Pattern spot survey data acquisition method and system based on unmanned aerial vehicle shooting

Abstract

The invention provides a pattern spot survey data acquisition method and system based on unmanned aerial vehicle shooting. The method comprises the following steps: acquiring a plurality of pattern spot data and shooting angles; generating a corresponding unmanned aerial vehicle route according to the plurality of pattern spot data and preset camera parameters; determining a target unmanned aerial vehicle executing a shooting task according to the unmanned aerial vehicle route; sending the unmanned aerial vehicle route to a corresponding target unmanned aerial vehicle, so that the target unmanned aerial vehicle sequentially sails to each shooting point according to the unmanned aerial vehicle route and shoots corresponding image data; establishing a corresponding relationship between each piece of image data and each piece of pattern spot data; according to the camera parameters, the shooting angles, the geographic coordinates of the pattern spot data and the corresponding relation, the boundary range lines of the pattern spot data are mapped to the corresponding image data, survey result maps are obtained, and the efficiency and accuracy of data collection and data processing in pattern spot survey work are improved.

Description

Technical Field

[0001] This application relates to the fields of unmanned aerial vehicle (UAV) applications and GIS data processing technology, and in particular to a method and system for collecting map spot survey data based on UAV photography. Background Technology

[0002] Map patch surveys, a fundamental task in land resource management, agricultural monitoring, environmental protection, and disaster assessment, aim to obtain accurate spatial and attribute information by identifying, delineating, and analyzing specific areas of the land surface to support relevant decision-making and management. Traditional map patch surveys typically follow a sequential workflow: preliminary delineation in the office—data collection via drones in the field—data processing and verification in the office. However, this model heavily relies on manual operation and switching between multiple platforms, resulting in a fragmented workflow and low collaborative efficiency, becoming a major bottleneck restricting the scale, precision, and timeliness of survey work.

[0003] Specifically, existing technologies and methods have several prominent drawbacks. First, the automation level of the process is low and the connections are not smooth. From map patch drawing and field flight route planning to image matching and attribute entry in the office, each step is often completed by different personnel in different software. Relying on manual data transfer and conversion is not only inefficient but also prone to introducing errors during handover. Second, the heavy reliance on manual work easily leads to problems such as incorrect matching of map patches and images, misjudgment of boundaries, and omission of attributes, making it difficult to guarantee data consistency and accuracy. Third, the task scheduling and management methods are outdated. The allocation, distribution, and status tracking of UAV tasks are mostly achieved through manual communication or simple file sharing, lacking a unified and intelligent management platform, which is particularly chaotic when multiple airports and multiple fleets are working together. Fourth, the timeliness of data processing is poor. The massive amounts of images collected in the field need to be manually exported, organized, and associated with map patches one by one, which is time-consuming and cannot meet the business needs of emergency monitoring or rapid response.

[0004] In summary, the fundamental challenge currently facing the field of map feature surveys lies in the fragmentation and low level of intelligence throughout the entire process. This not only increases operational costs and time but also affects the reliability and application value of survey results. Therefore, the industry urgently needs to build an integrated technical solution that can connect all aspects of map feature surveys, achieve intelligent task scheduling, and automatically associate and process data. This will drive map feature surveys towards automation, intelligence, and high consistency, better serving the needs of rapid acquisition and precise governance of modern geographic information. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a method and system for collecting map patch survey data based on drone photography, which improves the efficiency and accuracy of data collection and processing in map patch survey work.

[0006] In a first aspect, embodiments of this application provide a method for collecting map patch survey data based on drone photography, including: Acquire several image patch data and shooting angles; A corresponding UAV flight path is generated based on the aforementioned patch data and preset camera parameters. The UAV flight path includes several shooting points, and each shooting point corresponds one-to-one with each patch data. Based on the drone flight path, determine the target drone to perform the shooting mission; The drone flight path is sent to the corresponding target drone, so that the target drone travels to each shooting point in sequence according to the drone flight path, and captures corresponding image data at each shooting point at the shooting angle. Acquire each of the image data and establish the correspondence between each of the image data and each of the patch data; Based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the corresponding relationship, the boundary lines of each patch data are mapped to the corresponding image data to obtain each survey result map.

[0007] This application provides a method for collecting map patch survey data based on drone photography. Through automated process design, it significantly improves the overall efficiency and accuracy of data collection and processing in map patch surveys. Traditional map patch surveys rely on manual operation and multi-platform switching, resulting in fragmented processes that are prone to errors. This embodiment, however, achieves full automation and intelligence through integrated steps—from acquiring map patch data and shooting angles, generating drone flight paths, automatically capturing image data, to establishing data correspondences and generating survey result maps—creating a closed loop from office planning to field execution and then to office processing. Specifically, this embodiment accurately plans drone flight paths containing various shooting points based on each map patch data, ensuring that the drone completes the capture of all map patch data in a single flight, avoiding omissions or duplicate collections, and improving data collection efficiency and accuracy. Simultaneously, by establishing a correspondence between image data and map patch data, automatic mapping of map patch boundary lines is achieved, significantly shortening the time from data collection to result generation, and improving data processing efficiency and accuracy. This embodiment is particularly suitable for large-scale map patch surveys or emergency monitoring scenarios, enabling rapid response to business needs and improving data timeliness.

[0008] In one possible implementation, generating the corresponding UAV flight path based on the plurality of map patch data includes: Traverse each of the said patch data to generate each corresponding shooting point. For any patch data, generate a corresponding bounding rectangle based on the boundary range of the patch data, determine the corresponding minimum shooting height based on the area of ​​the bounding rectangle and the camera parameters, determine the corresponding two-dimensional shooting coordinates based on the center point coordinates of the bounding rectangle and the shooting angle, and generate the shooting point corresponding to the patch data based on the minimum shooting height and the two-dimensional shooting coordinates. The drone flight path is generated based on each shooting point using a preset path planning algorithm.

[0009] This application provides a method for generating UAV flight paths, ensuring both the quality and efficiency of data acquisition through scientific calculation and path planning. Traditional flight path planning often relies on experience or simple rules, making it difficult to simultaneously consider patch shape, camera parameters, and flight efficiency. This embodiment, however, traverses the patch data, generates a bounding rectangle for each patch, and then calculates the minimum shooting height and two-dimensional shooting coordinates for each patch based on the bounding rectangle, achieving refined flight path design. Specifically, the minimum shooting height is determined based on the area of ​​the bounding rectangle and camera parameters, ensuring that the image resolution meets survey requirements and avoiding detail loss due to excessive flight or incomplete coverage due to excessive flight height. Simultaneously, the shooting point coordinates are determined by the center point of the bounding rectangle and the shooting angle, ensuring alignment between the image center and the patch center, improving the accuracy of data acquisition and subsequent patch mapping. Finally, a preset path planning algorithm is used to generate flight paths covering all shooting points, reducing the number of unnecessary movements and turns by the UAV, further reducing flight time and energy consumption, and improving data acquisition efficiency.

[0010] Furthermore, determining the target drone for the shooting mission based on the drone flight path includes: determining the corresponding drone airport based on the geographical locations of the first and last shooting points in the drone flight path, and determining the corresponding target drone based on the idle status of each drone in the drone airport.

[0011] This application further refines the method for determining the target UAV. By combining the geographical locations of the first and last shooting points with the idle status of the UAV airport, intelligent task scheduling and resource optimization are achieved. In traditional multi-airport, multi-fleet collaboration, task allocation often relies on manual communication, which can easily lead to scheduling chaos, resource idleness, or conflicts. This method, through geographical location matching and status awareness, can automatically select the most suitable UAV to perform the task, improving operational collaboration efficiency. Specifically, determining the nearest UAV airport based on the shooting point reduces the invalid flight distance and time of UAVs, lowering energy consumption and operating costs. At the same time, dynamically allocating tasks according to the idle status of UAVs avoids equipment idleness or overload, improving the overall fleet utilization rate. This intelligent scheduling mechanism is particularly suitable for large-scale, distributed map survey tasks, enabling multi-UAV parallel operations and significantly shortening the task cycle. In addition, the automated scheduling process reduces manual intervention, lowers task delays or errors caused by communication failures, and improves the efficiency and accuracy of data collection in map survey work.

[0012] In one possible implementation, when the target drone travels to any current shooting point according to the drone flight path and captures corresponding current image data at the shooting angle at the current shooting point, the target drone sequentially travels to each shooting point according to the drone flight path and captures corresponding image data at each shooting point at the shooting angle, including: When the target drone reaches the preset range of the current shooting point, it adjusts its flight altitude according to the current shooting point and the shooting angle, and moves to the current shooting point; When the target drone reaches the current shooting point, it adjusts the yaw angle and gimbal angle according to the shooting angle, and then shoots the current geographical area at the shooting angle to obtain the corresponding initial current image data. Determine the corresponding current patch data based on the current shooting point; Based on the identification information of the current patch data, corresponding identification information is added to the initial current image data to obtain the current image data.

[0013] This application provides a specific method for acquiring image data using a drone. By automatically adjusting flight parameters and adding identification information, the standardization and traceability of image acquisition are improved. Specifically, by automatically adjusting the flight altitude, yaw angle, and gimbal angle, the shooting angle is ensured to be consistent with the preset angle, avoiding deviations introduced by manual operation and improving the geometric consistency of various image data. Simultaneously, identification information is automatically added based on the patch data associated with the shooting point, providing key metadata for subsequent data processing, reducing the workload and error risk of manual annotation, and providing an efficient data acquisition solution for large-scale patch surveys.

[0014] In one possible implementation, acquiring each of the image data and establishing the correspondence between each of the image data and each of the patch data includes: Obtain each of the image data from a preset cloud or preset storage device; Based on the identification information in each of the image data and the identification information in each of the patch data, a first correspondence between each of the image data and each of the patch data is established; Establish a corresponding shooting timeline based on the shooting time of each of the aforementioned image data; Based on the shooting timeline, the order of shooting points in the UAV flight path, and the correspondence between each shooting point and each patch data, a second correspondence between each image data and each patch data is established. Based on the location information of each image data, the location information of each shooting point in the UAV flight path, and the correspondence between each shooting point and each patch data, a third correspondence between each image data and each patch data is established. If the first correspondence, the second correspondence, and the third correspondence are all consistent, then the first correspondence shall be used as the correspondence between each of the image data and each of the patch data.

[0015] This application provides a method for establishing a correspondence between image data and patch data. By constructing a multi-dimensional correspondence and performing joint verification, the efficiency and accuracy of data acquisition and processing are significantly improved. In traditional methods, the matching of images and patches often relies on a single identifier or manual verification, which is prone to data misalignment due to identifier errors or omissions. This method, however, ensures the robustness of the matching results through triple verification of a first correspondence (identifier information matching), a second correspondence (timeline and flight path sequence matching), and a third correspondence (location information matching). Specifically, the first correspondence, based on the direct association of identifier information, provides a basic matching basis; the second correspondence, through logical verification of the shooting timeline and flight path sequence, can detect anomalies caused by time discrepancies or reversed sequences; the third correspondence, through spatial verification of geographical location, further eliminates matching errors in cases of identifier or time information anomalies. If all three are consistent, the matching result is highly reliable, effectively avoiding data chaos caused by the failure of a single information source. This multi-verification mechanism is particularly suitable for large-scale, long-term survey tasks. It can cope with complex situations such as equipment malfunctions, network delays, or human errors, ensuring the integrity and consistency of the data chain. At the same time, it provides a reliable data foundation for subsequent map mapping, improving the efficiency and accuracy of data collection and processing in map survey work.

[0016] In one possible implementation, when mapping the boundary lines of the current patch data to the corresponding current image data based on the camera parameters, the shooting angle, the geographic coordinates of any current patch data, and the correspondence, to obtain the current survey result map, the step of mapping the boundary lines of each patch data to the corresponding image data based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the correspondence, to obtain each survey result map, includes: The current image data is analyzed to obtain the shooting height, image center point coordinates, and yaw angle at the time of shooting. Based on the coordinates of the center point of the image and the geographic coordinates of the center point of the current patch data, the geographic coordinates of the boundary line of the current patch data are converted into local rectangular coordinates in a local rectangular coordinate system. The local rectangular coordinate system is constructed with the coordinates of the center point of the image as the center. Based on the camera parameters, the shooting angle, and the yaw angle, each of the local Cartesian coordinates is converted into a three-dimensional coordinate in the camera coordinate system; Using a preset camera model, each of the three-dimensional coordinates is projected onto the two-dimensional image plane of the current image data to obtain the corresponding pixel coordinates. Based on the coordinates of each pixel, a closed polygon is superimposed on the current image data to obtain the current survey result map.

[0017] This application describes in detail the technical process of mapping the boundary lines of geographic patches to image data, achieving precise spatial overlay through geometric calculations and coordinate transformations. In traditional methods, patch boundaries often exist as independent layers, and overlay with the image relies on manual registration, which is time-consuming and prone to errors. This embodiment, however, achieves automatic and accurate boundary overlay by parsing image parameters, constructing a local coordinate system, transforming to the camera coordinate system, and projecting onto a two-dimensional image plane, thus improving the efficiency and accuracy of data processing. Specifically, based on the image's shooting height, center point coordinates, and yaw angle, combined with camera parameters and shooting angle, the geometric relationships at the time of shooting can be accurately reconstructed, thereby accurately converting the geographic coordinates of the patch boundary lines into image pixel coordinates. Through the overlay of closed polygons, the patch range is automatically marked on the image, generating ready-to-use survey results. This eliminates the need for professional personnel to perform GIS overlay operations again, allowing users to directly compare the image with the boundary range, lowering the technical threshold and improving the user experience.

[0018] Furthermore, the image patch survey data acquisition method also includes: after the target UAV completes the shooting task of the UAV flight path, transmitting each image data to a preset cloud or preset storage device.

[0019] This application provides steps for image data transmission. By automatically uploading to the cloud or storage device, it achieves real-time backup and centralized management of data, improving data security and accessibility. In traditional surveys, image data is often stored locally on the drone, requiring manual export and organization, and is prone to data loss due to equipment failure or operational errors. This method automatically transmits data after the shooting task is completed, ensuring data integrity and timeliness. Specifically, the pre-set cloud or storage device provides a stable and scalable data storage environment, supporting concurrent access by multiple users and remote collaboration, facilitating real-time processing and analysis by field personnel, and improving the efficiency and accuracy of data collection and processing in map survey work. Furthermore, centralized data management facilitates subsequent data mining, model training, or long-term monitoring, promoting the value-added utilization of data.

[0020] Secondly, embodiments of this application provide a data acquisition system for map patch surveys based on UAV photography, including a first acquisition module, a flight path generation module, a task allocation module, a photography module, a second acquisition module, and a data processing module; The first acquisition module is used to acquire several patch data and shooting angles; The flight path generation module is used to generate a corresponding UAV flight path based on the plurality of patch data and preset camera parameters. The UAV flight path includes a plurality of shooting points, and each shooting point corresponds one-to-one with each patch data. The task allocation module is used to determine the target drone to perform the shooting task based on the drone's flight path; The shooting module is used to send the drone flight path to the corresponding target drone, so that the target drone can travel to each shooting point in sequence according to the drone flight path, and capture corresponding image data at each shooting point at the shooting angle. The second acquisition module is used to acquire each of the image data and establish a correspondence between each of the image data and each of the patch data; The data processing module is used to map the boundary lines of each patch data to the corresponding image data according to the camera parameters, the shooting angle, the geographic coordinates of each patch data and the corresponding relationship, so as to obtain each survey result map.

[0021] In one possible implementation, the second acquisition module acquires each of the image data and establishes a correspondence between each of the image data and each of the patch data, including: Obtain each of the image data from a preset cloud or preset storage device; Based on the identification information in each of the image data and the identification information in each of the patch data, a first correspondence between each of the image data and each of the patch data is established; Establish a corresponding shooting timeline based on the shooting time of each of the aforementioned image data; Based on the shooting timeline, the order of shooting points in the UAV flight path, and the correspondence between each shooting point and each patch data, a second correspondence between each image data and each patch data is established. Based on the location information of each image data, the location information of each shooting point in the UAV flight path, and the correspondence between each shooting point and each patch data, a third correspondence between each image data and each patch data is established. If the first correspondence, the second correspondence, and the third correspondence are all consistent, then the first correspondence shall be used as the correspondence between each of the image data and each of the patch data.

[0022] In one possible implementation, when mapping the boundary lines of the current patch data to the corresponding current image data based on the camera parameters, the shooting angle, the geographic coordinates of any current patch data, and the correspondence, to obtain the current survey result map, the data processing module maps the boundary lines of each patch data to the corresponding image data based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the correspondence, to obtain each survey result map, including: The current image data is analyzed to obtain the shooting height, image center point coordinates, and yaw angle at the time of shooting. Based on the coordinates of the center point of the image and the geographic coordinates of the center point of the current patch data, the geographic coordinates of the boundary line of the current patch data are converted into local rectangular coordinates in a local rectangular coordinate system. The local rectangular coordinate system is constructed with the coordinates of the center point of the image as the center. Based on the camera parameters, the shooting angle, and the yaw angle, each of the local Cartesian coordinates is converted into a three-dimensional coordinate in the camera coordinate system; Using a preset camera model, each of the three-dimensional coordinates is projected onto the two-dimensional image plane of the current image data to obtain the corresponding pixel coordinates. Based on the coordinates of each pixel, a closed polygon is superimposed on the current image data to obtain the current survey result map. Attached Figure Description

[0023] Figure 1 A flowchart illustrating a method for collecting map patch survey data based on drone photography, provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for collecting map patch survey data in the prior art; Figure 3 This is a schematic diagram of the structure of a map spot survey data acquisition system based on drone photography, provided in an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] It should be noted that the step numbers in this document are only for the convenience of explaining the specific embodiments and are not intended to limit the order in which the steps are performed. In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0026] Example 1: like Figure 1 As shown, Embodiment 1 provides a method for collecting map patch survey data based on drone photography, including steps S1-S6: Step S1: Obtain several patch data and shooting angles; Step S2: Generate a corresponding UAV flight path based on the plurality of patch data and preset camera parameters. The UAV flight path includes a plurality of shooting points, and each shooting point corresponds one-to-one with each patch data. Step S3: Determine the target drone to perform the shooting mission based on the drone flight path; Step S4: Send the drone flight path to the corresponding target drone so that the target drone can travel to each shooting point in sequence according to the drone flight path and take corresponding image data at each shooting point at the shooting angle. Step S5: Acquire each of the image data and establish the correspondence between each of the image data and each of the patch data; Step S6: Based on the camera parameters, the shooting angle, the geographic coordinates of each patch data and the corresponding relationship, map the boundary lines of each patch data to the corresponding image data to obtain each survey result map.

[0027] In existing technologies, such as Figure 2 As shown, the data collection for the map patch survey mainly relies on manual work by professionals and includes the following six stages: 1. Manual Mapping and Task Assignment: Office staff typically use professional GIS software (such as ArcGIS, QGIS) to manually draw the areas to be surveyed on an electronic map based on satellite imagery, change clues, or other data, generating a list or layer file containing multiple maps. The field survey team receives the survey task through internal communication (such as meetings or emails) and obtains printed map maps or electronic map files (such as Shapefiles or KML files).

[0028] 2. Manual Flight Route Planning: Based on the received map features, field personnel use another professional drone flight route planning software (such as DJI Pilot, Pix4Dcapture, etc.) to manually trace or re-import the map feature boundaries on the software map. Then, they manually set flight route parameters such as flight altitude and overlap rate for each map feature within the software. If there are many map features or their shapes are complex, this process is time-consuming and prone to inconsistencies.

[0029] 3. Manual transmission and task execution: After the flight path is planned, field personnel need to transmit the flight path task from the planning computer to the drone remote controller via physical means such as data cable, USB flash drive or Bluetooth.

[0030] The pilot carries the equipment to the field, manually finds a take-off and landing point, operates the drone to take off, and monitors its flight mission.

[0031] 4. Manual Data Copying and Organization: After the flight, the pilot manually copies the photos from the drone's SD card to the computer's hard drive using a card reader or data cable. Then, based on memory or on-site notes, the pilot manually organizes and renames the massive amount of photos to distinguish data from different image patches. This process is extremely prone to confusion, and once confused, it is difficult to recover.

[0032] 5. Manually linking map features with imagery (a highly error-prone core step): After obtaining photo data from the field team, office staff need to rely on experience to manually link the photo folder with the map feature objects in the GIS software by visually comparing the photo content with the location of the map features. Alternatively, a slightly more advanced but still cumbersome method can be used: generating points in the GIS software using the GPS information of the photos, and then performing semi-automatic matching through spatial queries, but this process still requires a lot of manual checking and intervention.

[0033] 6. Manual plotting and result production: After determining the correspondence between the image and the map features, the office staff need to manually draw the boundary lines of the map features on the orthophoto again, or display them through the layer overlay function of GIS software, in order to produce the final survey result map.

[0034] Therefore, this application provides a method for collecting map patch survey data based on UAV (unmanned aerial vehicle) photography. Through automated process design, it significantly improves the overall efficiency and accuracy of data collection and processing in map patch surveys. Traditional map patch surveys rely on manual operation and multi-platform switching, resulting in fragmented processes that are prone to errors. This embodiment, however, achieves full automation and intelligence through integrated steps—from acquiring map patch data and shooting angles, generating UAV flight paths, automatically capturing image data, to establishing data correspondences and generating survey result maps—creating a closed loop from office planning to field execution and then to office processing. Specifically, this embodiment accurately plans UAV flight paths containing various shooting points based on each map patch data, ensuring that the UAV completes the capture of all map patch data in a single flight, avoiding omissions or duplicate collections, and improving data collection efficiency and accuracy. Simultaneously, by establishing a correspondence between image data and map patch data, automatic mapping of map patch boundary lines is achieved, significantly shortening the time from data collection to result generation, and improving data processing efficiency and accuracy. This embodiment is particularly suitable for large-scale map patch surveys or emergency monitoring scenarios, enabling rapid response to business needs and improving data timeliness.

[0035] In a preferred embodiment, in step S1, the patch data is mainly obtained by creating and storing one or more patch objects on the map interface of the system platform through manual drawing by the user or importing boundary files. Each patch object has unique identification information (such as ID, name, type, creation time, etc.). The patch data is usually fence data, such as POLYGON(102.678955 25.065506, 102.679084 25.065183, 102.68285325.067055, 102.682640 25.067357, 102.678955 25.065506). The shooting angle can be an orthographic shooting angle or an oblique shooting angle.

[0036] In one possible implementation, step S2, generating the corresponding UAV flight path based on the plurality of map patch data, includes: Traverse each of the said patch data to generate each corresponding shooting point. For any patch data, generate a corresponding bounding rectangle based on the boundary range of the patch data, determine the corresponding minimum shooting height based on the area of ​​the bounding rectangle and the camera parameters, determine the corresponding two-dimensional shooting coordinates based on the center point coordinates of the bounding rectangle and the shooting angle, and generate the shooting point corresponding to the patch data based on the minimum shooting height and the two-dimensional shooting coordinates. The drone flight path is generated based on each shooting point using a preset path planning algorithm.

[0037] This application provides a method for generating UAV flight paths, ensuring both the quality and efficiency of data acquisition through scientific calculation and path planning. Traditional flight path planning often relies on experience or simple rules, making it difficult to simultaneously consider patch shape, camera parameters, and flight efficiency. This embodiment, however, traverses the patch data, generates a bounding rectangle for each patch, and then calculates the minimum shooting height and two-dimensional shooting coordinates for each patch based on the bounding rectangle, achieving refined flight path design. Specifically, the minimum shooting height is determined based on the area of ​​the bounding rectangle and camera parameters, ensuring that the image resolution meets survey requirements and avoiding detail loss due to excessive flight or incomplete coverage due to excessive flight height. Simultaneously, the shooting point coordinates are determined by the center point of the bounding rectangle and the shooting angle, ensuring alignment between the image center and the patch center, improving the accuracy of data acquisition and subsequent patch mapping. Finally, a preset path planning algorithm is used to generate flight paths covering all shooting points, reducing the number of unnecessary movements and turns by the UAV, further reducing flight time and energy consumption, and improving data acquisition efficiency.

[0038] In a preferred embodiment, the system automatically calculates and generates a drone flight path covering the region of each map patch based on the patch boundaries. The drone flight path includes several shooting points and several flight path parameters, such as flight altitude, forward overlap, lateral overlap, and shooting mode. These parameters can be set according to preset rules or user-defined settings. The specific steps for generating the drone flight path based on the map patch boundaries are as follows: 1. Calculate the bounding rectangle of the patch based on its shape.

[0039] 2. Based on the camera's CMOS parameters, the minimum height of the circumscribed rectangle can be calculated using the principle of similar triangles.

[0040] 3. Determine the corresponding two-dimensional shooting coordinates based on the shooting angle and the center point of the circumscribed rectangle. When the shooting angle is a normal shooting angle (the gimbal tilt angle defaults to -90 degrees, which is vertically downward), the center point of the circumscribed rectangle is the normal point of the image patch, and the two-dimensional shooting coordinates can be directly determined; when the shooting angle is an oblique shooting angle, after calculating the minimum shooting height, move northward from the current center point by a multiple of the shooting height (e.g., height * 1.2) to obtain the corresponding two-dimensional shooting coordinates.

[0041] 4. After determining the two-dimensional shooting coordinates and shooting height in orthophoto and oblique shooting modes, generate the shooting points of the patch data, and set the corresponding shooting actions at the shooting points.

[0042] 5. Generate drone flight paths based on each shooting point using a preset path planning algorithm.

[0043] Furthermore, in step S3, determining the target drone to perform the shooting task based on the drone flight path includes: determining the corresponding drone airport based on the geographical locations of the first and last shooting points in the drone flight path, and determining the corresponding target drone based on the idle status of each drone in the drone airport.

[0044] This application further refines the method for determining the target UAV. By combining the geographical locations of the first and last shooting points with the idle status of the UAV airport, intelligent task scheduling and resource optimization are achieved. In traditional multi-airport, multi-fleet collaboration, task allocation often relies on manual communication, which can easily lead to scheduling chaos, resource idleness, or conflicts. This method, through geographical location matching and status awareness, can automatically select the most suitable UAV to perform the task, improving operational collaboration efficiency. Specifically, determining the nearest UAV airport based on the shooting point reduces the invalid flight distance and time of UAVs, lowering energy consumption and operating costs. At the same time, dynamically allocating tasks according to the idle status of UAVs avoids equipment idleness or overload, improving the overall fleet utilization rate. This intelligent scheduling mechanism is particularly suitable for large-scale, distributed map survey tasks, enabling multi-UAV parallel operations and significantly shortening the task cycle. In addition, the automated scheduling process reduces manual intervention, lowers task delays or errors caused by communication failures, and improves the efficiency and accuracy of data collection in map survey work.

[0045] In a preferred embodiment, the system automatically distributes the generated flight path task to a designated drone airport via a communication network. The airport can be an unmanned, fully automated airport or a manned, conventional airport. The task distribution information includes the flight path file, task priority, and estimated execution time. Specifically, this embodiment first retrieves the coordinates of each preset drone airport, adds the distances from the drone airport coordinates to the first and last shooting points, and uses this as the comprehensive distance from the drone airport to the shooting flight path. Then, it determines the target drone airport with the closest comprehensive distance and sends the generated flight path task to the target drone airport. Upon receiving the task, the airport, based on the current idle status of each drone, designates the target drone to perform the shooting task and distributes the flight path to the target drone for execution.

[0046] In one possible implementation, in step S4, when the target drone travels to any current shooting point according to the drone flight path and captures corresponding current image data at the shooting angle at the current shooting point, the target drone sequentially travels to each shooting point according to the drone flight path and captures corresponding image data at each shooting point at the shooting angle, including: When the target drone reaches the preset range of the current shooting point, it adjusts its flight altitude according to the current shooting point and the shooting angle, and moves to the current shooting point; When the target drone reaches the current shooting point, it adjusts the yaw angle and gimbal angle according to the shooting angle, and then shoots the current geographical area at the shooting angle to obtain the corresponding initial current image data. Determine the corresponding current patch data based on the current shooting point; Based on the identification information of the current patch data, corresponding identification information is added to the initial current image data to obtain the current image data.

[0047] This application provides a specific method for acquiring image data using a drone. By automatically adjusting flight parameters and adding identification information, the standardization and traceability of image acquisition are improved. Specifically, by automatically adjusting the flight altitude, yaw angle, and gimbal angle, the shooting angle is ensured to be consistent with the preset angle, avoiding deviations introduced by manual operation and improving the geometric consistency of various image data. Simultaneously, identification information is automatically added based on the patch data associated with the shooting point, providing key metadata for subsequent data processing, reducing the workload and error risk of manual annotation, and providing an efficient data acquisition solution for large-scale patch surveys.

[0048] In a preferred embodiment, after receiving the mission, the UAV airport controls the UAV to take off along a predetermined route and perform the shooting mission, acquiring orthophotos of the image patch area. During flight, the UAV can transmit status information in real time (such as location, battery level, and number of shots). Specifically, the route includes the shooting points to be executed by the UAV and the corresponding shooting actions (including gimbal rotation angle, aircraft yaw angle, and shooting action). When the UAV flies to the shooting point in the route file, it performs certain specified actions (such as taking a picture). After completing the route mission, the aircraft uploads the media files generated by the mission to a file storage object.

[0049] In one possible implementation, step S5, acquiring each of the image data and establishing the correspondence between each of the image data and each of the patch data, includes: Obtain each of the image data from a preset cloud or preset storage device; Based on the identification information in each of the image data and the identification information in each of the patch data, a first correspondence between each of the image data and each of the patch data is established; Establish a corresponding shooting timeline based on the shooting time of each of the aforementioned image data; Based on the shooting timeline, the order of shooting points in the UAV flight path, and the correspondence between each shooting point and each patch data, a second correspondence between each image data and each patch data is established. Based on the location information of each image data, the location information of each shooting point in the UAV flight path, and the correspondence between each shooting point and each patch data, a third correspondence between each image data and each patch data is established. If the first correspondence, the second correspondence, and the third correspondence are all consistent, then the first correspondence shall be used as the correspondence between each of the image data and each of the patch data.

[0050] This application provides a method for establishing a correspondence between image data and patch data. By constructing a multi-dimensional correspondence and performing joint verification, the efficiency and accuracy of data acquisition and processing are significantly improved. In traditional methods, the matching of images and patches often relies on a single identifier or manual verification, which is prone to data misalignment due to identifier errors or omissions. This method, however, ensures the robustness of the matching results through triple verification of a first correspondence (identifier information matching), a second correspondence (timeline and flight path sequence matching), and a third correspondence (location information matching). Specifically, the first correspondence, based on the direct association of identifier information, provides a basic matching basis; the second correspondence, through logical verification of the shooting timeline and flight path sequence, can detect anomalies caused by time discrepancies or reversed sequences; the third correspondence, through spatial verification of geographical location, further eliminates matching errors in cases of identifier or time information anomalies. If all three are consistent, the matching result is highly reliable, effectively avoiding data chaos caused by the failure of a single information source. This multi-verification mechanism is particularly suitable for large-scale, long-term survey tasks. It can cope with complex situations such as equipment malfunctions, network delays, or human errors, ensuring the integrity and consistency of the data chain. At the same time, it provides a reliable data foundation for subsequent map mapping, improving the efficiency and accuracy of data collection and processing in map survey work.

[0051] In a preferred embodiment, the system automatically matches and links uploaded image data with user-created patch data through pre-defined association logic. This association logic includes, but is not limited to: spatiotemporal matching: matching the geographic range and time attributes of the patch based on the GPS location information (latitude and longitude) and shooting timestamp of the photo; and task ID matching: generating a unique task ID for each patch route task when the task is issued. This ID is used throughout the entire process, and is contained in the photo file metadata or filename, which the system uses for association. Taking task ID matching as an example, since each patch has a fixed ID, this patch ID is bound to the corresponding photo filename suffix when generating the patch route. When the airport uploads the image to the miniio file storage object, the system parses the image suffix to determine the patch data corresponding to the image. Finally, the system uses the patch ID contained in the photo name to associate with the specified patch for photo-patch matching.

[0052] In one possible implementation, in step S6, when mapping the boundary lines of the current patch data to the corresponding current image data based on the camera parameters, the shooting angle, the geographic coordinates of any current patch data, and the correspondence, to obtain the current survey result map, the step of mapping the boundary lines of each patch data to the corresponding image data based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the correspondence, to obtain each survey result map, includes: The current image data is analyzed to obtain the shooting height, image center point coordinates, and yaw angle at the time of shooting. Based on the coordinates of the center point of the image and the geographic coordinates of the center point of the current patch data, the geographic coordinates of the boundary line of the current patch data are converted into local rectangular coordinates in a local rectangular coordinate system. The local rectangular coordinate system is constructed with the coordinates of the center point of the image as the center. Based on the camera parameters, the shooting angle, and the yaw angle, each of the local Cartesian coordinates is converted into a three-dimensional coordinate in the camera coordinate system; Using a preset camera model, each of the three-dimensional coordinates is projected onto the two-dimensional image plane of the current image data to obtain the corresponding pixel coordinates. Based on the coordinates of each pixel, a closed polygon is superimposed on the current image data to obtain the current survey result map.

[0053] This application describes in detail the technical process of mapping the boundary lines of geographic patches to image data, achieving precise spatial overlay through geometric calculations and coordinate transformations. In traditional methods, patch boundaries often exist as independent layers, and overlay with the image relies on manual registration, which is time-consuming and prone to errors. This embodiment, however, achieves automatic and accurate boundary overlay by parsing image parameters, constructing a local coordinate system, transforming to the camera coordinate system, and projecting onto a two-dimensional image plane, thus improving the efficiency and accuracy of data processing. Specifically, based on the image's shooting height, center point coordinates, and yaw angle, combined with camera parameters and shooting angle, the geometric relationships at the time of shooting can be accurately reconstructed, thereby accurately converting the geographic coordinates of the patch boundary lines into image pixel coordinates. Through the overlay of closed polygons, the patch range is automatically marked on the image, generating ready-to-use survey results. This eliminates the need for professional personnel to perform GIS overlay operations again, allowing users to directly compare the image with the boundary range, lowering the technical threshold and improving the user experience.

[0054] In a preferred embodiment, after successfully associating the image with the patch, the system automatically overlays and draws the boundary line of the patch onto the corresponding orthophoto, forming an intuitive survey result map. This boundary line can be directly mapped from the patch boundary data in the patch data. Specifically, during the patch data mapping process, the patch fence data, camera parameters, and aircraft yaw angle are all known parameters. Simultaneously, the center point coordinates and shooting height of the image can be parsed from the image's EXIF ​​information. Then, coordinate transformation is performed based on the aforementioned known parameters, converting the geographic coordinates of the patch boundary line into local rectangular coordinates. The transformed local rectangular coordinates are then further rotated to convert them into camera coordinates, where the heading angle is converted to radians. Using the camera model, each three-dimensional point (x, y, z) in the camera coordinate system is projected onto the two-dimensional image plane (px, py), calculating each pixel. Finally, the calculated pixels are connected to form a polygon in the image, achieving the overlay and drawing of the patch boundary line in the image, thus obtaining the survey result map.

[0055] Furthermore, the image patch survey data acquisition method also includes: after the target UAV completes the shooting task along the UAV flight path, transmitting each image data to a preset cloud or preset storage device. Specifically, after completing the flight mission, the UAV lands at the airport and automatically uploads all the raw photo data collected in this mission to a cloud or local file storage object (Minio) via a high-speed data transmission interface (wired or wireless).

[0056] This application provides steps for image data transmission. By automatically uploading to the cloud or storage device, it achieves real-time backup and centralized management of data, improving data security and accessibility. In traditional surveys, image data is often stored locally on the drone, requiring manual export and organization, and is prone to data loss due to equipment failure or operational errors. This method automatically transmits data after the shooting task is completed, ensuring data integrity and timeliness. Specifically, the pre-set cloud or storage device provides a stable and scalable data storage environment, supporting concurrent access by multiple users and remote collaboration, facilitating real-time processing and analysis by field personnel, and improving the efficiency and accuracy of data collection and processing in map survey work. Furthermore, centralized data management facilitates subsequent data mining, model training, or long-term monitoring, promoting the value-added utilization of data.

[0057] Example 2: like Figure 3 As shown, Embodiment 2 provides a map patch survey data acquisition system based on UAV photography, including a first acquisition module 10, a flight path generation module 20, a task allocation module 30, a shooting module 40, a second acquisition module 50, and a data processing module 60. The first acquisition module is used to acquire several patch data and shooting angles; The flight path generation module is used to generate a corresponding UAV flight path based on the plurality of patch data and preset camera parameters. The UAV flight path includes a plurality of shooting points, and each shooting point corresponds one-to-one with each patch data. The task allocation module is used to determine the target drone to perform the shooting task based on the drone's flight path; The shooting module is used to send the drone flight path to the corresponding target drone, so that the target drone can travel to each shooting point in sequence according to the drone flight path, and capture corresponding image data at each shooting point at the shooting angle. The second acquisition module is used to acquire each of the image data and establish a correspondence between each of the image data and each of the patch data; The data processing module is used to map the boundary lines of each patch data to the corresponding image data according to the camera parameters, the shooting angle, the geographic coordinates of each patch data and the corresponding relationship, so as to obtain each survey result map.

[0058] Furthermore, the route generation module 20 generates corresponding UAV routes based on the plurality of map patch data, including: Traverse each of the said patch data to generate each corresponding shooting point. For any patch data, generate a corresponding bounding rectangle based on the boundary range of the patch data, determine the corresponding minimum shooting height based on the area of ​​the bounding rectangle and the camera parameters, determine the corresponding two-dimensional shooting coordinates based on the center point coordinates of the bounding rectangle and the shooting angle, and generate the shooting point corresponding to the patch data based on the minimum shooting height and the two-dimensional shooting coordinates. The drone flight path is generated based on each shooting point using a preset path planning algorithm.

[0059] Furthermore, the task allocation module 30 determines the target drone to perform the shooting task based on the drone flight path, including: determining the corresponding drone airport based on the geographical location of the first and last shooting points in the drone flight path, and determining the corresponding target drone based on the idle status of each drone in the drone airport.

[0060] In one possible implementation, when the target drone travels to any current shooting point according to the drone's flight path and captures corresponding current image data at the shooting angle at that current shooting point, the shooting module 40 sequentially travels to each shooting point according to the drone's flight path and captures corresponding image data at each shooting point at the shooting angle, including: When the target drone reaches the preset range of the current shooting point, it adjusts its flight altitude according to the current shooting point and the shooting angle, and moves to the current shooting point; When the target drone reaches the current shooting point, it adjusts the yaw angle and gimbal angle according to the shooting angle, and then shoots the current geographical area at the shooting angle to obtain the corresponding initial current image data. Determine the corresponding current patch data based on the current shooting point; Based on the identification information of the current patch data, corresponding identification information is added to the initial current image data to obtain the current image data.

[0061] In one possible implementation, the second acquisition module 50 acquires each of the image data and establishes a correspondence between each of the image data and each of the patch data, including: Obtain each of the image data from a preset cloud or preset storage device; Based on the identification information in each of the image data and the identification information in each of the patch data, a first correspondence between each of the image data and each of the patch data is established; Establish a corresponding shooting timeline based on the shooting time of each of the aforementioned image data; Based on the shooting timeline, the order of shooting points in the UAV flight path, and the correspondence between each shooting point and each patch data, a second correspondence between each image data and each patch data is established. Based on the location information of each image data, the location information of each shooting point in the UAV flight path, and the correspondence between each shooting point and each patch data, a third correspondence between each image data and each patch data is established. If the first correspondence, the second correspondence, and the third correspondence are all consistent, then the first correspondence shall be used as the correspondence between each of the image data and each of the patch data.

[0062] In one possible implementation, when mapping the boundary lines of the current patch data to the corresponding current image data based on the camera parameters, the shooting angle, the geographic coordinates of any current patch data, and the correspondence, to obtain the current survey result map, the data processing module 60 maps the boundary lines of each patch data to the corresponding image data based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the correspondence, to obtain each survey result map, including: The current image data is analyzed to obtain the shooting height, image center point coordinates, and yaw angle at the time of shooting. Based on the coordinates of the center point of the image and the geographic coordinates of the center point of the current patch data, the geographic coordinates of the boundary line of the current patch data are converted into local rectangular coordinates in a local rectangular coordinate system. The local rectangular coordinate system is constructed with the coordinates of the center point of the image as the center. Based on the camera parameters, the shooting angle, and the yaw angle, each of the local Cartesian coordinates is converted into a three-dimensional coordinate in the camera coordinate system; Using a preset camera model, each of the three-dimensional coordinates is projected onto the two-dimensional image plane of the current image data to obtain the corresponding pixel coordinates. Based on the coordinates of each pixel, a closed polygon is superimposed on the current image data to obtain the current survey result map.

[0063] Furthermore, the image patch survey data acquisition system also includes a data transmission module, which is used to transmit each image data to a preset cloud or preset storage device after the target UAV completes the shooting task of the UAV flight path.

[0064] This application provides a data acquisition system for map patch surveys based on drone photography. Through automated process design, it significantly improves the overall efficiency and accuracy of data acquisition and processing in map patch surveys. Traditional map patch surveys rely on manual operation and multi-platform switching, resulting in fragmented processes prone to errors. This embodiment, however, achieves full automation and intelligence through integrated steps—from acquiring map patch data and shooting angles, generating drone flight paths, automatically capturing image data, to establishing data correspondences and generating survey result maps—creating a closed loop from office planning to field execution and then to office processing. Specifically, this embodiment accurately plans drone flight paths containing various shooting points based on each map patch data, ensuring that the drone completes the capture of all map patch data in a single flight, avoiding omissions or duplicate collections, and improving data acquisition efficiency and accuracy. Simultaneously, by establishing a correspondence between image data and map patch data, automatic mapping of map patch boundary lines is achieved, significantly shortening the time from data acquisition to result generation, and improving data processing efficiency and accuracy. This embodiment is particularly suitable for large-scale map patch surveys or emergency monitoring scenarios, enabling rapid response to business needs and improving data timeliness.

[0065] For a more detailed explanation of the working principle and procedures of this embodiment, please refer to the relevant description in Embodiment 1.

[0066] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for collecting map patch survey data based on drone photography, characterized in that, include: Acquire several image patch data and shooting angles; A corresponding UAV flight path is generated based on the aforementioned patch data and preset camera parameters. The UAV flight path includes several shooting points, and each shooting point corresponds one-to-one with each patch data. Based on the drone flight path, determine the target drone to perform the shooting mission; The drone flight path is sent to the corresponding target drone, so that the target drone travels to each shooting point in sequence according to the drone flight path, and captures corresponding image data at each shooting point at the shooting angle. Acquire each of the image data and establish the correspondence between each of the image data and each of the patch data; Based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the corresponding relationship, the boundary lines of each patch data are mapped to the corresponding image data to obtain each survey result map.

2. The method for collecting map patch survey data based on UAV photography as described in claim 1, characterized in that, The step of generating corresponding UAV flight paths based on the plurality of map patch data includes: Traverse each of the said patch data to generate each corresponding shooting point. For any patch data, generate a corresponding bounding rectangle based on the boundary range of the patch data, determine the corresponding minimum shooting height based on the area of ​​the bounding rectangle and the camera parameters, determine the corresponding two-dimensional shooting coordinates based on the center point coordinates of the bounding rectangle and the shooting angle, and generate the shooting point corresponding to the patch data based on the minimum shooting height and the two-dimensional shooting coordinates. The drone flight path is generated based on each shooting point using a preset path planning algorithm.

3. The method for collecting map patch survey data based on UAV photography as described in claim 1, characterized in that, The step of determining the target drone to perform the shooting mission based on the drone flight path includes: determining the corresponding drone airport based on the geographical location of the first and last shooting points in the drone flight path, and determining the corresponding target drone based on the idle status of each drone in the drone airport.

4. The method for collecting map patch survey data based on UAV photography as described in claim 1, characterized in that, When the target drone travels to any current shooting point according to the drone flight path and captures corresponding current image data at the shooting angle at the current shooting point, the target drone sequentially travels to each shooting point according to the drone flight path and captures corresponding image data at each shooting point at the shooting angle, including: When the target drone reaches the preset range of the current shooting point, it adjusts its flight altitude according to the current shooting point and the shooting angle, and moves to the current shooting point; When the target drone reaches the current shooting point, it adjusts the yaw angle and gimbal angle according to the shooting angle, and then shoots the current geographical area at the shooting angle to obtain the corresponding initial current image data. Determine the corresponding current patch data based on the current shooting point; Based on the identification information of the current patch data, corresponding identification information is added to the initial current image data to obtain the current image data.

5. The method for collecting map patch survey data based on UAV photography as described in claim 1, characterized in that, The step of acquiring each of the image data and establishing the correspondence between each of the image data and each of the patch data includes: Obtain each of the image data from a preset cloud or preset storage device; Based on the identification information in each of the image data and the identification information in each of the patch data, a first correspondence between each of the image data and each of the patch data is established; Establish a corresponding shooting timeline based on the shooting time of each of the aforementioned image data; Based on the shooting timeline, the order of shooting points in the UAV flight path, and the correspondence between each shooting point and each patch data, a second correspondence between each image data and each patch data is established. Based on the location information of each image data, the location information of each shooting point in the UAV flight path, and the correspondence between each shooting point and each patch data, a third correspondence between each image data and each patch data is established. If the first correspondence, the second correspondence, and the third correspondence are all consistent, then the first correspondence shall be used as the correspondence between each of the image data and each of the patch data.

6. The method for collecting map patch survey data based on UAV photography as described in claim 1, characterized in that, When mapping the boundary lines of the current patch data to the corresponding current image data based on the camera parameters, the shooting angle, the geographic coordinates of any current patch data, and the correspondence, to obtain the current survey result map, the step of mapping the boundary lines of each patch data to the corresponding image data based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the correspondence, to obtain each survey result map, includes: The current image data is analyzed to obtain the shooting height, image center point coordinates, and yaw angle at the time of shooting. Based on the coordinates of the center point of the image and the geographic coordinates of the center point of the current patch data, the geographic coordinates of the boundary line of the current patch data are converted into local rectangular coordinates in a local rectangular coordinate system. The local rectangular coordinate system is constructed with the coordinates of the center point of the image as the center. Based on the camera parameters, the shooting angle, and the yaw angle, each of the local Cartesian coordinates is converted into a three-dimensional coordinate in the camera coordinate system; Using a preset camera model, each of the three-dimensional coordinates is projected onto the two-dimensional image plane of the current image data to obtain the corresponding pixel coordinates. Based on the coordinates of each pixel, a closed polygon is superimposed on the current image data to obtain the current survey result map.

7. A method for collecting map patch survey data based on UAV photography as described in any one of claims 1-6, characterized in that, The method for collecting image patch survey data further includes: after the target UAV completes the shooting task of the UAV flight path, transmitting each image data to a preset cloud or preset storage device.

8. A data acquisition system for map patch surveys based on drone photography, characterized in that, It includes a first acquisition module, a route generation module, a task allocation module, a shooting module, a second acquisition module, and a data processing module; The first acquisition module is used to acquire several patch data and shooting angles; The flight path generation module is used to generate a corresponding UAV flight path based on the plurality of patch data and preset camera parameters. The UAV flight path includes a plurality of shooting points, and each shooting point corresponds one-to-one with each patch data. The task allocation module is used to determine the target drone to perform the shooting task based on the drone flight path; The shooting module is used to send the drone flight path to the corresponding target drone, so that the target drone can travel to each shooting point in sequence according to the drone flight path, and capture corresponding image data at each shooting point at the shooting angle. The second acquisition module is used to acquire each of the image data and establish a correspondence between each of the image data and each of the patch data; The data processing module is used to map the boundary lines of each patch data to the corresponding image data according to the camera parameters, the shooting angle, the geographic coordinates of each patch data and the corresponding relationship, so as to obtain each survey result map.

9. The data acquisition system for map patch surveys based on UAV photography as described in claim 8, characterized in that, The second acquisition module acquires each of the image data and establishes a correspondence between each of the image data and each of the patch data, including: Obtain each of the image data from a preset cloud or preset storage device; Based on the identification information in each of the image data and the identification information in each of the patch data, a first correspondence between each of the image data and each of the patch data is established; Establish a corresponding shooting timeline based on the shooting time of each of the aforementioned image data; Based on the shooting timeline, the order of shooting points in the UAV flight path, and the correspondence between each shooting point and each patch data, a second correspondence between each image data and each patch data is established. Based on the location information of each image data, the location information of each shooting point in the UAV flight path, and the correspondence between each shooting point and each patch data, a third correspondence between each image data and each patch data is established. If the first correspondence, the second correspondence, and the third correspondence are all consistent, then the first correspondence shall be used as the correspondence between each of the image data and each of the patch data.

10. The data acquisition system for map patch surveys based on UAV photography as described in claim 8, characterized in that, When the boundary lines of the current patch data are mapped to the corresponding current image data based on the camera parameters, the shooting angle, the geographic coordinates of any current patch data, and the correspondence, to obtain the current survey result map, the data processing module maps the boundary lines of each patch data to the corresponding image data based on the camera parameters, the shooting angle, the geographic coordinates of each patch data, and the correspondence, to obtain each survey result map, including: The current image data is analyzed to obtain the shooting height, image center point coordinates, and yaw angle at the time of shooting. Based on the coordinates of the center point of the image and the geographic coordinates of the center point of the current patch data, the geographic coordinates of the boundary line of the current patch data are converted into local rectangular coordinates in a local rectangular coordinate system. The local rectangular coordinate system is constructed with the coordinates of the center point of the image as the center. Based on the camera parameters, the shooting angle, and the yaw angle, each of the local Cartesian coordinates is converted into a three-dimensional coordinate in the camera coordinate system; Using a preset camera model, each of the three-dimensional coordinates is projected onto the two-dimensional image plane of the current image data to obtain the corresponding pixel coordinates. Based on the coordinates of each pixel, a closed polygon is superimposed on the current image data to obtain the current survey result map.

Images