Low-altitude economic unmanned aerial vehicle data processing method and system based on large model

Abstract

The invention discloses a low-altitude economic unmanned aerial vehicle data processing method and system based on a large model, and relates to the technical field of low-altitude economic data process.The method comprises the steps of aerial video screening analysis, unmanned aerial vehicle shooting analysis, aerial photography effect evaluation analysis and database analysis, the method comprises the following steps: selecting unmanned aerial vehicles for aerial photography, analyzing shooting techniques correspondingly acquired by unmanned aerial vehicle aerial photography, screening out unmanned aerial vehicles correspondingly acquired by unmanned aerial vehicle aerial photography, executing urban characteristic aerial photography tasks, and judging shooting effects of aerial photography videos corresponding to the unmanned aerial vehicles, so that efficient urban aerial photography is realized, and the purposes of effectively propagandizing a city and promoting tourism economic development of the city are achieved. Meanwhile, the unmanned aerial vehicle is used for collecting the styles and features of the city, the shooting efficiency can be effectively improved, the shooting burden and cost can be effectively reduced, highlights are added for propaganda, new visual experience is brought, and more tourists are attracted.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude economic data processing, and in particular to a low-altitude economic UAV data processing method and system based on a large model. Background Art

[0002] As the scope of drone applications continues to expand, it can effectively promote the development of cities and increase the economic benefits of cities. By using drones to take special photos of cities and increase the promotion of cities, cities can be led to achieve more comprehensive business development. First, select reference shooting techniques for aerial photography of cities, and then promote and shoot your own city based on the reference shooting techniques, so as to promote the city’s tourism economy and further enhance the city’s popularity and attractiveness.

[0003] Prior art, such as the invention patent application with announcement number CN115085967A, discloses a method and system for integrated processing of multi-sensor data of ultra-low altitude UAVs. The method sets initial data including GPS / IMU, basic sensor data and initial sensor placement parameters, and performs post-processing on multi-sensor joint data based on the initial data; defines the data flow process and interface file specifications for multi-sensor post-processing; uses GPS / IMU as a reference benchmark, and uniformly calculates the joint data processing mode between multiple sensors to the same absolute reference coordinate system through a placement error registration model. The technical solution of the present invention can realize post-processing of geometric data for multiple sensors.

[0004] With respect to the above scheme, the applicant of the present invention has found that the above technical problems have at least the following technical problems: the above invention is mainly for the post-processing of geometric data of multiple sensors, and does not use drones to shoot cities, thereby failing to achieve the purpose of promoting urban publicity and the development of tourism low-altitude economy. At the same time, the drones are not analyzed based on reference videos to obtain corresponding aerial photography techniques, and efficient photography and publicity of the city cannot be achieved. The shooting effect after the drone executes aerial photography commands on the city cannot be predicted, which reduces the efficiency and visibility of urban aerial photography and fails to effectively promote urban economic development. Summary of the invention

[0005] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a low-altitude economical UAV data processing method and system based on a large model.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a low-altitude economic UAV data processing method based on a large model, including: Step 1, aerial video screening and analysis: Collect the corresponding characteristic features in the city through UAV aerial photography, first screen and obtain the corresponding reference videos collected by the UAV aerial photography, and extract the corresponding reference clips collected by the UAV aerial photography.

[0007] Step 2: Analysis of drone photography: Based on the reference clips collected by drone aerial photography, the shooting techniques collected by drone aerial photography are screened out, and the reference application editing techniques and reference music selection methods after the drone aerial photography are screened out. At the same time, the drones collected by drone aerial photography are screened out.

[0008] Step 3: Aerial photography effect evaluation and analysis: Control the drone to collect the corresponding aerial photography features of the city, and then edit and synthesize the corresponding aerial video of the drone, and judge the shooting effect of the corresponding aerial video of the drone.

[0009] In a second aspect, the present invention provides a low-altitude economical UAV data processing system based on a large model, including: an aerial video screening and analysis module, which is used to collect corresponding characteristic features in the city through UAV aerial photography, first screening out the corresponding reference videos collected by the UAV aerial photography, and extracting the corresponding reference clips collected by the UAV aerial photography.

[0010] The drone shooting analysis module is used to filter out the shooting techniques corresponding to the drone aerial photography based on the reference clips collected by the drone aerial photography, and to filter out the reference application editing techniques and reference music selection methods after the drone aerial photography is collected, and at the same time, to filter out the drones corresponding to the drone aerial photography.

[0011] The aerial photography effect evaluation and analysis module is used to control the drone to perform corresponding aerial photography feature collection on the city, and then edit and synthesize the corresponding aerial video of the drone, and judge the shooting effect of the corresponding aerial video of the drone.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a low-altitude economic UAV data processing method and system based on a large model, collects corresponding characteristic features in the city through UAV aerial photography, first screens out the reference videos corresponding to the collection of UAV aerial photography, and analyzes the shooting techniques corresponding to the collection of UAV aerial photography, thereby screening out the UAVs corresponding to the collection of UAV aerial photography, performing the city characteristic aerial photography task, and judging the shooting effect of the corresponding aerial photography video of the UAV, thereby realizing efficient urban aerial photography, thereby achieving effective publicity of the city, promoting the development of the city's tourism economy, and solving the shortcomings of the current technology. At the same time, using UAVs to collect the city's style can effectively improve the efficiency of shooting, reduce the burden and cost of shooting, save shooting time and required manpower, and based on the shooting method obtained from the reference video, it can further control the UAV to realize various aspects of city characteristic style publicity shooting, better show the special features of the city, add highlights to the publicity, bring new visual experience, attract more tourists, effectively promote urban economic development, enhance the public users' efficient attention to the city, enhance the city's popularity and attractiveness, promote the development of the city's low-altitude economy led by UAVs, and inject new economic vitality into the city. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0015] Figure 2 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1As shown, a low-altitude economic UAV data processing method based on a large model includes step one, aerial video screening and analysis: by collecting corresponding characteristic features in the city through UAV aerial photography, firstly, the reference videos corresponding to the UAV aerial photography are screened, and then the reference clips corresponding to the UAV aerial photography are extracted.

[0018] It should be noted that the characteristic features include ancient buildings, scenic spots and special foods, etc., and the characteristic features are obtained from the public information of the city’s corresponding official account.

[0019] As an optional implementation method, the screening obtains the reference videos corresponding to the drone aerial photography, and the specific screening process is as follows: the background viewing data of each promotional video corresponding to each platform is obtained, and the switching points corresponding to each promotional video are extracted from the background viewing data of each promotional video corresponding to each platform, and the switching points of each promotional video corresponding to each platform are compared with the switching point interval corresponding to the high popularity stored in the database. If the switching point of a promotional video corresponding to a certain platform is within the switching point interval corresponding to the high popularity stored in the database, then the promotional video corresponding to the platform is recorded as the reference video corresponding to the drone aerial photography, so as to obtain the reference videos corresponding to the drone aerial photography by screening.

[0020] As an optional implementation, the extraction obtains each reference segment corresponding to the drone aerial photography, and the specific extraction process is as follows: by extracting the browsing dwell time and segment search times corresponding to each video segment of each reference video from the background heat map of each reference video corresponding to the drone aerial photography, the browsing dwell time corresponding to each video segment of each reference video is compared with the reference dwell time corresponding to each high heat stored in the database, and the segment search times corresponding to each video segment of each reference video is also compared with the reference segment search times corresponding to each high heat stored in the database. If the browsing dwell time corresponding to a certain video segment of a reference video is greater than or equal to the reference dwell time corresponding to each high heat stored in the database, and the segment search times corresponding to the video segment of the reference video is greater than or equal to the reference segment search times corresponding to the high heat stored in the database, then the video segment corresponding to the reference video is used as the reference segment corresponding to the drone aerial photography, so as to obtain the reference segments corresponding to the drone aerial photography.

[0021] Step 2: Analysis of drone photography: Based on the reference clips collected by drone aerial photography, the shooting techniques collected by drone aerial photography are screened out, and the reference application editing techniques and reference music selection methods after the drone aerial photography are screened out. At the same time, the drones collected by drone aerial photography are screened out.

[0022] As an optional implementation manner, the screening obtains the shooting techniques corresponding to the drone aerial photography, and the specific screening process is as follows: based on the video display of each reference segment corresponding to the drone aerial photography, the objects in the reference segments corresponding to the drone aerial photography are extracted, and the morphological information of each object corresponding to each reference segment is obtained by performing portrait analysis on the objects corresponding to each reference segment, wherein the portrait analysis information includes portrait static, portrait dynamic, portrait distant view, portrait close-up, portrait focus and portrait divergence, and the amount of likes obtained when each object corresponding to each reference segment displays each portrait form is obtained from the background, and the amount of likes obtained when each object corresponding to each reference segment displays each portrait form is compared with the like amount interval corresponding to the high popularity stored in the database. If the amount of likes obtained when a certain object corresponding to a certain reference segment displays a certain portrait form is within the like amount interval corresponding to the high popularity stored in the database, then the shooting technique corresponding to the object is used as the shooting technique corresponding to the drone aerial photography, thereby screening to obtain the shooting techniques corresponding to the drone aerial photography.

[0023] As an optional implementation manner, the reference application editing techniques collected after drone aerial photography are screened out, and the specific analysis process is as follows: based on the shooting techniques collected corresponding to drone aerial photography, each reference video corresponding to the shooting technique is obtained, and the video playback corresponding to each reference video is analyzed to obtain the playback display data corresponding to each reference video, wherein the playback display data includes the home page picture color, video portrait area layout and video portrait connection interval, and the playback display data corresponding to each reference video is compared with the playback display data set corresponding to each high-heat editing technique corresponding to each current platform stored in the database. If the playback display data corresponding to a reference video is within the playback display data set corresponding to a high-heat editing technique corresponding to a current platform stored in the database, then the editing technique is used as the reference application editing technique collected after drone aerial photography, thereby screening to obtain the reference application editing technique collected after drone aerial photography.

[0024] As an optional implementation manner, the method for selecting reference soundtracks corresponding to drone aerial photography is as follows: A1. Acquire the soundtrack information corresponding to each current reference video, wherein the soundtrack information includes the usage of the sound source and the type of population using each sound source, compare the type of population using each sound source corresponding to each reference video with the preset set of population types corresponding to the city aerial photography, if the type of population using a certain sound source corresponding to a certain reference video is within the preset set of population types corresponding to the city aerial photography, then record the sound source as the target sound source, and so on to obtain the target sound sources.

[0025] It should be noted that a set of crowd types is preset: the browsing group types corresponding to the city’s historical aerial videos and the group types of historical tourism in each year are obtained, and then a unified statistics of the number of groups of each type is performed to obtain the number of each group type, and the crowd type set is preset based on the maximum and minimum values ​​of the number of each group type.

[0026] A2. Compare the usage of the sound source corresponding to each target sound source with the usage interval of the sound source corresponding to the high popularity stored in the database. If the usage of the sound source corresponding to a target sound source is within the usage interval of the sound source corresponding to the high popularity stored in the database, then use the target sound source as the reference sound source after the drone aerial photography is collected, so as to screen out the reference soundtrack selection method after the drone aerial photography is collected.

[0027] As an optional implementation method, the screening obtains drones corresponding to the drone aerial photography, and the specific screening process is as follows: obtain basic information of the characteristic features corresponding to the drone aerial photography, wherein the basic information includes position distance and altitude, compare the position distance of the characteristic features corresponding to the drone aerial photography with the flight distance position interval corresponding to each drone stored in the database, and at the same time compare the height of the characteristic features corresponding to the drone aerial photography with the flight altitude interval corresponding to each drone stored in the database; if the position distance of the characteristic features corresponding to the drone aerial photography is within the flight distance position interval corresponding to a certain drone stored in the database, and the height of the characteristic features corresponding to the drone aerial photography is also within the flight altitude interval corresponding to a certain drone stored in the database, then the drone is used as the drone corresponding to the drone aerial photography, thereby screening to obtain drones corresponding to the drone aerial photography.

[0028] It should be noted that the flight distance position range and flight altitude range: the current position distance and altitude corresponding to each drone are obtained from the certificate of qualification issued by the seller. Before purchasing the required aerial photography drone, you need to find a seller to purchase the corresponding drone. When selling a drone, the seller needs to conduct corresponding capability tests on the drone in advance, set various types of shooting tasks for each drone, and observe the functional performance of the drone when completing each shooting task, so as to obtain the corresponding flight distance position range and flight altitude range of the drone.

[0029] Step 3: Aerial photography effect evaluation and analysis: Control the drone to collect the corresponding aerial photography features of the city, and then edit and synthesize the corresponding aerial video of the drone, and judge the shooting effect of the corresponding aerial video of the drone.

[0030] It should be noted that the aerial video corresponding to the drone is synthesized by inputting the architectural images, scenic spot images and food images taken by the drone in the corresponding shooting method into the deep learning model, and based on the automatic processing and editing generation technology corresponding to the deep learning model, the aerial video corresponding to the drone is obtained, where the deep learning model includes generative adversarial networks GANs, variational autoencoders VAEs, etc.

[0031] It should be noted that by watching the synthesized aerial video and seeing whether there is any connection jam during the playback of the aerial video, the smoothness of the corresponding aerial video of the drone can be obtained. When the aerial video is playing, see if the music appears but the image does not, or the image appears but the music does not. Use a timer to see how long the delay is, get the synchronization of audio and video, and obtain the visual color value from the specification parameters of the aerial video.

[0032] It should also be noted that the corresponding large model is selected: based on the aerial video corresponding to the drone, the shooting effect demand judgment features are extracted, and the extracted demand judgment features are compared with the evaluation feature sets corresponding to the large models. If the extracted demand judgment features are in the evaluation feature set corresponding to a large model, the large model is used as a predictive evaluation large model for judging the shooting effect of the drone's corresponding aerial video, and then input into the effect information of the drone's corresponding aerial video for prediction and evaluation.

[0033] As an optional implementation manner, the specific judgment process of judging the shooting effect of the aerial video corresponding to the drone is as follows: obtaining the effect information of the aerial video corresponding to the synthesized drone, wherein the effect information includes smoothness, audio and video synchronization value and visual color value, and selecting the corresponding large model, inputting the effect information of the aerial video corresponding to the drone after synthesis into the large model, and predicting the effect evaluation coefficient of the aerial video corresponding to the drone. When the output value of the large model evaluation result is 0, it is judged that the shooting effect of the aerial video corresponding to the drone is unqualified. When the output value of the large model evaluation result is 1, it is judged that the shooting effect of the aerial video corresponding to the drone is qualified, thereby judging the shooting effect of the aerial video corresponding to the drone.

[0034] As an optional implementation, the prediction obtains the effect evaluation coefficient of the drone corresponding to the aerial video. The specific prediction process is as follows: the effect information of the drone corresponding to the aerial video is standardized, and then the smoothness, audio and video synchronization value and visual color value of the drone corresponding to the aerial video are recorded as , and .

[0035] By calculating the formula , predict the effect evaluation coefficient of the drone corresponding to the aerial video , , and They are the weight factors of the set fluency, the audio-visual synchronization value, and the visual color value. is the effect coefficient of the reference video corresponding to the drone aerial photography, , , .

[0036] It should be noted that the weight factors of fluency, audio-visual synchronization value, and visual color value are calculated using the CRITIC weight method, and the contrast intensity and conflict index are multiplied and normalized to obtain the final weight.

[0037] See also Figure 2 As shown, the present invention provides a low-altitude economic UAV data processing system based on a large model in the second aspect, including: an aerial video screening and analysis module, a UAV shooting analysis module, an aerial effect evaluation and analysis module and a database.

[0038] The drone shooting analysis module is respectively connected to the aerial video selection analysis module, the aerial photography effect evaluation analysis module and the database.

[0039] The aerial video screening and analysis module is used to collect corresponding characteristic features in the city through drone aerial photography. First, the reference videos corresponding to the drone aerial photography are screened, and then the reference clips corresponding to the drone aerial photography are extracted.

[0040] The drone shooting analysis module is used to filter out the shooting techniques corresponding to the drone aerial photography based on the reference clips collected by the drone aerial photography, and to filter out the reference application editing techniques and reference music selection methods after the drone aerial photography is collected, and at the same time, to filter out the drones corresponding to the drone aerial photography.

[0041] The aerial photography effect evaluation and analysis module is used to control the drone to perform corresponding aerial photography feature collection on the city, and then edit and synthesize the corresponding aerial video of the drone, and judge the shooting effect of the corresponding aerial video of the drone.

[0042] The database is used to store switching points, switching point intervals, browsing duration, number of clip searches, portrait analysis information, number of likes, playback display data, music information and effect information.

[0043] In the embodiment of the present invention, the corresponding characteristic features in the city are collected by drone aerial photography. First, the reference videos corresponding to the drone aerial photography are screened, and the shooting techniques corresponding to the drone aerial photography are analyzed, so as to screen the drones corresponding to the drone aerial photography, perform the city characteristic aerial photography task, and judge the shooting effect of the drone corresponding aerial photography video, so as to realize efficient city aerial photography, thereby achieving effective publicity of the city, promoting the development of the city's tourism economy, and solving the shortcomings existing in the current technology. At the same time, the drone is used to collect the city's style, which can effectively improve the efficiency of shooting, reduce the burden and cost of shooting, save shooting time and required manpower, and based on the shooting method obtained from the reference video, the drone can be further controlled to realize various aspects of city characteristic style publicity shooting, better show the special features of the city, add highlights to the publicity, bring new visual experience, attract more tourists, effectively promote the development of the city's economy, enhance the public users' efficient attention to the city, enhance the city's popularity and attractiveness, promote the development of the city's low-altitude economy led by drones, and inject new economic vitality into the city.

[0044] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. A method for processing low-altitude economic UAV data based on a large model, characterized in that: include: Step 1: Aerial video screening and analysis: The corresponding characteristics of the city are collected through drone aerial photography. First, the reference videos corresponding to the drone aerial photography are screened, and the reference clips corresponding to the drone aerial photography are extracted; Step 2: Analysis of drone photography: Based on the reference clips collected by drone aerial photography, the shooting techniques collected by drone aerial photography are screened out, and the reference application editing techniques and reference soundtrack selection methods after the drone aerial photography are screened out. At the same time, the drones collected by drone aerial photography are screened out; Step 3: Aerial photography effect evaluation and analysis: Control the drone to collect the corresponding aerial photography features of the city, and then edit and synthesize the corresponding aerial video of the drone, and judge the shooting effect of the corresponding aerial video of the drone.

2. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 1, characterized in that: The screening obtains the reference videos corresponding to the drone aerial photography. The specific screening process is as follows: Obtain the background viewing data of each promotional video corresponding to each platform, and extract the switching points corresponding to each promotional video from the background viewing data of each promotional video corresponding to each platform, and compare the switching points of each promotional video corresponding to each platform with the switching point interval corresponding to the high popularity stored in the database. If the switching point of a promotional video corresponding to a certain platform is within the switching point interval corresponding to the high popularity stored in the database, then the promotional video corresponding to the platform is recorded as the reference video collected by drone aerial photography, so as to screen out the reference videos collected by drone aerial photography.

3. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 2, characterized in that: The extraction obtains the reference fragments corresponding to the drone aerial photography, and the specific extraction process is as follows: By extracting the browsing dwell time and the number of fragment searches corresponding to each video segment of each reference video from the background heat map of each reference video collected by drone aerial photography, the browsing dwell time corresponding to each video segment of each reference video is compared with the reference dwell time corresponding to each high popularity stored in the database. At the same time, the number of fragment searches corresponding to each video segment of each reference video is also compared with the reference fragment search number corresponding to each high popularity stored in the database. If the browsing dwell time corresponding to a video segment of a reference video is greater than or equal to the reference dwell time corresponding to each high popularity stored in the database, and the number of fragment searches corresponding to the video segment of the reference video is greater than or equal to the reference fragment search number corresponding to the high popularity stored in the database, then the video segment corresponding to the reference video is used as the reference segment collected by drone aerial photography, so as to obtain the reference segments collected by drone aerial photography.

4. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 3, characterized in that: The screening obtains the shooting techniques corresponding to the drone aerial photography, and the specific screening process is as follows: Based on the video display of each reference clip collected by drone aerial photography, the objects in the reference clips collected by drone aerial photography are extracted, and the morphological information of each object corresponding to each reference clip is obtained by portrait analysis of each object corresponding to each reference clip, wherein the portrait analysis information includes portrait static, portrait dynamic, portrait long-range, portrait close-up, portrait focus and portrait divergence, and the number of likes for each object corresponding to each reference clip when showing each portrait form is obtained from the background, and the number of likes for each object corresponding to each reference clip when showing each portrait form is compared with the range of likes corresponding to high popularity stored in the database. If the number of likes for a certain object corresponding to a reference clip when showing a certain portrait form is within the range of likes corresponding to high popularity stored in the database, then the shooting technique corresponding to the object is used as the shooting technique collected by drone aerial photography, so as to screen the shooting techniques collected by drone aerial photography.

5. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 4, characterized in that: The specific analysis process of selecting the reference application editing techniques corresponding to the drone aerial photography after collection is as follows: Based on the shooting techniques collected corresponding to drone aerial photography, reference videos corresponding to the shooting techniques are obtained, and the video playback corresponding to each reference video is analyzed to obtain the playback display data corresponding to each reference video, wherein the playback display data includes the homepage picture color, video portrait area layout and video portrait connection interval. The playback display data corresponding to each reference video is compared with the playback display data set corresponding to each high-heat editing technique corresponding to each current platform stored in the database. If the playback display data corresponding to a reference video is in the playback display data set corresponding to a high-heat editing technique corresponding to a current platform stored in the database, then the editing technique is used as the reference application editing technique after the drone aerial photography is collected, so as to screen and obtain the reference application editing technique after the drone aerial photography is collected.

6. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 5, characterized in that: The method for selecting reference music after filtering out the corresponding collection of drone aerial photography: A1. Obtain the music information corresponding to each current reference video, wherein the music information includes the usage amount of the sound source and the type of user group of each sound source, and compare the type of user group of each sound source corresponding to each reference video with the preset set of target groups corresponding to the city aerial photography. If the type of user group of a sound source corresponding to a reference video is within the preset set of target groups corresponding to the city aerial photography, then the sound source is recorded as the target sound source, and so on to obtain each target sound source; A2. Compare the usage of the sound source corresponding to each target sound source with the usage interval of the sound source corresponding to the high popularity stored in the database. If the usage of the sound source corresponding to a target sound source is within the usage interval of the sound source corresponding to the high popularity stored in the database, then use the target sound source as the reference sound source after the drone aerial photography is collected, so as to screen out the reference soundtrack selection method after the drone aerial photography is collected.

7. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 1, characterized in that: The screening obtains the drones corresponding to the drone aerial photography, and the specific screening process is as follows: The basic information of the characteristic features corresponding to the drone aerial photography is obtained, wherein the basic information includes the position distance and the height. The position distance of the characteristic features corresponding to the drone aerial photography is compared with the flight distance position interval corresponding to each drone stored in the database. At the same time, the height of the characteristic features corresponding to the drone aerial photography is compared with the flight altitude interval corresponding to each drone stored in the database. If the position distance of the characteristic features corresponding to the drone aerial photography is within the flight distance position interval corresponding to a certain drone stored in the database, and the height of the characteristic features corresponding to the drone aerial photography is also within the flight altitude interval corresponding to a certain drone stored in the database, then the drone is used as the drone collected corresponding to the drone aerial photography, so as to screen the drones collected corresponding to the drone aerial photography.

8. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 1, characterized in that: The specific judging process of judging the shooting effect of the drone corresponding to the aerial video is as follows: The effect information of the drone's corresponding aerial video after editing and synthesis is obtained, wherein the effect information includes smoothness, audio-visual synchronization value and visual color value, and the corresponding large model is selected, and the effect information of the drone's corresponding aerial video after synthesis is input into the large model, and the effect evaluation coefficient of the drone's corresponding aerial video is predicted. When the output value of the large model evaluation result is 0, it is determined that the shooting effect of the drone's corresponding aerial video is unqualified. When the output value of the large model evaluation result is 1, it is determined that the shooting effect of the drone's corresponding aerial video is qualified, thereby judging the shooting effect of the drone's corresponding aerial video.

9. A method for processing low-altitude economical UAV data based on a large model as claimed in claim 1, characterized in that: The prediction obtains the effect evaluation coefficient of the drone corresponding to the aerial video. The specific prediction process is as follows: The effect information of the drone’s corresponding aerial video is standardized, and then the smoothness, audio and video synchronization value, and visual color value of the drone’s corresponding aerial video are recorded as , and ; By calculating the formula , predict the effect evaluation coefficient of the drone corresponding to the aerial video , , and They are the weight factors of the set fluency, the audio-visual synchronization value, and the visual color value. is the effect coefficient of the reference video corresponding to the drone aerial photography, , , .

10. A low-altitude economical UAV data processing system for executing the large model-based low-altitude economical UAV data processing method according to any one of claims 1 to 9, characterized in that: include: The aerial video screening and analysis module is used to collect the corresponding characteristics of the city through drone aerial photography. First, the reference videos corresponding to the drone aerial photography are screened and the reference clips corresponding to the drone aerial photography are extracted. The drone shooting analysis module is used to screen the shooting techniques corresponding to the drone aerial photography based on the reference clips collected by the drone aerial photography, and screen the reference application editing techniques and reference soundtrack selection methods after the drone aerial photography is collected, and at the same time screen the drones corresponding to the drone aerial photography; The aerial photography effect evaluation and analysis module is used to control the drone to perform corresponding aerial photography feature collection of the city, and then edit and synthesize the corresponding aerial video of the drone, and judge the shooting effect of the corresponding aerial video of the drone.

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