A multi-element coupling driven land scene temperature field simulation method

Abstract

This invention provides a multi-factor coupled temperature field simulation method for land scenes, relating to the field of infrared scene simulation technology. The method includes: first, based on a ground object heat transfer model, statistically analyzing the temperature variation patterns of ground cover and vegetation under different environmental parameters, and extracting multi-factor coupled temperature field modulation parameters; second, segmenting ground object regions in visible light remote sensing images using cluster analysis to generate ground cover marker textures; subsequently, constructing a 3D land scene with a vegetation interface using a procedural vegetation generation method based on the marker textures; finally, importing the modulation parameters and the 3D scene into a rendering engine, and determining shadow areas based on environmental parameters such as sunlight, altitude, and wind field, combined with a ray projection self-intersection algorithm, to achieve real-time temperature modulation of the 3D land scene and individual vegetation. This invention solves the problems of insufficient physical basis for temperature modulation, imprecise shadow judgment, and lack of vegetation cover in existing technologies.

Description

Technical Field

[0001] This invention relates to the field of infrared scene simulation technology, and in particular to a method for simulating the temperature field of a land scene driven by multi-element coupling. Background Technology

[0002] The importance of infrared-guided weapons in modern combat systems continues to rise, making their operational effectiveness assessment and capability boundary verification critical issues. Influenced by various spatiotemporal factors such as weather conditions, seasonal changes, and surface and atmospheric radiation characteristics, the infrared characteristics of targets and backgrounds vary significantly under different conditions. This situation often necessitates long waiting times and multiple rounds of field tests to obtain measured data covering typical and extreme scenarios, resulting in passively extended assessment cycles and a significant increase in personnel and financial investment.

[0003] With advancements in computer graphics, radiative transfer, and multiphysics numerical simulation, it has become possible to rapidly synthesize infrared images of targets and backgrounds under controlled conditions, encompassing multiple poses, viewpoints, sensor parameters, and weather visibility scenarios. This high-fidelity, reproducible experimental data can be used for algorithm development, parameter sensitivity analysis, and scheme comparison, significantly reducing field acquisition costs, shortening development and evaluation cycles, and improving test coverage and the repeatability of conclusions. For land backgrounds, the temperature field of the surface and covering materials is crucial for infrared scene simulation, greatly affecting the realism of the scene.

[0004] Currently, existing technical approaches typically begin by obtaining terrain geometry based on terrain elevation data, then use heat conduction physical equations and environmental parameters to obtain the uniform ground temperature under specific conditions, and finally obtain the terrain surface temperature field by superimposing temperature modulation textures or determining the shadow area based on the angle of sunlight.

[0005] However, existing simulation methods still suffer from problems in practical applications, such as insufficient physical basis for surface temperature modulation, low accuracy in judging land background shadow areas, and failure to consider vegetation cover, which makes it impossible to accurately simulate the terminal working environment of guided weapons. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a multi-element coupled-driven land scene temperature field simulation method. This invention solves the problems that existing simulation methods still have in practical applications, such as insufficient physical basis for surface temperature modulation, low accuracy in judging land background shadow areas, and failure to consider vegetation cover, which leads to the inability to accurately simulate the terminal working environment of guided weapons.

[0007] To achieve the above objectives, the present invention provides the following solution: A multi-factor coupled-driven method for simulating the temperature field of a land scene includes: Based on the ground heat transfer model, statistical analysis was conducted on the temperature change patterns of various ground cover and vegetation under preset environmental parameter conditions to determine the temperature field modulation parameters driven by multi-factor coupling. The acquired visible light remote sensing images are segmented into land cover regions to obtain cover marker textures used to record the distribution of land cover in different regions of the land background. Based on the texture of the cover markings, vegetation is constructed on the three-dimensional geometric mesh of the terrain using a procedural vegetation generation method to obtain a three-dimensional land scene with vegetation. The temperature field modulation parameters and the 3D land scene with vegetation are imported into the 3D rendering engine. The 3D rendering engine modulates the temperature of the 3D land scene in real time according to the environmental parameters and the cover marking texture to obtain the land surface and cover temperature field based on physical processes.

[0008] The present invention discloses the following technical effects: This invention provides a multi-element coupled-driven method for simulating the temperature field of land scenes. It incorporates research results based on heat transfer theory into a 3D rendering engine to achieve fine modulation of surface temperature through multi-element coupling, significantly improving the realism and consistency of the surface temperature field in 3D rendering. A lightweight, procedural method is used to generate vegetation, ensuring both the quantity of generated vegetation and computational efficiency, while also modulating the temperature field of different individuals. A self-intersection algorithm based on ray projection is implemented to accurately obtain the shadow areas of the terrain, thereby achieving temperature modulation based on solar radiation. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart of a multi-element coupled-driven land scene temperature field simulation method provided in an embodiment of the present invention; Figure 2 Detailed flowcharts provided for embodiments of the present invention; Figure 3 The logic flow diagram provided for embodiments of the present invention; Figure 4 A flowchart of vegetation construction provided in an embodiment of the present invention; Figure 5 The temperature modulation workflow provided in this embodiment of the invention; Figure 6A schematic diagram of the texture markings on the beach and soil coverings provided in an embodiment of the present invention; Figure 7 The visible light rendering results of a land background at different angles provided in the embodiments of the present invention, wherein, Figure 7 (a) is a schematic diagram of the first visible light rendering result. Figure 7 (b) is a schematic diagram of the second visible light rendering result; Figure 8 This is a schematic diagram illustrating the results of whether different locations on a land background are illuminated by sunlight, provided in an embodiment of the present invention. Figure 8 (a) is a schematic diagram of the first irradiation result. Figure 8 (b) is a schematic diagram of the second irradiation result; Figure 9 This is a schematic diagram illustrating the temperature field modulation effect on the land surface under solar irradiation conditions, provided as an embodiment of the present invention. Figure 9 (a) is a schematic diagram of the modulation effect of the first temperature field. Figure 9 (b) is a schematic diagram of the modulation effect of the second temperature field; Figure 10 This is a schematic diagram illustrating the temperature field modulation effect of a land background and surface cover after temperature modulation based on different environmental parameters, provided as an embodiment of the present invention. Figure 10 (a) is a schematic diagram of the modulation effect of the third temperature field. Figure 10 (b) is a schematic diagram of the modulation effect of the fourth temperature field. Detailed Implementation

[0011] 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, and 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.

[0012] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0013] like Figure 1 As shown, this invention provides a multi-element coupled-driven method for simulating the temperature field of a land scene, including: Step 100: Based on the ground heat transfer model, statistical analysis is performed on the temperature change patterns of various ground cover and vegetation under preset environmental parameter conditions to determine the temperature field modulation parameters driven by multi-factor coupling. Step 200: Perform land feature region segmentation on the acquired visible light remote sensing image to obtain cover marker textures used to record the distribution of land features in different regions of the land background; Step 300: Based on the texture of the cover marker, vegetation is constructed on the terrain three-dimensional geometric mesh using a procedural vegetation generation method to obtain a three-dimensional land scene with vegetation. Step 400: Import the temperature field modulation parameters and the 3D land scene with vegetation into the 3D rendering engine. The 3D rendering engine modulates the temperature of the 3D land scene in real time according to the environmental parameters and the cover marking texture to obtain the land surface and cover temperature field based on physical processes.

[0014] Specifically, such as Figure 2-3 As shown, the core process of this invention is as follows: First, based on a mature ground heat transfer model, the temperature change patterns of various ground cover and vegetation are studied, and multi-factor coupled temperature field modulation parameters are statistically obtained, providing theoretical data support for temperature field modulation. Second, based on visible light remote sensing images and K-class algorithms, the distribution of different ground cover and vegetation in the land background is analyzed to obtain cover marker textures, providing basic information constraints for land scene construction. Then, based on the obtained cover marker textures, a lightweight procedural vegetation generation method is adopted to construct vegetation, ensuring that the amount of vegetation meets the requirements while reducing the overall computational cost, and providing an interface for individual vegetation in the subsequent temperature modulation process. Finally, the temperature field modulation parameters and the 3D land scene with vegetation are imported into a 3D rendering engine. Based on environmental parameters such as sun, altitude, and wind speed, combined with the cover and temperature field modulation parameters of different land areas, the temperature field modulation of the 3D land scene is realized. Furthermore, the ground cover heat transfer model based on the coupling of multiple meteorological elements and ground cover thermal parameters statistically analyzes the temperature change patterns of various ground cover and vegetation under preset environmental parameter conditions to determine the temperature field modulation parameters driven by multi-element coupling, including: Set typical environmental parameters and input them into the ground heat transfer model to calculate the average temperature of the ground surface or corresponding cover output by the ground heat transfer model, and obtain the reference temperature. Based on the temperature field calculation results of the ground heat transfer model under the typical environmental parameters, the difference between the highest and lowest temperatures of the ground surface or corresponding cover is extracted to obtain temperature modulation parameters based on visible light information. The slope of the average temperature of the land surface or corresponding cover as a function of altitude is calculated using the aforementioned ground heat transfer model, thereby obtaining temperature modulation parameters based on altitude. The absolute value of the difference between the mean temperature of the surface normal and the direction of solar radiation is calculated using the aforementioned ground heat transfer model, respectively, when they are set in the same direction and opposite directions, to obtain temperature modulation parameters based on solar radiation conditions. The slope of the average temperature of the land surface or corresponding cover as a function of wind speed is calculated using the aforementioned ground feature heat transfer model to obtain the temperature modulation coefficient based on wind speed, and the temperature modulation coefficient based on wind direction is obtained from the aforementioned ground feature heat transfer model.

[0015] Specifically, based on mature ground heat transfer models, the temperature changes of various ground cover and vegetation under different environmental parameter conditions are studied and statistically analyzed to obtain temperature field modulation parameters.

[0016] First, determine the reference temperature. For specific latitude and longitude coordinates and the date and time of the study, typical environmental parameters are set, and the average temperature of the land surface or corresponding cover under the typical environmental parameters is calculated as the reference temperature.

[0017] Secondly, determine the temperature modulation parameters based on visible light information. Based on the temperature field calculations under typical environmental parameters, the difference between the highest and lowest temperatures on the land surface or corresponding cover is determined and used as a temperature modulation parameter based on visible light information. ; Next, determine the temperature modulation parameters based on altitude. Based on typical environmental parameters, the altitude and ambient temperature were adjusted, with the ambient temperature decreasing by 6°C for every 1000m increase in altitude. The slope of the average temperature of the land surface or corresponding cover as a function of altitude was calculated and used as an altitude-based temperature modulation parameter. ; Then, determine the temperature modulation parameters based on solar irradiance. If the sun has not yet risen, Set to 0. If the sun rises, based on typical environmental parameters, the slope is set to make the land surface normal and the solar radiation direction vector move in the same direction and opposite directions, respectively. The absolute value of the difference between the mean values ​​of the land surface or corresponding cover under these two settings is calculated as the temperature modulation based on solar radiation conditions. .

[0018] Finally, the temperature modulation coefficient based on wind speed was determined. and wind direction-based temperature modulation coefficient Based on typical environmental parameters, the wind direction vector is set to be perpendicular to the land normal direction. By adjusting the wind speed, the slope of the average temperature of the land surface or corresponding cover as a function of wind speed is calculated, which serves as the wind speed-based temperature modulation coefficient. .

[0019] More specifically, the underlying principle of the ground feature heat transfer model is as follows: Heat exchange at the Earth's surface mainly consists of: thermal radiation from the object itself, absorbed solar radiation and atmospheric background radiation, sensible and latent heat exchange between the Earth's surface and the atmosphere, and heat conduction to lower layers. The specific energy heat balance equation is shown below: ; in, The item is the absorbed solar radiation; The term refers to absorbed atmospheric radiation; It is surface thermal radiation; It is the sensible heat exchange flux between the Earth's surface and the atmosphere; It is the latent heat exchange flux between the Earth's surface and the atmosphere; The term for heat conduction from the Earth's surface to the lower layers; and These represent the absorptivity of solar and atmospheric radiation, respectively.

[0020] The specific calculation equations for each heat term are as follows: ① Surface thermal radiation According to Planck's formula, surface thermal radiation is mainly concentrated in the longwave band, which is related to its temperature and emissivity. If The surface temperature of the object at time t is Its thermal radiation intensity can be calculated using the Stefan-Boltzmann formula: in, It is the Stefan-Boltzmann constant; The emissivity of the object's surface across the entire wavelength range.

[0021] ② Sensible heat exchange flux Sensible heat exchange characterizes the heat exchange between the Earth's surface and the surrounding atmosphere due to convection, and is related to factors such as surface temperature, altitude, air temperature, and wind speed. The aerodynamic impedance method can ideally determine various parameters. Its principle is to treat heat as a heat flow, the value of which is directly proportional to the potential difference of the heat source and inversely proportional to aerodynamic drag, as follows: ; in, This refers to the air density near the ground. The specific heat of air at constant pressure; For aerodynamic drag; At the reference altitude, the atmospheric temperature is shown.

[0022] ③ Latent heat exchange flux Air currents above the Earth's surface cause phenomena such as condensation of moisture in the air at the surface and evaporation of moisture from the surface and interior of the Earth. The resulting thermal process is called latent heat exchange. The latent heat exchange flux is related to factors such as surface temperature, altitude, atmospheric temperature, relative humidity, and wind speed, and can be calculated using the following formula: ; in, This is the constant of the wet / dry meter; The surface temperature is The saturated vapor pressure at that time; This refers to the water vapor pressure of the air near the ground.

[0023] when > At times, air movement causes moisture to condense; when < Furthermore, when the ground contains moisture, air movement causes the moisture to evaporate.

[0024] ④ Heat conduction flux Temperature differences exist between different depths of ground features, and heat conduction occurs when these features come into contact. The heat conduction at the Earth's surface is mainly related to the temperature difference, thickness, and thermal conductivity of the different layers of the object, and can be calculated using the following formula: ; in, Absolute temperature; Thermal conductivity of the medium; These are depth coordinates.

[0025] Furthermore, the step of segmenting the acquired visible light remote sensing image into land cover regions to obtain a cover marker texture for recording the distribution of land cover in different areas of the land background includes: Obtain the histogram distribution of the visible light remote sensing image; Based on the histogram distribution, the gray values ​​of each peak are calculated and arranged in descending order, and the first few gray levels are selected as the initial values ​​of the cluster centers. Calculate the Euclidean distance between all sample data and the initial value of the cluster center, and assign each sample to the cluster with the smallest distance; Recalculate the center values ​​of each cluster until the new cluster centers no longer change or the change is less than the preset judgment coefficient, and obtain the overlay marker texture.

[0026] Specifically, remote sensing images are a direct reflection of the real world, realistically and vividly representing the distribution and detailed changes of various natural landforms and scenes. Therefore, generating land cover textures based on visible light remote sensing images can efficiently record the distribution of ground cover in different areas of the land background. By segmenting land cover regions in remote sensing images, accurate and natural material distribution data can be obtained, correctly reflecting the characteristics of different land cover types in the real natural world, such as their often intertwined nature and the irregular and tortuous boundaries of regions. This not only solves the problem of overly subjective and arbitrary planning of land cover distribution that may not conform to objective natural laws, but also effectively simplifies the cumbersome, time-consuming, and labor-intensive nature of manual planning.

[0027] To isolate the distribution and texture features of various land features reflected in visible light remote sensing images, image segmentation is necessary. Since pixel-based image segmentation methods produce relatively stable results, this paper uses clustering analysis within this type of algorithm to classify land features in remote sensing images.

[0028] K-means clustering is the most basic unsupervised clustering algorithm. It is computationally simple and suitable for handling large amounts of data. Its principle is to manually assign K initial cluster centers, calculate the distance between each sample and each center, and assign each sample to the cluster with the smallest distance. The cluster centers of the resulting K clusters are then recalculated. The iteration returns to the first step, terminating when the cluster centers no longer change or change very little, thus ending the clustering process. The specific steps are: a) Select K initial values ​​for cluster centers ,in, This is the scripture The i-th cluster center value after the nth iteration. If the initial value is arbitrarily set, the number of iterations is usually large, resulting in low efficiency. This paper uses the histogram distribution of remote sensing images to obtain the gray values ​​of each peak, sorts the gray levels corresponding to the peaks from largest to smallest, and selects the first K gray levels as the initial values ​​of the sample vector, thus improving computational efficiency.

[0029] b) Classify all sample vectors based on their similarity to cluster centers. To classify each sample vector... ( To assign a vector (feature dimension) to one of K cluster centers, the classification criteria are set as follows: ,when: ; For all .in, Representing the Clustering at the next iteration The sample. The distance in the above formula is calculated using Euclidean distance: ; c) Update cluster center values. Use the data from the newly created clusters in step b) to recalculate the cluster centers, minimizing the weighted sum of distances from the new cluster centers to the vectors within their respective clusters. That is, minimize... : ; in, , These are all samples that minimize the above equation. Therefore, the cluster centers are updated using the following equation: ; in, It is classified as The number of sample vectors.

[0030] d) Convergence condition. The calculated new cluster centers no longer change, or change very little, i.e.: ; in, , If the cluster center change coefficient is used as the criterion, the clustering is considered converged. If it has not converged, return to step b) to continue iterating.

[0031] Furthermore, based on the overlying texture markers, a procedural vegetation generation method is used to construct vegetation on the terrain's three-dimensional geometric mesh, resulting in a three-dimensional land scene with vegetation, including: The texture marked by the cover is input as a density field mask into the three-dimensional geometry of the terrain to control the regional distribution of different vegetation categories; Obtain a preset vegetation prototype asset, wherein the individual vegetation in the three-dimensional land scene is generated by referencing the preset vegetation prototype asset; Create an association data packet for the vegetation individual, wherein the association data packet is used to record the unique identification code, transformation matrix and temperature adjustment coefficient of the vegetation individual; During the rendering process, the associated data packets are indexed based on the unique identification code, and the pose, temperature field and optical characteristics of different vegetation individuals are set independently in batches to obtain a three-dimensional land scene with vegetation.

[0032] Specifically, lightweight procedural vegetation generation produces a land scene with vegetation.

[0033] In 3D rendering engines, every object in a scene is broken down into thousands of meshes, each containing data necessary for rendering, such as vertex coordinates, texture indexes, and normal information. When there are too many objects in a scene, this places significant strain on computational resources and rendering speed. For the same type of vegetation, their geometric appearance and texture are largely similar; therefore, a lightweight, procedural vegetation generation method is adopted to reduce unnecessary data processing while maintaining vegetation realism.

[0034] like Figure 4As shown, the process begins with preparing the 3D geometry of the terrain and the various vegetation types to be placed in the scene. The ground cover texture obtained in the previous step is used as a density field mask and input into the 3D geometric mesh of the terrain to control the regional distribution of different vegetation types, achieving texture-driven regional distribution. Secondly, when placing a large number of vegetation types, a lightweight referencing approach is adopted. The mesh data (such as vertex positions, normals, etc.) and material settings of each type of vegetation serve as a unique prototype asset. All individual vegetation types in the scene are simply used to call this prototype asset without causing additional data storage. Then, separate data packages are created for individual vegetation types placed in different locations in the scene to record necessary data such as unique IDs, transformation matrices, and temperature adjustment coefficients. These data are indexed by unique IDs, enabling accurate representation of differences between individuals. Finally, during the rendering process, individual vegetation types are batch-copied, and data packages are indexed based on different IDs. The position, size, temperature field, and optical characteristics of different individual vegetation types are then independently set.

[0035] Furthermore, the types of real-time temperature modulation include: Temperature modulation based on visible light information, temperature modulation based on altitude, temperature modulation based on solar illumination, and temperature modulation based on wind field.

[0036] Specifically, such as Figure 5 As shown, the 3D rendering engine, as the core support for modulating the land background temperature field, can efficiently store and retrieve terrain elevation, normal, and visible light information, and can also record data such as wind speed and direction, and solar radiation vectors. Based on this data, the 3D rendering engine can quantify the geometric information at different locations on the terrain and its relative relationship with wind field and sunlight. Combined with the temperature change range corresponding to each element in the study of ground heat transfer, it can obtain the multi-element coupled terrain temperature field modulation result. Finally, the modulation result and the reference temperature are input into the 3D rendering engine, which can accurately present the land background temperature field under multi-element coupled conditions through texture mapping.

[0037] Temperature modulation based on visible light information: In a terrestrial background, similar ground cover objects at the same location exhibit fluctuating temperatures within a small range around the mean temperature, displaying undulating infrared texture characteristics. According to the surface energy thermal balance model of the ground background, since the environmental parameters of ground cover objects at the same location or with little variation can be considered consistent, the main reason for the temperature fluctuations can be attributed to the non-uniformity and random variations in the thermal properties of the material. Visible light remote sensing images of ground cover objects can reflect their visible light band reflection and absorption characteristics to a certain extent. Since solar radiation energy in the visible light band (0.38-0.76 μm) accounts for approximately 46% of the total solar energy, the solar energy absorptivity in this band has certain reference value for assessing the heat absorption capacity of ground cover objects. Therefore, a temperature modulation model based on visible light information is established by utilizing the correlation between visible light band absorptivity and material temperature.

[0038] Let the response functions of each color band of the panchromatic image sensor from the remote sensing satellite be: , ( (Representing the red, green, and blue bands), then according to the visible light energy transfer and sensor imaging model, the grayscale value of the sensor pixel in each band can be expressed as: ; In the formula, It is the sensor gain; It is sensor bias; It is the visible light reflectance of the corresponding wavelength band.

[0039] Generally speaking, the properties of all multispectral sensors on the same satellite should be similar, and can be approximated as follows: And under normal circumstances Furthermore, assuming that the sensor can achieve its highest grayscale level when the reflectivity is 1 (the highest value is 255 when the grayscale precision is 8 bits), we can obtain: ; The reflectivity of the visible light band can be obtained by combining the reflectivity of the red, green, and blue bands: ; In the formula, the coefficients This refers to the proportion of solar radiation in the red, green, and blue bands relative to the visible light band, and it depends on the specific detector's wavelength range. If we assume the red, green, and blue detector wavelength ranges are 0.6-0.7, 0.5-0.6, and 0.4-0.5 μm, respectively, The values ​​were set to 0.34, 0.36, and 0.3 respectively.

[0040] According to Kirchhoff's laws, the absorption rate in the visible light band In summary, the temperature modulation model based on visible light information is shown in the following equation: ; In the formula, Based on the temperature modulation coefficient of visible light, This represents the visible light absorption rate of a pixel in the terrain or ground cover texture. and These represent the maximum and minimum values ​​of the visible light absorption rate in the terrain or ground cover texture, respectively.

[0041] Temperature modulation based on altitude: For terrestrial regions with similar latitude and longitude, differences in altitude modulate near-surface air and surface temperature fields by altering the energy balance between the atmosphere and the surface. The main mechanisms include: with increasing altitude, air pressure and density decrease, adiabatic expansion cools air masses, resulting in a nearly stable vertical temperature lapse rate; shortened optical paths and variations in cloud and fog frequency jointly affect direct and scattered shortwave radiation; decreased atmospheric water vapor content enhances effective longwave radiation; and roughness and turbulent exchange are constrained by topographic features, altering the distribution of sensible and latent heat fluxes. Although different land cover types exhibit significant differences in albedo, emissivity, heat capacity, and thermal conductivity, under conditions of similar latitude and longitude and consistent climatic background, altitude dominates as the first-order control of surface temperature. With increasing altitude, decreased air pressure and water vapor content, weakened back radiation, and systematic modulation of radiation by topography cause surface temperature to exhibit an approximately monotonic variation along the altitude direction. Therefore, the temperature modulation model based on altitude is shown in the following equation: ; In the formula, Indicates the altitude of a certain point in the terrain. It is a temperature modulation parameter that varies with altitude.

[0042] Temperature modulation based on solar radiation: Unlike wind fields, changes in the land background temperature field caused by solar radiation are affected not only by the angle of illumination and the direction of the land normal, but more significantly by whether a particular area is actually illuminated by the sun. To accurately characterize the distribution of solar radiation on the land background, a self-intersection algorithm based on ray projection and a visibility encoding process were implemented in the 3D rendering engine. First, using the vertices of the object surface as the ray origin, the reverse unit vector of the sun's incident direction is taken as the unified ray direction, and test rays are emitted into the scene to test for occlusion. Subsequently, Boolean variables are assigned to the surface vertices to record the intersection of rays: if the test ray intersects with the land itself or other geometry in the scene for the first time, the variable is set to False; if no intersection event is detected within the tolerance range, it is set to True. The former indicates that the point is self-occluded or blocked by other objects and cannot receive sunlight, while the latter indicates that the point is directly illuminated by sunlight. Finally, based on the 3D terrain texture coordinates, the Boolean results of each vertex are mapped to the texture space, and after interpolation and rasterization, a binary texture with a value of 0 or 1 is generated for each pixel to encode the distribution of sunlight.

[0043] After obtaining the solar radiation distribution, the temperature modulation model based on solar radiation is shown below: ; In the formula, This indicates the temperature change caused by sunlight. This represents the sunlight exposure at a point on the land geometry; 1 indicates a point that is illuminated, and 0 indicates a point that is not illuminated. This represents the temperature modulation parameters based on solar irradiance. and Let x, y, and z represent the unit normal vector and the unit vector of sunlight at a point on the three-dimensional geometry of the land, respectively. The direction of sunlight refers to the direction of light propagation, with due east, due north, and vertically upward as the positive directions of the x, y, and z components of the unit vector of sunlight, respectively.

[0044] Temperature modulation based on wind field: In a terrestrial context where latitude, longitude, and altitude are approximately the same, wind fields can still significantly influence the temperature field distribution of the land surface and its cover through multiple mechanisms. A temperature modulation model based on wind fields is shown below: ; In the formula, This indicates temperature changes caused by wind. This represents the temperature modulation coefficient based on wind speed; Indicates wind speed; This represents the temperature modulation coefficient based on wind direction; and Let these represent the unit normal vector and the unit wind direction vector at a point on the three-dimensional geometry of the land, respectively. The direction from which the wind comes is defined as the wind direction. If the wind is due west, the unit wind direction vector is (1,0,0); if the wind is due south, the unit wind direction vector is (0,1,0); vertically upward is the positive direction of the z-component of the wind direction vector.

[0045] In summary, after temperature field modulation based on the 3D rendering engine, the temperature value of a certain surface or corresponding covering in the land background is as follows: ; in, Temperature changes caused by wind location, Temperature changes caused by solar radiation T base As the reference temperature, T h This is due to temperature changes caused by altitude. T rgb This refers to the temperature change caused by visible light information.

[0046] More specifically, the following shows the temperature field modulation parameters for the beach and soil on the summer solstice.

[0047] Table 1. Beach temperature field modulation parameters

[0048] Table 2 Soil temperature field modulation parameters

[0049] Figure 6 The text displays the texture markings of the coastal land background after terrain segmentation and material mapping, with black representing sand and white representing soil.

[0050] Figure 7 The image showcases the visible light rendering results of the land background from different angles after adding detailed visible light textures to the sand and soil.

[0051] Figure 8 This demonstrates the results of a self-intersection algorithm based on ray projection under certain conditions, showing whether different locations on the land background are illuminated by sunlight. White represents the illuminated areas, and black represents the shadowed areas.

[0052] Figure 9 This demonstrates the effect of temperature field modulation on the land surface under only the condition of solar radiation.

[0053] Figure 10The effect of temperature field modulation on the land background and surface cover after temperature modulation is achieved according to different environmental parameters is demonstrated.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0055] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A multi-factor coupled-driven method for simulating the temperature field of a land scene, characterized in that, include: Based on the ground heat transfer model, statistical analysis was conducted on the temperature change patterns of various ground cover and vegetation under preset environmental parameter conditions to determine the temperature field modulation parameters driven by multi-factor coupling. The acquired visible light remote sensing images are segmented into land cover regions to obtain cover marker textures used to record the distribution of land cover in different regions of the land background. Based on the texture of the cover markings, vegetation is constructed on the three-dimensional geometric mesh of the terrain using a procedural vegetation generation method to obtain a three-dimensional land scene with vegetation. The temperature field modulation parameters and the 3D land scene with vegetation are imported into the 3D rendering engine. The 3D rendering engine modulates the temperature of the 3D land scene in real time according to the environmental parameters and the cover marking texture to obtain the land surface and cover temperature field based on physical processes.

2. The method for simulating the temperature field of a land scene driven by multi-element coupling according to claim 1, characterized in that, The ground-based heat transfer model statistically analyzes the temperature variation patterns of various ground cover and vegetation under preset environmental parameter conditions to determine the temperature field modulation parameters driven by multi-factor coupling, including: Set typical environmental parameters and input them into the ground heat transfer model to calculate the average temperature of the ground surface or corresponding cover output by the ground heat transfer model, and obtain the reference temperature. Based on the temperature field calculation results of the ground heat transfer model under the typical environmental parameters, the difference between the highest and lowest temperatures of the ground surface or corresponding cover is extracted to obtain temperature modulation parameters based on visible light information. The slope of the average temperature of the land surface or corresponding cover as a function of altitude is calculated using the aforementioned ground heat transfer model, thereby obtaining temperature modulation parameters based on altitude. The absolute value of the difference between the mean temperature of the surface normal and the direction of solar radiation is calculated using the aforementioned ground heat transfer model, respectively, when they are set in the same direction and opposite directions, to obtain temperature modulation parameters based on solar radiation conditions. The slope of the average temperature of the land surface or corresponding cover as a function of wind speed is calculated using the aforementioned ground feature heat transfer model to obtain the temperature modulation coefficient based on wind speed, and the temperature modulation coefficient based on wind direction is obtained from the aforementioned ground feature heat transfer model.

3. The method for simulating the temperature field of a land scene driven by multi-element coupling according to claim 1, characterized in that, The process of segmenting the acquired visible light remote sensing image into land cover regions to obtain cover marker textures for recording the distribution of land cover in different regions of the land background includes: Obtain the histogram distribution of the visible light remote sensing image; Based on the histogram distribution, the gray values ​​of each peak are calculated and arranged in descending order, and the first few gray levels are selected as the initial values ​​of the cluster centers. Calculate the Euclidean distance between all sample data and the initial value of the cluster center, and assign each sample to the cluster with the smallest distance; Recalculate the center values ​​of each cluster until the new cluster centers no longer change or the change is less than the preset judgment coefficient, and obtain the overlay marker texture.

4. The method for simulating the temperature field of a land scene driven by multi-element coupling according to claim 1, characterized in that, Based on the overlying texture markers, vegetation is constructed on the terrain's 3D geometric mesh using a procedural vegetation generation method, resulting in a 3D land scene with vegetation, including: The texture marked by the cover is input as a density field mask into the three-dimensional geometry of the terrain to control the regional distribution of different vegetation categories; Obtain a preset vegetation prototype asset, wherein the individual vegetation in the three-dimensional land scene is generated by referencing the preset vegetation prototype asset; Create an association data packet for the vegetation individual, wherein the association data packet is used to record the unique identification code, transformation matrix and temperature adjustment coefficient of the vegetation individual; During the rendering process, the associated data packets are indexed based on the unique identification code, and the pose, temperature field and optical characteristics of different vegetation individuals are set independently in batches to obtain a three-dimensional land scene with vegetation.

5. The method for simulating the temperature field of a land scene driven by multi-element coupling according to claim 1, characterized in that, The types of real-time temperature modulation include: Temperature modulation based on visible light information, temperature modulation based on altitude, temperature modulation based on solar illumination, and temperature modulation based on wind field.

6. The method for simulating the temperature field of a land scene driven by multi-element coupling according to claim 1, characterized in that, The expression for calculating temperature values ​​in the land surface and cover temperature field based on physical processes is as follows: ； in, Temperature changes caused by wind location, Temperature changes caused by solar radiation T base As the reference temperature, T h This is due to temperature changes caused by altitude. T rgb This refers to the temperature change caused by visible light information.

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