An intelligent control system for drones based on radio signal interference

Abstract

The present invention relates to the technical field of unmanned aerial vehicles (UAVs). Specifically, it relates to an intelligent UAV control system based on radio signal interference. It includes a radio signal interference detection unit for monitoring radio signal interference in the environment, an emergency response unit that, according to the received interference signals, implements emergency measures through pre-set emergency strategies; a power management and energy efficiency unit for managing the power usage of the UAV; a control center unit for the overall control and coordination of the UAV; through a signal analysis algorithm, it can simultaneously process and analyze various types of interference signals; this multi-level signal analysis ability enables the system to accurately identify impulse interference, continuous interference, and intermittent interference, improving the recognition rate and accuracy of radio interference; through the TOA algorithm, it can calculate the source location of the interference signal in real time, enabling the UAV to better avoid potential risks in complex environments and enhancing safety.

Description

Technical Field

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

[0002] This system uses a radio signal interference detection unit, which, through the combination of a signal collection, signal analysis, and interference signal detection module, realizes the precise monitoring and classification of radio signal interference in the environment, and can timely identify pulse interference, continuous interference, and intermittent interference; adopts the TOA algorithm, and by measuring the signal propagation time, can accurately determine the position of the interference signal source, thereby providing a basis for subsequent emergency responses; the integration of various system units such as interference detection, emergency response, and energy management modules enables the unmanned aerial vehicle to comprehensively utilize various technologies when facing radio interference, improving the overall performance;

[0003] With the rapid development of unmanned aerial vehicle technology, its applications in various fields are becoming more and more extensive. However, the accompanying radio signal interference problems may lead to the loss of control signals or communication interruptions of unmanned aerial vehicles, thereby affecting the safety and reliability of unmanned aerial vehicles; the sources of radio signal interference are extensive. In the prior art, many unmanned aerial vehicle systems lack effective interference detection and response mechanisms, resulting in the inability to quickly respond when being interfered; therefore, an intelligent control system for unmanned aerial vehicles based on radio signal interference is designed. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control system for unmanned aerial vehicles based on radio signal interference to solve the problems raised in the above background art, that is, with the rapid development of unmanned aerial vehicle technology, its applications in various fields are becoming more and more extensive. However, the accompanying radio signal interference problems may lead to the loss of control signals or communication interruptions of unmanned aerial vehicles, thereby affecting the safety and reliability of unmanned aerial vehicles; the sources of radio signal interference are extensive. In the prior art, many unmanned aerial vehicle systems lack effective interference detection and response mechanisms, resulting in the inability to quickly respond when being interfered.

[0005] To achieve the above purpose, the present invention aims to provide an intelligent control system for unmanned aerial vehicles based on radio signal interference, including

[0006] A radio signal interference detection unit, which is used to monitor radio signal interference in the environment. The radio signal interference detection unit includes a signal collection module, a signal analysis module, and an interference signal detection module;

[0007] An emergency response unit, which implements emergency measures according to the received interference signals through a pre-set emergency strategy;

[0008] 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;

[0009] Control Center Unit, which is used for the overall control and coordination of the drone. The Control Center Unit includes a flight control module, a data transmission module, and a path planning module.

[0010] As a further improvement of this technical solution, the radio signal interference detection unit is used to judge the type of interference signal, and according to the phase characteristics of the signal, identify the influence of phase change on the signal. The types of interference signals include pulse interference, continuous interference, and intermittent interference;

[0011] Among them, the steps involved in judging the type of interference signal are as follows:

[0012] S1.1. The signal collection module is used to collect control instruction signals from the ground control station and other drones, as well as radio signals from the surrounding environment;

[0013] S1.2. The signal analysis module, based on the discrete Fourier transform, judges the type of signal by carefully analyzing the data collected by the signal collection module, and distinguishes normal signals and interference signals;

[0014] The inverse discrete Fourier transform is used to reconstruct the time-domain signal from the frequency-domain signal, which is defined as follows:

[0015] ;

[0016] In the formula, represents the nth sample of the reconstructed time-domain signal; represents a constant factor of frequency resolution; represents the time index.

[0017] S1.3. According to the interference signal analyzed by the signal analysis module, the interference signal detection module uses the TOA algorithm to determine the position by measuring the propagation time of the signal from the transmission source to the receiving device; the TOA algorithm is an algorithm based on the time of arrival of the signal to calculate the distance between the signal source and the receiving station; the TOA algorithm directly measures the absolute time of the signal from the source to the receiving station, calculates the distance between the signal source and the receiving station, and then obtains the position of the signal source through the data of multiple receiving stations.

[0018] As a further improvement of this technical solution, in the above S1.3, the TOA algorithm is specifically as follows:

[0019] The known signal source position is , and the drone position is , the signal propagates from the source to the position of the drone in a time of , then:

[0020] ;

[0021] In the formula, represents the arrival time of the signal from the source to the position of the drone ; represents the distance from the signal source to the position of the drone ; represents the propagation speed of the signal.

[0022] As a further improvement of this technical solution, the emergency response unit selects corresponding emergency strategies according to the information provided by the interference signal detection module. The emergency strategies include switching communication frequency band strategy, adjusting flight path strategy, automatic return strategy, and landing and hovering strategy;

[0023] Among them, the switching communication frequency band strategy is adopted when the interference signal is concentrated in the communication frequency band currently used by the drone; the adjusting flight path strategy is adopted when the position of the interference signal source is detected; the automatic return strategy is adopted when a severe interference signal is detected; the landing and hovering strategy is adopted when the detected interference signal is extremely severe and the control signal has been lost;

[0024] Switching communication frequency band strategy: When the interference signal is concentrated in the communication frequency band currently used by the drone, the system will automatically switch to a standby frequency band less affected by interference; it can quickly restore the communication between the drone and the control center and reduce the risk of signal interruption;

[0025] Adjusting flight path strategy: If the system detects the position of the interference signal source, the system 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;

[0026] Automatic return strategy: When a severe 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; it ensures that the drone can return to the safe area under control and avoids being lost or crashing due to signal loss;

[0027] Landing and hovering strategy: If the interference signal detected by the drone is extremely severe and the control signal has been lost, the system will perform an emergency landing at the current or designated position, or hover in a safe area and wait; it can ensure the safe landing of the drone and reduce the flight risk caused by loss of control.

[0028] As a further improvement of the present technical solution, the power management and energy efficiency unit includes a power monitoring module, which monitors the battery status in real time based on an energy consumption model, provides battery status feedback, and ensures that the drone operates within a safe battery power range;

[0029] It also includes an energy efficiency optimization module, which optimizes power distribution using the genetic MAX algorithm and dynamically adjusts the energy usage strategy according to the flight mission to extend the endurance time.

[0030] As a further improvement of the present technical solution, the power monitoring module estimates the energy consumption of the drone during flight based on an energy consumption model. The energy consumption model specifically is:

[0031] When the drone flies from the starting point A to the ending point B in a straight line, with a constant horizontal flight speed of v and a vertical speed of zero, the total energy consumption model is:

[0032] ;

[0033] In the formula, represents the total energy consumption of the drone from the starting point to the ending point; represents the energy consumption coefficient related to gravity; represents the mass of the drone; represents the acceleration due to gravity; represents the energy consumption coefficient related to air resistance; represents the horizontal flight speed of the drone; represents the horizontal flight distance of the drone from the starting point A to the ending point B.

[0034] As a further improvement of the present technical solution, the energy efficiency optimization module executes a flight path adjustment strategy based on the interference signal received by the interference signal detection module, and optimizes the task allocation of the drone by optimizing power distribution using the genetic MAX algorithm. The specific formula involved is:

[0035] ;

[0036] In the formula, represents the total power consumption function; represents the power consumption of flight power caused by path adjustment; represents the communication power consumption affected by signal interference; represents the motor power consumption for attitude and speed adjustment; represents the sensor working power consumption; is the interference coefficient for adjusting communication and path optimization. When the signal interference increases, increases to adapt to the power distribution requirements in different interference environments; Indicates the flight power consumption caused by path adjustment; Indicates the communication power consumption affected by radio signal interference; Indicates the motor power consumption for attitude and speed adjustment; Indicates the sensor operating power consumption.

[0037] As a further improvement of this technical solution, the control center unit includes a flight control module. The flight control module is based on the PID control algorithm. The purpose of the PID control algorithm is to precisely control the flight attitude, heading, and speed of the UAV, ensure that the UAV maintains stable flight under various environmental conditions, and be able to quickly respond to external environmental changes, improving flight safety and flexibility;

[0038] It includes a data transmission module. The data transmission module is based on the LoRa wireless communication protocol. The LoRa wireless communication protocol can real-time transmit the flight status and environmental information of the UAV to the ground control center, ensure the timely feedback of control instructions, and support remote monitoring and data analysis;

[0039] It also includes a path planning module. The path planning module optimizes the path based on the shortest path algorithm. The purpose of the shortest path algorithm is to calculate the optimal flight path, avoid obstacles, and improve flight efficiency.

[0040] As a further improvement of this technical solution, the flight control module is used to adjust the flight attitude, heading, and speed of the UAV affected by signal interference. The specific PID control algorithm adopted is:

[0041] Flight attitude control:

[0042] ;

[0043] In the formula, represents the pitch angle control output, which is used to drive the motor to adjust the pitch angle of the UAV; represents the pitch angle error, that is, the difference between the target pitch angle and the current pitch angle; represents the attitude proportional gain, which determines the sensitivity of the error to the pitch angle adjustment; represents the attitude integral gain, which is used to eliminate small and persistent errors and avoid long-term deviation; represents the attitude derivative gain, which is used to reduce oscillation and make the attitude adjustment smoother; represents the time variable; represents the time derivative of the pitch angle error;

[0044] Heading control:

[0045] ;

[0046] In the formula, represents the yaw angle control output, which is used to adjust the rotation of the UAV so that it faces 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; represents the heading integral gain, which eliminates long-term heading offsets; represents the heading derivative 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;

[0047] Speed control:

[0048] ;

[0049] 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, that is, the difference between the target speed and the current speed; represents the speed proportional gain, which determines the influence of the current speed error on the adjustment; represents the speed integral gain, which is used to eliminate the accumulated speed deviation; represents the speed derivative gain, which suppresses the sudden changes in the speed adjustment process and makes the speed smoother; represents the time derivative of the speed error.

[0050] As a further improvement of this technical solution, when the UAV is continuously interfered, the path planning module optimizes the path based on the energy consumption model using the shortest path algorithm, and samples the shortest path algorithm to minimize the flight distance and energy consumption. The specific steps are as follows:

[0051] S2.1. Initialize and determine the starting point and the ending point , and record the initial remaining battery power of the UAV , add the starting point to the open list OpenList. For each node , initialize , , and set a new heuristic function ;

[0052] S2.2. On the basis of the original cost function of the shortest path algorithm, add energy consumption calculation:

[0053] ;

[0054] In the formula, represents the static energy consumption; Indicates dynamic energy consumption; Indicates the distance from node i to node j in the path segment; Indicates the cumulative energy consumption when the UAV reaches node n;

[0055] S2.3, Heuristic function It is necessary to consider the remaining power and the energy consumption to reach the target point, and is used to estimate the distance from the current node n to the target node G:

[0056] ;

[0057] In the formula, Indicates the traditional heuristic function; Indicates the weight factor; Indicates the straight-line distance from the current node n to the target node G; Indicates the heuristic function;

[0058] S2.4, Select a node. Select the node with the lowest value from the unprocessed nodes ; Check the target. If is the end point , then end the algorithm; Move the node to the closed list ClosedList; For all neighbor nodes of the node :

[0059] If is already in the closed list, skip this neighbor;

[0060] If is not in the open list, calculate and , add to the open list, and set as the parent node of ;

[0061] If is already in the open list, check if there is a better path to reach , that is, ;

[0062] If so, update the parent node of to , and recalculate and ;

[0063] S2.5, Check the exit condition. If the open list is empty, there is no path to reach the target, the algorithm ends, and reconstruct the path from the end point Start from the end node and trace back along the parent node pointers until the starting point to obtain the shortest path.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0065] 1. In the intelligent control system for unmanned aerial vehicles based on radio signal interference, through the signal analysis algorithm, it is possible to simultaneously process and analyze multiple types of interference signals; this multi-level signal analysis ability 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, it is possible to calculate the source location of the interference signal in real time, providing key data support for the unmanned aerial vehicle to formulate corresponding emergency measures; enabling the unmanned aerial vehicle to better avoid potential risks in complex environments and enhancing safety.

[0066] 2. In the intelligent control system for unmanned aerial vehicles based on radio signal interference, the emergency response unit can select the most appropriate emergency measure according to the type and intensity of the interference detected in real time through a pre-set emergency strategy; significantly improving the response ability of the unmanned aerial vehicle in the face of 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

[0067] Figure 1 is the overall flow block diagram of the present invention;

[0068] The meanings of the reference numerals in the figure are as follows:

[0069] 1. Radio signal interference detection unit; 2. Emergency response unit; 3. Power management and energy efficiency unit; 4. Control center unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0071] Embodiment

[0072] Please refer to Figure 1 as shown, there is provided an intelligent control system for unmanned aerial vehicles based on radio signal interference, including:

[0073] Radio signal interference detection unit, which is used to monitor radio signal interference in the environment. The radio signal interference detection unit includes a signal collection module, a signal analysis module, and an interference signal detection module;

[0074] Emergency response unit, which implements emergency measures according to the received interference signal through a pre-set emergency strategy;

[0075] 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;

[0076] Control center unit, which is used for the overall control and coordination of the drone. The control center unit includes a flight control module, a data transmission module, and a path planning module.

[0077] The radio signal interference detection unit is used to judge the type of interference signal, and based on the phase characteristics of the signal, identify the impact of phase changes on the signal. The types of interference signals include pulse interference, continuous interference, and intermittent interference;

[0078] Among them, the steps involved in judging the type of interference signal are:

[0079] S1.1. The signal collection module is used to collect control command signals from the ground control station and other drones, as well as radio signals from the surrounding environment;

[0080] S1.2. The signal analysis module, based on the discrete Fourier transform, judges the type of signal and distinguishes normal signals and interference signals through a detailed analysis of the data collected by the signal collection module;

[0081] The inverse discrete Fourier transform is used to reconstruct the time-domain signal from the frequency-domain signal, which is defined as follows:

[0082] ;

[0083] In the formula, represents the nth sample of the reconstructed time-domain signal; represents a constant factor of the frequency resolution; represents the time index.

[0084] S1.3. Analyze the interference signal 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 transmission source to the receiving device; the TOA algorithm is an algorithm for calculating the distance between the signal source and the receiving site based on the time of arrival of the signal; the TOA algorithm directly measures the absolute time for the signal to reach the receiving site from the source, calculates the distance between the signal source and the receiving site based on this, and then determines the position of the signal source through the data of multiple receiving sites.

[0085] Among them, the TOA algorithm is specifically as follows:

[0086] The known signal source position is , the UAV position is , the signal propagates from the source to the UAV position at the time of , then:

[0087] ;

[0088] In the formula, represents the time of arrival of the signal from the source to the UAV position ; represents the distance from the signal source to the UAV position ; represents the propagation speed of the signal.

[0089] The emergency response unit selects corresponding emergency strategies according to the information provided by the interference signal detection module. The emergency strategies include switching communication frequency band strategy, adjusting flight path strategy, automatic return strategy, and landing and hovering strategy;

[0090] Among them, the switching communication frequency band strategy is adopted when the interference signal is concentrated in the communication frequency band currently used by the UAV; the adjusting flight path strategy is adopted when the position of the interference signal source is detected; the automatic return strategy is adopted when a severe interference signal is detected; the landing and hovering strategy is adopted when the detected interference signal is extremely severe and the control signal has been lost;

[0091] Switching communication frequency band strategy: When the interference signal is concentrated in the communication frequency band currently used by the UAV, the system will automatically switch to the standby frequency band with less interference; it can quickly restore the communication between the UAV and the control center and reduce the risk of signal interruption;

[0092] Adjusting flight path strategy: If the system detects the position of the interference signal source, the system 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;

[0093] Automatic Return Strategy: When a severe interference signal is detected, which may cause the drone to lose control or communication interruption, the system will trigger the automatic return function, enabling the drone to return to the starting point according to the preset route; ensuring that the drone can return to a safe area under controlled conditions and avoiding being lost or crashing due to signal loss;

[0094] Landing and Hovering Strategy: If the interference signal detected by the drone is extremely severe and the control signal has been lost, the system will perform an emergency landing at the current or designated location, or hover within a safe area and wait; this can ensure the safe landing of the drone and reduce the flight risks brought about by losing control.

[0095] The power management and energy efficiency unit includes a power monitoring module. The power monitoring module based on the energy consumption model monitors the battery status in real time, provides battery status feedback, and ensures that the drone operates within a safe power range;

[0096] It also includes an energy efficiency optimization module. The energy efficiency optimization module uses the genetic MAX algorithm to optimize power distribution and dynamically adjusts the energy usage strategy according to the flight mission to extend the flight duration.

[0097] The power monitoring module estimates the energy consumption of the drone during flight based on the energy consumption model. Based on the energy consumption model specifically:

[0098] When the drone flies from starting point A to ending point B in a straight line, with a constant horizontal flight speed of v and a vertical speed of zero, the total energy consumption model is:

[0099] ;

[0100] In the formula, represents the total energy consumption of the drone from the starting point to the ending point; represents the energy consumption coefficient related to gravity; represents the mass of the drone; represents the acceleration due to gravity; represents the energy consumption coefficient related to air resistance; represents the horizontal flight speed of the drone; represents the horizontal flight distance of the drone from starting point A to ending point B;

[0101] The role of the total energy consumption model is to quantify the energy consumed by the drone during flight; this 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 drone during horizontal flight, it can help design more efficient flight paths and optimize energy distribution strategies, thereby enhancing the flight duration and mission efficiency of the drone; this model has important guiding significance in the energy consumption management and performance optimization of drones.

[0102] Based on the interference signals received by the interference signal detection module, the energy efficiency optimization module executes the flight path adjustment strategy, uses the genetic MAX algorithm to optimize the power distribution, and optimizes the task allocation of the UAV. The specific formula involved is as follows:

[0103] ;

[0104] In the formula, represents the total power consumption function; represents the flight power consumption caused by path adjustment; represents the communication power consumption affected by signal interference; represents the motor power consumption for attitude and speed adjustment; represents the sensor working power consumption; is the interference coefficient for adjusting communication and path optimization. When the signal interference increases, increases to adapt to the power distribution requirements in different interference environments; represents the flight power consumption caused by path adjustment; represents the communication power consumption affected by radio signal interference; represents the motor power consumption for attitude and speed adjustment; represents the sensor working power consumption.

[0105] The control center unit includes a flight control module. The flight control module is based on the PID control algorithm. The purpose of the PID control algorithm is to precisely control the flight attitude, heading, and speed of the UAV, ensure that the UAV maintains stable flight under various environmental conditions, and can quickly respond to external environmental changes, improving flight safety and flexibility;

[0106] The flight control module is used to adjust the flight attitude, heading, and speed of the UAV affected by signal interference. The specific PID control algorithm used is as follows:

[0107] Flight attitude control:

[0108] ;

[0109] In the formula, represents the pitch angle control output, which is used to drive the motor to adjust the pitch angle of the UAV; represents the pitch angle error, that is, the difference between the target pitch angle and the current pitch angle; represents the attitude proportional gain, which determines the sensitivity of the error to pitch angle adjustment; represents the attitude integral gain, which is used to eliminate small and persistent errors and avoid long-term offsets; represents the attitude derivative gain, which is used to reduce oscillations and make the attitude adjustment smoother; represents the time variable; represents the time derivative of the pitch angle error;

[0110] Yaw control:

[0111] ;

[0112] wherein, represents the yaw angle control output, which is used to adjust the rotation of the UAV so that it faces the target direction; represents the yaw angle error, that is, the difference between the target yaw angle and the current yaw angle; represents the yaw proportional gain, which controls the responsiveness of the yaw angle error; represents the yaw integral gain, which eliminates long-term yaw offsets; represents the yaw derivative 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;

[0113] Speed control:

[0114] ;

[0115] wherein, represents the speed control output, which is used to adjust the motor thrust to achieve the target speed; represents the speed error, that is, the difference between the target speed and the current speed; represents 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; represents the speed derivative gain, which suppresses the sudden changes during the speed adjustment process and makes the speed smoother; represents the time derivative of the speed error.

[0116] includes a data transmission module. The data transmission module is based on the LoRa wireless communication protocol, and the LoRa wireless communication protocol can transmit the flight status and environmental information of the UAV to the ground control center in real time, ensuring the timely feedback of control instructions and supporting remote monitoring and data analysis;

[0117] also includes a path planning module. The path planning module optimizes the path based on the shortest path algorithm. The purpose of the shortest path algorithm is to calculate the optimal flight path, avoid obstacles, and improve flight efficiency.

[0118] The path planning module, when the UAV is continuously interfered, optimizes the path based on the energy consumption model using the shortest path algorithm. The shortest path algorithm minimizes the flight distance and energy consumption. The specific steps are as follows:

[0119] S2.1. Initialize and determine the starting point and the end point and record the initial remaining power of the UAV Add the starting point to the open list OpenList. For each node Initialize and set a new heuristic function ;

[0120] The open list OpenList is an important data structure for dynamically managing and tracking device status and fault information; it provides effective support for the system's fault detection and response through real-time storage, priority management, and rapid update, enhancing the overall system's reliability and efficiency;

[0121] S2.2. On the basis of the cost function of the original shortest path algorithm , add energy consumption calculation:

[0122] ;

[0123] 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;

[0124] S2.3. The heuristic function needs to consider the remaining power and the energy consumption to reach the target point, and is used to estimate the distance from the current node n to the target node G:

[0125] ;

[0126] 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;

[0127] S2.4. Select a node by selecting the node with the lowest value from the unprocessed nodes ; Check the target. If is the end point , then end the algorithm; Move the node to the closed list ClosedList; For all neighbor nodes of the node :

[0128] If​ Already in the closed list, skip this neighbor;

[0129] If is not in the open list, calculate and , add to the open list, and set as the parent node of ;

[0130] If is already in the open list, check if there is a better path to reach , that is ;

[0131] If so, update the parent node of to , and recalculate and ;

[0132] 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 reconstructed starting from the end point , and trace back along the parent node pointer until the starting point , so as to obtain the shortest path.

[0133] The advantage of path optimization using the shortest path algorithm 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 the operation efficiency; by real-time evaluating the energy consumption of the flight path, the system can select the path with the minimum energy consumption, which not only improves the endurance of the UAV in complex environments, but also enhances the reliability of task execution, making it more efficient and economical in long-term tasks, and ensuring the stability and sustainability of the UAV.

[0134] 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 in the specification are only preferred examples of the present invention, which do not limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed.

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) is used to monitor radio signal interference in an environment, the radio signal interference detection unit (1) comprises 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 UAV, and the control center unit (4) includes a flight control module, a data transmission module and a path planning module; The power management and energy efficiency unit (3) comprises a power monitoring module, and 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 usage strategies according to flight missions, and extends flight time; the power monitoring module estimates the energy consumption of the drone during flight based on an energy consumption model, which is specifically based on the energy consumption model: 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, E total represents the total energy consumption of the UAV from the starting point to the end point; K1 represents the gravity-related energy consumption coefficient; m represents the mass of the UAV; g represents the gravity acceleration; K2 represents the energy consumption coefficient related to air resistance; v represents the horizontal flight speed of the UAV; d represents the horizontal flight distance of the UAV from the starting point A to the end point B; 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 optimize the task allocation of the UAV. The specific formulas involved are: f=(α+∈)P path +(β+∈)P comm +γP motor +δP sensor ; Where, f represents the total power consumption function; P path represents the flight power consumption caused by path adjustment; P comm Indicates the communication power consumption affected by signal interference; P motor Represents the motor power consumption for attitude and speed adjustment; P sensor represents the power consumption of the sensor; ∈ is the interference coefficient used to adjust communication and path optimization; α represents the flight power consumption caused by path adjustment; β represents the communication power consumption affected by radio signal interference; γ represents the motor power consumption for attitude and speed adjustment; δ represents the sensor power consumption; 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. Initialize the starting point S and the end point G, and record the initial remaining power E of the drone. Add the starting point S to the open list OpenList. For each node n, initialize g′(n)=∞, g′(S)=0, and set a new heuristic function h′(n); S2.

2. Based on the cost function g(n) of the original shortest path algorithm, energy consumption calculation is added: In the formula, K1·m·g represents the static energy consumption; K2·v 3 represents dynamic energy consumption; d ij represents the distance from node i to node j in the path segment; g ′ (n) represents the cumulative energy consumption when the UAV reaches node n; S2.3, the heuristic function h(n) needs to consider the remaining power and the energy consumption to reach the target point, and is used to estimate the distance from the current node n to the target node G: In the formula, h(n) represents the traditional heuristic function; ω represents the weight factor; d nG represents the straight-line distance from the current node n to the target node G; h′(n) represents the heuristic function; S2.

4. Select a node by selecting the node n with the lowest f′(n)=g′(n)+h′(n) value from the unprocessed nodes; check the target. If n is the end point G, end the algorithm; move the node n to the closed list ClosedList; for all neighbor nodes m of node n: If m is already in the closed list, skip this neighbor; If m is not in the open list, calculate g′(m) and f′(m), add m to the open list, and set n as the parent node of m; If m is already in the open list, check whether there is a better path to m, that is, If yes, update the parent node of m to n, and recalculate g′(m) and f′(m); 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 reconstructed starting from the end point G and tracing back along the parent node pointer until the starting point S, thus obtaining the shortest path.

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 position is S, and the drone position is R i , the signal propagates from the source S to the drone position R i The time is t i ,but: d i =c·t i ; Where, t i Represents the signal from source S to drone position R i Arrival time; d i Indicates the signal source S to the drone position R i distance; c represents the propagation speed of the signal.

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, wherein 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 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 control center unit (4) includes 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 drone; 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.

6. The UAV intelligent control system for radio signal interference according to claim 5 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, u pitch (t) represents the pitch angle control output; e pitch (t) represents the pitch angle error; K p_att Indicates attitude proportional gain; K i_att Indicates attitude integral gain; K d_att represents attitude differential gain; t represents time variable; represents the time derivative of the pitch angle error; Heading control: In the formula, u yaw (t) represents the yaw angle control output; e yaw (t) represents the yaw angle error; K p_yaw Indicates heading proportional gain; K i_yaw Indicates heading integral gain; K d_yaw represents the heading differential gain; Represents the time derivative of the yaw angle error; Speed ​​Control: In the formula, u vel (t) represents the speed control output; e vel (t) represents the speed error; K p_vel Indicates speed proportional gain; K i_vel Indicates speed integral gain; K d_vel represents the speed differential gain; Represents the time derivative of the velocity error.

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