A control method and system for bucket-slinging operation of a bridge grab unloader

Abstract

This invention discloses a control method and system for the bucket-slinging operation of a bridge-type grab unloader, belonging to the field of intelligent control technology for port machinery. The method includes: calculating the six-degree-of-freedom motion parameters of the hull and the trolley following error based on real-time hull pose data to generate hull compensation parameters and trolley deviation compensation; obtaining initial grab motion control commands based on the bucket-slinging motion parameter commands; performing dynamic trajectory correction by superimposing the hull compensation parameters and trolley deviation compensation to generate grab motion control commands; executing the grab motion control commands; evaluating the bucket-slinging operation effect using a dynamic time warping algorithm; and dynamically performing secondary dynamic trajectory correction. This invention, by using a dynamic time warping algorithm to compare the spatiotemporal differences between the actual trajectory and the target trajectory, and combining this with the material residue to calculate the effect score and trigger secondary correction, forms a closed-loop control mechanism of "execution-evaluation-optimization," ensuring operational accuracy under complex disturbances.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for port machinery, and in particular to a control method and system for the bucket-slinging operation of a bridge grab unloader. Background Technology

[0002] As a core piece of equipment in bulk cargo terminals, the bridge-type grab unloader's bucket-swing operation control primarily relies on preset motion parameters and basic hull compensation technology. Existing methods typically generate bucket-swing action commands by detecting fluctuations in the drive motor current and changes in the grab's tilt angle, combined with preset rules; simultaneously, they employ GNSS / IMU fusion positioning technology to acquire the hull's attitude, predict hull displacement based on a kinematic model, and generate trolley compensation amounts. This type of technology, through static parameter mapping and linear compensation mechanisms, can achieve grab trajectory control under normal operating conditions.

[0003] Existing methods still have room for improvement in terms of dynamic adaptability: First, relying solely on bucket parameters makes it difficult to dynamically match the characteristic changes of materials with different viscosities, which can easily cause bucket shaking or residue due to sudden changes in resistance when using high-viscosity materials; Second, the lack of a dynamic feedback adjustment mechanism based on the actual bucket effect makes it difficult to cope with the cumulative error under complex working conditions after a single trajectory correction. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a control method and system for the bucket-slinging operation of a bridge grab unloader, which solves the problems of weak correlation of material parameters and insufficient closed-loop control levels in the prior art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a control method for bucket-swinging operations of a bridge-type grab unloader, comprising: acquiring three-phase current data of the switching motor and extracting current harmonic distortion rate characteristics; constructing a viscosity identification model, evaluating the material viscosity grade based on the current harmonic distortion rate characteristics and grab swing amplitude parameters, and dynamically generating bucket-swinging motion parameter commands in conjunction with bucket stabilization constraints; calculating the six-degree-of-freedom motion parameters of the hull and the trolley following error based on real-time hull pose data, and generating hull compensation parameters and trolley deviation compensation amounts; acquiring initial grab motion control commands based on the bucket-swinging motion parameter commands, performing dynamic trajectory correction by superimposing hull compensation parameters and trolley deviation compensation amounts, and generating grab motion control commands; executing the grab motion control commands, evaluating the bucket-swinging operation effect using a dynamic time warping algorithm, and dynamically performing secondary dynamic trajectory correction.

[0008] As a preferred embodiment of the control method for the bucket-slinging operation of a bridge grab unloader described in this invention, the extraction of current harmonic distortion rate characteristics refers to filtering, denoising, and normalizing the three-phase current data, and analyzing each harmonic component through fast Fourier transform to extract current harmonic distortion rate characteristics.

[0009] As a preferred embodiment of the control method for the bucket-slinging operation of a bridge-type grab unloader according to the present invention, the steps for constructing the viscosity recognition model are as follows:

[0010] By fusing the current harmonic distortion rate characteristics and the grab bucket swing amplitude parameters through feature cascading, a material viscosity feature vector is generated.

[0011] Using SVR as the base model and training it with historical material operation data, the mapping relationship between material viscosity feature vector and material viscosity grade is established through the trained SVR.

[0012] A viscosity recognition model is constructed based on the mapping relationship between the material viscosity feature vector and the material viscosity grade, as well as the trained SVR.

[0013] As a preferred embodiment of the control method for bucket-swinging operation of a bridge grab unloader according to the present invention, the steps of evaluating the material viscosity grade and dynamically generating bucket-swinging action parameter commands in combination with bucket stabilization constraints are as follows:

[0014] Input the material viscosity feature vector into the viscosity recognition model to predict the material viscosity index;

[0015] Define low viscosity threshold and high viscosity threshold, compare them with the material viscosity index, and evaluate the material viscosity grade based on the comparison results;

[0016] Based on historical operation data, the stable swing range of the grab bucket under different viscosity grades was statistically analyzed, and the stability constraint conditions were generated through multibody dynamics simulation.

[0017] Based on the bucket stabilization constraints and the material viscosity grade, a condition matching and interpolation optimization algorithm is used to generate bucket throwing action parameter commands.

[0018] As a preferred embodiment of the control method for the bucket-slinging operation of a bridge-type grab unloader described in this invention, the steps for calculating the six-degree-of-freedom motion parameters of the hull and the trolley following error to generate hull compensation parameters and trolley deviation compensation are as follows:

[0019] Real-time hull pose data is collected, and the six-degree-of-freedom motion parameters of the hull are obtained through the time difference method.

[0020] Based on the six-degree-of-freedom motion parameters, the future hull position is predicted by kinematic extrapolation, and a linear quadratic regulator is used to identify the ideal following trajectory of the trolley.

[0021] Based on the actual position of the vehicle and the ideal following trajectory of the vehicle, the following error of the vehicle is obtained, and the following error of the vehicle is converted into a speed compensation command using a PID controller.

[0022] The anti-sway compensation angle of the grab bucket is obtained based on the angular velocity of the hull in three-dimensional space.

[0023] As a preferred embodiment of the control method for the bucket-slinging operation of a bridge-type grab unloader according to the present invention, the steps of obtaining the initial grab motion control command and generating the grab motion control command by dynamically correcting the trajectory by superimposing hull compensation parameters and trolley deviation compensation amount are as follows:

[0024] A dynamic pose compensation algorithm is used to superimpose the hull compensation parameters onto the initial grab trajectory to correct the grab offset caused by hull swaying.

[0025] Based on the trolley deviation compensation amount, the trolley following error is dynamically compensated;

[0026] The corrected initial grab trajectory and the compensated trolley following error were verified through multibody dynamics simulation, and grab motion control commands were dynamically generated.

[0027] As a preferred embodiment of the control method for bucket-slinging operation of a bridge grab unloader according to the present invention, the steps of using a dynamic time warping algorithm to evaluate the bucket-slinging operation effect and dynamically performing secondary dynamic trajectory correction are as follows:

[0028] During the execution of the grab bucket motion control command, the actual motion trajectory of the grab bucket and the amount of material residue after the bucket swing operation are collected, and the dynamic time warping algorithm is used to calculate the bucket swing operation effect score.

[0029] Define a threshold for the bucket-swing effect and compare it with the bucket-swing operation effect score. Evaluate the bucket-swing operation effect based on the comparison results.

[0030] When the effect of the grab bucket operation is not up to standard, secondary correction parameters are generated based on the deviation direction of the grab bucket's motion trajectory, and then iteratively optimized by superimposing them on the grab bucket motion control command in real time through the PID controller.

[0031] Secondly, the present invention provides a control system for the bucket-slinging operation of a bridge grab unloader, including a data acquisition module for acquiring three-phase current data of the switching motor and extracting current harmonic distortion rate characteristics.

[0032] The bucket-swinging instruction generation module is used to build a viscosity recognition model. Based on the characteristics of current harmonic distortion rate and the parameters of bucket swing amplitude, it evaluates the viscosity grade of the material and dynamically generates bucket-swinging action parameter instructions in combination with bucket stabilization constraints.

[0033] The instruction correction module is used to calculate the six-degree-of-freedom motion parameters of the hull and the tracking error of the trolley based on the real-time pose data of the hull, and generate the hull compensation parameters and the trolley deviation compensation.

[0034] The motion command generation module is used to obtain the initial grab motion control command based on the grab action parameter command, and to generate the grab motion control command by dynamically correcting the trajectory by superimposing the hull compensation parameters and the trolley deviation compensation amount.

[0035] The operation evaluation module is used to execute the grab bucket motion control commands, use a dynamic time warping algorithm to evaluate the effect of the grab bucket operation, and dynamically perform secondary dynamic trajectory correction.

[0036] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the control method for the bucket-slinging operation of a bridge grab unloader as described in the first aspect of the present invention.

[0037] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the control method for the bucket-slinging operation of a bridge grab unloader as described in the first aspect of the present invention.

[0038] The beneficial effects of this invention are as follows: by integrating the current harmonic distortion rate characteristics with the grab bucket swing amplitude parameters into a material viscosity feature vector, and establishing a viscosity grade mapping relationship based on the SVR model, real-time quantitative evaluation of material characteristics is achieved, enabling the bucket swing action parameters to adapt to different viscosity conditions; by using a dynamic time warping algorithm to compare the spatiotemporal differences between the actual trajectory and the target trajectory, and combining the material residue to calculate the effect score and trigger secondary correction, a closed-loop control mechanism of "execution-evaluation-optimization" is formed, ensuring the operational accuracy under complex disturbances. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a control method for the bucket-slinging operation of a bridge-type grab unloader.

[0041] Figure 2 This is a schematic diagram of the control system used for the bucket-slinging operation of a bridge grab unloader.

[0042] Figure 3 A flowchart for constructing a viscosity identification model.

[0043] Figure 4 This is a flowchart for the closed-loop control of the bucket-slinging operation. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0047] Reference Figure 1 As one embodiment of the present invention, this embodiment provides a control method for the bucket-swinging operation of a bridge grab unloader, comprising the following steps:

[0048] S1: Obtain the three-phase current data of the switching motor and extract the current harmonic distortion rate characteristics.

[0049] The three-phase current data includes real-time current waveform signals of phases A, B, and C;

[0050] It should be noted that the three-phase current data (real-time current waveform signals of phases A, B, and C) of the opening and closing motor of the bridge grab unloader are acquired in the following way: Hall effect current sensors are installed in the motor drive circuit to synchronously acquire the instantaneous values ​​of the three-phase current. After isolation amplification and anti-aliasing filtering by the signal conditioning circuit, the analog signal is digitized by a 16-bit high-precision ADC analog-to-digital converter, finally forming a time-aligned three-phase current waveform signal sequence. Phases A, B, and C refer to the three independent phases of the three-phase AC power supply to the opening and closing motor of the bridge grab unloader, constituting a 120° phase difference symmetrical power supply system for motor drive.

[0051] The three-phase current data is filtered, denoised, and normalized.

[0052] Furthermore, the three-phase current data is first subjected to high-frequency noise suppression through a Butterworth low-pass filter, with the cutoff frequency set to 1kHz to preserve the current harmonic characteristics; then, a wavelet threshold denoising algorithm is used to decompose the real-time current waveform signals of phases A, B, and C into time-frequency domains, and a soft threshold function is used to eliminate power frequency interference and random noise; finally, the filtered and denoised three-phase current data is subjected to linear normalization processing, mapping the amplitude to the interval [-1, 1].

[0053] Based on the preprocessed three-phase current data, the harmonic components are analyzed by fast Fourier transform, and the current harmonic distortion rate characteristics are extracted.

[0054] Furthermore, spectral analysis was performed using a 2048-point fast Fourier transform to identify the amplitude of each harmonic component in the 0-2kHz frequency band of the real-time current waveform signals of phases A, B, and C. For each phase current, the amplitude ratio of the harmonic component to the fundamental component was extracted to form a current harmonic distortion rate feature vector containing 75-dimensional features (three phases × 25 harmonics).

[0055] S2, please refer to Figure 3 A viscosity identification model is constructed to evaluate the viscosity grade of materials based on the characteristics of current harmonic distortion rate and the swing amplitude parameters of the grab bucket, and to dynamically generate bucket swing action parameter commands in combination with bucket stabilization constraints.

[0056] The parameters of the grab swing amplitude include the maximum tilt angle and swing kinetic energy of the grab;

[0057] It should be noted that the grab swing amplitude parameter is obtained by real-time acquisition of swing angle data by the tilt sensor on the grab, and then by peak detection and kinetic energy integration.

[0058] By fusing the current harmonic distortion rate characteristics with the grab bucket swing amplitude parameters through feature cascading, a material viscosity feature vector is generated.

[0059] Furthermore, the current harmonic distortion rate characteristics of phases A, B, and C are first arranged into a 75-dimensional vector in phase order. Then, the maximum tilt angle and swing kinetic energy of the grab bucket are added as additional features to the end of the vector, ultimately generating a material viscosity feature vector. The fusion process retains the physical meaning and dimensional consistency of all features. The current harmonic distortion rate characteristics maintain the calculated values ​​according to the IEEE Std 1459-2010 standard, and the grab bucket swing amplitude parameters maintain the units of angle (radians) and energy (joules).

[0060] SVR (Support Vector Regression) was used as the base model and trained using historical material handling data.

[0061] It should be noted that a grid search method is used to optimize the hyperparameters of SVR (penalty coefficient, hyperparameters for controlling material viscosity and calculating decay rate based on eigenvector similarity). The optimal parameter combination is selected through 5-fold cross-validation; the training process uses the sequence minimum optimization algorithm to solve for the Lagrange multipliers. and We establish a regression mapping relationship between the material viscosity feature vector and the viscosity grade, and finally generate an SVR model with the minimum generalization error.

[0062] Historical material handling data consists of current harmonic distortion characteristics and grab swing angle-energy parameters;

[0063] The mapping relationship between the material viscosity feature vector and the material viscosity grade is established by training the SVR;

[0064] Furthermore, the trained SVR maps the material viscosity feature vector to a high-dimensional space based on the kernel function, and establishes the mapping relationship between the material viscosity feature vector and the viscosity grade through support vectors.

[0065] A viscosity recognition model is constructed based on the mapping relationship between the material viscosity feature vector and the material viscosity grade, as well as the trained SVR.

[0066] Furthermore, the trained SVR and the mapping relationship between the material viscosity feature vector and the viscosity grade together constitute the core component of the viscosity recognition model. The workflow of the viscosity recognition model is as follows: First, it receives the real-time acquired material viscosity feature vector input; then, it calculates the similarity with historical support vectors through the kernel function built into the SVR; and finally, it outputs the viscosity index based on the mapping relationship.

[0067] Input the material viscosity feature vector into the viscosity recognition model to predict the material viscosity index. The expression is as follows:

[0068] ;

[0069] in, It is the viscosity index of the material. It is the number of support vectors in SVR. It is the index variable of the support vector. It is The Lagrange multipliers corresponding to each support vector It is The dual Lagrange multipliers corresponding to each support vector It is a hyperparameter that controls the decay rate of the viscosity eigenvector similarity calculation of materials. This is the first step in the SVR training process. The historical material handling data corresponding to each support vector. It is the viscosity characteristic vector of the material. It is a global bias term in SVR;

[0070] Based on statistical analysis of historical material handling data, a low viscosity threshold is defined. 1 (value range: 0.15-0.25) and high viscosity threshold 2 (Value range: 0.3-0.45);

[0071] It should be noted that, based on the current harmonic distortion characteristics and grab bucket swing angle-energy parameters in historical material handling data, a statistical analysis was performed on the distribution of material viscosity index. The K-means clustering algorithm was used to divide the historical data into three viscosity grade intervals, with the boundary between grade I and grade II viscosity taken as the low viscosity threshold Y1, and the boundary between grade II and grade III viscosity taken as the high viscosity threshold Y2.

[0072] when When the viscosity is less than Y1, the current material viscosity is a first-order viscosity; for example, when the material viscosity index is... When the viscosity is 0.15 (Y1=0.2), it is judged to be a first-level viscosity material similar to dry coal (low viscosity material).

[0073] When Y1≤ When <Y2, the current material viscosity is a second-order viscosity; for example, when the material viscosity index... When the viscosity is 0.25 (Y1=0.2, Y2=0.35), it is judged to be a secondary viscosity (medium viscosity material) similar to wet sand.

[0074] when When Y2 is greater than or equal to 2, the current material viscosity is a third-order viscosity; for example, the material viscosity index. When the viscosity is 0.4 (Y2=0.35), it is judged to be a third-degree viscosity similar to clay (high viscosity material).

[0075] Among them, the viscosity of grade 1 < viscosity of grade 2 < viscosity of grade 3;

[0076] Based on historical operation data, the stable swing range of the grab bucket under different viscosity grades was statistically analyzed, and the stability constraint conditions were generated through multibody dynamics simulation.

[0077] Furthermore, based on the grab swing amplitude parameters recorded in historical operation data, the stability of the grab's motion trajectory under first-level, second-level, and third-level viscosity conditions was analyzed. Multibody dynamics simulation was used to reproduce the operation scenarios under different viscosity levels, observing the dynamic response of the grab under changes in maximum tilt angle and swing kinetic energy. By comparing the simulation results with historical operation data, the boundaries of the stable swing range corresponding to each viscosity level were determined, forming stable grab constraints that limit the grab's swing motion parameters. These constraints include the maximum allowable velocity and acceleration, ensuring that the grab remains stable when completing the swing motion at the specified viscosity level.

[0078] Based on the stabilization constraints and the viscosity grade of the material, the bucket-swinging action parameter instructions (bucket-swinging speed, bucket-swinging angle, and delay parameters) are generated using a condition matching and interpolation optimization algorithm.

[0079] Furthermore, the maximum tilt angle limit and swing kinetic energy limit of the current material viscosity grade are used as basic parameters in the stabilization constraint conditions. These parameters are matched with the historical best bucket swinging action parameter library to screen out candidate bucket swinging speed, bucket swinging angle, and delay parameter combinations that meet the current viscosity grade and stabilization constraint conditions. A cubic spline interpolation algorithm is used to optimize and adjust the candidate parameters, generating a smooth and continuous bucket swinging action parameter command curve to ensure that the bucket swinging speed, bucket swinging angle, and delay parameters maximize material unloading efficiency while meeting the stabilization constraint conditions.

[0080] S3, based on the real-time hull pose data, calculates the hull's six-degree-of-freedom motion parameters and the trolley's following error, and generates hull compensation parameters and trolley deviation compensation.

[0081] Real-time hull pose data (including 3D position and Euler angles) is acquired through a combination of GNSS and IMU.

[0082] Based on the real-time hull pose data, the six-degree-of-freedom motion parameters of the hull are obtained by the time difference method.

[0083] Furthermore, the time-difference method is used to identify the pose changes between adjacent time steps, and the linear velocity and angular velocity components are solved separately. A filtering algorithm is used to eliminate the influence of sensor noise, and after fusing multiple sets of difference results, the six-degree-of-freedom motion parameters of the hull are output. The six-degree-of-freedom motion parameters refer to the hull's linear velocity (translation along the X / Y / Z axes) and angular velocity (roll / pitch / yaw rotation) in three-dimensional space.

[0084] Based on the six-degree-of-freedom motion parameters, the future position of the hull is predicted by kinematic extrapolation, and a linear quadratic regulator is used to identify the ideal following trajectory of the trolley.

[0085] Furthermore, the linear velocity and angular velocity components in the degrees of freedom motion parameters are used to calculate the future motion trend of the hull, and the changes in the hull position are extrapolated based on kinematic principles. The trajectory optimization method in optimal control theory is used to process the extrapolation results, and the tracking accuracy and energy consumption factors are comprehensively considered to generate an ideal following trajectory for the vehicle that includes position, velocity, and acceleration.

[0086] The expression for predicting the future position of the ship is:

[0087] ;

[0088] in, It is the current moment. The position of the hull, It is the future moment The position of the hull, This is the current linear velocity of the ship. It predicts the time step;

[0089] Based on the actual position of the vehicle and the ideal following trajectory of the vehicle, the following error of the vehicle is obtained, and the following error of the vehicle is converted into a speed compensation command using a PID controller.

[0090] Furthermore, the actual position of the vehicle is acquired in real time using a high-precision absolute encoder, and compared spatiotemporally with the planned position-velocity-acceleration third-order parameters in the vehicle's ideal following trajectory to obtain the following error, including horizontal position and velocity deviations. The PID controller responds quickly to position deviations through a proportional element, eliminates steady-state errors through an integral element, and predicts trend changes through a derivative element, ultimately outputting a speed compensation command with smooth transition characteristics.

[0091] The anti-sway compensation angle of the grab bucket is obtained based on the angular velocity of the hull in three-dimensional space;

[0092] Furthermore, the angular velocity of the hull in three-dimensional space is measured in real time by an inertial measurement unit, and the measured angular velocity components are converted into the coordinate system of the grab bucket's swing plane. Based on the converted angular velocity components and the dynamic characteristics of the grab bucket's swing, the compensation torque required to suppress the swing is analyzed. According to the compensation torque and the geometric parameters of the grab bucket's wire rope suspension, the anti-sway compensation angle of the grab bucket is obtained. The anti-sway compensation angle of the grab bucket is achieved by controlling the speed of the wire rope's deployment and retraction, which in real time counteracts the grab bucket's swing caused by the hull's motion and maintains the stability of the grab bucket.

[0093] It should be noted that the speed compensation command is the trolley deviation compensation amount, and the grab anti-sway compensation angle is the hull compensation parameter.

[0094] S4, please refer to Figure 4Based on the bucket swing action parameter command, the initial grab motion control command is obtained, and the dynamic trajectory is corrected by superimposing the hull compensation parameter and the trolley deviation compensation amount to generate the grab motion control command.

[0095] Based on the instructions of the grabbing motion parameters, cubic spline interpolation is used to generate the initial grabbing motion trajectory (position, velocity, acceleration).

[0096] Furthermore, the swing speed, swing angle, and delay parameters in the swing action parameter command are used as control point inputs for cubic spline interpolation calculation. First, a control point sequence containing timestamps and position coordinates is established. Then, a tridiagonal matrix is ​​constructed to solve for the coefficients of the cubic polynomial in each interval. Finally, the initial grab trajectory composed of continuous piecewise cubic polynomials is output.

[0097] A dynamic pose compensation algorithm is used to superimpose the hull compensation parameters onto the initial grab trajectory to correct the grab offset caused by hull swaying.

[0098] Furthermore, a geometric mapping relationship between the ship's swaying motion and the grab's posture is established, and the compensation angle is converted into the position correction amount of the grab's end effector in three-dimensional space. Then, the position correction amount is superimposed on the corresponding coordinate points of the initial grab's motion trajectory in a time sequence, and quaternion interpolation is used to ensure smooth attitude transition, thereby generating a grab offset that eliminates the swaying motion caused by the ship's swaying.

[0099] The trolley following error is dynamically compensated based on the trolley deviation compensation amount.

[0100] Furthermore, the trolley deviation compensation value is input to a digital PID controller. After processing through proportional, integral, and derivative operations, the controller outputs a trolley speed compensation command. This command is transmitted via the CAN bus to the trolley drive inverter to adjust the speed of the trolley's walking motor. For example, when the deviation between the trolley's actual position coordinates and its ideal following trajectory reaches ±50 mm, the PID controller outputs a corresponding trolley speed compensation command, driving the trolley to eliminate the position deviation within a set time. Simultaneously, a wire rope tension sensor monitors the force changes during the compensation process.

[0101] The corrected initial grab trajectory and the compensated trolley following error were verified through multibody dynamics simulation, and grab motion control commands were dynamically generated.

[0102] Furthermore, the multibody dynamics simulation platform receives the corrected initial grab trajectory and the compensated trolley following error data, establishing a rigid-flexible coupled virtual prototype including the grab, wire rope, trolley, and hull. During the simulation, the corrected initial grab trajectory is used as the driving input, and the compensated trolley following error is used as the boundary condition to solve the multibody dynamics equations and verify whether the grab swing amplitude parameters meet the stabilization constraints. When the simulation results show that the actual grab trajectory deviates from the target trajectory beyond the allowable range, the velocity curve and acceleration parameters in the grab motion control command are automatically adjusted, and the simulation is repeated for verification. After iterative optimization, the final grab motion control command that satisfies all stabilization constraints is output.

[0103] It should be noted that the trolley refers to the load-bearing mechanism on the bridge grab unloader that moves horizontally along the main beam track, and is connected to the grab bucket and controls its horizontal position via steel wire ropes.

[0104] S5, please continue reading Figure 4 It executes the grab bucket motion control command, uses a dynamic time warping algorithm to evaluate the effect of the bucket-slinging operation, and dynamically performs secondary dynamic trajectory correction.

[0105] During the execution of grab bucket motion control commands, the actual motion trajectory (position, velocity, acceleration) of the grab bucket and the amount of material residue after the bucket-slinging operation are collected. A dynamic time warping algorithm is then used to calculate the bucket-slinging operation effectiveness score, expressed as:

[0106]

[0107] in, It is the first grabber The coordinates of the actual trajectory points It is the first grabber The coordinates of the actual trajectory points It is the set of all time-aligned paths in the Dynamic Time Warping (DTW) algorithm. It is the maximum permissible trajectory deviation threshold (the value range is 200-500 mm). It refers to the amount of residual material. It is the residual penalty coefficient (with a value range of 0.1-0.3). It is a score for the effectiveness of bucket-swing operations. It is the optimal alignment path with the smallest cumulative distance in the set of alignment paths;

[0108] The time alignment path refers to the optimal sequence of corresponding points that allows for non-linear scaling of the time axis when matching the actual trajectory of the grab with the target trajectory of the grab in dynamic time warping. .

[0109] The target trajectory of the grab bucket refers to the ideal motion path (position, velocity, acceleration) generated by cubic spline interpolation based on the parameters of the grab bucket action (grab bucket speed, angle, delay).

[0110] Furthermore, the process of generating the ideal motion path using cubic spline interpolation is as follows: First, the swing speed, swing angle, and delay parameters in the swing motion parameter command are converted into discrete trajectory control points. Each discrete trajectory control point contains a timestamp and corresponding spatial coordinates. Then, a piecewise cubic polynomial curve is constructed based on the control point sequence to ensure a smooth transition in the position, velocity, and acceleration parameters of adjacent curve segments at the connection points. Finally, the generated ideal motion path consists of continuously differentiable cubic polynomial curves.

[0111] Based on the statistical analysis of historical bucket-swing operation data, a bucket-swing effect threshold S1 is defined (with a value range of 0.8-0.9).

[0112] Furthermore, based on historical bucket-swing operation data, the score corresponding to the best performance under typical working conditions is selected as the benchmark reference for bucket-swing effect. The benchmark reference value of bucket-swing effect is compared with the multibody dynamics simulation verification results to confirm that the corresponding grab bucket motion state meets the requirements of the bucket stabilization constraint. Finally, the benchmark reference value is determined as the bucket-swing effect threshold S1, which is used as the qualified standard for evaluating the bucket-swing operation effect.

[0113] when If the score is ≥S1, the bucket-slinging operation is considered to be up to standard. For example, when the bucket-slinging operation score is S=92% (S1=85%), the bucket-slinging operation is considered to be up to standard, the deviation between the actual trajectory of the grab bucket and the target trajectory of the grab bucket is less than 5cm and the material residue rate is less than 3%.

[0114] when If the value is less than S1, the bucket-slinging operation is considered to be substandard. For example, when the bucket-slinging operation effect score is S=78% (S1=85%), the bucket-slinging operation effect is judged to be substandard, the actual trajectory of the grab bucket deviates by 10cm and the material residue rate reaches 8%, triggering the secondary correction mechanism.

[0115] When the bucket-slinging operation effect is not up to standard, secondary correction parameters (incremental bucket-slinging angle or speed adjustment) are generated based on the deviation direction (horizontal / vertical) of the bucket's motion trajectory and the amount of material residue. The secondary correction parameters are then superimposed on the bucket motion control command in real time through the PID controller for iterative optimization until the bucket-slinging operation effect meets the standard or the maximum number of iterations is reached.

[0116] Furthermore, when the bucket-swing operation effect is not up to standard, the direction of the grab's motion trajectory deviation (horizontal / vertical) and the distribution characteristics of the material residue are first analyzed to generate incremental bucket-swing angle adjustment and incremental bucket-swing speed adjustment as secondary correction parameters. Then, the secondary correction parameters are input into the digital PID controller, and the compensation control quantity is output through proportional-integral-derivative operation. The compensation control quantity is superimposed on the bucket-swing angle parameter and bucket-swing speed parameter in the current grab motion control command in real time to form an optimized new command. After the new command is issued and executed, the actual trajectory of the grab and the material residue are collected again for effect evaluation. The cycle is iterated until the bucket-swing operation effect score reaches the bucket-swing effect threshold S1 or the maximum number of iterations is reached.

[0117] Please see Figure 2 This embodiment also provides a control system for the bucket-slinging operation of a bridge grab unloader, including: a data acquisition module for acquiring three-phase current data of the switching motor and extracting current harmonic distortion rate characteristics;

[0118] The bucket-swinging instruction generation module is used to build a viscosity recognition model. Based on the characteristics of current harmonic distortion rate and the parameters of bucket swing amplitude, it evaluates the viscosity grade of the material and dynamically generates bucket-swinging action parameter instructions in combination with bucket stabilization constraints.

[0119] The instruction correction module is used to calculate the six-degree-of-freedom motion parameters of the hull and the tracking error of the trolley based on the real-time pose data of the hull, and generate the hull compensation parameters and the trolley deviation compensation.

[0120] The motion command generation module is used to obtain the initial grab motion control command based on the grab action parameter command, and to generate the grab motion control command by dynamically correcting the trajectory by superimposing the hull compensation parameters and the trolley deviation compensation amount.

[0121] The operation evaluation module is used to execute the grab bucket motion control commands, use a dynamic time warping algorithm to evaluate the effect of the grab bucket operation, and dynamically perform secondary dynamic trajectory correction.

[0122] This embodiment also provides a computer device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the control method for the bucket-slinging operation of the bridge grab unloader proposed in the above embodiment.

[0123] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0124] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the control method for the bucket-slinging operation of a bridge grab unloader as described in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0125] In summary, this invention integrates the current harmonic distortion rate characteristics with the grab bucket swing amplitude parameters into a material viscosity feature vector, establishes a viscosity grade mapping relationship based on the SVR model, and realizes real-time quantitative evaluation of material characteristics, enabling the bucket swing motion parameters to adapt to different viscosity conditions. By employing a dynamic time warping algorithm to compare the spatiotemporal differences between the actual trajectory and the target trajectory, and combining the material residue to calculate the effect score and trigger secondary correction, a closed-loop control mechanism of "execution-evaluation-optimization" is formed, ensuring operational accuracy under complex disturbances.

[0126] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A control method for the bucket-slinging operation of a bridge-type grab unloader, characterized in that: include: Acquire the three-phase current data of the switching motor and extract the current harmonic distortion rate characteristics; The three-phase current data includes real-time current waveform signals of phase A, phase B, and phase C; A viscosity identification model is constructed to evaluate the viscosity grade of the material based on the characteristics of current harmonic distortion rate and the swing amplitude parameters of the grab bucket, and to dynamically generate the bucket swing action parameter instructions in combination with the bucket stabilization constraint conditions. Based on the real-time hull pose data, the six-degree-of-freedom motion parameters of the hull and the tracking error of the trolley are calculated, and the hull compensation parameters and the trolley deviation compensation are generated. Based on the bucket swing action parameter command, the initial grab bucket motion control command is obtained, and dynamic trajectory correction is performed by superimposing the hull compensation parameter and the trolley deviation compensation amount to generate the grab bucket motion control command. The system executes grab bucket motion control commands, uses a dynamic time warping algorithm to evaluate the effectiveness of the grab bucket operation, and dynamically performs secondary dynamic trajectory correction. The steps for constructing the viscosity recognition model are as follows: By fusing the current harmonic distortion rate characteristics and the grab bucket swing amplitude parameters through feature cascading, a material viscosity feature vector is generated. The support vector regression model is used as the base model and trained using historical material operation data. The training support vector regression model is used to establish the mapping relationship between the material viscosity feature vector and the material viscosity grade. A viscosity identification model is constructed based on the mapping relationship between material viscosity feature vectors and material viscosity grades, as well as the trained support vector regression model. The steps for evaluating the viscosity grade of the material and dynamically generating bucket-throwing action parameter commands based on bucket stabilization constraints are as follows: Input the material viscosity feature vector into the viscosity recognition model to predict the material viscosity index; Define low viscosity threshold and high viscosity threshold, compare them with the material viscosity index, and evaluate the material viscosity grade based on the comparison results; Based on historical operation data, the stable swing range of the grab bucket under different viscosity grades was statistically analyzed, and the stability constraint conditions were generated through multibody dynamics simulation. Based on the bucket stabilization constraints and the material viscosity grade, a condition matching and interpolation optimization algorithm is used to generate bucket throwing action parameter instructions; The steps for calculating the six-degree-of-freedom motion parameters of the hull and the tracking error of the trolley, and generating the hull compensation parameters and the trolley deviation compensation amount, are as follows: Real-time hull pose data is collected, and the six-degree-of-freedom motion parameters of the hull are obtained through the time difference method. Based on the six-degree-of-freedom motion parameters, the future hull position is predicted by kinematic extrapolation, and a linear quadratic regulator is used to identify the ideal following trajectory of the trolley. Based on the actual position of the vehicle and the ideal following trajectory of the vehicle, the following error of the vehicle is obtained, and the following error of the vehicle is converted into a speed compensation command using a PID controller. The anti-sway compensation angle of the grab bucket is obtained based on the angular velocity of the hull in three-dimensional space; The steps for obtaining the initial grab motion control command and generating the grab motion control command by dynamically correcting the trajectory by superimposing hull compensation parameters and trolley deviation compensation amount are as follows: A dynamic pose compensation algorithm is used to superimpose the hull compensation parameters onto the initial grab trajectory to correct the grab offset caused by hull swaying. Based on the trolley deviation compensation amount, the trolley following error is dynamically compensated; The corrected initial grab trajectory and the compensated trolley following error were verified through multibody dynamics simulation, and grab motion control commands were dynamically generated. The steps for evaluating the effectiveness of the bucket-slinging operation using a dynamic time warping algorithm and dynamically performing secondary dynamic trajectory correction are as follows: During the execution of the grab bucket motion control command, the actual motion trajectory of the grab bucket and the amount of material residue after the bucket swing operation are collected, and the dynamic time warping algorithm is used to calculate the bucket swing operation effect score. Define a threshold for the bucket-swing effect and compare it with the bucket-swing operation effect score. Evaluate the bucket-swing operation effect based on the comparison results. When the effect of the grab bucket operation is not up to standard, secondary correction parameters are generated based on the deviation direction of the grab bucket's motion trajectory, and then iteratively optimized by superimposing them on the grab bucket motion control command in real time through the PID controller.

2. The control method for the bucket-swing operation of a bridge grab unloader as described in claim 1, characterized in that: The extraction of current harmonic distortion rate features refers to filtering, denoising, and normalizing the three-phase current data, and then analyzing each harmonic component through fast Fourier transform to extract the current harmonic distortion rate features.

3. A control system for bucket-swing operations of a bridge grab unloader, based on the control method for bucket-swing operations of a bridge grab unloader as described in any one of claims 1 to 2, characterized in that: include: The data acquisition module is used to acquire the three-phase current data of the switching motor and extract the current harmonic distortion rate characteristics. The bucket-swinging instruction generation module is used to build a viscosity recognition model. Based on the characteristics of current harmonic distortion rate and the parameters of bucket swing amplitude, it evaluates the viscosity grade of the material and dynamically generates bucket-swinging action parameter instructions in combination with bucket stabilization constraints. The instruction correction module is used to calculate the six-degree-of-freedom motion parameters of the hull and the tracking error of the trolley based on the real-time pose data of the hull, and generate the hull compensation parameters and the trolley deviation compensation. The motion command generation module is used to obtain the initial grab motion control command based on the grab action parameter command, and to generate the grab motion control command by dynamically correcting the trajectory by superimposing the hull compensation parameters and the trolley deviation compensation amount. The operation evaluation module is used to execute the grab bucket motion control commands, use a dynamic time warping algorithm to evaluate the effect of the grab bucket operation, and dynamically perform secondary dynamic trajectory correction.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the control method for the bucket-slinging operation of a bridge grab unloader as described in any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the control method for the bucket-slinging operation of a bridge grab unloader as described in any one of claims 1 to 2.

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