Multi-source data fusion method, device, computer equipment and storage medium

Abstract

The present application relates to a multi-source data fusion method, device, computer equipment, and storage medium. The method includes: obtaining terrain data and landform data; wherein the terrain data includes: multi-beam data and GIS terrain data, and the landform data includes: side-scan data and GIS image data; converting the terrain data into terrain longitude and latitude data; wherein the terrain longitude and latitude data includes multiple unified gridded data with the same longitude and latitude coordinate system; converting the landform data into landform longitude and latitude data; wherein the landform longitude and latitude data includes multiple image data; fusing the multiple unified gridded data to obtain a whole block of terrain data; fusing the multiple image data to obtain a whole block of image data; and obtaining fused data based on the whole block of terrain data and the whole block of image data. By using the above-mentioned fused data for three-dimensional modeling, the constructed three-dimensional model of terrain and landform can be made more comprehensive and accurate.

Description

Technical Field

[0001] The present application relates to the technical field of terrain modeling, and in particular to a multi-source data fusion method, apparatus, computer equipment and storage medium. Background Art

[0002] Due to the regional and phased nature of multi-beam detection data, there are often blank areas in the bathymetric data when conducting three-dimensional modeling. In order to make the three-dimensional terrain and landform model more comprehensive and accurate, it is necessary to supplement the terrain and landform data from other sources. However, the accuracy of these data is different from that of multi-beam data. Therefore, how to integrate multi-source data is a technical problem that needs to be solved urgently in constructing three-dimensional terrain and landform models. Summary of the Invention

[0003] Based on this, it is necessary to provide a multi-source data fusion method, device, computer equipment and storage medium that can fuse terrain and landform data from multiple sources to address the above technical problems.

[0004] In a first aspect, the present application provides a multi-source data fusion method. The method comprises:

[0005] Acquire terrain data and landform data; wherein the terrain data includes: multi-beam data and GIS terrain data, and the landform data includes: side scan data and GIS image data;

[0006] Converting the terrain data into terrain longitude and latitude data; wherein the terrain longitude and latitude data includes a plurality of unified gridded data with the same longitude and latitude coordinate system;

[0007] Converting the landform data into landform longitude and latitude data; wherein the landform longitude and latitude data includes a plurality of image data;

[0008] Fusing the plurality of unified gridded data to obtain the entire terrain data;

[0009] fusing the plurality of image data to obtain a whole block of image data;

[0010] The fused data is obtained according to the entire terrain data and the entire image data.

[0011] In one embodiment, the step of converting the terrain data into terrain longitude and latitude data includes:

[0012] Gridding the multi-beam data to obtain standard gridded data;

[0013] Performing coordinate system conversion on the standard gridded data to obtain the unified gridded data;

[0014] The GIS terrain data is subjected to coordinate system conversion to obtain the unified gridded data.

[0015] In one embodiment, the step of converting the landform data into landform latitude and longitude data includes:

[0016] The side scan data and the GIS image data are respectively subjected to coordinate conversion to obtain a plurality of image data.

[0017] In one embodiment, the step of fusing the plurality of unified gridded data to obtain the entire terrain data includes:

[0018] Calculating the resolution of all the unified gridded data to obtain a first minimum resolution;

[0019] Calculating the envelope range of all the unified gridded data to obtain a first overall envelope range;

[0020] Obtaining unfilled overall grid data according to the first minimum resolution and the first overall envelope range;

[0021] The unfilled overall grid data is mapped and filled according to the plurality of unified gridded data to obtain the entire terrain data.

[0022] In one embodiment, the step of fusing the plurality of image data to obtain a whole block of image data includes:

[0023] Calculating the resolution of all the image data to obtain a second minimum resolution;

[0024] Calculating the envelope range of all the image data to obtain a second overall envelope range;

[0025] Obtaining unfilled overall image data according to the second minimum resolution and the second overall envelope range;

[0026] The unfilled whole image data is mapped and filled according to the plurality of image data to obtain the whole block of image data.

[0027] In one embodiment, the method further comprises:

[0028] Cutting the entire block of terrain data and the entire block of image data respectively to obtain a plurality of small blocks of terrain data and a plurality of small blocks of image data; wherein each small block of terrain data corresponds to each small block of image data;

[0029] Converting the latitude and longitude coordinate systems of each of the small pieces of terrain data and each of the small pieces of image data into a geocentric rectangular coordinate system;

[0030] Calculating the common center point of all small-piece terrain data and all small-piece image data, and converting the geocentric rectangular coordinate system into a local rectangular coordinate system;

[0031] Constructing a triangular surface according to the local rectangular coordinate system to obtain a three-dimensional terrain model;

[0032] Texture mapping is performed on the three-dimensional terrain model to obtain a three-dimensional terrain model.

[0033] In one embodiment, the method further comprises:

[0034] Performing triangulation processing on the three-dimensional model of the terrain to obtain compressed three-dimensional models of multiple different levels of detail;

[0035] Redundancy removal and precision processing are performed on the material map of the compressed three-dimensional model, and texture mapping relationship repair is performed to obtain a plurality of low-resolution three-dimensional models with different resolutions.

[0036] In a second aspect, the present application further provides a multi-source data fusion device. The device comprises:

[0037] A data acquisition module, configured to acquire terrain data and landform data; wherein the terrain data includes multi-beam data and GIS terrain data, and the landform data includes side scan data and GIS image data;

[0038] A terrain data conversion module, configured to convert the terrain data into terrain longitude and latitude data; wherein the terrain longitude and latitude data includes a plurality of unified gridded data with the same longitude and latitude coordinate system;

[0039] A landform data conversion module, configured to convert the landform data into landform longitude and latitude data; wherein the landform longitude and latitude data includes a plurality of image data;

[0040] A first data fusion module is used to fuse the plurality of unified gridded data to obtain the entire terrain data;

[0041] A second data fusion module is used to fuse the plurality of image data to obtain a whole block of image data;

[0042] The fusion data acquisition module is used to obtain fusion data according to the entire terrain data and the entire image data.

[0043] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0044] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0045] The multi-source data fusion method, apparatus, computer device, and storage medium described above convert terrain data and landform data to obtain terrain longitude and latitude data and landform longitude and latitude data in a unified coordinate system. Then, through data fusion processing, the terrain longitude and latitude data are fused into a complete block of terrain data, and the landform longitude and latitude data are fused into a complete block of image data, thereby obtaining fused data after the fusion of terrain and landform data. Using this fused data for 3D modeling can make the constructed 3D terrain and landform model more comprehensive and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 1. A diagram showing an application environment of a multi-source data fusion method in one embodiment;

[0047] Figure 2 1 is a flow chart of a multi-source data fusion method according to an embodiment;

[0048] Figure 3 A schematic diagram of a process for calculating terrain latitude and longitude data in one embodiment;

[0049] Figure 4 A schematic diagram of a process for calculating entire terrain data in one embodiment;

[0050] Figure 5 A schematic diagram of a process for calculating entire block image data in one embodiment;

[0051] Figure 6 A schematic diagram of a process for calculating a three-dimensional model of terrain and landforms in one embodiment;

[0052] Figure 7 Schematic diagram of a process for calculating a low-resolution three-dimensional model in one embodiment;

[0053] Figure 8 Schematic diagram of the process steps of a multi-source data fusion method in one embodiment;

[0054] Figure 9 1. A schematic diagram of modules of a multi-source data fusion device in one embodiment;

[0055] Figure 10 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0057] The multi-source data fusion method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 obtains the terrain data and landform data stored in the server 104, and converts the terrain data into terrain longitude and latitude data, and converts the landform data into landform longitude and latitude data, and then fuses the converted data respectively, thereby obtaining fused data including the entire terrain data and the entire image data. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0058] In one embodiment, Figure 2 As shown in the figure, a multi-source data fusion method is provided, which is applied to Figure 1 Taking the terminal 102 in FIG. 1 as an example, the method includes the following steps:

[0059] Step S110: Acquire terrain data and landform data, wherein the terrain data includes multi-beam data and GIS terrain data, and the landform data includes side scan data and GIS image data.

[0060] Specifically, terminal 102 can obtain terrain data and landform data that need to be fused from server 104. For terrain data, multi-beam data can be obtained by measuring the underwater terrain using a multi-beam echo sounder, and GIS (Geographic Information System) terrain data is height data detected by other means. For example, it can include acoustic or optical point cloud data, or terrain data obtained by scanning using a laser scanner, depth camera, or binocular camera. For landform data, side-scan data can be obtained by scanning the underwater terrain using side-scan sonar, and GIS image data is a color map similar to a photograph detected by other means, which is used to combine with terrain data to generate a three-dimensional model of the terrain and landform.

[0061] Step S120 converts the terrain data into terrain longitude and latitude data. The terrain longitude and latitude data includes multiple unified gridded data with the same longitude and latitude coordinate system. Specifically, the multibeam data and GIS terrain data are aligned to obtain terrain longitude and latitude data comprising multiple unified gridded data. The multiple unified gridded data have the same longitude and latitude coordinate system, facilitating subsequent 3D modeling.

[0062] Step S130: converting the landform data into landform longitude and latitude data, wherein the landform longitude and latitude data includes a plurality of image data. Specifically, the side scan data and the GIS image data are aligned with the coordinate system to obtain the landform longitude and latitude data including the plurality of image data.

[0063] Step S140: fuse the plurality of unified gridded data to obtain the entire terrain data. Specifically, the plurality of unified gridded data after the coordinate system is unified are fused to obtain the fused entire terrain data.

[0064] Step S150: fuse the multiple image data to obtain the whole block of image data. Specifically, fuse the multiple image data after unifying the coordinate system to obtain the fused whole block of image data.

[0065] In step S160, fused data is generated based on the entire terrain data and the entire image data. Specifically, the fused entire terrain data and the entire image data are used as the final fused data, which is then used to construct a three-dimensional terrain model. Because the fused data includes terrain data from multiple sources, using this fused data for 3D modeling can make the resulting 3D model more comprehensive and accurate.

[0066] The multi-source data fusion method converts terrain data and landform data separately to obtain terrain longitude and latitude data and landform longitude and latitude data in a unified coordinate system. Then, through data fusion processing, the terrain longitude and latitude data are fused into a complete block of terrain data, and the landform longitude and latitude data are fused into a complete block of image data, thereby obtaining fused data after the fusion of terrain and landform data. Using this fused data for 3D modeling can make the constructed 3D terrain and landform model more comprehensive and accurate.

[0067] In one embodiment, Figure 3 As shown, in step S120, the step of converting terrain data into terrain longitude and latitude data includes:

[0068] Step S121: gridding the multi-beam data to obtain standard gridded data. Specifically, firstly gridding the multi-beam data to convert it into standard gridded data, namely standard gridded data.

[0069] Step S122, coordinate system conversion is performed on the standard gridded data to obtain unified gridded data. Specifically, the obtained standard gridded data is subjected to coordinate system conversion to obtain multiple unified gridded data with the same longitude and latitude coordinate system. For example, under normal circumstances, the data acquisition equipment collects multi-beam data in the CGCS2000 coordinate system and obtains the water depth of the point. In order to obtain the elevation information of the multi-beam data for 3D scene modeling, the coordinate system needs to be converted to the WGS-84 coordinate system. It can be understood that the standard gridded data after the coordinate system conversion includes multiple gridded data with the same longitude and latitude coordinate system.

[0070] Step S123: Convert the GIS terrain data to a coordinate system to obtain unified gridded data. Specifically, GIS terrain data is already standard gridded data, so only coordinate system conversion is required to obtain multiple gridded data with the same latitude and longitude coordinate system, i.e., unified gridded data.

[0071] In one embodiment, the step of converting the landform data into landform latitude and longitude data includes performing coordinate conversion on the side scan data and the GIS image data to obtain a plurality of image data. Specifically, the side scan data and the GIS image data only need to be coordinate converted to obtain a plurality of image data having the same latitude and longitude coordinate system. It is understood that the latitude and longitude coordinate systems of the unified gridded data and the image data are the same coordinate system after conversion.

[0072] In one embodiment, Figure 4 As shown, in step S140, the step of fusing multiple unified gridded data to obtain the entire terrain data includes:

[0073] Step S141: Calculate the resolution of all unified gridded data to obtain a first minimum resolution. Specifically, sort the resolutions of each unified gridded data to select the first minimum resolution with the smallest resolution.

[0074] Step S142: Calculate the envelope of all unified gridded data to obtain a first overall envelope. Specifically, cross-overlap the envelopes of each unified gridded data to obtain the overall envelope of all unified gridded data, i.e., the first overall envelope.

[0075] Step S143: Obtain unfilled overall grid data based on the first minimum resolution and the first overall envelope. Specifically, after obtaining the first overall envelope, the large grid block is evenly divided into a plurality of identical small grid blocks according to the first minimum resolution, without assigning values ​​to the grid blocks, thereby obtaining the unfilled overall grid data.

[0076] Step S144 maps and fills the unfilled grid data using the multiple unified gridded data sets to obtain the complete terrain data. Specifically, all values ​​of each unified gridded data set are mapped and filled into the unfilled grid data to obtain the filled complete terrain data. Specifically, during the mapping and filling process, if a fill value is repeated, the subsequent value overwrites the previous value. If grid gaps are generated due to inconsistent resolution, the gaps are calculated using linear interpolation and filled. Through the above steps, the terrain data fusion is completed.

[0077] In one embodiment, Figure 5 As shown, in step S150, the step of fusing multiple image data to obtain a whole block of image data includes:

[0078] Step S151: Calculate the resolution of all image data to obtain the second minimum resolution. Specifically, sort the resolution of each image data and select the second minimum resolution with the smallest resolution.

[0079] Step S152: Calculate the envelope range of all image data to obtain a second overall envelope range. Specifically, cross-overlap the envelope ranges of each image data to obtain the overall envelope range of all image data, namely, the second overall envelope range.

[0080] Step S153: Obtain unfilled overall image data based on the second minimum resolution and the second overall envelope. Specifically, after obtaining the second overall envelope, the large grid block is evenly divided into a plurality of identical small grid blocks at the second minimum resolution without assigning values ​​to the grid blocks, thereby obtaining the unfilled overall image data.

[0081] Step S154 maps and fills the unfilled overall image data based on the multiple image data to obtain the entire block of image data. Specifically, all values ​​of each image data are mapped and filled into the unfilled overall image data to obtain the filled entire block of image data. Specifically, during the mapping and filling process, if the fill value is repeated, the subsequent value overwrites the previous value. When image gaps are generated due to inconsistent resolution, the value of the gap is calculated through linear interpolation and filled. In addition, the elevation image generated by layering and coloring the entire block of terrain data according to elevation can fill the missing portions of the unfilled overall image data. Through the above steps, the fusion of the entire block of landform image data is completed.

[0082] In one embodiment, Figure 6 As shown, the multi-source data fusion method also includes:

[0083] In step S210, the entire block of terrain data and the entire block of image data are segmented to obtain multiple small blocks of terrain data and multiple small blocks of image data. Each small block of terrain data corresponds one-to-one to each small block of image data. Specifically, because computer hardware conditions such as storage and memory are not unlimited, segmenting the entire block of terrain data and the entire block of image data is required during the subsequent modeling process. The entire block of terrain data is segmented to obtain multiple small blocks of terrain data. The entire block of image data is segmented to obtain multiple small blocks of image data. Furthermore, each small block of terrain data necessarily corresponds to a small block of image data.

[0084] Step S220: convert the longitude and latitude coordinates of each small piece of terrain data and each small piece of image data into a geocentric rectangular coordinate system. Specifically, when the longitude and latitude coordinates of the small piece of terrain data and the small piece of image data are WGS-84, convert them into the WGS-84 geocentric rectangular coordinate system.

[0085] Step S230: Calculate the common center point of all small terrain data and all small image data, and convert the geocentric rectangular coordinate system into a local rectangular coordinate system. Specifically, calculate the common center point of all blocks, and convert the geocentric rectangular coordinate system into a local rectangular coordinate system with the center point as the origin.

[0086] Step S240: construct triangular faces based on the local rectangular coordinate system to obtain a three-dimensional terrain model. Specifically, construct triangular faces based on all blocks and the local rectangular coordinate system to form a three-dimensional terrain model.

[0087] Step S250 performs texture mapping on the three-dimensional terrain model to obtain a three-dimensional terrain model. Specifically, the corresponding small image data is defined as a material, and texture mapping coordinates are calculated for the three-dimensional terrain model to complete the texture mapping, thereby obtaining the three-dimensional terrain model. The above steps can conserve computer computing resources during the process of modeling the three-dimensional terrain model.

[0088] In one embodiment, Figure 7 As shown, the multi-source data fusion method also includes:

[0089] Step S310 involves performing triangulation processing on the 3D terrain model to obtain compressed 3D models at multiple levels of detail. Specifically, the multi-beam data has a resolution of 0.2 meters and covers several hundred square kilometers. Considering network transmission and rendering efficiency, the obtained 3D terrain model needs to be processed at different levels of detail. First, the 3D terrain model is subjected to triangulation processing with multiple different parameter settings to obtain compressed 3D models at multiple levels of detail. It will be appreciated that the parameters and number of triangulation processing can be set according to different needs.

[0090] Step S320 removes redundancy and performs precision processing on the material maps of the compressed 3D model, and performs texture mapping repair to obtain multiple low-resolution 3D models of varying resolutions. Specifically, after obtaining the compressed 3D model, the material mapping information of each compressed 3D model must be de-redundant and precision processed. For example, the precision of low-precision material images is correspondingly reduced. Texture mapping repair is then performed to correct any problematic texture mapping relationships. Through these steps, 3D terrain models of varying resolutions, i.e., low-resolution 3D models, are obtained. This facilitates network transmission and computer rendering.

[0091] like Figure 8 As shown, the multi-source data fusion method of the present application is described in detail below with a specific embodiment. After the multi-beam data is gridded and coordinate converted, and the GIS terrain data is coordinate converted, a plurality of unified gridded data with the same latitude and longitude coordinate system (i.e., gridded block 1 to gridded block N) are generated, and the unified gridded data are subjected to resolution unification, deduplication and linear interpolation to obtain the entire terrain data. After the side scan data and the GIS image data are coordinate converted respectively, a plurality of image data (i.e., image 1 to image N) are generated, and the plurality of image data are subjected to the steps of resolution unification, resolution matching with terrain resolution (4 times), deduplication, linear interpolation and cropping supplement (range consistent with terrain), and the elevation image obtained by layered coloring of the entire terrain data is combined to make up for the missing part and linear interpolation to obtain the entire image data. After cutting the fused data including the entire terrain data and the entire image data, one-to-one corresponding small-block terrain data and small-block image data (i.e., small-block terrain 1 and small-block image 1 to small-block terrain N and small-block image N) are obtained. Then, all the small-block data are subjected to geocentric coordinate conversion to local coordinates, triangulation, edge processing (gaps, black edges) and texture mapping to obtain a three-dimensional model of the terrain and landform (i.e., terrain model 1 to terrain model N). Finally, the edge-collapsed surface of the three-dimensional model of the terrain and landform is subjected to LOD (Levels of Detail) processing to obtain compressed three-dimensional models of multiple different levels of detail. In some embodiments, the obtained model file is converted into gltf format and assembled (calculating the model envelope range, center point position and screen error, etc.) to generate an overall three-dimensional model of the terrain and landform in 3D-tiles format.

[0092] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0093] Based on the same inventive concept, the present application also provides a multi-source data fusion device for implementing the multi-source data fusion method described above. The solution provided by this device is similar to the solution described in the method described above. Therefore, the specific limitations in one or more embodiments of the multi-source data fusion device provided below can be found in the above-mentioned limitations on the multi-source data fusion method and will not be repeated here.

[0094] In one embodiment, Figure 9 As shown, a multi-source data fusion device is provided, including: a data acquisition module 410, a terrain data conversion module 420, a landform data conversion module 430, a first data fusion module 440, a second data fusion module 450 and a fused data acquisition module 460, wherein:

[0095] The data acquisition module 410 is used to acquire terrain data and landform data; the terrain data includes multi-beam data and GIS terrain data, and the landform data includes side scan data and GIS image data;

[0096] A terrain data conversion module 420 is used to convert terrain data into terrain longitude and latitude data; wherein the terrain longitude and latitude data includes a plurality of unified gridded data with the same longitude and latitude coordinate system;

[0097] A landform data conversion module 430 is used to convert landform data into landform longitude and latitude data; wherein the landform longitude and latitude data includes a plurality of image data;

[0098] The first data fusion module 440 is used to fuse multiple unified gridded data to obtain the entire terrain data;

[0099] The second data fusion module 450 is used to fuse multiple image data to obtain a whole block of image data;

[0100] The fusion data acquisition module 460 is used to obtain fusion data according to the entire terrain data and the entire image data.

[0101] Each module in the multi-source data fusion device described above may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0102] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 10 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a multi-source data fusion method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0103] Those skilled in the art will understand that Figure 10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0104] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0105] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented.

[0106] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0107] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A multi-source data fusion method, characterized in that: The method comprises: Acquire terrain data and landform data; wherein the terrain data includes: multi-beam data and GIS terrain data, and the landform data includes: side scan data and GIS image data; Converting the terrain data into terrain longitude and latitude data; wherein the terrain longitude and latitude data includes a plurality of unified gridded data with the same longitude and latitude coordinate system; Converting the landform data into landform longitude and latitude data; wherein the landform longitude and latitude data includes a plurality of image data; Fusing the plurality of unified gridded data to obtain the entire terrain data; fusing the plurality of image data to obtain a whole block of image data; Obtaining fused data according to the entire terrain data and the entire image data; Cutting the entire block of terrain data and the entire block of image data respectively to obtain a plurality of small blocks of terrain data and a plurality of small blocks of image data; wherein each small block of terrain data corresponds to each small block of image data; Converting the latitude and longitude coordinate systems of each of the small pieces of terrain data and each of the small pieces of image data into a geocentric rectangular coordinate system; Calculating the common center point of all small-piece terrain data and all small-piece image data, and converting the geocentric rectangular coordinate system into a local rectangular coordinate system; Constructing a triangular surface according to the local rectangular coordinate system to obtain a three-dimensional terrain model; Performing texture mapping on the three-dimensional terrain model to obtain a three-dimensional terrain model; Among them, the step of fusing multiple unified gridded data to obtain a whole block of terrain data includes: sorting the resolution size of each unified gridded data, and screening out the first minimum resolution with the smallest resolution; cross-overlapping according to the envelope range of each unified gridded data to obtain the overall envelope range of all unified gridded data, and using it as the first overall envelope range; dividing the large grid block into several identical small grid blocks according to the first minimum resolution, and not assigning values ​​to the grid blocks, to obtain unfilled overall grid data; mapping all values ​​of each unified gridded data to fill the unfilled overall grid data, to obtain the filled whole block of terrain data.

2. The method according to claim 1, characterized in that The step of converting the terrain data into terrain longitude and latitude data comprises: Gridding the multi-beam data to obtain standard gridded data; Performing coordinate system conversion on the standard gridded data to obtain the unified gridded data; The GIS terrain data is subjected to coordinate system conversion to obtain the unified gridded data.

3. The method according to claim 1, characterized in that The step of converting the landform data into landform longitude and latitude data comprises: The side scan data and the GIS image data are respectively subjected to coordinate conversion to obtain a plurality of image data.

4. The method according to claim 1, wherein The step of fusing the plurality of image data to obtain a whole block of image data includes: Calculating the resolution of all the image data to obtain a second minimum resolution; Calculating the envelope range of all the image data to obtain a second overall envelope range; Obtaining unfilled overall image data according to the second minimum resolution and the second overall envelope range; The unfilled whole image data is mapped and filled according to the plurality of image data to obtain the whole block of image data.

5. The method according to claim 1, wherein The method further comprises: Performing triangulation processing on the three-dimensional model of the terrain to obtain compressed three-dimensional models of multiple different levels of detail; Redundancy removal and precision processing are performed on the material map of the compressed three-dimensional model, and texture mapping relationship repair is performed to obtain a plurality of low-resolution three-dimensional models with different resolutions.

6. A multi-source data fusion device, characterized in that: For executing the multi-source data fusion method according to any one of claims 1 to 5, the device comprises: A data acquisition module, configured to acquire terrain data and landform data; wherein the terrain data includes multi-beam data and GIS terrain data, and the landform data includes side scan data and GIS image data; A terrain data conversion module, configured to convert the terrain data into terrain longitude and latitude data; wherein the terrain longitude and latitude data includes a plurality of unified gridded data with the same longitude and latitude coordinate system; A landform data conversion module, configured to convert the landform data into landform longitude and latitude data; wherein the landform longitude and latitude data includes a plurality of image data; A first data fusion module is used to fuse the plurality of unified gridded data to obtain the entire terrain data; A second data fusion module is used to fuse the plurality of image data to obtain a whole block of image data; The fusion data acquisition module is used to obtain fusion data according to the entire terrain data and the entire image data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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