Unmanned aerial vehicle flight shooting quality optimization method and device, and storage medium

Abstract

The application relates to the technical field of video processing, in particular to a UAV flight shooting quality optimization method and device and a storage medium, the method comprising the following steps: inputting UAV basic shooting parameters through interaction and collecting UAV flight data in real time; performing space modeling on a flight attitude curve according to the UAV flight data, and constructing a motion disturbance model to output a real-time attitude disturbance index; constructing an anti-disturbance model according to signal interference data and outputting a real-time anti-interference index; coupling the real-time anti-interference index and the real-time attitude disturbance index to determine the video shooting parameters of the UAV in a current control period; shooting with the video shooting parameters in the current control period to obtain a shooting video, and optimizing the shooting video in combination with the shooting difficulty of the UAV. The application effectively improves the quality of video shooting in the flight process of the UAV.

Description

Technical Field

[0001] This invention relates to the field of video processing technology, and in particular to a method, apparatus, and storage medium for optimizing the quality of drone flight footage. Background Technology

[0002] With the popularization of drone technology, aerial photography has become an important means of video creation. Current drone shooting systems typically rely on gimbal physical stabilization and basic electronic image stabilization algorithms, using gyroscope data to adjust the lens angle or crop the image in real time to counteract shaking during flight. In addition, existing automatic exposure (AE) and automatic white balance (AWB) algorithms can also adapt to changes in ambient light to a certain extent, meeting basic shooting needs.

[0003] However, existing technologies still have significant shortcomings in complex flight environments. First, relying solely on a physical gimbal cannot completely eliminate the impact of high-frequency vibrations on image clarity. Second, current shooting parameter adjustments often depend on a single light sensor, ignoring the combined effects of electromagnetic interference, wind resistance, and other factors on image quality. This results in videos that are prone to blurring, increased noise, or color distortion in strong interference or high dynamic scenes, failing to meet professional-grade shooting quality requirements. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and storage medium for optimizing the quality of drone flight photography, so as to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, the present invention provides a method for optimizing the quality of drone flight photography, comprising:

[0006] Interactive input of basic drone shooting parameters and real-time acquisition of drone flight data;

[0007] Based on the UAV flight data, spatial modeling of the flight attitude curve is performed, and a motion disturbance model is constructed to output the real-time attitude disturbance index.

[0008] A counter-interference model is constructed based on signal interference data, and a real-time counter-interference index is output.

[0009] The real-time anti-jamming index and the real-time attitude disturbance index are coupled to determine the video capture parameters of the UAV in the current control cycle.

[0010] Optionally, a spatial rectangular coordinate system is established with the current spatial coordinates of the UAV as the origin, and a simulation is performed in the spatial rectangular coordinate system based on the current UAV flight speed vd, the flight speed va of the flight command, and the flight angle θ of the flight command to obtain the flight attitude curve in the spatial rectangular coordinate system.

[0011] Let D1 denote the distance from the starting point of the flight attitude curve to the subject in focus, D2 denote the distance from the ending point of the flight attitude curve to the subject in focus, θ1 denote the shooting angle from the starting point of the flight attitude curve to the subject in focus, and θ2 denote the shooting angle from the starting point of the flight attitude curve to the subject in focus. Construct the motion disturbance index α, and set it as follows:

[0012] α = a1 × |D2 - D1| / D1 + a2 × |θ1 - θ2| / (θ1 + θ2); where a1 and a2 are attitude perturbation weights.

[0013] Optionally, the bimodal peak FF and vibration frequency ZV of the UAV vibration waveform in the previous control cycle are extracted, and the vibration disturbance factor ZY is constructed based on the extraction results. ZY is set as 0.7×max{(ZV-yv) / yv,0}+0.3×FF / YF; where yv is the jelly effect vibration frequency threshold and YF is the jelly effect vibration bimodal threshold.

[0014] The motion disturbance index within the control cycle is compensated by combining the drone's highest shooting frame rate (FPS) and vibration disturbance factor (ZY).

[0015] When ZY / FPS is greater than the preset vibration disturbance constant, the motion disturbance index within the control cycle is updated to α×{1+(ZY / FPS-preset vibration disturbance constant) / preset vibration disturbance constant}; otherwise, the motion disturbance index within the control cycle is not updated.

[0016] Optionally, the average value of the background spectral energy collected within the background update cycle is taken and denoted as μ. The standard deviation of the background spectral energy σ is calculated. Then, the spectral energy in the preset sensitive band within the previous control cycle is collected and denoted as P. The spectral peak intensity RIR is calculated and set as RIR=(P-μ) / σ.

[0017] Set an abnormal peak intensity threshold, and when the RIR is greater than the abnormal peak intensity threshold, construct a spectral interference factor and set the value of the spectral interference factor to (RIR - abnormal peak intensity threshold) / abnormal peak intensity threshold; otherwise, set the value of the spectral interference factor to 0.

[0018] Optionally, the signal-to-noise ratio time series of the UAV communication frequency band with a sampling rate of 1 kHz and a window length of 1 s is collected, denoted as SNR, and the Hearst exponent H of SNR is extracted.

[0019] The Hearst exponent H is coupled with the spectral interference factor to construct a real-time anti-interference exponent β.

[0020] Optionally, when JD is less than or equal to the polarization amplitude threshold, the gimbal offset state is determined to be normal.

[0021] When JD is greater than the polarization amplitude threshold, the gimbal offset state is determined to be abnormal. At this time, a compensation factor for the abnormal peak intensity threshold is set. The value of the compensation factor is set to [1-(JD-polarization amplitude threshold) / polarization amplitude threshold]. The product of the abnormal peak intensity threshold and the compensation factor is used as the compensated abnormal peak intensity threshold.

[0022] Optionally, the difficulty of drone photography can be determined by combining the real-time anti-interference index and the real-time attitude disturbance index.

[0023] Determine the video shooting parameters within the current control cycle based on the difficulty of drone shooting;

[0024] The video blur coefficient γ is constructed based on the real-time anti-interference index and the real-time attitude perturbation index, and γ is set as c1×α+c2×β;

[0025] Set various shooting quality difficulty ranges, and match the video blur coefficient within the control period with the shooting quality difficulty range:

[0026] When γ falls within the first range of shooting quality difficulty, the drone shooting difficulty is determined to be normal;

[0027] When γ falls within the second shooting quality difficulty range, the drone shooting difficulty is determined to be difficult.

[0028] When γ falls within the third shooting quality difficulty range, the drone shooting difficulty is determined to be unsuitable for shooting.

[0029] Optionally, when the difficulty of drone shooting is normal, the exposure time corresponding to FPS×50% is used as the exposure time for drone video shooting within the control period;

[0030] When the drone shooting difficulty is difficult, the exposure time corresponding to FPS is used as the exposure time for drone video shooting within the control period;

[0031] When the drone's shooting difficulty is deemed unsuitable, it is not recommended for users to attempt shooting.

[0032] According to another aspect of this application, a device for optimizing the quality of drone flight photography is provided, comprising:

[0033] The data acquisition unit is used for interactive input of basic drone shooting parameters and real-time acquisition of drone flight data;

[0034] The attitude disturbance unit is used to spatially model the flight attitude curve based on UAV flight data and construct a motion disturbance model to output the real-time attitude disturbance index.

[0035] The signal jamming unit is used to construct an anti-jamming model based on signal jamming data and output a real-time anti-jamming index.

[0036] The shooting parameter determination unit is used to couple the real-time anti-interference index and the real-time attitude disturbance index to determine the video shooting parameters of the UAV in the current control cycle.

[0037] The video optimization unit is used to shoot with the video shooting parameters within the current control cycle to obtain the captured video, and to optimize the captured video based on the difficulty of drone shooting.

[0038] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the UAV flight photography quality optimization method during runtime.

[0039] Compared with existing technologies, the advantages of this invention are as follows: by integrating multi-dimensional evaluation of vibration disturbance, anti-interference, and environmental parameters, it achieves precise classification and adaptive optimization of the difficulty of drone shooting. This not only effectively suppresses image quality degradation caused by rolling shutter effect and signal interference, but also balances computational power consumption and image quality through a graded processing strategy, significantly improving the stability and clarity of video in complex environments, and enhancing the drone's adaptability and reliability in shooting in varied scenarios. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating the drone flight photography quality optimization method in this embodiment.

[0042] Figure 2 This is a flowchart illustrating the motion perturbation model construction method in this embodiment.

[0043] Figure 3 This is a flowchart illustrating the video shooting optimization method in this embodiment.

[0044] Figure 4 This is a schematic diagram of the structure of the drone flight photography quality optimization device provided in this embodiment. Detailed Implementation

[0045] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0046] It should be noted that although the terms first, second, third, etc., may be used in the embodiments of this application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of this application, first can also be referred to as second, and similarly, second can also be referred to as first.

[0047] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0048] Specifically, the drone flight photography quality optimization method, apparatus, and storage medium described in this application are applied to the quality optimization of videos taken by drones in industrial field inspections. The drone shooting scenario in this application is an indoor enclosed industrial site. In this application scenario, it is necessary to accurately image the subject and reduce the problem of low video quality caused by blurry images and interference with optical signals in the industrial site. It is worth noting that the video shooting object in this application is within 5m of the drone. Therefore, image blur processing is necessary when the drone shoots the object during close-range flight.

[0049] To apply to the above-mentioned application scenarios, this application provides a method for optimizing the quality of drone flight photography, the flowchart of which can be found in the document. Figure 1 As shown, it includes:

[0050] Step S101: Interactively input the basic shooting parameters of the drone and collect drone flight data in real time; the basic shooting parameters of the drone include: the drone's maximum shooting frame rate (FPS);

[0051] The UAV flight data includes: flight commands, current UAV flight speed, the double peak value of the UAV vibration waveform, and vibration frequency.

[0052] It is worth noting that the data collected in step S101 of this application is processed by the drone controller.

[0053] Specifically, in this application, the data acquisition frequency for real-time UAV flight data is based on the control cycle of the UAV executing flight commands as one data acquisition cycle; in this application, the control cycle is determined by the basic parameters of the UAV.

[0054] Please continue reading. Figure 1As shown, the method for optimizing the quality of drone flight photography also includes:

[0055] Step S102: Based on the UAV flight data, perform spatial modeling of the flight attitude curve and construct a motion disturbance model to output the real-time attitude disturbance index.

[0056] Please see Figure 2 The diagram shown is a flowchart illustrating the motion perturbation model construction method provided in this application, including:

[0057] Step S201: Spatial modeling of the UAV's flight attitude curve is performed based on the UAV flight data received during the control cycle to output the real-time attitude disturbance index.

[0058] Specifically, the implementation process of step S201 is as follows:

[0059] Using the current spatial coordinates of the UAV as the origin, a spatial rectangular coordinate system is established. Based on the current UAV flight speed vd, the flight speed va of the flight command, and the flight angle θ of the flight command, a simulation is performed in the spatial rectangular coordinate system to obtain the flight attitude curve in the spatial rectangular coordinate system.

[0060] Let D1 denote the distance from the starting point of the flight attitude curve to the subject in focus, D2 denote the distance from the ending point of the flight attitude curve to the subject in focus, θ1 denote the shooting angle from the starting point of the flight attitude curve to the subject in focus, and θ2 denote the shooting angle from the starting point of the flight attitude curve to the subject in focus. Construct the motion disturbance index α, and set it as follows:

[0061] α = a1 × |D2 - D1| / D1 + a2 × |θ1 - θ2| / (θ1 + θ2); where a1 and a2 are attitude perturbation weights.

[0062] Specifically, the object to be focused in this application is the object focused by the focal point of the drone camera, and a1=0.6, a2=0.4. At the same time, the spatial rectangular coordinate system in this application is established based on the standard direction, and the unit length is m.

[0063] Please continue reading. Figure 2 As shown, the motion perturbation model construction method further includes:

[0064] Step S202: Compensate for the motion disturbance index within the control cycle based on the UAV vibration data.

[0065] Specifically, the implementation process of step S202 is as follows:

[0066] Extract the bimodal peak FF and vibration frequency ZV of the UAV vibration waveform in the previous control cycle, and construct the vibration disturbance factor ZY based on the extraction results. Set ZY=0.7×max{(ZV-yv) / yv,0}+0.3×FF / YF; where yv is the jelly effect vibration frequency threshold and YF is the jelly effect vibration bimodal threshold.

[0067] The motion disturbance index within the control cycle is compensated by combining the drone's highest shooting frame rate (FPS) and vibration disturbance factor (ZY).

[0068] When ZY / FPS is greater than the preset vibration disturbance constant, the motion disturbance index within the control cycle is updated to α×{1+(ZY / FPS-preset vibration disturbance constant) / preset vibration disturbance constant}; otherwise, the motion disturbance index within the control cycle is not updated.

[0069] Specifically, the preset vibration disturbance constant mentioned in this application is 0.05, and the values ​​of the jelly effect vibration frequency threshold and the jelly effect vibration bimodal threshold are 30Hz and 0.5mm, respectively. It is worth noting that in this application, only the numerical value of "ZY / FPS" is used, therefore, the unit of the preset vibration disturbance constant is not set.

[0070] Specifically, by setting vibration disturbance constants, jelly effect frequency thresholds, and bimodal thresholds, the system achieves accurate quantification and classification of high-frequency vibrations; effectively distinguishes between normal flight vibrations and abnormal disturbances that cause image distortion, avoids misjudgments and omissions in traditional thresholding methods, significantly improves the targeting and reliability of vibration detection, provides high-confidence input for subsequent compensation algorithms, suppresses the jelly effect from the source, and ensures the basic clarity of the video.

[0071] Please continue reading. Figure 1 As shown, the method for optimizing the quality of drone flight photography also includes:

[0072] Step S103: Construct an anti-interference model based on signal interference data and output the real-time anti-interference index.

[0073] Specifically, the implementation process of step S103 is as follows:

[0074] Take the average value of the background spectral energy collected within the background update cycle, denoted as μ, and calculate the standard deviation σ of the background spectral energy. Then, collect the spectral energy in the preset sensitive band within the previous control cycle, denoted as P, calculate the spectral peak intensity RIR, and set RIR=(P-μ) / σ.

[0075] Set an abnormal peak intensity threshold, and when the RIR is greater than the abnormal peak intensity threshold, construct a spectral interference factor and set the value of the spectral interference factor to (RIR - abnormal peak intensity threshold) / abnormal peak intensity threshold; otherwise, set the value of the spectral interference factor to 0.

[0076] The signal-to-noise ratio (SNR) time series of the UAV communication frequency band was collected at a sampling rate of 1 kHz with a window length of 1 s, denoted as SNR, and the Hearst exponent H of SNR was extracted.

[0077] The Hearst exponent H is coupled with the spectral interference factor to construct a real-time anti-interference exponent β. β is set as b1 × spectral interference factor + b2 × H / 0.5, where b1 and b2 are weighting factors, and b1 + b2 = 1.

[0078] Specifically, the preset sensitive wavelength band described in this application is set according to the shooting environment. For example, when blue light is a commonly used wavelength band, the preset sensitive wavelength band can be set to 450nm±10nm. At the same time, the value of the abnormal peak intensity threshold described in this application is 10. Meanwhile, the duration of the background update cycle described in this application is 5 minutes. The calculation process of the Hearst exponent H in this application is a technical means well known to those skilled in the art, and will not be described in detail in this application.

[0079] Specifically, step S103 further includes:

[0080] The gimbal offset state is analyzed based on the abrupt change in polarization angle JD during the previous control cycle:

[0081] When JD is less than or equal to the polarization amplitude threshold, the gimbal offset status is determined to be normal.

[0082] When JD is greater than the polarization amplitude threshold, the gimbal offset state is determined to be abnormal. At this time, a compensation factor for the abnormal peak intensity threshold is set. The value of the compensation factor is set to [1-(JD-polarization amplitude threshold) / polarization amplitude threshold]. The product of the abnormal peak intensity threshold and the compensation factor is used as the compensated abnormal peak intensity threshold.

[0083] Specifically, the polarization amplitude threshold value described in this application is 10° / ms; it can be understood that the calculation process of the abrupt change amplitude JD of the polarization angle described in this application is: the ratio of the absolute value of the difference between the starting polarization angle and the ending polarization angle of the previous control cycle to the duration of the control cycle.

[0084] Specifically, by integrating the signal-to-noise ratio (SNR) Hurst exponent and spectral interference parameters, the impact of latent interference such as electromagnetic and signal interference on image quality is quantified. This breaks through the limitations of traditional methods that only focus on physical jitter, enabling the system to accurately diagnose the root causes of image quality degradation even in highly interference scenarios such as high-voltage power line areas and urban canyons. The environmental robustness of the evaluation model is enhanced, providing a key supplementary dimension for calculating the difficulty coefficient, significantly improving the overall reliability and practicality of the solution in real-world, complex environments.

[0085] Please continue reading. Figure 1 As shown, the method for optimizing the quality of drone flight photography also includes:

[0086] Step S104: Couple the real-time anti-interference index with the real-time attitude disturbance index to determine the video shooting parameters of the UAV in the current control cycle.

[0087] For details, please refer to Figure 3 As shown, it is a flowchart illustrating the video shooting optimization method provided in this application, including:

[0088] Step S401: Determine the difficulty of drone shooting by combining the real-time anti-interference index and the real-time attitude disturbance index;

[0089] Step S402: Determine the video shooting parameters within the current control cycle based on the difficulty of drone shooting.

[0090] Specifically, the implementation process of step S401 is as follows:

[0091] The video blur coefficient γ is constructed based on the real-time adversarial interference index and the real-time attitude perturbation index, and γ is set as c1×α+c2×β; c1 and c2 are both interference weights, and c1+c2=1;

[0092] Set various shooting quality difficulty ranges, and match the video blur coefficient within the control period with the shooting quality difficulty range:

[0093] When γ falls within the first range of shooting quality difficulty, the drone shooting difficulty is determined to be normal;

[0094] When γ falls within the second shooting quality difficulty range, the drone shooting difficulty is determined to be difficult.

[0095] When γ falls within the third shooting quality difficulty range, the drone shooting difficulty is determined to be unsuitable for shooting.

[0096] Specifically, in this application, the values ​​of the first shooting quality difficulty range, the second shooting quality difficulty range, and the third shooting quality difficulty range are [0, 0.2), [0.2, 0.5), and [0.5, 1], respectively.

[0097] Specifically, a quantifiable and reproducible evaluation standard is established to intuitively map the level of environmental challenge, eliminate subjective experience bias, and enable the system to quickly match processing strategies. This avoids both "over-optimization" that wastes computing power and "under-processing" that leads to image quality degradation, achieving a precise balance between resource allocation and image quality improvement, and significantly enhancing the algorithm's adaptability and decision-making efficiency in multiple scenarios.

[0098] Specifically, the implementation process of step S402 is as follows:

[0099] Please continue reading. Figure 1 As shown, the method for optimizing the quality of drone flight photography also includes:

[0100] When the difficulty of drone shooting is normal, the exposure time corresponding to 50% of FPS is used as the exposure time for drone video shooting within the control period;

[0101] When the drone shooting difficulty is difficult, the exposure time corresponding to FPS is used as the exposure time for drone video shooting within the control period;

[0102] When the drone's shooting difficulty is deemed unsuitable, it is not recommended for users to attempt shooting.

[0103] Step S105: Shoot with the video shooting parameters within the current control cycle to obtain the shot video, and optimize the shot video based on the difficulty of drone shooting.

[0104] Specifically, the implementation process of step S105 is as follows:

[0105] When the drone shooting difficulty is normal, no optimization is performed on the captured video;

[0106] When the drone shooting difficulty is high, the dynamic range compression ratio of the captured video is increased to 1.5 times, and a multi-frame synthesis noise reduction algorithm is enabled. By performing pixel-level alignment and weighted fusion on three consecutive frames, the noise level in moving scenes is reduced. At the same time, edge sharpening processing is performed on dynamic areas in the video. The sharpening intensity is dynamically adjusted according to the speed of movement of the area. For every 0.5m / s increase in movement speed, the sharpening coefficient increases by 0.1 to ensure the clarity of the outline of fast-moving targets.

[0107] Please see Figure 4 As shown, the device for optimizing the quality of drone flight photography provided in this application includes:

[0108] The data acquisition unit is used for interactive input of basic drone shooting parameters and real-time acquisition of drone flight data;

[0109] The attitude disturbance unit is used to spatially model the flight attitude curve based on UAV flight data and construct a motion disturbance model to output the real-time attitude disturbance index.

[0110] The signal jamming unit is used to construct an anti-jamming model based on signal jamming data and output a real-time anti-jamming index.

[0111] The shooting parameter determination unit is used to couple the real-time anti-interference index and the real-time attitude disturbance index to determine the video shooting parameters of the UAV in the current control cycle.

[0112] The video optimization unit is used to shoot with the video shooting parameters within the current control cycle to obtain the captured video, and to optimize the captured video based on the difficulty of drone shooting.

[0113] The UAV flight photography quality optimization device provided in this application embodiment can execute the UAV flight photography quality optimization method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of executing the method.

[0114] In this application, the computer-readable storage medium is a tangible physical storage medium that can store the aforementioned computer program and various types of data used in the program; the physical storage medium includes, but is not limited to, existing physical storage media or combinations thereof, such as random access memory, read-only memory, optical disk, and hard disk.

[0115] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the quality of drone flight photography, characterized in that, include: Interactive input of basic drone shooting parameters and real-time acquisition of drone flight data; Based on the UAV flight data, spatial modeling of the flight attitude curve is performed, and a motion disturbance model is constructed to output the real-time attitude disturbance index. A counter-interference model is constructed based on signal interference data, and a real-time counter-interference index is output. The real-time anti-jamming index and the real-time attitude disturbance index are coupled to determine the video capture parameters of the UAV in the current control cycle.

2. The method for optimizing the quality of drone flight photography according to claim 1, characterized in that, Using the current spatial coordinates of the UAV as the origin, a spatial rectangular coordinate system is established. Based on the current UAV flight speed vd, the flight speed va of the flight command, and the flight angle θ of the flight command, a simulation is performed in the spatial rectangular coordinate system to obtain the flight attitude curve in the spatial rectangular coordinate system. Let D1 denote the distance from the starting point of the flight attitude curve to the subject in focus, D2 denote the distance from the ending point of the flight attitude curve to the subject in focus, θ1 denote the shooting angle from the starting point of the flight attitude curve to the subject in focus, and θ2 denote the shooting angle from the starting point of the flight attitude curve to the subject in focus. Construct the motion disturbance index α, and set it as follows: α = a1 × |D2 - D1| / D1 + a2 × |θ1 - θ2| / (θ1 + θ2); where a1 and a2 are attitude perturbation weights.

3. The method for optimizing the quality of drone flight photography according to claim 2, characterized in that, Extract the bimodal peak FF and vibration frequency ZV of the UAV vibration waveform in the previous control cycle, and construct the vibration disturbance factor ZY based on the extraction results. Set ZY=0.7×max{(ZV-yv) / yv,0}+0.3×FF / YF; where yv is the jelly effect vibration frequency threshold and YF is the jelly effect vibration bimodal threshold. The motion disturbance index within the control cycle is compensated by combining the drone's highest shooting frame rate (FPS) and vibration disturbance factor (ZY). When ZY / FPS is greater than the preset vibration disturbance constant, the motion disturbance index within the control cycle is updated to α×{1+(ZY / FPS-preset vibration disturbance constant) / preset vibration disturbance constant}; otherwise, the motion disturbance index within the control cycle is not updated.

4. The method for optimizing the quality of drone flight photography according to claim 3, characterized in that, Take the average value of the background spectral energy collected within the background update cycle, denoted as μ, and calculate the standard deviation σ of the background spectral energy. Then, collect the spectral energy in the preset sensitive band within the previous control cycle, denoted as P, calculate the spectral peak intensity RIR, and set RIR=(P-μ) / σ. Set an abnormal peak intensity threshold, and when the RIR is greater than the abnormal peak intensity threshold, construct a spectral interference factor and set the value of the spectral interference factor to (RIR - abnormal peak intensity threshold) / abnormal peak intensity threshold; otherwise, set the value of the spectral interference factor to 0.

5. The method for optimizing the quality of drone flight photography according to claim 4, characterized in that, The signal-to-noise ratio (SNR) time series of the UAV communication frequency band was collected at a sampling rate of 1 kHz with a window length of 1 s, denoted as SNR, and the Hearst exponent H of SNR was extracted. The Hearst exponent H is coupled with the spectral interference factor to construct a real-time anti-interference exponent β.

6. The method for optimizing the quality of drone flight photography according to claim 5, characterized in that, When JD is less than or equal to the polarization amplitude threshold, the gimbal offset status is determined to be normal. When JD is greater than the polarization amplitude threshold, the gimbal offset state is determined to be abnormal. At this time, a compensation factor for the abnormal peak intensity threshold is set. The value of the compensation factor is set to [1-(JD-polarization amplitude threshold) / polarization amplitude threshold]. The product of the abnormal peak intensity threshold and the compensation factor is used as the compensated abnormal peak intensity threshold.

7. The method for optimizing the quality of drone flight photography according to claim 6, characterized in that, The difficulty of drone photography is determined by combining the real-time anti-interference index and the real-time attitude disturbance index. Determine the video shooting parameters within the current control cycle based on the difficulty of drone shooting; The video blur coefficient γ is constructed based on the real-time anti-interference index and the real-time attitude perturbation index, and γ is set as c1×α+c2×β; Set various shooting quality difficulty ranges, and match the video blur coefficient within the control period with the shooting quality difficulty range: When γ falls within the first range of shooting quality difficulty, the drone shooting difficulty is determined to be normal; When γ falls within the second shooting quality difficulty range, the drone shooting difficulty is determined to be difficult. When γ falls within the third shooting quality difficulty range, the drone shooting difficulty is determined to be unsuitable for shooting.

8. The method for optimizing the quality of drone flight photography according to claim 7, characterized in that, When the difficulty of drone shooting is normal, the exposure time corresponding to 50% of FPS is used as the exposure time for drone video shooting within the control period; When the drone shooting difficulty is difficult, the exposure time corresponding to FPS is used as the exposure time for drone video shooting within the control period; When the drone's shooting difficulty is deemed unsuitable, it is not recommended for users to attempt shooting.

9. A device for optimizing the quality of drone flight photography, applied to the drone flight photography quality optimization method as described in any one of claims 1-8, characterized in that, include: The data acquisition unit is used for interactive input of basic drone shooting parameters and real-time acquisition of drone flight data; The attitude disturbance unit is used to spatially model the flight attitude curve based on UAV flight data and construct a motion disturbance model to output the real-time attitude disturbance index. The signal jamming unit is used to construct an anti-jamming model based on signal jamming data and output a real-time anti-jamming index. The shooting parameter determination unit is used to couple the real-time anti-interference index and the real-time attitude disturbance index to determine the video shooting parameters of the UAV in the current control cycle. The video optimization unit is used to shoot with the video shooting parameters within the current control cycle to obtain the captured video, and to optimize the captured video based on the difficulty of drone shooting.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to perform the UAV flight photography quality optimization method according to any one of claims 1-8 during runtime.

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