Flight control method, device, equipment, storage medium and product

Abstract

This application discloses a flight control method, apparatus, device, storage medium, and product, relating to the field of flight control technology. The flight control method includes: acquiring noise information of a UAV at a current moment, wherein the noise information includes a velocity quantity; determining the acceleration at the current moment based on the noise signal; determining whether the acceleration exceeds a threshold; and, based on the determination result, outputting a target acceleration that meets noise requirements, so that the UAV performs flight control based on the target acceleration. After determining the acceleration of the noise signal, this application outputs a target acceleration that meets noise requirements by judging the acceleration, so that the UAV performs flight control based on the target acceleration that meets noise requirements. By controlling the output of acceleration, noise is controlled, thereby avoiding the problem of increased development costs caused by Kalman filtering in related technologies.

Description

Technical Field

[0001] This application relates to the field of flight control technology, and in particular to a flight control method, apparatus, equipment, storage medium, and product. Background Technology

[0002] As drones are increasingly used in military and civilian fields, many fixed-wing drones are equipped with inertial navigation systems that use low-cost sensors due to cost issues. These sensors produce high noise levels, making it difficult for drones to follow commands. Filtering out this noise has become a problem.

[0003] In related technologies, Kalman filtering is used to filter noise. However, Kalman filtering requires an accurate system dynamic model and noise statistical characteristics, which increases development costs. Summary of the Invention

[0004] The main objective of this application is to provide a flight control method, apparatus, device, storage medium, and product, which aims to solve the technical problem that Kalman filtering requires accurate system dynamic models and noise statistical characteristics, leading to increased development costs.

[0005] To achieve the above objectives, this application proposes a flight control method, the flight control method comprising:

[0006] Obtain the noise information of the drone at the current moment, wherein the noise information includes a velocity quantity;

[0007] Based on the noise signal, determine the acceleration at the current moment;

[0008] Determine whether the acceleration exceeds a threshold, and based on the determination result, output a target acceleration that meets the noise requirements so that the UAV can perform flight control based on the target acceleration.

[0009] In one embodiment, the step of outputting the target acceleration that meets the noise requirements based on the judgment result includes:

[0010] If the acceleration is greater than the threshold, the output threshold is the target acceleration;

[0011] If the acceleration is less than or equal to the threshold, then the acceleration is output as the target acceleration.

[0012] In one embodiment, the step of determining the acceleration at the current moment based on the noise signal includes:

[0013] Extract the first velocity quantity of the noise signal;

[0014] Obtain the second velocity of the noise signal at the previous moment, wherein the previous moment is separated from the current moment by a preset time interval;

[0015] The acceleration at the current moment is determined based on the first velocity and the second velocity.

[0016] In one embodiment, the threshold includes a target threshold, and the step of determining whether the acceleration exceeds the threshold, and based on the determination result, outputting a target acceleration that meets noise requirements so that the UAV can perform flight control based on the target acceleration includes:

[0017] Determine the flight status of the drone;

[0018] If the flight state is a takeoff state, then the target threshold corresponding to the flight state is determined;

[0019] It is determined whether the acceleration exceeds the target threshold. Based on the determination result, a target acceleration that meets the noise requirements is output so that the UAV can perform flight control based on the target acceleration.

[0020] In one embodiment, the step of determining the flight status of the drone includes:

[0021] Acquire flight status data;

[0022] Based on the aforementioned data, a sliding window is generated;

[0023] Calculate the state characteristics of the sliding window;

[0024] Based on the aforementioned state characteristics, the flight state of the UAV is determined.

[0025] In one embodiment, the flight state includes a takeoff state, and the step of determining the flight state of the UAV based on the state characteristics includes:

[0026] If the flight state corresponding to the sliding window is the takeoff state, then the target flight state of the set of a preset number of active windows after the sliding window is determined;

[0027] If all the target flight states are in the take-off state, then the flight state of the UAV is determined to be the take-off state.

[0028] Furthermore, to achieve the above objectives, this application also proposes a flight control device, which includes:

[0029] The acquisition module is used to acquire the noise information of the UAV at the current moment, wherein the noise information includes a velocity quantity;

[0030] A determination module is used to determine the acceleration at the current moment based on the noise signal;

[0031] The judgment module is used to determine whether the acceleration exceeds a threshold, and based on the judgment result, outputs a target acceleration that meets the noise requirements, so that the UAV can perform flight control based on the target acceleration.

[0032] In addition, to achieve the above objectives, this application also proposes a flight control device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the flight control method as described above.

[0033] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the flight control method described above.

[0034] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the flight control method described above.

[0035] One or more technical solutions proposed in this application have at least the following technical effects:

[0036] Compared to related technologies that use Kalman filtering to filter noise, which requires accurate system dynamic models and noise statistical characteristics, leading to increased development costs, this application obtains the noise information of the UAV at the current moment, wherein the noise information includes velocity; determines the acceleration at the current moment based on the noise signal; determines whether the acceleration exceeds a threshold, and obtains a judgment result; and outputs a target acceleration that meets the noise requirements based on the judgment result, so that the UAV can perform flight control based on the target acceleration. It can be understood that after determining the acceleration of the noise signal, this application outputs a target acceleration that meets the noise requirements by judging the acceleration, so that the UAV can perform flight control based on the target acceleration that meets the noise requirements. By controlling the output of acceleration, noise is controlled, thus avoiding the problem of increased development costs caused by Kalman filtering in related technologies. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating an embodiment of the flight control method of this application.

[0040] Figure 2 This is a speed feedback diagram of the flight control method of this application;

[0041] Figure 3 This is a diagram of the engine command without noise for the flight control method of this application;

[0042] Figure 4 This is the unfiltered velocity feedback diagram of the flight control method of this application;

[0043] Figure 5 This is a diagram of the unfiltered engine command for the flight control method of this application.

[0044] Figure 6 This is a flowchart illustrating Embodiment 2 of the flight control method of this application;

[0045] Figure 7 This is a flowchart illustrating the threshold determination process for the normal flight state in the flight control method of this application.

[0046] Figure 8 This is a schematic diagram of the module structure of the flight control device according to an embodiment of this application;

[0047] Figure 9 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the flight control method in the embodiments of this application.

[0048] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0050] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The main solution of this application embodiment is: to obtain the noise information of the UAV at the current moment, wherein the noise information includes a velocity quantity; to determine the acceleration at the current moment based on the noise signal; to determine whether the acceleration exceeds a threshold and obtain a determination result; and to output a target acceleration that meets the noise requirements based on the determination result, so that the UAV can perform flight control based on the target acceleration.

[0052] In related technologies, Kalman filtering is used to filter noise. However, Kalman filtering requires an accurate system dynamic model and noise statistical characteristics, which increases development costs.

[0053] After determining the acceleration of the noise signal, this application outputs a target acceleration that meets the noise requirements by judging the acceleration, so that the UAV can perform flight control based on the target acceleration that meets the noise requirements. By controlling the output of acceleration, the noise can be controlled, thereby avoiding the problem of increased development costs caused by Kalman filtering in related technologies.

[0054] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following description uses a flight control device as an example to illustrate this embodiment and the subsequent embodiments.

[0055] Based on this, the embodiments of this application provide a flight control method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the flight control method of this application.

[0056] In this embodiment, the flight control method includes steps S100~S300:

[0057] Step S100: Obtain the noise information of the UAV at the current moment, wherein the noise information includes a velocity quantity;

[0058] It should be noted that the execution entity in this embodiment is a flight control device. This flight control device is equipped with sensors to collect noise signals from the UAV at regular intervals. The noise signals may be subject to additional signal fluctuations from the accelerometer due to vibration or other interference.

[0059] Understandably, the velocity quantity refers to the speed information of the drone, where the drone is a fixed-wing drone. The flight control equipment acquires additional noise signals generated by the accelerometer due to vibration or other interference, and these noise signals contain the drone's speed information.

[0060] Step S200: Determine the acceleration at the current moment based on the noise signal;

[0061] It should be noted that the acceleration refers to the drone's acceleration at the current moment. The flight control equipment determines the drone's current acceleration by comparing the velocity quantity in the noise signal at the current moment with the velocity quantity at the previous moment.

[0062] Specifically, all accelerations mentioned in this application are absolute values.

[0063] Step S300: Determine whether the acceleration exceeds a threshold. Based on the determination result, output a target acceleration that meets the noise requirements so that the UAV can perform flight control based on the target acceleration.

[0064] Understandably, the flight control equipment determines whether the absolute value of the current acceleration exceeds a threshold and uses this result to decide on the target acceleration to ensure it meets noise requirements. Setting a reasonable threshold is crucial for ensuring flight safety. If the detected acceleration exceeds a preset safety range, it indicates a potential anomaly, such as a sudden strong wind or mechanical failure. In this case, the system should immediately take measures to limit further actions until the problem is resolved. Specifically, this can be determined by comparing the actual measured acceleration with the theoretical maximum allowable value. Only when the absolute value of the acceleration is less than or equal to the threshold is it considered to meet noise requirements and output as the target acceleration for subsequent flight control system reference.

[0065] Understandably, due to the inherent limitations of low-cost sensors (such as accelerometer drift and gyroscope bias) and the influence of the external environment (such as electromagnetic interference), actual measurements often contain a certain degree of noise. If left untreated, this noise will directly affect the accuracy of state estimation, thereby affecting the stability of engine commands and reducing flight performance. Furthermore, in related technologies, noise filtering through Kalman filtering increases development costs. Therefore, step S300 is performed to obtain the current acceleration of the UAV and determine whether the current acceleration meets the noise requirements. The final output target acceleration is the acceleration that meets the noise requirements, eliminating the need for noise filtering through Kalman filtering, thus improving filtering efficiency and reducing development costs.

[0066] Specifically, refer to Figure 2 , Figure 3 , Figure 2 A velocity feedback graph without noise was provided. Figure 3 Provides an engine command diagram without noise, for Figure 2 , Figure 3 Add 10Hz noise, where, Figure 3 In this context, dtc refers to signals or parameters related to discrete-time control (such as the output form of control commands), see reference. Figure 4 , Figure 5 , Figure 4 The unfiltered velocity feedback graph is provided. Figure 5 An unfiltered engine command diagram is provided, demonstrating that noise signals can affect speed and engine commands.

[0067] Furthermore, the flight control equipment transmits the verified, limited target acceleration to the flight controller, generating smooth engine throttle commands to adjust propeller speed and other actuator operating parameters, thereby achieving a smooth and efficient flight experience.

[0068] Furthermore, the flight controller will obtain the overall control information based on the target acceleration and map this control information to the range of engine throttle commands. Based on the engine's characteristic curve, the acceleration command will be converted into the corresponding throttle opening command. Through smoothing processing (such as low-pass filtering), the high-frequency fluctuations of the command will be further reduced to ensure the smoothness of the engine throttle command and avoid engine vibration or instability caused by sudden changes in the command.

[0069] Furthermore, considering the low-altitude flight missions of drones in complex urban environments, in this scenario, drones need to navigate between tall buildings while avoiding collisions and signal interference. Moreover, drones flying in urban environments face the following challenges:

[0070] Obstruction by tall buildings: This may cause GPS signals to be lost or weakened, increasing the difficulty of navigation.

[0071] Strong electromagnetic interference: Communication base stations and wireless networks in the city may affect sensor signals.

[0072] Frequent acceleration changes: Due to the need to avoid obstacles and adapt to rapidly changing environments, drones experience significant acceleration changes.

[0073] High safety requirements: The flight altitude is low, and loss of control could lead to serious safety accidents.

[0074] Therefore, flight control equipment dynamically sets thresholds based on the following factors:

[0075] 1. Dynamic threshold setting based on environmental complexity

[0076] Environmental complexity assessment:

[0077] Use LiDAR or visual sensors to scan the surrounding environment in real time to assess environmental complexity. For example, quantify complexity by calculating the density and distribution of surrounding obstacles.

[0078] Complexity levels are defined as: low complexity (open areas), medium complexity (partially obscured areas), and high complexity (densely built areas).

[0079] Threshold adjustment strategy:

[0080] Low complexity region: The threshold is set at 1.5g. In this region, the drone flies relatively smoothly, allowing for smaller acceleration variations.

[0081] Medium complexity region: The threshold is set at 1.8g. In this region, the drone needs a certain degree of agility to avoid obstacles, but the acceleration change still needs to be controlled within a safe range.

[0082] High-complexity areas: The threshold is set at 2.0g. In these areas, drones need greater agility to quickly avoid obstacles, while ensuring that they do not exceed structural strength and safety requirements.

[0083] 2. Adaptive threshold setting based on electromagnetic interference intensity

[0084] Electromagnetic interference monitoring:

[0085] Electromagnetic interference sensors (such as radio frequency sensors) are used to monitor the intensity of electromagnetic interference in the flight environment in real time.

[0086] Interference intensity levels are defined as: low interference (no obvious interference), medium interference (slight interference), and high interference (strong interference).

[0087] Threshold adjustment strategy:

[0088] Low-interference environment: Threshold set at 1.5g. In this environment, the sensor signal is relatively stable, and a lower threshold can be used to improve filtering accuracy.

[0089] Medium interference environment: Threshold set at 1.8g. In this environment, the sensor signal may be affected to some extent; appropriately increasing the threshold can reduce false alarms.

[0090] High-interference environment: Threshold set at 2.0g. In this environment, the sensor signal may be severely interfered with, requiring a higher threshold to avoid frequent clipping.

[0091] 3. Threshold setting based on the urgency of flight missions

[0092] Mission urgency assessment:

[0093] The threshold is dynamically adjusted based on the urgency of the flight mission (such as emergency rescue, rapid transportation, etc.).

[0094] Define the urgency level of a task: low urgency (routine task), medium urgency (time-sensitive task), and high urgency (emergency rescue task).

[0095] Threshold adjustment strategy:

[0096] Low-urgency missions: The threshold is set at 1.5g. In such missions, flight stability is the primary consideration.

[0097] Medium-urgency tasks: The threshold is set at 1.8g. For these tasks, a degree of flexibility is needed to ensure timely completion.

[0098] High-urgency missions: Threshold set at 2.0g. These missions require greater flexibility to respond quickly to emergencies while ensuring flight safety.

[0099] 4. Threshold setting based on safe distance

[0100] Safe distance monitoring:

[0101] Use lidar or visual sensors to monitor the distance between the drone and surrounding obstacles in real time.

[0102] Define safety distance levels: large safety distance (no risk of collision), medium safety distance (some risk of collision), and small safety distance (high risk of collision).

[0103] Threshold adjustment strategy:

[0104] Large safe distance: Threshold set at 1.5g. In this case, the drone can use a lower threshold to improve filtering accuracy.

[0105] In the safe distance setting: the threshold is set at 1.8g. In this case, the threshold needs to be appropriately increased to ensure flight safety.

[0106] Small safe distance: Threshold set at 2.0g. In this case, a higher threshold is needed to avoid flight instability caused by frequent throttling.

[0107] Furthermore, in order to determine the threshold by comprehensively considering the above-mentioned parameters (environmental complexity, electromagnetic interference intensity, flight mission urgency, and safe distance), the flight control equipment can adopt a multi-factor comprehensive evaluation model. This model can dynamically calculate an optimal threshold based on various parameters monitored in real time, thereby achieving the best filtering effect and flight safety under different flight scenarios. The following are the specific methods and steps for determining the threshold by comprehensively considering these parameters:

[0108] 1. Define weighting factors

[0109] Based on the characteristics of emergency rescue missions, weighting factors are assigned to each parameter. The sum of the weighting factors is 1. Considering the urgency and safety of the mission, the weights of mission urgency and safe distance can be appropriately increased.

[0110] The environmental complexity weight w1 = 0.25;

[0111] Electromagnetic interference intensity weight w2=0.25;

[0112] The urgency weight of the flight mission is w3 = 0.3;

[0113] The safety distance weight w4 = 0.2;

[0114] 2. Parameter standardization

[0115] Standardize the value of each parameter to a uniform range (e.g., 0 to 1) for comprehensive evaluation. For example:

[0116] Environment complexity: Low complexity (0.0), Medium complexity (0.5), High complexity (1.0)

[0117] Open area: 0.0;

[0118] Partially obscured area: 0.5;

[0119] Densely built-up areas: 1.0;

[0120] Electromagnetic interference intensity: Low interference (0.0), Medium interference (0.5), High interference (1.0)

[0121] No significant interference: 0.0;

[0122] Slight interference: 0.5;

[0123] Strong interference: 1.0;

[0124] Flight mission urgency: Low urgency (0.0), Medium urgency (0.5), High urgency (1.0)

[0125] Routine task: 0.0;

[0126] Time-sensitive tasks: 0.5;

[0127] Emergency Rescue Mission: 1.0;

[0128] Safe distance: Large safe distance (0.0), Medium safe distance (0.5), Small safe distance (1.0)

[0129] No collision risk: 0.0;

[0130] There is a certain risk of collision: 0.5;

[0131] High risk of collision: 1.0;

[0132] 3. Comprehensive evaluation model

[0133] According to the standardized parameter values and weight factors, calculate a comprehensive evaluation value S:

[0134] S = w1×C + w2×I + w3×E + w4×D

[0135] Where: C is the standardized value of environmental complexity; I is the standardized value of electromagnetic interference intensity; E is the standardized value of flight mission urgency; D is the standardized value of safety distance.

[0136] 4. Threshold mapping

[0137] The flight control device dynamically adjusts the threshold according to the comprehensive evaluation value S. A threshold mapping function f(S) can be defined to map the comprehensive evaluation value to a specific threshold. For example:

[0138] When S ≤ 0.3, the threshold is 1.5g (low complexity / low interference / low urgency / large safety distance);

[0139] When 0.3 < S ≤ 0.6, the threshold is 1.7g (medium complexity / medium interference / medium urgency / medium safety distance);

[0140] When 0.6 < S ≤ 0.8, the threshold is 1.9g (high complexity / high interference / high urgency / small safety distance);

[0141] When S > 0.8, the threshold is 2.0g (extremely high complexity / extremely high interference / extremely high urgency / extremely small safety distance).

[0142] Furthermore, after calculating the comprehensive evaluation value S, the flight control device will make the following judgments:

[0143] Acceleration check: If the acceleration caused by the current environmental complexity and electromagnetic interference intensity exceeds the set maximum value (the maximum value can be determined according to the flight effect), the threshold is adjusted to a more conservative value (such as 1.5g) to ensure flight safety.

[0144] Safety distance check: If the current safety distance is less than the maximum value (determined according to the physical range of the UAV, that is, the size of the UAV and the size of the wings, and the braking ability of the UAV), the threshold is adjusted to a more conservative value (such as 1.5g) to avoid the risk of collision.

[0145] Furthermore, the flight control equipment adjusts the threshold based on the results of the intermediate judgment conditions. If the intermediate judgment conditions are not triggered, the original threshold mapping function f(S) is used to determine the threshold; if the intermediate judgment conditions are triggered, a more conservative threshold is used.

[0146] In one feasible implementation, step S300 may include the following steps:

[0147] Extract the first velocity quantity of the noise signal;

[0148] It should be noted that the noise signal may come from inertial measurement units (IMUs) such as accelerometers and gyroscopes, which can provide key information about the drone's motion status. For example, during flight, the accelerometer can detect speed changes caused by wind or other external factors, so the flight control equipment can extract the first velocity quantity from the noise signal at the current moment.

[0149] Obtain the second velocity of the noise signal at the previous moment, wherein the previous moment is separated from the current moment by a preset time interval;

[0150] Understandably, the preset time interval is 0.005 seconds, which directly affects the accuracy of acceleration calculation. If the time interval is too short, it may introduce the influence of high-frequency noise; while if the time interval is too long, it may cause the dynamic response to slow down and fail to reflect the rapidly changing motion state in time. Therefore, choosing an appropriate time interval is one of the keys to ensuring the accuracy of acceleration calculation.

[0151] It should be noted that the flight control equipment acquires the second velocity quantity from the noise signal at the previous sampling time, that is, 0.005 seconds before the current time.

[0152] The acceleration at the current moment is determined based on the first velocity and the second velocity.

[0153] Understandably, the flight control equipment can determine the acceleration at the current moment by determining the first velocity at the current moment and the second velocity at the previous moment.

[0154] Furthermore, before extracting the velocity data, the flight control equipment needs to filter the raw data to reduce the impact of random noise. For example, a low-pass filter can be used to smooth changes in acceleration, thereby improving the accuracy of subsequent judgments.

[0155] In this embodiment, a limiting method is used after the sensor to determine, based on physical mechanisms, that the maximum acceleration generated by the aircraft itself will not exceed a threshold, thereby filtering out noise.

[0156] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 6 In step S300, the flight control method further includes steps A01 to A03:

[0157] Step A01: Determine the flight status of the drone;

[0158] It should be noted that flight status includes takeoff status and normal flight status. This is because the UAV in this application is a fixed-wing UAV, and such fixed-wing UAVs typically require a runway or catapult for takeoff; that is, the autonomous takeoff time for fixed-wing UAVs is relatively long. Therefore, takeoff status is a crucial flight status for fixed-wing UAVs, hence the division of flight status into takeoff status and normal flight status. The flight control equipment determines the current flight status of the fixed-wing UAV.

[0159] Step A02: If the flight state is a takeoff state, then determine the target threshold corresponding to the flight state;

[0160] Understandably, since the engine thrust is greater during takeoff due to the need to overcome gravity, the acceleration will also be higher. Therefore, the target threshold should be slightly higher than the acceleration threshold during normal cruise to meet the power requirements during takeoff.

[0161] It should be noted that the flight control equipment will determine the acceleration threshold corresponding to the current flight state, i.e., the target threshold, based on the current flight state of the drone.

[0162] Specifically, during takeoff, the drone needs to pay special attention to the balance between lift and gravity to ensure a smooth takeoff. At this time, a specific target threshold can be set to limit the maximum allowable acceleration, avoiding instability caused by excessive thrust. The acceleration threshold for takeoff is 3g, and the acceleration threshold for normal flight is 2g. (Refer to...) Figure 7 , Figure 7 A flowchart for threshold determination in normal flight conditions is provided. The acceleration threshold can be dynamically adjusted according to the specific model of the UAV, the payload weight, and environmental conditions to ensure optimal performance and safety.

[0163] Step A03: Determine whether the acceleration exceeds the target threshold. Based on the determination result, output the target acceleration that meets the noise requirements so that the UAV can perform flight control based on the target acceleration.

[0164] Understandably, the flight control equipment compares the acceleration with the target threshold. If the acceleration is less than or equal to the target threshold, it means that the current acceleration is within the normal range. In this case, the original measured acceleration is directly used as the target acceleration output for the UAV's flight control. If the acceleration is greater than the target threshold, it means that the current acceleration may be abnormal, which may lead to flight instability or exceed noise requirements. In this case, the target threshold is used as the target acceleration output to limit the UAV's acceleration and avoid excessively violent movements. This allows the UAV to adjust the rotational speed or thrust of each rotor according to the target acceleration, thereby achieving precise flight control of the UAV.

[0165] It should be noted that, through the above steps, the flight control equipment enables the UAV to monitor acceleration in real time during takeoff and determine whether any abnormalities exist based on preset target thresholds. Based on the judgment results, the flight control system can output a target acceleration that meets noise requirements, thereby achieving precise flight control of the UAV. This method not only improves the accuracy of flight control but also enhances the safety and reliability of the system.

[0166] In one feasible implementation, the step of determining the flight status of the drone includes the following steps:

[0167] Acquire flight status-related data, and generate a sliding window based on the data;

[0168] Understandably, flight control equipment needs to collect real-time data related to the drone's flight status from multiple sensors to accurately determine its flight status. Since the drone's flight status is a dynamic process, data from a single point in time may not fully reflect the current state. Therefore, a sliding window is needed to capture trend changes over a period of time. This multi-window verification mechanism helps improve the accuracy of status recognition and prevents operational errors caused by misjudgments. Simultaneously, by optimizing the drone's trajectory, the optimal location for emitting artificial noise can be found, thereby enhancing the interference effect on eavesdropping channels and ensuring communication security.

[0169] Specifically, a sliding window is a fixed-length time interval containing data from a number of sampling points. The window length should be selected based on specific requirements.

[0170] For rapidly changing situations (such as takeoff or emergency stop), a shorter window (such as 0.1 seconds) can be selected.

[0171] For steady conditions (such as cruising), a longer window (such as 1 second) can be selected.

[0172] Calculate the state characteristics of the sliding window;

[0173] It should be noted that the flight control equipment arranges the acquired flight status data in chronological order and generates windows by sliding them with a fixed step size. For example, assuming the data sampling frequency is 100Hz (100 data points are collected per second) and the window length is 0.1 seconds, each window contains 10 data points. As time goes on, the latest data points are continuously added to the window while the oldest points are removed, thus achieving the "sliding" of the window.

[0174] Specifically, the flight control equipment extracts features from the data within a sliding window to more intuitively reflect the flight status of the drone.

[0175] Based on the aforementioned state characteristics, the flight state of the UAV is determined.

[0176] Understandably, flight control equipment uses classification algorithms or rule engines to determine the flight status of the drone based on the extracted state features.

[0177] Example rules:

[0178] If the altitude increases rapidly from zero with a large acceleration, it is considered to be in takeoff mode.

[0179] If the altitude remains stable and the speed changes little, it is determined to be in a hovering state;

[0180] If the altitude gradually decreases and the speed decreases, it is determined to be a descent.

[0181] Furthermore, different acceleration thresholds can be set according to more detailed flight conditions.

[0182] In one feasible implementation, the step of determining the flight status of the UAV based on the state characteristics includes:

[0183] If the flight state corresponding to the sliding window is the takeoff state, then the target flight state of the set of a preset number of active windows after the sliding window is determined;

[0184] It should be noted that the preset number is 3. If the flight control equipment determines that the current sliding window's state characteristics indicate that the drone may be in takeoff mode, it needs to further verify the reliability of this judgment. To verify the judgment result of the current sliding window, it is necessary to observe the flight state change trend over a subsequent period of time.

[0185] If all the target flight states are in the take-off state, then the flight state of the UAV is determined to be the take-off state.

[0186] Understandably, after completing the flight status judgment of the active window set, the flight control equipment checks whether the target flight status of all active windows is consistent. If all target flight statuses in the active window set are in takeoff status, it can be confirmed that the UAV is indeed in the takeoff phase. When the above conditions are met, the flight control system can officially determine the UAV's flight status as takeoff status and enter the corresponding control logic.

[0187] Specifically, if the flight control equipment determines that there are a concentrated number of non-takeoff states within the activity window, further analysis of the reasons is needed:

[0188] Temporary disturbances: These may be temporary fluctuations caused by external environmental factors (such as sudden changes in wind speed). In such cases, the observation period can be extended, and the number of active windows increased for re-verification.

[0189] State transition: If a transition from takeoff to other states (such as hovering or cruise) is detected, the flight control strategy needs to be updated in a timely manner.

[0190] In this embodiment, the concept of an active window set is introduced to enhance the accuracy of flight status judgment, which is particularly suitable for monitoring the takeoff phase of UAVs in complex dynamic environments.

[0191] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the flight control method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0192] This application also provides a flight control device, please refer to... Figure 8 The flight control device includes:

[0193] The acquisition module 10 is used to acquire the noise information of the UAV at the current moment, wherein the noise information includes a velocity quantity;

[0194] The determining module 20 is used to determine the acceleration at the current moment based on the noise signal;

[0195] The judgment module 30 is used to determine whether the acceleration exceeds a threshold, and based on the judgment result, outputs a target acceleration that meets the noise requirements so that the UAV can perform flight control based on the target acceleration.

[0196] Optionally, the determination module includes:

[0197] The judgment submodule is used to output the threshold as the target acceleration if the acceleration is greater than the threshold, and to output the acceleration as the target acceleration if the acceleration is less than or equal to the threshold.

[0198] The flight state determination submodule is used to determine the flight state of the UAV; if the flight state is a takeoff state, then determine the target threshold corresponding to the flight state; determine whether the acceleration exceeds the target threshold, and based on the determination result, output the target acceleration that meets the noise requirements, so that the UAV can perform flight control based on the target acceleration.

[0199] Optionally, the determining module includes:

[0200] An extraction submodule is used to extract a first velocity quantity of the noise signal; obtain a second velocity quantity of the noise signal at the previous moment, wherein the previous moment is separated from the current moment by a preset time interval; and determine the acceleration at the current moment based on the first velocity quantity and the second velocity quantity.

[0201] Optionally, the flight status determination submodule includes:

[0202] A computing unit is used to acquire flight status-related data; generate a sliding window based on the relevant data; calculate the state characteristics of the sliding window; and determine the flight status of the UAV based on the state characteristics.

[0203] Optionally, the computing unit includes:

[0204] The flight state determination subunit is used to determine the target flight state of a preset number of active windows after the sliding window if the flight state corresponding to the sliding window is the take-off state; and to determine the flight state of the UAV as the take-off state if all the target flight states are the take-off state.

[0205] The flight control device provided in this application, employing the flight control method described in the above embodiments, can solve the technical problems of flight control. Compared with the prior art, the beneficial effects of the flight control device provided in this application are the same as those of the flight control method provided in the above embodiments, and other technical features in the flight control device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0206] This application provides a flight control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the flight control method in Embodiment 1 above.

[0207] The following is for reference. Figure 9The diagram illustrates a structural schematic of a flight control device suitable for implementing embodiments of this application. The flight control device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, tablets, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 9 The flight control device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0208] like Figure 9 As shown, the flight control device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the flight control device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the flight control equipment to communicate wirelessly or wiredly with other equipment to exchange data. Although the figure shows flight control equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0209] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0210] The flight control device provided in this application, employing the flight control method described in the above embodiments, can solve the technical problems of flight control. Compared with the prior art, the beneficial effects of the flight control device provided in this application are the same as those of the flight control method provided in the above embodiments, and other technical features of the flight control device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0211] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0212] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0213] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the flight control method in the above embodiments.

[0214] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0215] The aforementioned computer-readable storage medium may be included in the flight control equipment; or it may exist independently and not be assembled into the flight control equipment.

[0216] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a flight control device, cause the flight control device to: acquire noise information of the UAV at the current moment, wherein the noise information includes a velocity quantity; determine the acceleration at the current moment based on the noise signal; determine whether the acceleration exceeds a threshold; and, based on the determination result, output a target acceleration that meets the noise requirements, so that the UAV performs flight control based on the target acceleration.

[0217] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0218] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0219] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0220] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the above-described flight control method, thereby solving the technical problems of flight control. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the flight control method provided in the above embodiments, and will not be repeated here.

[0221] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the flight control method described above.

[0222] The computer program product provided in this application can solve the technical problems of flight control. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the flight control method provided in the above embodiments, and will not be repeated here.

[0223] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A flight control method, characterized in that, The flight control method includes: Obtain the noise information of the drone at the current moment, wherein the noise information includes a velocity quantity; Based on the noise information, determine the acceleration at the current moment; Determine whether the acceleration exceeds a threshold. Based on the determination result, output a target acceleration that meets noise requirements so that the UAV can perform flight control based on the target acceleration. The threshold is determined based on environmental complexity, electromagnetic interference intensity, flight mission urgency and safe distance. If the safe distance is less than a preset maximum value, the threshold is set to a conservative value. The preset maximum value is determined based on the safe range of the UAV. The threshold includes the target threshold. The steps of determining whether the acceleration exceeds a threshold, and outputting a target acceleration that meets noise requirements based on the determination result, so that the UAV can perform flight control based on the target acceleration, include: Determine the flight status of the drone; If the flight state is a takeoff state, then a target threshold corresponding to the flight state is determined, wherein the target threshold is greater than the acceleration threshold of the normal cruise phase; Determine whether the acceleration exceeds the target threshold, and based on the determination result, output the target acceleration that meets the noise requirements so that the UAV can perform flight control based on the target acceleration; The steps for determining the flight status of the drone include: Acquire flight status data; Based on the relevant data, a sliding window is generated, wherein the window length of the sliding window is set according to specific requirements; Calculate the state characteristics of the sliding window; Based on the aforementioned state characteristics, the flight state of the UAV is determined; The flight state includes takeoff state, and the step of determining the flight state of the UAV based on the state characteristics includes: If the flight state corresponding to the sliding window is the takeoff state, then the target flight state of the set of a preset number of active windows after the sliding window is determined; If all the target flight states are in the take-off state, then the flight state of the UAV is determined to be the take-off state.

2. The flight control method as described in claim 1, characterized in that, The step of outputting the target acceleration that meets the noise requirements based on the judgment result includes: If the acceleration is greater than the threshold, the output threshold is the target acceleration; If the acceleration is less than or equal to the threshold, then the acceleration is output as the target acceleration.

3. The flight control method as described in claim 1, characterized in that, The step of determining the acceleration at the current moment based on the noise information includes: The first velocity quantity from which the noise information is extracted; The second velocity quantity of the noise information at the previous moment is obtained, wherein the previous moment and the current moment are separated by a preset time period; The acceleration at the current moment is determined based on the first velocity and the second velocity.

4. A flight control device, characterized in that, The device includes: The acquisition module is used to acquire the noise information of the UAV at the current moment, wherein the noise information includes a velocity quantity; A determination module is used to determine the acceleration at the current moment based on the noise information; The judgment module is used to determine whether the acceleration exceeds a threshold. Based on the judgment result, it outputs a target acceleration that meets the noise requirements so that the UAV can perform flight control based on the target acceleration. The threshold is determined based on environmental complexity, electromagnetic interference intensity, flight mission urgency and safe distance. If the safe distance is less than a preset maximum value, the threshold is set to a conservative value. The preset maximum value is determined based on the safe range of the UAV. The threshold includes the target threshold. The judgment module includes: The flight status determination submodule is used to determine the flight status of the UAV; if the flight status is takeoff, then the target threshold corresponding to the flight status is determined, wherein the target threshold is greater than the acceleration threshold of the normal cruise phase; it is determined whether the acceleration exceeds the target threshold, and based on the determination result, the target acceleration that meets the noise requirements is output so that the UAV can perform flight control based on the target acceleration; The flight status determination submodule includes: A calculation unit is used to acquire flight status-related data; generate a sliding window based on the relevant data, wherein the window length of the sliding window is set according to specific requirements; calculate the state characteristics of the sliding window; and determine the flight status of the UAV based on the state characteristics. The computing unit includes: The flight state determination subunit is used to determine the target flight state of a preset number of active windows after the sliding window if the flight state corresponding to the sliding window is the take-off state; and to determine the flight state of the UAV as the take-off state if all the target flight states are the take-off state.

5. A flight control device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the flight control method as described in any one of claims 1 to 3.

6. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the flight control method as described in any one of claims 1 to 3.

7. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the flight control method as described in any one of claims 1 to 3.

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