Ship ladder virtual and real symbiotic land-based swinging system and device

Abstract

The invention relates to the technical field of ship performance testing, in particular to a ship ladder virtual-real symbiotic land-based swing system and device.The system comprises a digital simulation module, a ship ladder virtual-real symbiotic land-based swing system and device.The digital simulation module constructs a virtual digital model of a ship body according to sea wave parameters and ship type data and simulates the motion states of the ship body in different sea conditions; control signals of the ship ladder on the rolling servo motor, the pitching servo motor and the heaving servo motor are obtained, and optimization processing is conducted on the control signals; three servo motors of the physical simulation module respectively control three-degree-of-freedom motions of rolling, pitching and heaving through control signals optimized and processed by the digital simulation module. The invention aims to improve the accuracy of the ship ladder land-based swing test bed for simulating the physical sea condition, and establishes a scientific research system and platform which can carry out ship ladder technical research at any time on the land.

Description

Technical Field

[0001] This application relates to the technical field of ship performance testing, and particularly relates to a virtual-real symbiotic land-based swing system and device for ship ladders. Background Art

[0002] Marine elevators are special electromechanical equipment fixedly installed on ships to provide vertical or inclined transportation services for passengers, crew, or cargo. Ship ladders during ocean voyages need to have strong stability to cope with complex sea conditions such as typhoons, huge waves, and local abnormal weather. In these extreme environments, due to the ship experiencing multi-directional uncertain motions such as rolling, pitching, and heaving during navigation, it may interfere with the shaft facilities in these complex marine environments, resulting in ladder-related safety problems such as elevator stops, jams, and entrapment of people.

[0003] By establishing a land-based swing test bench for ship ladders, simulating the sea wave interference environment, and analyzing the dynamic laws of the ship ladder with the hull swing, problems such as 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 sea waves, the dynamic characteristics of the ship ladder swing have the characteristics of random mutations. It is difficult to carry out research on the swing characteristics of the ship ladder in the physical environment of sea conditions, the various processes of the experiment are uncontrollable, and it is difficult for the ship-based swing test bench to simulate the actual sea conditions, resulting in analysis deviations of the swing behaviors of each component of the ship ladder and affecting the accuracy of obtaining the results of its swing dynamic laws. Summary of the Invention

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

[0005] In the first aspect of this application, a virtual-real symbiotic land-based swing system for ship ladders is provided. The system includes a digital simulation module and a physical simulation module: The digital simulation module uses a marine system simulator to simulate the motion state of the hull, converts the motion amplitudes of the three degrees of freedom of rolling, pitching, and heaving at each moment into the rotational speeds of the corresponding servo motors; forms a first rotational speed sequence with the rotational speeds of each servo motor for a preset time length, and evenly divides the first rotational speed sequence to obtain the difference sequences of each subsequence after division; based on the element distributions in the difference sequences of each subsequence, combined with the element dispersion degrees in each subsequence, determines the rotational speed mutation characteristic values of each subsequence; Based on the first rotational speed sequence, rotational speed points are constructed, the value range of the number of control points in the NURBS curve is preset, combined with the element distributions of each subsequence and the rotational speed mutation characteristic values, determines the number of control points for adjusting each subsequence by the NURBS curve, randomly obtains all the control points of the rotational speed sequence to form a control point vector, obtains the NURBS curve corresponding to each control point vector and the second rotational speed sequence; Compare the first rotational speed sequence with the second rotational speed sequence, combine the distribution of elements in the difference sequence of the second rotational speed sequence, confirm the fitness of the control point vector, adopt an optimization algorithm to obtain the optimal second rotational speed sequence, and combine the PID control algorithm to obtain the control signals of each servo motor in the physical simulation module; The physical simulation module consists of a three-degree-of-freedom swing table. The control signals obtained by the three servo motors through the digital simulation module respectively control the movements of the three degrees of freedom of roll, pitch, and heave.

[0006] Preferably, the steps of confirming the rotational speed mutation eigenvalue of each subsequence are as follows: Calculate the sum of the absolute values of all elements in each subsequence, and perform positive fusion with the degree of dispersion of the elements in each subsequence to obtain the rotational speed mutation eigenvalue of each subsequence.

[0007] Preferably, the rotational speed mutation eigenvalue is specifically the product of the sum and the degree of dispersion of each subsequence.

[0008] Preferably, the step of constructing the rotational speed points based on the first rotational speed sequence is: taking the serial number value of the elements in the first rotational speed sequence as the abscissa of the rotational speed points, and taking the element value of the first rotational speed sequence as the ordinate of the rotational speed points.

[0009] Preferably, the steps of confirming the number of control points for adjusting each subsequence by the NURBS curve are as follows: Calculate the ratio of the rotational speed mutation eigenvalue of each subsequence to the sum value of all elements in each subsequence, and perform normalization; Calculate the ceiling value of the product of the range difference of the number of control points in the NURBS curve and the obtained normalized value; Take the sum of the minimum number of control points corresponding to the value range and the ceiling value as the number of control points for adjusting each subsequence by the NURBS curve.

[0010] Preferably, the second rotational speed sequence is obtained by sampling the NURBS curve at a preset sampling frequency.

[0011] Preferably, the steps of confirming the fitness of the control point vector are as follows: 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 greater than the upper quartile in the difference sequence of the second rotational speed sequence and the upper quartile, and record it as the first difference; accumulate all the first differences in the second rotational speed sequence to obtain the second difference; Calculate the difference between the first rotational speed sequence and the second rotational speed sequence, and record it as the third difference; Perform weighted summation on the second difference and the third difference to obtain the fitness of each control point vector.

[0012] Preferably, the first difference is specifically determined by the square of the difference between each element greater than the upper quartile in the difference sequence of the second rotation speed sequence and the upper quartile.

[0013] Preferably, the third difference is determined by the mean square error between the first rotation speed sequence and the second rotation speed sequence.

[0014] In a second aspect, an embodiment of the present application further provides a ship ladder virtual-real symbiotic land-based rocking device, which is implemented by the ship ladder virtual-real symbiotic land-based rocking system.

[0015] The present application has at least the following beneficial effects: The ship ladder virtual-real symbiotic land-based rocking system in the present application combines the digital simulation module and the physical simulation module. By constructing a virtual digital model of the hull under the disturbance of sea waves in the digital simulation module, the motion state of the ship ladder under the corresponding disturbance and the control signal for driving the physical simulation model are obtained, so as to accurately simulate the rocking state of the ship ladder under different sea conditions, and establish a scientific research system and platform for conducting ship ladder technology research at any time on land.

[0016] Furthermore, the first rotation speed sequence is segmented to confirm the rotation speed mutation characteristic value of each subsequence, which reflects the degree of rotation speed mutation within a period of time; the number of control points of the NURBS curve is determined by the rotation speed mutation characteristic value, which helps to reasonably select the number and position of the control points according to the intensity and frequency of mutations in the rotation speed sequence. The second rotation speed sequence is obtained on the resulting NURBS curve, and an optimization algorithm is used to optimize the control signal, reducing the control vibration generated by the non-linear parts such as backlash of the electric cylinder and the servo motor when the rotation speed changes too fast, thereby reducing the situation where the actual rotation speed of the servo motor deviates too much from the control signal due to the vibration of the device, and improving the accuracy of the ship ladder land-based rocking test bench in simulating the actual sea conditions. Description of the Drawings

[0017] Figure 1 It is a block diagram of a ship ladder virtual-real symbiotic land-based rocking system provided by an embodiment of the present application; Figure 2 It is a flowchart for optimizing the control signal provided by an embodiment of the present application. Detailed Embodiments

[0018] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary", "or", "for example" aims to present relevant concepts in a specific way.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. 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.

[0020] In addition, it should be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or the methods shown in the flowcharts, including one or more steps for implementing the methods, without departing from the scope of protection of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of this application.

[0022] The following specifically describes the specific solutions of a ship ladder virtual-real symbiotic land-based swing system and device provided by this application in conjunction with the accompanying drawings.

[0023] Please refer to Figure 1 , which shows a step flowchart of a ship ladder virtual-real symbiotic land-based swing system provided by an embodiment of this application. The system includes: a digital simulation module and a physical simulation module.

[0024] The digital simulation module constructs a virtual digital model of the hull according to the wave parameters and ship type data. This application uses the Marine System Simulator (MSS) to simulate the motion state of the hull under different sea conditions. The platform used for the development of MSS is Matlab / Simulink, and then the motion state of the ship ladder at different moments is obtained. Through the inverse kinematic position solution, the motion amplitudes of the ship ladder in the three degrees of freedom of roll, pitch, and heave are obtained, and the motion control computer converts the motion amplitudes of the three degrees of freedom into control signals for three servo motors. The control signal is the rotational speed of the servo motor at this moment. In this embodiment, the frequency of obtaining the control signal is 2 kHz.

[0025] Due to the large random disturbance and strong impact of the waves, the dynamic characteristics of the ship ladder swing have the characteristics of random mutation, resulting in the problem of sudden change in the rotational speed of the servo motor control signal output by the digital simulation module. Nonlinear parts such as the servo motor and the backlash in the electric cylinder in the three-degree-of-freedom swing table will have unstable contact when the rotational speed changes too fast, resulting in mechanical vibration. In addition, when the motor suddenly accelerates or decelerates, the inertia of the load will cause additional vibration in the mechanical system.

[0026] The additional vibration generated by the three-degree-of-freedom swing table due to the excessively rapid change in rotational speed is amplified through transmission by the mechanical device on one hand, resulting in a large deviation between the actual swinging state of the swing table and the virtual simulation model. On the other hand, excessive and strong additional vibration increases the difficulty of rotational speed control, affects the accuracy of the subsequent servo driver in controlling the servo motor, and reduces the accuracy of the simulation of the land-based swing system.

[0027] To reduce the impact of sudden rotational speed changes on the three-degree-of-freedom swing table while the physical simulation module simulates the virtual digital model of the ship ladder, the control signals output by the digital simulation model are adjusted to moderately reduce the degree of rotational speed mutation. In this application, the adjustment methods for the control signals of the three servo motors of the three-degree-of-freedom swing table are the same. Taking the servo motor that controls the roll of the three-degree-of-freedom swing table as an example, the adjustment method for its control signal is as follows: In this embodiment, the control signals output by the digital simulation module per second are arranged in chronological order to form a first rotational speed sequence, which is evenly divided into 10 subsequences, and the length of each subsequence is 200. Backward difference processing is performed on each subsequence to obtain a subsequence difference sequence, which reflects the rotational speed change of the corresponding subsequence.

[0028] The swing of the ship ladder may be caused by various factors such as random impacts of sea waves and the swinging inertia of the hull. Dividing the sequence into 10 subsequences, each with a length of 200, can better capture the details of rotational speed fluctuations within these short time periods, providing richer information for subsequent calculation of rotational speed mutation characteristic values and optimization of control signals. If the divided time period is too long, some rapid rotational speed changes may be smoothed out, thus affecting the accurate judgment of the degree of rotational speed mutation.

[0029] Although dividing too finely (such as dividing into more subsequences with shorter lengths for each subsequence) can capture more subtle rotational speed changes, it will greatly increase the computational amount and computational complexity, possibly resulting in an overly long running time of the optimization algorithm, even exceeding the real-time requirements of the system. Dividing into 10 subsequences, each with a length of 200, achieves a good balance between computational accuracy and complexity, can not only meet the need to capture the details of rotational speed fluctuations but also avoid excessive computational amount, ensuring the reliability and efficiency of system operation.

[0030] Secondly, considering that the degree of sudden speed change is positively correlated with the discreteness of the subsequence on the one hand; on the other hand, the absolute value of the difference sequence of the subsequence represents the speed change at adjacent moments, and the larger the cumulative sum of the absolute values of the difference sequence of the subsequence, the greater the degree of sudden speed change of the corresponding subsequence. Based on this, the sudden speed change characteristic value is calculated: the cumulative sum of the absolute values of all elements in each subsequence is positively fused with the discreteness of the elements of each subsequence to obtain the sudden speed change characteristic value of each subsequence. In this embodiment, the discreteness of the sequence elements is calculated by the standard deviation; the positive fusion of multiple variables is performed by multiplication. The sudden speed change characteristic value reflects the degree of sudden speed change within the subsequence, and the larger the sudden speed change characteristic value, the greater the degree of sudden speed change of the corresponding subsequence.

[0031] Further, in this embodiment, by using the NURBS curve to adjust the first speed sequence, the speed change can 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 the control points and usually lower than the speed of the input control points, and can effectively reduce the sudden change of the speed control signal.

[0032] Specifically, the speeds of the first speed sequence are numbered in chronological order to obtain the serial number of each speed. The serial number values of the elements in the first speed sequence are used as the abscissa of the speed points, and the element values of the speed sequence are used as the ordinate of the speed points to obtain 2000 speed points in the first speed sequence.

[0033] Since the sudden speed change characteristic value reflects the degree of sudden speed change of the subsequence speed, when the sudden speed change characteristic value of the subsequence is relatively large, more control points are set to improve the flexibility of adjusting the control signal and enhance the effect of reducing the sudden speed change. In addition, the speed close to zero in the subsequence can reflect that the servo motor is in the process of preparing to turn or starting to turn. At this time, the probability of mechanical vibration of the servo motor due to non-linear parts such as backlash is greater, and it is necessary to enhance the degree of reducing the sudden speed change. Therefore, the smaller the cumulative sum of the speeds in the subsequence, the more control points are set to improve the adjustment effect of the control signal.

[0034] Preset the value range of the number of control points in the NURBS curve. Since the degree of the NURBS curve determines the minimum number of control points, in this embodiment, a cubic NURBS curve is adopted, and at least N = 4 control points are required to output an effective curve. Otherwise, the first rotational speed sequence cannot be correctly fitted. In addition, too many control points will cause the NURBS curve to be too complex, which may introduce unnecessary fitting errors and even cause the curve to be overly smoothed, making it impossible to effectively capture the mutation characteristics of the rotational speed sequence. Therefore, in this embodiment, the maximum number of control points is proportional to the length of the subsequence and is set to 0.05M, where M represents the number of elements in the subsequence; in order to prevent too many control points and at the same time make the maximum number of control points match the length of the subsequence of the first rotational speed sequence, the length of the subsequence reflects the number of rotational speed mutation characteristics to a certain extent. Therefore, a certain proportion of the maximum number of control points is set to capture these characteristics. In addition, too many control points will increase the computational complexity, especially when the particle swarm algorithm is subsequently used to optimize multiple control point vectors. The maximum number of control points limits the amount of calculation and ensures that the optimization process is completed within a reasonable time.

[0035] Based on this, calculate the ratio of the rotational speed mutation characteristic value of each subsequence to the sum value of all elements in each subsequence and perform normalization; calculate the ceiling value of the product of the range difference of the number of control points in the NURBS curve and the obtained normalized value; use the sum of the minimum number of control points corresponding to the value range and the ceiling value as the number of control points for the NURBS curve to adjust each subsequence. In this embodiment, the normalization method adopted is the maximum-minimum normalization method.

[0036] Randomly select the corresponding number of control points from the rotational speed points of each subsequence, thereby obtaining the control points of the entire rotational speed sequence, and arrange the rotational speed points in ascending order according to the corresponding serial number values to form the control point vector of the first rotational speed sequence. In this embodiment, the weight factor of each control point is set to 1, obtain the NURBS curve under the control point vector, and sample it to obtain a new rotational speed sequence as the second rotational speed sequence, where the sampling frequency is 2 kHz. It should be noted that the sampling frequency of the second rotational speed sequence needs to be consistent with that of the first rotational speed sequence.

[0037] On the one hand, taking the weight factors of the control points as 1 can greatly simplify the calculation process of the NURBS curve. When calculating the NURBS curve, the introduction of weight factors will increase the computational complexity and amount of calculation. Taking the weight factors uniformly as 1 can avoid complex weight value calculations. At the same time, for subsequent optimization algorithms such as the particle swarm algorithm, the unified weight factor value can make the optimization process more simple and direct. During the optimization process, there is no need to consider the influence of the change of weight factors on the optimization results, so it is easier to find the optimal combination of control point vectors, improving the convergence speed and optimization effect of the optimization algorithm.

[0038] On the other hand, a weight factor of 1 means that all control points have a uniform influence on the NURBS curve, and each control point can affect the shape of the curve to the same extent. This can avoid the curve being too close to or too far from some control points in a local area due to the excessive or too small weights of some control points, thus ensuring the overall uniformity of the curve. When the weight factors of all control points are equal, the transition of the NURBS curve between control points will be smoother and more natural. This helps to make the rotational speed change of the servo motor more stable when adjusting the control signal, reduce mechanical vibration caused by curve mutation, and improve the stability and simulation accuracy of the ship ladder land-based swing system.

[0039] Since the selection of control points for each subsequence has strong randomness, and the optimization effects of the control signals obtained by different control points vary complexly. Therefore, the particle swarm optimization algorithm is used to optimize the control signals obtained by different control point selections.

[0040] Randomly obtain a preset number of different control point vectors and the corresponding second rotational speed sequences. In this embodiment, the preset number is 30, and the 30 different control point vectors obtained are denoted as the initial population of the particle swarm optimization algorithm.

[0041] Secondly, considering that the optimization of the control signal should, on the one hand, reduce the degree of rotational speed mutation, and on the other hand, achieve the swing simulation of the ship ladder virtual digital model. Therefore, according to the rotational speed sequences before and after adjustment, calculate the fitness of the corresponding control point vectors: 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 greater than the upper quartile in the difference sequence of the second rotational speed sequence and the upper quartile, and denote it as the first difference; accumulate all the first differences in the second rotational speed sequence to obtain the second difference; calculate the difference between the first rotational speed sequence and the second rotational speed sequence, and denote it as the third difference; perform a weighted sum of the second difference and the third difference to obtain the fitness of each control point vector. In this embodiment, the first difference is calculated by the square of the difference; the third difference is calculated by the mean square error between sequences; it should be noted that the sum of the weights of the second difference and the weight of the third difference is equal to 1. In this embodiment, the weights of the second difference and the third difference are equal, both taking the value of 0.5.

[0042] It should be understood that the smaller the fitness, the better the optimization effect of the control point vector on the second rotational speed sequence.

[0043] Finally, set the maximum number of iterations to 200 and output the optimal second rotational speed sequence. Using the optimal second rotational speed sequence as the input of the servo driver, the control signals of each servo motor in the physical simulation module are obtained by using the PID algorithm. Among them, the method for tuning the PID algorithm parameters adopts the Ziegler-Nichols method, and its specific process is well-known to those skilled in the art and will not be elaborated here.

[0044] Among them, the flowchart for optimizing the control signal is as Figure 2 shown.

[0045] The physical simulation module is a ship ladder land-based sway test bench, which consists of a three-degree-of-freedom sway table. The three servo motors control the motions of the three degrees of freedom of roll, pitch, and heave respectively through the control signals obtained by the digital simulation module, and complete the simulation of the sway state of the ship ladder in the actual sea conditions by the ship ladder land-based sway test bench.

[0046] The ship ladder virtual-real symbiotic land-based sway system combines the digital simulation module with the physical simulation module, and drives the physical simulation model through the control signals obtained in the digital simulation module to simulate the sway state of the ship ladder under different sea conditions.

[0047] Based on the same inventive concept as the above method, the embodiment of the present application also provides a ship ladder virtual-real symbiotic land-based sway device, and the device is implemented by the above-mentioned ship ladder virtual-real symbiotic land-based sway system.

[0048] In summary, the ship ladder virtual-real symbiotic land-based sway system in the present application combines the digital simulation module with the physical simulation module, constructs a virtual digital model of the hull under the disturbance of sea waves in the digital simulation module, obtains the motion state of the ship ladder under the corresponding disturbance and the control signals for driving the physical simulation model, so as to accurately simulate the sway state of the ship ladder under different sea conditions, and establish a scientific research system and platform for ship ladder technology research that can be carried out at any time on land.

[0049] Furthermore, the first rotational speed sequence is segmented to confirm the rotational speed mutation characteristic values of each subsequence, which reflect the degree of rotational speed mutation within a period of time; the number of control points of the NURBS curve is determined through the rotational speed mutation characteristic values, which helps to reasonably select the number and position of the control points according to the intensity and frequency of mutations in the rotational speed sequence. The second rotational speed sequence is obtained on the obtained NURBS curve, and the control signal is optimized by using an optimization algorithm to reduce the control vibration generated by the non-linear parts such as backlash of the electric cylinder and the servo motor when the rotational speed changes too fast, thereby reducing the situation where the actual rotational speed of the servo motor deviates too much from the control signal due to the vibration of the device, and improving the accuracy of the ship ladder land-based sway test bench for simulating the actual sea conditions.

[0050] The specific implementation process is as follows: Select supply vessel for the ship model in MSS; select PID tracking controller for the Autopilots module and Nonlinear PID setpoint controller for the DP module. This application only considers the change of the hull motion state under the action of waves. Therefore, in this embodiment, an environment without wind and ocean currents is set in MSS, and only the influence of waves is considered. For the Waves module, select Linear 2nd-order wave spectrum, set the significant wave height to 2.5 m, the encounter angle to 45°, and set the noise power to 0.2 in Band-limited White loise. Set the simulation step size to s. Thus, the simulation settings of the virtual digital model of the hull in the digital simulation module are completed.

[0051] Obtain the control signals of the servo motors that control the roll of the three-degree-of-freedom swing table in the digital simulation module respectively, and form the first rotational speed sequence, denoted as ; , ; where , respectively represent the 1st, 2nd, and nth subsequences of the first rotational speed sequence regarding the roll control signal; , , represent the 1st, 2nd, and Mth element values in the nth subsequence; M represents the length of the subsequence, with a value of 200; n represents the number of subsequences, with a value of 10.

[0052] For the nth subsequence, denote the standard deviation of all elements in the nth subsequence as , and denote the sum of the absolute values of all elements in the nth subsequence as , and the rotational speed mutation value = .

[0053] Take the rotational speed point coordinates corresponding to each element in , and the rotational speed point coordinate of element is .

[0054] Preset the value range of the control point N of the NURBS curve: ; where and respectively represent the maximum and minimum values of the number of control points, and set to 4; The value range is 0.05M to 0.1M, and the value in this embodiment is 0.05M.

[0055] Parameter selection description: On the one hand, the subsequence length M reflects the data point density of the rotational speed sequence within a certain time. When M is large, it indicates that there is more information about the rotational speed change within the corresponding time period, and it may contain more details of rotational speed mutations and fluctuations. At this time, appropriately increasing the upper limit of the number of control points (i.e., taking a larger ), the complex rotational speed changes can be fitted more meticulously, avoiding the curve being too smooth due to too few control points and missing important rotational speed features.

[0056] On the other hand, if is too large relative to M, it will lead to too dense control points, making the NURBS curve too complex, not only increasing the computational complexity but also possibly introducing too many local fluctuations, which is instead not conducive to smoothing the rotational speed sequence and reducing the degree of rotational speed mutation. Therefore, setting as a value proportional to M (such as 0.05M to 0.1M) can dynamically adjust the upper limit of the number of control points when the subsequence length changes, ensuring that the number of control points can meet the fitting accuracy requirements without overly increasing the computational burden and curve complexity.

[0057] Calculate the number of control points for adjusting the nth subsequence by the X-NURBS curve: ; where, ceil[] represents the ceiling function; The value of ; in this embodiment, the degree of the NURBS curve is three, and M is the length of the subsequence; norm() represents the maximum-minimum normalization function; represents the sum of the rotational speeds in the nth subsequence; represents the rotational speed mutation eigenvalue of the nth subsequence; represents the number of control points for adjusting the nth subsequence by the NURBS curve.

[0058] Randomly select from the rotational speed point coordinates corresponding to the elements to form the control point vector , , where ; , , respectively represent the rotational speed point coordinates corresponding to the 1st, 2nd, th elements selected from the subsequence.

[0059] For the control point vector Perform NURBS curve fitting and set the weight factor of each control point to 1 during fitting; obtain the second rotational speed sequence at the same sampling frequency for the obtained NURBS curve, denoted as .

[0060] Randomly obtain a different control point vectors to form the initial population of the particle algorithm , ; where, , , respectively represent the 1st, 2nd, and a-th control point vectors; a represents the number of control point vectors, and the value is 30.

[0061] Parameter selection description: First of all, the performance of the particle swarm algorithm depends to a large extent on the scale and diversity of the initial population. 30 control point vectors can form an initial population with a certain scale and diversity. A larger population scale can increase the diversity of solutions, enabling the algorithm to have a wider exploration ability in the search space, and thus more likely to find the global optimal solution. If the population scale is too small, it may cause the algorithm to fall into a local optimum and be unable to effectively search for the global optimal solution.

[0062] Secondly, the complexity of the ship ladder land-based swing system determines that a certain number of control points are required to achieve effective control and optimization of the rotational speed signal. 30 control point vectors can achieve a better balance between the system complexity and control accuracy in this embodiment. Too many control point vectors may lead to an overly complex system, increasing the burden on the control system; while too few control point vectors may not meet the system's requirements for control accuracy and cannot effectively reduce the degree of rotational speed mutation and improve the accuracy of simulation.

[0063] Finally, the value of the particle swarm population size can be adjusted according to the complexity of the problem to be solved. The implementer can also specifically adjust according to its simulation conditions and requirements.

[0064] Calculate the fitness of each control point vector in the particle swarm algorithm: ; where, MSE represents the mean square error between the rotational speed sequence formed by the new adjusted control signal and the rotational speed sequence output by the digital simulation module ; represents the upper quartile of ; represents the i-th rotational speed difference in that exceeds the upper quartile; U represents and both take 0.5; J represents the fitness of the control point vector.

[0065] Set the maximum number of iterations of the particle swarm optimization algorithm. In this embodiment, 200 is selected, and finally the particle swarm optimization algorithm outputs the optimal second rotational speed sequence. It should be noted that this maximum number of iterations is not fixed and can be adjusted according to the needs of the problem in actual use.

[0066] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, and the module, the segment of a program, or the part of code contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than that disclosed in the descriptions. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0067] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the basic characteristics 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 recorded in the foregoing embodiments, or equivalent replacements of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A virtual-real symbiotic land-based swing system for a ship ladder, characterized in that The system includes a digital simulation module and a physical simulation module: The digital simulation module simulates the hull motion state, converts the motion amplitudes of the three degrees of freedom of roll, pitch, and heave at each moment into the rotational speeds of the corresponding servo motors; forms a first rotational speed sequence from the rotational speeds of each servo motor for a preset time length, and evenly divides the first rotational speed sequence to obtain the difference sequences of each subsequence after division; based on the element distribution in the difference sequence of each subsequence, combined with the element dispersion degree in each subsequence, determines the rotational speed mutation characteristic value of each subsequence; Based on the first rotational speed sequence, rotational speed points are constructed, the value range of the number of control points in the preset NURBS curve is set, combined with the element distribution of each subsequence and the rotational speed mutation characteristic value, determines the number of control points for adjusting each subsequence by the NURBS curve, randomly obtains all the control points of the rotational speed sequence to form a control point vector, and obtains the NURBS curve corresponding to each control point vector and the second rotational speed sequence; Compare the first rotational speed sequence with the second rotational speed sequence, combined with the element distribution in the difference sequence of the second rotational speed sequence, determines the fitness of the control point vector, and uses an optimization algorithm to obtain the optimal second rotational speed sequence, and obtains the control signals of each servo motor in the physical simulation module by combining the PID control algorithm; The physical simulation module consists of a three-degree-of-freedom shaking table, and the three servo motors control the motions of the three degrees of freedom of roll, pitch, and heave respectively through the control signals obtained by the digital simulation module.

2. The virtual-real symbiotic land-based swing system of a ship ladder according to claim 1, characterized in that, The determination of the rotational speed mutation characteristic value of each subsequence is specifically as follows: Calculate the cumulative sum of the absolute values of all elements in each subsequence, and perform positive fusion with the element dispersion degree of each subsequence to obtain the rotational speed mutation characteristic value of each subsequence.

3. A virtual-real symbiotic land-based swing system for a ship ladder according to claim 2, characterized in that, The rotational speed mutation characteristic value is specifically the product of the cumulative sum and the dispersion degree of each subsequence.

4. A virtual-real symbiotic land-based rocking system for a ship ladder as claimed in claim 1, wherein, The step of constructing rotational speed points based on the first rotational speed sequence is: Use the serial number value of the elements in the first rotational speed sequence as the abscissa of the rotational speed points, and use the element values of the first rotational speed sequence as the ordinate of the rotational speed points.

5. A virtual-real symbiotic land-based rocking system for a ship ladder as claimed in claim 1, characterized in that, The determination of the number of control points for adjusting each subsequence by the NURBS curve is specifically as follows: Calculate the ratio of the rotational speed mutation characteristic value of each subsequence to the sum value of all elements in each subsequence, and perform normalization; Calculate the ceiling value of the product of the extreme difference of the value range of the number of control points in the NURBS curve and the obtained normalized value; Use the sum value of the minimum number of control points corresponding to the value range and the ceiling value as the number of control points for adjusting each subsequence by the NURBS curve.

6. A virtual-real symbiotic land-based rocking system for a ship ladder as described in claim 1, characterized in that, The second rotational speed sequence is obtained by sampling the NURBS curve at a preset sampling frequency.

7. A virtual-real symbiotic land-based swing system for a ship ladder as claimed in claim 1, characterized in that The determination of the fitness of the control point vector is specifically as follows: 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 greater than the upper quartile in the difference sequence of the second rotational speed sequence and the upper quartile, and record it as the first difference; Accumulate all the first differences in the second rotational speed sequence to obtain the second difference; Calculate the difference between the first rotational speed sequence and the second rotational speed sequence, and record it as the third difference; The second difference and the third difference are weighted and summed to obtain the fitness of each control point vector.

8. A virtual-real symbiotic land-based rocking system for a ship ladder as claimed in claim 7, 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 difference sequence of the second rotational speed sequence and the upper quartile.

9. The land-based swing system with virtual-real symbiosis of a ship ladder according to claim 7, characterized in that, The third difference is determined by the mean square error between the first rotational speed sequence and the second rotational speed sequence.

10. A land-based swing device for a ship ladder with virtual-real symbiosis, characterized in that, The device is implemented by a ship ladder virtual-real symbiotic land-based swaying system as described in claim 1.

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