A high-precision dynamic simulation method and system for anti-interference UAV operation trajectory

Abstract

The present invention relates to the field of trajectory simulation, and in particular to a high-precision dynamic simulation method and system for the running trajectory of an anti-interference unmanned aerial vehicle. The system comprises: an instruction issuing module, which is used to issue an execution instruction and transmit a completion feedback signal; a response monitoring module, which is used to monitor the execution time required to complete the execution instruction; an efficiency monitoring module, which is used to preheat the execution time to form corresponding time processing data, select corresponding indicator features, learn the time processing data, and generate an execution efficiency map; a control module, which is used to compare the numerical value in the execution efficiency map with the efficiency threshold, and when the numerical value is lower than the efficiency threshold, determine whether the anti-interference unmanned aerial vehicle will collide when executing a built-in action, and control it to execute the built-in action. Through machine learning, the safety and stability of the anti-interference unmanned aerial vehicle flight are improved, the operating efficiency and adaptability of the anti-interference unmanned aerial vehicle are optimized, and the operating difficulty and operating cost are reduced.

Description

Technical Field

[0001] The present invention relates to the field of trajectory simulation, and in particular to a high-precision dynamic simulation method and system for anti-interference unmanned aerial vehicle operation trajectory. Background Art

[0002] With the continuous development of UAV technology and the continuous expansion of its application fields, UAVs are increasingly used in military, civil and commercial fields. In these applications, the communication links and control systems of UAVs are threatened by various interference signals, which may come from the natural environment, such as electromagnetic interference, multipath effects, etc., or human interference, such as enemy electronic jamming equipment, etc. Therefore, improving the anti-interference ability of UAVs and ensuring their stable operation in complex environments has become an important direction for the development of UAV technology.

[0003] China Patent Authorization Announcement No.: CN116280254B discloses a UAV anti-interference ability test device, which relates to the technical field of UAV test devices; including: a base, on which a plurality of support frames are installed, and a pulling simulation mechanism is installed on the support frame; a detection module, which is installed on the UAV fuselage; wherein the pulling simulation mechanism includes: an annular elastic bag, which is fixed to the outer wall of one side of the support frame. The invention is provided with a plurality of pulling simulation mechanisms, and can clamp the corresponding clamping part on the UAV fuselage according to the test requirements. When the UAV is flying, the elastic rope gradually straightens, thereby restricting the flight of the UAV. The UAV is pulled by the elastic rope, and the flight is naturally disturbed to a certain extent. The detection module can detect the flight state of the UAV, and achieve interference with the UAV at different angles through different pulling simulation mechanisms, so as to achieve the purpose of the test.

[0004] China Patent Authorization Announcement No.: CN114355986B discloses an adaptive anti-interference control system for unmanned aerial vehicles, including a load balancing unit and a ground receiving station, wherein the ground receiving station is used to send control signals to a four-wing unmanned aerial vehicle and receive feedback signals from the four-wing unmanned aerial vehicle, and the ground receiving station includes a test unit, a modeling sample unit and a template control unit. The invention relates to the field of unmanned aerial vehicle control technology. The adaptive anti-interference control system for unmanned aerial vehicles adjusts the center of gravity of the four-wing unmanned aerial vehicle by assembling a load balancing unit on the four-wing unmanned aerial vehicle, and cooperates with the simulation test of the change in wind force. By adjusting the center of gravity of the four-wing unmanned aerial vehicle, the driving energy consumption in the state without wind interference is ensured to control the four-wing unmanned aerial vehicle to reach the set pitch angle, thereby reducing the control difficulty of the four-wing unmanned aerial vehicle, ensuring that the four-wing unmanned aerial vehicle achieves an adaptive anti-interference effect when flying in the air, and further improving the stability of the four-wing unmanned aerial vehicle when making different flight postures.

[0005] However, the above method has the following problem: it is impossible to timely intervene and adjust the running trajectory of the UAV when the UAV is interfered with by a signal. Summary of the invention

[0006] To this end, the present invention provides a high-precision dynamic simulation method and system for the operation trajectory of an anti-interference UAV, so as to overcome the problem in the prior art that the operation trajectory of the UAV cannot be intervened and adjusted in time when the UAV is subject to interference signals.

[0007] To this end, the present invention provides a high-precision dynamic simulation method and system for the operation trajectory of an anti-interference UAV, so as to overcome the problem in the prior art that the operation trajectory of the UAV cannot be intervened and adjusted in time when the UAV is subject to interference signals.

[0008] To achieve the above object, the present invention provides a high-precision dynamic simulation system for anti-interference UAV running trajectory, comprising:

[0009] An instruction issuing module, which is used to issue an execution instruction to the anti-interference UAV and transmit a completion feedback signal when the anti-interference UAV completes the execution instruction;

[0010] A response monitoring module, which is connected to the instruction issuing module and is used to monitor the execution time required for the anti-interference UAV to complete the execution instruction;

[0011] An efficiency monitoring module, which is connected to the response monitoring module, is used to preheat the execution time to form corresponding time processing data, select corresponding indicator features, use the efficiency learning model to learn the time processing data, and generate an execution efficiency map corresponding to the anti-interference UAV;

[0012] a control module connected to the efficiency monitoring module, for comparing the value in the execution efficiency map with an efficiency threshold, and determining whether a collision will occur when the anti-interference UAV performs a built-in action when the value in the execution efficiency map is lower than the efficiency threshold, and controlling the anti-interference UAV to perform the built-in action when it is determined that no collision will occur;

[0013] The built-in action is an action automatically performed by the anti-interference UAV according to a preset program when receiving an interference signal;

[0014] The indicator characteristics include the flight speed, execution time and / or execution efficiency of the anti-interference UAV;

[0015] The efficiency threshold is the minimum standard of execution efficiency corresponding to the anti-interference UAV, and is related to the operating performance of the anti-interference UAV.

[0016] Furthermore, the instruction issuing module includes:

[0017] An operation interactive interface is used to test the user inputting the execution instruction;

[0018] A control device connected to the operation interaction interface is used to convert the execution instruction into a standard format, wherein:

[0019] The standard format is a standard format that can be recognized by the anti-interference UAV;

[0020] An instruction transmitting device, connected to the control device, for issuing the standard format and sending a timing instruction to the response monitoring module at the moment of issuance;

[0021] A signal transmitting device is connected to the instruction transmitting device and is used to transmit a completion feedback signal when the anti-interference UAV completes the execution instruction.

[0022] Furthermore, the response monitoring module includes:

[0023] A timer, which is used to monitor the execution time corresponding to the anti-interference drone;

[0024] An instruction receiving device, connected to the timer, for receiving the timing instruction and controlling the timer to start timing;

[0025] A signal receiving device is connected to the timer and is used to receive the completion feedback signal and control the timer to stop timing.

[0026] Furthermore, the efficiency monitoring module includes:

[0027] A preheating processor, which is used to perform preheating processing on the execution time, select a number of indicator features, and generate corresponding time processing data;

[0028] A trainer connected to the preheating processor, for training the efficiency learning model using the time processing data;

[0029] A learner connected to the trainer, configured to learn the time processing data according to the efficiency learning model and generate a corresponding execution efficiency graph;

[0030] The efficiency learning model is generated through training based on a time processing data set formed by all time processing data.

[0031] Furthermore, the control module comprises:

[0032] A comparison device, which is used to obtain the value in the execution efficiency map and compare it with the efficiency threshold to form a corresponding comparison result;

[0033] A rehearsal device, which is connected to the comparison device, is used to obtain the real-time acceleration and real-time speed of the anti-interference UAV, and obtain a rehearsal trajectory of the anti-interference UAV performing the built-in action, and determine whether a collision will occur according to the rehearsal trajectory;

[0034] An automatic execution device is connected to the rehearsal device and is used to control the anti-interference UAV to automatically execute the built-in action when no collision occurs in the rehearsal trajectory.

[0035] Further, the preheating processor performs preheating processing on the execution time to generate the corresponding time processing data, and the preheating processing includes:

[0036] Based on several indicator characteristics of the execution time, the execution time is standardized to form corresponding time processing data;

[0037] The standardization process is to divide the execution time according to a standard sampling rate;

[0038] The standard sampling rate is a sampling rate that can be recognized by the efficiency learning model, and for a single sampling, the corresponding standard sampling rate is a single sampling rate.

[0039] Furthermore, the control module compares the value in the execution efficiency map with the efficiency threshold, wherein:

[0040] When the value in the execution efficiency map is higher than the efficiency threshold, the comparison device marks the corresponding anti-interference UAV as not interfered with.

[0041] Furthermore, when the value in the execution efficiency map is lower than the efficiency threshold, the comparison device marks the corresponding anti-interference UAV as having been interfered with, and the rehearsal device uses a collision detection algorithm based on the real-time acceleration and real-time speed of the anti-interference UAV to obtain the corresponding rehearsal trajectory when the anti-interference UAV performs the built-in action.

[0042] Furthermore, the rehearsal device determines whether the anti-interference UAV will collide based on the rehearsal trajectory. If no collision occurs, the automatic execution device controls the anti-interference UAV to execute according to a preset built-in action.

[0043] On the other hand, the present invention provides a high-precision dynamic simulation method for anti-interference UAV running trajectory, comprising:

[0044] The trainer iteratively optimizes the efficiency learning model until the loss value of the efficiency learning model converges to the preset loss value;

[0045] Test the current efficiency learning model using the time processing dataset;

[0046] When the test result meets the preset test result, the learning parameters of the efficiency learning model are saved.

[0047] Compared with the prior art, the present invention sets an instruction issuing module to issue execution instructions and transmit a completion feedback signal, a response monitoring module to monitor the execution time required to complete the execution of the instructions, an efficiency monitoring module to preheat the execution time to form corresponding time processing data, and select corresponding indicator features to learn the time processing data to generate an execution efficiency map, and a control module to compare the numerical value in the execution efficiency map with the efficiency threshold, and when the numerical value is lower than the efficiency threshold, determine whether the anti-interference UAV will collide when executing the built-in action, and control it to execute the built-in action. Through machine learning, the safety and stability of the anti-interference UAV's flight are improved, the operating efficiency and adaptability of the anti-interference UAV are optimized, and the operating difficulty and cost are reduced.

[0048] Furthermore, by setting up a command issuing module and utilizing the operation interaction interface to receive commands input by the test user, the control device converts these commands into a standard format, the command transmitting device sends the standard format commands to the UAV and starts the timing at the moment of sending, and the signal transmitting device sends a completion feedback signal after the anti-interference UAV completes the command, thereby ensuring that the test user's commands can be accurately and timely conveyed to the anti-interference UAV, and can monitor the execution status of the anti-interference UAV in real time, thereby improving the reliability and accuracy of the system.

[0049] Furthermore, by setting up a response monitoring module, a timer is used to record the time it takes for the anti-interference UAV to execute instructions. The instruction receiving device receives the timing instruction and starts the timer, and the signal receiving device receives the completion feedback signal and stops the timer. This ensures that the system can accurately monitor the response time of the anti-interference UAV to instructions, thereby evaluating the performance and reliability of the system and providing important time data support for high-precision dynamic simulation.

[0050] Furthermore, by setting up an efficiency monitoring module, using a preheating processor to preprocess the execution time data, the trainer uses the processed data to train the efficiency learning model, and the learner uses the efficiency learning model to generate an execution efficiency map, it is ensured that the system can accurately evaluate and visualize the execution efficiency of the anti-interference UAV, provide important performance data support for high-precision dynamic simulation, and help test users better understand and optimize the operating status of the anti-interference UAV.

[0051] Furthermore, a comparison device is set up to evaluate the execution efficiency of the anti-interference UAV, a rehearsal device simulates the trajectory of the anti-interference UAV executing built-in actions and detects collisions, and an automatic execution device controls the anti-interference UAV to automatically execute actions while ensuring safety. This ensures that the anti-interference UAV can perform tasks safely and efficiently during high-precision dynamic simulation, thereby improving the reliability and practicality of the system.

[0052] Furthermore, the control module compares the value in the execution efficiency map with the efficiency threshold, and when the value in the execution efficiency map is lower than the efficiency threshold, it is determined whether a collision will occur when the anti-interference UAV executes the built-in action. When it is determined that no collision will occur, the anti-interference UAV is controlled to execute the built-in action, and the automatic execution device is used to monitor the status of the anti-interference UAV in real time to ensure the smooth execution of the built-in action, thereby ensuring that the anti-interference UAV can perform tasks safely and efficiently during the high-precision dynamic simulation process.

[0053] Furthermore, by iteratively optimizing the efficiency learning model, testing it with a time-processing dataset, and saving the learning parameters when the performance of the efficiency learning model meets the preset standards, the high accuracy and reliability of the efficiency learning model are ensured, providing strong support for the efficient operation of anti-interference UAVs, enabling the system to continuously optimize the model and improve the prediction and evaluation capabilities of the execution efficiency of anti-interference UAVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the structure of a high-precision dynamic simulation system for anti-interference UAV running trajectory according to an embodiment of the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of the instruction issuing module according to an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of a response monitoring module according to an embodiment of the present invention;

[0057] Figure 4 The present invention is a flowchart of a method for high-precision dynamic simulation of the trajectory of an anti-interference UAV according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0060] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0061] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] See also Figure 1 As shown, it is a structural schematic diagram of a high-precision dynamic simulation system for anti-interference UAV running trajectory according to an embodiment of the present invention, including:

[0063] A command issuing module, which is used to issue execution commands to the anti-interference UAV and send a completion feedback signal when the anti-interference UAV completes the execution of the command;

[0064] A response monitoring module, which is connected to the command issuing module, is used to monitor the execution time required for the anti-interference UAV to complete the execution of the command;

[0065] The efficiency monitoring module is connected to the response monitoring module to preheat the execution time, form the corresponding time processing data, select the corresponding indicator features, and use the efficiency learning model to learn the time processing data to generate the execution efficiency map corresponding to the anti-interference UAV;

[0066] a control module connected to the efficiency monitoring module, for comparing the value in the execution efficiency map with the efficiency threshold, judging whether a collision will occur when the anti-interference UAV performs a built-in action according to the comparison result that the value in the execution efficiency map is lower than the efficiency threshold, and controlling the anti-interference UAV to perform the built-in action based on the judgment result that no collision will occur, wherein the built-in action is an action automatically performed by the anti-interference UAV according to a preset program when receiving an interference signal;

[0067] The indicator characteristics include the flight speed, execution time and execution efficiency of the anti-jamming UAV;

[0068] The efficiency threshold is the minimum standard of execution efficiency corresponding to the anti-interference UAV, which is determined according to the operating performance of the anti-interference UAV.

[0069] By setting up an instruction issuing module, it is used to issue execution instructions and transmit completion feedback signals; a response monitoring module is used to monitor the execution time required to complete the execution instructions; an efficiency monitoring module is used to preheat the execution time to form corresponding time processing data, and select corresponding indicator features, learn the time processing data, and generate an execution efficiency map; a control module is used to compare the numerical value in the execution efficiency map with the efficiency threshold, and when the numerical value is lower than the efficiency threshold, it is determined whether the anti-interference UAV will collide when executing the built-in action, and control it to execute the built-in action. Through machine learning, the safety and stability of the anti-interference UAV's flight are improved, the operating efficiency and adaptability of the anti-interference UAV are optimized, and the operating difficulty and cost are reduced.

[0070] See also Figure 2 As shown, it is a schematic diagram of the structure of the instruction issuing module of an embodiment of the present invention, including:

[0071] An operation interactive interface is used to obtain execution instructions input by the test user;

[0072] A control device connected to the operation interaction interface, used to convert the execution instruction into a standard format, wherein the standard format is a standard format that can be recognized by the anti-interference UAV;

[0073] An instruction transmitting device, which is connected to the control device and is used to issue a standard format and issue a timing instruction to the response monitoring module at the same time as the standard format is issued;

[0074] The signal transmitting device is connected to the command transmitting device and is used to transmit a completion feedback signal when the anti-interference UAV completes the execution of the command.

[0075] In a specific implementation, the operation interaction interface can be a variety of input controls such as buttons, text boxes, and sliders to adapt to different types of command input requirements. For example, the test user can input the take-off command by clicking the "take-off" button and adjust the flight speed by using the slider.

[0076] The control device contains command parsing and format conversion logic. It can recognize various forms of commands input by the test user and convert them into a standard format that the drone can recognize. The standard format includes specific encoding, protocols, and data structures to ensure that the commands can be accurately received and executed by the anti-interference drone. For example, the "take-off" command input by the test user is converted into a binary command code that the anti-interference drone control system can recognize.

[0077] When the anti-interference UAV completes the corresponding operation according to the command, such as reaching the designated location, completing a specific action, etc., the signal transmitter will send a feedback signal to inform the system that the anti-interference UAV has successfully executed the command. It can also be used for the system to monitor the operating status of the anti-interference UAV and to issue subsequent instructions for adjustment and other operations. For example, the signal transmitter can receive feedback information from the anti-interference UAV through the communication link with the anti-interference UAV, and send the completion feedback signal back to the system to achieve closed-loop control of the anti-interference UAV operation trajectory simulation process.

[0078] By setting up the command issuing module and using the operation interactive interface to receive the commands input by the test user, the control device converts these commands into a standard format, the command transmitting device sends the standard format commands to the UAV and starts the timing at the moment of sending, and the signal transmitting device sends a completion feedback signal after the anti-interference UAV completes the command, ensuring that the test user's commands can be accurately and timely conveyed to the anti-interference UAV, and can monitor the execution status of the anti-interference UAV in real time, thereby improving the reliability and accuracy of the system.

[0079] See also Figure 3 As shown, it is a schematic diagram of the structure of the response monitoring module according to an embodiment of the present invention, including:

[0080] A timer, which is used to monitor the execution time corresponding to the anti-jamming drone;

[0081] An instruction receiving device, which is connected to the timer, is used to receive the timing instruction and control the timer to start timing;

[0082] The signal receiving device is connected to the timer to receive the completion feedback signal and control the timer to stop timing.

[0083] In a specific implementation, the timer is a high-precision electronic timing device that can be accurate to milliseconds or even microseconds. It has the functions of starting, stopping and reading time, and can accurately record the entire process time of instruction execution. The instruction receiving device can identify the timing instruction sent from the instruction transmitting device. When the timing instruction is received, it will immediately trigger the timer to start timing. The instruction receiving device is connected to the timer through wireless communication or wired connection to ensure that the instruction can be transmitted quickly and accurately. For example, the instruction receiving device can receive the timing instruction through Wi-Fi or a dedicated radio frequency, and control the start of the timer through an internal logic circuit. The signal receiving device can identify the completion feedback signal sent from the signal transmitting device. When the completion feedback signal is received, it will immediately trigger the timer to stop timing. The signal receiving device is also connected to the timer through wireless communication or wired connection to ensure that the feedback signal can be transmitted quickly and accurately.

[0084] By setting up a response monitoring module and using a timer to record the time it takes for the anti-interference UAV to execute instructions, the instruction receiving device receives the timing instruction and starts the timer, and the signal receiving device receives the completion feedback signal and stops the timer. This ensures that the system can accurately monitor the response time of the anti-interference UAV to instructions, thereby evaluating the performance and reliability of the system and providing important time data support for high-precision dynamic simulation.

[0085] Specifically, the efficiency monitoring module includes:

[0086] A preheating processor, which is used to preheat the execution time according to a number of indicator characteristics to eliminate irrelevant data and generate corresponding time processing data;

[0087] A trainer connected to the preheating processor for utilizing time processing data to train an efficient learning model;

[0088] A learner, which is connected to the trainer and is used to learn the time processing data according to the efficiency learning model and generate a corresponding execution efficiency map;

[0089] Among them, the indicator characteristics are determined according to the operational performance of the anti-interference UAV;

[0090] The efficiency learning model is trained and generated based on the time processing data set formed by all the time processing data.

[0091] In specific implementations, machine learning algorithms, such as linear regression, decision trees, neural networks, etc., can be used to train time processing data and generate efficiency learning models.

[0092] The learner uses the trained efficiency learning model to analyze and predict the new time processing data, and visualizes the analysis results to generate an execution efficiency map. The execution efficiency map can be a chart, a curve chart, etc., which intuitively shows the changing trend and efficiency of the execution time, ensuring that the system can dynamically reflect the execution efficiency of the anti-interference UAV, and evaluates the performance of the efficiency learning model through cross-validation and other methods to ensure the accuracy and generalization ability of the efficiency learning model.

[0093] Preheat processor: preprocess the execution time data, extract features, and generate time processing data.

[0094] Trainer: Use the time processing data to train the efficiency learning model and adjust the efficiency learning model parameters to minimize the loss function.

[0095] Learner: Use the trained efficiency learning model to analyze and predict new time processing data, generate an execution efficiency map, and intuitively display the changing trend and efficiency of execution time.

[0096] This method not only improves the prediction and evaluation capabilities of the system, but also ensures the dynamics and real-time nature of the system, providing strong support for the efficient operation of anti-interference UAVs.

[0097] By setting up the efficiency monitoring module and using the preheating processor to preprocess the execution time data, the trainer uses the processed data to train the efficiency learning model, and the learner uses the efficiency learning model to generate an execution efficiency map. This ensures that the system can accurately evaluate and visualize the execution efficiency of the anti-interference UAV, provide important performance data support for high-precision dynamic simulation, and help test users better understand and optimize the operating status of the anti-interference UAV.

[0098] Specifically, the control module includes:

[0099] A comparison device, which is used to obtain the value in the execution efficiency map and compare it with the efficiency threshold to form a corresponding comparison result;

[0100] A rehearsal device, which is connected to the comparison device, is used to obtain the real-time acceleration and real-time speed of the anti-interference UAV, determine the rehearsal trajectory of the anti-interference UAV to perform the built-in action, and determine whether the anti-interference UAV will collide according to the rehearsal trajectory;

[0101] The automatic execution device is connected to the rehearsal device and is used to control the anti-interference UAV to automatically execute the corresponding built-in action based on the judgment result that no collision will occur in the rehearsal trajectory.

[0102] In a specific implementation, real-time acceleration and real-time speed data are obtained from the sensors of the anti-interference UAV, and a dynamic model and a collision detection algorithm are used to simulate the trajectory of the anti-interference UAV performing built-in actions according to the real-time acceleration and real-time speed data. A dynamic model, such as Newton's laws of motion, Lagrange's equations, etc., is used to simulate the motion trajectory of the UAV; and a collision detection algorithm, such as geometric analysis, a physics engine, etc., is used to detect whether the simulated trajectory collides with the surrounding environment.

[0103] Through the coordinated work of the comparison device, the rehearsal device and the automatic execution device, the system can ensure the safety and reliability of the anti-interference UAV when performing built-in actions.

[0104] The specific steps include:

[0105] Comparison device: obtains the value in the execution efficiency map and compares it with the efficiency threshold to mark whether the drone is interfered with.

[0106] Drill device: obtains real-time acceleration and velocity data, simulates execution trajectory, and detects whether a collision will occur.

[0107] Automatic execution device: When the drill trajectory is safe, the drone is controlled to automatically execute built-in actions.

[0108] A comparison device is set up to evaluate the execution efficiency of the anti-interference UAV, the rehearsal device simulates the trajectory of the anti-interference UAV executing built-in actions and detects collisions, and the automatic execution device controls the anti-interference UAV to automatically execute actions while ensuring safety. This ensures that the anti-interference UAV can perform tasks safely and efficiently during high-precision dynamic simulation, thereby improving the reliability and practicality of the system.

[0109] Specifically, the preheating processor normalizes the execution time based on several indicator characteristics of the execution time to form corresponding time processing data;

[0110] The normalization process is to divide the execution time according to the standard sampling rate;

[0111] The standard sampling rate is a sampling rate that can be recognized by the efficiency learning model, and for a single sampling, the corresponding standard sampling rate is a single sampling rate.

[0112] Preferably, setting the sampling rate to 170 / second has a better learning effect on time processing data, and for the system described in this application, the corresponding analysis effect is optimal. An appropriate sampling rate can ensure the accuracy and real-time nature of the data, which is crucial for the training and prediction of machine learning models. Too high a sampling rate may lead to data redundancy and waste of computing resources, while too low a sampling rate may lead to information loss.

[0113] In the specific implementation, the execution time data is segmented according to the preset standard sampling rate. The standard sampling rate is the sampling rate that the efficiency learning model can recognize, ensuring the accuracy and real-time nature of the data. For a single sampling, the corresponding standard sampling rate is a single sampling rate, and the same sampling rate is used for each sampling to ensure data consistency.

[0114] Preset standard sampling rate: Select a standard sampling rate to ensure data accuracy and real-time performance.

[0115] Data segmentation: The execution time data is segmented according to the standard sampling rate to ensure that the same sampling rate is used for each sampling.

[0116] Data consistency: Verify whether the processed data is segmented according to the preset sampling rate to ensure data consistency.

[0117] This process not only improves the quality of the data, but also provides a reliable data foundation for the training and prediction of the efficiency learning model, ensuring the high accuracy and reliability of the system.

[0118] Specifically, the comparison device compares the value in the execution efficiency map with the efficiency threshold, wherein:

[0119] When the value in the execution efficiency map is higher than the efficiency threshold, the comparison device marks that the corresponding anti-interference UAV is not interfered with.

[0120] In some possible implementations, the efficiency threshold is set to 85%. When the value in the execution efficiency map is 90%, the value is higher than the efficiency threshold, and the comparison device marks that the corresponding anti-interference UAV is not interfered with.

[0121] Specifically, based on the comparison result that the value in the execution efficiency map is lower than the efficiency threshold, the comparison device marks the corresponding anti-interference UAV as having been interfered with, and transmits it to the rehearsal device, so that the rehearsal device can obtain the corresponding rehearsal trajectory when the anti-interference UAV performs the built-in action according to the real-time acceleration and real-time speed of the anti-interference UAV and the collision detection algorithm.

[0122] In some possible implementations, the efficiency threshold is set to 85%. When the value in the execution efficiency map is 80%, the value is lower than the efficiency threshold, and the comparison device marks that the corresponding anti-interference UAV has been interfered.

[0123] In a specific implementation, when it is determined that the anti-interference UAV has been interfered with, it is queried whether the test user has issued an execution instruction. When the test user has issued an execution instruction, the built-in action executed by the anti-interference UAV is the action issued by the execution instruction. When the test user has not issued an execution instruction, the built-in action executed by the anti-interference UAV is a pre-set built-in action.

[0124] For example, anti-interference drone A keeps moving at a constant speed. There are four anti-interference drones in front, behind, to the left and to the right of anti-interference drone A, respectively, which keep moving at the same speed as anti-interference drone A. If anti-interference drone A is judged to have been interfered with, and the execution instruction it receives is to accelerate, a collision will occur. In this case, anti-interference drone A will execute the built-in action and continue to move forward at a constant speed.

[0125] Specifically, the rehearsal device determines whether the anti-interference UAV will collide based on the rehearsal trajectory, and transmits the judgment result that no collision will occur to the automatic execution device, so that the automatic execution device controls the anti-interference UAV to execute according to the preset built-in action.

[0126] If a collision is detected, the execution is stopped and an alarm is issued; if no collision is detected, the next step is continued, the execution command is sent, the drone is controlled to perform the built-in action, and the execution process is monitored in real time to ensure the smooth execution of the anti-interference drone action.

[0127] The control module compares the value in the execution efficiency map with the efficiency threshold, and when the value in the execution efficiency map is lower than the efficiency threshold, it is determined whether a collision will occur when the anti-interference UAV executes the built-in action. When it is determined that no collision will occur, the anti-interference UAV is controlled to execute the built-in action, and the automatic execution device is used to monitor the status of the anti-interference UAV in real time to ensure the smooth execution of the built-in action, thereby ensuring that the anti-interference UAV can perform tasks safely and efficiently during the high-precision dynamic simulation process.

[0128] See also Figure 4 As shown, it is a flow chart of a high-precision dynamic simulation method for anti-interference UAV running trajectory according to an embodiment of the present invention, including:

[0129] Step S1, the trainer iteratively optimizes the efficiency learning model until the loss value of the efficiency learning model converges to a preset loss value;

[0130] Step S2, testing the current efficiency learning model using the time processing data set;

[0131] Step S3: When the test result meets the preset test result, the learning parameters of the efficiency learning model are saved.

[0132] In the specific implementation, the trainer selects a suitable optimization algorithm, such as gradient descent, stochastic gradient descent, Adam, etc., and performs multiple iterative training on the efficiency learning model. Each iteration uses appropriate evaluation indicators, such as mean square error, R² score, etc. to calculate the loss value of the efficiency learning model, and updates the parameters of the efficiency learning model according to the optimization algorithm. It continues to iterate until the loss value of the efficiency learning model reaches or is lower than the preset loss value threshold, indicating that the prediction error of the efficiency learning model is within an acceptable range.

[0133] By iteratively optimizing the efficiency learning model, testing it with a time-processing data set, and saving the learning parameters when the performance of the efficiency learning model meets the preset standards, the high accuracy and reliability of the efficiency learning model are ensured, providing strong support for the efficient operation of anti-interference UAVs, enabling the system to continuously optimize the model and improve the prediction and evaluation capabilities of the execution efficiency of anti-interference UAVs.

[0134] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-precision dynamic simulation system for anti-interference UAV operation trajectory, characterized in that: include: An instruction issuing module is used to issue an execution instruction to the anti-interference UAV, and send a completion feedback signal when the anti-interference UAV completes the execution instruction; A response monitoring module, connected to the command issuing module, for monitoring the execution time required for the anti-interference UAV to complete the execution command; An efficiency monitoring module, connected to the response monitoring module, is used to preheat the execution time to form corresponding time processing data, select corresponding indicator features, use an efficiency learning model to learn the time processing data, and generate an execution efficiency map of the anti-interference UAV; a control module connected to the efficiency monitoring module, for comparing the value in the execution efficiency map with the efficiency threshold, judging whether a collision will occur when the anti-interference UAV performs a built-in action according to a comparison result that the value in the execution efficiency map is lower than the efficiency threshold, and controlling the anti-interference UAV to perform the built-in action based on the judgment result that no collision will occur, wherein the built-in action is an action automatically performed by the anti-interference UAV according to a preset program when receiving an interference signal; The indicator characteristics include the flight speed, execution time and execution efficiency of the anti-interference UAV; The efficiency threshold is the minimum standard of the execution efficiency of the anti-interference UAV, which is determined according to the operating performance of the anti-interference UAV; The preheating processor performs standardization processing on the execution time based on several indicator characteristics of the execution time to form corresponding time processing data; The standardization process is to divide the execution time according to a standard sampling rate; The standard sampling rate is a sampling rate that can be recognized by the efficiency learning model. For a single sampling, the corresponding standard sampling rate is a single sampling rate; The comparison device, based on the comparison result that the value in the execution efficiency map is lower than the efficiency threshold, marks the corresponding anti-interference UAV as having been interfered with, and transmits it to the rehearsal device, so that the rehearsal device uses a collision detection algorithm according to the real-time acceleration and real-time speed of the anti-interference UAV to obtain the corresponding rehearsal trajectory when the anti-interference UAV executes the built-in action; The rehearsal device determines whether the anti-interference UAV will collide based on the rehearsal trajectory, and transmits the judgment result that no collision will occur to the automatic execution device, so that the automatic execution device controls the anti-interference UAV to execute according to the preset built-in action.

2. The high-precision dynamic simulation system for anti-interference UAV running trajectory according to claim 1 is characterized in that: The instruction issuing module includes: An operation interactive interface is used to obtain the execution instruction input by the test user; A control device connected to the operation interaction interface, used to convert the execution instruction into a standard format; An instruction transmitting device, connected to the control device, for issuing the standard format and issuing a timing instruction to the response monitoring module at the same time as the standard format is issued; A signal transmitting device is connected to the instruction transmitting device and is used to transmit a completion feedback signal when the anti-interference UAV completes the execution instruction.

3. The high-precision dynamic simulation system for anti-interference UAV running trajectory according to claim 2 is characterized in that: The response monitoring module comprises: A timer, which is used to monitor the execution time corresponding to the anti-interference drone; An instruction receiving device, connected to the timer, for receiving the timing instruction and controlling the timer to start timing; A signal receiving device is connected to the timer and is used to receive the completion feedback signal and control the timer to stop timing.

4. The high-precision dynamic simulation system for anti-interference UAV running trajectory according to claim 3 is characterized in that: The efficiency monitoring module comprises: A preheating processor, which is used to perform preheating processing on the execution time according to a number of indicator characteristics to eliminate irrelevant data and generate corresponding time processing data; A trainer connected to the preheating processor, for training the efficiency learning model using the time processing data; A learner connected to the trainer, configured to learn the time processing data according to the efficiency learning model and generate a corresponding execution efficiency graph; Wherein, the index characteristics are determined according to the operational performance of the anti-interference UAV; The efficiency learning model is generated by training based on a time processing data set formed by all time processing data.

5. The high-precision dynamic simulation system for anti-interference UAV running trajectory according to claim 4 is characterized in that: The control module comprises: A comparison device, which is used to obtain the value in the execution efficiency map and compare it with the efficiency threshold to form a corresponding comparison result; A rehearsal device, connected to the comparison device, for obtaining the real-time acceleration and real-time speed of the anti-interference UAV, determining a rehearsal trajectory of the anti-interference UAV performing the built-in action, and judging whether the anti-interference UAV will collide according to the rehearsal trajectory; An automatic execution device is connected to the rehearsal device and is used to control the anti-interference UAV to automatically execute the corresponding built-in action based on the judgment result that the rehearsal trajectory will not collide.

6. The high-precision dynamic simulation system for anti-interference UAV running trajectory according to claim 5 is characterized in that: The comparison device compares the value in the execution efficiency map with the efficiency threshold, wherein: When the value in the execution efficiency map is higher than the efficiency threshold, the comparison device marks the corresponding anti-interference UAV as not interfered with.

7. A dynamic simulation method based on the high-precision dynamic simulation system for anti-interference UAV operation trajectory according to any one of claims 1 to 6, characterized in that: include: The trainer iteratively optimizes the efficiency learning model until the loss value of the efficiency learning model converges to the preset loss value; Test the current efficiency learning model using the time processing dataset; When the test result meets the preset test result, the learning parameters of the efficiency learning model are saved.

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