An intelligence data visual analysis method and system combined with a knowledge graph

Abstract

The application discloses an intelligence data visual analysis method and system combined with a knowledge graph, and the method comprises the following steps: acquiring a satellite image, performing gray processing on the satellite image, generating a two-dimensional gray image, and extracting image information of the two-dimensional gray image, wherein the image information comprises pixel values of each pixel point, terrain height, ground reflectivity, solar incident angle, ground temperature and ground humidity; a terrain and landform feature value extraction model is set, terrain and landform feature values are calculated according to the image information, the terrain and landform feature values are clustered, a plurality of terrain and landform categories are generated, and a terrain and landform knowledge graph is formed; and the terrain and landform knowledge graph is used as intelligence data for visual display, thereby providing visual intelligence support.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligence data analysis, and more particularly relates to an intelligence data visual analysis method and system combined with a knowledge graph. BACKGROUND

[0002] Satellite-based intelligence data refers to the analysis of images and other data obtained by satellites to extract valuable information. These data can be used in multiple fields, such as military, meteorology, agriculture, environmental monitoring, urban planning, etc. Here are some common applications:

[0003] 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.

[0004] Weather forecasting: Images and data provided by meteorological satellites can help predict weather changes such as storms, hurricanes, and other extreme weather events.

[0005] Agricultural monitoring: The health and growth of crops can be monitored through satellite images, helping farmers optimize farming methods and increase yields.

[0006] Environmental protection: Satellite data can be used to monitor deforestation, pollution emissions, water resource management, etc., to help develop environmental protection policies and measures.

[0007] Urban planning: Through satellite images, urban expansion, infrastructure construction, and traffic flow planning and management can be carried out.

[0008] Disaster response: After natural disasters such as earthquakes, floods, and fires, satellite data can help assess the impact of disasters and guide rescue and recovery efforts.

[0009] However, the existing technology does not display satellite intelligence data intuitively, and it is difficult to highlight the key data to be displayed. SUMMARY

[0010] To solve the above technical problems, the present application proposes an intelligence data visual analysis method combined with a knowledge graph, which includes:

[0011] Obtain satellite images, perform grayscale processing on the satellite images 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, terrain height, surface reflectivity, solar incident angle, surface temperature and surface humidity;

[0012] Set up a terrain and landform feature value extraction model, calculate terrain and landform feature values ​​based on the image information, cluster the terrain and landform feature values ​​to generate multiple terrain and landform categories, and form a terrain and landform knowledge graph.

[0013] The topographic knowledge graph is visualized as intelligence data, thereby providing visualized intelligence support.

[0014] Furthermore, the terrain and geomorphological feature value extraction model includes:

[0015] ,

[0016] in, These are topographic and geomorphological feature values. Two-dimensional grayscale image At pixel The gradient vector at that point, For pixels x-coordinate For pixels The ordinate, This represents the number of pixels in the local neighborhood. For the first The weights of local directional eigenvectors, Two-dimensional grayscale image At pixel The gradient vector at that point, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The The offset of the neighborhood of the vertical coordinate. For pixels Adjacent pixels The Local directional feature vectors The weights of the terrain gradient, For at pixel Terrain height at the location Topographic gradient, As the weight of surface reflectance, For at pixel Surface reflectance at that location The weight of the solar incidence angle. For at pixel The angle of incidence of the sun at that location, For at pixel The surface temperature at that location. The weighting of surface humidity, For at pixel the ground surface humidity at the location.

[0017] Further, the weight of the first local direction feature vector comprises:

[0018] ,

[0019] wherein, is a first horizontal coordinate neighborhood offset relative to the pixel point is a first vertical coordinate neighborhood offset relative to the pixel point .

[0020] Further, the weight of the terrain gradient comprises:

[0021] ,

[0022] wherein, is a comprehensive adjustment factor of the weight of the terrain gradient is an adjustment factor of the terrain gradient, is a direction angle of the terrain gradient is an adjustment factor of the terrain gradient direction angle. Further, the weight of the ground surface reflectivity comprises:

[0023]

[0024] ,

[0025] wherein, is a comprehensive adjustment factor of the weight of the ground surface reflectivity is an adjustment factor of the ground surface reflectivity, is a direction angle of the ground surface reflectivity is an adjustment factor of the ground surface reflectivity direction angle.

[0026] Further, the terrain types comprise: mountains, plains, rivers, lakes, artificial roads and bridges.

[0027] The application further provides an intelligence data visualization analysis system combined with a knowledge graph, comprising:

[0028] ​​​​​​​​The information acquisition module is used to acquire satellite images, perform grayscale processing on the satellite images to generate a two-dimensional grayscale image, and extract image information from the two-dimensional grayscale image. The image information includes: pixel value of each pixel, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity.

[0029] The model setting module is used to set up a terrain feature value extraction model, calculate terrain feature values ​​based on the image information, cluster the terrain feature values ​​to generate multiple terrain categories, and form a terrain knowledge graph.

[0030] The visualization module is used to visualize the topographic knowledge graph as intelligence data, thereby providing visualized intelligence support.

[0031] Furthermore, the terrain and geomorphological feature value extraction model includes:

[0032] ,

[0033] in, These are topographic and geomorphological feature values. Two-dimensional grayscale image At pixel The gradient vector at that point, For pixels x-coordinate For pixels The ordinate, This represents the number of pixels in the local neighborhood. For the first The weights of local directional eigenvectors, Two-dimensional grayscale image At pixel The gradient vector at that point, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The The offset of the neighborhood of the vertical coordinate. For pixels Adjacent pixels The Local directional feature vectors The weights of the terrain gradient, For at pixel Terrain height at the location Topographic gradient, As the weight of surface reflectance, For at pixel Surface reflectance at that location is a weight of the solar incident angle, is a solar incident angle at a pixel point , is a ground surface temperature at a pixel point , is a weight of the ground surface humidity, is a ground surface humidity at a pixel point .

[0034] Further, the weight of the first local direction feature vector includes:

[0035] ,

[0036] wherein, is a first horizontal coordinate neighborhood offset relative to a pixel point , is a first vertical coordinate neighborhood offset relative to a pixel point .

[0037] Further, the weight of the terrain gradient includes:

[0038] ,

[0039] wherein, is a comprehensive adjustment factor of the weight of the terrain gradient , is an adjustment factor of the terrain gradient, is a direction angle of the terrain gradient , is an adjustment factor of the terrain gradient direction angle.

[0040] Compared with the prior art, the above technical scheme conceived by the present application has the following beneficial effects:

[0041] The present application sets a terrain and topography feature value extraction model, and calculates terrain and topography feature values according to the image information, clusters the terrain and topography feature values, generates multiple terrain and topography categories, and forms a terrain and topography knowledge graph; the terrain and topography knowledge graph is used as intelligence data for visualized display, thereby providing visualized intelligence support. Through the above technical scheme, the present application can extract terrain and topography features on a satellite image, form a knowledge graph, and generate visualized intelligence data, thereby improving intelligence use efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a method flowchart of embodiment 1 of the present application;​​​

[0043] Figure 2 Figure 1 is a system structure diagram of embodiment 2 of the present application. DETAILED DESCRIPTION

[0044] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in combination with the drawings in the specification and specific embodiments.

[0045] The method provided by the present application can be implemented in a terminal environment, which can 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, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0046] The processor can include one or more processing cores. The processor connects various parts in the entire terminal through various interfaces and lines, 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.

[0047] The storage medium can include random access memory (RAM) and read-only memory (ROM). The storage medium can be used to store instructions, programs, codes, code sets or instructions.

[0048] The display screen is used to display the interaction section of each application program.

[0049] All subscripts in the formula of the present application are only for distinguishing parameters and have no actual meaning.

[0050] In addition, those skilled in the art can understand that the structure of the terminal described above does not constitute a limitation on the terminal, and the terminal can include more or fewer components, or combine certain components, or different component arrangements. For example, the terminal also includes radio frequency circuit, input unit, sensor, audio circuit, power supply and other components, which will not be described here.

[0051] Embodiment 1

[0052] As shown in Figure 1 The present application proposes an intelligence data visualization analysis method combined with a knowledge graph, which includes:

[0053] Step 101, acquiring a satellite image, performing gray processing on the satellite image to generate a two-dimensional gray image, and extracting image information of the two-dimensional gray image, wherein the image information includes pixel value of each pixel point, terrain height, ground reflectivity, solar incident angle, ground temperature and ground humidity;

[0054] In step 102, a topographic feature value extraction model is set, and a topographic feature value is calculated according to the image information, the topographic feature value is clustered, a plurality of topographic categories are generated, and a topographic knowledge graph is formed;

[0055] Specifically, the topographic feature value extraction model comprises:

[0056]

[0057] wherein, is a topographic feature value, is a two-dimensional gray image a gradient vector at a pixel point , is an abscissa of the pixel point , is an ordinate of the pixel point , is a number of pixels in a local neighborhood, is a weight of an m-th local directional feature vector, is a two-dimensional gray image a gradient vector at a pixel point , is an m-th abscissa neighborhood offset relative to the pixel point , is an m-th ordinate neighborhood offset relative to the pixel point , is an m-th local directional feature vector of a pixel point adjacent to the pixel point , is a weight of a topographic gradient, is a topographic gradient of a topographic height at a pixel point , is a weight of a surface albedo, is a surface albedo at a pixel point , is a weight of a solar incident angle, is a solar incident angle at a pixel point , is a surface temperature at a pixel point , is a weight of a surface humidity, is a surface humidity at a pixel point . Specifically, the m-th local directional feature vector comprises:

[0058] Specifically, the m-th local directional feature vector comprises:​​​​ weight of the local direction feature vector comprises:

[0059] ,

[0060] wherein, is the first horizontal coordinate neighborhood offset relative to the pixel point , is the first vertical coordinate neighborhood offset relative to the pixel point . Specifically, the weight of the terrain gradient comprises:

[0061] ,

[0062] wherein, is the comprehensive adjustment factor of the weight of the terrain gradient

[0063] , is the adjustment factor of the terrain gradient, is the direction angle of the terrain gradient , is the adjustment factor of the terrain gradient direction angle. Specifically, the weight of the surface reflectivity comprises:

[0064] ,

[0065] wherein, is the comprehensive adjustment factor of the weight of the surface reflectivity

[0066] , is the adjustment factor of the surface reflectivity, is the direction angle of the surface reflectivity , is the adjustment factor of the surface reflectivity direction angle. Specifically, the terrain and landscape categories include mountains, plains, rivers, lakes, artificial roads and bridges.

[0067] Step 103, the terrain and landscape knowledge graph is visualized as intelligence data, thereby providing visual intelligence support.

[0068] Embodiment 2

[0069] As shown in the figure, the embodiment of the present application also proposes an intelligence data visualization analysis system combined with a knowledge graph, comprising:

[0070] Figure 2

[0071] ​​​The information acquisition module is used to acquire satellite images, perform grayscale processing on the satellite images to generate a two-dimensional grayscale image, and extract image information from the two-dimensional grayscale image. The image information includes: pixel value of each pixel, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity.

[0072] The model setting module is used to set up a terrain feature value extraction model, calculate terrain feature values ​​based on the image information, cluster the terrain feature values ​​to generate multiple terrain categories, and form a terrain knowledge graph.

[0073] Specifically, the terrain and landform feature value extraction model includes:

[0074] ,

[0075] in, These are topographic and geomorphological feature values. Two-dimensional grayscale image At pixel The gradient vector at that point, For pixels x-coordinate For pixels The ordinate, This represents the number of pixels in the local neighborhood. For the first The weights of local directional eigenvectors, Two-dimensional grayscale image At pixel The gradient vector at that point, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The The offset of the neighborhood of the vertical coordinate. For pixels Adjacent pixels The Local directional feature vectors The weights of the terrain gradient, For at pixel Terrain height at the location Topographic gradient, As the weight of surface reflectance, For at pixel Surface reflectance at that location The weight of the solar incidence angle. For at pixel The angle of incidence of the sun at that location, is a ground temperature at a pixel point is a weight of ground humidity, is a ground humidity at a pixel point .

[0076] Specifically, the weight of the first local directional feature vector includes:

[0077] ,

[0078] wherein, is a first horizontal coordinate neighborhood offset relative to the pixel point is a first vertical coordinate neighborhood offset relative to the pixel point .

[0079] Specifically, the weight of the terrain gradient includes:

[0080] ,

[0081] wherein, is a comprehensive adjustment factor of the weight of the terrain gradient is an adjustment factor of the terrain gradient, is a direction angle of the terrain gradient is an adjustment factor of the terrain gradient direction angle.

[0082] Specifically, the weight of the ground reflectivity includes:

[0083] ,

[0084] wherein, is a comprehensive adjustment factor of the weight of the ground reflectivity is an adjustment factor of the ground reflectivity, is a direction angle of the ground reflectivity is an adjustment factor of the ground reflectivity direction angle.

[0085] Specifically, the terrain and landscape categories include: mountains, plains, rivers, lakes, artificial roads and bridges.

[0086] The visualization module is configured to visualize the terrain and landscape knowledge graph as intelligence data, thereby providing visual intelligence support. ​​​​​​​​​

[0087] Example 3

[0088] This invention also proposes a storage medium storing multiple instructions for implementing the aforementioned knowledge graph-based intelligence data visualization and analysis method.

[0089] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0090] Optionally, in this embodiment, the storage medium is configured to store program code for performing the method steps: Step 101, acquire a satellite image, perform grayscale processing on the satellite image to generate a two-dimensional grayscale image, and extract image information from the two-dimensional grayscale image, wherein the image information includes: pixel value of each pixel, terrain height, surface reflectivity, solar incidence angle, surface temperature and surface humidity;

[0091] Step 102: Set up a terrain feature value extraction model, calculate terrain feature values ​​based on the image information, cluster the terrain feature values ​​to generate multiple terrain categories, and form a terrain knowledge graph.

[0092] Specifically, the terrain and landform feature value extraction model includes:

[0093] ,

[0094] in, These are topographic and geomorphological feature values. Two-dimensional grayscale image At pixel The gradient vector at that point, For pixels x-coordinate For pixels The ordinate, This represents the number of pixels in the local neighborhood. For the first The weights of local directional eigenvectors, Two-dimensional grayscale image At pixel The gradient vector at that point, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The The offset of the neighborhood of the vertical coordinate. For pixels Adjacent pixels The Local directional feature vectors The weights of the terrain gradient, For at pixel Terrain height at the location Topographic gradient, As the weight of surface reflectance, For at pixel Surface reflectance at that location The weight of the solar incidence angle. For at pixel The angle of incidence of the sun at that location, For at pixel The surface temperature at that location. The weighting of surface humidity, For at pixel Surface humidity at the location.

[0095] Specifically, the first Weights of local directional eigenvectors include:

[0096] ,

[0097] in, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The Each vertical coordinate neighborhood offset.

[0098] Specifically, the weights of the terrain gradient include:

[0099] ,

[0100] in, Weights of terrain gradient The comprehensive adjustment factor, This is an adjustment factor for the terrain gradient. Terrain gradient Direction and angle, This is an adjustment factor for the direction and angle of the terrain gradient.

[0101] Specifically, the weight of surface reflectance include:

[0102] ,

[0103] in, Weight of surface reflectance a comprehensive adjustment factor of the surface reflectivity, an adjustment factor of the surface reflectivity, a surface reflectivity a direction angle, an adjustment factor of the surface reflectivity direction angle.

[0104] Specifically, the topographic feature categories include mountains, plains, rivers, lakes, artificially constructed roads, and bridges.

[0105] In step 103, the topographic feature knowledge graph is visualized as intelligence data, thereby providing visualized intelligence support.

[0106] Embodiment 4

[0107] The electronic device according to the embodiment of the present application can be a computer terminal, which can include one or more processors and a storage medium.

[0108] Specifically, the electronic device according to the embodiment of the present application can be a computer terminal, which can include one or more processors and a storage medium.

[0109] The storage medium can be used to store software programs and modules, such as the intelligence data visualization analysis method combined with the knowledge graph according to the embodiment of the present application, and the corresponding program instructions / modules. The processor executes various functions, applications, and data processing by running the software programs and modules stored in the storage medium, that is, implements the intelligence data visualization analysis method combined with the knowledge graph. The storage medium can include a high-speed random storage medium, and can also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memories, or other non-volatile solid-state storage media. In some examples, the storage medium can further include storage media remotely arranged relative to the processor, which can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0110] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the method steps: in step 101, a satellite image is acquired, the satellite image is subjected to gray scale processing to generate a two-dimensional gray scale image, and image information of the two-dimensional gray scale image is extracted, wherein the image information includes pixel values, terrain heights, surface reflectivities, solar incident angles, surface temperatures, and surface humidities of each pixel point.

[0111] In step 102, a topographic feature value extraction model is set, and a topographic feature value is calculated according to the image information, the topographic feature value is clustered, a plurality of topographic categories are generated, and a topographic knowledge graph is formed.

[0112] Specifically, the topographic feature value extraction model comprises:

[0113] ,

[0114] Among them, is a topographic feature value, is a two-dimensional gray image Gradient vector at pixel point , is the horizontal coordinate of pixel point , is the vertical coordinate of pixel point , is the number of pixels in the local neighborhood, is the weight of the mth local directional feature vector, is the gradient vector of the two-dimensional gray image at pixel point , is the mth horizontal coordinate neighborhood offset relative to pixel point , is the mth vertical coordinate neighborhood offset relative to pixel point , is the mth local directional feature vector of the pixel point adjacent to pixel point , is the weight of the topographic gradient, is the topographic gradient of the topographic height at pixel point , is the weight of the surface albedo, is the surface albedo at pixel point , is the weight of the solar incident angle, is the solar incident angle at pixel point , is the surface temperature at pixel point , is the weight of the surface humidity, is the surface humidity at pixel point . Specifically, the mth local directional feature vector comprises:

[0115] Specifically, the mth local directional feature vector comprises: ​​​Weight of a local direction feature vector Comprises:

[0116] ,

[0117] Wherein, The first horizontal coordinate neighborhood offset of the pixel point , The first vertical coordinate neighborhood offset of the pixel point , The first vertical coordinate neighborhood offset of the pixel point .

[0118] Specifically, the weight of the terrain gradient Comprises:

[0119] ,

[0120] Wherein, The comprehensive adjustment factor of the weight of the terrain gradient , The adjustment factor of the terrain gradient, The direction angle of the terrain gradient , The adjustment factor of the terrain gradient direction angle.

[0121] Specifically, the weight of the surface reflectivity Comprises:

[0122] ,

[0123] Wherein, The comprehensive adjustment factor of the weight of the surface reflectivity , The adjustment factor of the surface reflectivity, The direction angle of the surface reflectivity , The adjustment factor of the surface reflectivity direction angle.

[0124] Specifically, the terrain type includes: mountains, plains, rivers, lakes, artificial roads and bridges.

[0125] Step 103, the terrain knowledge graph is visualized as intelligence data, thereby providing visual intelligence support.

[0126] The above-mentioned embodiment number of the application only for description, not represent the pros and cons of the embodiment.

[0127] In the above-mentioned embodiments of the application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0128] In several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Among them, the above-mentioned system embodiments are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division mode, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection between the units or components through some interfaces, and can be electrical or other forms.

[0129] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0130] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0131] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or all or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a read-only storage medium (ROM, Read-Only Memory), a random access storage medium (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various storage program codes.

[0132] Obviously, the above embodiments are only examples for clear illustration, and not limitation of the embodiments. For ordinary skilled persons in the art, other different forms of changes or variations can be made on the basis of the above description. Here, all the embodiments need not and cannot be exhausted. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method for visualizing and analyzing intelligence data using knowledge graphs, characterized in that, include: Acquire satellite images, perform grayscale processing on the satellite images to generate a two-dimensional grayscale image, and extract image information from the two-dimensional grayscale image, wherein the image information includes: pixel value of each pixel, terrain height, surface reflectivity, solar incidence angle, surface temperature and surface humidity; Set up a terrain and landform feature value extraction model, calculate terrain and landform feature values ​​based on the image information, cluster the terrain and landform feature values ​​to generate multiple terrain and landform categories, and form a terrain and landform knowledge graph. The terrain and geomorphological feature value extraction model includes: ， in, These are topographic and geomorphological feature values. Two-dimensional grayscale image At pixel The gradient vector at that point, For pixels x-coordinate For pixels The ordinate, This represents the number of pixels in the local neighborhood. For the first The weights of local directional eigenvectors, Two-dimensional grayscale image At pixel The gradient vector at that point, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The The offset of the neighborhood of the vertical coordinate. For pixels Adjacent pixels The Local directional feature vectors The weights of the terrain gradient, For at pixel Terrain height at the location Topographic gradient, As the weight of surface reflectance, For at pixel Surface reflectance at that location The weight of the solar incidence angle. For at pixel The angle of incidence of the sun at that location, For at pixel The surface temperature at that location. The weighting of surface humidity, For at pixel Surface humidity at the location; The topographic knowledge graph is visualized as intelligence data, thereby providing visualized intelligence support.

2. The intelligence data visualization and analysis method combining knowledge graphs as described in claim 1, characterized in that, No. Weights of local directional eigenvectors include: ， in, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The Each vertical coordinate neighborhood offset.

3. The intelligence data visualization and analysis method combining knowledge graphs as described in claim 1, characterized in that, Weights of terrain gradient include: ， in, Weights of terrain gradient The comprehensive adjustment factor, This is an adjustment factor for the terrain gradient. Terrain gradient Direction and angle, This is an adjustment factor for the direction and angle of the terrain gradient.

4. The intelligence data visualization and analysis method combining knowledge graphs as described in claim 1, characterized in that, Weight of surface reflectance include: ， in, Weight of surface reflectance The comprehensive adjustment factor, As an adjustment factor for surface reflectance, Surface reflectance Direction and angle, This is an adjustment factor for the direction angle of surface reflectivity.

5. The intelligence data visualization and analysis method combining knowledge graphs as described in claim 1, characterized in that, The topographical categories include: mountains, plains, rivers, lakes, and man-made roads and bridges.

6. An intelligence data visualization and analysis system combining knowledge graphs, characterized in that, include: The information acquisition module is used to acquire satellite images, perform grayscale processing on the satellite images to generate a two-dimensional grayscale image, and extract image information from the two-dimensional grayscale image. The image information includes: pixel value of each pixel, terrain height, surface reflectivity, solar incidence angle, surface temperature, and surface humidity. The model setting module is used to set up a terrain feature value extraction model, calculate terrain feature values ​​based on the image information, cluster the terrain feature values ​​to generate multiple terrain categories, and form a terrain knowledge graph. The terrain and geomorphological feature value extraction model includes: ， in, These are topographic and geomorphological feature values. Two-dimensional grayscale image At pixel The gradient vector at that point, For pixels x-coordinate For pixels The ordinate, This represents the number of pixels in the local neighborhood. For the first The weights of local directional eigenvectors, Two-dimensional grayscale image At pixel The gradient vector at that point, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The The offset of the neighborhood of the vertical coordinate. For pixels Adjacent pixels The Local directional feature vectors The weights of the terrain gradient, For at pixel Terrain height at the location Topographic gradient, As the weight of surface reflectance, For at pixel Surface reflectance at that location The weight of the solar incidence angle. For at pixel The angle of incidence of the sun at that location, For at pixel The surface temperature at that location. The weighting of surface humidity, For at pixel Surface humidity at the location; The visualization module is used to visualize the terrain and landform knowledge graph as intelligence data, thereby providing visualized intelligence support.

7. The intelligence data visualization and analysis system combining knowledge graphs as described in claim 6, characterized in that, No. Weights of local directional eigenvectors include: ， in, Relative to pixel The The offset of the x-coordinate neighborhood. Relative to pixel The Each vertical coordinate neighborhood offset.

8. The intelligence data visualization and analysis system combining knowledge graphs as described in claim 6, characterized in that, Weights of terrain gradient include: ， in, Weights of terrain gradient The comprehensive adjustment factor, This is an adjustment factor for the terrain gradient. Terrain gradient Direction and angle, This is an adjustment factor for the direction and angle of the terrain gradient.

Images