Method and System for Managing UAV Photos and Videos Based on Geographical Location

Abstract

This application relates to the technical field of data processing, and discloses a method and system for managing drone photos and videos based on geographical location. The method includes: extracting geographical attribute data from drone photo EXIF and video captions; establishing a spatial database, storing photos as point layers and videos as line layers; renaming files according to shooting time and camera model; importing the point and line layers into GIS software to generate a visualization map; querying and screening media in the target area through spatial analysis; and performing statistical analysis on the media set to generate a comprehensive report. This application can manage drone photo and video data simultaneously, make full use of geographical location information, support multi-dimensional spatial retrieval, and is applicable to management methods in various application scenarios, thereby eliminating the cumbersome and errors of manual collation and improving the management efficiency and retrieval accuracy of drone media data.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular to a method and system for managing drone photos and videos based on geographical location. Background Art

[0002] In recent years, with the popularization of DJI consumer drones, more and more field workers have applied drones to on-site investigations and evidence collection, greatly improving work efficiency and obtaining more detailed field data. The photos and videos taken by drones are not only large in quantity but also carry rich geographical location information. Currently, most users mainly manage the photo and video data obtained by drones in the form of folders according to time and tasks.

[0003] However, the existing drone media management methods have obvious deficiencies: First, the obtained photos and videos usually need to be manually sorted, which is time-consuming and prone to errors and omissions when the data volume is large; Second, when the number of photos and videos reaches a certain level, due to the lack of database support, rapid retrieval cannot be performed; Third, the existing methods cannot effectively utilize the location information in drone photos and videos, especially cannot quickly retrieve relevant media files according to geographical location. Summary of the Invention

[0004] This application provides a method and system for managing drone photos and videos based on geographical location, which is a management method that can manage drone photo and video data simultaneously, make full use of geographical location information, support multi-dimensional spatial retrieval, and be applicable to various application scenarios, thereby eliminating the cumbersome and errors of manual sorting and improving the management efficiency and retrieval accuracy of drone media data.

[0005] First aspect, the present application provides a method for managing drone photos and videos based on geographical location. The method for managing drone photos and videos based on geographical location includes: extracting the shooting time, camera model, and geographical location coordinates from the EXIF information of drone photos, and parsing the trajectory coordinate information at fixed time intervals from the video subtitles to obtain the geographical attribute data of drone media; establishing an ArcGIS spatial relationship database according to the geographical attribute data of drone media, storing the photo information as a spatial point layer, and storing the video information as a spatial line layer to obtain a drone media spatial database; setting the storage path and renaming the photo and video files according to the shooting time and camera model in the geographical attribute data of drone media to obtain a structured media file set; importing the spatial point layer and spatial line layer in the drone media spatial database into GIS software, overlaying topographic maps or remote sensing images, and performing symbolic setting on the layers according to the camera model to obtain a visualization map of drone shooting distribution; performing area query and attribute screening on the visualization map of drone shooting distribution through a spatial analysis tool, and quickly locating the photo and video files according to the storage path in the structured media file set to obtain a target area media set; calculating the time distribution and spatial density of the target area media set, and generating a comprehensive analysis report including statistical charts and spatial distribution based on the attribute information in the drone media spatial database.

[0006] Second aspect, the present application provides a system for managing drone photos and videos based on geographical location. The system for managing drone photos and videos based on geographical location includes:

[0007] A parsing module, configured to extract the shooting time, camera model, and geographical location coordinates from the EXIF information of drone photos, and parse the trajectory coordinate information at fixed time intervals from the video subtitles to obtain the geographical attribute data of drone media;

[0008] A storage module, configured to establish an ArcGIS spatial relationship database according to the geographical attribute data of drone media, store the photo information as a spatial point layer, and store the video information as a spatial line layer to obtain a drone media spatial database;

[0009] A naming module, configured to set the storage path and rename the photo and video files according to the shooting time and camera model in the geographical attribute data of drone media to obtain a structured media file set;

[0010] An import module, configured to import the spatial point layer and spatial line layer in the drone media spatial database into GIS software, overlay topographic maps or remote sensing images, and perform symbolic setting on the layers according to the camera model to obtain a visualization map of drone shooting distribution;

[0011] A screening module, configured to perform area query and attribute screening on the visualized map of the distribution of the drone-captured images through a spatial analysis tool, and quickly locate photo and video files according to the storage paths in the structured media file set, so as to obtain a media set for the target area;

[0012] A calculation module, configured to perform time distribution and spatial density calculations on the media set for the target area, and generate a comprehensive analysis report including statistical charts and spatial distributions based on the attribute information in the drone media space database.

[0013] A third aspect of the present invention provides a computer device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor invokes the instructions in the memory to enable the computer device to execute the above-mentioned method for managing drone photos and videos based on geographical location.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions run on a computer, the computer is enabled to execute the above-mentioned method for managing drone photos and videos based on geographical location.

[0015] In the technical solution provided by this application, by extracting geographical location data from drone photo EXIF information and video captions and establishing an ArcGIS spatial relationship database, the complete spatial management of drone media files is realized, enabling users to no longer need to carry out cumbersome manual sorting work and greatly improving the data management efficiency. The spatial point layer and spatial line layer constructed based on the extracted media geographical attribute data visualize photos and videos in the geographical space, providing an intuitive reference for understanding the spatial distribution of drone shooting activities. In particular, the design of storing photo information as a spatial point layer and video information as a spatial line layer fully adapts to the essential characteristics of the two media types, making the data structure highly match the media type. The mechanism of setting paths and renaming files according to shooting time and camera model establishes a clear and unified file organizational structure, facilitating quick browsing and searching at the file system level. In terms of spatial visualization, importing the spatial point layer and spatial line layer into GIS software and overlaying topographic maps or remote sensing images, and performing symbolic settings according to the camera model, the generated visualization map of drone shooting distribution greatly enhances the interpretability of the data, making complex spatial relationships clear at a glance. The spatial analysis tools applied in the solution perform regional queries and attribute filtering on the shooting distribution, quickly locating photo and video files based on the storage paths in the structured media file set, realizing a seamless link from the spatial location to the actual media files and solving the problem that media files cannot be retrieved based on location under traditional methods. In addition, the ability to calculate the time distribution and spatial density of the media set in the target area and generate a comprehensive analysis report based on the attribute information in the media spatial database provides users with an analysis tool for deeply understanding the rules of drone shooting activities. It is particularly worth mentioning that the application of the kernel density estimation algorithm in the media spatial distribution analysis in the solution transforms discrete photo points and video trajectories into a continuous density surface through scientific spatial statistical methods, effectively identifying the hot spots of drone activities and providing strong support for decision-making. At the same time, this solution has wide applicability, not only applicable to various DJI drone models, but also suitable for a variety of application scenarios, such as urban planning, agricultural monitoring, disaster assessment, cultural relic protection and other fields, enabling the full and efficient utilization of the massive geographical spatial media resources collected by drones. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of an embodiment of the method for managing drone photos and videos based on geographical location in the embodiments of this application;

[0018] Figure 2 This is the structure diagram of the UAV photo and video management database in the embodiment of the present application;

[0019] Figure 3 This is the schematic diagram of the storage directory and file naming of UAV photos / video files in the embodiment of the present application;

[0020] Figure 4 This is the schematic diagram of an embodiment of the UAV photo and video management system based on geographical location in the embodiment of the present application;

[0021] Figure 5 This is the structural schematic block diagram of the computer device in the embodiment of the present invention. Detailed implementation manners

[0022] The embodiment of the present application provides a method and system for managing UAV photos and videos based on geographical location. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order different from that shown or described here. In addition, the term "comprising" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 , an embodiment of the method for managing UAV photos and videos based on geographical location in the embodiment of the present application includes:

[0024] Step S101: Extract the shooting time, camera model and geographical location coordinates from the EXIF information of the UAV photos, and parse the trajectory coordinate information at fixed time intervals from the video subtitles to obtain the UAV media geographical attribute data;

[0025] Step S102: Establish an ArcGIS spatial relationship database according to the UAV media geographical attribute data, store the photo information as a spatial point layer, and store the video information as a spatial line layer to obtain the UAV media spatial database;

[0026] Step S103: Set the storage path and rename the photo and video files according to the shooting time and camera model in the UAV media geographical attribute data to obtain a structured media file set;

[0027] Step S104: Import the spatial point layer and spatial line layer in the UAV media space database into the GIS software, overlay the topographic map or remote sensing image, and perform symbolic setting on the layer according to the camera model to obtain the visualization map of the UAV shooting distribution;

[0028] Step S105: Perform area query and attribute filtering on the visualization map of the UAV shooting distribution through the spatial analysis tool, quickly locate the photo and video files according to the storage path in the structured media file set, and obtain the target area media set;

[0029] Step S106: Calculate the time distribution and spatial density of the target area media set, and generate a comprehensive analysis report including statistical charts and spatial distribution based on the attribute information in the UAV media space database.

[0030] It can be understood that the execution subject of this application can be a UAV photo and video management system based on geographical location, or a terminal or a server. Specifically, it is not limited here. In this embodiment of the application, the server is used as the execution subject for illustration.

[0031] Specifically, extract the key attributes from the EXIF information of the UAV photos. The EXIF of the photos contains rich geographical location and device information, such as longitude and latitude coordinates, altitude, shooting angle, shooting time, camera model, etc. At the same time, parse the longitude and latitude coordinate information recorded at fixed time intervals from the video subtitles. The DJI UAV videos usually record the flight position once per second in the subtitles to form complete trajectory data. The combination of these two parts of information forms the UAV media geographical attribute data, covering the spatial and device characteristics of the media files. Import the extracted UAV media geographical attribute data into the ArcGIS spatial relationship database. As Figure 2 shown, it is the structure diagram of the UAV photo and video management database in this embodiment of the application. Among them, create a PersonalGeodatabase spatial database, set the WGS84 coordinate system, convert the photo point information into a spatial point feature class, and each photo corresponds to a point object in the database. The attribute table of the point object contains information such as shooting time, camera model, photo size, shooting angle, etc.; convert the video trajectory coordinate sequence into a spatial line feature class, and each video corresponds to a line object in the database. The attribute table of the line object contains information such as video creation time, camera model, video size, duration, etc. Improve the subsequent spatial query efficiency by creating a spatial index, so as to build the UAV media space database.

[0032] Set the storage paths and rename the photo and video files according to the shooting time and camera model in the UAV media geographic attribute data. The specific process is to construct a multi-level directory structure in the format of "camera model\year\month\date". Rename the photo files in the format of "yearmonthday_hhmmss_camera model_4-digit random code.JPG", and rename the video files in the format of "yearmonthday_hhmmss_camera model_4-digit random code.MOV". For example, a photo taken by a Phantom 4 RTK UAV at 10:02:35 on October 21, 2019 is renamed to "20191021_100235_P4RTK_6779.JPG" and stored in the directory "P4RTK\2019\10\21". As Figure 3 shown, it is a schematic diagram of the storage directory and file naming of UAV photos / video files in the embodiment of this application. This organization method ensures the uniqueness of file names and contains core information, forming a structured media file set. Import the spatial point layer and spatial line layer in the UAV media spatial database into the ArcGIS software, load a high-resolution topographic map or remote sensing image as the base map, and set different symbols according to different camera models. Photo points are assigned point symbols of different colors and sizes according to the camera model, and video track lines are assigned different colors and line types according to the camera model. Add cartographic elements such as legends, scales, and north arrows to generate a visualization map of the UAV shooting distribution, intuitively showing the spatial distribution characteristics of UAV shooting activities.

[0033] Use the ArcGIS spatial analysis tool to perform spatial queries on the UAV shooting distribution visualization map. By drawing rectangular, circular, or polygonal areas, select the photo points and video line elements within the area of interest, and perform precise filtering in combination with attribute conditions such as shooting time and camera model. The screening results are associated with the storage paths in the structured media file set to quickly locate the original photo and video files. For example, query all photos and videos taken by a Mavic 2 Pro UAV in a certain area in June 2020 with a height above 100 meters. The system immediately locates these files and provides access paths to form a media set for the target area.

[0034] Statistical analysis is carried out on the target area media set, including time distribution statistics (counting the shooting volume grouped by year, month, day, and hour), spatial density calculation (generating a heat map using the kernel density estimation algorithm), equipment usage analysis (counting the usage frequency of each model of drone), and trajectory feature analysis (calculating indicators such as trajectory length, average flight altitude, and flight direction). These statistical results are integrated into charts, combined with representative photos and video thumbnails, and necessary explanations are added to generate a comprehensive analysis report. For example, the report shows that the drone shooting activities in a certain area from 2019 to 2020 were mainly concentrated in summer, the Phantom4 RTK model had the highest usage frequency, accounting for 65% of the total, the shooting density was the largest in the northeastern region, and the average flight altitude was 120 meters.

[0035] In the embodiments of the present application, by extracting geographical location data from the EXIF information of drone photos and video captions and establishing an ArcGIS spatial relationship database, the complete spatial management of drone media files is realized, enabling users to no longer need to carry out cumbersome manual sorting work and greatly improving the data management efficiency. The spatial point layer and spatial line layer constructed based on the extracted media geographical attribute data visualize photos and videos in the geographical space, providing an intuitive reference for understanding the spatial distribution of drone shooting activities. In particular, the design of storing photo information as a spatial point layer and video information as a spatial line layer fully adapts to the essential characteristics of the two media types, making the data structure highly match the media type. The mechanism of setting paths and renaming files according to the shooting time and camera model establishes a clear and unified file organizational structure, facilitating fast browsing and searching at the file system level. In terms of spatial visualization, importing the spatial point layer and spatial line layer into GIS software and overlaying topographic maps or remote sensing images, and performing symbolic settings according to the camera model, the generated visualization map of drone shooting distribution greatly enhances the interpretability of the data, making complex spatial relationships clear at a glance. The spatial analysis tools applied in the solution perform regional queries and attribute filtering on the shooting distribution, quickly locating photo and video files based on the storage paths in the structured media file set, realizing a seamless link from the spatial location to the actual media files and solving the problem of being unable to retrieve media files based on location under traditional methods. In addition, the ability to calculate the time distribution and spatial density of the media set in the target area and generate a comprehensive analysis report based on the attribute information in the media spatial database provides users with an analysis tool for deeply understanding the laws of drone shooting activities. It is particularly worth mentioning that the application of the kernel density estimation algorithm in the media spatial distribution analysis in the solution transforms discrete photo points and video trajectories into a continuous density surface through scientific spatial statistical methods, effectively identifying the hot spots of drone activities and providing strong support for decision-making. At the same time, this solution has wide applicability, being applicable not only to various DJI drone models but also to multiple application scenarios such as urban planning, agricultural monitoring, disaster assessment, cultural relic protection and other fields, enabling the full and efficient utilization of the massive geographical spatial media resources collected by drones.

[0036] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0037] (1) Read the exchangeable image file format data in the drone photo file through a photo EXIF information parser, and extract the photo shooting time, camera model, photo width, photo height, longitude, latitude, altitude, shooting angle and camera tilt angle information to form a photo geographical attribute set;

[0038] (2) Obtain the creation time, camera model, video width, video height, and video duration information in the UAV video file through a video attribute reader to form a video basic attribute set;

[0039] (3) Identify the subtitles at fixed time intervals in the UAV video through a video subtitle parser, extract the longitude, latitude, and altitude coordinate information contained in the subtitles, and generate a video trajectory coordinate sequence;

[0040] (4) Convert the location information in the photo geographical attribute set into spatial point data in a unified coordinate system to generate a photo spatial attribute table;

[0041] (5) Associate the video basic attribute set with the video trajectory coordinate sequence, and connect and construct it into spatial line data in chronological order to generate a video trajectory attribute table;

[0042] (6) Merge and organize the photo spatial attribute table and the video trajectory attribute table to generate UAV media geographical attribute data containing spatio-temporal information.

[0043] Specifically, extract key information such as geographical location from UAV media files. The photo EXIF information parser locates and parses the EXIF information block by reading the binary data stream of the JPG file. EXIF (Exchangeable Image File Format) is a metadata standard embedded in image files, and photos taken by DJI UAVs all contain such information. The parser first reads the file header to identify the EXIF marker (usually 0xFFE1), and then parses the TAG ID to locate the key information fields, such as GPS information (TAG 0x8825), camera information (TAG 0x0110), etc. Extract information such as the photo shooting time (usually in GMT time format), camera model (such as "P4RTK" marked for Phantom 4 RTK), photo width (such as 5472 pixels), photo height (such as 3648 pixels), longitude, latitude, altitude, shooting angle, and camera tilt angle to form a photo geographical attribute set.

[0044] The video attribute reader reads the metadata header information for MOV format video files. Videos recorded by DJI UAVs usually use the MOV container format encoded with H.264, and the metadata is stored in a specific block of the file header. The reader analyzes the atom structure of the MOV file (such as'moov','mvhd', 'trak', etc.) to find the block containing the meta information. Extract basic attribute information such as the video creation time (accurate to seconds), camera model (the same model identification as in the photo), video width, video height, and video duration to form a video basic attribute set. These information reflect the basic characteristics of the video file but do not contain spatial location information.

[0045] The video subtitle parser specifically processes the subtitle information embedded in DJI drone videos. After the subtitle function is enabled in DJI drone videos, flight information such as GPS coordinates, altitude, and speed will be superimposed and displayed at the bottom of the video frame, and updated at fixed time intervals (usually once per second). The parser first samples the video frame by frame, extracts the images with subtitles at a fixed interval (such as one frame per second). Then, through optical character recognition (OCR) technology, it identifies the text content in the subtitles and extracts the formatted coordinate information. The GPS coordinates in DJI video subtitles are usually displayed in the format of "GPS(longitude, latitude, altitude)", which includes longitude, latitude, and relative altitude values. The parser arranges these coordinate points in the order of the video time code to generate a video trajectory coordinate sequence, describing the flight path of the drone in three-dimensional space.

[0046] The location information in the photo geographic attribute set needs to be converted into spatial point data in a unified coordinate system. DJI drones usually use the WGS84 coordinate system to record GPS information, but for compatibility with GIS software, coordinate conversion is required. The conversion process follows the following formula: ; where represents the projection function, and an appropriate projection algorithm is selected according to the target coordinate system (such as UTM, Web Mercator, etc.). The converted coordinate points, together with other attribute information of the photo, form a photo spatial attribute table, and each record represents the position and its attributes of a photo in space.

[0047] The video basic attribute set and the video trajectory coordinate sequence need to be associated to establish the corresponding relationship between time and position. The association process is based on timestamp matching. Taking the video creation time as the reference point and the time code of the trajectory coordinate point as the relative offset, the absolute time of each coordinate point is calculated. Then, these points are connected into line features in chronological order to form a flight trajectory. At the same time, the basic attribute information of the video (such as camera model, resolution, duration, etc.) is associated with this line feature to generate a video trajectory attribute table, representing the spatial trajectory and its attribute characteristics of the video file. The photo spatial attribute table and the video trajectory attribute table are merged and sorted to construct a unified data structure, including a photo point feature set, a video line feature set, and their associated attribute information, forming the drone media geographic attribute data. This data integrates the spatial positions, time information, and device parameters of all media files, laying a foundation for the subsequent establishment of a spatial database.

[0048] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0049] (1) Create a spatial relationship database in the format of ArcGIS Personal Geodatabase, set the coordinate system and spatial reference, and generate an initial media space container;

[0050] (2) Convert the photo information in the UAV media geographic attribute data into spatial point geometric objects, construct a photo point feature class, and form a photo spatial point attribute table;

[0051] (3) Extract the shooting time, camera model, photo width, photo height, shooting angle, and camera tilt angle information from the photo spatial point attribute table, establish a photo attribute domain and index, and generate a photo spatial point layer;

[0052] (4) Connect the video track coordinate information in the UAV media geographic attribute data in time series, construct a video line feature class, and form a video track line attribute table;

[0053] (5) Extract the video creation time, camera model, video width, video height, and video duration information from the video track line attribute table, establish a video attribute domain and index, and generate a video spatial line layer;

[0054] (6) Integrate the photo spatial point layer and the video spatial line layer into the initial media space container, establish a spatial index and attribute association, and obtain the UAV media space database.

[0055] Specifically, create a spatial relationship database in the Personal Geodatabase format through ArcGIS software. This database is based on the Microsoft Access (.mdb) file format and has the characteristics of being lightweight and highly portable. During the creation process, specify the database name and storage location, and then set the coordinate system and spatial reference. Usually, select the WGS84 geographic coordinate system (EPSG:4326) because the GPS coordinates collected by DJI UAVs default to using this coordinate system. The settings include parameters such as horizontal and vertical datum planes, angular units, and length units. After creation, an empty database file is formed as the initial media space container to prepare for subsequent data import. When converting the photo information in the UAV media geographic attribute data into spatial point geometric objects, the longitude and latitude coordinate information of each photo needs to be processed. Perform a point feature creation operation on each photo record, use the longitude value as the X coordinate, the latitude value as the Y coordinate, and the height value as the Z coordinate to construct a three-dimensional point geometric object. Use the geometric API of ArcGIS to create point features, with each photo corresponding to a point feature, and the position of the point feature being the photo shooting location. Subsequently, create a FeatureClass (feature class) in the database, define its geometric type as Point (point), create the necessary field structure, and batch import all point geometric objects into this feature class to form a photo spatial point attribute table, where each record contains a photo ID, geometric object, and reserved attribute fields.

[0056] After the photo spatial point attribute table is established, more detailed photo information is extracted from the UAV media geographic attribute data to populate the attribute fields. Specifically, multiple attribute fields are added to the photo spatial point attribute table: DATE_TIME (date and time type, storing the shooting time), CAM_MODEL (text type, storing the camera model), IMG_WIDTH (integer type, storing the pixel value of the photo width), IMG_HEIGHT (integer type, storing the pixel value of the photo height), HEADING (floating point type, storing the shooting direction angle), PITCH (floating point type, storing the camera pitch angle), FILE_PATH (text type, storing the photo file path), etc. Appropriate domain constraints are set for these fields. For example, the camera model is limited to a list of known DJI UAV models, and the shooting angle is limited within the range of 0 - 360 degrees. At the same time, multiple indexes are created, especially indexes for the shooting time and camera model, to accelerate subsequent query operations. After completing the attribute population and index establishment, a photo spatial point layer is formed, containing the photo spatial location and attribute information. The video data processing is more complex. The video trajectory coordinate sequence in the UAV media geographic attribute data is converted into a line geometry object. For each video file, its trajectory coordinate sequence (usually multiple three-dimensional coordinate points arranged in time order) is extracted, and these points are connected in time order to form a line feature. In the specific implementation, first, an empty line geometry object is created, and then the trajectory points are added one by one to form a line geometry. Subsequently, a FeatureClass is created in the database, the geometry type is defined as Polyline (line), the necessary field structure is created, and all line geometry objects are imported in batches to form a video trajectory line attribute table, where each record represents the complete flight trajectory of a video file.

[0057] After the video trajectory line attribute table is established, it is also necessary to populate detailed attribute information. Multiple attribute fields are added to this table: CREATE_TIME (date and time type, storing the video creation time), CAM_MODEL (text type, storing the camera model), VID_WIDTH (integer type, storing the pixel value of the video width), VID_HEIGHT (integer type, storing the pixel value of the video height), DURATION (floating point type, storing the video duration in seconds), FILE_PATH (text type, storing the video file path), etc. Appropriate domain constraints and indexes are set, especially creating a time index and a spatial index to optimize the query efficiency. After completion, a video spatial line layer is formed, containing the video trajectory and attribute information.

[0058] Integrate the photo spatial point layer and the video spatial line layer into the initial media spatial container. In the ArcGIS Personal Geodatabase, the two layers are stored as independent feature classes, and at the same time, the association relationship between them is established. The association is based on the shooting time and spatial location, allowing queries of all photos and videos within a certain time period or spatial range. In addition, a spatial index is established to accelerate spatial query operations, and an attribute index is established to accelerate attribute query operations. After completion, an integrated UAV media spatial database is obtained, which includes the photo point layer, the video line layer, and their association relationships.

[0059] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0060] (1) Extract the shooting time information of photos and videos from the UAV media geographic attribute data, perform time stratification by year, month, and day to form a time hierarchical structure;

[0061] (2) Extract the camera model information from the UAV media geographic attribute data, establish a camera model classification directory, and generate an equipment classification structure;

[0062] (3) Combine the time hierarchical structure with the equipment classification structure to construct a multi-level storage path and generate a media storage path template;

[0063] (4) Rename the photo files according to the media storage path template and the photo shooting time to generate a uniquely identified photo file name;

[0064] (5) Rename the video files according to the media storage path template and the video creation time to generate a uniquely identified video file name;

[0065] (6) Move the renamed photo and video files to the corresponding directories according to the media storage path template, and at the same time write the new storage path back to the UAV media spatial database to obtain a structured media file set.

[0066] Specifically, the shooting time information of photos and videos is extracted from the drone media geographical attribute data. These time information are usually stored as timestamps in a standard format, such as UTC time in the format of "2019-10-21T09:34:15Z". The extraction operation filters the DATE_TIME field from the drone media geographical attribute data table through an SQL query statement to obtain the time information of all media files. After extraction, time decomposition is performed, and each timestamp is decomposed into three levels: year, month, and day. For example, "2019-10-21" is decomposed into the year "2019", the month "10", and the day "21". Then, a three-level nested time directory structure is created. The top layer is the year directory, under which is the month directory, and then the day directory, forming a time hierarchy structure for subsequent file organization and storage. The camera model information is extracted from the same dataset. The DJI drone product line is rich, and the file parameters and quality of the files taken by cameras of different models vary, so classification management is required. The CAM_MODEL field is queried from the drone media geographical attribute data table through an SQL statement to extract all the camera models that appear, such as "P4RTK" (Phantom 4 RTK), "M2P" (Mavic 2 Pro), "MINI2" (DJI Mini 2), etc. The extracted models are de-duplicated to obtain a list of unique camera models. Then, a corresponding directory name is created for each camera model to form a device classification structure, which serves as the top-level classification basis.

[0067] The time hierarchy structure and the device classification structure are combined to construct a multi-level storage path. In the specific implementation, a four-level directory structure of "camera model\year\month\day" is adopted, such as "P4RTK\2019\10\21". This structure design takes into account the dual requirements of classification by device and organization by time. It can not only quickly find all the files taken by a specific camera but also view the shooting results of a certain period in chronological order. The combination process is achieved through string concatenation. First, the camera model is used as the root directory, and the year, month, and day subdirectories are added in sequence, and a separator is inserted between each level of directories to generate a multi-level path string, forming a media storage path template.

[0068] For renaming photo files, a unified format of "YYYYMMDD_HHMMSS_Camera Model_4-digit Random Code.JPG" is adopted. During the renaming process, first, six values of year, month, day, hour, minute, and second are extracted from the photo's shooting time and formatted into the form of "YYYYMMDD_HHMMSS", such as "20191021_093415". Then, the camera model identifier is added, such as "P4RTK". To ensure the uniqueness of the file name, a 4-digit random number code is generated, ranging from 1000 to 9999, such as "6779". These elements are connected by underscores and the JPG suffix is added to form the file name "20191021_093415_P4RTK_6779.JPG". The naming scheme retains the shooting time and device information, and at the same time, the random code is used to avoid file name conflicts for multiple photos taken in the same second.

[0069] A similar strategy is adopted for renaming video files, but based on the creation time of the video rather than the shooting time. The format is also "YYYYMMDD_HHMMSS_Camera Model_4-digit Random Code.MOV". The year, month, day, hour, minute, and second are extracted from the video creation time and formatted into "YYYYMMDD_HHMMSS", such as "20191021_094022". The camera model identifier and a 4-digit random code are added and connected into a complete file name, such as "20191021_094022_P4RTK_3371.MOV". This naming method is consistent with that of photos, facilitating unified management. The renamed photo and video files are moved to the corresponding directory structure. First, check whether the target directory exists. If not, create the directory tree. Then, copy or move the original media file to the target path while retaining all the metadata of the original file. After the file movement is completed, update the file path information in the UAV media space database, and update the FILE_PATH field in the photo space point layer and the video space line layer to the new file storage path. This update operation is implemented through SQL update statements to ensure that the spatial data is synchronized with the actual file location, forming a structured media file set.

[0070] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0071] (1) Start the ArcGIS software environment, create a new map document, set appropriate map coordinate systems and projection parameters, and generate a basic map container;

[0072] (2) Load the spatial point layer and the spatial line layer in the UAV media space database into the basic map container to form an initial media data view;

[0073] (3) Obtain high - resolution topographic maps, administrative division maps or remote sensing image maps from the map service, and add them as base map data below the initial media data view to generate a geographic background reference layer;

[0074] (4) Extract the camera model field from the spatial point layer, classify the photo points according to different models, and assign symbols of different colors and sizes to the photo points of each camera model to generate a classified expression of photo points;

[0075] (5) Extract the camera model field from the spatial line layer, classify the video trajectory lines according to different models, and assign symbols of different colors and line types to the video trajectory lines of each camera model to generate a classified expression of video lines;

[0076] (6) Overlay the classified expression of photo points and the classified expression of video lines on the geographic background reference layer, adjust the layer transparency and display order, and add cartographic elements such as a legend, scale bar and north arrow to obtain a visualization map of the drone shooting distribution.

[0077] Specifically, start the ArcGIS Desktop software, select "File - New - Blank Map" through the menu bar to create a new map document (.mxd file). During the process of creating the new map document, it is necessary to set the coordinate system and projection parameters of the map, which directly affect the spatial display effect of the data. Usually, select the same coordinate system as the drone media spatial database, that is, the WGS84 coordinate system (EPSG:4326), or select a suitable projected coordinate system according to the characteristics of the project area, such as the UTM projection. The coordinate system setting is completed through the "Data Frame Properties - Coordinate System" dialog box, and it is necessary to specify the geographic coordinate system or projected coordinate system, as well as parameters such as units and central meridian. After the setting is completed, a blank base map container is formed to prepare for the subsequent data loading. Next, load the layers in the drone media spatial database into the map. Through the "Add Data" function of ArcGIS, browse and select the previously created Personal Geodatabase database file (.mdb), and select the photo spatial point layer and video spatial line layer from it and add them to the map. During the loading process, the software will read the geometric information and attribute information of the layers and draw the positions of all photo points and video trajectory lines in the map window. After the layer loading is completed, the system will automatically calculate and adjust the map display range to ensure that all data points and lines can be displayed in the view, forming the initial media data view.

[0078] To enhance geographical background information, it is necessary to add basemap data. ArcGIS provides various ways to obtain basemaps, including local basemap data and online map services. Through the "Add Basemap" function, you can connect to the ArcGIS Online map service and select an appropriate basemap type, such as high-resolution satellite imagery, topographic maps, or administrative division maps. The selection process needs to consider the characteristics of the project area and the data analysis requirements. For example, satellite imagery can be selected in rural areas to show the distribution of farmland, and detailed street maps can be selected in urban areas. After the basemap data is downloaded, it is automatically added to the bottom layer of the map view as a geographical background reference layer, providing a geographical context for the drone shooting points and trajectories. Subsequently, the photo point layer is classified and expressed. Using the "Layer Properties - Symbology" function in ArcGIS, select the "Classified Symbols" method and select the "CAM_MODEL" (camera model) field in the classification field. The system will automatically count and list all the unique values in this field, such as "P4RTK", "M2P", "MINI2", etc. Different symbol characteristics are assigned to each camera model, including symbol shapes (such as circles, squares, triangles), symbol colors (such as red for P4RTK, blue for M2P), and symbol sizes (such as 6 points, 8 points, 10 points). After the classification settings are completed, the photo points on the map will display different symbols according to the camera model, intuitively reflecting the shooting distribution of different devices and forming a classified expression of photo points. The video line layer is also classified and expressed. Similarly, use the "Layer Properties - Symbology" function to classify based on the "CAM_MODEL" field. Different line type characteristics are assigned to the video trajectories of different camera models, including line colors (such as dark red for P4RTK, dark blue for M2P), line thicknesses (such as 1.5 pounds, 2 pounds, 2.5 pounds), and line styles (such as solid lines, dashed lines, dotted lines). After the settings are completed, the video trajectory lines on the map will display different line type symbols according to the camera model, forming a visual coordination with the photo points and jointly reflecting the activity characteristics of the drone, forming a classified expression of video lines.

[0079] Finally, layer integration and map layout settings are carried out. Set the photo point layer and the video line layer to be above the basemap layer, and ensure that the media data is clearly visible by adjusting the layer order. Appropriately set the layer transparency, such as setting the photo point transparency to 0% to remain completely opaque and the video line transparency to 30% to show the terrain features below. Add necessary cartographic elements, including a legend (explaining the meaning of various symbols), a scale (showing the distance relationship), a north arrow (indicating the direction), and a title (describing the map content), etc. Complete the addition of these elements through functions such as "Insert - Legend" and "Insert - Scale". After all the settings are completed, a visualization map of the drone shooting distribution is formed, showing the spatial distribution characteristics of the media files and the equipment usage situation.

[0080] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0081] (1) Draw a rectangular, circular or polygonal spatial query range on the visualization map of the drone's captured distribution, perform a spatial selection operation on the spatial point layer and the spatial line layer, and obtain a preliminary spatial screening result;

[0082] (2) Apply buffer analysis to the preliminary spatial screening result, set an appropriate buffer radius, expand the query range, obtain all photo points and video line features within the buffer, and generate a buffer screening set;

[0083] (3) Extract the attribute fields of shooting time, camera model, shooting height, and camera tilt angle from the buffer screening set, construct an attribute condition expression, and perform multi-condition combination screening to form an accurate screening result;

[0084] (4) Associate the photo points and video line features in the accurate screening result with the storage path field in the drone media spatial database, extract the file storage path information, and establish a media file index table;

[0085] (5) Access the structured media file set through the media file index table, locate and extract the actual photo and video files that meet the conditions, and generate a media file link set;

[0086] (6) Integrate the spatial element attributes of the accurate screening result with the media file link set, construct an associated data set containing spatial location, attribute information, and file links, and obtain the media set of the target area.

[0087] Specifically, for spatial range selection on the visualization map of drone-shot distributions, ArcGIS provides a variety of spatial selection tools, including the rectangle selection tool, the circle selection tool, and the polygon selection tool. When using the rectangle selection, a rectangular area is defined by dragging the mouse, determined by the coordinates of two diagonal points; when using the circle selection, a circular area is defined by specifying the center point and radius; when using the polygon selection, an irregular polygon area is defined by clicking multiple vertices in sequence. After the selection area is determined, the spatial selection operation is executed, and the system will automatically filter out all photo points and video line features that intersect with the selected area based on the spatial position relationship. This process accelerates the query through spatial indexing to quickly obtain the preliminary spatial screening results. Further apply buffer analysis to the preliminary spatial screening results to expand the query range to handle data near the boundary or meet the needs of fuzzy queries. Buffer analysis is a classic spatial analysis method in GIS. For point features, a circular area is created with each point as the center and a specified radius; for line features, a strip area is created with each line as the center line and a specified distance. In ArcGIS, it is implemented through the "Analysis Tools - Proximity Analysis - Buffer" function, and the buffer distance (such as 50 meters) and the buffer generation method (such as retaining overlapping areas or dissolving overlapping areas) need to be set. After the buffer is generated, execute a spatial query to extract all photo points and video line features that intersect with the buffer. This process includes not only the original screening results but also additional features within the buffer expansion range, forming a buffer screening set.

[0088] Perform attribute condition filtering on the buffered filter set to further refine the query results. Extract the key attribute fields from the buffered filter set, including shooting time (DATE_TIME), camera model (CAM_MODEL), shooting altitude (ALTITUDE), and camera tilt angle (PITCH), etc. Construct an SQL conditional expression for attribute filtering, such as "DATE_TIME >= '2020-07-01' AND DATE_TIME <= '2020-07-31' AND CAM_MODEL = 'P4RTK' AND ALTITUDE > 100". This expression filters out all media files taken in July 2020 using a P4RTK model drone at an altitude above 100 meters. The SQL query process is optimized through the index of the database engine to efficiently complete the multi-condition combination filtering and obtain the precise filtering results. Associate the precise filtering results with the storage path information to establish a media file index table. Each photo point and video line feature has a corresponding FILE_PATH field in the drone media spatial database, which stores the location of the actual media file. Through attribute table operations, extract the ID and FILE_PATH fields of all features in the precise filtering results to generate a media file index table containing the mapping relationship of "feature ID - file path". This index table is a bridge between spatial features and actual files, stored using a relational database table structure, and contains fields such as feature ID, feature type (photo / video), file name, file format, and complete storage path.

[0089] According to the path information in the media file index table, access the structured media file set and extract the actual media files that meet the conditions. This process is implemented through the file system API. Locate and verify the existence of the file according to the path in the index table, and read the basic file information (such as size, modification date, etc.). To improve the user experience, thumbnails or preview information of the media files can be further generated. All accessible media file paths and preview information are aggregated to form a media file link set. This set contains the links and preview information of all actual photo and video files that meet the spatial and attribute conditions. Integrate the spatial feature attributes of the precise filtering results with the media file links to construct a target area media set. The integration process is associated through the feature ID, and the geometric information (such as coordinates, shape), attribute information (such as shooting time, camera parameters), and file link information (such as storage path, preview image) of the spatial features are merged into a unified data structure. The generated target area media set is a multi-dimensional comprehensive data set, which contains both spatial information, attribute information, and file access information, supporting subsequent statistical analysis and visualization display.

[0090] In a specific embodiment, the process of performing step S106 may specifically include the following steps:

[0091] (1)Extract the shooting time information of photos and videos from the target area media set, perform time grouping statistics by year, month, day, and hour, and generate a media time distribution histogram;

[0092] (2)Extract the photo shooting locations and video track information from the target area media set, calculate the media space distribution density through the kernel density estimation algorithm, and generate a media density heat map;

[0093] (3)Extract the camera model, shooting height, and camera tilt angle attribute fields from the UAV media space database, classify and count the usage of different camera models, and generate a pie chart of device usage frequency;

[0094] (4)Measure the length, calculate the speed, and analyze the direction of the video tracks in the target area media set, and generate a track feature statistical table;

[0095] (5)Integrate and typeset the media time distribution histogram, media density heat map, device usage frequency pie chart, and track feature statistical table to form a multi-page statistical chart set;

[0096] (6)Combine the multi-page statistical chart set with representative photos and video thumbnails in the target area media set, add regional overview and analysis description text, and generate a comprehensive analysis report containing statistical charts and spatial distributions.

[0097] Specifically, extract time information from the target area media set for analysis. Use SQL query statements to extract the DATE_TIME field data from the photo and video attribute tables, and parse the time stamp into numerical values at four levels: year, month, day, and hour. After data extraction, perform grouping statistics.

[0098] Group the records according to the statement "GROUP BY YEAR(DATE_TIME), MONTH(DATE_TIME), DAY(DATE_TIME), HOUR(DATE_TIME)", and use the "COUNT(*)" function to calculate the number of media files in each time period. The statistical results are visually expressed through a histogram. The X-axis represents the time dimension (which can be switched to display the granularity of year, month, day, or hour), and the Y-axis represents the number of media files in the corresponding time period, intuitively showing the time distribution law of drone shooting activities, such as the differences between weekdays and weekends, and the comparison of activity frequencies between day and night. The spatial distribution analysis uses the kernel density estimation algorithm, which is a spatial statistical analysis method used to evaluate the density distribution of point features in space. The core of the algorithm is to apply a distance attenuation function (kernel function) to each point, and then divide the entire study area into regular grids, and calculate the cumulative value of the kernel functions of all points within each grid cell as the density value of the cell. When specifically implemented, first extract the coordinate data of the photo points and the vertex coordinates of the video lines from the media set in the target area. For line features, they need to be converted into a set of constituent points.

[0099] Then set appropriate analysis parameters, including the bandwidth (search radius, which determines the smoothness), usually set to 1 / 20 to 1 / 10 of the data range, and the grid size (output resolution), generally set to 1 / 10 of the bandwidth. The kernel function usually selects the Gaussian kernel: ;

[0100] where is the distance from the point to the center of the grid, is the bandwidth. For each grid cell (x, y) in the study area, calculate the density value: ;

[0101] where is the distance from the th point to the center of the grid, is the total number of points. The calculation results are converted into a heat map, and the density level is represented by a gradient color, for example, red represents high density and blue represents low density, visually showing the spatial hot spots of UAV activities. The equipment usage analysis is achieved by classifying and counting the camera model field. Select the camera model (CAM_MODEL), shooting altitude (ALTITUDE), and camera tilt angle (PITCH) fields from the UAV media space database, and group the records by the "GROUP BY CAM_MODEL" statement to count the usage frequency, average shooting altitude, and average tilt angle of each model of equipment. The statistical results are presented in a pie chart to show the usage proportion of each model of equipment, and at the same time, a bar chart is combined to show the comparison of the average shooting parameters of each model of equipment. This multi-dimensional equipment analysis can reflect the usage scenarios and parameter characteristics of different models of UAVs, providing data support for equipment configuration optimization. The video trajectory feature analysis involves multiple spatial and temporal calculations. The length measurement is achieved by calculating the geometric length of the trajectory line. ArcGIS provides the "Calculate Geometry" function, which can directly calculate the length of line features. The speed calculation combines the trajectory length and video duration: speed = trajectory length / video duration. The direction analysis is performed by calculating the dominant direction of the trajectory, that is, the azimuth angle from the start point to the end point of the trajectory, and the complexity of the trajectory, such as the number of turns and the average turning angle. These calculation results are summarized in a trajectory feature statistical table, including indicators such as video ID, trajectory length, average speed, dominant direction, highest point altitude, lowest point altitude, and elevation change, comprehensively describing the characteristics of the video flight trajectory.

[0102] Integrate and layout the generated media time distribution histogram, media density heat map, equipment usage frequency pie chart, and trajectory feature statistical table to form a multi-page statistical chart set. The integration process uses the layout view function of ArcGIS to create multiple layouts. Each layout shows a type of statistical chart, sets the appropriate chart size, position, and style, and adds titles, legends, and explanatory texts. After completing the layout, it can be exported as a PDF document to maintain the integrity of the layout, facilitating browsing and printing.

[0103] Finally, integrate the statistical chart set with representative media files to generate a comprehensive analysis report. Select representative photos and videos from the media collection in the target area based on factors such as spatial distribution (selecting representative points in different regions), time distribution (selecting representative points at different time periods), and content characteristics (selecting important or special scenes). Extract the thumbnails of the selected photos and key frames of the videos, integrate them with the statistical chart set, and add regional overview and analysis explanatory texts, including the geographical location description of the research area, shooting task background, data statistics summary, and main features found, etc. The integrated document forms a comprehensive analysis report, which includes both quantitative statistical analysis and intuitive media display, comprehensively reflecting the characteristics and laws of UAV shooting activities.

[0104] The method for managing drone photos and videos based on geographical location in the embodiments of the present application has been described above. Next, the system for managing drone photos and videos based on geographical location in the embodiments of the present application will be described. Please refer to Figure 4 , an embodiment of the system for managing drone photos and videos based on geographical location in the embodiments of the present application includes:

[0105] A parsing module, configured to extract the shooting time, camera model, and geographical location coordinates from the EXIF information of drone photos, and parse the trajectory coordinate information at fixed time intervals from video captions to obtain drone media geographical attribute data;

[0106] A storage module, configured to establish an ArcGIS spatial relationship database according to the drone media geographical attribute data, store photo information as a spatial point layer, and store video information as a spatial line layer to obtain a drone media spatial database;

[0107] A naming module, configured to set the storage path and rename photo and video files according to the shooting time and camera model in the drone media geographical attribute data to obtain a structured media file set;

[0108] An import module, configured to import the spatial point layer and spatial line layer in the drone media spatial database into GIS software, overlay topographic maps or remote sensing images, and perform symbolic setting on the layers according to the camera model to obtain a visualization map of drone shooting distribution;

[0109] A screening module, configured to perform area query and attribute screening on the visualization map of drone shooting distribution through a spatial analysis tool, and quickly locate photo and video files according to the storage paths in the structured media file set to obtain a target area media set;

[0110] A calculation module, configured to calculate the time distribution and spatial density of the target area media set, and generate a comprehensive analysis report including statistical charts and spatial distribution based on the attribute information in the drone media spatial database.

[0111] Through the collaborative cooperation of the above-mentioned various components, by extracting geographical location data from the EXIF information of drone photos and video captions, establishing an ArcGIS spatial relationship database, the complete spatial management of drone media files is realized, enabling users to no longer need to carry out cumbersome manual sorting work, and greatly improving the data management efficiency. The spatial point layer and spatial line layer constructed based on the extracted media geographical attribute data visualize photos and videos in the geographical space, providing an intuitive reference for understanding the spatial distribution of drone shooting activities. In particular, the design of storing photo information as a spatial point layer and video information as a spatial line layer fully adapts to the essential characteristics of the two media types, making the data structure highly match the media type. The mechanism of setting paths and renaming files according to the shooting time and camera model establishes a clear and unified file organizational structure, facilitating quick browsing and searching at the file system level. In terms of spatial visualization, importing the spatial point layer and spatial line layer into GIS software and overlaying topographic maps or remote sensing images, and performing symbolic setting according to the camera model, the generated visualization map of drone shooting distribution greatly enhances the interpretability of the data, making complex spatial relationships clear at a glance. The spatial analysis tools applied in the solution perform area queries and attribute filtering on the shooting distribution, quickly locating photo and video files based on the storage paths in the structured media file set, realizing a seamless link from the spatial location to the actual media files, and solving the problem that media files cannot be retrieved based on location under traditional methods. In addition, the ability to calculate the time distribution and spatial density of the media set in the target area and generate a comprehensive analysis report based on the attribute information in the media spatial database provides users with an analysis tool for deeply understanding the rules of drone shooting activities. It is particularly worth mentioning that the application of the kernel density estimation algorithm in the media spatial distribution analysis in the solution transforms discrete photo points and video trajectories into a continuous density surface through scientific spatial statistical methods, effectively identifying the hot spots of drone activities and providing strong support for decision-making. At the same time, this solution has wide applicability, not only applicable to various DJI drone models, but also suitable for a variety of application scenarios, such as urban planning, agricultural monitoring, disaster assessment, cultural relic protection and other fields, making full and efficient use of the massive geographical spatial media resources collected by drones.

[0112] Referring to Figure 5 , in the embodiment of the present invention, a computer device is further provided. This computer device can be a server, and its internal structure can be as Figure 5As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0113] Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0114] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0115] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0116] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0117] 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 storage medium. Based on such an understanding, the technical solution of the present invention, in essence, 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. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for managing drone photos and videos based on geographical location, characterized in that, The method for managing drone photos and videos based on geographical location includes: Extracting the shooting time, camera model, and geographical location coordinates from the EXIF information of drone photos, and parsing the trajectory coordinate information at fixed time intervals from the video captions to obtain the geographical attribute data of drone media; Establishing an ArcGIS spatial relationship database based on the geographical attribute data of drone media, storing the photo information as a spatial point layer, and storing the video information as a spatial line layer to obtain a drone media spatial database; Setting the storage path and renaming the photo and video files according to the shooting time and camera model in the geographical attribute data of drone media to obtain a structured media file set; Importing the spatial point layer and spatial line layer in the drone media spatial database into GIS software, overlaying topographic maps or remote sensing images, and performing symbolic setting on the layers according to the camera model to obtain a visualization map of drone shooting distribution; Performing area query and attribute screening on the visualization map of drone shooting distribution through spatial analysis tools, and quickly locating photo and video files based on the storage paths in the structured media file set to obtain a media set for the target area; Performing time distribution statistics, spatial density calculation, equipment usage analysis, and trajectory feature analysis on the media set for the target area, and generating a comprehensive analysis report including statistical charts and spatial distribution based on the attribute information in the drone media spatial database; The step of establishing an ArcGIS spatial relationship database based on the geographical attribute data of drone media, storing the photo information as a spatial point layer, and storing the video information as a spatial line layer to obtain a drone media spatial database includes: Creating a spatial relationship database in the format of ArcGIS Personal Geodatabase, setting the coordinate system and spatial reference, and generating an initial media space container; Converting the photo information in the geographical attribute data of drone media into spatial point geometric objects, constructing a photo point feature class, and forming a photo spatial point attribute table; Extracting the shooting time, camera model, photo width, photo height, shooting angle, and camera tilt angle information from the photo spatial point attribute table, establishing photo attribute domains and indexes, and generating a photo spatial point layer; Connecting the video trajectory coordinate information in the geographical attribute data of drone media in time series, constructing a video line feature class, and forming a video trajectory line attribute table; Extracting the video creation time, camera model, video width, video height, and video duration information from the video trajectory line attribute table, establishing video attribute domains and indexes, and generating a video spatial line layer; Integrating the photo spatial point layer and the video spatial line layer into the initial media space container, establishing a spatial index and attribute association, and obtaining a drone media spatial database; The step of setting the storage path and renaming the photo and video files according to the shooting time and camera model in the geographical attribute data of drone media to obtain a structured media file set includes: Extract the shooting time information of photos and videos from the UAV media geographic attribute data, and perform time stratification by year, month, and day to form a time hierarchical structure; Extract the camera model information from the UAV media geographic attribute data, establish a camera model classification directory, and generate an equipment classification structure; Combine the time hierarchical structure with the equipment classification structure to construct a multi-level storage path and generate a media storage path template; Rename the photo files according to the media storage path template and the photo shooting time to generate a uniquely identified photo file name; Rename the video files according to the media storage path template and the video creation time to generate a uniquely identified video file name; Move the renamed photo and video files to the corresponding directories according to the media storage path template, and write the new storage path back to the UAV media space database to obtain a structured media file set; Import the spatial point layer and spatial line layer in the UAV media space database into GIS software, overlay the topographic map or remote sensing image, and perform symbolic setting on the layer according to the camera model to obtain a visualization map of UAV shooting distribution, including: Start the ArcGIS software environment, create a new map document, set appropriate map coordinate systems and projection parameters, and generate a basic map container; Load the spatial point layer and spatial line layer in the UAV media space database into the basic map container to form an initial media data view; Obtain high-resolution topographic maps, administrative division maps, or remote sensing image maps from map services, and add them as base map data below the initial media data view to generate a geographic background reference layer; Extract the camera model field from the spatial point layer, classify the photo points by different models, and assign symbols of different colors and sizes to the photo points of each camera model to generate a classified expression of photo points; Extract the camera model field from the spatial line layer, classify the video track lines by different models, and assign symbols of different colors and line types to the video track lines of each camera model to generate a classified expression of video lines; Overlay the classified expression of photo points and the classified expression of video lines on the geographic background reference layer, adjust the layer transparency and display order, and add map elements such as legends, scales, and north arrows to obtain a visualization map of UAV shooting distribution.

2. The method for managing drone photos and videos based on geographical location according to claim 1, characterized in that, Extract the shooting time, camera model, and geographic location coordinates from the EXIF information of UAV photos, and parse the track coordinate information at fixed time intervals from the video subtitles to obtain UAV media geographic attribute data, including: Read the exchangeable image file format data in the UAV photo file through a photo EXIF information parser, and extract photo shooting time, camera model, photo width, photo height, longitude, latitude, altitude, shooting angle, and camera tilt angle information to form a photo geographic attribute set; Obtain the creation time, camera model, video width, video height, and video duration information in the UAV video file through a video attribute reader to form a video basic attribute set; The subtitle parser of the drone video identifies the subtitles at fixed time intervals in the drone video, extracts the longitude, latitude, and altitude coordinate information contained in the subtitles, and generates a video trajectory coordinate sequence; Convert the location information in the photo geographic attribute set into spatial point data in a unified coordinate system to generate a photo spatial attribute table; Associate the video basic attribute set with the video trajectory coordinate sequence, and connect them in chronological order to construct spatial line data, generating a video trajectory attribute table; Merge and organize the photo spatial attribute table and the video trajectory attribute table to generate drone media geographic attribute data containing spatio-temporal information.

3. The method for managing drone photos and videos based on geographical location according to claim 1, wherein, The regional query and attribute filtering of the drone shooting distribution visualization map are performed through a spatial analysis tool, and the photo and video files are quickly located based on the storage paths in the structured media file set to obtain a target area media set, including: Draw a rectangular, circular, or polygonal spatial query range on the drone shooting distribution visualization map, and perform a spatial selection operation on the spatial point layer and the spatial line layer to obtain a preliminary spatial screening result; Apply buffer analysis to the preliminary spatial screening result, set an appropriate buffer radius, expand the query range, and obtain all photo points and video line features within the buffer to generate a buffer screening set; Extract the shooting time, camera model, shooting altitude, and camera tilt angle attribute fields from the buffer screening set, construct an attribute condition expression, and perform multi-condition combination screening to form an accurate screening result; Associate the photo points and video line features in the accurate screening result with the storage path field in the drone media spatial database, extract the file storage path information, and establish a media file index table; Access the structured media file set through the media file index table, locate and extract the actual photos and video files that meet the conditions, and generate a media file link set; Integrate the spatial element attributes of the accurate screening result with the media file link set to construct an associated data set containing spatial location, attribute information, and file links, obtaining a target area media set.

4. The method for managing drone photos and videos based on geographical location according to claim 1, wherein Perform time distribution statistics, spatial density calculation, device usage analysis, and trajectory feature analysis on the target area media set, and generate a comprehensive analysis report containing statistical charts and spatial distribution based on the attribute information in the drone media spatial database, including: Extract the shooting time information of photos and videos from the target area media set, perform time grouping statistics by year, month, day, and hour, and generate a media time distribution histogram; Extract the photo shooting locations and video trajectory information from the target area media set, calculate the media spatial distribution density through the kernel density estimation algorithm, and generate a media density heat map; Extract the camera model, shooting altitude, and camera tilt angle attribute fields from the drone media spatial database, classify and count the usage of different camera models, and generate a device usage frequency pie chart; Measure the length, calculate the speed, and analyze the direction of the video trajectories in the target area media set to generate a trajectory feature statistical table; Integrate and typeset the media time distribution histogram, the media density heat map, the device usage frequency pie chart, and the trajectory feature statistical table to form a multi-page statistical chart set; Combine the multi-page statistical chart set with representative photos and video thumbnails in the target area media set, and add regional overview and analysis description text to generate a comprehensive analysis report containing statistical charts and spatial distribution.

5. A drone photo and video management system based on geographical location, which is used to implement the drone photo and video management method based on geographical location according to any one of claims 1 to 4, characterized in that, The drone photo and video management system based on geographical location includes: An analysis module for extracting shooting time, camera model, and geographical location coordinates from the EXIF information of drone photos, and parsing the trajectory coordinate information at fixed time intervals from video captions to obtain drone media geographical attribute data; A storage module for establishing an ArcGIS spatial relationship database according to the drone media geographical attribute data, storing photo information as a spatial point layer, and storing video information as a spatial line layer to obtain a drone media spatial database; A naming module for setting the storage path and renaming photo and video files according to the shooting time and camera model in the drone media geographical attribute data to obtain a structured media file set; An import module for importing the spatial point layer and the spatial line layer in the drone media spatial database into GIS software, overlaying topographic maps or remote sensing images, and performing symbolic setting on the layers according to the camera model to obtain a visualization map of drone shooting distribution; A screening module for performing regional query and attribute screening on the visualization map of drone shooting distribution through spatial analysis tools, and quickly locating photo and video files based on the storage paths in the structured media file set to obtain a target area media set; A calculation module for performing time distribution statistics, spatial density calculation, device usage analysis, and trajectory feature analysis on the target area media set, and generating a comprehensive analysis report containing statistical charts and spatial distribution based on the attribute information in the drone media spatial database.

6. A computer device, characterized in that, It includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the drone photo and video management method based on geographical location according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, A computer program is stored thereon. When the computer program is run by the processor, the processor is caused to execute the drone photo and video management method based on geographical location according to any one of claims 1 to 4.

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