Information data visualization analysis method and system combined with knowledge graph

Abstract

The invention discloses an information data visualization analysis method and system combined with a knowledge graph, and the method comprises the steps: obtaining a satellite image, carrying out the gray processing of the satellite image, generating a two-dimensional gray image, and extracting the image information of the two-dimensional gray image, the image information comprises the pixel value of each pixel point, the terrain height, the surface reflectance, the solar incident angle, the surface temperature and the surface humidity; setting a landform feature value extraction model, calculating landform feature values according to the image information, clustering the landform feature values, generating a plurality of landform categories, and forming a landform knowledge graph; and taking the landform knowledge graph as information data for visual display so as to provide visual information support.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligence data analysis, and more specifically, relates to an intelligence data visualization analysis method and system combined with a knowledge graph. Background Art

[0002] Satellite-based intelligence data refers to the use of satellite-acquired images and other data for analysis to extract valuable information. This data can be used in a variety of fields, such as military, meteorology, agriculture, environmental monitoring, urban planning, etc. The following are some common applications: Military intelligence: Satellite images can be used to monitor and identify enemy military activities and facilities. By analyzing these images, the location, size, and activities of military bases can be understood.

[0003] Weather Forecasting: Weather satellites provide images and data that can help predict weather changes such as storms, hurricanes, and other extreme weather events.

[0004] Agricultural Monitoring: The health and growth of crops can be monitored through satellite imagery, helping farmers optimize farming practices and increase yields.

[0005] Environmental protection: Satellite data can be used to monitor deforestation, pollution emissions, water resource management, etc., and help formulate environmental protection policies and measures.

[0006] Urban planning: Satellite imagery can be used to plan and manage urban expansion, infrastructure development, and traffic flow.

[0007] Disaster response: After natural disasters such as earthquakes, floods and fires, satellite data can help assess the impact and guide rescue and recovery efforts.

[0008] However, the display of satellite intelligence data in the existing technology is not intuitive enough, and it is difficult to highlight the key data that needs to be displayed. Summary of the invention

[0009] In order to solve the above technical problems, the present invention proposes a visualization analysis method of intelligence data combined with knowledge graph, including: Acquire a satellite image, perform grayscale processing on the satellite image to generate a two-dimensional grayscale image, and extract image information of the two-dimensional grayscale image, wherein the image information includes: a pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity; Setting a terrain and geomorphic feature value extraction model, and calculating terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; The topographic knowledge graph is visualized as intelligence data to provide visualized intelligence support.

[0010] Furthermore, the terrain and geomorphic feature value extraction model includes: , in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

[0011] Further, The weight of the local directional eigenvector include: , in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

[0012] Furthermore, the weight of the terrain gradient include: , in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

[0013] Furthermore, the weight of the surface reflectivity include: , in, is the weight of the surface reflectivity The comprehensive adjustment factor is is the adjustment factor for the surface reflectivity, is the surface reflectivity The direction angle, is the adjustment factor for the surface reflectivity direction angle.

[0014] Furthermore, the landform categories include: mountains, plains, rivers, lakes, artificial roads and bridges.

[0015] The present invention also proposes an intelligence data visualization analysis system combined with a knowledge graph, comprising: An information acquisition module is used to acquire satellite images, perform grayscale processing on the satellite images to generate two-dimensional grayscale images, and extract image information of the two-dimensional grayscale images, wherein the image information includes: pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature and surface humidity; Setting a model module, used for setting a terrain and geomorphic feature value extraction model, and calculating the terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; The visualization module is used to visualize the topographic knowledge map as intelligence data, thereby providing visualized intelligence support.

[0016] Furthermore, the terrain and landform feature value extraction model includes: , in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

[0017] Further, The weight of the local directional eigenvector include: , in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

[0018] Furthermore, the weight of the terrain gradient include: , in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

[0019] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects: The present invention sets a terrain and geomorphic feature value extraction model, and calculates the terrain and geomorphic feature values ​​according to the image information, clusters the terrain and geomorphic feature values, generates multiple terrain and geomorphic categories, and forms a terrain and geomorphic knowledge map; the terrain and geomorphic knowledge map is visualized as intelligence data, thereby providing visualized intelligence support. Through the above technical solution, the present invention can extract terrain and geomorphic features on satellite images, form a knowledge map, and generate visualized intelligence data, thereby improving the efficiency of intelligence use. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0023] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0024] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0025] The display is used to show the interactive cross-section of each application.

[0026] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0027] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0028] Example 1 like Figure 1 As shown, the present invention proposes a method for visual analysis of intelligence data combined with a knowledge graph, comprising: Step 101, acquiring a satellite image, gray-processing the satellite image to generate a two-dimensional gray-scale image, and extracting image information of the two-dimensional gray-scale image, wherein the image information includes: a pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity; Step 102, setting a terrain and geomorphic feature value extraction model, and calculating terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; Specifically, the terrain and landform feature value extraction model includes: , in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

[0029] Specifically, The weight of the local directional eigenvector include: , in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

[0030] Specifically, the weight of the terrain gradient include: , in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

[0031] Specifically, the weight of the surface reflectivity include: , in, is the weight of the surface reflectivity The comprehensive adjustment factor is is the adjustment factor for the surface reflectivity, is the surface reflectivity The direction angle, is the adjustment factor for the surface reflectivity direction angle.

[0032] Specifically, the landform categories include: mountains, plains, rivers, lakes, and artificially constructed roads and bridges.

[0033] Step 103, visually displaying the topographic knowledge graph as intelligence data, thereby providing visual intelligence support.

[0034] Example 2 like Figure 2 As shown, the embodiment of the present invention also proposes an intelligence data visualization analysis system combined with a knowledge graph, including: An information acquisition module is used to acquire satellite images, perform grayscale processing on the satellite images to generate two-dimensional grayscale images, and extract image information of the two-dimensional grayscale images, wherein the image information includes: pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature and surface humidity; Setting a model module, used for setting a terrain and geomorphic feature value extraction model, and calculating the terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; Specifically, the terrain and landform feature value extraction model includes: , in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

[0035] Specifically, The weight of the local directional eigenvector include: , in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

[0036] Specifically, the weight of the terrain gradient include: , in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

[0037] Specifically, the weight of the surface reflectivity include: , in, is the weight of the surface reflectivity The comprehensive adjustment factor is is the adjustment factor for the surface reflectivity, is the surface reflectivity The direction angle, is the adjustment factor for the surface reflectivity direction angle.

[0038] Specifically, the landform categories include: mountains, plains, rivers, lakes, and artificially constructed roads and bridges.

[0039] The visualization module is used to visualize the topographic knowledge map as intelligence data, thereby providing visualized intelligence support.

[0040] Example 3 An embodiment of the present invention also proposes a storage medium storing multiple instructions, wherein the instructions are used to implement the intelligence data visualization analysis method combined with the knowledge graph.

[0041] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0042] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the method steps: Step 101, acquiring a satellite image, grayscale processing the satellite image to generate a two-dimensional grayscale image, and extracting image information of the two-dimensional grayscale image, wherein the image information includes: a pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity; Step 102, setting a terrain and geomorphic feature value extraction model, and calculating terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; Specifically, the terrain and landform feature value extraction model includes: , in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

[0043] Specifically, The weight of the local directional eigenvector include: , in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

[0044] Specifically, the weight of the terrain gradient include: , in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

[0045] Specifically, the weight of the surface reflectivity include: , in, is the weight of the surface reflectivity The comprehensive adjustment factor is is the adjustment factor for the surface reflectivity, is the surface reflectivity The direction angle, is the adjustment factor for the surface reflectivity direction angle.

[0046] Specifically, the landform categories include: mountains, plains, rivers, lakes, and artificially constructed roads and bridges.

[0047] Step 103, visually displaying the topographic knowledge graph as intelligence data, thereby providing visual intelligence support.

[0048] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute a method for visualizing and analyzing intelligence data combined with a knowledge graph.

[0049] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0050] Among them, the storage medium can be used to store software programs and modules, such as a method for visualizing and analyzing intelligence data combined with a knowledge graph in an embodiment of the present invention, and corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned method for visualizing and analyzing intelligence data combined with a knowledge graph. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0051] The processor can call the information and application program stored in the storage medium through the transmission system to execute the method steps: Step 101, obtain a satellite image, perform grayscale processing on the satellite image to generate a two-dimensional grayscale image, and extract image information of the two-dimensional grayscale image, wherein the image information includes: pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature and surface humidity; Step 102, setting a terrain and geomorphic feature value extraction model, and calculating terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; Specifically, the terrain and landform feature value extraction model includes: , in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

[0052] Specifically, The weight of the local directional eigenvector include: , in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

[0053] Specifically, the weight of the terrain gradient include: , in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

[0054] Specifically, the weight of the surface reflectivity include: , in, is the weight of the surface reflectivity The comprehensive adjustment factor is is the adjustment factor for the surface reflectivity, is the surface reflectivity The direction angle, is the adjustment factor for the surface reflectivity direction angle.

[0055] Specifically, the landform categories include: mountains, plains, rivers, lakes, and artificially constructed roads and bridges.

[0056] Step 103, visually displaying the topographic knowledge graph as intelligence data, thereby providing visual intelligence support.

[0057] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0058] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0059] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0060] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0062] If the integrated unit is implemented in the form of a software functional unit 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 the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0063] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for visual analysis of intelligence data combined with knowledge graph, characterized in that: include: Acquire a satellite image, perform grayscale processing on the satellite image to generate a two-dimensional grayscale image, and extract image information of the two-dimensional grayscale image, wherein the image information includes: a pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity; Setting a terrain and geomorphic feature value extraction model, and calculating terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; The topographic knowledge graph is visualized as intelligence data to provide visualized intelligence support.

2. The method for visualizing and analyzing intelligence data combined with a knowledge graph as claimed in claim 1, characterized in that: The terrain and geomorphic feature value extraction model includes: ， in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

3. The method for visualizing and analyzing intelligence data combined with a knowledge graph as claimed in claim 2, characterized in that: No. The weight of the local directional eigenvector include: ， in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

4. The method for visualizing and analyzing intelligence data combined with a knowledge graph as claimed in claim 2, characterized in that: Weight of terrain gradient include: ， in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

5. The method for visualizing and analyzing intelligence data combined with a knowledge graph as claimed in claim 2, characterized in that: Weight of surface reflectance include: ， in, is the weight of the surface reflectivity The comprehensive adjustment factor is is the adjustment factor for the surface reflectivity, is the surface reflectivity The direction angle, is the adjustment factor for the surface reflectivity direction angle.

6. The method for visualizing and analyzing intelligence data combined with a knowledge graph as claimed in claim 1, characterized in that: The landform categories include: mountains, plains, rivers, lakes, and artificially constructed roads and bridges.

7. An intelligence data visualization analysis system combined with knowledge graph, characterized in that: include: An information acquisition module is used to acquire satellite images, perform grayscale processing on the satellite images to generate two-dimensional grayscale images, and extract image information of the two-dimensional grayscale images, wherein the image information includes: pixel value of each pixel point, terrain height, surface reflectivity, solar incidence angle, surface temperature and surface humidity; Setting a model module, used for setting a terrain and geomorphic feature value extraction model, and calculating the terrain and geomorphic feature values ​​according to the image information, clustering the terrain and geomorphic feature values, generating multiple terrain and geomorphic categories, and forming a terrain and geomorphic knowledge graph; The visualization module is used to visualize the topographic knowledge map as intelligence data, thereby providing visualized intelligence support.

8. The information data visualization analysis system combined with knowledge graph according to claim 7, characterized in that: The terrain and geomorphic feature value extraction model includes: ， in, is the topographic characteristic value, is a two-dimensional grayscale image In pixels The gradient vector at Pixel The horizontal axis of Pixel The vertical coordinate of is the number of pixels in the local neighborhood, For the The weight of the local directional eigenvector, is a two-dimensional grayscale image In pixels The gradient vector at Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset, For pixels Adjacent pixels No. local directional eigenvectors, is the weight of the terrain gradient, For the pixel Terrain height The terrain gradient, is the weight of the surface reflectivity, For the pixel The surface reflectivity at is the weight of the sun’s incident angle, For the pixel The solar incident angle at For the pixel The surface temperature at is the weight of surface moisture, For the pixel The surface humidity of the ground.

9. The information data visualization analysis system combined with knowledge graph according to claim 8, characterized in that: No. The weight of the local directional eigenvector include: ， in, Relative to the pixel No. The horizontal axis neighborhood offset, Relative to the pixel No. The vertical coordinate neighborhood offset.

10. The intelligence data visualization analysis system combined with knowledge graph according to claim 8, characterized in that: Weight of terrain gradient include: ， in, is the weight of the terrain gradient The comprehensive adjustment factor is is the adjustment factor for terrain gradient, The terrain gradient The direction angle, is the adjustment factor for the terrain gradient direction angle.

Images