A ship ladder virtual and real symbiotic land-based swing system and device

Abstract

This application relates to the technical field of ship performance testing, and specifically to a land-based ship ladder virtual-real symbiotic rocking system and device. The system comprises: a digital simulation module that constructs a virtual digital model of the ship hull based on wave parameters and ship type data, simulates the motion of the hull in different sea conditions, obtains control signals from the three servo motors of the ship ladder in roll, pitch, and heave, and optimizes the control signals; and a physical simulation module in which the three servo motors control the three degrees of freedom of roll, pitch, and heave, respectively, using the control signals optimized by the digital simulation module. This application aims to improve the accuracy of the ship ladder land-based rocking test bench in simulating real sea conditions, and to establish a scientific research system and platform that can conduct ship ladder technology research at any time on land.

Description

Technical Field

[0001] The present application relates to the technical field of ship performance testing, and in particular to a ship ladder virtual-real symbiotic land-based swinging system and device. Background Art

[0002] Marine elevators are specialized electromechanical equipment installed on ships, providing vertical or oblique transport for passengers, crew, or cargo. Ocean-going elevators require robust stability to withstand complex sea conditions such as typhoons, high waves, and localized weather events. In these extreme environments, vessels experience multi-directional, uncertain motions such as roll, pitch, and heave. These complex marine environments can interfere with shaft equipment, leading to elevator stalls, obstructions, and entrapment, among other safety issues.

[0003] By establishing a land-based sway test rig for ship ladders, simulating the wave disturbance environment and analyzing the dynamic laws of the ship ladder's sway with the ship, the challenges of uncontrollable sea conditions, high test risks, and high costs during sea trials can be avoided. Due to the large random disturbances and strong impacts of waves, the dynamic characteristics of ship ladder sway exhibit random mutations. This makes it difficult to study the sway characteristics of ship ladders in a physical sea environment, as the experimental process is uncontrollable. Furthermore, the ship-based sway test rig struggles to simulate actual sea conditions, resulting in deviations in the analysis of the sway behavior of various ship ladder components and affecting the accuracy of the results obtained regarding the dynamic laws of the sway. Summary of the Invention

[0004] In view of the above, it is necessary to provide a ship ladder virtual and real symbiotic land-based swing system and device to solve the above problems.

[0005] In a first aspect, the present application provides a virtual-real symbiotic land-based swaying system for a ship ladder, the system comprising a digital simulation module and a physical simulation module: the digital simulation module uses an ocean system simulator to simulate the motion state of the ship hull, converting the motion amplitudes of the three degrees of freedom of roll, pitch, and heave at each moment into the speeds of corresponding servo motors; the speeds of each servo motor for a preset time length are combined into a first speed sequence, and the first speed sequence is evenly divided to obtain a differential sequence of each subsequence after the division; based on the distribution of elements in the differential sequence of each subsequence and the degree of discreteness of the elements in each subsequence, a speed mutation characteristic value of each subsequence is determined;

[0006] Based on the first speed sequence, speed points are constructed, the range of the number of control points in the NURBS curve is preset, and the number of control points for adjusting each subsequence of the NURBS curve is determined based on the element distribution and speed mutation characteristic value of each subsequence. All control points of the speed sequence are randomly obtained to form a control point vector, and the NURBS curve and the second speed sequence corresponding to each control point vector are obtained;

[0007] Compare the first speed sequence with the second speed sequence, combine the distribution of elements in the differential sequence of the second speed sequence, confirm the fitness of the control point vector, use the optimization algorithm to obtain the optimal second speed sequence, and combine the PID control algorithm to obtain the control signal of each servo motor in the physical simulation module;

[0008] The physical simulation module consists of a three-degree-of-freedom rocking platform. The three servo motors obtain control signals from the digital simulation module to control the three degrees of freedom of roll, pitch and heave.

[0009] Preferably, the determining of the speed mutation characteristic value of each subsequence is specifically as follows:

[0010] The cumulative sum of the absolute values ​​of all elements in each subsequence is calculated and forward-fused with the discrete degree of the elements of each subsequence to obtain the speed mutation characteristic value of each subsequence.

[0011] Preferably, the speed mutation characteristic value is specifically the product of the cumulative sum of each subsequence and the discrete degree.

[0012] Preferably, the step of constructing the speed point based on the first speed sequence is: using the sequence number value of the element in the first speed sequence as the horizontal coordinate of the speed point, and using the element value of the first speed sequence as the vertical coordinate of the speed point.

[0013] Preferably, the number of control points of each subsequence to be adjusted by the NURBS curve is determined as follows:

[0014] Calculate the ratio of the speed mutation characteristic value of each subsequence to the sum of all elements in each subsequence and normalize it;

[0015] Calculate the rounded-up value of the product of the range of the control point number in the NURBS curve and the obtained normalized value;

[0016] The sum of the minimum number of control points corresponding to the value range and the rounded-up value is used as the number of control points for adjusting each subsequence of the NURBS curve.

[0017] Preferably, the second rotation speed sequence is obtained by sampling the NURBS curve at a preset sampling frequency.

[0018] Preferably, the fitness of the control point vector is confirmed as follows:

[0019] For each control point vector, obtain the upper quartile of the difference sequence of the second speed sequence, calculate the difference between each element in the difference sequence of the second speed sequence that is greater than the upper quartile and the upper quartile, and record it as a first difference; accumulate all the first differences in the second speed sequence to obtain a second difference;

[0020] Calculate the difference between the first speed sequence and the second speed sequence, and record it as the third difference;

[0021] The second difference and the third difference are weighted summed to obtain the fitness of each control point vector.

[0022] Preferably, the first difference is determined by the square of the difference between each element greater than the upper quartile in the differential sequence of the second rotation speed sequence and the upper quartile.

[0023] Preferably, the third difference is determined by a mean square error between the first rotational speed sequence and the second rotational speed sequence.

[0024] On the second aspect, an embodiment of the present application further provides a ship ladder virtual-real symbiotic land-based swing device, which is implemented by the ship ladder virtual-real symbiotic land-based swing system.

[0025] This application has at least the following beneficial effects:

[0026] The ship ladder virtual-reality symbiotic land-based swaying system in this application combines a digital simulation module with a physical simulation module. By constructing a virtual digital model of the hull disturbed by waves in the digital simulation module, the motion state of the ship ladder under the corresponding disturbance and the control signal that drives the physical simulation model are obtained to accurately simulate the swaying state of the ship ladder under different sea conditions, and establish a scientific research system and platform for carrying out ship ladder technology research at any time on land.

[0027] Furthermore, the first speed sequence is segmented, and the speed mutation characteristic value of each subsequence is confirmed to reflect the degree of speed mutation within a period of time; the number of control points of the NURBS curve is determined by the speed mutation characteristic value, which helps to reasonably select the number and position of control points according to the intensity and frequency of the mutation in the speed sequence. The second speed sequence is obtained on the obtained NURBS curve, and the optimization algorithm is used to optimize the control signal to reduce the control vibration of the electric cylinder and servo motor caused by nonlinear parts such as tooth backlash when the speed changes too quickly, thereby reducing the occurrence of excessive deviation between the actual speed of the servo motor and the control signal due to device vibration, and improving the accuracy of the ship ladder land-based sway test bench in simulating real sea conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A block diagram of a ship ladder virtual-real symbiotic land-based swaying system provided in one embodiment of the present application;

[0029] Figure 2 A flowchart for optimizing a control signal is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0030] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0032] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of protection of this application, the order of executing multiple steps can be interchanged with each other, and some steps can also be deleted.

[0033] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] The specific scheme of the ship ladder virtual-real symbiotic land-based swing system and device provided by the present application is described in detail below with reference to the accompanying drawings.

[0035] See also Figure 1 , which shows a step flow chart of a ship ladder virtual-real symbiotic land-based swaying system provided by an embodiment of the present application, the system includes: a digital simulation module and a physical simulation module.

[0036] The digital simulation module constructs a virtual digital model of the ship's hull based on wave parameters and ship form data. This application uses the Marine System Simulator (MSS) to simulate the ship's motion in different sea conditions. The MSS was developed using the Matlab / Simulink platform, thereby capturing the motion of the lower ladder at different times. Kinematic position inversion is used to determine the ladder's motion amplitudes in roll, pitch, and heave. A motion control computer then converts these amplitudes into control signals for the three servo motors. The control signals represent the servo motor's speed at that moment. In this embodiment, the frequency of the control signals is 2 kHz.

[0037] Due to the large random disturbances and strong impacts of waves, the dynamic characteristics of the ship ladder's swaying exhibit random mutations, resulting in sudden speed changes in the servo motor control signal output by the digital simulation module. Rapid speed changes in the servo motor and the nonlinear components of the electric cylinder, such as backlash, can lead to contact instability, causing mechanical vibration. Furthermore, when the motor accelerates or decelerates suddenly, the inertia of the load can cause additional vibration in the mechanical system.

[0038] The additional vibration generated by the rapid change in speed of the three-degree-of-freedom swing table is amplified through the mechanical device, causing a large deviation between the actual swing state of the swing table and the virtual simulation model; on the other hand, excessive and strong additional vibration will increase the difficulty of speed control, affect the accuracy of the subsequent servo drive controlling the servo motor, and reduce the accuracy of the land-based swing system simulation.

[0039] To enable the physical simulation module to simulate the virtual digital model of the ship ladder while reducing the impact of sudden speed changes on the three-degree-of-freedom rocking platform, the control signal output by the digital simulation model is adjusted to moderately reduce the degree of sudden speed changes. The adjustment method for the control signals of the three servo motors of the three-degree-of-freedom rocking platform in this application is the same. Taking the servo motor that controls the panning of the three-degree-of-freedom rocking platform as an example, the adjustment method for its control signal is as follows:

[0040] In this embodiment, the control signals output by the digital simulation module every second are arranged in chronological order to form a first speed sequence, which is then evenly divided into 10 subsequences, each of which has a length of 200. Backward differencing is performed on each subsequence to obtain a subsequence difference sequence, which reflects the speed change of the corresponding subsequence.

[0041] The sway of a ship's ladder can be caused by a variety of factors, including the random impact of waves and the swaying inertia of the ship's hull. Dividing the sequence into 10 subsequences, each 200 bytes long, can better capture the details of these short-term speed fluctuations, providing richer information for subsequent calculation of speed mutation characteristic values ​​and control signal optimization. If the time period is too long, some rapid speed changes may be smoothed out, affecting the accurate assessment of the speed mutation severity.

[0042] While overly detailed division (e.g., into more subsequences, each with a shorter length) can capture even subtler speed changes, it significantly increases the amount of computation and complexity, potentially causing the optimization algorithm to run excessively long, even exceeding the system's real-time requirements. Dividing the data into 10 subsequences, each 200 characters long, achieves a better balance between computational accuracy and complexity, meeting the need to capture detailed speed fluctuations without excessive computational effort, thus ensuring system reliability and efficiency.

[0043] Secondly, considering that the degree of speed mutation is positively correlated with the degree of discreteness of the subsequence on the one hand; on the other hand, the absolute value of the subsequence difference sequence represents the change in speed between adjacent moments, the larger the cumulative sum of the absolute values ​​of the subsequence difference sequence, the greater the speed mutation degree of the corresponding subsequence. Based on this, the speed mutation characteristic value is calculated: the cumulative sum of the absolute values ​​of all elements in each subsequence is forward fused with the degree of discreteness of the elements of each subsequence to obtain the speed mutation characteristic value of each subsequence. In this embodiment, the discreteness of the sequence elements is calculated by standard deviation; multiple variables are forward fused by multiplication. The speed mutation characteristic value reflects the degree of speed mutation within the subsequence. The larger the speed mutation characteristic value, the greater the speed mutation degree of the corresponding subsequence.

[0044] Furthermore, this embodiment uses a NURBS curve to adjust the first speed sequence, allowing the speed changes to be refitted by selecting different control points. The NURBS curve does not directly pass through the control points, which makes the adjusted speed smoother between control points and is typically lower than the input control point speed, effectively reducing sudden changes in the speed control signal.

[0045] Specifically, the speeds of the first speed sequence are numbered in chronological order to obtain the sequence number of each speed. The sequence number of the element in the first speed sequence is used as the horizontal coordinate of the speed point, and the element value of the speed sequence is used as the vertical coordinate of the speed point to obtain 2000 speed points in the first speed sequence.

[0046] Since the speed mutation eigenvalue reflects the degree of speed mutation within a subsequence, setting more control points when the subsequence speed mutation eigenvalue is large increases the flexibility of control signal adjustment and enhances the effectiveness of speed mutation reduction. Furthermore, speeds close to zero within a subsequence can indicate that the servo motor is preparing for or beginning to turn. During these times, the servo motor is more likely to experience mechanical vibration due to nonlinearities such as backlash, necessitating enhanced speed mutation reduction. Therefore, the smaller the cumulative sum of speeds within a subsequence, the more control points are required to improve control signal adjustment.

[0047] The number of control points in a NURBS curve is predefined within a range of values. Since the degree of a NURBS curve determines the minimum number of control points, this embodiment uses a cubic NURBS curve, requiring at least N = 4 control points to output a valid curve; otherwise, the first speed sequence cannot be correctly fitted. Furthermore, an excessive number of control points can lead to overly complex NURBS curves, potentially introducing unnecessary fitting errors or even causing the curve to be overly smooth, failing to effectively capture the abrupt changes in the speed sequence. Therefore, in this embodiment, the maximum number of control points is proportional to the subsequence length and set to 0.05M, where M represents the number of elements in the subsequence. To prevent an excessive number of control points and ensure that the maximum number of control points matches the subsequence length of the first speed sequence, a certain ratio of the maximum number of control points is set to capture these features. Furthermore, an excessive number of control points increases computational complexity, especially when the particle swarm optimization algorithm (PSO) requires optimizing multiple control point vectors. The maximum number of control points limits the computational effort, ensuring that the optimization process completes within a reasonable timeframe.

[0048] Based on this, the ratio of the speed mutation characteristic value of each subsequence to the sum of all elements in each subsequence is calculated and normalized. The product of the range of the control point number in the NURBS curve and the obtained normalized value is rounded up. The sum of the minimum number of control points corresponding to the range and the rounded-up value is used as the number of control points for adjusting the NURBS curve for each subsequence. In this embodiment, the normalization method used is the maximum and minimum value normalization method.

[0049] A corresponding number of control points are randomly selected from the speed points of each subsequence to obtain the control points of the entire speed sequence. The speed points are then arranged in ascending order according to their corresponding sequence numbers to form a control point vector for the first speed sequence. In this embodiment, a weight factor of 1 is set for each control point. The NURBS curve underlying the control point vector is obtained and sampled to obtain a new speed sequence as the second speed sequence, where the sampling frequency is 2 kHz. It should be noted that the sampling frequency of the second speed sequence must be consistent with the sampling frequency of the first speed sequence.

[0050] On the one hand, setting the weight factors of all control points to 1 greatly simplifies the calculation process of NURBS curves. When calculating NURBS curves, the introduction of weight factors increases the complexity and computational effort. However, setting the weight factors to 1 uniformly avoids complex weight calculations. Furthermore, for subsequent optimization algorithms such as the particle swarm optimization algorithm, the unified weight factor value makes the optimization process simpler and more direct. During the optimization process, there is no need to consider the impact of changes in the weight factors on the optimization results, making it easier to find the optimal control point vector combination, thereby improving the convergence speed and optimization effect of the optimization algorithm.

[0051] On the other hand, a weight factor of 1 means that all control points have an even influence on the NURBS curve, with each control point affecting the curve's shape to the same degree. This prevents the curve from being too close to or too far from certain control points in certain areas due to excessively large or small weights, thus ensuring the overall uniformity of the curve. When all control points have equal weight factors, the transitions between control points in the NURBS curve are smoother and more natural. This helps to stabilize the servo motor's speed when adjusting the control signal, reduces mechanical vibration caused by sudden curve changes, and improves the stability and simulation accuracy of the ship ladder land-based sway system.

[0052] Since the selection of control points for each subsequence is highly random and the optimization effects of control signals obtained from different control points vary complexly, a particle swarm optimization algorithm is used to optimize the control signals obtained from different control points.

[0053] A preset number of different control point vectors and corresponding second speed sequences are randomly obtained. In this embodiment, the preset number is 30, and the 30 different control point vectors obtained are recorded as the initial population of the particle swarm algorithm.

[0054] Secondly, considering that optimizing the control signal requires both reducing the degree of speed fluctuations and simulating the sway of the virtual digital model of the ship ladder, the fitness of the corresponding control point vector is calculated based on the speed sequences before and after adjustment: for each control point vector, the upper quartile of the difference sequence of the second speed sequence is obtained. The difference between each element in the difference sequence of the second speed sequence that is greater than the upper quartile and the upper quartile is calculated, denoted as the first difference; all first differences in the second speed sequence are accumulated to obtain the second difference; the difference between the first speed sequence and the second speed sequence is calculated, denoted as the third difference; and the weighted sum of the second and third differences is taken to obtain the fitness of each control point vector. In this embodiment, the first difference is calculated by squaring the difference; the third difference is calculated by the mean squared error between the sequences. It should be noted that the sum of the weights of the second and third differences is equal to 1. In this embodiment, the weights of the second and third differences are equal, both taking a value of 0.5.

[0055] It should be understood that the smaller the fitness is, the better the optimization effect of the control point vector on the second speed sequence is.

[0056] Finally, the maximum number of iterations was set to 200, and the optimal second speed sequence was output. This optimal second speed sequence served as the input for the servo driver, and a PID algorithm was used to generate control signals for each servo motor in the physical simulation module. The PID algorithm parameter tuning method uses the Ziegler-Nichols method, the specific process of which is well known to those skilled in the art and will not be detailed here.

[0057] Among them, the flow chart for optimizing the control signal is as follows: Figure 2 shown.

[0058] The physical simulation module is a land-based ship ladder swing test bench, which consists of a three-degree-of-freedom swing platform. The three servo motors obtain control signals from the digital simulation module to control the three-degree-of-freedom movements of roll, pitch and heave respectively, completing the ship ladder land-based swing test bench's simulation of the ship ladder's swing state in actual sea conditions.

[0059] The ship ladder virtual-reality symbiotic land-based swaying system combines the digital simulation module with the physical simulation module. It drives the physical simulation model through the control signal obtained from the digital simulation module to simulate the swaying state of the ship ladder under different sea conditions.

[0060] Based on the same inventive concept as the above method, an embodiment of the present application also provides a ship ladder virtual-real symbiotic land-based swing device, which is implemented by the ship ladder virtual-real symbiotic land-based swing system.

[0061] To sum up, the virtual-reality symbiotic land-based swaying system of the ship ladder in this application combines the digital simulation module with the physical simulation module. By constructing a virtual digital model of the hull disturbed by waves in the digital simulation module, the motion state of the ship ladder under the corresponding disturbance and the control signal that drives the physical simulation model are obtained to accurately simulate the swaying state of the ship ladder under different sea conditions, and establish a scientific research system and platform for carrying out ship ladder technology research at any time on land.

[0062] Furthermore, the first speed sequence is segmented, and the speed mutation characteristic value of each subsequence is confirmed to reflect the degree of speed mutation within a period of time; the number of control points of the NURBS curve is determined by the speed mutation characteristic value, which helps to reasonably select the number and position of control points according to the intensity and frequency of the mutation in the speed sequence. The second speed sequence is obtained on the obtained NURBS curve, and the optimization algorithm is used to optimize the control signal to reduce the control vibration of the electric cylinder and servo motor caused by nonlinear parts such as tooth backlash when the speed changes too quickly, thereby reducing the occurrence of excessive deviation between the actual speed of the servo motor and the control signal due to device vibration, and improving the accuracy of the ship ladder land-based sway test bench in simulating real sea conditions.

[0063] The specific implementation process is as follows:

[0064] In the MSS, select the supply vessel model for the ship model (Model); select the PID tracking controller for the autopilot module (Autopilots); and select the Nonlinear PID setpoint controller for the dynamic positioning module (DP). This application only considers the changes in the ship's motion state under the action of waves. Therefore, this embodiment sets a windless and currentless environment in the MSS and only considers the impact of waves. In the Waves module (Waves), select Linear 2nd-order wave spectrum, set the significant wave height to 2.5m, the encounter angle to 45°, and set the noise power to 0.2 in the Band-limited White loise. The simulation step size is set to At this point, the simulation settings of the hull virtual digital model in the digital simulation module are completed.

[0065] The control signals of the servo motors that control the panning of the three-degree-of-freedom rocking platform in the digital simulation module are obtained respectively to form the first speed sequence, which is recorded as ; , ;in, 、 represent the 1st, 2nd, and nth subsequences of the first speed sequence of the roll control signal, respectively; 、 、 Represents the first, second, and Mth element values ​​in the nth subsequence; M represents the length of the subsequence, which is 200; n represents the number of subsequences, which is 10.

[0066] For the nth subsequence, the standard deviation of all elements in the nth subsequence is recorded as , the sum of the absolute values ​​of all elements in the nth subsequence is recorded as , the speed mutation value of the nth subsequence = .

[0067] Will The coordinates of the speed point corresponding to each element in The coordinates of the speed point are .

[0068] The value range of the control point N of the preset NURBS curve is: ;in, and Respectively represent the maximum and minimum number of control points, set The value of is 4; The value range is 0.05M~0.1M, and the value in this embodiment is 0.05M.

[0069] Parameter selection instructions:

[0070] On the one hand, the subsequence length M reflects the density of data points in the speed sequence within a certain period of time. When M is larger, it means that the amount of information about the speed change in the corresponding time period is greater, and it may contain more speed mutations and fluctuation details. In this case, it is appropriate to increase the upper limit of the number of control points (i.e., take a larger ), which can fit these complex speed changes more carefully and avoid the curve being too smooth due to too few control points, thus avoiding the omission of important speed features.

[0071] On the other hand, if If M is too large, the control points will be too dense, making the NURBS curve too complex, which will not only increase the computational complexity, but also introduce too many local fluctuations, which is not conducive to smoothing the speed sequence and reducing the speed mutation degree. By setting it to a value proportional to M (such as 0.05M to 0.1M), the upper limit of the number of control points can be dynamically adjusted when the subsequence length changes, ensuring that the number of control points can meet the fitting accuracy requirements without excessively increasing the computational burden and curve complexity.

[0072] Calculate the number of control points of the X-NURBS curve to adjust the nth subsequence: ;Where, ceil[] represents the rounding up function; The value is ; In this embodiment, the number of NURBS curves used is three, M is the length of the subsequence; norm() represents the maximum and minimum value normalization function; represents the cumulative sum of the rotation speeds in the nth subsequence; Indicates the speed mutation characteristic value of the nth subsequence; Indicates the number of control points used to adjust the nth subsequence using the NURBS curve.

[0073] from Randomly selected The coordinates of the speed points corresponding to the elements are composed of The control point vector , ,in, ; 、 、 Respectively represent the selection of the first, second, and The elements correspond to the coordinates of the rotational speed points.

[0074] right The control point vector Perform NURBS curve fitting, and set the weight factor of each control point to 1 during fitting; obtain the second speed sequence of the obtained NURBS curve at the same sampling frequency, which is recorded as .

[0075] Randomly obtain a different control point vectors to form the initial population of the particle algorithm , ;in, 、 、 They represent the first, second, and ath control point vectors respectively; a represents the number of control point vectors, and its value is 30.

[0076] Parameter selection instructions:

[0077] First, the performance of the particle swarm optimization algorithm depends heavily on the size and diversity of the initial population. Thirty control point vectors constitute a sufficiently large and diverse initial population. A larger population size increases the diversity of solutions, enabling the algorithm to explore a wider range of the search space and thus increasing the likelihood of finding the global optimal solution. However, if the population size is too small, the algorithm may become trapped in a local optimum and be unable to effectively search for the global optimal solution.

[0078] Secondly, the complexity of the land-based ship ladder swaying system dictates that a certain number of control points are required to effectively control and optimize the speed signal. Thirty control point vectors achieve a good balance between system complexity and control accuracy in this embodiment. Too many control point vectors may lead to excessive system complexity and increase the burden on the control system; too few control point vectors may not meet the system's control accuracy requirements, failing to effectively reduce the degree of sudden speed changes and improve simulation accuracy.

[0079] Finally, the number of particle swarms can be adjusted based on the complexity of the problem being solved. Implementers can also adjust it based on their simulation conditions and requirements.

[0080] Calculate the fitness of each control point vector in the particle swarm algorithm: ; Among them, MSE represents the speed sequence composed of the new control signal after adjustment and the speed sequence output by the digital simulation module The mean square error between express The upper quartile of express The i-th speed difference value that exceeds the upper quartile; U represents The number of speed differences exceeding the upper quartile; weight and Both are set to 0.5; J represents the fitness of the control point vector.

[0081] The maximum number of iterations of the particle swarm algorithm is set. In this embodiment, 200 is selected. Finally, the particle swarm algorithm outputs the optimal second speed sequence. It should be noted that the maximum number of iterations is not fixed and can be adjusted according to the needs of the problem in actual use.

[0082] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0083] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifications to the technical solutions described in the above embodiments, or equivalent replacement of some of the technical features therein, do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application.

Claims

1. A ship ladder virtual and real symbiotic land-based swing system, characterized in that: The system includes a digital simulation module and a physical simulation module. The digital simulation module simulates the motion state of the hull and converts the motion amplitudes of the three degrees of freedom of roll, pitch, and heave at each moment into the speeds of the corresponding servo motors. The speeds of each servo motor for a preset time length are combined into a first speed sequence, and the first speed sequence is evenly divided to obtain the difference sequence of each subsequence after the division. Based on the distribution of elements in the difference sequence of each subsequence and the degree of element discreteness in each subsequence, the speed mutation characteristic value of each subsequence is determined. Based on the first speed sequence, speed points are constructed, the range of the number of control points in the NURBS curve is preset, and the number of control points for adjusting each subsequence of the NURBS curve is determined based on the element distribution and speed mutation characteristic value of each subsequence. All control points of the speed sequence are randomly obtained to form a control point vector, and the NURBS curve and the second speed sequence corresponding to each control point vector are obtained; Compare the first speed sequence with the second speed sequence, combine the distribution of elements in the differential sequence of the second speed sequence, confirm the fitness of the control point vector, use the optimization algorithm to obtain the optimal second speed sequence, and combine the PID control algorithm to obtain the control signal of each servo motor in the physical simulation module; The physical simulation module consists of a three-degree-of-freedom rocking platform. The three servo motors control the roll, pitch, and heave motions using control signals obtained from the digital simulation module. The number of control points for adjusting each subsequence of the NURBS curve is determined as follows: The ratio of the speed mutation characteristic value of each subsequence to the sum of all elements in each subsequence is calculated and normalized. The rounded-up value of the product of the range of the control point number in the NURBS curve and the obtained normalized value is calculated. The sum of the minimum number of control points corresponding to the range and the rounded-up value is used as the number of control points for adjusting the NURBS curve for each subsequence.

2. The ship ladder virtual-real symbiotic land-based swaying system according to claim 1, characterized in that: The determination of the speed mutation characteristic value of each subsequence is specifically as follows: The cumulative sum of the absolute values ​​of all elements in each subsequence is calculated and forward-fused with the discrete degree of the elements of each subsequence to obtain the speed mutation characteristic value of each subsequence.

3. The ship ladder virtual-real symbiotic land-based swaying system according to claim 2, characterized in that: The speed mutation characteristic value is specifically the product of the cumulative sum of each subsequence and the discrete degree.

4. The ship ladder virtual-real symbiotic land-based swaying system according to claim 1, characterized in that: The step of constructing the speed point based on the first speed sequence is: using the sequence number value of the element in the first speed sequence as the horizontal coordinate of the speed point, and using the element value of the first speed sequence as the vertical coordinate of the speed point.

5. The ship ladder virtual-real symbiotic land-based swaying system according to claim 1, characterized in that: The second speed sequence is obtained by sampling the NURBS curve at a preset sampling frequency.

6. The ship ladder virtual-real symbiotic land-based swaying system according to claim 1, characterized in that: The fitness of the confirmed control point vector is specifically: For each control point vector, obtain the upper quartile of the difference sequence of the second rotational speed sequence, calculate the difference between each element in the difference sequence of the second rotational speed sequence that is greater than the upper quartile and the upper quartile, and record it as the first difference; Accumulating all the first differences in the second speed sequence to obtain a second difference; Calculate the difference between the first speed sequence and the second speed sequence, and record it as the third difference; The second difference and the third difference are weighted summed to obtain the fitness of each control point vector.

7. The ship ladder virtual-real symbiotic land-based swaying system according to claim 6, characterized in that: The first difference is specifically determined by the square of the difference between each element greater than the upper quartile in the differential sequence of the second rotation speed sequence and the upper quartile.

8. The ship ladder virtual-real symbiotic land-based swaying system according to claim 6, characterized in that: The third difference is determined by a mean square error between the first rotational speed sequence and the second rotational speed sequence.

9. A ship ladder virtual and real symbiotic land-based swing device, characterized in that: The device is realized by a ship ladder virtual-real symbiotic land-based swaying system as described in claim 1.

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