Building material texture tile data generation method, device and equipment in a simulation scene

Abstract

This invention relates to a method, apparatus, and device for generating tile data of ground features in simulated scenarios. The method includes: acquiring relevant data on ground feature materials; generating a set of tile images in tile format based on the relevant data; when performing close-range ground feature background simulation, loading high-resolution tile images within a small area of ​​the detection field of view from the tile image set; and when performing long-range ground feature background simulation, loading low-resolution tile images within a large area of ​​the detection field of view from the tile image set. The technical solution of this application generates a set of tile images in tile format based on the relevant data on ground feature materials; the tile format data helps reduce loading time and improves infrared simulation performance.

Description

Technical Field

[0001] This invention relates to the field of infrared simulation technology, and in particular to a method, apparatus, and equipment for generating data on the material properties of ground features in a simulated scene. Background Technology

[0002] In infrared simulation of ground scenes, ground feature material data is crucial for simulating ground infrared radiation characteristics and is one of the key factors affecting the accuracy of ground scene infrared radiation simulation. Current infrared scene simulation systems have high real-time requirements for infrared image simulation capabilities. In dynamic ground scene simulations, the detection platform and detection line of sight change constantly, and the ground detection range frequently changes, requiring the reloading of new ground data for each frame. High-resolution ground feature material data and large-scale ground scene simulation result in a very large amount of ground data loaded per frame, causing a significant amount of simulation time to be consumed in data loading, thus reducing the simulation performance of the infrared scene simulation system. Summary of the Invention

[0003] The technical problem to be solved by this invention is that loading a large amount of data leads to a decrease in infrared simulation performance. In view of the defects in the prior art, this invention provides a method, apparatus and equipment for generating ground feature material tile data in a simulation scene.

[0004] A method for generating tile data of ground features in a simulated scenario includes: Obtain relevant data on the material properties of ground features, and generate a set of tile images in tile format based on the relevant data on the material properties of ground features; Wherein, any one tile image in the tile image set is used to represent the relevant data of the material of the land features within the actual geographical area corresponding to the tile image; When performing close-range ground feature background simulation, load a set of tile images, including high-resolution tile images within a small area of ​​the detection field of view; When performing long-distance ground feature background simulation, load a set of tile images and detect a large area of ​​low-resolution tile images within the field of view.

[0005] Secondly, this application proposes a data processing device for a simulated scenario, comprising: The tile module is used to acquire relevant data on the material properties of ground features and generate a set of tile images in tile format based on the relevant data on the material properties of ground features; Wherein, any one tile image in the tile image set is used to represent the relevant data of the material of the land features within the actual geographical area corresponding to the tile image; The first processing module is used to load high-resolution tile images within a small area of ​​the detection field of view from the tile image set when performing close-range ground feature background simulation. The second processing module loads a set of tile images and detects a large area of ​​low-resolution tile images within the field of view when performing long-distance ground background simulation. The tile image is used to represent relevant data on the material properties of land features within the actual geographical area corresponding to the tile image.

[0006] Thirdly, this application proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a data processing method in a simulation scenario as described above.

[0007] Fourthly, this application proposes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the data processing method in the simulation scenario described above.

[0008] Implementing this invention has the following beneficial effects: The technical solution of this application acquires relevant data on the material properties of ground features and generates a set of tile images in tile format based on the relevant data. When performing close-range ground feature background simulation, high-resolution tile images within a small range of the detection field of view are loaded from the tile image set. When performing long-range ground feature background simulation, low-resolution tile images within a large range of the detection field of view are loaded from the tile image set. In this way, the problem of a significant reduction in the simulation performance of the scene infrared radiation simulation system caused by dynamically loading large-scale high-resolution ground feature material classification data can be avoided. Attached Figure Description

[0009] Figure 1 This is a flowchart of a method for generating tile data of ground features in a simulated scene, provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a collection of tile images provided in an embodiment of the present invention; Figure 3 This is a flowchart of a method for determining pixel channel data provided in an embodiment of the present invention; Figure 4 This is a flowchart of a storage bit range storage ratio coefficient provided in an embodiment of the present invention; Figure 5 This is a flowchart of a device for generating tile data of ground features in a simulated scene, provided by an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] In ground scene simulation, ground feature classification data is crucial for simulating ground infrared radiation characteristics and is one of the key factors affecting the accuracy of ground scene infrared radiation simulation. Conducting large-scale ground scene simulations requires the prior preparation of ground feature classification data. To improve the accuracy of ground scene simulations, high-resolution ground feature classification data is often needed. For large-scale ground scene simulations, such as those based on spaceborne or spaceborne exploration, the covered ground area is often large, and the required amount of high-precision ground feature classification data is often very large.

[0012] Current infrared radiation simulation systems have high real-time requirements for infrared image simulation capabilities. The common approach is to use a graphics rendering engine to simulate radiation images, with simulation performance typically reaching over 50Hz. However, in dynamic ground scene simulations, the detection platform and line of sight change constantly, and the ground detection range also frequently changes. This necessitates reloading new ground data for infrared radiation characteristic simulation every frame. High-resolution ground feature classification data and large-scale ground scene simulation result in a very large amount of ground data loaded per frame, causing a significant portion of simulation time to be consumed in data loading, thus reducing the simulation performance of the infrared scene simulation system.

[0013] In practice, the required resolution of terrain feature classification data in ground scene simulation varies with the detection distance of the platform sensors. When the platform sensors are at a shorter distance, higher resolution is required, while when the platform sensors are at a greater distance, a relatively lower resolution is permissible. Therefore, when simulating close-range terrain features, only high-resolution terrain feature classification data within a small area of ​​the detection field of view can be loaded. When the platform sensors are at a greater distance, the detection range increases, and the loaded data can be switched to relatively lower resolution data. Choosing an appropriate data resolution tiered loading scheme can potentially avoid the problem of reduced simulation performance caused by loading a large amount of data due to a larger detection range.

[0014] Based on this, this application proposes a method for generating tile data of ground features in a simulated scenario, see appendix. Figure 1 The method includes: In step S100, relevant data on the material of ground features are obtained, and a set of tile images in tile format is generated based on the relevant data on the material of ground features. Among them, any one tile image in the tile image set is used to represent the relevant data of the material of the land features within the actual geographical area corresponding to the tile image.

[0015] In step S102, when performing close-range ground feature background simulation, a high-resolution tile image within a small area of ​​the detection field of view is loaded from the tile image set.

[0016] In step S104, when performing long-distance ground background simulation, a large area of ​​low-resolution tile images within the field of view is detected from the tile image set.

[0017] The technical solution of this application acquires relevant data on the material properties of ground features and generates a set of tile images in tile format based on the data. When performing close-range ground feature background simulation, high-resolution tile images within a small area of ​​the detection field of view are loaded from the tile image set. When performing long-range ground feature background simulation, low-resolution tile images within a large area of ​​the detection field of view are loaded from the tile image set. This reduces loading time and improves infrared simulation performance. It minimizes the negative impact on the simulation performance of the scene infrared simulation system caused by dynamically loading large-area, high-resolution ground feature classification data.

[0018] In some embodiments, see Appendix Figure 2 The tile image set has multiple levels; for any two adjacent tile images, the number of tiles in the next level is four times the number of tiles in the current level, and the resolution of the tiles in the next level is twice the resolution of the tiles in the current level.

[0019] In this embodiment, the tile images are stored in a quadtree format, with the top-level tile image (lowest resolution) being the level 0 tile image. The global material classification data is divided into two 512-level images. In a 512-level image, the number of level 1 tiles is four times that of level 0 tiles, and the resolution is twice that of level 0 tiles; the number of level 2 tiles is four times that of level 1 tiles, and the resolution is twice that of level 1 tiles; and so on. For each level increase in tile level, the number of tiles becomes four times the original level, and the resolution becomes twice the original level. That is, the... i The number of +1 level tile images is the first i Four times the level, the resolution is the first i Twice the level.

[0020] In some embodiments, the dynamic process of generating a set of tile images is as follows: According to theI Level 1 tile image, calculate to generate the 1st level tile image I- Level 1 tile image.

[0021] Specifically, it can be based on the first I The image of the tile with the mixed material classification ratio and the image of the tile with the mixed material classification number are used to calculate and generate the first mixed material classification tile. I- Level 1 mixed material classification ratio tile image and mixed material classification number tile image.

[0022] No. I- The pixel values ​​of a Level 1 tile image are determined by the first... I The four adjacent pixels of the first-level tile image are re-statistically generated: based on the first... I The tile image is used to determine the material IDs and area proportions of all materials contained within 4 pixels. The material IDs and proportion coefficients of the top few materials by area proportion are then determined, and new pixel values ​​are generated.

[0023] This process continues, generating higher-level tile images based on the current-level tile image, until all levels of tile images are generated.

[0024] In some embodiments, the relevant data of the land feature material includes: the classification number of the land feature material, and / or the classification ratio of the land feature material.

[0025] The tile image includes multiple pixels, where each pixel corresponds to an actual geographic region unit.

[0026] The classification number of the ground feature material is represented by the pixel channel value of the pixel point corresponding to the actual geographical area unit where the ground feature material is located.

[0027] In this embodiment, for example, a certain community has two types of ground features: grassland and limestone ground. The classification number of the grassland is 01, and the classification number of the limestone ground is set to 10.

[0028] The classification ratio of the land feature material refers to the ratio of the coverage area of ​​the land feature material to the total area of ​​the geographical area unit corresponding to any pixel.

[0029] For example, in a certain residential area, the area of ​​grass accounts for 0.3, and the area of ​​lime-paved ground accounts for 0.7.

[0030] In some embodiments, the above method may further include the following steps: The pre-generated set of tile images specifically includes: Determine the highest resolution in the set of background images of ground features.

[0031] In this embodiment, the maximum resolution is generally set according to actual business needs, such as 1 meter / pixel or 5 meters / pixel. Here, resolution means the actual distance corresponding to the side length of each pixel. The smaller the actual distance corresponding to the side length of each pixel, the higher the resolution. The larger the actual distance corresponding to the side length of each pixel, the lower the resolution.

[0032] The number of tile levels in the tile image set is determined based on the highest resolution and the preset resolution of each tile level.

[0033] In this embodiment, each level of tile is assigned a fixed resolution. For example, the resolution of the second-level tile is 2 meters per pixel, the resolution of the third-level tile is 4 meters per pixel, and the resolution of the fourth-level tile is 8 meters per pixel. Since the resolution of adjacent levels of tiles is multiplied by 2, the number of tile levels can be derived from the highest resolution and the preset resolution of each level of tile.

[0034] The channel data for each pixel is determined based on the relevant data of the texture of the land features in the actual geographic area unit corresponding to each pixel.

[0035] The tile image set is generated based on the channel data of each pixel and the number of tile layers.

[0036] For example, assuming the cell is elliptical, to perform infrared simulation on the cell, the first step is to establish the set of tiles for the cell. The first step is to determine the minimum bounding rectangle of the ellipse.

[0037] The major axis of the elliptical cell is 3800m (Y direction), and the minor axis is 2000m (X direction).

[0038] Step 1, determine the dimensions of the circumscribed rectangle as follows: Length (Y direction): 3800m; Width (X direction): 2000m; Step 2: Calculate the minimum resolution based on the longest side, as shown in the example below: Longest side = 3800m (Y direction); Resolution calculation: 3800m ÷ 256 pixels ≈ 14.84m / pixel; Take the nearest power of 2 integer up to get 16m / pixel.

[0039] In other words, the minimum resolution is 16m / pixel.

[0040] Step 3: Calculate the number of tiles in the length / width direction. If the result is a decimal, rounding is allowed, as shown in the example below: The number of tiles in a certain direction is equal to the length in that direction divided by the product of 256 pixels and the resolution, as shown in the example below: X direction (width 2000m): 2000 ÷ (256 × 16) = 2000 ÷ 4096 ≈ 0.488; Round up to the nearest integer, with a quantity of 1. Y direction (length 3800m): 3800 ÷ (256 × 16) = 3800 ÷ 4096 ≈ 0.927; Round up to the nearest integer, with a quantity of 1. Therefore, the total number of level 0 tiles is 1×1=1, covering an area of ​​4096m×4096m. The area outside the ellipse can be represented transparently.

[0041] Step 4, for higher-level tiles, such as level 4 with a resolution of 1m / pixel, the calculation process is as follows: Number of X-direction items: 2000 ÷ (256 × 1) ≈ 7.81; after rounding, 8 items. Number of items in the Y direction: 3800 ÷ (256 × 1) ≈ 14.84; after rounding, 15 items. Total number of level 4 tiles: 8 × 15 = 120, seamlessly spliced ​​to cover the outer rectangle, with the outer ellipse transparent.

[0042] The above explains how to determine the number of tiles in any given layer using layers 0 and 4.

[0043] The following explains in detail how to determine the number of tile layers. The first step in determining the tile layers is to calculate the minimum precision, 16 pixels per pixel. Specifically, this is calculated by dividing the longest side of the cell's bounding rectangle by the pixel count of one side of a single tile. Since a single tile has 256 pixels... 256 pixels, with 256 pixels on a single side, so the longest side of the outer rectangle of the cell is divided by 256, and then matched with the nearest power of 2 value.

[0044] Determine the highest precision, or highest resolution, based on actual business needs, for example, 1m / pixel: The number of levels is equal to the quotient of the lowest resolution divided by the highest resolution, plus a base-2 logarithmic operation.

[0045] For example, C = log2(16÷1) = 4, so the number of layers is 4.

[0046] The number of rows (Y direction) and columns (X direction) of each layer of tiles are calculated by dividing the length / width of the circumscribed rectangle by the actual coverage area of ​​that layer of tiles.

[0047] The dimensions of the circumscribed rectangle are as follows: Length (Y direction) = 3800m, width (X direction) = 2000m; The tile dimensions are as follows: 256×256 pixels, fixed setting; The layer resolution is set as follows: Level 0 corresponds to 16m / pixel; Level 1 corresponds to 8m / pixel; Level 2 corresponds to 4m / pixel; Level 3 corresponds to 2m / pixel; Level 4 corresponds to 1m / pixel.

[0048] The actual coverage area of ​​a single tile is equal to the tile size multiplied by the resolution of that layer.

[0049] The number of tiles in a certain direction is equal to the length of the circumscribed rectangle in that direction, divided by the actual coverage area of ​​a single tile, and then rounded up. See Table 1 for specific data.

[0050] Table 1

[0051] The number of columns in the X direction is 1. The calculation formula is: 2000 ÷ 2048 ≈ 0.97; rounding 0.97 to the nearest integer gives 1.

[0052] The number of columns in the X direction is 2. The calculation formula is: 2000 ÷ 1024 ≈ 1.95; take the integer part of 1.95 and get 2.

[0053] The number of columns in the X direction is 4. The calculation formula is: 2000 ÷ 512 ≈ 3.91; taking the integer part of 3.91, we get 4.

[0054] The number of columns in the X direction is 8. The calculation formula is: 2000 ÷ 256 ≈ 7.81; taking the integer part of 7.81, we get 8.

[0055] In some embodiments, see Appendix Figure 3 The channel data for each pixel is determined based on the relevant data of the land cover material in the actual geographic area unit corresponding to each pixel, including: In step S202, for any pixel, the types of materials involved in the actual geographical area unit corresponding to the pixel, and the coverage area of ​​each material are determined.

[0056] In step S204, the total area of ​​the actual geographic region unit corresponding to the pixel is determined.

[0057] In step S206, the proportional coefficient of the coverage area of ​​each material is determined based on the coverage area of ​​each material and the total area of ​​the actual geographical area unit corresponding to the pixel.

[0058] For example, in a certain residential community, the area covered by grassland is 25 square meters, the area occupied by buildings is 60 square meters, and the area covered by asphalt roads is 15 square meters, for a total area of ​​100 square meters. The proportion coefficient of grassland is 0.25, the proportion coefficient of asphalt roads is 0.15, and the proportion coefficient of buildings is 0.8.

[0059] In step S208, the material coverage area ratio coefficient set is sorted according to the size of the ratio coefficient to obtain the sequence of material type area ratio coefficients.

[0060] For example, the order of material type area ratio coefficients is: buildings, grass, asphalt roads.

[0061] In step S210, the material type area ratio coefficients that are ranked first in the sequence of material type area ratio coefficients are determined.

[0062] For example, the area ratio coefficients of the top two material types can be determined to be 0.8 and 0.25.

[0063] In step S212, the first few material type area ratio coefficients and their numbers are stored in a predetermined storage bit range of the storage data bits of the pixel channel.

[0064] In some embodiments, the length of a predetermined storage bit range for the channel storage data bits of the pixel is 30 bits.

[0065] In this embodiment, the data bit width of the pixel channel is 32 bits. Two bits can be reserved for extended functions, and the remaining 30 bits can be flexibly set to store a range of bits according to actual needs.

[0066] For example, the 30 bits can be divided according to the number of material types that rank in the top few positions. For example, if the number of material types is 5, then the 30 bits can be divided into 5 sub-segments, and each sub-segment stores the area ratio coefficient of one material type.

[0067] For example, if the quantity is 3, then the 30 bits are divided into 3 sub-intervals, and each sub-interval stores the area ratio coefficient of a material type.

[0068] See Figure 4 In step S212, storing the first few material type area ratio coefficients within a predetermined storage bit range of the storage data bits in the channel of the pixel may further include the following steps: In step S302, for any material type area ratio coefficient, the material type area ratio coefficient is rounded down to obtain the converted integer of the material type area ratio.

[0069] In this embodiment, the actual area ratio coefficient of the material type area that is less than 1 is rounded down to an integer and stored in the sub-interval corresponding to the material type.

[0070] The conversion integer is equal to the area ratio coefficient multiplied by the difference, where the difference is the difference between 2 raised to the power of N and 1, and N is the number of bits in the sub-interval corresponding to the material type.

[0071] ; Where K2 is the area ratio coefficient; K1 is the converted integer; N is the number of bits in the sub-interval corresponding to this material type.

[0072] In step S304, within the predetermined storage bit range, the sub-storage bit range of the material type is determined according to the sorting position of the material type in the sorting of the area ratio coefficients of the first few material types.

[0073] In step S306, the converted integer of the area ratio of the material type is stored in the sub-storage bit range of the material type.

[0074] In this embodiment, taking the 32-bit pixel storage of the coverage ratio coefficients of three materials—grass, buildings, and lime roads—in infrared detection simulation, and rounding using the ROUND function as an example, the specific steps are as follows: 32-bit pixel storage is used. To adapt to three materials, the 32 bits are divided into three 10-bit intervals to store the ratio coefficients, with the remaining 2 bits reserved for backup. The first 10 bits are set to store the grass ratio, the 11th to 20th bits to store the building ratio, and the 21st to 30th bits to store the lime road ratio. The value range of the 10-bit interval is 0-1023, which perfectly maps the coverage ratio from 0-100%.

[0075] Assume that in the simulation, the ground corresponding to a certain pixel has 60% grass coverage, 30% building coverage, and 10% lime road coverage. The corresponding 10-bit storage values ​​are as follows: The percentage of grassland coverage, converted into a storage value for a storage area, is calculated as follows: 60 × 10.23 ≈ 614.

[0076] Similarly, the storage value for the building is 30×10.23≈307, and for the lime road it is 10×10.23≈102. These three values ​​will be stored in the corresponding 10-bit intervals to form the 32-bit data of the pixel.

[0077] The ROUND function's rounding calculation is explained below. If 36 is used as the quantization coefficient, it will be used for subsequent infrared radiation intensity conversion. The calculations for the three materials are as follows.

[0078] The integer conversion for grassland is: ROUND(36×60%)=ROUND(21.6)=22; The conversion integer for the building is: ROUND(36×30%)=ROUND(10.8)=11; The integer conversion for Lime Road is: ROUND(36×10%)=ROUND(3.6)=4.

[0079] The rounded integers 22, 11, and 4 not only avoid the precision errors of floating-point arithmetic but also allow for quick integration with the integer arithmetic interface of the simulation system. When calculating the overall infrared radiation value of this pixel, these integers can be directly multiplied by the corresponding material's infrared radiation reference value, significantly improving simulation efficiency.

[0080] In some embodiments, the color of each pixel in the mixed-material classification ratio tile image is encoded as follows: The last 30 bits of the RGBA four-channel 32-bit data store the proportion coefficients of the five materials with the largest area coverage in the pixel coverage area, assuming that the area of ​​each material accounts for a certain percentage of the pixel coverage area. Then the proportionality coefficient ,in This is the rounding function. Bits 3-8 of the 32-bit RGBA four-channel data store the scaling factor for the first material, bits 9-14 store the scaling factor for the second material, bits 15-20 store the scaling factor for the third material, bits 21-26 store the scaling factor for the fourth material, and bits 27-32 store the scaling factor for the fifth material.

[0081] In step S212, the numbers of the first few material types are stored in a predetermined storage bit range of the storage data bits of the pixel channel, including: Within the predetermined storage bit range, the material type number is determined based on its sorting position in the sorting of the area ratio coefficients of the first few material types.

[0082] The material type number is stored in the sub-storage bit range of the material type.

[0083] In this embodiment, the color of each pixel in the mixed-material classification tile image is encoded in the following way: The last 30 bits of the RGBA four-channel 32-bit data store the material numbers of the five materials with the largest area coverage in the pixel area. , The value range is [0, 63], and the material arrangement order corresponds to the material arrangement order in the material classification ratio tile image. The 3rd to 8th bits of the RGBA four-channel 32-bit data store the material number of the first material, the 9th to 14th bits store the material number of the second material, the 15th to 20th bits store the material number of the third material, the 21st to 26th bits store the material number of the fourth material, and the 27th to 32nd bits store the material number of the fifth material.

[0084] Pixel values ​​are generated based on the top 5 material numbers and scale coefficients of the area ratio mentioned above.

[0085] In some embodiments, the temperature of the actual area corresponding to a pixel can be calculated based on each material stored in the channel of each pixel and the area ratio of each material, and then the color of the pixel can be determined based on the temperature to achieve real-time infrared simulation.

[0086] For example, in a residential area, if the temperature in a certain area is very high, the pixel cluster corresponding to that area will be displayed in dark red in the infrared simulation image to indicate that the temperature in that area is very high.

[0087] This application proposes a technical solution to redesign the data storage format for ground feature material classification. The ground feature material classification data is encoded into material classification number data tile images and material classification ratio data tile images at different resolution levels. This allows for the real-time loading of local tile images at the corresponding resolution level based on sensor distance and detection range during simulation, enabling rapid loading of ground feature material classification data and ensuring the real-time performance of the scene infrared radiation simulation system.

[0088] Secondly, this application proposes a device for generating tile data of ground features in a simulated scenario, see appendix. Figure 5 ,include: The tile module 20 is used to acquire relevant data on the material properties of ground features and generate a set of tile images in tile format based on the relevant data on the material properties of ground features; Wherein, any one tile image in the tile image set is used to represent the relevant data of the material of the land features within the actual geographical area corresponding to the tile image; The first processing module 21 is used to load high-resolution tile images within a small range of the detection field of view from the tile image set when performing close-range ground background simulation. The second processing module 22, when performing long-distance ground background simulation, loads a set of tile images and detects a large area of ​​low-resolution tile images within the field of view. The tile module 20 specifically includes: a maximum resolution determination submodule, used to determine the maximum resolution in the set of ground feature background images; The tile level determination submodule is used to determine the number of tile levels in the tile image set based on the highest resolution and the preset resolution of each tile level. The channel data processing submodule is used to determine the channel data of each pixel based on the relevant data of the land cover material in the actual geographic area unit corresponding to each pixel. The tile generation submodule is used to generate the tile image set based on the channel data of each pixel and the number of tile layers.

[0089] The channel data processing submodule is also used to determine, for any pixel, the type of material involved in the actual geographical area unit corresponding to the pixel, and the coverage area of ​​each material; Determine the total area of ​​the actual geographic region unit corresponding to the pixel; Based on the coverage area of ​​each material and the total area of ​​the actual geographic region unit corresponding to the pixel, determine the ratio coefficient of the coverage area of ​​each material; In the set of material coverage area ratio coefficients, sort them according to the size of the ratio coefficients to obtain the sequence of material type area ratio coefficients; The area ratio coefficients of the material types are determined from the sequence of the first few material types. Within a predetermined range of storage bits in the storage data bits of the pixel channel, the first few material type area ratio coefficients and their numbers are stored.

[0090] The channel data processing submodule also performs a rounding operation on any material type area ratio coefficient to obtain the converted integer of the material type area ratio. Within the predetermined storage bit range, the sub-storage bit range of the material type is determined according to the sorting position of the material type in the sorting of the area ratio coefficients of the first few material types. Within the sub-storage bit range of the material type, the converted integer of the area ratio of the material type is stored.

[0091] Thirdly, this application proposes an electronic device, see appendix. Figure 6The system includes a memory 32, a processor 31, and a computer program stored in the memory and executable on the processor. When the processor 31 executes the computer program, it implements the method for generating land feature material tile data in the simulation scenario as described in any of the above.

[0092] The aforementioned electronic devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. These electronic devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that the figures are merely examples of electronic devices and do not constitute a limitation on the electronic devices. They may include more or fewer components than illustrated, or combine certain components, or different components. For example, the aforementioned electronic devices may also include input / output devices, network access devices, buses, etc.

[0093] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0094] Fourthly, this application proposes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the data processing method in the simulation scenario described above.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating tile data of ground features in a simulated scene, characterized in that, include: Obtain relevant data on the material properties of ground features, and generate a set of tile images in tile format based on the relevant data on the material properties of ground features; Wherein, any one tile image in the tile image set is used to represent the relevant data of the material of the land features within the actual geographical area corresponding to the tile image; When performing close-range ground feature background simulation, load a set of tile images, including high-resolution tile images within a small area of ​​the detection field of view; When performing long-distance ground feature background simulation, load a set of tile images and detect a large area of ​​low-resolution tile images within the field of view.

2. The method for generating tile data of ground features in a simulated scene according to claim 1, characterized in that, The relevant data on the material of the land feature includes: the classification number of the land feature material, and / or the classification ratio of the land feature material; The tile image includes multiple pixels, where each pixel corresponds to an actual geographic region unit; The classification number of the land feature material is represented by the pixel channel value of the pixel point corresponding to the actual geographical area unit where the land feature material is located; The classification ratio of the land feature material refers to the ratio of the coverage area of ​​the land feature material to the total area of ​​the geographical area unit corresponding to any pixel.

3. The method for generating tile data of ground features in a simulated scenario according to claim 1, characterized in that, The tile image set has multiple levels; for any two adjacent tile images, the number of tiles in the next level is four times the number of tiles in the current level, and the resolution of the tiles in the next level is twice the resolution of the tiles in the current level.

4. The method for generating tile data of ground features in a simulated scenario according to claim 3, characterized in that, Acquire relevant data on the material properties of ground features, and generate a set of tile images in tile format based on the relevant data, including: Determine the highest resolution in the set of background images of ground features; The number of tile levels in the tile image set is determined based on the highest resolution and the preset resolution of each tile level. The channel data for each pixel is determined based on the relevant data of the material of the ground features in the actual geographic area unit corresponding to each pixel. The tile image set is generated based on the channel data of each pixel and the number of tile layers.

5. The method for generating tile data of ground features in a simulated scene according to claim 4, characterized in that, The channel data for each pixel is determined based on the relevant data of the land cover material in the actual geographic area unit corresponding to each pixel, including: For any pixel, determine the types of materials involved in the actual geographic area unit corresponding to the pixel, and the coverage area of ​​each material; Determine the total area of ​​the actual geographic region unit corresponding to the pixel; Based on the coverage area of ​​each material and the total area of ​​the actual geographic region unit corresponding to the pixel, determine the ratio coefficient of the coverage area of ​​each material; In the set of material coverage area ratio coefficients, sort them according to the size of the ratio coefficients to obtain the sequence of material type area ratio coefficients; The area ratio coefficients of the material types are determined from the sequence of the first few material types. Within a predetermined range of storage bits in the storage data bits of the pixel channel, the first few material type area ratio coefficients and their numbers are stored.

6. The method for generating tile data of ground features in a simulated scene according to claim 5, characterized in that, Within a predetermined range of storage bits in the channel storage data of the pixel, the aforementioned material type area ratio coefficients are stored, including: For any material type area ratio coefficient, the area ratio coefficient of the material type is rounded down to obtain the converted integer of the area ratio of the material type. Within the predetermined storage bit range, the sub-storage bit range of the material type is determined according to the sorting position of the material type in the sorting of the area ratio coefficients of the first few material types. Within the sub-storage bit range of the material type, the converted integer of the area ratio of the material type is stored.

7. The method for generating tile data of ground features in a simulated scene according to claim 6, characterized in that, Within a predetermined range of storage bits in the storage data bits of the pixel's channel, the numbers of the first plurality of material types are stored, including: Within the predetermined storage bit range, the material type number is determined based on its sorting position in the sorting of the area ratio coefficients of the first few material types. The material type number is stored in the sub-storage bit range of the material type.

8. The method for generating tile data of ground features in a simulated scenario according to claim 5, characterized in that, The length of the predetermined storage bit range for the channel storage data bits of the pixel is 30 bits.

9. A device for generating tile data of ground features in a simulated scene, characterized in that, include: The tile module is used to acquire relevant data on the material properties of ground features and generate a set of tile images in tile format based on the relevant data on the material properties of ground features; Wherein, any one tile image in the tile image set is used to represent the relevant data of the material of the land features within the actual geographical area corresponding to the tile image; The first processing module is used to load high-resolution tile images within a small area of ​​the detection field of view from the tile image set when performing close-range ground feature background simulation. The second processing module, when performing long-distance ground feature background simulation, loads a set of tile images and detects a large area of ​​low-resolution tile images within the field of view.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for generating tile data of ground features in a simulated scenario as described in any one of claims 1 to 7.

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