Unmanned aerial vehicle intelligent control system based on radio signal interference

Abstract

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle intelligent control system based on radio signal interference. The system comprises a radio signal interference detection unit for monitoring radio signal interference in an environment, and an emergency response unit for implementing emergency measures through a preset emergency strategy according to a received interference signal; the power management and energy efficiency unit is used for managing power use of the unmanned aerial vehicle; the control center unit is used for overall control and coordination of the unmanned aerial vehicle; through a signal analysis algorithm, various types of interference signals can be processed and analyzed at the same time; the multi-level signal analysis capability enables the system to accurately identify pulse interference, continuous interference and intermittent interference, and the identification rate and accuracy of radio interference are improved. The source position of the interference signal can be calculated in real time through the TOA algorithm, so that the unmanned aerial vehicle can better avoid potential risks in a complex environment, and the safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to an intelligent control system for unmanned aerial vehicles (UAVs) based on radio signal interference. Background Art

[0002] The system uses a radio signal interference detection unit, which realizes accurate monitoring and classification of radio signal interference in the environment through the combination of signal collection, signal analysis and interference signal detection modules, and can timely identify pulse interference, continuous interference and intermittent interference; the TOA algorithm can accurately determine the location of the interference signal source by measuring the signal propagation time, thereby providing a basis for subsequent emergency response; the integration of various system units such as interference detection, emergency response and energy management modules enables the drone to comprehensively apply various technologies to improve overall performance when facing radio interference; With the rapid development of UAV technology, its application in various fields is becoming more and more extensive. However, the accompanying radio signal interference problem may cause the loss of UAV control signals or communication interruption, thus affecting the safety and reliability of UAVs. Radio signal interference comes from a wide range of sources. In the existing technology, many UAV systems lack effective interference detection and response mechanisms, resulting in the inability to respond quickly when interfered. Therefore, an intelligent control system for UAVs based on radio signal interference is designed. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent control system for unmanned aerial vehicles based on radio signal interference, so as to solve the problem mentioned in the above background technology that with the rapid development of unmanned aerial vehicle technology, its application in various fields is becoming more and more extensive, but the accompanying radio signal interference problem may cause the control signal of the unmanned aerial vehicle to be lost or the communication to be interrupted, thereby affecting the safety and reliability of the unmanned aerial vehicle; the sources of radio signal interference are wide, and in the prior art, many unmanned aerial vehicle systems lack effective interference detection and response mechanisms, resulting in the problem that they cannot respond quickly when interfered; To achieve the above object, the present invention provides an intelligent control system for unmanned aerial vehicles based on radio signal interference, comprising: A radio signal interference detection unit, the radio signal interference detection unit is used to monitor radio signal interference in the environment, and the radio signal interference detection unit includes a signal collection module, a signal analysis module and an interference signal detection module; An emergency response unit, wherein the emergency response unit implements emergency measures according to a pre-set emergency strategy based on the received interference signal; A power management and energy efficiency unit, which is used to manage the power usage of the drone, improve energy utilization efficiency, and extend flight time; the power management and energy efficiency unit includes a power monitoring module and an energy efficiency optimization module; The control center unit is used for the overall control and coordination of the UAV, and includes a flight control module, a data transmission module and a path planning module.

[0004] As a further improvement of the technical solution, the radio signal interference detection unit is used to determine the type of interference signal, and identify the impact of phase change on the signal according to the phase characteristics of the signal, wherein the types of interference signals include pulse interference, continuous interference and intermittent interference; The steps involved in determining the type of interference signal are: S1.1, the signal collection module is used to collect control command signals from the ground control station and other UAVs, as well as radio signals from the surrounding environment; S1.2, the signal analysis module is based on discrete Fourier transform, and through detailed analysis of the data collected by the signal collection module, it determines the type of signal and distinguishes between normal signals and interference signals; The inverse discrete Fourier transform is used to reconstruct the time domain signal from the frequency domain signal and is defined as follows: ; In the formula, represents the nth sample of the reconstructed time domain signal; A constant factor representing the frequency resolution; Represents a time index.

[0005] S1.3. The interference signal is analyzed according to the signal analysis module. The interference signal detection module adopts the TOA algorithm to determine the position by measuring the propagation time of the signal from the transmitting source to the receiving device. The TOA algorithm is an algorithm that calculates the distance between the signal source and the receiving site based on the signal arrival time. The TOA algorithm directly measures the absolute time when the signal arrives at the receiving site from the source, thereby calculating the distance from the signal source to the receiving site, and then the position of the signal source is obtained through the data of multiple receiving sites.

[0006] As a further improvement of the technical solution, in S1.3, the TOA algorithm is specifically: The known signal source location is , the drone’s position is , the signal from the source Propagate to drone location The time is ,but: ; In the formula, Indicates that the signal comes from the source To the drone location Arrival time; Indicates the signal source To the drone location distance; Indicates the speed at which a signal travels.

[0007] As a further improvement of the technical solution, the emergency response unit selects a corresponding emergency strategy according to the information provided by the interference signal detection module, and the emergency strategy includes a communication frequency band switching strategy, a flight path adjustment strategy, an automatic return strategy, and a landing and hovering strategy; Among them, when the interference signal is concentrated in the communication frequency band currently used by the drone, the communication frequency band switching strategy is adopted; when the location of the interference signal source is detected, the flight path adjustment strategy is adopted; when a serious interference signal is detected, the automatic return strategy is adopted; when the detected interference signal is extremely serious and the control signal has been lost, the landing and hovering strategy is adopted; Switching communication frequency band strategy: When the interference signal is concentrated on the communication frequency band currently used by the drone, the system will automatically switch to the backup frequency band that is less interfered with; it can quickly restore the communication between the drone and the control center and reduce the risk of signal interruption; Adjust flight path strategy: If the system detects the location of the interference signal source, it will re-plan the flight path to avoid the area where the interference source is located; it can avoid entering the strong interference area and maintain the stability of communication and flight; Automatic return strategy: When a serious interference signal is detected, which may cause the drone to lose control or communication interruption, the system will trigger the automatic return function to make the drone return to the starting point according to the preset route; ensuring that the drone can return to a safe area under control to avoid loss of connection or crash due to signal loss; Landing and hovering strategy: If the interference signal detected by the drone is extremely serious and the control signal has been lost, the system will perform an emergency landing at the current or specified location, or hover and wait in a safe area; this can ensure the drone lands safely and reduce the flight risks caused by loss of control.

[0008] As a further improvement of the technical solution, the power management and energy efficiency unit includes a power monitoring module, which monitors the battery status in real time based on the energy consumption model and provides battery status feedback to ensure that the drone operates within a safe power range;

[0009] It also includes an energy efficiency optimization module, which uses a genetic MAX algorithm to optimize power distribution, dynamically adjusts energy use strategies according to flight missions, and extends flight time.

[0010] As a further improvement of the technical solution, the power monitoring module estimates the energy consumption of the drone during flight based on the energy consumption model, which is specifically: When the drone flies from starting point A to end point B, it flies in a straight line. The horizontal flight speed is a constant v, and the vertical speed is zero. The total energy consumption model is: ; In the formula, Represents the total energy consumption of the drone from the starting point to the end point; represents the gravity-related energy consumption coefficient; Indicates the quality of the drone; represents the acceleration due to gravity; Indicates the energy consumption coefficient related to air resistance; Indicates the horizontal flight speed of the drone; Indicates the horizontal flight distance of the drone from starting point A to end point B.

[0011] As a further improvement of the technical solution, the energy efficiency optimization module executes the flight path adjustment strategy based on the interference signal received by the interference signal detection module, and uses the genetic MAX algorithm to optimize the power distribution and the task allocation of the UAV. The specific formula involved is: ; In the formula, represents the total electricity consumption function; Indicates the flight power consumption caused by path adjustment; Indicates the communication power consumption affected by signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the working power consumption of the sensor; It is used to adjust the interference coefficient for communication and path optimization. When the signal interference increases, Increased to meet the power distribution needs in different interference environments; Indicates the flight power consumption caused by path adjustment; Indicates the power consumption of communications affected by radio signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the operating power consumption of the sensor.

[0012] As a further improvement of the technical solution, the control center unit includes a flight control module, which is based on a PID control algorithm. The PID control algorithm aims to accurately control the flight attitude, heading and speed of the drone, ensure that the drone maintains stable flight under various environmental conditions, and can quickly respond to changes in the external environment, thereby improving flight safety and flexibility. It includes a data transmission module, which is based on the LoRa wireless communication protocol. The LoRa wireless communication protocol can transmit the flight status and environmental information of the drone to the ground control center in real time, ensure timely feedback of control commands, and support remote monitoring and data analysis; It also includes a path planning module, which performs path optimization based on a shortest path algorithm. The purpose of the shortest path algorithm is to calculate the optimal flight path, avoid obstacles, and improve flight efficiency.

[0013] As a further improvement of the technical solution, the flight control module is used to adjust the flight attitude, heading and speed of the UAV subject to signal interference, and the PID control algorithm used is specifically: Flight attitude control: ; In the formula, Indicates the pitch angle control output, which is used to drive the motor to adjust the pitch angle of the drone; Indicates the pitch angle error, that is, the difference between the target pitch angle and the current pitch angle; It represents the attitude proportional gain, which determines the sensitivity of the error to the pitch angle adjustment; Indicates the attitude integral gain, which is used to eliminate small but persistent errors and avoid long-term offsets; Indicates the attitude differential gain, which is used to reduce oscillation and make attitude adjustment smoother; represents the time variable; represents the time derivative of the pitch angle error; Heading control: ; In the formula, Represents the yaw angle control output, which is used to adjust the rotation of the drone to face the target direction; Represents the yaw angle error, that is, the difference between the target yaw angle and the current yaw angle; Represents the heading proportional gain, which controls the responsiveness of the yaw angle error; Indicates the heading integral gain, which eliminates long-term heading deviation; Represents the heading differential gain, which is used to smooth the yaw adjustment process and avoid drastic changes in direction; Represents the time derivative of the yaw angle error; Speed ​​Control: ; In the formula, Represents the speed control output, which is used to adjust the motor thrust to achieve the target speed; Represents the speed error, which is the difference between the target speed and the current speed; Indicates the speed proportional gain, which determines the impact of the current speed error on the adjustment; Represents the speed integral gain, which is used to eliminate the accumulated speed deviation; It represents the speed differential gain, which suppresses the sudden change in speed adjustment process and makes the speed smoother; Represents the time derivative of the velocity error.

[0014] As a further improvement of the technical solution, the path planning module, when the UAV is subjected to continuous interference, uses the shortest path algorithm based on the energy consumption model to optimize the path, and the sampling shortest path algorithm minimizes the flight distance and energy consumption. The specific steps are: S2.1. Initialization to determine the starting point and end point , and record the initial remaining power of the drone , the starting point Add the open list OpenList, for each node ,initialization , , and set a new heuristic function ; S2.2. Cost function of the original shortest path algorithm Based on this, add energy consumption calculation: ; In the formula, represents static energy consumption; represents dynamic energy consumption; represents the distance from node i to node j in the path segment; represents the cumulative energy consumption when the UAV reaches node n; S2.3. Heuristic Function The remaining power and the energy consumption to reach the target point need to be considered to estimate the distance from the current node n to the target node G: ; In the formula, represents the traditional heuristic function; represents the weight factor; Represents the straight-line distance from the current node n to the target node G; represents the heuristic function; S2.4. Select nodes by selecting the nodes with the lowest Value Node ; Check target if It is the end , then the algorithm ends; the node Move to the closed list ClosedList; for node All neighbor nodes of : if Already in the closed list, skip this neighbor; if Not in the open list, calculate and ,Will Add to open list and set As The parent node of if Already in the open list, check if there is a better path to reach ,Right now ; If yes, then update The parent node is , and recalculate and ; S2.5. Check the exit condition. If the open list is empty, there is no path to the target. The algorithm ends and the path is rebuilt from the end point. Start by following the parent node pointer backwards until you reach the starting point. , thus obtaining the shortest path.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In this UAV intelligent control system based on radio signal interference, the signal analysis algorithm can simultaneously process and analyze multiple types of interference signals; this multi-level signal analysis capability enables the system to accurately identify pulse interference, continuous interference and intermittent interference, improving the recognition rate and accuracy of radio interference; through the TOA algorithm, the source location of the interference signal can be calculated in real time, providing key data support for the UAV to formulate corresponding emergency measures; enabling the UAV to better avoid potential risks in complex environments and improve safety.

[0016] 2. In this kind of UAV intelligent control system based on radio signal interference, the emergency response unit can select the most appropriate emergency measures through a pre-set emergency strategy according to the interference type and intensity monitored in real time; it significantly improves the UAV's response capability when facing radio interference. Compared with the fixed emergency response mechanism in the prior art, the present invention is more adaptable and practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Radio signal interference detection unit; 2. Emergency response unit 3. Power management and energy efficiency unit; 4. Control center unit. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example

[0020] See also Figure 1 As shown, a UAV intelligent control system based on radio signal interference is provided, comprising: A radio signal interference detection unit, the radio signal interference detection unit is used to monitor radio signal interference in the environment, and the radio signal interference detection unit includes a signal collection module, a signal analysis module and an interference signal detection module; An emergency response unit, wherein the emergency response unit implements emergency measures according to a pre-set emergency strategy based on the received interference signal; A power management and energy efficiency unit, which is used to manage the power usage of the drone, improve energy utilization efficiency, and extend flight time; the power management and energy efficiency unit includes a power monitoring module and an energy efficiency optimization module; The control center unit is used for the overall control and coordination of the UAV, and includes a flight control module, a data transmission module and a path planning module.

[0021] The radio signal interference detection unit is used to determine the type of interference signal and identify the impact of phase change on the signal according to the phase characteristics of the signal, wherein the types of interference signals include pulse interference, continuous interference and intermittent interference; The steps involved in determining the type of interference signal are: S1.1, the signal collection module is used to collect control command signals from the ground control station and other UAVs, as well as radio signals from the surrounding environment; S1.2, the signal analysis module is based on discrete Fourier transform, and through detailed analysis of the data collected by the signal collection module, it determines the type of signal and distinguishes between normal signals and interference signals; The inverse discrete Fourier transform is used to reconstruct the time domain signal from the frequency domain signal and is defined as follows: ; In the formula, represents the nth sample of the reconstructed time domain signal; A constant factor representing the frequency resolution; Represents a time index.

[0022] S1.3. The interference signal is analyzed according to the signal analysis module. The interference signal detection module adopts the TOA algorithm to determine the position by measuring the propagation time of the signal from the transmitting source to the receiving device. The TOA algorithm is an algorithm that calculates the distance between the signal source and the receiving site based on the signal arrival time. The TOA algorithm directly measures the absolute time when the signal arrives at the receiving site from the source, thereby calculating the distance from the signal source to the receiving site, and then the position of the signal source is obtained through the data of multiple receiving sites.

[0023] The TOA algorithm is specifically as follows: The known signal source location is , the drone’s position is , the signal from the source Propagate to drone location The time is ,but: ; In the formula, Indicates that the signal comes from the source To the drone location Arrival time; Indicates the signal source To the drone location distance; Indicates the speed at which a signal travels.

[0024] The emergency response unit selects the corresponding emergency strategy based on the information provided by the interference signal detection module. The emergency strategy includes switching communication frequency band strategy, adjusting flight path strategy, automatic return strategy and landing and hovering strategy. Among them, when the interference signal is concentrated in the communication frequency band currently used by the drone, the communication frequency band switching strategy is adopted; when the location of the interference signal source is detected, the flight path adjustment strategy is adopted; when a serious interference signal is detected, the automatic return strategy is adopted; when the detected interference signal is extremely serious and the control signal has been lost, the landing and hovering strategy is adopted; Switching communication frequency band strategy: When the interference signal is concentrated on the communication frequency band currently used by the drone, the system will automatically switch to the backup frequency band that is less interfered with; it can quickly restore the communication between the drone and the control center and reduce the risk of signal interruption; Adjust flight path strategy: If the system detects the location of the interference signal source, it will re-plan the flight path to avoid the area where the interference source is located; it can avoid entering the strong interference area and maintain the stability of communication and flight; Automatic return strategy: When a serious interference signal is detected, which may cause the drone to lose control or communication interruption, the system will trigger the automatic return function to make the drone return to the starting point according to the preset route; ensuring that the drone can return to a safe area under control to avoid loss of connection or crash due to signal loss; Landing and hovering strategy: If the interference signal detected by the drone is extremely serious and the control signal has been lost, the system will perform an emergency landing at the current or specified location, or hover and wait in a safe area; this can ensure the drone lands safely and reduce the flight risks caused by loss of control.

[0025] The power management and energy efficiency unit includes a power monitoring module, which monitors the battery status in real time based on the energy consumption model and provides battery status feedback to ensure that the drone operates within a safe power range; It also includes an energy efficiency optimization module, which uses a genetic MAX algorithm to optimize power distribution, dynamically adjusts energy use strategies according to flight missions, and extends flight time.

[0026] The power monitoring module estimates the energy consumption of the drone during flight based on the energy consumption model. The specific energy consumption model is: When the drone flies from starting point A to end point B, it flies in a straight line. The horizontal flight speed is a constant v, and the vertical speed is zero. The total energy consumption model is: ; In the formula, Represents the total energy consumption of the drone from the starting point to the end point; represents the gravity-related energy consumption coefficient; Indicates the quality of the drone; represents the acceleration due to gravity; Indicates the energy consumption coefficient related to air resistance; Indicates the horizontal flight speed of the drone; Indicates the horizontal flight distance of the drone from starting point A to end point B; The role of the total energy consumption model is to quantify the energy consumed by the UAV during flight; the model comprehensively considers the influence of gravity and air resistance, and provides an accurate assessment of energy consumption; by analyzing the total energy consumption required by the UAV during horizontal flight, it can help design more efficient flight paths and optimize energy allocation strategies, thereby improving the UAV's endurance and mission efficiency; this model has important guiding significance in the energy consumption management and performance optimization of UAVs.

[0027] The energy efficiency optimization module executes the flight path adjustment strategy based on the interference signal received by the interference signal detection module, and uses the genetic MAX algorithm to optimize the power distribution and the task allocation of the UAV. The specific formula involved is: ; In the formula, represents the total electricity consumption function; Indicates the flight power consumption caused by path adjustment; Indicates the communication power consumption affected by signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the working power consumption of the sensor; It is used to adjust the interference coefficient for communication and path optimization. When the signal interference increases, Increased to meet the power distribution needs in different interference environments; Indicates the flight power consumption caused by path adjustment; Indicates the power consumption of communications affected by radio signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the operating power consumption of the sensor.

[0028] The control center unit includes a flight control module, which is based on a PID control algorithm. The PID control algorithm aims to accurately control the flight attitude, heading and speed of the drone, ensure that the drone maintains stable flight under various environmental conditions, and can quickly respond to changes in the external environment, thereby improving flight safety and flexibility. The flight control module is used to adjust the flight attitude, heading and speed of the UAV that is subject to signal interference. The PID control algorithm used is as follows: Flight attitude control: ; In the formula, Indicates the pitch angle control output, which is used to drive the motor to adjust the pitch angle of the drone; Indicates the pitch angle error, that is, the difference between the target pitch angle and the current pitch angle; It represents the attitude proportional gain, which determines the sensitivity of the error to the pitch angle adjustment; Indicates the attitude integral gain, which is used to eliminate small but persistent errors and avoid long-term offsets; Indicates the attitude differential gain, which is used to reduce oscillation and make attitude adjustment smoother; represents the time variable; represents the time derivative of the pitch angle error; Heading control: ; In the formula, Represents the yaw angle control output, which is used to adjust the rotation of the drone to face the target direction; Represents the yaw angle error, that is, the difference between the target yaw angle and the current yaw angle; Represents the heading proportional gain, which controls the responsiveness of the yaw angle error; Indicates the heading integral gain, which eliminates long-term heading deviation; Represents the heading differential gain, which is used to smooth the yaw adjustment process and avoid drastic changes in direction; Represents the time derivative of the yaw angle error; Speed ​​Control: ; In the formula, Represents the speed control output, which is used to adjust the motor thrust to achieve the target speed; Represents the speed error, which is the difference between the target speed and the current speed; Indicates the speed proportional gain, which determines the impact of the current speed error on the adjustment; Represents the speed integral gain, which is used to eliminate the accumulated speed deviation; It represents the speed differential gain, which suppresses the sudden change in speed adjustment process and makes the speed smoother; Represents the time derivative of the velocity error.

[0029] It includes a data transmission module, which is based on the LoRa wireless communication protocol. The LoRa wireless communication protocol can transmit the flight status and environmental information of the drone to the ground control center in real time, ensure timely feedback of control commands, and support remote monitoring and data analysis; It also includes a path planning module, which performs path optimization based on a shortest path algorithm. The purpose of the shortest path algorithm is to calculate the optimal flight path, avoid obstacles, and improve flight efficiency.

[0030] Path planning module, when the UAV is subject to continuous interference, the shortest path algorithm is used to optimize the path based on the energy consumption model, and the shortest path algorithm is sampled to minimize the flight distance and energy consumption. The specific steps are: S2.1. Initialization to determine the starting point and end point , and record the initial remaining power of the drone , the starting point Add the open list OpenList, for each node ,initialization , , and set a new heuristic function ; OpenList is an important data structure used to dynamically manage and track device status and fault information. It provides effective support for system fault detection and response through real-time storage, priority management and rapid update, thus enhancing the reliability and efficiency of the overall system. S2.2. Cost function of the original shortest path algorithm Based on this, add energy consumption calculation: ; In the formula, represents static energy consumption; represents dynamic energy consumption; represents the distance from node i to node j in the path segment; represents the cumulative energy consumption when the UAV reaches node n; S2.3. Heuristic Function The remaining power and the energy consumption to reach the target point need to be considered to estimate the distance from the current node n to the target node G: ; In the formula, represents the traditional heuristic function; represents the weight factor; Represents the straight-line distance from the current node n to the target node G; represents the heuristic function; S2.4. Select nodes by selecting the nodes with the lowest Value Node ; Check target if It is the end , then the algorithm ends; the node Move to the closed list ClosedList; for node All neighbor nodes of : if Already in the closed list, skip this neighbor; if Not in the open list, calculate and ,Will Add to open list and set As The parent node of if Already in the open list, check if there is a better path to reach ,Right now ; If yes, then update The parent node is , and recalculate and ; S2.5. Check the exit condition. If the open list is empty, there is no path to the target. The algorithm ends and the path is rebuilt from the end point. Start by following the parent node pointer backwards until you reach the starting point. , thus obtaining the shortest path.

[0031] The advantage of using the shortest path algorithm for path optimization based on the energy consumption model is that it can shorten the flight path and effectively reduce the energy consumption of the UAV during flight, thereby extending the flight time and improving operational efficiency; by real-time evaluation of the energy consumption of the flight path, the system can select the path with the least energy consumption, which not only improves the UAV's endurance in complex environments, but also enhances the reliability of mission execution, making it more efficient and economical in long-term missions, ensuring the stability and sustainability of the UAV.

[0032] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. An intelligent control system for unmanned aerial vehicles based on radio signal interference, characterized in that: include A radio signal interference detection unit (1), the radio signal interference detection unit (1) being used to monitor radio signal interference in an environment, the radio signal interference detection unit (1) comprising a signal collection module, a signal analysis module and an interference signal detection module; An emergency response unit (2), wherein the emergency response unit (2) implements emergency measures according to a pre-set emergency strategy based on the received interference signal; A power management and energy efficiency unit (3), wherein the power management and energy efficiency unit (3) is used to manage the power usage of the drone; the power management and energy efficiency unit (3) comprises a power monitoring module and an energy efficiency optimization module; A control center unit (4), the control center unit (4) is used for overall control and coordination of the unmanned aerial vehicle, and the control center unit (4) comprises a flight control module, a data transmission module and a path planning module.

2. The UAV intelligent control system for radio signal interference according to claim 1 is characterized by: The radio signal interference detection unit (1) is used to determine the type of interference signal and identify the influence of phase change on the signal according to the phase characteristics of the signal, wherein the types of interference signal include pulse interference, continuous interference and intermittent interference; The steps involved in determining the type of interference signal are: S1.1, the signal collection module is used to collect control command signals from the ground control station and other UAVs, as well as radio signals from the surrounding environment; S1.2, the signal analysis module is based on discrete Fourier transform, and through detailed analysis of the data collected by the signal collection module, it determines the type of signal and distinguishes between normal signals and interference signals; S1.

3. The signal analysis module analyzes the interference signal. The interference signal detection module uses the TOA algorithm to determine the location by measuring the propagation time of the signal from the sending source to the receiving device.

3. The UAV intelligent control system for radio signal interference according to claim 2 is characterized by: In S1.3, the TOA algorithm is specifically: The known signal source location is , the drone’s position is , the signal from the source Propagate to drone location The time is ,but: ； In the formula, Indicates that the signal comes from the source To the drone location Arrival time; Indicates the signal source To the drone location distance; Indicates the speed at which a signal travels.

4. The UAV intelligent control system for radio signal interference according to claim 1 is characterized by: The emergency response unit (2) selects a corresponding emergency strategy according to the information provided by the interference signal detection module, the emergency strategy including a communication frequency band switching strategy, a flight path adjustment strategy, an automatic return strategy and a landing and hovering strategy; Among them, when the interference signal is concentrated in the communication frequency band currently used by the UAV, the strategy of switching the communication frequency band is adopted; when the location of the interference signal source is detected, the strategy of adjusting the flight path is adopted; when a serious interference signal is detected, the automatic return strategy is adopted; when the detected interference signal is extremely serious and the control signal has been lost, the landing and hovering strategy is adopted.

5. The UAV intelligent control system for radio signal interference according to claim 1 is characterized by: The power management and energy efficiency unit (3) comprises a power monitoring module, wherein the power monitoring module monitors the battery status in real time based on an energy consumption model; It also includes an energy efficiency optimization module, which uses a genetic MAX algorithm to optimize power distribution, dynamically adjusts energy use strategies according to flight missions, and extends flight time.

6. The UAV intelligent control system for radio signal interference according to claim 5 is characterized by: The power monitoring module estimates the energy consumption of the drone during flight based on the energy consumption model, which is specifically: When the drone flies from starting point A to end point B, it flies in a straight line. The horizontal flight speed is a constant v, and the vertical speed is zero. The total energy consumption model is: ； In the formula, Represents the total energy consumption of the drone from the starting point to the end point; represents the gravity-related energy consumption coefficient; Indicates the quality of the drone; represents the acceleration due to gravity; Indicates the energy consumption coefficient related to air resistance; Indicates the horizontal flight speed of the drone; Indicates the horizontal flight distance of the drone from starting point A to end point B.

7. The UAV intelligent control system for radio signal interference according to claim 5 is characterized by: The energy efficiency optimization module executes the flight path adjustment strategy based on the interference signal received by the interference signal detection module, and uses the genetic MAX algorithm to optimize the power distribution and the task allocation of the UAV. The specific formula involved is: ； In the formula, represents the total electricity consumption function; Indicates the flight power consumption caused by path adjustment; Indicates the communication power consumption affected by signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the working power consumption of the sensor; is the interference coefficient used to adjust communication and path optimization; Indicates the flight power consumption caused by path adjustment; Indicates the power consumption of communications affected by radio signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the operating power consumption of the sensor.

8. The UAV intelligent control system for radio signal interference according to claim 1 is characterized by: The control center unit (4) comprises a flight control module, which is based on a PID control algorithm, wherein the PID control algorithm is intended to accurately control the flight attitude, heading and speed of the UAV; It includes a data transmission module, which is based on the LoRa wireless communication protocol. The LoRa wireless communication protocol can transmit the flight status and environmental information of the drone to the ground control center in real time; It also includes a path planning module, which performs path optimization based on a shortest path algorithm, wherein the shortest path algorithm is intended to calculate an optimal flight path.

9. The UAV intelligent control system for radio signal interference according to claim 8 is characterized by: The flight control module is used to adjust the flight attitude, heading and speed of the UAV that is subject to signal interference. The PID control algorithm used is specifically: Flight attitude control: ； In the formula, Indicates the pitch angle control output; Indicates the pitch angle error; Indicates attitude proportional gain; represents attitude integral gain; represents the attitude differential gain; represents the time variable; represents the time derivative of the pitch angle error; Heading control: ； In the formula, Indicates the yaw angle control output; Indicates the yaw angle error; Indicates heading proportional gain; Indicates heading integral gain; represents the heading differential gain; Represents the time derivative of the yaw angle error; Speed ​​Control: ； In the formula, Indicates speed control output; Indicates speed error; represents the speed proportional gain; Indicates the speed integral gain; represents the speed differential gain; Represents the time derivative of the velocity error.

10. The UAV intelligent control system for radio signal interference according to claim 8 is characterized by: The path planning module, when the UAV is subjected to continuous interference, uses the shortest path algorithm based on the energy consumption model to optimize the path, and the shortest path algorithm is sampled to minimize the flight distance and energy consumption. The specific steps are: S2.

1. Initialization to determine the starting point and end point , and record the initial remaining power of the drone , the starting point Add the open list OpenList, for each node ,initialization , , and set a new heuristic function ; S2.

2. Cost function of the original shortest path algorithm Based on this, add energy consumption calculation: ； In the formula, represents static energy consumption; represents dynamic energy consumption; represents the distance from node i to node j in the path segment; represents the cumulative energy consumption when the UAV reaches node n; S2.

3. Heuristic Function The remaining power and the energy consumption to reach the target point need to be considered to estimate the distance from the current node n to the target node G: ； In the formula, represents the traditional heuristic function; represents the weight factor; Represents the straight-line distance from the current node n to the target node G; represents the heuristic function; S2.

4. Select nodes by selecting the nodes with the lowest Value Node ; Check target if It is the end , then the algorithm ends; the node Move to the closed list ClosedList; for node All neighbor nodes of : if Already in the closed list, skip this neighbor; if Not in the open list, calculate and ,Will Add to open list and set As The parent node of if Already in the open list, check if there is a better path to reach ,Right now ; If yes, then update The parent node is , and recalculate and ; S2.

5. Check the exit condition. If the open list is empty, there is no path to the target. The algorithm ends and the path is rebuilt from the end point. Start by following the parent node pointer backwards until you reach the starting point. , thus obtaining the shortest path.

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