Unmanned aerial vehicle flight control system and method

Abstract

The invention relates to the technical field of unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle flight control system and method, and the method comprises the steps: obtaining the real-time flight data and communication data of an unmanned aerial vehicle, and detecting whether communication abnormality exists through comparing the relation between the communication data and preset communication data. Once communication abnormity is detected, environment data can be further obtained to assist in decision making. Meanwhile, the height, the speed, the time, the position and other information in the flight data are utilized, the flight channel is combined, the preset flight position is calculated, the difference between the real-time position and the preset position is compared, and whether the unmanned aerial vehicle needs to be controlled or not is judged. If control is needed, the control strategy is optimized by considering the residual energy, the flight height, the flight speed and the environmental data so as to ensure the flight safety and task completion. The communication state, the flight data and the environmental factors of the unmanned aerial vehicle are monitored in real time, and the flight control strategy is adjusted in time, so that the flight safety and the task completion reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) flight control, and more particularly, to a UAV flight control system and method. Background Art

[0002] With the rapid development of UAV technology, UAVs have been widely used in many fields such as agricultural monitoring, environmental monitoring, express delivery logistics, and mapping. However, during the actual flight of UAVs, they face multiple challenges such as communication stability, flight control accuracy, and energy management.

[0003] Currently, a major problem faced by UAVs is communication anomalies, which can lead to the loss of effective connection between UAVs and ground control stations, thus affecting the completion of tasks and flight safety. How to effectively monitor communication quality, timely detect communication anomalies and take corresponding countermeasures has become an important topic for improving the reliability and flight safety of UAVs. At the same time, traditional UAV flight control systems rely on flight data such as flight altitude, speed, and real-time position for path planning and control. However, with the increasing complexity of the flight environment, traditional methods face problems such as lag in data processing and low flight accuracy.

[0004] Therefore, there is an urgent need to invent a UAV flight control technology to solve the problems of lag in data processing and insufficient flight accuracy of the flight control system of UAVs in complex flight environments in the prior art. Summary of the Invention

[0005] In view of this, the present invention proposes a UAV flight control system and method, aiming to solve the problems of lag in data processing and insufficient flight accuracy of the flight control system of UAVs in complex flight environments in the current technology.

[0006] The present invention proposes a UAV flight control method, including:

[0007] Obtaining communication data between the UAV and the base station, and obtaining flight data and flight routes of the UAV;

[0008] Determining whether there is a communication anomaly between the UAV and the base station according to the relationship between the communication data and pre-configured preset communication data, where:

[0009] If it is determined that there is a communication anomaly between the UAV and the base station, then obtaining environmental data of the UAV;

[0010] Obtain the real-time flight altitude, flight speed, flight time, and real-time position in the flight data. Determine the preset position of the flight data according to the relationship between the flight time, flight speed, and the flight path, and determine whether to control the drone according to the relationship between the real-time position and the preset position, where:

[0011] When it is determined to control the drone, obtain the remaining energy of the drone, and control the drone according to the relationship between the remaining energy of the drone, flight altitude, flight speed, and environmental data.

[0012] Further, when determining whether there is a communication anomaly between the drone and the base station according to the relationship between the communication data and the pre-configured preset communication data, it includes:

[0013] Obtain each historical data during the flight of the drone, and determine the data mean value between each historical data as the preset communication data;

[0014] Obtain the real-time communication data of the drone, and determine the communication score according to the relationship between the real-time communication data and the preset communication data:

[0015]

[0016] Wherein, S is the communication score, m is the number of scoring indicators, Wj is the weight coefficient of the jth type of scoring indicator data, n is the total number of the jth type of scoring indicators in each historical data, Ki is the ith type of historical data, and k is the indicator data of the real-time communication data;

[0017] Determine whether there is a communication anomaly between the drone and the base station according to the communication score.

[0018] Further, when determining whether there is a communication anomaly between the drone and the base station according to the communication score, it includes:

[0019] Determine whether there is a communication anomaly between the drone and the base station according to the relationship between the communication score and the pre-configured preset communication score;

[0020] When the communication score is less than the preset communication score, it is determined that there is a communication anomaly between the drone and the base station;

[0021] When the communication score is greater than or equal to the preset communication score, it is determined that there is no communication anomaly between the drone and the base station.

[0022] Further, when determining whether to control the drone according to the relationship between the real-time position and the preset position, it includes:

[0023] When there is a discrepancy between the real-time position and the preset position, it is determined to control the UAV.

[0024] When the real-time position is consistent with the preset position, it is determined not to control the UAV.

[0025] Further, when controlling the UAV according to the relationship among the remaining energy, flight altitude, flight speed and environmental data of the UAV, it includes:

[0026] Determine the control mode when controlling the UAV according to the relationship between the remaining energy and the preset remaining energy;

[0027] When the remaining energy is less than the preset remaining energy, the UAV is controlled according to glide control;

[0028] When the remaining energy is greater than or equal to the remaining energy, the UAV is controlled according to the position distance between the real-time position and the preset position.

[0029] Further, the environmental data is specifically the real-time wind speed, real-time air pressure, real-time air density and real-time temperature on the periphery of the UAV.

[0030] Further, when presetting the remaining energy, it includes:

[0031] Obtain the initial energy of the UAV and the flight angle of the UAV, and determine the preset remaining energy according to the real-time wind speed, real-time air pressure, real-time temperature on the periphery of the UAV, the initial energy of the UAV, the flight angle of the UAV, the flight speed and flight altitude of the UAV:

[0032]

[0033] Among them, E remaining(t) is the preset remaining energy, E initial is the initial energy of the UAV, P aero is the aerodynamic power consumption, P drag is the air resistance power consumption, P wind is the influence of wind speed on flight energy consumption, P thermal is the influence of temperature and air pressure on flight energy consumption, P angl is the correction of the energy consumption by the flight angle θ, v is the flight speed, h is the flight altitude, ρ is the air density, W is the real-time wind speed, T is the real-time temperature, P is the real-time air pressure, and dt is the time increment.

[0034] Furthermore, when the drone is controlled according to the position distance between the real-time position and the preset position, it includes:

[0035] A displacement error between the real-time position and the preset position is obtained, and the UAV is controlled to return to the flight trajectory according to the displacement error.

[0036] Furthermore, when obtaining the displacement error between the real-time position and the preset position, it includes:

[0037]

[0038] Among them, Error x is the displacement error of the drone in the x direction, Error y is the displacement error of the drone in the y direction, Error z is the displacement error of the UAV in the z direction, Kp, Ki and Kd are gain coefficients, and Kp, Ki and Kd are not zero.

[0039] Compared with the prior art, the beneficial effect of the present invention is that by real-time monitoring of the communication quality between the UAV and the base station, the impact of communication interruption on the flight mission can be effectively avoided. When a communication anomaly occurs between the UAV and the base station, the system can automatically detect and respond quickly, and take appropriate emergency control measures to ensure that the UAV can always maintain effective contact with the ground control station. This real-time anomaly detection and response mechanism significantly improves the communication reliability of the UAV and reduces the potential risks of communication failures to mission execution and flight safety. In addition, the flight path and control strategy are optimized by integrating multiple factors such as flight data, flight route, real-time position and environmental data. By analyzing the relationship between real-time flight data and preset flight positions, the flight trajectory can be effectively corrected to ensure that the UAV flies along the predetermined route and avoids route deviation caused by environmental changes or flight errors. In complex weather conditions or large environmental changes, the flight parameters can be dynamically adjusted to ensure that the UAV continues to stay on a safe trajectory. Finally, by introducing real-time monitoring and management of residual energy, it can ensure that the energy consumption of the UAV during flight is always within a controllable range. When the residual energy of the UAV reaches a certain threshold, a warning will be issued in time or the flight strategy will be adjusted to avoid flight interruptions or accidents caused by insufficient power. This energy management mechanism greatly improves the safety during flight, especially in scenarios with long-duration flights or complex missions, and can ensure that the UAV successfully completes the mission and returns safely.

[0040] On the other hand, the present application also provides a drone flight control system, comprising:

[0041] An acquisition module, configured to acquire communication data between a drone and a base station, and further configured to acquire flight data and a flight path of the drone; the acquisition module is further configured to determine whether there is a communication anomaly between the drone and the base station according to the relationship between the communication data and pre-configured preset communication data, where: when it is determined that there is a communication anomaly between the drone and the base station, the acquisition module acquires environmental data of the drone;

[0042] A central control module, electrically connected to the acquisition module, configured to acquire the real-time flight altitude, flight speed, flight time, and real-time position in the flight data, determine a preset position of the flight data according to the relationship between the flight time, flight speed, and the flight path, and determine whether to control the drone according to the relationship between the real-time position and the preset position, where: when it is determined to control the drone, the central control module acquires the remaining energy of the drone and controls the drone according to the relationship between the remaining energy of the drone, flight altitude, flight speed, and environmental data.

[0043] It can be understood that the above-mentioned various embodiments of a drone flight control system and method in the present invention have the same beneficial effects and will not be elaborated here. Description of the Drawings

[0044] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0045] Figure 1 It is a flowchart of a drone flight control method provided by an embodiment of the present invention;

[0046] Figure 2 It is a functional block diagram of a drone flight control system provided by an embodiment of the present invention. Detailed Embodiments

[0047] Hereinafter, the exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in combination with the embodiments.

[0048] As Figure 1 shown, in some embodiments of the present application, this embodiment provides a method for controlling the flight of a drone, including:

[0049] Step S100: Obtain the communication data between the drone and the base station, and obtain the flight data and flight path of the drone.

[0050] Step S200: Determine whether there is a communication anomaly between the drone and the base station according to the relationship between the communication data and the pre-configured preset communication data, where: if it is determined that there is a communication anomaly between the drone and the base station, then obtain the environmental data of the drone.

[0051] Specifically, when determining whether there is a communication anomaly between the drone and the base station according to the relationship between the communication data and the pre-configured preset communication data, it includes: obtaining each historical data during the flight of the drone, and determining the data mean value between each historical data as the preset communication data. Obtain the real-time communication data of the drone, and determine the communication score according to the relationship between the real-time communication data and the preset communication data:

[0052]

[0053] Among them, S is the communication score, m is the number of scoring indicators, Wj is the weight coefficient of the jth type of scoring indicator data, n is the total number of the jth type of scoring indicators in each historical data, Ki is the ith type of historical data, and k is the indicator data of the real-time communication data. Determine whether there is a communication anomaly between the drone and the base station according to the communication score.

[0054] Specifically, when determining whether there is a communication anomaly between the drone and the base station according to the communication score, it includes: determining whether there is a communication anomaly between the drone and the base station according to the relationship between the communication score and the pre-configured preset communication score: when the communication score is less than the preset communication score, it is determined that there is a communication anomaly between the drone and the base station. When the communication score is greater than or equal to the preset communication score, it is determined that there is no communication anomaly between the drone and the base station.

[0055] It is understandable that by obtaining the historical communication data during the flight of the drone and performing mean processing on this data, a set of preset communication data is obtained as a standard. These preset communication data serve as a comparison benchmark for real-time communication data, providing a reference value for evaluating the quality of the current communication state. The mean value of the historical data is a summary of the long-term communication situation and represents the communication characteristics during normal flight. Secondly, the real-time communication data is obtained by monitoring the communication state between the drone and the base station. By comparing the real-time communication data with the preset communication data, according to specific scoring criteria and combining multiple scoring indicators, a communication score is calculated. The calculation process of the communication score takes into account multiple influencing factors, such as signal strength, delay time, packet loss rate, etc. The weight coefficient of each scoring indicator is determined by the actual situation. In the calculation process of the communication score, the number of scoring indicators (m) and the weight (Wj) of each scoring indicator determine the importance of each indicator in the final score. The data of each scoring indicator comes from historical data and real-time data. The number of historical data is n, and the number of real-time data is k. Through this weighted synthesis method, it can accurately reflect the influence of different factors on the communication quality, thus obtaining a comprehensive communication score. Next, by comparing the result of the communication score with the preset communication score, it can be judged whether the communication state between the current drone and the base station is normal. When the calculated communication score is less than the preset communication score, it indicates that the communication quality does not meet the requirements and there is communication abnormality. Necessary emergency measures will be taken based on this result to ensure the flight safety of the drone. When the communication score is greater than or equal to the preset communication score, it indicates that the communication state is normal and the communication abnormality is excluded. Finally, the comparison between the communication score and the preset communication score provides a decision-making basis for the flight control of the drone. When communication abnormality is detected, environmental data will be automatically obtained, and the flight path will be dynamically adjusted in combination with flight data. During this process, the communication score, as a key parameter for measuring communication quality, ensures that the drone can respond in a timely manner when communication abnormality occurs, avoiding unstable flight control caused by communication failures, thereby improving the flight reliability and safety of the drone in complex environments.

[0056] Step S300: Obtain the real-time flight altitude, flight speed, flight time, and real-time position in the flight data. According to the relationship between the flight time, flight speed, and flight path, determine the preset position of the flight data. And according to the relationship between the real-time position and the preset position, determine whether to control the drone, where: when it is determined to control the drone, obtain the remaining energy of the drone, and control the drone according to the relationship between the remaining energy of the drone, flight altitude, flight speed, and environmental data.

[0057] Specifically, when determining whether to control the drone according to the relationship between the real-time position and the preset position, it includes: when the real-time position is inconsistent with the preset position, it is determined to control the drone. When the real-time position is consistent with the preset position, it is determined not to control the drone.

[0058] Specifically, when controlling the drone according to the relationship between the remaining energy, flight altitude, flight speed of the drone and environmental data, it includes: determining the control method when controlling the drone according to the relationship between the remaining energy and the preset remaining energy: when the remaining energy is less than the preset remaining energy, the drone is controlled according to glide control. When the remaining energy is greater than or equal to the remaining energy, the drone is controlled according to the position distance between the real-time position and the preset position.

[0059] Specifically, the environmental data is specifically the real-time wind speed, real-time air pressure, real-time air density and real-time temperature on the periphery of the drone.

[0060] Specifically, when presetting the remaining energy, it includes: obtaining the initial energy of the drone and the flight angle of the drone, and determining the preset remaining energy according to the real-time wind speed, real-time air pressure, real-time temperature on the periphery of the drone, the initial energy of the drone, the flight angle of the drone, the flight speed and flight altitude of the drone:

[0061]

[0062] Among them, E remaining(t) is the preset remaining energy, E initial is the initial energy of the drone, P aero is the aerodynamic power consumption, P drag is the air resistance power consumption, P wind is the influence of wind speed on flight energy consumption, P thermal is the influence of temperature and air pressure on flight energy consumption, P angl is the correction of the energy consumption by the flight angle θ, v is the flight speed, h is the flight altitude, ρ is the air density, W is the real-time wind speed, T is the real-time temperature, P is the real-time air pressure, and dt is the time increment.

[0063] Specifically, when controlling the drone according to the position distance between the real-time position and the preset position, it includes: obtaining the displacement error between the real-time position and the preset position, and controlling the drone to return to the flight track according to the displacement error. Further, when obtaining the displacement error between the real-time position and the preset position, it includes:

[0064]

[0065] Among them, Error xis the displacement error of the drone in the x - direction, Error y is the displacement error of the drone in the y - direction, Error z is the displacement error of the drone in the z - direction. Kp, Ki, and Kd are gain coefficients, and Kp, Ki, and Kd are all non - zero.

[0066] It can be understood that by obtaining the real - time flight data of the drone, such as flight altitude, flight speed, flight time, and real - time position, the preset position of the flight data is determined. This process calculates the preset position where the drone should be by analyzing the relationship between flight time, flight speed, and flight path. Then, by comparing the real - time position with the preset position, it can be judged whether the drone has deviated from the predetermined flight path. If there is a difference between the real - time position and the preset position, the flight control mechanism will be triggered for dynamic adjustment to ensure the accuracy and stability of the drone's flight trajectory. Secondly, by real - time monitoring the remaining energy of the drone, as well as its flight altitude, flight speed, and environmental data, the control strategy of the drone can be determined. Especially when the remaining energy is less than the preset remaining energy, a glide control strategy will be adopted to optimize energy consumption. Glide control can help the drone adjust parameters such as flight angle and speed in low - energy situations to achieve longer - duration flight without causing flight interruption. When the remaining energy is sufficient, the flight trajectory is adjusted according to the distance between the real - time position and the preset position to ensure that the drone accurately returns to the preset flight path. In the control strategy, environmental data, especially factors such as real - time wind speed, air pressure, air density, and temperature, are taken into account. These environmental factors directly affect the flight energy efficiency of the drone. For example, changes in wind speed may lead to increased energy consumption, and changes in air pressure and temperature may affect air density, thereby affecting flight performance. By combining these real - time environmental data, the flight strategy can be adjusted more precisely to improve flight stability and efficiency. Further, in order to precisely control the flight path of the drone, correction needs to be made based on the displacement error between the real - time position and the preset position. The calculation of the displacement error is achieved through the errors in the x, y, and z directions in a three - dimensional coordinate system, and the PID control algorithm is used to adjust the flight path of the drone. The PID controller can achieve fast and precise adjustment in different flight states by adjusting the proportional (Kp), integral (Ki), and derivative (Kd) coefficients, enabling the drone to dynamically adjust the flight path according to the error and ensuring the successful completion of the flight mission. Finally, the calculation of the preset remaining energy is the key in the whole control process. It combines multiple factors such as the initial energy of the drone, flight angle, wind speed, air pressure, and temperature, and comprehensively considers factors such as aerodynamic power consumption, air resistance, and wind speed effect during flight. Through this dynamic calculation, the possible energy consumption of the drone during flight can be accurately predicted, so as to formulate a reasonable flight strategy to ensure that the drone can maintain sufficient energy to execute tasks under different flight conditions and can return safely.

[0067] In the above embodiments, by monitoring the communication quality between the UAV and the base station in real time, the impact of communication interruption on the flight mission can be effectively avoided. When communication anomalies occur between the UAV and the base station, the system can automatically detect and respond quickly, taking appropriate emergency control measures to ensure that the UAV can always maintain effective communication with the ground control station. This real-time anomaly detection and response mechanism significantly improves the communication reliability of the UAV and reduces the potential risks brought by communication failures to mission execution and flight safety. In addition, by integrating multiple factors such as flight data, flight path, real-time position, and environmental data, the flight path and control strategy are optimized. By analyzing the relationship between the real-time flight data and the preset flight position, the flight trajectory can be effectively corrected to ensure that the UAV flies along the predetermined flight path and avoid flight path deviation caused by environmental changes or flight errors. In the case of complex weather conditions or large environmental changes, the flight parameters can be dynamically adjusted to ensure that the UAV continuously maintains a safe trajectory. Finally, by introducing real-time monitoring and management of the remaining energy, it can be ensured that the energy consumption of the UAV during flight is always within a controllable range. When the remaining energy of the UAV reaches a certain threshold, a warning will be issued in a timely manner or the flight strategy will be adjusted to avoid flight interruption or accidents caused by insufficient power. This energy management mechanism greatly improves the safety during flight, especially in scenarios of long-term flight or complex tasks, and can ensure that the UAV successfully completes the mission and returns safely.

[0068] In another preferred manner based on the above embodiments, as Figure 2 shown, this embodiment provides a UAV flight control system, including: an acquisition module and a central control module.

[0069] Specifically, the acquisition module is configured to acquire communication data between the UAV and the base station. The acquisition module is further configured to acquire the flight data and flight path of the UAV. The acquisition module is also configured to determine whether there is a communication anomaly between the UAV and the base station according to the relationship between the communication data and the pre-configured preset communication data, where: if it is determined that there is a communication anomaly between the UAV and the base station, the acquisition module acquires the environmental data of the UAV. The central control module is electrically connected to the acquisition module. The central control module is configured to acquire the real-time flight altitude, flight speed, flight time, and real-time position in the flight data, determine the preset position of the flight data according to the relationship between the flight time, flight speed, and flight path, and determine whether to control the UAV according to the relationship between the real-time position and the preset position, where: when it is determined to control the UAV, the central control module acquires the remaining energy of the UAV and controls the UAV according to the relationship between the remaining energy of the UAV, flight altitude, flight speed, and environmental data.

[0070] It is understandable that a drone flight control system and method in each of the above embodiments of the present invention have the same beneficial effects and will not be elaborated herein.

[0071] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0072] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0073] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A UAV flight control method, characterized in that: include: Acquire communication data between the UAV and the base station, and acquire flight data and flight path of the UAV; According to the relationship between the communication data and the pre-configured preset communication data, it is determined whether there is a communication anomaly between the UAV and the base station, wherein: If it is determined that there is a communication anomaly between the UAV and the base station, obtaining environmental data of the UAV; The real-time flight altitude, flight speed, flight time and real-time position in the flight data are obtained, a preset position of the flight data is determined according to the relationship between the flight time, flight speed and the flight path, and whether to control the UAV is determined according to the relationship between the real-time position and the preset position, wherein: When it is determined to control the drone, the remaining energy of the drone is obtained, and the drone is controlled according to the relationship between the remaining energy, flight altitude, flight speed and environmental data of the drone.

2. A UAV flight control method as claimed in claim 1, characterized in that: When determining whether there is a communication anomaly between the drone and the base station according to the relationship between the communication data and the pre-configured preset communication data, it includes: Acquire various historical data of the UAV during flight, and determine the data mean between the various historical data as the preset communication data; Acquire the real-time communication data of the drone, and determine the communication score according to the relationship between the real-time communication data and the preset communication data: Wherein, S is the communication score, m is the number of scoring indicators, Wj is the weight coefficient of the j-th scoring indicator data, n is the total number of the j-th scoring indicators in each of the historical data, Ki is the i-th historical data, and k is the indicator data of the real-time communication data; According to the communication score, it is determined whether there is a communication anomaly between the drone and the base station.

3. A UAV flight control method as claimed in claim 2, characterized in that: Determining whether there is a communication anomaly between the drone and the base station according to the communication score includes: Determining whether there is a communication anomaly between the drone and the base station according to a relationship between the communication score and a preconfigured preset communication score; When the communication score is less than the preset communication score, it is determined that there is a communication anomaly between the drone and the base station; When the communication score is greater than or equal to the preset communication score, it is determined that there is no communication abnormality between the drone and the base station.

4. The method for controlling the flight of an unmanned aerial vehicle according to claim 1, characterized in that: Determining whether to control the drone according to the relationship between the real-time position and the preset position includes: When the real-time position is inconsistent with the preset position, determining to control the drone; When the real-time position is consistent with the preset position, it is determined that the drone is not to be controlled.

5. A UAV flight control method as claimed in claim 4, characterized in that: When controlling the UAV according to the relationship between the remaining energy, the flight altitude, the flight speed and the environmental data of the UAV, the method includes: Determining a control method for controlling the drone according to a relationship between the remaining energy and a preset remaining energy; When the remaining energy is less than the preset remaining energy, the UAV is controlled according to gliding control; When the remaining energy is greater than or equal to the remaining energy, the drone is controlled according to the position distance between the real-time position and the preset position.

6. A UAV flight control method as claimed in claim 5, characterized in that: The environmental data specifically includes real-time wind speed, real-time air pressure, real-time air density and real-time temperature around the drone.

7. A UAV flight control method as claimed in claim 6, characterized in that: When the remaining energy is preset, it includes: The initial energy of the drone and the flight angle of the drone are obtained, and the preset remaining energy is determined according to the real-time wind speed, real-time air pressure, real-time temperature around the drone, the initial energy of the drone, the flight angle of the drone, the flight speed of the drone and the flight altitude of the drone: Among them, E remaining(t) is the preset remaining energy, E initial is the initial energy of the UAV, P aero is the aerodynamic power consumption, P drag P is the power consumed by air resistance, wind is the effect of wind speed on flight energy consumption, P thermal is the effect of temperature and air pressure on flight energy consumption, P angl is the correction of the energy consumption by the flight angle θ, v is the flight speed, h is the flight altitude, ρ is the air density, W is the real-time wind speed, T is the real-time temperature, P is the real-time air pressure, and dt is the time increment.

8. A UAV flight control method as claimed in claim 7, characterized in that: When the drone is controlled according to the position distance between the real-time position and the preset position, the method includes: A displacement error between the real-time position and the preset position is obtained, and the UAV is controlled to return to the flight trajectory according to the displacement error.

9. A UAV flight control method as claimed in claim 8, characterized in that: When obtaining the displacement error between the real-time position and the preset position, it includes: Among them, Error x is the displacement error of the drone in the x direction, Error y is the displacement error of the drone in the y direction, Error z is the displacement error of the UAV in the z direction, Kp, Ki and Kd are gain coefficients, and Kp, Ki and Kd are not zero.

10. A UAV flight control system, using a UAV flight control method as claimed in any one of claims 1 to 9, characterized in that: include: An acquisition module is configured to acquire communication data between the drone and the base station, and the acquisition module is further configured to acquire flight data and a flight path of the drone; the acquisition module is further configured to determine whether there is a communication anomaly between the drone and the base station based on a relationship between the communication data and pre-configured preset communication data, wherein: if it is determined that there is a communication anomaly between the drone and the base station, the acquisition module acquires environmental data of the drone; A central control module is electrically connected to the acquisition module, and is configured to acquire the real-time flight altitude, flight speed, flight time and real-time position in the flight data, determine the preset position of the flight data according to the relationship between the flight time, flight speed and the flight path, and determine whether to control the UAV according to the relationship between the real-time position and the preset position, wherein: when it is determined to control the UAV, the central control module acquires the remaining energy of the UAV, and controls the UAV according to the relationship between the remaining energy, flight altitude and flight speed of the UAV and environmental data.

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