Image processing method and device based on mooring unmanned aerial vehicle

Abstract

The invention discloses an image processing method and device based on a mooring unmanned aerial vehicle, and relates to the technical field of image processing. The method comprises the following steps: receiving a construction request sent by a user side; controlling the mooring unmanned aerial vehicle to perform image acquisition on the target area according to the construction request; determining a target transmission mode based on the target area, obtaining a plurality of target images sent by the mooring unmanned aerial vehicle through the target transmission mode, and determining a first point location corresponding to the mooring unmanned aerial vehicle according to the target images; processing the plurality of target images to obtain a plurality of features; splicing the plurality of features to obtain a first sub-model corresponding to the first point location; and obtaining a plurality of first sub-models in sequence, and combining the plurality of first sub-models to construct a three-dimensional model corresponding to the target area. By implementing the technical scheme provided by the invention, the problem of insufficient real-time transmission capability of image data in the three-dimensional model construction process of a traditional unmanned aerial vehicle is solved, and the overall time of three-dimensional model construction is shortened.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an image processing method and device based on a tethered drone. Background Art

[0002] With the rapid progress of computer vision and deep learning technology, 3D reconstruction technology has increasingly become a core component of the digital world. In the current surveying and mapping, Geographic Information System (GIS), Building Information Modeling (BIM) and other industries, high-precision 3D modeling technology is receiving more and more widespread application and close attention.

[0003] The traditional 3D reconstruction process mainly relies on fixed-wing aircraft or rotary-wing drones, which are responsible for capturing large amounts of 2D image data in the air. After completing the image acquisition, the drone needs to return to the ground workstation, and then professionals will retrieve the image data from the drone. Next, the ground workstation will use high-performance computing resources to perform large-scale data processing to build an accurate 3D model. Although this method can successfully generate a 3D model, the lack of real-time transmission capability of image data makes the entire 3D model construction process relatively lengthy.

[0004] Therefore, there is an urgent need for an image processing method and device based on a tethered drone that can solve the above technical problems. Summary of the invention

[0005] The present application provides an image processing method and device based on a tethered drone, which solves the problem of insufficient real-time transmission capability of image data in a traditional drone during the three-dimensional model construction process and shortens the overall time of three-dimensional model construction.

[0006] In a first aspect, the present application provides an image processing method based on a tethered drone, the method comprising: receiving a construction request sent by a user terminal; controlling the tethered drone to collect images of a target area according to the construction request; determining a target transmission mode based on the target area, and obtaining multiple target images sent by the tethered drone through the target transmission mode, determining a first point position corresponding to the tethered drone according to the target image, the first point position being the positioning information of the tethered drone in the target area; processing the multiple target images to obtain multiple features; splicing the multiple features to obtain a first sub-model corresponding to the first point position; obtaining multiple first sub-models in sequence, and combining the multiple first sub-models to construct a three-dimensional model corresponding to the target area.

[0007] By adopting the above technical solution, a tethered drone is used to collect images of the target area, and the target image is obtained in real time through target transmission. The image data can be processed immediately without waiting for the drone to return. After receiving the target image, the target image is processed to obtain multiple features, and the features of each image are gradually processed. These features are spliced ​​into sub-models in real time, and then these sub-models are combined into a complete three-dimensional model. The model begins to be constructed during the collection of other images of the target area, further shortening the overall time, determining the first point position of the tethered drone, and accurately placing and combining each sub-model during the construction process, thereby improving the accuracy of the three-dimensional model, and solving the problems of insufficient real-time transmission capability of image data and lengthy overall construction process of traditional drones in the three-dimensional model construction process.

[0008] Optionally, before determining the target transmission mode based on the target area and acquiring multiple target images sent by the tethered drone through the target transmission mode, the method also includes: dividing the target area to obtain multiple sub-areas; acquiring a first sub-area from the multiple sub-areas, and acquiring environmental information and facility information corresponding to the first sub-area; determining the target transmission mode based on the environmental information and facility information, the target transmission mode including a wireless transmission mode and a tethered cable transmission mode; when the target transmission mode is a wireless transmission mode, generating a target acquisition path for the first sub-area; and sending the target acquisition path to the tethered drone so that the tethered drone acquires the target image for the first sub-area according to the target acquisition path.

[0009] By adopting the above technical solution, the target area is divided into multiple sub-areas, and then the environmental information and facility information of the first sub-area are obtained, and the target transmission mode is determined. The transmission mode of the tethered drone is determined based on the actual situation, which can improve the transmission efficiency and collection quality. When it is determined to adopt the wireless transmission mode, the target collection path of the first sub-area can be generated and sent to the tethered drone so that the drone can collect images along the optimal path, avoiding unnecessary repeated collection and waste of resources on the flight path.

[0010] Optionally, generating a target collection path for the first sub-area specifically includes: obtaining a target power level corresponding to the tethered drone, and determining a first shooting duration based on the target power level; obtaining an area corresponding to the first sub-area, and determining a second shooting duration based on the area; determining whether the first shooting duration is greater than or equal to the second shooting duration; when the first shooting duration is greater than or equal to the second shooting duration, confirming the generation of a target collection path corresponding to the first sub-area.

[0011] By adopting the above technical solution, the target power of the tethered drone is obtained, and the first shooting duration is determined based on the power, which can ensure that the drone will not be interrupted due to insufficient power when performing the collection task; the second shooting duration is determined based on the area of ​​the first sub-area, which reflects the full consideration of the size of the shooting area. By comparing the first shooting duration and the second shooting duration, it can be determined whether the drone has enough power to complete the comprehensive collection of the first sub-area. When the first shooting duration is greater than or equal to the second shooting duration, it is confirmed that the target collection path is generated, which ensures that the drone can collect efficiently according to the planned path when the power is sufficient.

[0012] Optionally, a target transmission mode is determined based on the target area, and multiple target images sent by the tethered drone are obtained through the target transmission mode, specifically including: obtaining a target distance, where the target distance is the distance between a first point and a second point, and the second point is a position corresponding to a ground service station; if the target distance is greater than a preset distance, an encrypted instruction is sent to the tethered drone, so that the tethered drone encrypts the multiple target images according to the encrypted instruction to obtain a first image; and receiving the first image sent by the tethered drone via wireless transmission, and outputting the first image as multiple target images.

[0013] By adopting the above technical solution, the target distance (i.e., the distance between the first point and the ground service station) is obtained, and the relative position relationship between the UAV and the service station can be determined. When the target distance exceeds the preset value, an encrypted instruction is sent to the tethered UAV, requiring the UAV to encrypt multiple target images. This encryption process can ensure the security of the image data during the transmission process, and then the encrypted image (i.e., the first image) is sent to the ground service station via wireless transmission.

[0014] Optionally, multiple target images are processed to obtain multiple features, specifically including: decrypting the first image to obtain multiple second images; preprocessing the multiple second images to obtain preprocessed second images; and extracting features from the preprocessed second images to obtain multiple features.

[0015] By adopting the above technical solution, the first image is decrypted and the original multiple second images can be restored. This step is the basis for ensuring that subsequent image processing and analysis can be carried out correctly, because the encrypted image data cannot be directly used before decryption. Preprocessing the decrypted second image, such as denoising, contrast enhancement, distortion correction, etc., can significantly improve the image quality. Feature extraction on the preprocessed image can more effectively identify key points, edges, textures and other features in the image. These features are important bases for building a three-dimensional model because the features can reflect the shape, structure and texture information of the target area.

[0016] Optionally, multiple features are spliced ​​to obtain a first sub-model corresponding to the first point, specifically including: obtaining a third image and a fourth image from multiple second images; extracting the third image to obtain a first feature set; extracting the fourth image to obtain a second feature set; judging whether there are target features in the first feature set that are the same as those in the second feature set; when there are target features in the first feature set that are the same as those in the second feature set, splicing the third image and the fourth image according to the target features to obtain a fifth image, and generating a first sub-model based on the fifth image.

[0017] By adopting the above technical solution, the third image and the fourth image are feature extracted to obtain the first feature set and the second feature set, and it is determined whether there are the same target features in the two feature sets. This feature matching method can accurately identify the common parts of the two images and provide an accurate alignment basis for subsequent image stitching; when the first feature set contains the same target features as those in the second feature set, it means that the third image and the fourth image are consistent at the feature level. Image stitching based on these common features can generate a more complete and accurate fifth image. This fifth image, as the basis for constructing the first sub-model, can more realistically reflect the actual situation of the target area and improve the accuracy and reliability of the model.

[0018] Optionally, after sending the target acquisition path to the tethered drone so that the tethered drone acquires an image of the first sub-area according to the target acquisition path, the method further includes: if it is determined that the tethered drone is in a charging state, acquiring a second sub-area, the target transmission mode of the second sub-area is a tethered cable transmission mode, and the second sub-area is any sub-area among the multiple sub-areas except the first sub-area; sending an image acquisition instruction to the tethered drone so that the tethered drone acquires an image of the second sub-area according to the image acquisition instruction to obtain a sixth image, and sending the acquired sixth image via a tethered cable transmission mode, and outputting the sixth image as a target image.

[0019] By adopting the above technical solution, the charging status of the tethered drone can be monitored in real time, and the image acquisition and transmission methods can be dynamically adjusted according to the charging status. When the drone is in the charging state, the tethered cable transmission method can be used preferentially. During the charging of the drone, image transmission through the tethered cable can avoid the energy consumption and signal interference problems that may be caused by the wireless transmission method. This also enables the drone to continue to perform image acquisition tasks while charging, thereby optimizing resource utilization. And during the charging of the drone, using a tethered cable for image transmission can ensure the stable transmission of image data and avoid transmission interruptions or data loss due to signal interference or insufficient power.

[0020] In a second aspect of the present application, an image processing device based on a tethered drone is provided, the device comprising a receiving unit, a processing unit and a stitching unit, the receiving unit receiving a construction request sent by a user terminal; the processing unit controlling the tethered drone to collect images of a target area according to the construction request; determining a target transmission mode based on the target area, and acquiring multiple target images sent by the tethered drone through the target transmission mode, determining a first point position corresponding to the tethered drone according to the target image, the first point position being positioning information of the tethered drone in the target area; processing the multiple target images to obtain multiple features; a stitching unit stitching the multiple features to obtain a first sub-model corresponding to the first point position; sequentially acquiring multiple first sub-models, and combining the multiple first sub-models to construct a three-dimensional model corresponding to the target area.

[0021] Optionally, the processing unit is used to divide the target area into multiple sub-areas; the receiving unit is used to obtain a first sub-area from the multiple sub-areas, and obtain environmental information and facility information corresponding to the first sub-area; the processing unit is used to determine a target transmission mode based on the environmental information and facility information, and the target transmission mode includes a wireless transmission mode and a tethered cable transmission mode; when the target transmission mode is a wireless transmission mode, a target acquisition path for the first sub-area is generated; the target acquisition path is sent to the tethered drone, so that the tethered drone performs target image acquisition on the first sub-area according to the target acquisition path.

[0022] Optionally, the receiving unit is used to obtain a target power level corresponding to the tethered drone, and determine a first shooting duration based on the target power level; obtain an area corresponding to the first sub-area, and determine a second shooting duration based on the area; the processing unit is used to determine whether the first shooting duration is greater than or equal to the second shooting duration; when the first shooting duration is greater than or equal to the second shooting duration, confirm the generation of a target acquisition path corresponding to the first sub-area.

[0023] Optionally, the receiving unit is used to obtain the target distance, which is the distance between the first point and the second point, and the second point is the position corresponding to the ground service station; the processing unit is used to send an encrypted instruction to the tethered drone if the target distance is greater than a preset distance, so that the tethered drone encrypts multiple target images according to the encrypted instruction to obtain a first image; the receiving unit is used to receive the first image sent by the tethered drone via wireless transmission, and output the first image as multiple target images.

[0024] Optionally, the processing unit is used to decrypt the first image to obtain multiple second images; preprocess the multiple second images to obtain preprocessed second images; and extract features from the preprocessed second images to obtain multiple features.

[0025] Optionally, the receiving unit is used to obtain a third image and a fourth image from multiple second images; the processing unit is used to extract the third image to obtain a first feature set; extract the fourth image to obtain a second feature set; determine whether there are target features in the first feature set that are the same as those in the second feature set; the splicing unit is used to splice the third image and the fourth image according to the target features to obtain a fifth image when there are target features in the first feature set that are the same as those in the second feature set, and generate a first sub-model based on the fifth image.

[0026] Optionally, if it is determined that the tethered drone is in a charging state, the receiving unit is used to obtain a second sub-area, the target transmission mode of the second sub-area is the tethered cable transmission mode, and the second sub-area is any sub-area among the multiple sub-areas except the first sub-area; the processing unit is used to send an image acquisition instruction to the tethered drone, so that the tethered drone performs image acquisition on the second sub-area according to the image acquisition instruction to obtain a sixth image, and sends the acquired sixth image via the tethered cable transmission mode, and outputs the sixth image as the target image.

[0027] In a third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory, so that an electronic device executes any one of the methods described above in the present application.

[0028] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any one of the above methods of the present application is executed.

[0029] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Use a tethered drone to collect images of the target area, and obtain the target image in real time through target transmission. The image data can be processed immediately without waiting for the drone to return. After receiving the target image, the target image is processed to obtain multiple features. The features of each image are gradually processed, and these features are spliced ​​into sub-models in real time, and then these sub-models are combined into a complete three-dimensional model. The model begins to be constructed while other images of the target area are collected, further shortening the overall time, determining the first point position of the tethered drone, and accurately placing and combining each sub-model during the construction process, thereby improving the accuracy of the three-dimensional model, solving the problems of insufficient real-time transmission capability of image data and long overall construction process in the three-dimensional model construction process of traditional drones.

[0030] 2. Extract features from the third and fourth images to obtain the first and second feature sets, and determine whether the same target features exist in the two feature sets. This feature matching method can accurately identify the common parts of the two images and provide an accurate alignment basis for subsequent image stitching. When the first feature set contains the same target features as those in the second feature set, it means that the third and fourth images are consistent at the feature level. Image stitching based on these common features can generate a more complete and accurate fifth image. This fifth image, as the basis for building the first sub-model, can more realistically reflect the actual situation of the target area and improve the accuracy and reliability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flowchart of an image processing method based on a tethered drone provided in an embodiment of the present application; Figure 2 is a structural schematic diagram of an image processing device based on a tethered drone provided in an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0032] Description of reference numerals: 201, receiving unit; 202, processing unit; 203, splicing unit; 300, electronic device; 301, processor; 302, memory; 303, user interface; 304, network interface; 305, communication bus. DETAILED DESCRIPTION

[0033] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0034] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0035] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0036] With the rapid progress of computer vision and deep learning technology, 3D reconstruction technology has increasingly become a core component of the digital world. In the current surveying and mapping, geographic information system (GIS), building information model (BIM) and other industries, high-precision 3D modeling technology is receiving more and more widespread application and close attention. Among them, the use of unmanned aerial vehicles to perform aerial photography tasks has become a mainstream and efficient operation mode.

[0037] The traditional 3D reconstruction process mainly relies on fixed-wing aircraft or rotary-wing drones, which are responsible for capturing large amounts of 2D image data in the air. After completing the image acquisition, the drone needs to return to the ground workstation, and then professionals will retrieve the image data from the drone. Next, the ground workstation will use high-performance computing resources to perform large-scale data processing to build an accurate 3D model. Although this method can successfully generate a 3D model, the lack of real-time transmission capabilities of image data makes the entire 3D model construction process relatively long.

[0038] Therefore, how to solve the problem of insufficient real-time transmission capability of image data in the process of building a three-dimensional model of a traditional UAV. An image processing method based on a tethered UAV provided in an embodiment of the present application is applied to a server. Figure 1 is a flow chart of an image processing method based on a tethered drone provided in an embodiment of the present application, with reference to Figure 1 The method includes the following steps S101-S106.

[0039] S101: receiving a construction request sent by a user terminal.

[0040] In the above S101, the user submits a build request through a client (such as a computer, mobile phone APP, etc.). The request usually includes parameters such as the geographic location, range, expected accuracy, and possible special requirements (such as time limit, model resolution, etc.) of the target area. At this time, the user refers to the staff, and the server refers to the platform corresponding to the ground service station. After receiving the build request, the server performs preliminary verification and analysis on the build request to ensure that the request information is complete and meets the system requirements. Then, according to the build request, it is determined to collect images of the target area, and the collected images are processed to build a three-dimensional model that conforms to the target area.

[0041] S102: Controlling the tethered drone to collect images of the target area according to the build request.

[0042] In the above S102, after determining the location of the model to be constructed by the construction request, the location refers to the target area. According to the characteristics of the target area (such as size, terrain, weather conditions, etc.), and the special requirements in the user request, this application determines to use a tethered drone for image acquisition. A tethered drone is a special drone system that is connected to the ground or other carriers through an optoelectronic integrated cable to achieve power transmission, so that the drone can stay in the air for a long time without being restricted by power. Compared with ordinary drones, tethered drones have more stable flight and power supply, and are suitable for long-term, high-precision operations. In addition to transmitting the collected image data through cables, tethered drones can also use wireless transmission to transmit the collected image data. In order to better collect images of the target area, this application can carry multiple cameras on the tethered drone to meet the image acquisition requirements of different directions of the same point. The server generates a flight mission based on the construction request, and then sends the flight mission to the tethered drone so that the tethered drone can collect images of the entire target area to meet the needs of subsequent modeling.

[0043] In addition, in order to ensure that the image data collected by the tethered drone can cover the entire target area, the target area can be divided into multiple sub-areas, and then the transmission mode is determined according to the environmental information and facility information of each sub-area, and then the path for the tethered drone to collect images of the target area is planned, which specifically includes: dividing the target area to obtain multiple sub-areas; obtaining a first sub-area from the multiple sub-areas, and obtaining the environmental information and facility information corresponding to the first sub-area; determining the target transmission mode according to the environmental information and facility information, and the target transmission mode includes a wireless transmission mode and a tethered cable transmission mode; when the target transmission mode is a wireless transmission mode, generating a target collection path for the first sub-area; sending the target collection path to the tethered drone, so that the tethered drone collects target images of the first sub-area according to the target collection path.

[0044] Specifically, the target area is divided into multiple smaller and easier-to-manage sub-areas so that the environment and facility information of each sub-area can be obtained more accurately, so as to make more appropriate decisions on transmission methods and collection paths. Geographic Information System (GIS) or remote sensing technology can be used to obtain map data of the target area. According to factors such as the terrain, landform, and building distribution of the target area, algorithms (such as clustering algorithms, grid division, etc.) are used to divide the area into multiple sub-areas. Ensure that each sub-area has clear boundaries and identification for subsequent management and analysis. The first sub-area is obtained from multiple sub-areas. Next, the first sub-area is processed as an example to obtain the specific environment and facilities of the first sub-area, providing a basis for determining the transmission method and collection path. A ground vehicle can be used to conduct preliminary exploration of the first sub-area to collect environmental information (such as terrain, climate, vegetation, etc.) and facility information (such as buildings, roads, communication facilities, etc.). The collected information is sorted and analyzed to form a detailed environmental and facility report. According to the content of the report, the characteristics and restrictions of the first sub-area are evaluated to provide basic data for subsequent steps. According to the environment and facilities in the first sub-area, select the most appropriate transmission method to ensure the stability and efficiency of data transmission. Analyze the impact of factors such as terrain and climate in the first sub-area on wireless transmission methods (such as signal attenuation, interference, etc.). Evaluate the support degree of communication facilities (such as base stations, relay stations, etc.) in the first sub-area for wireless transmission methods. If the wireless transmission method is severely restricted or cannot meet the needs, consider using the tethered cable transmission method and use the wired connection between the drone and the ground station for data transmission. After comprehensive analysis, determine the target transmission method. The tethered cable refers to the connection between the tethered drone and the ground power supply through the tethered cable. The cable contains not only the power line, but also the communication line and data line. When the drone is hovering in the air, it obtains power and data transmission through the cable, so that it can continue to perform the task. When the target transmission method is determined to be the wireless transmission method, it is necessary to plan an efficient and safe collection path for the tethered drone to ensure that high-quality image data is obtained in the first sub-area. A path planning algorithm (such as sampling-based path planning method, A* algorithm, etc.) can be used to generate an optimal path from the starting point to the end point for the drone. Considering that the UAV is limited by power when using wireless transmission and needs to return to the ground server for charging, the starting point and the end point can be set to the same location. Considering factors such as obstacles and terrain undulations in the first sub-area, make necessary adjustments and optimizations to the path. Ensure that the path can cover all key areas and facilities in the first sub-area to obtain comprehensive image data. Verify the feasibility and safety of the path to ensure that the UAV will not encounter danger during the collection process. Use the communication link between the UAV and the ground station (such as wireless link, tethered cable, etc.) to send the target collection path to the UAV. Ensure that the UAV can correctly receive and parse the path information.The drone adjusts its flight attitude and speed based on the received path information and collects images along the predetermined path. During the collection process, the drone may need to fine-tune the path based on actual conditions to ensure that high-quality image data is collected.

[0045] Further, confirming the generation of the target acquisition path of the first sub-area specifically includes: obtaining the target power corresponding to the tethered drone, determining the first shooting duration according to the target power; obtaining the area corresponding to the first sub-area, determining the second shooting duration according to the area; judging whether the first shooting duration is greater than or equal to the second shooting duration; when the first shooting duration is greater than or equal to the second shooting duration, confirming the generation of the target acquisition path corresponding to the first sub-area. Specifically, when the tethered drone performs image acquisition on the first sub-area, the collected image data needs to be sent to the server via wireless transmission. At this time, the drone will be limited by the power through the wireless transmission mode, so it is necessary to generate the target acquisition path according to the power of the tethered drone. The current battery power is obtained through the battery management system or related sensors of the drone. If the drone is in a charging state or is about to be charged, the charging speed and the estimated full time need to be considered to calculate the target power. The target power is appropriately adjusted by comprehensively considering the flight power consumption of the drone and the influence of environmental factors (such as wind speed, temperature, etc.) on battery performance. Then, according to the power limit of the drone, determine the time it can continue shooting. The flight performance parameters of the drone (such as maximum flight time, average flight power consumption, etc.) can be referred to, and the first shooting duration can be calculated in combination with the target power. Since the drone of this application also involves real-time image transmission, the power consumption changes caused by image transmission need to be considered during the shooting process, and the power consumption changes caused by flight altitude, speed, and shooting mode need to be considered, and the first shooting duration is dynamically adjusted. Then use the geographic information system (GIS) or remote sensing technology to obtain the area data of the first sub-area. If the area data is not accurate enough or there are errors, it can be calibrated by field measurement or drone flight measurement. According to the shooting speed of the drone (such as the number of frames per second, the coverage of each frame of the image, etc.) and the shooting requirements (data transmission), calculate the second shooting time required to complete the shooting of the first sub-area. Taking into account the impact of obstacles, terrain undulations, and other factors that may be encountered during the shooting process on the shooting speed, the second shooting time is appropriately adjusted. Compare the first shooting time and the second shooting time to ensure whether the current power of the drone meets the requirements for comprehensive shooting of the first sub-area. Directly compare the first shooting time with the second shooting time. If the first shooting time is greater than or equal to the second shooting time, it means that the power of the drone is sufficient to support the shooting task of the first sub-area. When confirming that the power is sufficient, plan a suitable collection path for the drone. Use a path planning algorithm (such as sampling-based path planning method, A* algorithm, etc.) to generate an optimal path from the starting point to the end point for the drone. Considering the shooting requirements (such as resolution, overlap rate, etc.) and the characteristics of the first sub-area (such as terrain, obstacles, etc.), make necessary adjustments and optimizations to the path. Ensure that the path can cover all key areas and facilities in the first sub-area to obtain comprehensive image data.Verify the feasibility and safety of the path again to ensure that the drone will not encounter danger or exceed its performance limits during the collection process. For example, if the drone's battery power is 100, determine the first shooting duration according to the drone's shooting requirements and flight power consumption. The first shooting duration also needs to consider the time it takes for the drone to return to the starting point. If the first shooting duration is 20 minutes, then obtain the area corresponding to the first sub-area. If the area is 80 square meters, the required shooting duration is determined to be 15 minutes based on the area, that is, the second shooting duration is 15 minutes. Compare the first shooting duration with the second shooting duration. At this time, the first shooting duration is longer than the second shooting duration, so it is assumed that the drone's battery power can support the completion of the shooting task for the first sub-area.

[0046] Furthermore, when the first shooting time is less than the second shooting time, it is confirmed that the first sub-area is segmented again, and the first sub-area is divided into multiple specific areas, and then the multiple specific areas are processed according to the above-mentioned path planning method for the first sub-area, so as to obtain multiple paths, and the paths are sent to the drone in sequence according to the serial numbers corresponding to each specific area, so that the drone can collect images of each specific area in turn to complete the image collection task of the entire target area.

[0047] In a possible implementation, if the drone needs to be charged after completing the shooting task of the first sub-area, during the charging process, other sub-areas in the target area where image acquisition has not been performed can be checked. If there are other sub-areas where image acquisition has not been performed, the tethered drone can be charged using the tethered cable, and then the tethered drone can collect images of other sub-areas while charging, and transmit the collected image data to the ground server through the cable. That is, if it is determined that the tethered drone is in a charging state, the second sub-area is obtained, the target transmission mode of the second sub-area is the tethered cable transmission mode, and the second sub-area is any sub-area of ​​the multiple sub-areas except the first sub-area; an image acquisition instruction is sent to the tethered drone, so that the tethered drone performs image acquisition on the second sub-area according to the image acquisition instruction, obtains a sixth image, and sends the collected sixth image through the tethered cable transmission mode, and outputs the sixth image as the target image. Specifically, the service station or the drone management system continuously monitors the status of the tethered drone, including the power level, charging status, etc. This can be achieved through a communication link between the drone and the ground station (such as a communication channel in the tethered cable). According to the monitored data, determine whether the tethered drone is in a charging state. If the drone has sufficient power and is being charged through the tethered cable, it is confirmed that it is in a charging state. At this time, sufficient power means that the drone is still in a usable state, but the drone needs to be charged. The environmental information and facility information of multiple sub-areas have been obtained in advance, and the corresponding transmission mode of each sub-area is determined. At this time, the second sub-area that needs to use the tethered cable transmission mode is obtained in multiple sub-areas. The second sub-area represents any sub-area in the target area that needs to use the tethered cable transmission mode. The detailed information of the second sub-area needs to be obtained, including the geographical location, boundary coordinates, etc. Ensure that the information obtained is accurate to facilitate the subsequent sending of instructions and the accuracy of image acquisition. Generate an image acquisition instruction based on the information of the second sub-area and the image acquisition requirements. The instruction should contain key information such as the acquisition area, acquisition method (such as resolution, frame rate, etc.), and acquisition time. The communication link between the drone and the ground station (such as the communication channel in the tethered cable) sends the image acquisition instruction to the tethered drone. Ensure the integrity and accuracy of the instruction to avoid errors or losses during transmission. The tethered drone receives the image acquisition instruction sent by the ground station. According to the command requirements, adjust the camera or sensor parameters of the drone (such as focal length, exposure time, etc.). Collect images of the second sub-area to ensure that the quality and quantity of the collected images meet the task requirements. The tethered drone sends the collected sixth image to the ground service station through the data transmission channel in the tethered cable. During the transmission process, ensure the integrity and real-time of the image to avoid data loss or delay. The ground service station receives and stores the transmitted sixth image. Output the sixth image as the target image for subsequent processing or analysis.

[0048] S103: Determine a target transmission mode based on the target area, obtain multiple target images sent by the tethered drone through the target transmission mode, and determine a first point position corresponding to the tethered drone according to the target image.

[0049] In the above S103, the tethered drone transmits the collected images to the ground control station or server in real time through wired or wireless means (such as optical fiber, Wi-Fi, 4G / 5G, etc.). At the same time, the drone sends its own position information (i.e., the first point) in real time through GPS or other positioning technologies (such as visual positioning, laser ranging, etc.), which is crucial for subsequent three-dimensional modeling.

[0050] In addition, the target transmission method is used to obtain multiple target images sent by the tethered drone, specifically including: obtaining the target distance, the target distance is the distance between the first point and the second point, and the second point is the position corresponding to the ground service station; if the target distance is greater than the preset distance, an encryption instruction is sent to the tethered drone, so that the tethered drone encrypts the multiple target images according to the encryption instruction to obtain the first image; the first image sent by the tethered drone through the wireless transmission method is received, and the first image is output as multiple target images. Specifically, when the target transmission method is the wireless transmission method, the first point needs to be determined, at which time the first point is the current position of the drone, and then the second point of the ground service station is obtained, and then the distance between the first point and the second point is calculated, so as to determine whether the image needs to be encrypted and transmitted according to the distance. Use GPS or other positioning technology to obtain the precise coordinates of the first point and the second point (ground service station). If the first point and the second point are plane coordinates, the Euclidean distance formula can be used. Assuming the coordinates of the first point are (x1, y1) and the coordinates of the second point are (x1, y1), the straight-line distance d between the two points can be calculated using the following formula: .

[0051] If the first point and the second point are longitude and latitude coordinates, use the Haversine formula or other distance calculation formula to calculate the straight-line distance between the first point and the second point. Assume that the longitude and latitude of two points A and B are (lat1, lon2) and (lat2, lon2) respectively, where the latitude is expressed in radians (which can be converted by multiplying the degree by π / 180), and the longitude is also in radians. Then the straight-line distance d (in kilometers) between the two points can be calculated using the following formula: ;Where R is the radius of the Earth, it is usually set to 6371 km (this is a common average value for the WGS-84 ellipsoid). ; is the difference in latitude between the two points. ; is the difference between the longitudes of the two points. Since longitude and latitude are usually expressed in degrees, they need to be converted into radians before applying the Haversine formula. After the distance between the first point and the second point, i.e., the target distance, is calculated by the above formula, the target distance is compared with the preset distance. The preset distance is a reasonable preset distance threshold set according to factors such as communication security requirements and drone performance. The calculated target distance is compared with the preset distance. If the target distance is greater than the preset distance, it is determined that the image needs to be encrypted for transmission; otherwise, it is determined that encryption is not required. When encrypted transmission is required, an encryption instruction is sent to the drone to instruct it to perform image encryption processing. According to the encryption algorithm and security requirements, an encryption instruction containing information such as encryption key and encryption method is generated. The encryption instruction is securely sent to the drone through the communication link (wireless link in this case) between the drone and the ground service station. After the drone receives and confirms the encryption instruction, it is ready to perform image encryption processing. The drone collects target images according to the preset shooting plan or instruction. The encryption algorithm and key in the received encryption instruction are used to encrypt the multiple target images collected to obtain the encrypted first image. The encrypted image is stored in the storage device of the drone and waits for transmission. The drone sends the encrypted first image to the ground service station via wireless transmission (such as Wi-Fi, 4G / 5G network, etc.). The ground service station receives and stores the encrypted image. When necessary, the encrypted image is output as multiple target images.

[0052] S104: Process multiple target images to obtain multiple features.

[0053] In the above S104, the received target image is preprocessed, including denoising, contrast enhancement, distortion correction, etc., to improve the image quality. Computer vision algorithms (such as edge detection, corner detection, SIFT / SURF feature point extraction, etc.) are used to extract key feature points, edges, textures and other information from each image.

[0054] At this time, multiple target images are processed to obtain multiple features, specifically including: decrypting the first image to obtain multiple second images; preprocessing the multiple second images to obtain the preprocessed second images; and extracting features from the preprocessed second images to obtain multiple features. Specifically, since multiple target images corresponding to the first point of the first sub-area are received at this time, and since the distance between the first point and the second point is relatively far, the multiple target images received are the encrypted first images. The key used when encrypting the image corresponding to the first point is determined, and then the corresponding decryption key is determined according to the key. The corresponding decryption algorithm can be selected according to the encryption algorithm used during encryption. For example, if a bitwise XOR algorithm is used during encryption, the same bitwise XOR algorithm is also used during decryption. The decryption key and the encryption algorithm are applied to the first image to perform the decryption operation. During the decryption process, the integrity and accuracy of the data are ensured. After decryption, the obtained image is verified to ensure that the image content is complete, clear, and not damaged. If the decryption result does not meet expectations, the decryption key, algorithm, and encryption process are rechecked to find out the problem and correct it. The decrypted images (i.e., multiple second images) are stored in a safe location for subsequent processing. The color image can be converted to a grayscale image first to reduce the amount of data and increase the processing speed. Grayscale processing can be performed using methods such as component method, maximum method, average method, or weighted average method. The image is subjected to geometric transformations such as translation, rotation, and scaling to correct the distortion and error in the image. A grayscale interpolation algorithm is used to process non-integer coordinate pixels that may be generated after the transformation. The contrast of the image is enhanced by contrast stretching, histogram equalization, and other methods. Smoothing and sharpening algorithms are used to remove noise or enhance edge details. The preprocessed image is verified to ensure that the image quality meets the requirements of subsequent processing. Then, according to the characteristics of the image and the requirements of subsequent processing, a suitable feature extraction method is selected. Common feature extraction methods include SIFT (scale invariant feature transform), SURF (speeded up robust features), HOG (histogram of oriented gradients), LBP (local binary pattern), etc. The preprocessed image is subjected to feature extraction using the selected feature extraction method. During the extraction process, the accuracy and completeness of the features are ensured. The extracted features are screened and optimized to remove redundant and irrelevant features. Use feature dimensionality reduction techniques (such as PCA, LDA, etc.) to further reduce the number and dimensions of features. Store the extracted feature data in a secure location for subsequent analysis and processing. Verify the extracted features to ensure that they can accurately describe the content and characteristics of the image.

[0055] S105: Combine multiple features to obtain a first sub-model corresponding to the first point.

[0056] In the above S105, the features in different images are matched to determine the relative positional relationship between them. Based on the feature matching results, an image stitching algorithm (such as stitching based on feature points, stitching based on image blocks, etc.) is used to stitch multiple images into a larger image or a local three-dimensional model (i.e., the first sub-model).

[0057] In addition, since multiple cameras are deployed on the tethered drone, when the tethered drone collects images at a certain point, each camera will take an image, and multiple images can be obtained. One point corresponds to multiple images, one image corresponds to one camera, and one camera corresponds to one angle. After sending multiple images of the same point, the multiple images need to be processed and spliced ​​to obtain image data at different angles at the point, and then the sub-model corresponding to the point is constructed. Splicing multiple features to obtain the first sub-model corresponding to the first point specifically includes: obtaining a third image and a fourth image from multiple second images; extracting the third image to obtain a first feature set; extracting the fourth image to obtain a second feature set; judging whether there is a target feature in the first feature set that is the same as that in the second feature set; when there is a target feature in the first feature set that is the same as that in the second feature set, splicing the third image and the fourth image according to the target feature to obtain a fifth image, and generating the first sub-model according to the fifth image. Specifically, from the multiple second images, screening is performed based on factors such as image content, quality, and resolution. At this time, multiple second images all represent image data corresponding to the first point. First, the third image and the fourth image required for subsequent processing are selected from the multiple second images. The third image and the fourth image are any two images from the multiple second images. The number of images corresponding to a point is determined according to the number of cameras on the tethered drone. When the number of cameras is 4, the number of images corresponding to the first point is 4. Verify the selected third and fourth images to ensure that they meet the processing requirements. According to the characteristics of the third image and the needs of subsequent processing, select a suitable feature extraction method. Commonly used feature extraction methods include edge detection, corner detection, texture analysis, color histogram, etc. Use the selected feature extraction method to extract features from the third image. During the extraction process, ensure the accuracy and completeness of the features. Integrate the extracted features into the first feature set. Sort and optimize the feature set to remove redundant and irrelevant features. According to the characteristics of the fourth image and the needs of subsequent processing, select a suitable feature extraction method. Use the selected feature extraction method to extract features from the fourth image. Integrate the extracted features into the second feature set. Use feature matching algorithms (such as SIFT matching, SURF matching, template matching, etc.) to match the features in the first feature set with the features in the second feature set. During the matching process, calculate the similarity or distance between the features to determine whether there are the same target features. The same target features can also be understood as whether there are overlapping parts. If there are overlapping parts, the two images can be assumed to be adjacent images. Verify the matching results to ensure the accuracy and reliability of the matching. When the first feature set has the same target features as those in the second feature set, the third image and the fourth image are spliced ​​according to these target features. During the splicing process, it is necessary to ensure the accuracy and continuity of the images to avoid dislocation or overlap.The stitched images are optimized to improve the quality and clarity of the images. Image fusion, smoothing and other techniques can be used to eliminate stitching traces and noise. A first sub-model is generated based on the stitched fifth image. The sub-model can be a machine learning model or a deep learning model for tasks such as image recognition, classification, and detection. If there are other images in addition to the third image and the fourth image in the multiple second images, the other images are stitched with the third image or the fourth image in turn according to the above-mentioned processing method for the third image and the fourth image, and then the first point is a complete image, and a sub-model is constructed based on these images. At this time, the sub-model refers to the image data corresponding to a certain point, and one sub-model corresponds to one point.

[0058] S106: Acquire multiple first sub-models in sequence, and combine the multiple first sub-models to construct a three-dimensional model corresponding to the target area.

[0059] In the above S106, the above image acquisition, feature extraction and stitching process is repeated until a first sub-model is constructed corresponding to each point in the entire target area, that is, multiple first sub-models have covered the target area. Then all the first sub-models are globally aligned and optimized to ensure seamless connection and overall consistency between them. When multiple first sub-models are combined, they can be combined based on the distribution order of each point so that the finally constructed three-dimensional model matches the target area. Based on the optimized sub-model, a three-dimensional reconstruction algorithm (such as stereo vision, structured light, laser scanning, etc.) is used to generate a three-dimensional model of the target area. The three-dimensional model is post-processed, including texture mapping, lighting processing, model simplification, etc., to improve the visual effect and practicality of the model.

[0060] The present application also provides an image processing device based on a tethered drone. Figure 2 is a structural schematic diagram of an image processing device based on a tethered drone provided in an embodiment of the present application, with reference to Figure 2 The device includes a receiving unit 201, a processing unit 202 and a splicing unit 203.

[0061] The receiving unit 201 receives a construction request sent by a user terminal.

[0062] The processing unit 202 controls the tethered UAV to collect images of the target area according to the construction request; determines the target transmission mode based on the target area, and obtains multiple target images sent by the tethered UAV through the target transmission mode, and determines the first point position corresponding to the tethered UAV according to the target image, and the first point position is the positioning information of the tethered UAV in the target area; processes the multiple target images to obtain multiple features.

[0063] The stitching unit 203 stitches the multiple features to obtain a first sub-model corresponding to the first point; sequentially obtains multiple first sub-models, and combines the multiple first sub-models to construct a three-dimensional model corresponding to the target area.

[0064] In a possible implementation, the processing unit 202 is used to divide the target area into multiple sub-areas; the receiving unit 201 is used to obtain a first sub-area from the multiple sub-areas, and obtain environmental information and facility information corresponding to the first sub-area; the processing unit 202 is used to determine a target transmission mode according to the environmental information and facility information, and the target transmission mode includes a wireless transmission mode and a tethered cable transmission mode; when the target transmission mode is a wireless transmission mode, confirm the generation of a target acquisition path for the first sub-area; and send the target acquisition path to the tethered drone so that the tethered drone performs target image acquisition on the first sub-area according to the target acquisition path.

[0065] In a possible implementation, the receiving unit 201 is used to obtain a target power level corresponding to the tethered drone, and determine a first shooting duration based on the target power level; obtain an area corresponding to the first sub-area, and determine a second shooting duration based on the area; the processing unit 202 is used to determine whether the first shooting duration is greater than or equal to the second shooting duration; when the first shooting duration is greater than or equal to the second shooting duration, confirm the generation of a target acquisition path corresponding to the first sub-area.

[0066] In a possible implementation, the receiving unit 201 is used to obtain the target distance, where the target distance is the distance between the first point and the second point, and the second point is the position corresponding to the ground service station; the processing unit 202 is used to send an encrypted instruction to the tethered drone if the target distance is greater than a preset distance, so that the tethered drone encrypts the multiple target images according to the encrypted instruction to obtain a first image; the receiving unit 201 is used to receive the first image sent by the tethered drone via wireless transmission, and output the first image as multiple target images.

[0067] In a possible implementation, the processing unit 202 is used to decrypt the first image to obtain multiple second images; preprocess the multiple second images to obtain preprocessed second images; and perform feature extraction on the preprocessed second images to obtain multiple features.

[0068] In a possible implementation, the receiving unit 201 is used to obtain a third image and a fourth image from multiple second images; the processing unit 202 is used to extract the third image to obtain a first feature set; extract the fourth image to obtain a second feature set; determine whether there are target features in the first feature set that are the same as those in the second feature set; the stitching unit 203 is used to stitch the third image and the fourth image according to the target features to obtain a fifth image when there are target features in the first feature set that are the same as those in the second feature set, and generate a first sub-model based on the fifth image.

[0069] In a possible implementation, the receiving unit 201 is used to obtain the second sub-area if it is determined that the tethered drone is in a charging state, the target transmission mode of the second sub-area is the tethered cable transmission mode, and the second sub-area is any sub-area among the multiple sub-areas except the first sub-area; the processing unit 202 is used to send an image acquisition instruction to the tethered drone, so that the tethered drone performs image acquisition on the second sub-area according to the image acquisition instruction to obtain a sixth image, and sends the acquired sixth image through the tethered cable transmission mode, and outputs the sixth image as the target image.

[0070] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0071] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 302 , and at least one communication bus 305 .

[0072] The communication bus 305 is used to realize the connection and communication between these components.

[0073] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0074] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0075] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 302, and calling data stored in the memory 302. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application requests; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0076] Among them, the memory 302 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 302 includes a non-transitory computer-readable storage medium. The memory 302 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 302 may include a program storage area and a data storage area, wherein the program storage area. Instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. can be stored; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 302 can also be optionally at least one storage device located away from the aforementioned processor 301.

[0077] like Figure 3 As shown, the memory 302 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program based on image processing of a tethered drone.

[0078] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call the application program based on tethered drone image processing stored in the memory 302. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0079] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0080] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0081] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0082] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0085] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure.

Claims

1. An image processing method based on a tethered drone, characterized in that: The method comprises: Receive the build request sent by the client; Controlling the tethered drone to collect images of the target area according to the construction request; Determine a target transmission mode based on the target area, obtain multiple target images sent by the tethered drone through the target transmission mode, and determine a first point position corresponding to the tethered drone according to the target image, where the first point position is positioning information of the tethered drone in the target area; Processing a plurality of the target images to obtain a plurality of features; splicing a plurality of the features to obtain a first sub-model corresponding to the first point; A plurality of the first sub-models are acquired in sequence, and the plurality of the first sub-models are combined to construct a three-dimensional model corresponding to the target area.

2. The method according to claim 1, characterized in that Before determining the target transmission mode based on the target area and acquiring the multiple target images sent by the tethered drone through the target transmission mode, the method further includes: Dividing the target area to obtain multiple sub-areas; Acquire a first sub-area from the multiple sub-areas, and acquire environment information and facility information corresponding to the first sub-area; Determine the target transmission mode according to the environmental information and the facility information, wherein the target transmission mode includes a wireless transmission mode and a tethered cable transmission mode; When the target transmission mode is the wireless transmission mode, generating a target acquisition path for the first sub-area; The target acquisition path is sent to the tethered drone, so that the tethered drone acquires the target image of the first sub-area according to the target acquisition path.

3. The method according to claim 2, characterized in that The generating of the target acquisition path of the first sub-area specifically includes: Obtaining a target power level corresponding to the tethered drone, and determining a first shooting duration according to the target power level; Acquire an area corresponding to the first sub-region, and determine a second shooting duration according to the area; Determining whether the first shooting duration is greater than or equal to the second shooting duration; When the first shooting duration is greater than or equal to the second shooting duration, it is confirmed that the target acquisition path corresponding to the first sub-area is generated.

4. The method according to claim 2, characterized in that: The determining of the target transmission mode based on the target area, and obtaining the multiple target images sent by the tethered drone through the target transmission mode specifically includes: Acquire a target distance, where the target distance is the distance between the first point and a second point, where the second point is a position corresponding to a ground service station; If the target distance is greater than a preset distance, an encryption instruction is sent to the tethered drone, so that the tethered drone encrypts the plurality of target images according to the encryption instruction to obtain a first image; The first image sent by the tethered drone through the wireless transmission method is received, and the first image is output as a plurality of the target images.

5. The method according to claim 4, characterized in that The processing of the plurality of target images to obtain a plurality of features specifically includes: decrypting the first image to obtain a plurality of second images; Preprocessing the plurality of second images to obtain preprocessed second images; Feature extraction is performed on the preprocessed second image to obtain a plurality of features.

6. The method according to claim 5, characterized in that The step of combining the plurality of features to obtain a first sub-model corresponding to the first point specifically includes: acquiring a third image and a fourth image from the plurality of second images; Extracting the third image to obtain a first feature set; Extracting the fourth image to obtain a second feature set; Determine whether there is a target feature in the first feature set that is the same as that in the second feature set; When the first feature set contains the same target feature as that in the second feature set, the third image and the fourth image are spliced ​​according to the target feature to obtain a fifth image, and the first sub-model is generated according to the fifth image.

7. The method according to claim 2, characterized in that: After sending the target acquisition path to the tethered drone so that the tethered drone acquires images of the first sub-area according to the target acquisition path, the method further includes: If it is determined that the tethered drone is in a charging state, a second sub-area is acquired, the target transmission mode of the second sub-area is the tethered cable transmission mode, and the second sub-area is any one of the multiple sub-areas except the first sub-area; An image acquisition instruction is sent to the tethered drone so that the tethered drone acquires an image of the second sub-area according to the image acquisition instruction to obtain a sixth image, and the acquired sixth image is sent through the tethered cable transmission method, and the sixth image is output as the target image.

8. An image processing device based on a tethered drone, characterized in that: The device comprises a receiving unit (201), a processing unit (202) and a splicing unit (203). The receiving unit (201) receives a construction request sent by a user terminal; The processing unit (202) controls the tethered drone to collect images of the target area according to the construction request; determines a target transmission mode based on the target area, obtains multiple target images sent by the tethered drone through the target transmission mode, determines a first point position corresponding to the tethered drone according to the target image, and the first point position is the positioning information of the tethered drone in the target area; processes the multiple target images to obtain multiple features; The stitching unit (203) stitches the plurality of features to obtain a first sub-model corresponding to the first point; sequentially obtains a plurality of the first sub-models, and combines the plurality of the first sub-models to construct a three-dimensional model corresponding to the target area.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (302), a user interface (303) and a network interface (304), wherein the memory (302) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (302) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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