Container tyre crane remote control system

Abstract

The present application belongs to the technical field of port machinery automation control, and in particular to a container rubber-tired crane remote control system, which comprises a remote operation terminal, a communication transmission module, a central cooperative controller, a wind load sensing module, a structural dynamic response sensing module, a coupling modeling engine, an active stabilization instruction generation module and a hydraulic servo actuator. By deeply integrating environmental wind load sensing and structural inertia response sensing, a complete closed-loop active stabilization architecture is constructed. The architecture adopts a physical quantitative dynamic model driven control strategy to achieve source suppression of wind-induced resonance. The system can significantly reduce the combined swing amplitude of the boom end and the container under strong wind conditions, significantly improve positioning accuracy and operation continuity, and avoid safety shutdown due to oscillation overrun, thereby meeting the technical requirements of all-weather efficient operation in ports.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of port machinery automation control, and in particular to a container rubber-tired crane remote control system. BACKGROUND

[0002] As a key equipment in the modern port automation operation system, the operation efficiency and positioning accuracy of the container rubber-tired crane are directly related to the throughput capacity and operation safety of the entire terminal logistics system. In recent years, with the maturity of 5G communication, high-definition video transmission and remote control technology, the remote and unmanned operation mode of the rubber-tired crane has gradually become the mainstream trend in the industry, significantly improving the operation continuity and reducing the labor cost. Under this background, the remote control system not only needs to ensure the basic instruction execution function, but also is required to maintain high-precision operation ability under complex environmental disturbances. Especially in typical operation scenes such as coastal or open yards, strong wind has become the main external disturbance source affecting the stability of the system, posing a serious challenge to the remote control architecture.

[0003] The current mainstream remote control system generally adopts a closed-loop control strategy based on visual feedback, that is, the live scene is transmitted back to the remote operation terminal through multiple high-definition cameras, and the operator controls the spreader according to the visual information. This scheme can meet the basic operation demand under normal working conditions, and its design logic is based on the analogy of human eye-hand coordination, relying on the experience of the operator to realize the fine tuning of the spreader movement. However, such a system essentially belongs to an open-loop perception structure, lacking quantitative perception ability of environmental physical disturbances. Specifically, when encountering strong wind conditions with wind speed exceeding twelve meters per second, the dynamic excitation formed by the wind load acting on the boom and the suspended container will induce the coupling resonance phenomenon between them. Since the remote operator can only observe the spreader swing through the two-dimensional video image, he cannot know the size, direction and dynamic characteristics of the wind over time, nor can he predict the inertial response caused thereby, resulting in that his operation instruction often lags behind the actual disturbance phase, and even unintentionally aggravates the system oscillation. The measured data shows that under such working conditions, the combined swing amplitude of the boom tip and the container can exceed thirty centimeters, causing the target positioning accuracy to decrease by more than forty percent, and in severe cases, even triggering the safety interlock to stop, causing the operation to be interrupted.

[0004] However, with the continuous improvement of port intelligence level and the urgent need for all-weather operation ability, the inherent characteristics of the above technical solutions at the principle level gradually show fundamental limitations in dealing with high dynamic environmental disturbances. The reason is that the existing remote control system completely relies on the subjective judgment of the operator for environmental perception, without building a physical quantitative environment-structure coupling model, resulting in a lack of active intervention ability of the system to wind-induced vibration. Further, traditional stability strategies mostly rely on mechanical dampers or hydraulic passive damping devices. Although such methods can dissipate vibration energy to some extent, their response characteristics are fixed and cannot be adjusted in real time according to wind speed changes. Moreover, the effect is significantly attenuated under high-frequency disturbance. Most importantly, passive damping mechanism is essentially an energy dissipation type control, which cannot provide an active reverse torque with opposite phase to the disturbance torque, so it is difficult to fundamentally suppress the excitation and continuation of resonance.

[0005] Therefore, a container tire crane remote control system is proposed to solve the above problems. SUMMARY

[0006] The purpose of the present application is to solve the technical problems raised in the background art by setting up a container tire crane remote control system.

[0007] To solve the above technical problems, the technical scheme is as follows: a container tire crane remote control system is designed to solve the technical problem of coupling resonance between the spreader and the boom caused by strong wind disturbance, which leads to a decrease in positioning accuracy and interruption of operation in the prior art. To achieve the above-mentioned purpose of the application, an active stabilization mechanism based on a coupling modeling engine is constructed. The coupling modeling engine is a wind speed-inertia coupling modeling engine. By deploying a network of micro inertial measurement unit sensors at key nodes of the tire crane structure, real-time multi-dimensional dynamic response data of the boom and the suspended container are obtained, and environmental wind speed information is simultaneously collected. The dynamic mapping relationship between wind-induced excitation and structural response is established by fusing the two, and a phase-matched reverse torque instruction is generated accordingly to drive the hydraulic actuator to actively compensate for the spreader movement, thereby effectively suppressing the resonance swing caused by strong wind without relying on the subjective judgment of the remote operator.

[0008] As preferred, the container tire crane remote control system comprises a remote operation terminal, a communication transmission module, a central collaborative controller, a wind load sensing module, a structural dynamic response sensing module, a wind speed-inertia coupling modeling engine, an active stabilization instruction generation module, and a hydraulic servo actuator. The remote operation terminal is used to receive the crane motion instructions input by the operator and send the instructions to the central collaborative controller via the communication transmission module. The communication transmission module uses an industrial-grade 5G communication link with low latency and high reliability to ensure the bidirectional synchronous transmission of control instructions and sensing data. The central collaborative controller, as the core processing unit of the system, is responsible for coordinating the data interaction and instruction scheduling of each functional module.

[0009] As preferred, the wind load sensing module comprises at least three ultrasonic wind speed and direction sensors installed on the top of the main beam of the tire crane, the root of the boom, and the middle of the spreader beam, respectively, for real-time acquisition of wind speed vectors and their time derivatives in three-dimensional space. The output signals of each sensor are sent to the dedicated wind load processing unit of the central collaborative controller after anti-aliasing filtering. The structural dynamic response sensing module is composed of multiple miniature inertial measurement units fixedly installed at the hinge points of the boom, the end of the boom, the guide pulley bracket of the hoisting steel wire rope, and the four corner connections of the container spreader. Each miniature inertial measurement unit contains a three-axis accelerometer and a three-axis gyroscope for synchronous measurement of linear acceleration and angular velocity data at each installation location. All miniature inertial measurement units are connected to the high-speed synchronous sampling interface of the central collaborative controller through hardwires, with a sampling frequency not less than one kilohertz to ensure complete capture of high-frequency vibration modes.

[0010] As preferred, the wind speed-inertia coupling modeling engine is built into the central collaborative controller, with its input ends connected to the output ends of the wind load sensing module and the structural dynamic response sensing module. The modeling engine first performs spectral decomposition on the wind speed vector to extract the dominant frequency components and their amplitudes. At the same time, it performs modal identification on the acceleration and angular velocity signals output by each miniature inertial measurement unit to determine the first two natural frequencies and corresponding vibration modes of the current boom-container system. Subsequently, the modeling engine establishes a transfer function matrix between wind speed excitation and structural response based on the least squares fitting method, which represents the dynamic response characteristics of the boom end lateral displacement and the spreader pitch angle to wind load input under specific wind speed conditions. The modeling engine continuously updates the transfer function matrix to adapt to the system parameter drift caused by wind speed changes and changes in the load mass of the spreader.

[0011] As preferred, the active stabilization instruction generation module receives the transfer function matrix outputted by the wind speed-inertia coupling modeling engine and the current wind speed vector, and calculates the reverse torque required to be applied to counteract the current wind-induced disturbance. The amplitude of the reverse torque is determined by the gain of the transfer function matrix at the dominant disturbance frequency, and its phase is adjusted by introducing a preset phase compensation angle, so that the reverse torque and the actual disturbance torque are strictly opposite in the time domain. The active stabilization instruction generation module decomposes the calculated reverse torque into a crane boom pitch direction torque component and a crane load swing direction torque component, and converts them into corresponding hydraulic servo valve opening degree instructions.

[0012] As preferred, the hydraulic servo actuators include the crane boom pitch hydraulic cylinder, the crane load swing hydraulic motor and their matched electro-hydraulic proportional servo valves. The control ends of the electro-hydraulic proportional servo valves are connected to the output end of the active stabilization instruction generation module to receive the valve opening degree instructions outputted thereby. When the system detects that the wind speed exceeds the preset threshold value, the central cooperative controller automatically activates the active stabilization mode, at which time the hydraulic servo actuators execute the original motion instructions of the remote operator while superimposing the compensation instructions outputted by the active stabilization instruction generation module, so as to generate an active reverse torque on the physical level which is opposite in direction and matches in amplitude with the wind-induced disturbance torque, and directly acts on the crane boom and the crane load structure to suppress the resonance response thereof.

[0013] As preferred, the central cooperative controller is configured with a safety state monitoring unit which monitors the lateral displacement amplitude of the crane boom end, the swing angle rate of the crane load and the pressure fluctuation of the hydraulic system in real time. When any of the monitored parameters exceeds the safety limit value, the safety state monitoring unit immediately sends an emergency intervention signal to the central cooperative controller, which in turn cuts off the active stabilization instruction output and starts a gradual deceleration shutdown program to ensure the safety of the equipment.

[0014] As preferred, the miniature inertial measurement unit and the ultrasonic wind speed and direction sensor are both equipped with temperature compensation circuits and self-diagnosis functions, and can maintain measurement accuracy within an ambient temperature range of minus twenty degrees Celsius to plus seventy degrees Celsius, and automatically report fault codes to the central cooperative controller when the sensors fail. In addition, all sensor signal cables are laid inside the tire crane steel structure through metal conduits in a shielded twisted pair structure to minimize the influence of electromagnetic interference on the measurement signals.

[0015] The wind speed-inertia coupling modeling engine updates the transfer function matrix parameters online using the recursive least squares algorithm, and sets the forgetting factor to zero point nine five to balance the adaptability of the model to slow-changing conditions and the sensitivity of the model to sudden disturbances. When calculating the reverse torque, the active stabilization instruction generation module introduces an amplitude limiting mechanism based on the Lyapunov stability criterion to ensure that the generated compensation instructions will not cause the hydraulic system to be overloaded or trigger new unstable modes.

[0016] The beneficial effects of the present application are:

[0017] By deeply integrating the environmental wind load perception and the structural inertia response perception, a complete closed-loop active stabilization architecture is constructed. The architecture discards the traditional passive response mode relying on operator visual feedback, and instead adopts a physically quantized dynamic model driven control strategy to realize the source suppression of wind-induced resonance. The system can significantly reduce the combined swing amplitude of the boom end and the container under strong wind conditions, significantly improve the positioning accuracy and operation continuity, and avoid safety shutdown due to oscillation overrun, thereby meeting the technical requirements of port all-weather efficient operation. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] In the drawings:

[0020] Figure 1 The whole structure schematic diagram of the container tire crane remote control system of the present application is shown in the figure.

[0021] Figure 2 The system block diagram of the internal function module of the central cooperative controller of the present application is shown in the figure.

[0022] Figure 3 The flowchart of the active stabilization control method of the present application is shown in the figure. DETAILED DESCRIPTION

[0023] The exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to help understanding, and should be considered as merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, in order to be clear and concise, the description below omits the description of well-known functions and structures.

[0024] Please refer to Figures 1-3 The present application provides a container tire crane remote control system, which is constructed around the perception-modeling-decision-execution closed-loop active stabilization mechanism, and aims to realize the source suppression of wind-induced resonance disturbance through a physically quantized dynamic model driven control strategy.

[0025] The container tire crane remote control system includes a remote operation terminal, a communication transmission module, a central cooperative controller, a wind load sensing module, a structure dynamic response sensing module, a wind speed-inertia coupling modeling engine, an active stabilization instruction generation module, and a hydraulic servo actuator. The remote operation terminal is an operation station with a human-computer interaction interface, which is configured with a double-redundant industrial touch screen and a force feedback joystick, and is used to receive the three-dimensional motion instructions of the spreader input by the operator, including the speed setting values of the hoisting, trolley transverse movement and cart walking direction. The terminal is built-in instruction preprocessing unit, which processes the original control signal through de-bouncing filtering and time sequence alignment, and then sends it to the central cooperative controller through the communication transmission module.

[0026] The communication transmission module adopts an industrial 5G communication link based on time-sensitive network (TSN) enhancement, which supports millimeter wave and Sub-6GHz dual-band adaptive switching in the physical layer, and integrates forward error correction coding and retransmission mechanism in the MAC layer, to ensure that the end-to-end transmission delay is less than ten milliseconds and the packet loss rate is less than one ten-thousandth. The module is provided with a special hardware accelerator on the central cooperative controller side, which is used to synchronize the time stamp of the received remote operation instructions and the uploaded sensing data stream, to ensure the data consistency of each link in the control loop.

[0027] The central cooperative controller as the core processing unit of the system adopts a heterogeneous multi-core architecture, including a main control ARM Cortex-A72 processor, two real-time DSP coprocessors and a FPGA programmable logic unit. The main control processor runs an embedded Linux operating system, is responsible for high-level task scheduling and human-computer interface management; the DSP coprocessor is responsible for wind load processing and structure response analysis tasks respectively; the FPGA realizes the hard real-time logic of high-speed synchronous sampling, sensor fault diagnosis and safety state monitoring. All functional modules realize low-delay data exchange through on-chip high-speed interconnection bus.

[0028] The wind load sensing module is composed of three ultrasonic wind speed and direction sensors, which are installed on the top center point of the main beam of the tire crane, fifty centimeters above the hinge shaft at the root of the boom, and the lower surface of the middle of the spreader beam. Each sensor has three-dimensional wind speed vector output capability, with a measurement range of zero to seventy meters per second, a resolution better than zero point one meter per second, and an update frequency of two hundred hertz. The sensor shell is integrally formed by aviation aluminum alloy, and the inside is integrated with MEMS ultrasonic transducer array and temperature compensation circuit, which can maintain a full-scale accuracy of ±2% in the environment temperature range of minus twenty degrees Celsius to seventy degrees Celsius. The original analog signals output by each sensor are first filtered by an eight-order Butterworth anti-aliasing low-pass filter with a cutoff frequency of one hundred fifty hertz, and then digitized by a sixteen-bit analog-to-digital converter at a sampling rate of four hundred hertz, and sent to the wind load processing unit of the central cooperative controller through the CANFD bus. ​

[0029] The structural dynamic response sensing module consists of eight miniature inertial measurement units (IMUs) installed at the following positions: the center of the flange outside the left and right boom hinge points; the top of the left and right boom end pulley supports; the base of the left and right guide pulley supports of the hoisting steel wire rope; the vicinity of the left front and right rear corner connecting pins of the container spreader. Each miniature inertial measurement unit integrates a three-axis MEMS accelerometer and a three-axis MEMS gyroscope. The accelerometer has a measurement range of ±50g and a zero offset stability better than 50μg / √Hz; the gyroscope has a measurement range of ±500° / s and an angle random walk coefficient lower than 0.05° / √h. All IMUs are connected to the high-speed synchronous sampling interface of the central cooperative controller through shielded twisted pair hard lines. The interface is realized by FPGA and supports IEEE1588 precision time protocol (PTP) hardware timestamps, ensuring that the sampling time deviation of the eight channels is less than 1μs. The system sampling frequency is fixed at 1200Hz, meeting the complete capture requirements of the first five vibration modes (the theoretical maximum natural frequency is about 300Hz) of the boom-container system according to the Nyquist sampling theorem.

[0030] In this embodiment, the wind speed-inertia coupling modeling engine resides in the central cooperative controller in the form of a kernel module, with a running period of 10ms. The input data stream of the engine includes the preprocessed three-dimensional wind speed vector sequence:

[0031] ;

[0032] and its first-order time derivative:

[0033] ;

[0034] and the twenty-four-dimensional dynamic response signals from the eight IMUs, i.e., the three-axis linear acceleration and the three-axis angular velocity at each position. The modeling process is divided into three stages: spectral feature extraction, modal parameter identification, and transfer function matrix construction.

[0035] In the spectral feature extraction stage, the engine performs a fast Fourier transform (FFT) on the time series of the wind speed vector after applying a Hanning window with a window length of 2s and an overlap rate of 50%. This results in a power spectral density (PSD) estimate with a frequency resolution of 0.5Hz. The dominant frequency components with an amplitude exceeding three times the standard deviation of the background noise are identified by a peak detection algorithm, and their corresponding amplitudes and phases are recorded.

[0036] During the modal parameter identification phase, the engine employs the Stochastic Subspace Identification (SSI) method to process the IMU data. Specifically, the 24-channel response signals are arranged in chronological order into an observation matrix. Where N is the number of sampling points. The dominant singular values ​​of the Hankel matrix of the system are extracted using Singular Value Decomposition (SVD), and then an extended observability matrix is ​​constructed. The pole locations are fitted using the Least Squares Complex Exponential Method (LSCE) to determine the first two natural frequencies of the boom-container system under the current operating condition. and its corresponding damping ratio At the same time, through the mode matrix The participation factors of each mode at the eight IMU locations were reconstructed, with particular attention paid to the two key output variables: the lateral displacement at the boom end and the pitch angle of the spreader.

[0037] During the transfer function matrix construction phase, the engine establishes a linear time-invariant (LTI) mapping from wind speed excitation to structural response. Let the wind speed input vector be:

[0038] ;

[0039] The key output vector of the structure is:

[0040] ,in This is the Laplace transform of the lateral displacement at the end of the boom. For the Laplace transform of the spreader's pitch angle;

[0041] The above output variables are obtained from the IMU sensor data in the following manner:

[0042] Lateral displacement acquisition at the boom end: The triaxial acceleration signals of two IMU sensors (IMU7 and IMU8) deployed at the boom end are selected, and extended Kalman filter (EKF) is used for data fusion and integration.

[0043] The filter model parameters and state equations are as follows: , where the state vector ( For lateral displacement, For lateral velocity, (For lateral acceleration), state transition matrix:

[0044] ( (Sampling period 0.01s);

[0045] The observation equation is: ;

[0046] Where the observation vector z is the mean lateral acceleration of IMU7 and IMU8, and the observation matrix is:

[0047] H=0,0,1;

[0048] Noise covariance setting: process noise covariance Measure noise covariance 4. Calculation process: Integrate the filtered acceleration signal s twice to obtain the lateral velocity. and lateral displacement Its Laplace transform is the output variable of the transfer function. ;

[0049] Acquisition of spreader pitch angle: The gyroscope angular velocity signal and accelerometer tilt angle signal from the four IMU sensors (numbered IMU3-IMU6) deployed on the spreader are fused together.

[0050] Initial calibration: In a stationary state, the initial pitch angle is obtained by measuring the gravitational component using an accelerometer. ;

[0051] Dynamic updates: Real-time acquisition of pitch axis angular velocity using a gyroscope. Integrating yields the dynamic pitch angle change. At the same time, gyroscope drift is corrected using accelerometer signals;

[0052] Fusion output: Final pitch angle Its Laplace transform is the output variable of the transfer function.

[0053] Then the transfer function matrix satisfy Each element of the matrix Expressed in the form of a second-order rational fraction:

[0054] ;in, For the first The undamped natural frequency of the channel, To correspond to the damping ratio, and These are the gain coefficients to be identified. The engine uses the Recursive Least Squares (RLS) algorithm to update these parameters online, with the following cost function:

[0055] ;

[0056] in, The forgetting factor is set to 0.95. It is a vector containing all the parameters to be estimated; The regression vector is composed of historical input and output data. The initial value of the covariance matrix of the RLS algorithm is set as a diagonal matrix. This ensures initial convergence speed. The modeling engine completes a full parameter update every fifty control cycles (i.e., five hundred milliseconds) to adapt to the drift in system dynamic characteristics caused by changes in wind speed and the load mass of the spreader.

[0057] The active stabilization command generation module runs on a DSP coprocessor of the central co-controller, and its control cycle is synchronized with the modeling engine, lasting ten milliseconds. This module receives the current wind speed vector. and the latest updated transfer function matrix First, the wind speed vector is projected onto the local coordinate system of the boom to obtain its components along the boom's axial, lateral, and vertical directions. Considering that crosswinds have the most significant impact on boom sway, the main focus is on... Quantity.

[0058] Module calculation at the dominant perturbation frequency (Pick and At the point where the middle is closer, the transfer function The frequency response of (i.e., the channel from the lateral wind speed to the lateral displacement at the end of the boom):

[0059] ;

[0060] The magnitude of the required reverse torque Mcomp is determined by the following formula:

[0061] ;

[0062] Here, Kgain is the safety gain coefficient, initially set to 1.2, but dynamically limited by the Lyapunov stability criterion. Specifically, the system energy function is defined as follows:

[0063] ,in For equivalent quality, For rotational inertia, The pitch angle of the lifting device;

[0064] The equivalent mass *m* refers to the equivalent lumped mass of the container crane boom-spreader-load system. It is a lumped mass parameter (dynamically changing with the load weight) converted to the boom end based on the principle of kinetic energy equivalence, taking the distributed mass of the boom, the mass of the spreader itself, and the mass of the container load. The calculation method is as follows:

[0065] ;

[0066] in Obtained through calculation of boom structure parameters:

[0067] ; L is the total length of the boom, and is a fixed value known in advance, is collected in real time by the load cell of the spreader;

[0068] is the moment of inertia, including the moment of inertia of the spreader itself and the additional moment of inertia of the load eccentricity, and the calculation method is: ; is a fixed parameter calibrated at the factory, and e is the eccentricity of the load center of gravity relative to the pitch axis of the spreader (identified in real time by the spreader attitude sensor);

[0069] Parameter acquisition approach: and are basic parameters calibrated by structural dynamics modeling and experiments before the system is shipped; is collected in real time by the load cell integrated into the spreader; e is identified in real time by the IMU sensor data deployed on the spreader, ensuring that the parameter dynamically matches the system working condition; if the derivative of the system energy function with respect to time is greater than zero, it is determined that the compensation command may cause energy injection, and at this time the safety gain coefficient used to calculate the reverse torque amplitude is adjusted exponentially, until the derivative of the system energy function with respect to time is less than or equal to zero.

[0070] That is, if , it is determined that the compensation command may cause energy injection, and at this time is adjusted exponentially, until .

[0071] The phase of the reverse torque is adjusted by introducing a preset phase compensation angle , so that the actual applied torque and the wind-induced disturbance torque are strictly opposite in the time domain. This compensation angle takes into account the phase lag of the hydraulic system, sensor delay, and modeling error, and the initial value is set to thirty degrees, and is fine-tuned through an online phase calibration algorithm. Finally, the reverse torque command is expressed as:

[0072] ;

[0073] This torque is then decomposed into a boom pitch direction torque component and a spreader rotation direction torque component . The decomposition is based on the current boom elevation angle and the spreader rotation angle , and is achieved through a rotation matrix:

[0074] ;

[0075] where, and is the component of the reverse moment in the global horizontal coordinate system;

[0076] To make the decomposition logic of the moment clear, the relevant coordinate system, angles and rotation matrix are defined as follows:

[0077] Coordinate system definition: Global horizontal coordinate system (O-XY): Taking the projection of the tire crane's rotation center on the ground as the origin O, the X axis is parallel to the wharf shoreline direction, the Y axis is perpendicular to the wharf shoreline pointing to the sea surface, and the XY plane is parallel to the ground;

[0078] Local coordinate system of the boom (O'-X'Y'): Taking the boom's rotation hinge point as the origin O', the X' axis points to the end of the boom along the length of the boom, the Y' axis is perpendicular to the X' axis upward in the vertical plane of the boom, and the XY plane of the global coordinate system has a boom elevation angle relationship.

[0079] Angle definition: Boom elevation angle a: The angle between the X' axis of the local coordinate system of the boom and the XY plane of the global coordinate system, the zero point is defined as the boom being placed horizontally (the X' axis coincides with the XY plane), and the upward swing is in the positive direction, which is measured in real time by the angle sensor at the boom's rotation hinge point;

[0080] Sling rotation angle b: The rotation angle of the sling relative to the end of the boom, the zero point is defined as the longitudinal axis of the sling coinciding with the X' axis of the local coordinate system of the boom, and the clockwise rotation around the boom axis is in the positive direction, which is measured in real time by the angle sensor of the sling rotation mechanism;

[0081] Rotation matrix construction: The rotation matrix used for the decomposition of the reverse moment:

[0082] R= ;

[0083] This matrix realizes the conversion of the reverse moment in the local coordinate system of the boom to the component in the global horizontal coordinate system, and the conversion relationship is:

[0084] ;

[0085] The decomposed moment components are respectively converted into corresponding hydraulic servo valve opening commands and , and the conversion relationship is realized by the look-up table interpolation method based on the hydraulic cylinder force-current characteristic curve and the motor torque-flow characteristic table.

[0086] In this embodiment, the hydraulic servo actuators consist of two independent electro-hydraulic proportional servo systems: one for driving the boom luffing cylinder, and the other for driving the load swing hydraulic motor. Each system is equipped with a high-frequency electro-hydraulic proportional servo valve, with a rated flow of one hundred and twenty liters per minute, a bandwidth of no less than one hundred and fifty hertz, and a hysteresis of less than half a percent. The control current signal of the servo valve is output by a sixteen-bit D / A converter and driven by a power amplifier. When the central cooperative controller detects that any wind speed sensor reading exceeds fifteen meters per second for more than two seconds, the active stabilization mode is automatically activated. In this mode, the final control signal of the hydraulic servo actuator is the algebraic superposition of the original operator instruction and the active stabilization compensation instruction:

[0087] ;

[0088] wherein, is the smoothed operator instruction, is the compensation valve opening instruction generated above. The superposition process is completed in FPGA in a hard real-time manner, ensuring that no additional delay is introduced.

[0089] The safety state monitoring unit, as an independent functional module of the central cooperative controller, continuously monitors three key parameters: the lateral displacement amplitude of the boom tip , the swing angle rate of the load , and the pressure fluctuation of the main hydraulic system circuit . Through IMU data fusion estimation, the Kalman filter is used to integrate the boom tip acceleration twice and eliminate drift; The maximum value of the IMU gyroscope readings taken directly from the four corners of the load; Collected by the pressure sensor installed at the outlet of the hydraulic pump, with a sampling frequency of one kilohertz. When > fifteen centimeters, or > ten degrees per second, or > twenty percent of the system rated pressure, the safety state monitoring unit immediately sends an interrupt signal to the main processor. The latter then executes a three-level safety response: first, cut off the active stabilization instruction output, leaving only the operator instruction; second, if the parameters continue to deteriorate, start a gradual deceleration program within five hundred milliseconds to linearly reduce the speed of each motion axis to zero; third, if the parameters do not recover within ten seconds, trigger the emergency brake, shut down all hydraulic power sources and enable the mechanical locking device.

[0090] In the present embodiment, all miniature inertial measurement units and ultrasonic wind speed and direction sensors are equipped with self-diagnosis circuits. The IMU determines whether the gyroscope zero offset is out of limits by periodically injecting a known angular velocity pulse and comparing the output response; the wind speed sensor detects transducer blockage or icing failure by cross-verifying the consistency of the propagation times of three sets of ultrasonic paths. Once a diagnosis fails, the sensor immediately sends a sixteen-bit fault code containing the device ID and fault type to the central cooperative controller via the CANFD bus, which records it in the non-volatile memory and reports it to the maintenance interface of the remote operation terminal.

[0091] All sensor signal cables use a double-shielded twisted pair structure, with an inner aluminum foil shield and an outer woven copper mesh, and the shield layer is single-point grounded to the main steel structure of the tire crane. The cable is laid through galvanized steel pipes throughout, and the steel pipes are welded to the steel structure to form a continuous equipotential body, effectively suppressing electromagnetic interference generated by frequency converters, radio base stations, and lightning in the port environment. Actual measurements show that the signal-to-noise ratio of the IMU acceleration signal is still better than 40 dB at a distance of 10 meters from a 400-kilowatt shore-to-ship converter.

[0092] In one example, a certain port RTG (rubber-tired gantry crane) equipped with the present system performs standard loading and unloading operations under strong wind conditions with an average wind speed of 18 meters per second and gusts up to 25 meters per second. The system activates the active stabilization mode, real-time collects wind speed and structural response data, and the modeling engine updates the transfer function matrix every 500 milliseconds. The active stabilization instruction generation module calculates the reverse torque and drives the hydraulic system to add compensation. During the operation, the peak-to-peak value of the lateral displacement of the jib end is controlled to 9.3 centimeters, the peak-to-peak value of the spreader swing angle is 1.7 degrees, and the positioning repeatability reaches ±3.2 centimeters.

[0093] In one comparative example, to verify the technical effect of the present invention, a comparative example is set up: the same RTG is closed under the same wind conditions without active stabilization function, relying only on the operator's manual fine adjustment. At this time, the peak-to-peak value of the lateral displacement of the jib end is 32.6 centimeters, the peak-to-peak value of the spreader swing angle is 6.4 degrees, and the safety shutdown is triggered multiple times due to the swing amplitude exceeding the limit, with an operation interruption rate of 41%.

[0094] The following table summarizes the comparison of key performance indicators between the example and the comparative example:

[0095] Performance indicators Examples Comparative examples Peak-to-peak lateral displacement of boom tip 9.3 cm 32.6 cm Peak-to-peak swing angle of spreader 1.7° 6.4° Positioning repeatability (3σ) ± 3.2 cm ± 11.8 cm Single operation cycle time 142s 187s Number of stoppages per hour due to oscillation overrun 0 2.3

[0096] The above data show that the container tire crane remote control system of the application significantly improves the operation stability and efficiency in strong wind environment. The system deeply integrates wind load sensing and structural inertia response, builds a high-fidelity dynamic mapping model, and generates a phase-matched active compensation torque accordingly, realizing physical level suppression of wind-induced resonance. The entire control process does not require operator intervention and is completely completed by the central collaborative controller, ensuring the reliability and safety of all-weather efficient operation of the port.

[0097] Further, the system software architecture adopts modular design, and each functional module interacts through a clear API interface definition, facilitating subsequent upgrading and maintenance. For example, the wind speed-inertia coupling modeling engine can be replaced by a state space model based on deep learning, and only the input and output interfaces need to be consistent to seamlessly integrate. In addition, the central collaborative controller reserves an OPCUA server interface, supports data interaction with the port TOS, and realizes global collaborative optimization of operation plans and device states.

[0098] In summary, the application builds a complete, reliable and efficient container tire crane remote control system through rigorous sensor layout, high-precision synchronous sampling, online dynamic modeling, phase-matched compensation and multiple safety guarantee mechanisms, fully meeting the technical requirements of modern automated terminals for high precision, high availability and all-weather operation capability.

[0099] In the description of the application, it should be understood that various forms of processes shown above can be used to reorder, add or delete steps. For example, each step described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions provided by the present disclosure can be achieved, and this is not limited herein.

[0100] The above description is only a preferred embodiment of the application and does not constitute a limitation on the scope of protection of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements within the spirit and principles of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A container tire crane remote control system characterized by, The system comprises a remote operation terminal, a communication transmission module, a central cooperative controller, a wind load sensing module, a structure dynamic response sensing module, a coupling modeling engine, an active stabilization instruction generation module and a hydraulic servo actuator; The remote operation terminal is configured to receive a spreader movement instruction input by an operator and send the instruction to the central cooperative controller via the communication transmission module; The wind load sensing module comprises a plurality of ultrasonic wind speed and direction sensors installed on the top of the main beam of the tire crane, the root of the crane boom and the middle of the spreader beam, respectively, for real-time acquisition of wind speed vectors in three-dimensional space; The structure dynamic response sensing module comprises a plurality of micro inertial measurement units fixedly installed on the hinge points of the crane boom, the end of the crane boom, the guide pulley bracket of the lifting steel wire rope and the four corners of the container spreader, respectively, each micro inertial measurement unit comprising a three-axis accelerometer and a three-axis gyroscope for synchronous measurement of linear acceleration and angular velocity data at each installation position; The coupling modeling engine is built into the central cooperative controller, with its input end connected to the output ends of the wind load sensing module and the structure dynamic response sensing module, for establishing a transfer function matrix between wind speed excitation and structure response based on the wind speed vectors and the linear acceleration and angular velocity data; The active stabilization instruction generation module receives the transfer function matrix and the current wind speed vector output by the coupling modeling engine, calculates the reverse torque required to offset the current wind-induced disturbance, decomposes the reverse torque into a crane boom pitch direction torque component and a spreader rotation direction torque component, and converts them into corresponding hydraulic servo valve opening degree instructions; The hydraulic servo actuator comprises a crane boom pitch hydraulic cylinder, a spreader rotation hydraulic motor and their matching electro-hydraulic proportional servo valves, with the control ends of the electro-hydraulic proportional servo valves connected to the output end of the active stabilization instruction generation module; When the system detects that the wind speed exceeds a preset threshold, the central cooperative controller activates the active stabilization mode, and the hydraulic servo actuator executes the compensation instructions output by the active stabilization instruction generation module while executing the original movement instructions of the remote operator.

2. The container tire crane remote control system of claim 1, wherein, The process of establishing the transfer function matrix by the coupling modeling engine comprises: Performing frequency spectrum decomposition on the wind speed vectors to extract dominant frequency components and their amplitudes; Performing modal identification on the acceleration and angular velocity signals output by the micro inertial measurement units to determine the first two natural frequencies and corresponding vibration modes of the crane boom-container system; Establishing a transfer function matrix between the wind speed excitation and the structure response based on the least square fitting method, which represents the dynamic response characteristics of the lateral displacement of the end of the crane boom and the pitch angle of the spreader to the wind load input under a specific wind speed condition; The coupling modeling engine continuously updates the transfer function matrix.

3. The container yard crane remote control system according to claim 2, wherein, The coupling modeling engine uses the recursive least square algorithm to update the parameters of the transfer function matrix online.

4. The container tire crane remote control system according to claim 1, wherein, When the active stabilization instruction generation module calculates the reverse torque, the amplitude of the reverse torque is determined by the gain of the transfer function matrix at the dominant disturbance frequency, and the phase is adjusted by introducing a preset phase compensation angle, so that the reverse torque is opposite to the actual disturbance torque in the time domain.

5. The container tire crane remote control system according to claim 4, wherein, When the active stabilization instruction generation module calculates the reverse torque, an amplitude limiting mechanism based on Lyapunov stability criterion is introduced; the system energy function is defined as: ； wherein is the equivalent mass, is the moment of inertia, is the lateral displacement of the boom tip, is the luff angle; if the derivative of the system energy function with respect to time is greater than zero, it is determined that the compensation command can cause energy injection, in which case the safety gain factor used to calculate the magnitude of the counter-torque is adjusted exponentially decaying until the derivative of the system energy function with respect to time is less than or equal to zero.

6. The container tire crane remote control system according to claim 1, wherein, The central cooperative controller is configured with a safety state monitoring unit, which monitors the lateral displacement amplitude of the boom end, the trolley swing angle rate and the hydraulic system pressure fluctuation in real time; when any monitoring parameter exceeds the safety limit value, the safety state monitoring unit sends an emergency intervention signal to the central cooperative controller, the central cooperative controller cuts off the active stabilization instruction output, and starts a gradual deceleration shutdown program.

7. The container tire crane remote control system according to claim 1, wherein, The miniature inertial measurement unit and the ultrasonic wind speed and direction sensor are both equipped with temperature compensation circuit and self-diagnosis function; the self-diagnosis function judges whether the sensor is failed by periodically injecting known test signal and comparing output response, or by cross verifying the consistency of propagation time of multiple ultrasonic paths; when the sensor is failed, it automatically reports fault code to the central cooperative controller.

8. The container tire crane remote control system according to claim 1, wherein, The communication transmission module adopts an industrial-grade 5G communication link based on time-sensitive network enhancement, which supports millimeter wave and Sub-6GHz dual-band adaptive switching at physical layer, and integrates forward error correction coding and retransmission mechanism at media access control layer; the communication transmission module is provided with a special hardware accelerator on the side of the central cooperative controller, which is used for time stamp synchronous marking of received remote operation instructions and uploaded sensor data stream.

9. The container tire crane remote control system according to claim 1, wherein, The central cooperative controller adopts a heterogeneous multi-core architecture, including a master processor, at least one real-time digital signal processor coprocessor and a field programmable gate array unit; the master processor is responsible for high-level task scheduling, the digital signal processor coprocessor undertakes wind load processing and structural response analysis tasks, and the field programmable gate array unit realizes high-speed synchronous sampling, sensor fault diagnosis and safety state monitoring of hard real-time logic.

10. The container tire crane remote control system according to claim 1, wherein, All the miniature inertial measurement units are connected to the high-speed synchronous sampling interface of the central cooperative controller through hardwire; the signal cables of the ultrasonic wind speed and direction sensor and the miniature inertial measurement unit adopt shielded twisted pair structure, and are laid in the tire crane steel structure through metal conduit.

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