Intelligent unmanned aerial vehicle hoisting pendulum anti-swing control system based on industrial internet

Abstract

The invention relates to the technical field of intelligent anti-swing control of unmanned aerial vehicle hoisting, in particular to an industrial internet-based intelligent anti-swing control system for unmanned aerial vehicle hoisting. Comprising a multi-source dynamics data acquisition unit used for acquiring environmental dynamics prediction data of a predetermined flight path of an unmanned aerial vehicle from a cloud platform and acquiring real-time motion state data of the unmanned aerial vehicle from airborne equipment; the prospective stability evaluation unit is used for obtaining the predicted maximum swing angle of the load; the prospective stability evaluation unit is also used for comparing and analyzing the predicted maximum swing angle and a preset load maximum allowable swing angle to obtain a prospective stability index; the control parameter adjusting unit is used for judging whether the prospective stability index is greater than a preset stability threshold value or not; and finally, the system performs active correction control on the unmanned aerial vehicle according to the corrected controller gain. According to the system, the problem of control lag is solved, so that the safety and stability of unmanned aerial vehicle hoisting operation are greatly improved under complex meteorological conditions.

Description

Technical Field

[0001] This invention relates to the field of intelligent anti-sway control technology for drone hoisting, specifically to an intelligent anti-sway control system for drone hoisting based on the Industrial Internet. Background Technology

[0002] When a drone is performing a hoisting task, the load suspended below it may sway due to flight maneuvers and external environmental interference, such as high-altitude wind fields, which directly threatens flight safety and operational efficiency.

[0003] Most existing technologies employ passive anti-sway control strategies, which involve detecting load swaying through onboard sensors and then implementing corrective control. The limitation of this method lies in its lag response; it cannot anticipate environmental disturbances such as strong winds encountered along the flight path, thus failing to take preventative measures. This results in poor load sway suppression and slow control response under sudden strong disturbances, potentially posing a safety threat. Furthermore, the delayed control measures implemented to correct large swaying often require the UAV to perform violent maneuvers, which not only affects flight stability but also causes unnecessary energy consumption, reducing flight economics.

[0004] Therefore, there is an urgent need for a control scheme that can shift from passive response to active prediction in order to solve the problems of poor sway suppression, insufficient safety redundancy and high flight energy consumption caused by control lag in existing technologies.

[0005] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention discloses an intelligent unmanned aerial vehicle (UAV) crane sway and anti-sway control system based on the Industrial Internet. Specifically, the technical solution of this invention includes:

[0007] The multi-source dynamics data acquisition unit is used to acquire environmental dynamics prediction data of the UAV's predetermined flight path from the cloud platform and to acquire real-time motion state data of the UAV from the airborne equipment.

[0008] The forward-looking stability assessment unit is used to solve the predicted maximum swing angle of the load by using a preset dynamic model based on environmental dynamics prediction data and real-time motion state data.

[0009] The forward-looking stability assessment unit is also used to compare and analyze the predicted maximum swing angle with the preset maximum allowable swing angle of the load to obtain the forward-looking stability index.

[0010] The control parameter adjustment unit is used to determine whether the forward stability index is greater than the preset stability threshold.

[0011] When the forward stability index is greater than the preset stability threshold, the control parameter adjustment unit generates a control aggressive factor with a value greater than 1, and corrects the preset benchmark controller gain according to the control aggressive factor to obtain the corrected controller gain.

[0012] When the forward stability index is less than or equal to the preset stability threshold, the control parameter adjustment unit directly uses the reference controller gain as the corrected controller gain.

[0013] The system ultimately performs active corrective control of the UAV based on the corrected controller gain.

[0014] Preferably, the environmental dynamics prediction data is a time-series wind speed vector; the real-time motion state data includes the translational acceleration vector of the UAV body, as well as the real-time swing angle and real-time swing angular velocity of the load.

[0015] The preferred method for solving the forward-looking stability evaluation unit is as follows:

[0016] Obtain the planned trajectory acceleration sequence of the UAV within a future predicted time period;

[0017] The acceleration planning sequence, environmental dynamics prediction data, and real-time motion state data at the current moment are used as initial conditions and substituted into the dynamics model.

[0018] The predicted swing trajectory of the load during the future prediction period is obtained by solving the problem using numerical integration, and the predicted maximum swing angle is determined from the predicted swing trajectory.

[0019] Preferably, the forward stability index is the value obtained by normalizing and comparing the predicted maximum swing angle with the preset maximum allowable swing angle of the load.

[0020] Preferably, when the forward stability index is greater than the preset stability threshold, the value of the generated control radical factor is greater than 1, and it has a non-linear positive correlation with the value of the forward stability index.

[0021] Preferably, the process by which the control parameter adjustment unit corrects the gain of the reference controller is as follows: multiply the control aggressive factor with the proportional gain, integral gain and derivative gain of the reference controller gain respectively to generate the corrected proportional gain, integral gain and derivative gain.

[0022] Preferably, it also includes a risk level classification unit, which is used to compare and analyze the forward-looking stability index with the preset first-level risk threshold and the second-level risk threshold when the forward-looking stability index is greater than the preset stability threshold;

[0023] When the forward-looking stability index is greater than the first-level risk threshold and less than or equal to the second-level risk threshold, a first-level risk signal is generated.

[0024] A secondary risk signal is generated when the forward-looking stability index is greater than the secondary risk threshold.

[0025] Preferably, the control parameter adjustment unit responds to a primary risk signal or a secondary risk signal by generating control aggressive factors in different value ranges to execute corrective control strategies of different intensities.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. This system constructs a predictive dynamic model by integrating cloud-based environmental prediction data with real-time airborne data. This enables the system to anticipate disturbances such as wind shear on the future flight path and proactively correct control in advance. Compared to traditional passive anti-sway technology that relies solely on the current state, this system solves the fundamental problem of control lag by proactively suppressing load sway in its infancy, thereby significantly improving the safety and stability of UAV lifting operations under complex weather conditions.

[0028] 2. This system introduces a standardized forward-looking stability index to accurately quantify and assess future instability risks. The adjustment of control parameters is not a simple linear response, but rather achieved through a control aggressive factor that is non-linearly positively correlated with the risk index. This design allows the system to handle minor issues with minor adjustments and major issues with major ones. When the predicted risk is low, only fine-tuning is needed to ensure flight economy; high-intensity maneuvers are only performed when significant risks are anticipated, achieving a high degree of balance between safety and flight energy consumption.

[0029] 3. This system uniquely incorporates a risk level classification unit, which can categorize predicted risks into different levels, such as Level 1 and Level 2 risks, based on the level of the forward-looking stability index. The control system responds accordingly, generating control aggressive factors for different ranges and implementing differentiated correction strategies, ranging from gentle pre-suppression to strong counter-maneuvering. This tiered response mechanism enables refined management of control costs, ensuring optimal countermeasures are taken at any risk level and enhancing the system's intelligence level.

[0030] 4. The gain correction method of this system has high engineering practicality. By synchronously scaling the baseline PID gain through a unified aggressive factor, it can be seamlessly integrated into existing control frameworks without disruptive design. Simultaneously, it normalizes complex risk assessment results into a dimensionless stability exponent, greatly simplifying the decision-making logic. This modular and standardized design approach enhances the system's robustness and versatility, facilitating rapid deployment and application across different UAV platforms and mission scenarios. Attached Figure Description

[0031] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0032] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0034] Example 1:

[0035] Please see Figure 1 A smart unmanned aerial vehicle (UAV) crane based on the Industrial Internet of Things (IIoT) utilizes a sway prevention and control system, comprising:

[0036] The multi-source dynamics data acquisition unit is used to acquire environmental dynamics prediction data of the UAV's predetermined flight path from the cloud platform and to acquire real-time motion state data of the UAV from the airborne equipment.

[0037] The forward-looking stability assessment unit is used to solve the predicted maximum swing angle of the load by using a preset dynamic model based on environmental dynamics prediction data and real-time motion state data.

[0038] The forward-looking stability assessment unit is also used to compare and analyze the predicted maximum swing angle with the preset maximum allowable swing angle of the load to obtain the forward-looking stability index.

[0039] The control parameter adjustment unit is used to determine whether the forward stability index is greater than the preset stability threshold.

[0040] When the forward stability index is greater than the preset stability threshold, the control parameter adjustment unit generates a control aggressive factor with a value greater than 1, and corrects the preset benchmark controller gain according to the control aggressive factor to obtain the corrected controller gain.

[0041] When the forward stability index is less than or equal to the preset stability threshold, the control parameter adjustment unit directly uses the reference controller gain as the corrected controller gain.

[0042] The system ultimately performs active corrective control of the UAV based on the modified controller gain;

[0043] This embodiment provides an intelligent unmanned aerial vehicle (UAV) hoisting sway prevention and control system based on the Industrial Internet. It aims to solve the problems in the existing technology where UAV hoisting operations can only passively prevent swaying based on the current state, and cannot predict and respond to future environmental disturbances in advance, resulting in control lag, poor sway suppression effect and poor flight economy. This embodiment achieves advanced suppression of load sway by constructing a cloud-edge collaborative predictive control closed loop.

[0044] The core purpose of the multi-source dynamics data acquisition unit is to provide comprehensive and real-time input data for subsequent predictive dynamics modeling. In this embodiment, the unit acquires data through two parallel channels: it communicates with the cloud-based digital twin platform via an industrial internet interface to actively acquire environmental dynamics prediction data of the high-gradient wind shear region that the UAV will encounter on its predetermined flight path. This environmental dynamics prediction data is a quantitative description of the wind field within a specific time period and spatial region in the future. Its function is to serve as a forecast input to the control system for external disturbances. It is a time-series wind speed vector generated by data fusion and extrapolation from a large-scale meteorological model in the cloud or a sensor network deployed in the operational area. ;

[0045] This unit integrates an airborne high-precision inertial measurement unit (IMU) and a global positioning system (GPS) to monitor the motion state of the UAV itself and the swing state of the payload in real time. The real-time motion state data here refers to the physical state parameters of the UAV and the payload at the current moment. Its function is to provide initial conditions and real-time feedback for the dynamic model. It is a direct measurement by the airborne sensors.

[0046] The purpose of the forward-looking stability assessment unit is to quantitatively assess the potential instability risk of the UAV maintaining its original flight plan over a future period of time based on the collected multi-source data; in this embodiment, the unit constructs a predictive fusion dynamics model.

[0047] The core of this model is the dynamic equations based on Lagrange mechanics, which describe the oscillation of a load in an accelerating coordinate system:

[0048]

[0049] in, For the load swing angle, The effective length of the sling. It is the acceleration due to gravity. For load quality, and These represent the planned acceleration components of the UAV in the horizontal and vertical directions, respectively; the equivalent lateral force. It is achieved by using environmental dynamics prediction data, i.e., time-series wind speed vectors. Substitute into the wind resistance model, for example The calculation yielded, where air density, This is the drag coefficient. Let $\mathbf{a}$ be the windward area; to ensure dimensional consistency, the physical dimension of each independent term in this equation is acceleration, i.e., $\mathbf{a}$. In order to grasp the main contradiction, this embodiment simplifies the load swing into a two-dimensional swing in the plane perpendicular to the UAV's flight direction for analysis;

[0050] To clearly illustrate the core idea, this embodiment simplifies the model to two dimensions; in other embodiments, a model including a lateral swing angle can be established. The three-dimensional dynamic model, such as the two-degree-of-freedom swing model in spherical coordinates, is used to deal with more complex space wind fields and maneuvering situations, but this does not affect the core concept of the present invention to achieve active anti-sway through predictive control.

[0051] This equation is not an isolated physical model, but rather an equivalent lateral force of future wind disturbances predicted from the cloud. Future acceleration sequence of edge drones' native planning And the current measured swing state, i.e., the initial swing angle. and initial angular velocity A forward-looking fusion was implemented; by numerically integrating the equation, the unit can predict the load in the future. The oscillation trajectory inside And determine the predicted maximum swing angle from it. ;

[0052] The forward-looking stability assessment unit compares this prediction with a key engineering safety boundary, namely the preset maximum allowable swing angle under load. Perform comparative analysis; The value is a critical angle that the load swing must not exceed, set according to the safety operation specifications for a specific hoisting task. Its purpose is to provide a clear, non-volatile benchmark for risk assessment; it is an industry standard or a safety requirements document for a specific task. Through this normalized comparison, a dimensionless forward-looking stability index is ultimately obtained. ;

[0053] The control parameter adjustment unit aims to transform the abstract risk index derived from the forward-looking assessment into specific, executable control commands for adjustment. In this embodiment, the unit determines the forward-looking stability index. Whether it is greater than a preset stability threshold; this threshold is set to 1 in this embodiment, and its technical logic is as follows: The definition itself is the ratio of the predicted maximum swing angle to the safety threshold. This directly implies a prediction that the security boundary will be breached in the future;

[0054] When the forward stability index is greater than the preset stability threshold, it indicates that the system has foreseen the risk of instability. At this time, the control parameter adjustment unit generates a control aggressive factor with a value greater than 1. The system then modifies the preset baseline controller gain based on this factor, resulting in a set of modified controller gains for actively mitigating risks. When the forward stability index is less than or equal to the preset stability threshold, it indicates that flight as planned is safe within the predicted timeframe. In this case, the control parameter adjustment unit directly uses the baseline controller gain as the modified controller gain. The value is equal to 1, and no adjustments are made to maintain the economy and stability of the flight;

[0055] The system ultimately performs active corrective control of the UAV based on the corrected controller gain; this step closes the entire prediction-evaluation-control loop; the corrected gain is applied in real time to the UAV's position and attitude control loop, enabling the UAV to perform pre-acceleration, lateral maneuvering and other counter-flight maneuvers in advance before actually encountering strong wind disturbances, actively generating an inertial force opposite to the predicted disturbance, thereby suppressing the upcoming load swing in advance.

[0056] This embodiment transforms traditional passive and lagging anti-sway control into proactive and forward-looking predictive anti-sway control by establishing a collaborative mechanism between cloud prediction and edge control. This not only improves the safety of hoisting operations in complex wind fields, but also significantly optimizes flight energy consumption by adjusting the control strategy only when a definite risk is predicted, achieving a high degree of unity between safety and economy.

[0057] It should be noted that the model in this embodiment is an idealized model; in actual deployment, techniques such as Kalman filtering can be introduced to handle the uncertainty in the prediction data, and a more refined UAV dynamics model can be established to consider the dynamic response characteristics of the actuator, thereby improving the robustness and practicality of the system.

[0058] Example 2:

[0059] The environmental dynamics prediction data is a time-series wind speed vector; the real-time motion state data includes the translational acceleration vector of the UAV body, as well as the real-time swing angle and real-time swing angular velocity of the load.

[0060] In a preferred embodiment, the data type acquired by the multi-source dynamics data acquisition unit is further optimized and defined; the environmental dynamics prediction data is explicitly defined as a time-series wind speed vector. This temporal wind speed vector is a sequence of wind speed data containing information in both time and space. It not only provides the magnitude of the wind speed at a future moment but also indicates its direction, providing a basis for calculating the equivalent lateral force of wind disturbance. It provides more accurate raw input;

[0061] Real-time motion data specifically includes the translational acceleration vector of the UAV itself. and the real-time swing angle of the load. With real-time oscillation angular velocity This specific definition ensures the accuracy and completeness of the input parameters of the dynamic model. Translational acceleration is directly related to the forced vibration term of the model, while pendulum angle and angular velocity constitute the initial conditions necessary for solving the differential equation. By making such precise constraints on the input data, the data source of the dynamic model is ensured to have high physical relevance and real-time performance, which improves the accuracy and reliability of the forward stability assessment results and lays a solid foundation for the accuracy of subsequent control decisions.

[0062] Example 3:

[0063] The process of solving the forward-looking stability assessment unit is as follows:

[0064] Obtain the planned trajectory acceleration sequence of the UAV within a future predicted time period;

[0065] The acceleration planning sequence, environmental dynamics prediction data, and real-time motion state data at the current moment are used as initial conditions and substituted into the dynamics model.

[0066] The predicted swing trajectory of the load in the future prediction time period is obtained by solving the problem using numerical integration method, and the predicted maximum swing angle is determined from the predicted swing trajectory.

[0067] In a preferred embodiment, the specific process of solving the forward-looking stability assessment unit is described in detail; this unit obtains the UAV's future prediction time period. Pre-defined trajectory acceleration planning sequence This sequence is generated by the UAV's mission planning module and represents the pre-set maneuvers that the UAV will perform in order to complete its flight mission when there is no external interference.

[0068] The acceleration planning sequence and the environmental dynamics prediction data obtained from the cloud are converted into wind force. And the real-time motion state data measured by the airborne equipment at the current moment, i.e., the initial swing angle. and initial angular velocity As initial conditions, they are substituted into the dynamic response equation of the load swing angle;

[0069] This step fully constructs the planned organismal behavior and the predicted future environment in a mathematical model; the equation is solved using numerical integration methods to obtain the load over the predicted time period. Predicted oscillation trajectory within The maximum absolute value of the trajectory is then searched for to determine the predicted maximum swing angle. This implementation reveals a specific and reproducible technical path for fusing multi-source, heterogeneous, and cross-time-domain data to solve complex nonlinear dynamic models, realizing a shift from post-event remediation to pre-event prediction and improving the timeliness of risk assessment.

[0070] Example 4:

[0071] The forward stability index is the value obtained by normalizing and comparing the predicted maximum swing angle with the preset maximum allowable swing angle of the load.

[0072] In a preferred embodiment, the method for generating the forward-looking stability index is explicitly defined; the forward-looking stability index... The predicted maximum swing angle With the preset maximum allowable swing angle of the load The value obtained after normalization comparison; its mathematical expression is:

[0073]

[0074] in, : This is the absolute value of the predicted maximum swing angle, and its data type is floating point. It is calculated by the forward-looking stability assessment unit using the method in Example 3.

[0075] : This is the preset maximum allowable swing angle of the load. The data type is floating point, and it is preset according to the safety operation specifications of the hoisting task.

[0076] This implementation method uses normalization to transform a prediction result with physical dimensions that is relevant to a specific task. This is transformed into a dimensionless, universally applicable risk measurement indicator. This design allows subsequent control strategies to be entirely based on... Using this single indicator for decision-making simplifies the control logic and enhances the robustness and modularity of the system.

[0077] Example 5:

[0078] When the forward stability index is greater than the preset stability threshold, the value of the generated control radical factor is greater than 1, and it has a non-linear positive correlation with the value of the forward stability index.

[0079] In a preferred embodiment, the generation mechanism and characteristics of the control radical factor were designed in detail; when the forward-looking stability index Greater than the preset stability threshold At that time, the generated control radical factor The value is greater than 1, and it is consistent with the forward stability index. The values ​​exhibit a non-linear positive correlation; this non-linear relationship is achieved through a mapping function:

[0080]

[0081] in, : This is the control radical factor, which is dimensionless and calculated by this formula. Its function is to serve as the amplification factor for the downstream controller gain.

[0082] : This is the risk response gain coefficient, which is dimensionless and calibrated through hardware-in-the-loop simulation and flight experiments. It determines the overall severity of the control law adjustment.

[0083] : is a non-linear adjustment exponent, dimensionless and Through simulation and experimental calibration, it is used to achieve a nonlinear response characteristic that fine-tunes when the risk is small and drastically adjusts when the risk is large.

[0084] : is the Herveside step function, which acts as a switch to ensure that only when... The gain adjustment option only takes effect at that time;

[0085] To ensure that those skilled in the art can implement the complete procedure without excessive experimentation, parameters are specified. and The calibration process further clarifies that the data collection covers different wind field intensities. The measured maximum swing angle ultimately generated by the load during the corresponding flight mission Multiple sets of experimental data Based on this calibration dataset, the optimization objective is set as minimizing the simulated maximum swing angle predicted and intervened by the control system of this invention under all experimental conditions. With safety threshold The gap between them can be iteratively adjusted using numerical optimization methods such as particle swarm optimization or genetic algorithms. and The value of the parameter is calculated until the above optimization objective converges, thus obtaining an optimal combination of parameters.

[0086] The nonlinear mapping relationship of this design enables the system to delicately balance the stability of control with safety under extreme conditions when dealing with different levels of risk.

[0087] In addition, to ensure the robustness of the model, stress testing is required during the simulation phase; the test content includes, but is not limited to, inputting extremely small or extremely large sling lengths. Load quality The system inputs a sudden step wind speed signal and predicted data containing high-frequency noise to verify whether the stability and response of the system under various boundary and harsh conditions conform to physical laws and engineering requirements.

[0088] Example 6:

[0089] The process by which the control parameter adjustment unit corrects the gain of the reference controller is as follows: the control aggressive factor is multiplied by the proportional gain, integral gain and derivative gain in the reference controller gain to generate the corrected proportional gain, integral gain and derivative gain.

[0090] In a preferred embodiment, the specific mathematical process by which the control parameter adjustment unit corrects the gain of the reference controller is defined; taking a proportional-integral-derivative PID controller as an example, the correction process is as follows: the generated control aggressive factor is... Proportional gain in the reference controller gain Integral gain and differential gain Perform multiplication operations separately to generate the corrected proportional gain. Integral gain and differential gain ;

[0091] The specific calculation is as follows:

[0092]

[0093]

[0094]

[0095] in, : The baseline PID gain is dimensionless and is a parameter pre-tuned for a specific UAV platform using conventional methods;

[0096] : This is the corrected controller gain, dimensionless, calculated in real time by this formula, and immediately applied to the UAV's position and attitude control loop;

[0097] This implementation provides an efficient and easy-to-implement gain scheduling mechanism through a unified aggressive factor. By synchronously scaling all gain terms, the control system is able to respond to dynamic risk predictions in near instantaneous time, achieving a seamless transition from risk assessment to control execution.

[0098] Furthermore, to ensure the stability of the control system and comply with the physical execution capabilities of the UAV, the control aggressive factor is adjusted. Set a reasonable upper limit for its value. That is, the calculated If it exceeds this upper limit, then take... Similarly, the corrected controller gain They should also be limited to their respective safe ranges to prevent system instability due to excessive gain; among them, The safety range of each gain is determined through simulation and actual flight testing based on the maximum maneuverability of the UAV, the saturation limits of the motors and ESCs, and the response bandwidth of the actuators, in order to prevent problems such as control command saturation or high-frequency oscillation.

[0099] Example 7:

[0100] It also includes a risk level classification unit, which is used to compare and analyze the forward-looking stability index with the preset first-level risk threshold and second-level risk threshold when the forward-looking stability index is greater than the preset stability threshold.

[0101] When the forward-looking stability index is greater than the first-level risk threshold and less than or equal to the second-level risk threshold, a first-level risk signal is generated.

[0102] When the forward-looking stability index is greater than the secondary risk threshold, a secondary risk signal is generated;

[0103] The control parameter adjustment unit responds to a primary risk signal or a secondary risk signal by generating control aggressive factors in different value ranges to execute corrective control strategies of varying intensities.

[0104] In a preferred embodiment, a risk level classification unit and a linked graded correction strategy are further introduced to form a more refined risk response mechanism; the system also includes a risk level classification unit for use in forward-looking stability indices. When the risk exceeds the preset stability threshold, it is further compared and analyzed with the preset primary risk threshold and secondary risk threshold. The technical logic behind setting these risk thresholds is to match the optimal control cost for different levels of risk. The determination method is based on statistical analysis of a large amount of historical flight data and simulation results to identify key inflection points on the risk-reward curve.

[0105] When the forward-looking stability index When the risk threshold is greater than the Level 1 risk threshold but less than or equal to the Level 2 risk threshold, for example... The risk level classification unit generates a level one risk signal; when the forward-looking stability index When it exceeds the level 2 risk threshold, for example Then a secondary risk signal is generated;

[0106] Accordingly, the control parameter adjustment unit generates control aggressive factors in different value ranges in response to the received primary or secondary risk signals. To execute corrective control strategies of varying intensities; in response to a Level 1 risk signal, the control parameter adjustment unit calculates an appropriate... Values, for example, make it fall into Within the specified range, the drone is driven to perform smooth, pre-suppressed maneuvers;

[0107] When responding to a level 2 risk signal, the control parameter adjustment unit calculates a relatively large value. Value, for example, make it greater than It drives the drone to perform high-energy, powerful, and aggressive maneuvers; it has constructed a differentiated, hierarchical risk response system, enabling the system to take appropriate countermeasures according to the severity of the threat, achieving global optimization of flight energy consumption while ensuring absolute safety, and significantly improving the system's intelligence level and comprehensive mission efficiency.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A swing-prevention and anti-swing control system for intelligent unmanned aerial vehicles (UAVs) based on the Industrial Internet, characterized in that: include: The multi-source dynamics data acquisition unit is used to acquire environmental dynamics prediction data of the UAV's predetermined flight path from the cloud platform and to acquire real-time motion state data of the UAV from the airborne equipment. The forward-looking stability assessment unit is used to solve the predicted maximum swing angle of the load by using a preset dynamic model based on environmental dynamics prediction data and real-time motion state data. The forward-looking stability assessment unit is also used to compare and analyze the predicted maximum swing angle with the preset maximum allowable swing angle of the load to obtain the forward-looking stability index. The control parameter adjustment unit is used to determine whether the forward stability index is greater than the preset stability threshold. When the forward stability index is greater than the preset stability threshold, the control parameter adjustment unit generates a control aggressive factor with a value greater than 1, and corrects the preset benchmark controller gain according to the control aggressive factor to obtain the corrected controller gain. When the forward stability index is less than or equal to the preset stability threshold, the control parameter adjustment unit directly uses the reference controller gain as the corrected controller gain. The system ultimately performs active corrective control of the UAV based on the corrected controller gain.

2. The intelligent unmanned aerial vehicle (UAV) crane anti-sway control system based on the Industrial Internet as described in claim 1, characterized in that, The environmental dynamics prediction data is a time-series wind speed vector; the real-time motion state data includes the translational acceleration vector of the UAV body, as well as the real-time swing angle and real-time swing angular velocity of the load.

3. The intelligent unmanned aerial vehicle (UAV) crane anti-sway control system based on the Industrial Internet as described in claim 1, characterized in that, The process of solving the forward-looking stability assessment unit is as follows: Obtain the planned trajectory acceleration sequence of the UAV within a future predicted time period; The acceleration planning sequence, environmental dynamics prediction data, and real-time motion state data at the current moment are used as initial conditions and substituted into the dynamics model. The predicted swing trajectory of the load during the future prediction period is obtained by solving the problem using numerical integration, and the predicted maximum swing angle is determined from the predicted swing trajectory.

4. The intelligent unmanned aerial vehicle (UAV) crane anti-sway control system based on the Industrial Internet as described in claim 1, characterized in that, The forward stability index is the value obtained by normalizing and comparing the predicted maximum swing angle with the preset maximum allowable swing angle under load.

5. The intelligent unmanned aerial vehicle (UAV) crane anti-sway control system based on the Industrial Internet as described in claim 1, characterized in that, When the forward stability index is greater than the preset stability threshold, the value of the generated control radical factor is greater than 1, and it has a non-linear positive correlation with the value of the forward stability index.

6. The intelligent unmanned aerial vehicle (UAV) crane anti-sway control system based on the Industrial Internet according to claim 1, characterized in that, The process by which the control parameter adjustment unit corrects the gain of the reference controller is as follows: the control aggressive factor is multiplied by the proportional gain, integral gain and derivative gain of the reference controller gain to generate the corrected proportional gain, integral gain and derivative gain.

7. The intelligent unmanned aerial vehicle (UAV) crane anti-sway control system based on the Industrial Internet according to claim 1, characterized in that, It also includes a risk level classification unit, which is used to compare and analyze the forward-looking stability index with the preset first-level risk threshold and second-level risk threshold when the forward-looking stability index is greater than the preset stability threshold. When the forward-looking stability index is greater than the first-level risk threshold and less than or equal to the second-level risk threshold, a first-level risk signal is generated. A secondary risk signal is generated when the forward-looking stability index is greater than the secondary risk threshold.

8. A swing prevention and control system for intelligent unmanned aerial vehicles (UAVs) based on the Industrial Internet, as described in claim 7, is characterized in that... The control parameter adjustment unit responds to a primary risk signal or a secondary risk signal by generating control aggressive factors in different value ranges to execute corrective control strategies of varying intensities.

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