth dataset is a collection of high-precision, high-density data.
[0086] The third data in the third dataset generated in step 220 is... The elevation difference of each third data determined through step 250 is... , then and By adding them together, we can obtain the elevation value of the fourth data point in the fourth dataset. This allows us to obtain the fourth data. .
[0087] The solution provided in the above embodiments of this application improves the accuracy of low-precision point cloud data by fusing low-precision, high-density point cloud data with high-precision, low-density point cloud data, thereby forming high-density, high-precision point cloud data and achieving multi-source data fusion. Using the solution provided in the above embodiments of this application can improve the overall accuracy and reliability of the data, thus leveraging the value of low-precision, high-density data. Furthermore, it is easy to program in computers, allowing for rapid multi-source data fusion processing through software.
[0088] In yet another embodiment of this application, the data fusion method includes steps 310-380, as detailed below:
[0089] Step 310: Point cloud data of a certain survey area is obtained using UAV aerial photogrammetry. A small area of point cloud data is extracted for demonstration processing. This area contains a low-precision, high-density point cloud dataset. , There are 47 points in total. Bilinear interpolation was used to generate regular grid data with a grid spacing of 0.1 mm, resulting in a point cloud dataset of 6439 points. , .
[0090] Step 320: Use RTK to collect high-precision, low-density datasets within the same survey area. , A total of 7 points were collected, based on their planar positions. In sequence in the regular grid dataset Extract the elevation data from the corresponding location in the image. .
[0091] Step 330: Calculate the elevation difference at the same location - Obtain the elevation difference dataset There are a total of 7 points in this embodiment.
[0092] Step 340: Based on the elevation difference dataset Construct a triangular network.
[0093] Step 350: Construct the first polygons based on the triangulation. In this embodiment, there are a total of 7 first polygons.
[0094] Step 360: Extract the grid dataset generated in Step 310 Planar coordinates in Insert it point by point into the first polygon formed in step 350, reconstruct a new polygon, namely the second polygon, and calculate its weight based on the area of overlap with the first polygon. In the formula As weight, The area of a certain overlapping region. ).
[0095] Step 370: Weight Its elevation difference Sum after multiplication ( ), to obtain the grid datasets. Planar coordinates in The elevation difference below.
[0096] Step 380: Combine the elevation difference data from step 370 with the grid dataset from step 310. The elevation values are added together to obtain the final result, which is a set of high-precision, high-density data.
[0097] The devices and methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The device and method embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0098] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.
[0099] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
Claims
1. A data fusion method, characterized in that, Includes the following steps: Obtain a first dataset and a second dataset, wherein the first dataset is a collection of low-precision, high-density data, and the second dataset is a collection of high-precision, low-density data, and both the first dataset and the second dataset are point cloud data; A third dataset is generated by applying linear interpolation to the first dataset; Based on the second and third datasets, determine the elevation difference dataset; Based on the elevation difference dataset, determine the weight of each third data point in the third dataset; The elevation difference value of each third data is determined based on the weight of each third data and the elevation difference dataset; Based on the elevation differences of each third dataset and the third dataset, a fourth dataset is determined, wherein the fourth dataset is a collection of high-precision, high-density data. The low-precision, high-density data is obtained through UAV lidar or UAV aerial photogrammetry. The high-precision, low-density data is obtained through real-time dynamic differential positioning or total station measurement. The step of determining the weight of each third data point in the third dataset based on the elevation difference dataset includes: Based on the elevation difference dataset, construct a triangulation network; Based on the triangular mesh, construct the first polygon; The third data points are inserted point by point into the first polygon to construct the second polygon; The weights of each third data point are determined based on the overlapping area of the first and second polygons.
2. The data fusion method according to claim 1, characterized in that, The step of generating a third dataset by applying linear interpolation to the first dataset includes: A third dataset is generated by applying bilinear interpolation or kriging interpolation to the first dataset. The third dataset is a regular grid dataset, and the grid spacing is determined according to the measurement scale.
3. The data fusion method according to claim 1, characterized in that, The step of determining the elevation difference dataset based on the second dataset and the third dataset includes: Find the third data in the third dataset that corresponds to each second data in the second dataset; For each set of corresponding second and third data, subtract the elevation value of the third data from the elevation value of the second data to obtain the elevation difference of the second data; The elevation difference dataset is determined based on the elevation difference values of each second dataset.
4. The data fusion method according to claim 1, characterized in that, The step of constructing a triangulation network based on the elevation difference dataset includes: A triangle is constructed using points from the elevation difference dataset, wherein the circumcircle of the triangle does not contain any points from the elevation difference dataset. The triangles are combined to construct the triangular mesh.
5. The data fusion method according to claim 1, characterized in that, The step of constructing the first polygon based on the triangular mesh includes: If an edge in the triangulation is shared by two triangles in the triangulation, then the line connecting the centers of the circumcircles of the two triangles is taken as the interior edge of the first polygon. If an edge in the triangulation is used by only one triangle in the triangulation, then the perpendicular bisector from the circumcenter of the triangle to the edge is taken as the boundary edge of the first polygon. The first polygon is constructed based on the internal edges and the boundary edges.
6. The data fusion method according to claim 1, characterized in that, The step of determining the elevation difference value of each third data point based on its weight and elevation difference dataset includes: Based on the elevation difference dataset, determine the elevation difference value of each second data point in the second dataset; For each third data point, the weight of the third data point is multiplied by the elevation difference of the corresponding second data point, and then the results are summed to obtain the elevation difference of the third data point.
7. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store processor-executable instructions; The processor is configured to execute the data fusion method according to any one of claims 1-6.