Artificial Intelligence-Based UAV Control Method and System

Abstract

This application relates to the field of computer control technology and discloses an artificial intelligence-based drone control method and system. The method includes: sensing image data and environmental parameters of the drone's flight area through a sensor array installed on the drone; selecting a recognition model suitable for analyzing the image data of the flight area from a plurality of preset recognition models based on the environmental parameters of the flight area; using the recognition model to identify a target object in the flight area from the image data of the flight area; determining the location of the target object; calculating the drone's flight trajectory based on the location of the target object; and controlling the drone's flight based on the drone's flight trajectory. According to the present invention, not only is the drone's autonomous navigation capability enhanced in complex and changing environments, but it also improves work efficiency while ensuring higher safety standards, thus opening up a new direction for the development of drone technology.

Description

Technical Field

[0001] The present application relates to the field of computer control technology, and more specifically, to a method and system for controlling a drone based on artificial intelligence. Background Art

[0002] With the development of science and technology, the application of drones (UAVs) is becoming more and more extensive in various fields such as agriculture, logistics, monitoring, search and rescue, etc. In order to improve the safety and efficiency of drone operations, artificial intelligence-based technologies are increasingly being introduced into drone flight control.

[0003] Traditionally, drones have typically flown along pre-set routes or been manually controlled by ground operators using remote control devices. However, this approach presents numerous limitations and challenges. First, pre-set routes lack flexibility. If the environment changes or the target object moves, the drone may not be able to respond effectively, thus impacting mission performance.

[0004] Therefore, a new technical solution for controlling the flight of drones is needed, which enables drones to autonomously determine their flight paths and ensure that they will reach the target even if the environment changes or the target moves. Summary of the Invention

[0005] In order to solve the above technical problems, the present application is proposed to provide an artificial intelligence-based drone control method and system, which enables the drone to autonomously formulate a flight path and ensure that the drone flies to the location of the target object even if the environment changes or the target object moves.

[0006] In the first aspect, the present application provides an artificial intelligence-based drone control method, comprising: sensing the image data and environmental parameters of the drone's flight area through a sensor array installed on the drone; selecting a recognition model suitable for analyzing the image data of the flight area from a plurality of preset recognition models based on the environmental parameters of the flight area; using the recognition model to identify a target object in the flight area from the image data of the flight area; determining the position of the target object; and calculating the flight trajectory P(t) = {x(t), y(t), z(t)} of the drone based on the position of the target object, and making a preset optimization function

[0007] The value of is the smallest, where x(t), y(t), z(t) are the position components of the UAV on the x, y and z axes at time t, x′(t), y′(t), z′(t) are the velocity components of P(t) on the x, y and z axes, respectively, d obs(P(t)) represents the distance between the position of the UAV at time t and the target object, k1, k2, k3, k4 are preset weighting coefficients, T is the preset end time, and the position of P(t) at time T is the position of the target object; according to the flight trajectory P(t) of the UAV, the UAV is controlled to fly.

[0008] Optionally, the aforementioned artificial intelligence-based drone control method selects a recognition model suitable for analyzing the image data of the flight area from a plurality of preset recognition models according to the environmental parameters of the flight area, including: calculating the illumination coefficient of the flight area according to the environmental parameters of the flight area Wherein α, β, γ, and δ are preset weight values, and vis, T, and H are the visibility, temperature, and humidity of the flight environment included in the environmental parameters of the flight environment. According to the illumination coefficient Lc of the flight area, a recognition model suitable for analyzing the image data of the flight area is selected from the multiple recognition models corresponding to different illumination intensities.

[0009] Optionally, the aforementioned artificial intelligence-based drone control method, before using the recognition model to identify the target object in the flight area from the image data of the flight area, further includes: adjusting the pixel value of the image data of the flight area according to the illumination coefficient Lc of the flight area; inputting the image data of the flight area into a preset neural network-based image enhancement model to perform image enhancement processing on the image data of the flight area.

[0010] Optionally, the aforementioned artificial intelligence-based drone control method adjusts the pixel value of the image data of the flight area according to the illumination coefficient Lc of the flight area, including: for each pixel in the image data of the flight area, calculating the pixel mean of a preset size area centered on the pixel, wherein the pixel value of the i-th pixel is I i The pixel mean of the preset size area centered on the i-th pixel is I i,clahe ; Calculate the target pixel value of each pixel in the image data of the flight area, wherein the target pixel value of the i-th pixel

[0011] Optionally, the aforementioned artificial intelligence-based drone control method, before inputting the image data of the flight area into a preset neural network-based image enhancement model and performing image enhancement processing on the image data of the flight area, further includes: when training the image enhancement model, inputting a preset original image into the image enhancement model to obtain an enhanced image output by the image enhancement model; calculating the degree of structural similarity between the enhanced image and the expected image corresponding to the original image; calculating the value of the loss function of the image enhancement model based on the degree of structural similarity between the enhanced image and the expected image; and adjusting the image enhancement model based on the value of the loss function of the image enhancement model.

[0012] Optionally, the aforementioned artificial intelligence-based drone control method calculates the degree of structural similarity between the enhanced image and the expected image, including: calculating the mean and standard deviation of the pixels of the enhanced image, calculating the mean and standard deviation of the pixels of the expected image; calculating the covariance of the pixels of the enhanced image and the expected image; calculating the degree of structural similarity between the enhanced image and the expected image Wherein, A and B represent the enhanced image and the expected image, μ(A) and μ(S) are the means of the pixels of the enhanced image and the expected image, σ(A) and σ(S) are the standard deviations of the pixels of the expected image, σ(A, B) is the covariance of the pixels of the enhanced image and the expected image, and C1 and C2 are preset equilibrium constants.

[0013] Optionally, the aforementioned artificial intelligence-based drone control method calculates the value of the loss function of the image enhancement model according to the degree of structural similarity between the enhanced image and the expected image, including: calculating the mean square error of the pixels of the enhanced image and the expected image; calculating the value of the loss function of the image enhancement model Loss = w1M(A, B) + w2(1-S(a, B)), wherein A and B represent the enhanced image and the expected image, M(A, B) represents the mean square error of the pixels of the enhanced image and the expected image, S(A, B) represents the degree of structural similarity between the enhanced image and the expected image, and w1 and w2 represent preset weight values.

[0014] Optionally, the aforementioned artificial intelligence-based drone control method determines the position of the target object, including: calculating the size of the target object in the image data of the flight area; calculating the ratio of the size of the target object in the image data of the flight area to the known actual size of the target object; calculating the distance between the target object and the drone based on the ratio of the size of the target object in the image data of the flight area to the actual size of the target object; calculating the position of the target object based on the distance between the target object and the drone and the position of the drone.

[0015] In the second aspect, the present application provides an artificial intelligence-based drone control system, comprising: a perception module, which perceives the image data and environmental parameters of the drone's flight area through a sensor array installed on the drone; a model selection module, which selects a recognition model suitable for analyzing the image data of the flight area from a plurality of preset recognition models according to the environmental parameters of the flight area; an object recognition module, which uses the recognition model to identify the target object in the flight area from the image data of the flight area; a position determination module, which determines the position of the target object; a trajectory calculation module, which calculates the flight trajectory P(t) = {x(t), y(t), z(t)} of the drone according to the position of the target object, and makes a preset optimization function

[0016] The value of is the smallest, where x(t), y(t), z(t) are the position components of the UAV on the x, y and z axes at time t, x′(t), y′(t), z′(t) are the velocity components of P(t) on the x, y and z axes, respectively, d obs (P(t)) represents the distance between the position of the UAV at time t and the target object, k1, k2, k3, k4 are preset weighting coefficients, T is the preset end time, and the position of P(t) at time T is the position of the target object; the control module controls the UAV to fly according to the flight trajectory P(t) of the UAV.

[0017] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0018] According to the technical solution of the present invention, not only the autonomous navigation capability of UAVs in complex and changing environments is enhanced, but also higher safety standards are guaranteed while improving work efficiency, thus opening up a new direction for the development of UAV technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0020] Figure 1 Flowchart of an artificial intelligence-based drone control method according to an embodiment of the present application;

[0021] Figure 2 is another flow chart of an artificial intelligence-based drone control method according to an embodiment of the present application;

[0022] Figure 3 is another flow chart of an artificial intelligence-based drone control method according to an embodiment of the present application;

[0023] Figure 4 This is a partial flow chart of an artificial intelligence-based drone control method according to an embodiment of the present application;

[0024] Figure 5 is another partial flow chart of an artificial intelligence-based drone control method according to an embodiment of the present application;

[0025] Figure 6 4 is a block diagram of an artificial intelligence-based drone control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0027] like Figure 1 As shown, one embodiment of the present application provides a drone control method based on artificial intelligence, including:

[0028] Step S110 , sensing image data and environmental parameters of the UAV's flight area through a sensor array installed on the UAV.

[0029] Step S120 : selecting a recognition model suitable for analyzing the image data of the flight area from a plurality of preset recognition models according to the environmental parameters of the flight area.

[0030] In this embodiment, the most appropriate recognition model can be automatically selected according to the environmental parameters of different flight areas, which enables the drone to accurately perceive and understand the surrounding situation in a variety of environments, thereby improving the flexibility and applicability of the system.

[0031] Step S130 : using the recognition model to identify the target object in the flight area from the image data of the flight area.

[0032] In this embodiment, the target object may be a person or animal that moves at any time. In this embodiment, the use of a specially trained recognition model to process image data can more accurately locate the moving target object (such as a person or animal), thereby ensuring that the drone can efficiently track and respond to dynamically changing targets.

[0033] Step S140: Determine the location of the target object.

[0034] Step S150, calculate the flight trajectory P(t) = {x(t)y(t), z(t)} of the drone according to the position of the target object, and use the preset optimization function

[0035] The value of is the smallest, where x(t), y(t), z(t) are the position components of the drone on the x, y and z axes at time t, x′(t), y′(t), z′(t) are the velocity components of P(t) on the x, y and z axes, respectively. obs (P(t)) represents the distance between the drone's position at time t and the target object, k1, k2, k3, k4 are preset weighting coefficients, T is the preset end time, and the position of P(t) at time T is the position of the target object.

[0036] In this embodiment, a fast and stable flight trajectory is calculated by minimizing a preset optimization function. This path planning not only takes into account the influence of the velocity component but also pays special attention to the safe distance from obstacles, ensuring the safety and stability of the drone flight.

[0037] Step S160: Control the UAV to fly according to the UAV's flight trajectory P(t).

[0038] According to the technical solution of this embodiment, not only the autonomous navigation capability of the UAV in complex and changing environments is enhanced, but also higher safety standards are guaranteed while improving work efficiency, opening up a new direction for the development of UAV technology.

[0039] like Figure 2 As shown, one embodiment of the present application provides another method for controlling a drone based on artificial intelligence. Compared with the aforementioned embodiment, the method for controlling a drone based on artificial intelligence in this embodiment, step S120 includes:

[0040] Step S210: Calculate the illumination coefficient of the flight area according to the environmental parameters of the flight area. Wherein α, β, γ, and δ are preset weight values, and vis, T, and H are the visibility, temperature, and humidity of the flight environment included in the environmental parameters of the flight environment.

[0041] Step S220 , selecting a recognition model suitable for analyzing the image data of the flight area from a plurality of recognition models corresponding to different light intensities according to the light coefficient Lc of the flight area.

[0042] The technical solution of this embodiment effectively addresses the problem of inaccurate target recognition due to light variations by calculating the illumination coefficient of the flight area and selecting the most appropriate model from multiple recognition models optimized for different light intensities. This allows the drone to maintain a high recognition rate in a variety of lighting conditions, such as early morning, midday, evening, or on cloudy days.

[0043] like Figure 3 As shown, one embodiment of the present application provides another method for controlling a drone based on artificial intelligence. Compared with the aforementioned embodiment, the method for controlling a drone based on artificial intelligence in this embodiment further includes, before step S130:

[0044] Step S310 : adjusting the pixel values of the image data of the flight area according to the illumination coefficient Lc of the flight area.

[0045] This step specifically includes:

[0046] (1) For each pixel in the image data of the flight area, calculate the pixel mean of the preset size area centered on it, where the pixel value of the i-th pixel is I i , the pixel mean of the preset size area centered on the i-th pixel is I i，clahe .

[0047] (2) Calculate the target pixel value of each pixel in the image data of the flight area, where the target pixel value of the i-th pixel is

[0048] In this embodiment, by adjusting the target pixel value for each pixel based on the illumination factor and combining it with the average pixel value for the local area, image quality issues caused by uneven illumination can be effectively improved. This adaptive pixel value adjustment helps to even out the brightness distribution in the image, reducing the impact of shadows and highlights, resulting in a clearer and more consistent image.

[0049] Step S320: input the image data of the flight area into a preset neural network-based image enhancement model to perform image enhancement processing on the image data of the flight area.

[0050] According to the technical solution of this embodiment, further processing of the image using a neural network-based image enhancement model can extract more detailed information, making the subsequent target recognition process more accurate and reliable. Especially in poor lighting conditions, image enhancement processing can greatly improve the effectiveness of the recognition model.

[0051] like Figure 4 As shown, one embodiment of the present application provides another method for controlling a drone based on artificial intelligence. Compared with the aforementioned embodiment, the method for controlling a drone based on artificial intelligence in this embodiment further includes, before step S320:

[0052] Step S410: When training the image enhancement model, a preset original image is input into the image enhancement model to obtain an enhanced image output by the image enhancement model.

[0053] Step S420 , calculating the structural similarity between the enhanced image and the desired image corresponding to the original image.

[0054] Specifically include:

[0055] (1) Calculate the mean and standard deviation of the pixels of the enhanced image, and calculate the mean and standard deviation of the pixels of the expected image.

[0056] (2) Calculate the pixel covariance between the enhanced image and the desired image.

[0057] (3) Calculate the structural similarity between the enhanced image and the expected image Where A and B represent the enhanced image and the expected image, μ(A) and μ(B) are the means of the pixels of the enhanced image and the expected image, σ(A) and σ(B) are the standard deviations of the pixels of the expected image, σ(A, B) is the covariance of the pixels of the enhanced image and the expected image, and C1 and C2 are preset equilibrium constants.

[0058] In this embodiment, the degree of structural similarity is used to measure the similarity between the enhanced image and the desired image, which can better preserve the structural information in the image. This allows the processed image to not only improve brightness and contrast, but also maintain high clarity and authenticity in subtle features such as textures and edges.

[0059] Step S430: Calculate the value of the loss function of the image enhancement model according to the degree of structural similarity between the enhanced image and the expected image.

[0060] Specifically include:

[0061] (1) Calculate the mean square error between the pixels of the enhanced image and the desired image.

[0062] In this embodiment, by simultaneously considering pixel-level mean squared error and structural similarity in the loss function, the model can be guided to place greater emphasis on global and local consistency during training. This multi-objective optimization strategy helps accelerate convergence, reduces the risk of overfitting, and thus improves the overall efficiency of model training.

[0063] (2) Calculate the value of the loss function of the image enhancement model: Loss = w1M(A, B) + w2(1-S(A, B)), where A and B represent the enhanced image and the expected image, M(A, B) represents the mean square error of the pixels between the enhanced image and the expected image, S(A, B) represents the degree of structural similarity between the enhanced image and the expected image, and w1 and w2 represent the preset weight values.

[0064] In this embodiment, by calculating the degree of structural similarity between the enhanced image and the desired image and combining it with the mean squared error to define a loss function, the output quality of the image enhancement model can be more comprehensively evaluated. This comprehensive evaluation method helps ensure that the enhanced image retains the details of the original image while also being as close to the ideal visual effect as possible.

[0065] Step S440: Adjust the image enhancement model based on the value of the loss function of the image enhancement model.

[0066] The technical solution of this embodiment significantly improves the quality and robustness of the image enhancement model by introducing structural similarity and an optimized loss function, providing strong support for achieving higher levels of autonomous drone navigation. This approach not only improves the image enhancement effect but also enhances the stability and reliability of the entire system.

[0067] like Figure 5 As shown, one embodiment of the present application provides another method for controlling a drone based on artificial intelligence. Compared with the above-mentioned embodiment, the method for controlling a drone based on artificial intelligence in this embodiment includes step S140:

[0068] Step S510 , calculating the size of the target object in the image data of the flight area.

[0069] Step S520 , calculating the ratio between the size of the target object in the image data of the flight area and the known real size of the target object.

[0070] Step S530 , calculating the distance between the target object and the UAV based on the ratio of the size of the target object in the image data of the flight area to the actual size of the target object.

[0071] Step S540 : Calculate the position of the target object based on the distance between the target object and the drone and the position of the drone.

[0072] According to the technical solution of this embodiment, the proportional relationship between the target object's size in the image data and its known true size can be used to accurately estimate the actual distance between the target object and the drone. This visual distance measurement method is more intuitive and less susceptible to environmental interference than traditional distance measurement methods. By accurately calculating the target object's position, the drone can better plan its flight path and avoid collisions with obstacles. This is particularly important for operations in complex or changing environments, helping to reduce accident risks and protect equipment safety.

[0073] like Figure 6 As shown, one embodiment of the present application provides an artificial intelligence-based drone control system, comprising:

[0074] The perception module 610 perceives the image data and environmental parameters of the UAV's flight area through the sensor array installed on the UAV.

[0075] The model selection module 620 selects a recognition model suitable for analyzing the image data of the flight area from a plurality of preset recognition models according to the environmental parameters of the flight area.

[0076] In this embodiment, the most appropriate recognition model can be automatically selected according to the environmental parameters of different flight areas, which enables the drone to accurately perceive and understand the surrounding situation in a variety of environments, thereby improving the flexibility and applicability of the system.

[0077] The object recognition module 630 uses a recognition model to recognize a target object in the flight area from the image data of the flight area.

[0078] In this embodiment, the target object may be a person or animal that moves at any time. In this embodiment, the use of a specially trained recognition model to process image data can more accurately locate the moving target object (such as a person or animal), thereby ensuring that the drone can efficiently track and respond to dynamically changing targets.

[0079] The position determination module 640 determines the position of the target object.

[0080] The trajectory calculation module 650 calculates the flight trajectory P(t) = {x(t), y(t), z(t)} of the drone according to the position of the target object, and uses the preset optimization function

[0081] The value of is the smallest, where x(t), y(t), z(t) are the position components of the drone on the x, y and z axes at time t, x′(t), y′(t), z′(t) are the velocity components of P(t) on the x, y and z axes, respectively. obs (P(t)) represents the distance between the drone's position at time t and the target object, k1, k2, k3, k4 are preset weighting coefficients, T is the preset end time, and the position of P(t) at time T is the position of the target object.

[0082] In this embodiment, a fast and stable flight trajectory is calculated by minimizing a preset optimization function. This path planning not only takes into account the influence of the velocity component but also pays special attention to the safe distance from obstacles, ensuring the safety and stability of the drone flight.

[0083] The control module 660 controls the UAV to fly according to the UAV's flight trajectory P(t).

[0084] According to the technical solution of this embodiment, not only the autonomous navigation capability of the UAV in complex and changing environments is enhanced, but also higher safety standards are guaranteed while improving work efficiency, opening up a new direction for the development of UAV technology.

[0085] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0086] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0087] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0088] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0089] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. The UAV control method based on artificial intelligence is characterized by: include: sensing image data and environmental parameters of the flight area of the drone through a sensor array installed on the drone; selecting, according to environmental parameters of the flight area, a recognition model suitable for analyzing image data of the flight area from a plurality of preset recognition models; using the recognition model to identify a target object in the flight area from image data of the flight area; determining a location of the target object; Calculate the flight trajectory of the drone based on the position of the target object , and make the preset optimization function The value of is the smallest, among which, are the position components of the UAV on the x, y and z axes at time t, They are The velocity components in the x, y, and z axes, represents the distance between the position of the UAV at time t and the target object, is the preset weighting coefficient, T is the preset end time, The position at time T is the position of the target object; According to the flight trajectory of the UAV , controlling the UAV to fly; Selecting a recognition model suitable for analyzing image data of the flight area from a plurality of preset recognition models according to environmental parameters of the flight area includes: Calculate the illumination coefficient of the flight area according to the environmental parameters of the flight area ,in, is the preset weight value, The visibility, temperature and humidity of the flight area included in the environmental parameters of the flight area; According to the illumination coefficient of the flight area , selecting a recognition model suitable for analyzing the image data of the flight area from the multiple recognition models corresponding to different light intensities.

2. The method for controlling a drone based on artificial intelligence according to claim 1, characterized in that: Before using the recognition model to recognize the target object in the flight area from the image data of the flight area, the method further includes: According to the illumination coefficient of the flight area , adjusting the pixel value of the image data of the flight area; The image data of the flight area is input into a preset image enhancement model based on a neural network, and image enhancement processing is performed on the image data of the flight area.

3. The artificial intelligence-based drone control method according to claim 2, characterized in that: According to the illumination coefficient of the flight area , adjusting the pixel value of the image data of the flight area, comprising: For each pixel in the image data of the flight area, the pixel mean of the preset size area centered on it is calculated, where the pixel value of the i-th pixel is The pixel mean of the preset size area centered on the i-th pixel is ; Calculate the target pixel value of each pixel in the image data of the flight area, wherein the target pixel value of the i-th pixel is .

4. The artificial intelligence-based drone control method according to claim 3, characterized in that: Before inputting the image data of the flight area into a preset neural network-based image enhancement model to perform image enhancement processing on the image data of the flight area, the method further includes: When training the image enhancement model, a preset original image is input into the image enhancement model to obtain an enhanced image output by the image enhancement model; Calculating a degree of structural similarity between the enhanced image and an expected image corresponding to the original image; Calculating a loss function value of the image enhancement model according to a degree of structural similarity between the enhanced image and the expected image; The image enhancement model is adjusted based on a value of the loss function of the image enhancement model.

5. The artificial intelligence-based drone control method according to claim 4, characterized in that: Calculating the structural similarity between the enhanced image and the expected image includes: Calculating the mean and standard deviation of pixels of the enhanced image, and calculating the mean and standard deviation of pixels of the expected image; Calculating the covariance of pixels of the enhanced image and the desired image; Calculate the structural similarity between the enhanced image and the expected image ,in, represents the enhanced image and the desired image, is the mean value of the pixels of the enhanced image and the expected image, is the standard deviation of the pixels of the desired image, is the covariance of the pixels of the enhanced image and the desired image, is the preset equilibrium constant.

6. The artificial intelligence-based drone control method according to claim 5, characterized in that: Calculating a value of a loss function of the image enhancement model according to a degree of structural similarity between the enhanced image and the expected image includes: Calculating the mean square error between pixels of the enhanced image and the desired image; Calculate the value of the loss function of the image enhancement model ,in represents the enhanced image and the desired image, represents the mean square error between the pixels of the enhanced image and the desired image, represents the degree of structural similarity between the enhanced image and the desired image, Indicates the preset weight value.

7. The method for controlling a drone based on artificial intelligence according to claim 1, characterized in that: Determining the location of the target object includes: Calculating the size of the target object in the image data of the flight area; Calculating a ratio between a size of the target object in the image data of the flight area and a known real size of the target object; Calculating the distance between the target object and the drone based on a ratio between a size of the target object in the image data of the flight area and an actual size of the target object; The position of the target object is calculated according to the distance between the target object and the drone and the position of the drone.

8. The drone control system based on artificial intelligence is characterized by: include: A perception module, which perceives image data and environmental parameters of the UAV's flight area through a sensor array installed on the UAV; a model selection module, selecting a recognition model suitable for analyzing image data of the flight area from a plurality of preset recognition models according to environmental parameters of the flight area; an object recognition module, which uses the recognition model to recognize a target object in the flight area from image data of the flight area; A position determination module, for determining the position of the target object; The trajectory calculation module calculates the flight trajectory of the UAV according to the position of the target object. , and make the preset optimization function The value of is the smallest, among which, are the position components of the UAV on the x, y and z axes at time t, They are The velocity components in the x, y, and z axes, represents the distance between the position of the UAV at time t and the target object, is the preset weighting coefficient, T is the preset end time, The position at time T is the position of the target object; The control module controls the flight trajectory of the drone , controlling the UAV to fly; Selecting a recognition model suitable for analyzing image data of the flight area from a plurality of preset recognition models according to environmental parameters of the flight area includes: Calculate the illumination coefficient of the flight area according to the environmental parameters of the flight area ,in, is the preset weight value, The visibility, temperature and humidity of the flight area included in the environmental parameters of the flight area; According to the illumination coefficient of the flight area , selecting a recognition model suitable for analyzing the image data of the flight area from the multiple recognition models corresponding to different light intensities.

Images