Digital conversion sand table construction method and system based on aerial photography technology

Abstract

The invention discloses a digital conversion sand table construction method and system based on an aerial photography technology, and relates to the technical field of three-dimensional sand table construction, and the method comprises the steps: obtaining an aerial image, and carrying out the dynamic path planning and multi-modal data recording; splicing and correcting the aerial images to generate a digital elevation model; and a three-dimensional digital sand table is generated, and dynamic updating and multi-platform display functions are embedded. According to the method, the image acquisition precision is improved, repeated coverage and blind areas are reduced, the overall efficiency of aerial photography tasks is improved, splicing errors caused by factors such as complex terrain and illumination variation in a traditional method are reduced, the output overall image parameters are more accurate and consistent, the detail expression ability of the model is improved, and the method is suitable for large-scale popularization and application. A solid foundation is laid for subsequent three-dimensional digital sand table construction, the multi-platform display requirement is met, the sand table model has higher operability and sharing performance in actual scenes such as urban planning and emergency response, and the user interaction experience is effectively optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional sand table construction, and in particular to a digital conversion sand table construction method and system based on aerial photography technology. Background Art

[0002] With the rapid development of drone technology, data acquisition and processing based on aerial imagery have been widely used in fields such as geographic information systems (GIS), environmental monitoring, and urban planning. The precision and flexibility of aerial photography technology enable efficient image acquisition in complex terrain and wide areas, and it has demonstrated significant advantages in digital terrain modeling (DTM), orthophoto stitching, and three-dimensional reconstruction. In recent years, multimodal data fusion methods have further promoted the application scope of aerial photography data, expanding from single image acquisition to dynamic path optimization, real-time data recording, and comprehensive processing. At the same time, three-dimensional sandbox construction technology based on digital elevation models (DEMs) has also gradually emerged in scenarios such as simulation training, disaster response, and planning and design, providing researchers with innovative tools for the transition from two-dimensional data to multi-dimensional display. However, existing technologies still have considerable room for improvement in terms of accuracy, real-time performance, and diversified display functions.

[0003] Although aerial photography technology has become an important means of spatial data collection, existing technologies still have many limitations in dynamic path planning, multimodal data fusion and subsequent processing. In dynamic path planning, traditional methods mostly rely on preset fixed paths, which are difficult to adjust in real time to adapt to complex terrain or dynamic task requirements, resulting in limited image acquisition efficiency and integrity. The development of synchronous recording and processing technology for multimodal data lags behind. Existing methods find it difficult to simultaneously take into account the collection, matching and fusion of multi-source data, resulting in insufficient diversity and practicality of the results. In the subsequent image stitching and correction process, the existing stitching algorithm has poor robustness for complex scenes and is prone to introducing errors, affecting the accuracy of the digital elevation model. Especially in the construction of three-dimensional sand tables, the traditional system has a single function and cannot achieve real-time updating of dynamic data, nor can it meet the display requirements of multiple platforms and multiple scenes. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing three-dimensional sand table construction technology has the problems of low efficiency in dynamic path planning, incomplete multimodal data recording, insufficient dynamic update and multi-platform display functions, as well as how to achieve efficient processing of aerial images, accurate modeling, and dynamic update and multi-platform display of three-dimensional sand tables.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for constructing a digital conversion sandbox based on aerial photography technology, comprising acquiring aerial images, and performing dynamic path planning and multimodal data recording; splicing and correcting the aerial images to generate a digital elevation model; generating a three-dimensional digital sandbox, and embedding dynamic update and multi-platform display functions.

[0007] As a preferred solution of the digital conversion sandbox construction method based on aerial photography technology described in the present invention, the dynamic path planning includes adjusting the UAV flight path according to wind speed, temperature and humidity parameters, and automatically optimizing the shooting parameters in areas with insufficient light or strong reflections through the dynamic exposure adjustment function of the aerial photography equipment, which is expressed as:

[0008]

[0009] Among them, P(t) is the flight path, which represents the change of the trajectory of the UAV with time t, t0 represents the start time of the flight mission, and t f represents the end time of the flight mission, α is the speed weight, β is the adaptability weight, v(t) represents the flight speed of the UAV at time t, and η(t) is the environmental adaptability coefficient at time t, which is expressed as:

[0010]

[0011] Among them, w(t) is the wind speed at time t, T(t) is the real-time temperature at time t, T opt is the optimal temperature, κ1 is the wind speed weight parameter, and κ2 is the temperature deviation weight parameter.

[0012] As a preferred solution of the digital conversion sandbox construction method based on aerial photography technology described in the present invention, the multimodal data recording includes using a drone equipped with visible light, infrared light and LiDAR sensors to collect surface details and three-dimensional terrain data, and using global navigation satellite system and inertial measurement unit data to correct the image shooting position and direction.

[0013] As a preferred solution of the digital conversion sandbox construction method based on aerial photography technology described in the present invention, the aerial image stitching and correction includes the following steps: the drone completes image acquisition according to the optimized path, stitches and corrects the overlapping areas of adjacent images, extracts the feature points of the image using the SIFT algorithm, corrects the geometric distortion of the image in combination with the bundle adjustment, corrects the geometric errors caused by the drone movement and lens distortion based on the bundle adjustment algorithm, generates the corrected overall image, and converts the gradient information of the path optimization into the image. The bundle adjustment is introduced, and the output of stitching and correction is the corrected image parameter x, which is expressed as:

[0014] x=(ATWA)-1 A T W(l+e(τ i ))

[0015] Where x is the correction image parameter, A is the design matrix, including the design parameters of the bundle adjustment, W is the weighting matrix used to adjust the error weight, l is the observation value vector, representing the initial error of the image, e(τ i ) is the path correction vector, expressed as:

[0016]

[0017] Among them, τ i is the time point of path optimization, λ i is the path correction weight, is the path P(t) at time point τ i The gradient of , n is the number of known points.

[0018] As a preferred solution of the digital conversion sand table construction method based on aerial photography technology described in the present invention, wherein: the generation of digital elevation model includes combining LiDAR point cloud to generate high-precision digital elevation model (DEM), and fusing it with orthophoto to generate complete terrain data, the corrected image parameter x and LiDAR point cloud data Z i Together they form the input for elevation modeling.

[0019] The elevation of unknown points is calculated using interpolation algorithms, and the Laplace operator of image texture is used To improve the detail of the terrain model, the generated digital elevation model z(x,y) is expressed as:

[0020]

[0021] Among them, z(x,y) is the elevation value of the coordinate point 9x,y), w i (x, y) is the interpolation weight, which is used to measure the influence of point (x, y) on the known point i, z i is the elevation of the known point i in the LiDAR point cloud data, is the Laplacian operator of image texture, γ is the image texture fusion weight, and n is the number of known points.

[0022] As a preferred solution of the digital conversion sand table construction method based on aerial photography technology described in the present invention, wherein: the generation of a three-dimensional digital sand table includes generating a three-dimensional sand table model supported by LOD based on the digital elevation model z (x, y) and the orthophoto, and the LOD model adopts a hierarchical construction method, combined with the resolution parameter R ki and texture details T ki Describe the terrain accuracy at different levels through the Gaussian kernel function φ i(x,y) smooth texture changes make the model adaptable to multi-platform display requirements, expressed as:

[0023]

[0024] Among them, LOD k (x, y) is the detail weight of the k-th layer LOD sandbox model at the coordinate point (x, y), R ki is the k-th layer LOD resolution parameter, T ki is the k-th layer LOD texture detail weight, φ i (x,y) is the Gaussian kernel function used to smooth texture changes, x i ,y i is the center point coordinate of the Gaussian kernel function, σ is the smoothing width of the Gaussian kernel function, z(x,y) is the elevation data of the terrain, γ is the image texture fusion weight, and m is the number of texture points of the LOD model.

[0025] As a preferred solution of the digital conversion sandbox construction method based on aerial photography technology described in the present invention, the embedded dynamic update and multi-platform display function includes integrating a real-time data stream update module, dynamically updating the sandbox model through new aerial photography data, establishing a multi-user collaboration platform, supporting hierarchical authority management and collaborative editing, and providing a remote access interface based on a cloud platform to enable digital sandbox sharing and dynamic interaction between multiple devices.

[0026] Another object of the present invention is to provide a digital conversion sand table construction system based on aerial photography technology, which can generate a three-dimensional digital sand table and embed dynamic update and multi-platform display functions to solve the problems of current three-dimensional terrain modeling and visualization technology, such as model update lag, poor platform compatibility and insufficient user interaction experience.

[0027] As a preferred solution of the digital conversion sand table construction system based on aerial photography technology described in the present invention, it includes: an image acquisition module, a stitching and correction module, and a sand table update module.

[0028] The image acquisition module is used to acquire aerial images and perform dynamic path planning and multimodal data recording; the stitching and correction module is used to stitch and correct aerial images to generate a digital elevation model; the sand table update module is used to generate a three-dimensional digital sand table and embed dynamic update and multi-platform display functions.

[0029] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a digital conversion sandbox construction method based on aerial photography technology.

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a digital conversion sand table construction method based on aerial photography technology.

[0031] Beneficial effects of the present invention: The digital conversion sandbox construction method based on aerial photography technology provided by the present invention obtains aerial images, and performs dynamic path planning and multimodal data recording, ensuring the stability and clarity of aerial images. The dynamic exposure adjustment function optimizes the shooting effect in areas with insufficient light or strong reflection, improves the quality and reliability of aerial data, improves data acquisition efficiency and reduces the impact of environmental factors, splices and corrects aerial images, generates digital elevation models, realizes the conversion from discrete aerial images to continuous and accurate terrain models, reduces manual intervention, improves modeling efficiency and accuracy, generates a three-dimensional digital sandbox, and embeds dynamic update and multi-platform display functions, realizes real-time update of terrain information and multi-angle and multi-platform visual display, ensures the accuracy and display effect of the model at different resolutions, provides a three-dimensional terrain display with strong interactivity and rich information, improves user experience, and improves data sharing and collaboration efficiency. The present invention achieves better results in aerial image acquisition efficiency, digital elevation model accuracy and dynamic interaction and display of three-dimensional digital sandbox. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 This is an overall flow chart of a digital conversion sandbox construction method based on aerial photography technology provided in the first embodiment of the present invention.

[0034] Figure 2 This is an overall flow chart of a digital conversion sandbox construction system based on aerial photography technology provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0036] Example 1, with reference to Figure 1, as one embodiment of the present invention, provides a method for constructing a digital conversion sandbox based on aerial photography technology, comprising:

[0037] S1: Acquire aerial images and perform dynamic path planning and multimodal data recording.

[0038] Furthermore, dynamic path planning includes adjusting the drone's flight path based on wind speed, temperature, and humidity parameters. The dynamic exposure adjustment function of the aerial photography equipment automatically optimizes shooting parameters in areas with insufficient light or strong reflections, expressed as:

[0039]

[0040] Among them, P(t) is the flight path, which represents the change of the trajectory of the UAV with time t, t0 represents the start time of the flight mission, and t f represents the end time of the flight mission, α is the speed weight, β is the adaptability weight, v(t) represents the flight speed of the UAV at time t, and η(t) is the environmental adaptability coefficient at time t, which is expressed as:

[0041]

[0042] Among them, w(t) is the wind speed at time t, T(t) is the real-time temperature at time t, T opt is the optimal temperature, κ1 is the wind speed weight parameter, and κ2 is the temperature deviation weight parameter.

[0043] It should be noted that multimodal data recording includes the use of drones equipped with visible light, infrared light and LiDAR sensors to collect surface details and three-dimensional terrain data, and the use of global navigation satellite system and inertial measurement unit data to correct the image shooting position and direction.

[0044] It should also be noted that dynamic path planning and multimodal data recording have solved the problems of low image acquisition efficiency, unstable quality and single data in traditional aerial photography, laying a solid foundation for subsequent data processing and modeling. Dynamic path planning uses environmental parameters as the core input, adjusts the flight path of the drone in real time, optimizes flight efficiency and image coverage, introduces speed weights and adaptability weights, adjusts the flight trajectory of the drone according to different environmental conditions, and realizes task optimization in complex terrain and dynamic environments. In areas with high wind speeds, the drone automatically reduces the flight speed and adjusts the flight direction to ensure the stability and clarity of the aerial images. Real-time monitoring and weight distribution of temperature and humidity parameters enable the drone to choose the optimal shooting time to avoid image blur or data loss due to severe weather conditions. This path planning method not only improves the accuracy of image acquisition, but also reduces repeated coverage and blind spots, thereby improving the overall efficiency of aerial photography tasks.

[0045] S2: Stitch and correct the aerial images to generate a digital elevation model.

[0046] Furthermore, the aerial image stitching and correction includes the UAV completing image acquisition according to the optimized path, stitching and correcting the overlapping areas of adjacent images, extracting the feature points of the image using the SIFT algorithm, correcting the geometric distortion of the image in combination with the bundle adjustment, and correcting the geometric errors caused by the UAV movement and lens distortion based on the bundle adjustment algorithm to generate the corrected overall image, and converting the gradient information of the path optimization into the image. The bundle adjustment is introduced, and the output of stitching and correction is the corrected image parameter x, which is expressed as:

[0047] x=(A T WA) -1 A T W(l+e(τ i ))

[0048] Where x is the correction image parameter, A is the design matrix, including the design parameters of the bundle adjustment, W is the weighting matrix used to adjust the error weight, l is the observation value vector, representing the initial error of the image, e(τ i ) is the path correction vector, expressed as:

[0049]

[0050] Among them, τ i is the time point of path optimization, λ i is the path correction weight, is the path P(t) at time point τ i The gradient of , n is the number of known points.

[0051] It should be noted that the generation of digital elevation model includes combining LiDAR point cloud to generate high-precision digital elevation model (DEM), and fusing it with orthophoto to generate complete terrain data. The corrected image parameters x and LiDAR point cloud data Z i Together they form the input for elevation modeling; the elevation of unknown points is calculated using interpolation algorithms, and the Laplace operator of image textures is used To improve the detail of the terrain model, the generated digital elevation model z(x,y) is expressed as:

[0052]

[0053] Among them, z(x,y) is the elevation value of the coordinate point (x,y), wi(x,y) is the interpolation weight, which is used to measure the influence of the point (x,y) on the known point i, z i is the elevation of the known point i in the LiDAR point cloud data, is the Laplacian operator of image texture, γ is the image texture fusion weight, and n is the number of known points.

[0054] It should also be noted that the stitching and correction of image data obtained using the optimized path significantly improves the overall quality and geometric accuracy of the image. The SIFT algorithm is used to extract feature points of the image, making the feature matching of adjacent images more accurate. The bundle adjustment algorithm is combined to correct the geometric errors caused by drone movement and lens distortion. The path optimization gradient information is introduced, and the dynamic adjustment data of the flight path is integrated into the bundle adjustment process, which improves the accuracy of image correction. This processing method reduces the stitching errors caused by complex terrain, lighting changes and other factors in traditional methods, making the overall image parameters of the output more accurate and consistent. Combining LiDAR point cloud data and orthophotos, fine modeling of terrain data is achieved, the elevation values of unknown points are calculated through interpolation algorithms, and the Laplace operator of image texture is used to enhance the detail performance of the terrain model. The generated DEM not only has a higher spatial resolution, but also can more realistically reflect the terrain features and enhance the detail performance of the model, laying a solid foundation for the subsequent construction of a three-dimensional digital sand table.

[0055] S3: Generate a three-dimensional digital sandbox and embed dynamic update and multi-platform display functions.

[0056] Furthermore, the generation of a 3D digital sand table includes generating a 3D sand table model supported by LOD based on the digital elevation model z(x,y) and orthophoto. The LOD model is constructed in a layered manner, combined with the resolution parameter R ki and texture details T ki Describe the terrain accuracy at different levels through the Gaussian kernel function φ i (x,y) smooth texture changes make the model adaptable to multi-platform display requirements, expressed as:

[0057]

[0058] Among them, LOD k (x, y) is the detail weight of the k-th layer LOD sandbox model at the coordinate point (x, y), Rki is the k-th layer LOD resolution parameter, T ki is the k-th layer LOD texture detail weight, φi(x,y) is the Gaussian kernel function used to smooth texture changes, x i ,y i is the center point coordinate of the Gaussian kernel function, σ is the smoothing width of the Gaussian kernel function, z(x,y) is the elevation data of the terrain, γ is the image texture fusion weight, and m is the number of texture points of the LOD model.

[0059] It should be noted that the embedded dynamic update and multi-platform display functions include integrating a real-time data stream update module, dynamically updating the sand table model through new aerial photography data, establishing a multi-user collaboration platform, supporting hierarchical permission management and collaborative editing, and providing a cloud platform-based remote access interface for digital sand table sharing and dynamic interaction among multiple devices.

[0060] It should also be noted that the three-dimensional digital sand table is generated based on the digital elevation model (DEM) and orthophoto, which realizes the comprehensive digital transformation from two-dimensional to three-dimensional. The sandbox model supported by the 3D 3D Rendering (DVR) framework uses a layered construction approach to provide adaptive terrain accuracy and texture detail at different resolutions. This ensures that the sandbox model can display fine terrain details on high-performance devices while maintaining smooth operation on low-performance devices. The Gaussian kernel function is used to smooth texture changes, making the sandbox visual presentation more natural and avoiding the sense of model fragmentation caused by multi-resolution switching. This feature expands the applicable scenarios of the 3D sandbox, meeting user needs for both sophisticated simulation training and lightweight mobile display. The embedded dynamic update function further enhances the practicality of the system. Through the real-time data stream update module, the system can dynamically update the sandbox model based on the latest aerial photography data, making the sandbox real-time and adaptable. With the support of the cloud platform, the sandbox model can be operated collaboratively by multiple users, providing hierarchical permission management, collaborative editing, and other functions. This not only meets the display needs of multiple platforms, but also makes the sandbox model more operational and shareable in practical scenarios such as urban planning and emergency response, providing users with a better interactive experience.

[0061] Example 2 is an embodiment of the present invention, which provides a digital conversion sandbox construction method based on aerial photography technology. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0062] First, the experiment selected six representative areas, covering complex terrains such as plains, hills and mountains, to comprehensively evaluate the performance of the invention in different environments; the experiment used a drone equipped with multimodal sensors, including visible light, infrared cameras and LiDAR sensors, and was equipped with GNSS and IMU modules to ensure the positioning accuracy of image data. The experimental tasks were divided into four stages: data acquisition, image stitching and correction, digital elevation model generation, and three-dimensional digital sand table construction; in the experimental preparation stage, the meteorological conditions of the six target areas were first comprehensively evaluated, including factors such as wind speed, temperature and humidity, and light intensity. The dynamic path planning module was used to generate an optimized flight path based on the real-time environmental parameters in the area. The optimization of dynamic path planning took into account the wind speed weight and temperature and humidity adaptability, and adjusted the flight speed and path direction by real-time monitoring of environmental parameters. For example, in areas with high wind speeds, the system reduces the drone's flight speed and adjusts the flight altitude to reduce image jitter caused by wind speed. In areas with weak light or strong reflections, the dynamic exposure adjustment function optimizes the shooting parameters to ensure image clarity and color consistency. During the data collection process, the drone completes aerial photography tasks in six areas in turn. The collected multimodal data includes visible light images, thermal imaging data, and LiDAR point cloud data. Using GNSS and IMU modules, the system records the shooting position and attitude parameters in real time to ensure image positioning accuracy. The experiment also records the number and coverage of images collected in each area to measure the integrity of the data. In the image stitching and correction stage, the experiment inputs the collected images into the stitching and correction module. The SIFT algorithm is used to extract feature points from adjacent images, and the geometric distortion is corrected in combination with the bundle adjustment algorithm. The path optimization gradient information further improves the stitching accuracy. For example, in hilly areas (such as Area 3), the complex terrain undulations may cause overlapping errors in traditional stitching algorithms. However, by introducing path correction vectors, the system significantly reduces the error range. The corrected images are then fused with LiDAR point cloud data to generate a digital elevation model (DEM). During the DEM generation process, an interpolation algorithm is used to calculate the elevation of unknown points, and the Laplacian operator of the image texture is used to enhance the detail performance of the model. Finally, based on the digital elevation model and orthophotos, a three-dimensional sand table model supporting multiple levels of detail (LOD) is experimentally constructed, and dynamic update and multi-platform display functions are embedded. The real-time data stream update module is used in subsequent experiments to test the dynamic loading function of new data. At the same time, multi-user collaboration and remote access are achieved through the cloud platform. The experimental results show that the present invention is applicable to various terrain and climatic conditions, has advantages in high-precision image acquisition, rapid stitching correction, and dynamic three-dimensional sand table construction, and provides an efficient and innovative solution for geographic information processing and application.

[0063] Example 3, reference Figure 2, which is an embodiment of the present invention, provides a digital conversion sand table construction system based on aerial photography technology, including an image acquisition module, a stitching and correction module, and a sand table update module.

[0064] The image acquisition module is used to acquire aerial images and perform dynamic path planning and multimodal data recording; the stitching and correction module is used to stitch and correct aerial images to generate a digital elevation model; the sand table update module is used to generate a three-dimensional digital sand table and embed dynamic update and multi-platform display functions.

[0065] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0066] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0067] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0068] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc. It should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention and are not intended to be limiting. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced with equivalents without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications should be encompassed by the claims of the present invention.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A digital conversion sand table construction method based on aerial photography technology, characterized in that: include: Acquire aerial images and perform dynamic path planning and multimodal data recording; Stitching and correcting aerial images to generate digital elevation models; Generate a three-dimensional digital sandbox and embed dynamic update and multi-platform display functions.

2. The method for constructing a digital conversion sand table based on aerial photography technology according to claim 1, wherein: The dynamic path planning includes adjusting the UAV flight path according to wind speed, temperature and humidity parameters, and automatically optimizing the shooting parameters in low-light or highly reflective areas through the dynamic exposure adjustment function of the aerial photography equipment, which is expressed as: Among them, P(t) is the flight path, which represents the change of the trajectory of the UAV with time t, t0 represents the start time of the flight mission, and t f represents the end time of the flight mission, α is the speed weight, β is the adaptability weight, v(t) represents the flight speed of the UAV at time t, and η(t) is the environmental adaptability coefficient at time t, which is expressed as: Among them, w(t) is the wind speed at time t, T(t) is the real-time temperature at time t, T opt is the optimal temperature, κ1 is the wind speed weight parameter, and κ2 is the temperature deviation weight parameter.

3. The method for constructing a digital conversion sand table based on aerial photography technology according to claim 2, characterized in that: The multimodal data recording includes using drones equipped with visible light, infrared light and LiDAR sensors to collect surface details and three-dimensional terrain data, and using global navigation satellite system and inertial measurement unit data to correct the image shooting position and direction.

4. The method for constructing a digital conversion sand table based on aerial photography technology according to claim 3, wherein: The aerial image stitching and correction includes the UAV completing image acquisition according to the optimized path, stitching and correcting the overlapping areas of adjacent images, extracting the feature points of the image using the SIFT algorithm, correcting the geometric distortion of the image in combination with the bundle adjustment, correcting the geometric errors caused by the UAV movement and lens distortion based on the bundle adjustment algorithm, generating the corrected overall image, and converting the gradient information of the path optimization into the image. The bundle adjustment is introduced, and the output of stitching and correction is the corrected image parameter x, which is expressed as: x=(A T WA) -1 A T W(l+e(τ i )) Where x is the correction image parameter, A is the design matrix, including the design parameters of the bundle adjustment, W is the weighting matrix used to adjust the error weight, l is the observation value vector, representing the initial error of the image, e(τ i ) is the path correction vector, expressed as: Among them, τ i is the time point of path optimization, λ i is the path correction weight, is the path P(t) at time point τ i The gradient of , n is the number of known points.

5. The method for constructing a digital conversion sand table based on aerial photography technology according to claim 4, characterized in that: The generation of digital elevation model includes combining LiDAR point cloud to generate high-precision digital elevation model (DEM), and fusing it with orthophoto to generate complete terrain data. The corrected image parameters x and LiDAR point cloud data Z i Together they form the input for elevation modeling; The elevation of unknown points is calculated using interpolation algorithms, and the Laplace operator of image texture is used To improve the detail of the terrain model, the generated digital elevation model z(x,y) is expressed as: Among them, z(x,y) is the elevation value of the coordinate point (x,y), w i (x, y) is the interpolation weight, which is used to measure the influence of point (x, y) on the known point i, z i is the elevation of the known point i in the LiDAR point cloud data, is the Laplacian operator of image texture, γ is the image texture fusion weight, and n is the number of known points.

6. The method for constructing a digital conversion sand table based on aerial photography technology according to claim 5, characterized in that: The three-dimensional digital sand table generation includes generating a three-dimensional sand table model supported by LOD based on the digital elevation model z (x, y) and the orthophoto. The LOD model adopts a layered construction method and combines the resolution parameter R ki and texture details T ki Describe the terrain accuracy at different levels by using the Gaussian kernel function φ i (x,y) smooth texture changes make the model adaptable to multi-platform display requirements, expressed as: Among them, LOD k (x, y) is the detail weight of the k-th layer LOD sandbox model at the coordinate point (x, y), R ki is the k-th layer LOD resolution parameter, T ki is the k-th layer LOD texture detail weight, φ i (x,y) is the Gaussian kernel function used to smooth texture changes, x i ,y i is the center point coordinate of the Gaussian kernel function, σ is the smoothing width of the Gaussian kernel function, z(x,y) is the elevation data of the terrain, γ is the image texture fusion weight, and m is the number of texture points of the LOD model.

7. The method for constructing a digital conversion sand table based on aerial photography technology according to claim 6, characterized in that: The embedded dynamic update and multi-platform display functions include integrating a real-time data stream update module, dynamically updating the sandbox model through new aerial photography data, establishing a multi-user collaboration platform, supporting hierarchical permission management and collaborative editing, and providing a cloud platform-based remote access interface for digital sandbox sharing and dynamic interaction among multiple devices.

8. A system using the digital conversion sandbox construction method based on aerial photography technology according to any one of claims 1 to 7, characterized in that: Including image acquisition module, stitching and correction module, and sand table update module; The image acquisition module is used to acquire aerial images and perform dynamic path planning and multimodal data recording; The stitching and correction module is used to stitch and correct the aerial images to generate a digital elevation model; The sand table update module is used to generate a three-dimensional digital sand table and embed dynamic update and multi-platform display functions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the digital conversion sandbox construction method based on aerial photography technology according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the digital conversion sandbox construction method based on aerial photography technology according to any one of claims 1 to 7 are implemented.

Images