A dynamic load simulation test method and device for a cable winch

Abstract

The present invention discloses a method and device for dynamic load simulation testing of a mooring winch, which relates to the technical field of mooring winch simulation testing. The method comprises: retrieving historical working condition data associated with the target working scenario of the mooring winch, analyzing and constructing a typical load working condition set; obtaining design working condition information, and constructing a design load intensity sequence based on mapping rules; traversing the intensity sequence, matching and generating an initial test sequence in the typical working condition set; applying random perturbations to N typical load working condition segments, and iteratively optimizing through an optimization algorithm to obtain an iterative test sequence; smoothing the load switching points of the sequence, and inputting them into a simulation test device to perform dynamic load testing. This achieves the technical effects of diversifying test scenarios, simplifying parameter settings, improving matching with actual working conditions, and enhancing test accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of mooring machine simulation testing, and in particular to a method and device for dynamic load simulation testing of a mooring machine. Background Art

[0002] The mooring winch is a key device used for mooring operations in ships, offshore engineering platforms and other equipment. Its performance directly affects the mooring safety and operating efficiency of the ship. Traditional mooring winch testing methods usually rely on fixed load parameters or a single test scenario, which makes it difficult to fully simulate the dynamic load changes of the mooring winch in actual work. In the existing technology, mooring winch testing is usually carried out in the factory, but due to the limitations of the test scenario, it is impossible to truly reflect the load characteristics of the mooring winch under complex working conditions, resulting in a large deviation between the test results and actual usage. At the same time, when conducting strict restoration tests based on the design scenario, there are problems such as variable parameter settings and high testing costs. Summary of the Invention

[0003] The present invention provides a dynamic load simulation test method and device for a cable winch, so as to solve the technical problems in the prior art such as single test scenario, cumbersome parameter setting, low matching degree with actual working conditions, and influence on test accuracy, thereby achieving the technical effects of diversifying test scenarios, simplifying parameter setting, improving matching degree with actual working conditions, and enhancing test accuracy.

[0004] In a first aspect, the present invention provides a method for dynamic load simulation testing of a mooring winch, wherein the method for dynamic load simulation testing of a mooring winch comprises:

[0005] Retrieve historical operating condition data associated with the target operating scenario of the mooring winch, analyze the historical operating condition data, and construct a typical load condition set.

[0006] The design operating condition information under the working scenario is obtained, and the design operating condition information is mapped into a design load intensity sequence based on a preset mapping rule, wherein the mapping rule is constructed based on regression analysis, and takes the design operating condition information as input and the design load intensity sequence as output.

[0007] The designed load intensity sequence is traversed, and corresponding typical load conditions are matched and extracted from the typical load condition set to generate an initial test sequence.

[0008] Random perturbations are applied to N typical load condition segments in the initial test sequence, and an optimization algorithm is used to iteratively optimize the test sequence after the perturbation to obtain an iterative test sequence.

[0009] The load condition switching points in the iterative test sequence are smoothly transitioned, and the iterative test sequence after smoothing is input into the simulation test system of the mooring machine to perform a dynamic load simulation test.

[0010] In a feasible implementation, retrieving historical operating condition data associated with a target operating scenario of a mooring winch includes:

[0011] Get the predefined target working scenario description information of the winch.

[0012] Using the target working scenario description information as a homologous search constraint, the interactive cable winch data platform extracts matching historical operating parameters and corresponding historical environmental information, and outputs the historical working condition data.

[0013] In a feasible implementation, analyzing the historical operating condition data to construct a typical load condition set includes:

[0014] A multi-dimensional statistical analysis is performed on the historical operating condition data to extract a set of characteristic parameters including time domain characteristics and frequency domain characteristics.

[0015] The characteristic parameter set is clustered based on a density clustering algorithm.

[0016] A representative sample of the cluster center of each cluster in the clustering division result is extracted and defined as the typical load condition to form the typical load condition set.

[0017] In a feasible implementation, obtaining design operating condition information under the working scenario and mapping the design operating condition information into a design load intensity sequence based on a preset mapping rule includes:

[0018] Acquire an intensity evaluation sample with annotations, wherein the intensity evaluation sample includes an operating condition parameter sample, an environmental parameter sample and a corresponding intensity annotation value.

[0019] The mapping rule based on regression analysis is constructed based on the severity evaluation sample, and the design operating condition information is mapped into a design load severity set according to the mapping rule.

[0020] The design load intensity set is time-serialized, and a periodic analysis is performed on the serialization results to extract the most significant repeatable period and output it as the design load intensity sequence.

[0021] In a feasible implementation, the design load severity sequence is traversed, and corresponding typical load conditions are matched and extracted from the typical load condition set to generate an initial test sequence, including:

[0022] Based on the mapping rule, the typical load severity of each typical load condition in the typical load condition set is calculated and obtained to generate a typical load severity set.

[0023] According to the typical load intensity set, matching identification is performed in the design load intensity sequence, and the identification result is defined as the benchmark growth point set.

[0024] Taking N reference growth points in the reference growth point set as the center, bidirectional end growth is performed along the design load intensity sequence until the design load intensity sequence is completely covered, and N segments of growth results are output.

[0025] The intensity offsets of the N segments of the growth results are calculated respectively, and with the goal of minimizing the standard deviation of all the intensity offsets, the bidirectional end positions of the N segments of the growth results are optimized, and the optimization results are output as the initial test sequence, wherein the intensity offsets include an upper limit offset and a lower limit offset, and the initial test sequence includes N typical load condition segments.

[0026] In a feasible implementation, random perturbations are applied to N typical load condition segments in the initial test sequence, and an optimization algorithm is used to iteratively optimize the test sequence after the perturbation to obtain an iterative test sequence, including:

[0027] Random fluctuations are applied to each of the typical load condition sections in the initial test sequence, and the load severity of the load condition after the fluctuations is re-evaluated.

[0028] In combination with an optimization algorithm, with the goal of minimizing the differences between the N typical load condition sections and the designed load intensity sequence, an iterative fluctuation optimization is performed on the initial test sequence to obtain an iterative test sequence.

[0029] In a feasible implementation, after smoothly transitioning the load condition switching point in the iterative test sequence, the method further includes:

[0030] The smoothed iterative test sequence and the designed load intensity sequence are respectively integrated.

[0031] The size of the integral value is determined. If the integral value of the iterative test sequence after smoothing is greater than the integral value of the design load intensity sequence, the iterative test sequence after smoothing is run for testing.

[0032] In a feasible implementation, after smoothly transitioning the load condition switching point in the iterative test sequence, the method further includes:

[0033] The smoothed iterative test sequence is used as the upper limit of integration, the design load intensity sequence is used as the lower limit of integration, and integration is performed along the time direction of the sequence.

[0034] If the integral result is greater than zero, the difference sequence information between the smoothed iterative test sequence and the design load intensity sequence at each sequence point is calculated.

[0035] According to the preset difference control constraint, the significance check of the iterative test sequence and the load intensity sequence after smoothing is performed in combination with the difference sequence information.

[0036] In a feasible implementation, the difference control constraint includes a difference control threshold and a difference control ratio.

[0037] In a second aspect, the present invention further provides a dynamic load simulation test device for a mooring winch, wherein the dynamic load simulation test device for a mooring winch comprises:

[0038] The data retrieval and analysis module is used to retrieve historical working condition data associated with the target working scenario of the mooring machine, analyze the historical working condition data, and construct a typical load condition set.

[0039] A load intensity assessment module is used to obtain the design operating condition information under the working scenario and map the design operating condition information into a design load intensity sequence based on a preset mapping rule, wherein the mapping rule is constructed based on regression analysis, and takes the design operating condition information as input and the design load intensity sequence as output.

[0040] The initial test sequence generation module is used to traverse the design load intensity sequence, match and extract corresponding typical load conditions in the typical load condition set, and generate an initial test sequence.

[0041] The sequence optimization module is used to apply random perturbations to the N typical load condition segments in the initial test sequence, and perform iterative optimization of the test sequence after the perturbation in combination with an optimization algorithm to obtain an iterative test sequence.

[0042] The simulation test execution module is used to smoothly transition the load condition switching points in the iterative test sequence, and input the smoothed iterative test sequence into the simulation test system of the mooring machine to perform a dynamic load simulation test.

[0043] The present invention discloses a dynamic load simulation test method and device for a mooring winch, comprising: retrieving and analyzing historical operating condition data related to a target working scenario of the mooring winch, and constructing a typical load operating condition set; obtaining design operating condition information under the working scenario, and mapping the design operating condition information into a design load intensity sequence based on a preset mapping rule; on this basis, traversing the design load intensity sequence, matching and extracting corresponding typical load conditions in the typical load operating condition set, and generating an initial test sequence; then applying random perturbations to N typical load operating condition segments in the initial test sequence, and iteratively optimizing the perturbed test sequence in combination with an optimization algorithm to obtain an iterative test sequence; performing smooth transition processing on the load operating condition switching points in the iterative test sequence, and inputting the smoothed iterative test sequence into a mooring winch simulation test device to perform a dynamic load simulation test. The dynamic load simulation test method and device for a mooring winch disclosed in the present invention solve the technical problems of a single test scenario, cumbersome parameter settings, and low matching with actual operating conditions, which affect test accuracy, and achieve the technical effects of diversifying test scenarios, simplifying parameter settings, improving matching with actual operating conditions, and enhancing test accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The figure is a flow chart of a dynamic load simulation test method for a cable winch according to the present invention.

[0045] Figure 2 The figure is a structural schematic diagram of a dynamic load simulation test device for a cable winch according to the present invention.

[0046] Explanation of the reference numerals: data retrieval and analysis module 11 , load intensity assessment module 12 , initial test sequence generation module 13 , sequence optimization module 14 , simulation test execution module 15 . DETAILED DESCRIPTION

[0047] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0048] Example 1, as Figure 1 The present invention is a flow chart of a method for dynamic load simulation testing of a mooring winch, wherein the method for dynamic load simulation testing of a mooring winch includes:

[0049] S100: Retrieving historical operating condition data associated with a target operating scenario of a mooring winch, analyzing the historical operating condition data, and constructing a typical load operating condition set.

[0050] Specifically, historical operating condition data refers to the historical operating parameters (such as load force, speed, and time) and corresponding historical environmental information (such as wind speed, wave height, and water depth) accumulated during actual mooring winches' operation. This data is used for subsequent analysis and modeling. The typical load condition set is derived by statistically analyzing and clustering the historical operating condition data to identify a set of load characteristics that represent the common load characteristics of mooring winches in target operating scenarios. This typical load condition set covers the main load variation patterns experienced by mooring winches in actual operation.

[0051] Through the above steps, typical load conditions are extracted from historical operating condition data, which reduces redundant data, avoids the tedious parameter setting process in traditional methods, and significantly improves the efficiency of test preparation. At the same time, the typical load condition set can cover the main load change patterns of the mooring crane in the target working scenario, ensuring the diversity and comprehensiveness of the test scenarios, which helps to solve the problem of single test scenarios in traditional testing methods.

[0052] In some embodiments, retrieving historical operating condition data associated with a target operating scenario of a mooring winch includes:

[0053] Obtain predefined target working scenario description information of the mooring machine; using the target working scenario description information as a homologous search constraint, the interactive mooring machine data platform extracts matching historical operating parameters and corresponding historical environmental information, and outputs the historical working condition data.

[0054] Specifically, first, the description information of the target work scenario is obtained, including but not limited to parameters such as operation type (such as long cable pulling, broken cable recovery), cable length, terrain slope, ambient temperature and humidity, and operating time. Then, the description information is converted into structured retrieval constraints and interacts with the cable winch data platform to perform historical data screening based on attribute matching. For example, if the target scenario is described as "nighttime operation in a high-slope mountainous area, cable length greater than 1000 meters, and temperature below 0°C", historical records that meet the above conditions will be screened, and the operating parameters (such as current, voltage, tension, speed) and environmental information (such as temperature, humidity, wind speed) will be extracted and uniformly output as a historical working condition dataset.

[0055] Through the above process, it is possible to accurately extract the actual operating data in the target scenario, ensuring that the data used in subsequent modeling and analysis are highly relevant and representative, and avoiding model distortion or control strategy failure due to data deviation.

[0056] In some embodiments, analyzing the historical operating condition data to construct a typical load condition set includes:

[0057] Perform multidimensional statistical analysis on the historical operating condition data to extract a set of characteristic parameters including time domain characteristics and frequency domain characteristics; cluster the set of characteristic parameters based on a density clustering algorithm; extract a cluster center representative sample of each cluster in the clustering result, define it as the typical load condition, and form the typical load condition set.

[0058] Specifically, time domain features refer to the characteristics of historical operating condition data in the time domain, such as the load force variation trend over time, peak value, and mean value. Frequency domain features refer to the characteristics of historical operating condition data in the frequency domain, such as the frequency distribution of load changes and the main frequency components. The feature parameter set is a collection of time domain and frequency domain feature parameters, which is used for subsequent cluster analysis.

[0059] Specifically, the typical load condition is a representative load sample extracted, which can reflect the typical load characteristics of the mooring winch under specific working conditions.

[0060] Specifically, statistical analysis is first performed on historical operating condition data to extract a set of characteristic parameters, including time domain features (such as mean, variance, and fluctuation coefficient) and frequency domain features (such as main frequency and spectral energy distribution). Subsequently, a density clustering algorithm (such as DBSCAN) is used to cluster the characteristic parameter set, automatically identifying several operating condition clusters with similar operating characteristics. Next, for each cluster, the cluster center (the most representative sample) is selected as the representative of that type of operating condition, ultimately forming a typical load condition set. For example, the typical load condition set may include typical categories such as "low-speed and high-tension starting condition," "uniform speed and long-term cable twisting condition," and "high-frequency fluctuation impact condition."

[0061] Through the above process, working condition templates covering various operating states can be automatically summarized without manual labeling, which significantly improves the construction efficiency and adaptability of typical load conditions. At the same time, it has good scalability and is suitable for working condition identification under different types of cable winches or variable working conditions.

[0062] S200: Obtain design operating condition information under the working scenario, and map the design operating condition information into a design load intensity sequence based on a preset mapping rule, wherein the mapping rule is constructed based on regression analysis, and takes the design operating condition information as input and the design load intensity sequence as output.

[0063] Specifically, the design load intensity sequence refers to a load intensity change curve with time series characteristics derived from the design operating parameters and environmental conditions in the target working scenario, combined with the preset intensity mapping rules. In other words, this sequence is used to characterize the typical load intensity and periodic characteristics that the equipment may withstand under the design state, and to provide a basis for the formulation of subsequent testing and analysis strategies.

[0064] Specifically, the design operating condition information refers to the design parameters of the mooring winch under the target working scenario, including operating condition parameters (such as load force, speed, time, etc.) and environmental parameters (such as wind speed, wave height, water depth, etc.), which are set by the designer based on the intended use and working environment of the mooring winch.

[0065] In some embodiments, obtaining design operating condition information under the working scenario and mapping the design operating condition information into a design load intensity sequence based on a preset mapping rule includes:

[0066] Acquire an intensity evaluation sample with annotations, wherein the intensity evaluation sample includes an operating condition parameter sample, an environmental parameter sample and a corresponding intensity annotation value; construct the mapping rule based on regression analysis based on the intensity evaluation sample, and map the design operating condition information into a design load intensity set according to the mapping rule; time-serialize the design load intensity set, and perform periodic analysis on the serialization results, extract the most significant repeatable period, and output it as the design load intensity sequence.

[0067] Specifically, intensity is a comprehensive indicator used to evaluate the strength of load action, which can be reflected as a combination of parameters such as stress amplitude, impact frequency, and energy density, and is used to measure the force intensity level of equipment under specific working conditions.

[0068] Specifically, first, a set of labeled intensity evaluation samples is obtained, each sample contains working condition parameters (such as traction speed, load mass, acceleration, tension, etc.), environmental parameters (such as temperature, humidity, altitude, slope, etc.) and their corresponding intensity annotation values (for example, measured by stress amplitude, impact frequency or energy density, labeled by experts or calculated through stress analysis, used to quantify the load intensity under the working condition); then, based on the sample set formed by the intensity evaluation samples, a regression analysis method (such as multivariate regression, support vector regression SVR or neural network regression) is used to construct a mapping rule from working condition parameters to intensity values. This rule can be understood as a function model, which is used to map any input working condition information to a load intensity value.

[0069] Furthermore, the design operating condition information under the target working scenario is used as input parameters (including operating condition parameters and environmental parameters) and input into the mapping model (i.e., mapping rules), and the corresponding intensity values are output to form a design load intensity set arranged by time segments. This set reflects the intensity levels that the equipment may withstand at different time points.

[0070] Furthermore, the above-mentioned design load intensity set is time-series processed to construct a time-varying intensity variation sequence. Subsequently, periodic analysis methods (such as fast Fourier transform (FFT), sliding window autocorrelation analysis, and empirical mode decomposition (EMD)) are used to identify the most significant repeatable cycles in the sequence, thereby extracting representative periodic intensity patterns. Preferably, the periodic intensity pattern with the largest intensity variation rate within the cycle (corresponding to the most unfavorable operating condition) is selected as the design load intensity sequence. Finally, the above periodic intensity sequence is output as a standardized design load intensity sequence for subsequent analysis modules.

[0071] For example, in a typical mountain traction operation scenario, the design operating conditions are: traction speed 0.8m / s, cable length 1100m, ambient temperature -10°C, and slope 15°. This information is input into the trained mapping rules to generate a sequence of intensity values. FFT analysis then reveals significant 180-second periodic fluctuations in this sequence. Ultimately, a periodic design load intensity sequence is constructed for fatigue life analysis and control strategy simulation.

[0072] By acquiring design operating condition information and establishing mapping rules, the above steps convert the design parameters into a design load severity sequence, providing load severity data that matches the design operating conditions for subsequent steps. This ensures that the test sequence accurately reflects the load changes of the mooring winch under the design operating conditions, thereby improving the targetedness and accuracy of the test.

[0073] S300: traverse the design load severity sequence, match and extract corresponding typical load conditions in the typical load condition set, and generate an initial test sequence.

[0074] Specifically, the initial test sequence is a test condition sequence composed of multiple typical load conditions that are closest to each intensity value in the design load intensity sequence in the typical load condition set. The initial test sequence has a segmented characteristic, that is, a representative load segment set composed of multiple load segments corresponding to multiple typical load conditions, which is used to simulate the typical load change process that the mooring crane equipment may encounter during the entire life cycle of the target scenario in test verification, accelerated testing or virtual loading.

[0075] In other words, the core goal of generating the initial test sequence is to quickly construct a test sequence containing multiple typical load condition segments while ensuring coverage and representativeness, so that it can efficiently approximate the distribution characteristics of the original design load intensity sequence and have lower intensity offset volatility.

[0076] By matching each intensity value in the design load intensity sequence with samples in the typical load condition set and extracting typical load conditions, the tedious parameter setting process is avoided, the workload of manual parameter adjustment is reduced, and the test process is simplified, which helps to significantly improve the efficiency of test preparation; the generated initial test sequence can roughly cover the main load change patterns of the design conditions, helping to ensure the comprehensiveness and pertinence of the test.

[0077] In some embodiments, traversing the design load severity sequence, matching and extracting corresponding typical load conditions from the typical load condition set, and generating an initial test sequence includes:

[0078] Based on the mapping rule, the typical load intensity of each typical load condition in the typical load condition set is calculated to generate a typical load intensity set; according to the typical load intensity set, matching and identification are performed in the design load intensity sequence, and the identification result is defined as a benchmark growth point set; with N benchmark growth points in the benchmark growth point set as the center, bidirectional end growth is performed along the design load intensity sequence until the design load intensity sequence is completely covered, and N segments of growth results are output; the intensity offsets of the N segments of the growth results are calculated respectively, and with the goal of minimizing the standard deviation of all the intensity offsets, the bidirectional end positions of the N segments of the growth results are optimized, and the optimization results are output as the initial test sequence, wherein the intensity offset includes an upper limit offset and a lower limit offset, and the initial test sequence includes N typical load condition segments.

[0079] Specifically, first, based on the aforementioned mapping rules, the intensity of each typical load condition in the typical load condition set is calculated to obtain the corresponding typical load intensity value, forming a typical load intensity set, which is used for subsequent matching and positioning in the design load intensity sequence; then, the design load intensity sequence is traversed, and similarity matching is performed with the typical load intensity set, and the Euclidean distance or cosine similarity between the sequence point in the design load intensity sequence and multiple typical load intensity values in the typical load intensity set is calculated to identify the sequence point (time point) closest to each typical load intensity value, and define this group of matching results as a benchmark growth point set, where the number of benchmark growth points is N, and N is greater than or equal to the number of typical load conditions (that is, one typical load condition can match multiple sequence points in the design load intensity sequence).

[0080] Furthermore, with each benchmark growth point as the center and combined with the preset growth step, bidirectional end growth is performed along the time axis of the design load intensity sequence, and at the same time, it is expanded forward and backward until all N growth segments cover the entire design load intensity sequence without repetition, and the N-segment growth results can be determined, where each segment corresponds to an alternative typical load condition segment, and multiple typical load condition segments can correspond to the same typical load condition.

[0081] Furthermore, the offset of each growth result relative to the corresponding typical load intensity value is calculated separately, including the upper limit offset (i.e., the amount exceeding the typical load intensity value) and the lower limit offset (i.e., the amount lower than the typical load intensity value); then, the standard deviation of all offsets of the N-segment growth results is used as the optimization objective function, and the optimization direction is determined according to the deviation direction of the upper limit offset and the lower limit offset relative to the mean of the offset of each category, the bidirectional end growth boundary of each segment is adjusted, and the optimization is iterated; until the standard deviation is minimized (the standard deviation of the preset number of optimizations or the consecutive preset number of times is stable), the optimized N-segment growth results are output as the initial test sequence, where the sequence consists of N typical load condition segments, which can represent the main change mode of the design load intensity sequence and meet the needs of subsequent test loading or simulation verification.

[0082] In other words, the main goal of the above optimization process is to dynamically adjust the growth boundaries of each segment so that the generated test segments are as globally balanced as possible to approach the corresponding typical load intensity levels, minimize the volatility of the intensity offset, and thus construct an initial test sequence that is both representative and stable.

[0083] Specifically, the standard deviation of the upper limit offset and the lower limit offset is used as the optimization objective function to ensure that the offset degree of all growth segments is as consistent as possible in a statistical sense, to avoid excessive offset of individual segments (that is, the gap between the upper and lower limits of the intensity replaced by the typical load intensity value is too large) leading to representative distortion, so that each typical load condition segment can more accurately cover its actual performance segment in the design intensity sequence, thereby enhancing the representativeness and reproducibility of the test sequence.

[0084] Through this process, a representative initial test sequence can be constructed based on the structural similarities between the typical load condition set and the designed load intensity sequence, without completely traversing all load conditions. This sequence strikes a balance between coverage, representativeness, and intensity offset control, helping to improve the efficiency and reliability of the test solution.

[0085] S400: applying random disturbances to N typical load condition segments in the initial test sequence, and performing iterative optimization of the test sequence after the disturbance in combination with an optimization algorithm to obtain an iterative test sequence.

[0086] Specifically, random perturbation refers to the introduction of random changes within a certain range based on the original operating parameters in order to improve the coverage (equivalent substitution rate) of the test sequence for the load intensity sequence (i.e., design operating conditions), thereby improving the test confidence and accuracy.

[0087] In some embodiments, random perturbations are applied to N typical load condition segments in the initial test sequence, and an optimization algorithm is used to iteratively optimize the post-perturbation test sequence to obtain an iterative test sequence, including:

[0088] Random fluctuations are applied to each of the typical load condition segments in the initial test sequence, and the load intensity of the load condition after the fluctuations is re-evaluated; combined with an optimization algorithm, the initial test sequence is iteratively optimized with the goal of minimizing the differences between the N typical load condition segments and the designed load intensity sequence to obtain an iterative test sequence.

[0089] Specifically, random fluctuations are first applied to each typical load condition in the initial test sequence. These fluctuations can be randomly generated within a preset disturbance range to more closely resemble the load conditions that may occur in actual operation. For example, a disturbance within the ±5% range is set for the cable tension parameter, and corresponding disturbance amplitudes can also be set for parameters such as speed and current. After the disturbance is applied, the load intensity value is recalculated for each operating point in each typical load condition after the fluctuation according to the preset mapping rules.

[0090] Furthermore, after obtaining the load intensity values for all fluctuating operating conditions, an optimization algorithm (such as a genetic algorithm, particle swarm optimization, or simulated annealing) is used to iteratively optimize the perturbation parameters and iteratively update the initial test sequence, ensuring that the overall load intensity sequence more closely matches the design objectives and improving the representativeness and accuracy of the test. The optimization objective is to minimize the overall difference between the load intensity reassessment results of the perturbed load intensity sequence and the design load intensity sequence, such as minimizing the variance and standard deviation of the intensity values at corresponding points.

[0091] Specifically, when the optimization algorithm meets the convergence conditions (such as the convergence of the objective function value for a preset number of consecutive times) or reaches the preset number of iterations, the current optimal test sequence is output as the final iterative test sequence. This sequence covers a variety of disturbance combinations based on typical working conditions, which can more realistically simulate the dynamic load changes of the mooring machine during actual operation. At the same time, it facilitates the automatic setting of parameters, providing reliable support for subsequent test operations.

[0092] S500: Smoothly transition processing is performed on the load condition switching points in the iterative test sequence, and the smoothed iterative test sequence is input to a simulation test system of a mooring machine to perform a dynamic load simulation test.

[0093] Specifically, there may be large differences between multiple typical load conditions in the iterative test sequence, that is, there are discontinuous jump points between adjacent conditions. Therefore, smooth transition processing is required to reduce mechanical shock, current fluctuations and other problems caused by parameter jumps during the test process, and further improve the stability and executability of the test sequence in actual simulation tests.

[0094] Specifically, first, each group of two adjacent typical load condition segments in the iterative test sequence is analyzed to identify the jump values of key parameters (such as tension, speed, current, etc.). When the rate of change of at least one parameter in the adjacent condition segments exceeds a set threshold (such as 10%), the point is determined to be a load condition switching point. Then, for the identified load condition switching point, an interpolation algorithm or transition function is used for smoothing to avoid the sudden change of load parameters from impacting the mooring system. Optional smoothing methods include but are not limited to: linear interpolation, inserting several intermediate states between the two condition segments to transition key parameters in a linear manner; cubic spline interpolation, constructing a curve with continuous first-order and second-order derivatives to achieve smoother parameter transition; Sigmoid function transition, using an S-shaped function to slowly change the changing parameters to achieve nonlinear smooth transition.

[0095] For example, if the tension jumps from 1000N in working condition section A to 1500N in working condition section B, using the linear interpolation method, 3 to 5 transition points can be inserted between the two sections, and the tension values are: 1100N, 1200N, 1300N, 1400N, and finally transition to 1500N.

[0096] Furthermore, after smoothing all switching points, they are reassembled to create a smoothed iterative test sequence. This sequence maintains the continuity of parameter changes along the timeline, avoiding sudden changes, thereby improving the stability and authenticity of the simulation test. This smoothed test sequence is then input into the mooring winch's dynamic load simulation test system. The system adjusts actuators (such as the tension controller and motor drive) in real time based on the input sequence, simulating the mooring winch's operating state under different operating conditions, thereby enabling performance testing and response evaluation of the equipment under dynamic loads.

[0097] The above steps perform smooth transition processing on the load condition switching points in the iterative test sequence. The smoothed test sequence has better executableness when input into the control system, which facilitates stable operation of the system. At the same time, the continuous change of load parameters is conducive to the subsequent high-precision analysis of the response characteristics of the mooring machine (such as hysteresis, overshoot, steady-state error, etc.).

[0098] In some embodiments, after smoothly transitioning the load condition switching point in the iterative test sequence, the method further includes:

[0099] The smoothed iterative test sequence and the design load intensity sequence are respectively integrated; the integral value is determined, and if the integral value of the smoothed iterative test sequence is greater than the integral value of the design load intensity sequence, the smoothed iterative test sequence is run for testing.

[0100] Specifically, after completing the smooth transition processing of the load condition switching points in the iterative test sequence, in order to further evaluate the representativeness and effectiveness of the test sequence and ensure that its overall load intensity is not lower than the design requirements, a judgment mechanism based on integral value is adopted. This mechanism integrates the smoothed iterative test sequence and the design load intensity sequence, and compares their total integral amount to determine whether to use the test sequence for actual simulation testing.

[0101] Specifically, first, the test load intensity sequence of the smoothed iterative test sequence is calculated by the aforementioned mapping rule; then, the test load intensity sequence and the design load intensity sequence are integrated to obtain the test integral value It est and the design integral value I design Preferably, for the test load intensity sequence and the design load intensity sequence belonging to the discrete time series, the integral can be approximately represented by numerical summation; then, the sizes of the two integral values are compared and the following judgment logic is executed:

[0102] like It est Less than I design , it means that the overall load level of the test sequence is too low and may not fully cover the load requirements under the design conditions, and disturbance optimization or parameter adjustment is required.

[0103] like It est Greater than or equal to I design , it means that the overall load intensity of the test sequence after smoothing is not lower than the design target, and it has sufficient test strength and can be used for actual testing. Correspondingly, the smoothed iterative test sequence is input into the dynamic load simulation test system of the mooring winch, and the simulation test system is started. The dynamic load is gradually loaded according to the test sequence to complete the performance test and response collection of the mooring winch under complex working conditions.

[0104] By introducing an integral value discrimination mechanism, the test sequence is ensured to have sufficient total load intensity, avoiding a decrease in the overall strength of the test sequence due to smoothing or disturbance adjustment, which would affect the test effectiveness; at the same time, it helps to avoid resource waste and equipment wear caused by invalid or low-load tests.

[0105] In some embodiments, after smoothly transitioning the load condition switching point in the iterative test sequence, the method further includes:

[0106] The smoothed iterative test sequence is used as the upper limit of integration and the designed load intensity sequence is used as the lower limit of integration, and integration is performed along the time direction of the sequence; if the integration result is greater than zero, the difference sequence information between the smoothed iterative test sequence and the designed load intensity sequence at each sequence point is calculated; according to the preset difference control constraint, the significance check of the smoothed iterative test sequence and the load intensity sequence is performed in combination with the difference sequence information.

[0107] Specifically, in order to ensure that the generated test sequence is better than or not lower than the design target in terms of overall load intensity, the following steps can also be taken: first, the intensity evaluation value of the smoothed iterative test sequence, that is, the above-mentioned test load intensity sequence, is used as the upper limit of integration, and the design load intensity sequence is used as the lower limit of integration, and an integration operation is performed along the time axis to determine whether the overall load intensity meets the design requirements. If the integral value is greater than zero, it means that the test sequence is better than the design target in terms of overall load intensity, and the difference analysis and significance verification steps can be further entered.

[0108] Furthermore, while satisfying the overall integration conditions, the differences between the two sequences at each time point (i.e., sequence points) are calculated to generate differential sequence information. This differential sequence information characterizes the degree of deviation of the test sequence from the design target at each time point. Furthermore, a significance check is performed based on pre-set differential control constraints to prevent excessive deviations in the test sequence at certain local time periods.

[0109] In some implementations, the difference control constraint includes a difference control threshold and a difference control ratio.

[0110] Specifically, the difference control threshold refers to the maximum allowable difference value at any set time point; the difference control ratio is the upper limit of the proportion of time points at which the difference exceeds the threshold.

[0111] Specifically, based on the aforementioned difference control threshold and difference control ratio, the following significance check logic is executed: if the difference amount is less than the difference control threshold and this holds true for at least 100% - P (difference control ratio) time points, the test sequence is considered to have passed the significance check and can be used for actual testing; otherwise, the test sequence is considered to have excessive deviations in local load intensity and requires further adjustments (such as secondary perturbations, local optimization, etc.).

[0112] In summary, the dynamic load simulation test method for a cable winch provided by the present invention has the following technical effects:

[0113] By retrieving and analyzing historical operating condition data related to the target working scenario of the mooring winch, a typical load condition set is constructed; the design operating condition information under the working scenario is obtained, and the design operating condition information is mapped into a design load intensity sequence based on a preset mapping rule; on this basis, the design load intensity sequence is traversed, and the corresponding typical load conditions are matched and extracted in the typical load condition set to generate an initial test sequence; then random perturbations are applied to the N typical load condition segments in the initial test sequence, and the perturbed test sequence is iteratively optimized in combination with an optimization algorithm to obtain an iterative test sequence; the load condition switching points in the iterative test sequence are smoothly transitioned, and the smoothed iterative test sequence is input into the mooring winch simulation test device to perform a dynamic load simulation test, thereby achieving the technical effects of diversifying the test scenarios, simplifying parameter settings, improving the matching degree with the actual working conditions, and enhancing the test accuracy.

[0114] Example 2, as Figure 2 This is a structural diagram of a dynamic load simulation test device for a mooring machine of the present invention. For example, Figure 1 The flow chart of the dynamic load simulation test method of a mooring machine of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0115] Based on the same concept as the dynamic load simulation test method of a mooring winch in the above embodiment, the present invention also provides a dynamic load simulation test device for a mooring winch, comprising:

[0116] The data retrieval and analysis module 11 is used to retrieve historical working condition data associated with the target working scenario of the mooring machine, analyze the historical working condition data, and construct a typical load condition set.

[0117] The load intensity assessment module 12 is used to obtain the design operating condition information under the working scenario, and map the design operating condition information into a design load intensity sequence based on a preset mapping rule, wherein the mapping rule is constructed based on regression analysis, and takes the design operating condition information as input and the design load intensity sequence as output.

[0118] The initial test sequence generating module 13 is configured to traverse the designed load severity sequence, match and extract corresponding typical load conditions from the typical load condition set, and generate an initial test sequence.

[0119] The sequence optimization module 14 is configured to apply random perturbations to the N typical load condition segments in the initial test sequence, and perform iterative optimization of the test sequence after the perturbations in combination with an optimization algorithm to obtain an iterative test sequence.

[0120] The simulation test execution module 15 is used to smoothly transition the load condition switching points in the iterative test sequence, and input the smoothed iterative test sequence to the simulation test system of the mooring machine to perform a dynamic load simulation test.

[0121] In some embodiments, the data retrieval and analysis module 11 includes:

[0122] The target working scene description information acquisition unit is used to obtain the target working scene description information predefined by the mooring machine.

[0123] The historical working condition data extraction unit is used to use the target working scene description information as a homologous search constraint, and the interactive cable winch data platform extracts matching historical operating parameters and corresponding historical environmental information, and outputs the historical working condition data.

[0124] In some embodiments, the data retrieval and analysis module 11 includes:

[0125] The characteristic parameter extraction unit is used to perform multi-dimensional statistical analysis on the historical operating condition data to extract a characteristic parameter set including time domain characteristics and frequency domain characteristics.

[0126] The density clustering and typical load condition extraction unit is used to cluster the characteristic parameter set based on the density clustering algorithm, and extract the cluster center representative sample of each cluster cluster in the cluster division result, define it as the typical load condition, and form the typical load condition set.

[0127] In some embodiments, the load severity assessment module 12 includes:

[0128] The intensity assessment sample acquisition unit is used to acquire an intensity assessment sample with an annotation, wherein the intensity assessment sample includes an operating condition parameter sample, an environmental parameter sample and a corresponding intensity annotation value.

[0129] A mapping rule construction and design load intensity sequence generation unit is used to construct the mapping rule based on regression analysis based on the intensity evaluation sample, and map the design operating condition information into a design load intensity set according to the mapping rule, time-serialize the design load intensity set, and perform periodic analysis on the serialization results, extract the most significant repeatable period, and output it as the design load intensity sequence.

[0130] In some embodiments, the initial test sequence generation module 13 includes:

[0131] The typical load intensity calculation and benchmark growth point set definition unit is used to calculate and obtain the typical load intensity of each typical load condition in the typical load condition set based on the mapping rules, generate a typical load intensity set, and perform matching and identification in the design load intensity sequence based on the typical load intensity set, and define the identification result as the benchmark growth point set.

[0132] An initial test sequence generation unit is configured to perform bidirectional end growth along the design load intensity sequence, centered around N reference growth points in the reference growth point set, until the design load intensity sequence is completely covered, and output N segments of growth results. The unit is configured to calculate intensity offsets for each of the N segments of the growth results, optimize the bidirectional end positions of the N segments of the growth results with the goal of minimizing the standard deviation of all the intensity offsets, and output the optimized results as the initial test sequence, wherein the intensity offsets include an upper limit offset and a lower limit offset. The initial test sequence includes N typical load condition segments.

[0133] In some embodiments, the sequence optimization module 14 includes:

[0134] The random fluctuation and load severity re-evaluation unit is used to apply random fluctuation to each of the typical load condition sections in the initial test sequence, and to re-evaluate the load severity of the load condition after the fluctuation.

[0135] The iterative test sequence optimization unit is used to combine the optimization algorithm to minimize the difference between the N typical load condition sections and the designed load intensity sequence, perform iterative fluctuation optimization on the initial test sequence, and obtain an iterative test sequence.

[0136] In some embodiments, the dynamic load simulation test device for a mooring winch further includes:

[0137] The integral processing unit is used to perform integral processing on the smoothed iterative test sequence and the designed load intensity sequence respectively.

[0138] The integral value determination and test sequence selection unit is used to determine the size of the integral value. If the integral value of the iterative test sequence after smoothing is greater than the integral value of the design load intensity sequence, the iterative test sequence after smoothing is run for testing.

[0139] In some embodiments, the dynamic load simulation test device for a mooring winch further includes:

[0140] The integral range setting and integral calculation unit is used to integrate along the time direction of the sequence with the smoothed iterative test sequence as the integral upper limit and the designed load intensity sequence as the integral lower limit.

[0141] The difference sequence information calculation unit is used to calculate the difference sequence information between the smoothed iterative test sequence and the design load intensity sequence at each sequence point if the integration result is greater than zero.

[0142] A significance check execution unit is used to perform a significance check on the smoothed iterative test sequence and the load intensity sequence in combination with the difference sequence information according to a preset difference control constraint.

[0143] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the dynamic load simulation test device for a mooring machine described in embodiment two. For the sake of brevity of the specification, no further elaboration will be given here.

[0144] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A dynamic load simulation test method for a mooring machine, characterized in that: include: Retrieving historical operating condition data associated with a target operating scenario of the mooring winch, analyzing the historical operating condition data, and constructing a typical load condition set; obtaining design operating condition information under the working scenario, and mapping the design operating condition information into a design load intensity sequence based on a preset mapping rule, wherein the mapping rule is constructed based on regression analysis and takes the design operating condition information as input and the design load intensity sequence as output; Traversing the design load intensity sequence, matching and extracting corresponding typical load conditions in the typical load condition set, and generating an initial test sequence; Applying random perturbations to N typical load condition segments in the initial test sequence, and performing iterative optimization of the test sequence after the perturbation in combination with an optimization algorithm to obtain an iterative test sequence; The load condition switching points in the iterative test sequence are smoothly transitioned, and the iterative test sequence after smoothing is input into the simulation test system of the mooring machine to perform a dynamic load simulation test.

2. A dynamic load simulation test method for a mooring machine according to claim 1, characterized in that: Retrieve historical operating data associated with the target operating scenario of the winch, including: Obtain the predefined target working scenario description information of the mooring machine; Using the target working scenario description information as a homologous search constraint, the interactive cable winch data platform extracts matching historical operating parameters and corresponding historical environmental information, and outputs the historical working condition data.

3. A dynamic load simulation test method for a mooring machine according to claim 2, characterized in that: Analyze the historical operating condition data and construct a typical load condition set, including: Performing multidimensional statistical analysis on the historical operating condition data to extract a set of characteristic parameters including time domain characteristics and frequency domain characteristics; Clustering the characteristic parameter set based on a density clustering algorithm; A representative sample of the cluster center of each cluster in the clustering division result is extracted and defined as the typical load condition to form the typical load condition set.

4. A dynamic load simulation test method for a mooring machine according to claim 3, characterized in that: Obtaining design operating condition information under the working scenario, and mapping the design operating condition information into a design load intensity sequence based on a preset mapping rule, including: Acquire an intensity evaluation sample with annotations, wherein the intensity evaluation sample includes an operating condition parameter sample, an environmental parameter sample and a corresponding intensity annotation value; Constructing the mapping rule based on regression analysis based on the severity evaluation sample, and mapping the design operating condition information into a design load severity set according to the mapping rule; The design load intensity set is time-serialized, and a periodic analysis is performed on the serialization results to extract the most significant repeatable period and output it as the design load intensity sequence.

5. A dynamic load simulation test method for a mooring machine according to claim 4, characterized in that: Traversing the design load severity sequence, matching and extracting corresponding typical load conditions in the typical load condition set, and generating an initial test sequence, including: Based on the mapping rule, calculating and obtaining the typical load severity of each typical load condition in the typical load condition set to generate a typical load severity set; According to the typical load intensity set, matching and identifying are performed in the design load intensity sequence, and the identification result is defined as a benchmark growth point set; Taking N reference growth points in the reference growth point set as the center, bidirectional end growth is performed along the design load intensity sequence until the design load intensity sequence is completely covered, and N segments of growth results are output; The intensity offsets of the N segments of the growth results are calculated respectively, and with the goal of minimizing the standard deviation of all the intensity offsets, the bidirectional end positions of the N segments of the growth results are optimized, and the optimization results are output as the initial test sequence, wherein the intensity offsets include an upper limit offset and a lower limit offset, and the initial test sequence includes N typical load condition segments.

6. A method for dynamic load simulation testing of a mooring winch according to claim 5, characterized in that: Applying random perturbations to N typical load condition segments in the initial test sequence, and performing iterative optimization of the test sequence after the perturbation in combination with an optimization algorithm to obtain an iterative test sequence, including: applying random fluctuations to each of the typical load condition sections in the initial test sequence, and re-evaluating the load severity of the load condition after the fluctuations; In combination with an optimization algorithm, with the goal of minimizing the differences between the N typical load condition sections and the designed load intensity sequence, an iterative fluctuation optimization is performed on the initial test sequence to obtain an iterative test sequence.

7. A method for dynamic load simulation testing of a mooring machine according to claim 1, characterized in that: After smoothly transitioning the load condition switching point in the iterative test sequence, the method further includes: performing integration processing on the smoothed iterative test sequence and the designed load intensity sequence respectively; The size of the integral value is determined. If the integral value of the iterative test sequence after smoothing is greater than the integral value of the design load intensity sequence, the iterative test sequence after smoothing is run for testing.

8. A dynamic load simulation test method for a mooring machine according to claim 1, characterized in that: After smoothly transitioning the load condition switching point in the iterative test sequence, the method further includes: Taking the smoothed iterative test sequence as the upper limit of integration and the design load intensity sequence as the lower limit of integration, integrating along the time direction of the sequence; If the integral result is greater than zero, the difference sequence information between the smoothed iterative test sequence and the design load intensity sequence at each sequence point is calculated; According to the preset difference control constraint, the significance check of the iterative test sequence and the load intensity sequence after smoothing is performed in combination with the difference sequence information.

9. A method for dynamic load simulation testing of a mooring machine according to claim 8, characterized in that: The difference control constraint includes a difference control threshold and a difference control ratio.

10. A dynamic load simulation test device for a mooring machine, characterized in that: A method for implementing a dynamic load simulation test of a mooring machine according to any one of claims 1 to 9, comprising: A data retrieval and analysis module, configured to retrieve historical operating condition data associated with a target operating scenario of the mooring winch, analyze the historical operating condition data, and construct a typical load condition set; a load severity assessment module, configured to obtain design operating condition information under the working scenario and map the design operating condition information into a design load severity sequence based on a preset mapping rule, wherein the mapping rule is constructed based on regression analysis and takes the design operating condition information as input and the design load severity sequence as output; An initial test sequence generation module is used to traverse the design load intensity sequence, match and extract corresponding typical load conditions from the typical load condition set, and generate an initial test sequence; A sequence optimization module is used to apply random perturbations to N typical load condition segments in the initial test sequence, and perform iterative optimization of the test sequence after the perturbation in combination with an optimization algorithm to obtain an iterative test sequence; The simulation test execution module is used to smoothly transition the load condition switching points in the iterative test sequence, and input the smoothed iterative test sequence into the simulation test system of the mooring machine to perform a dynamic load simulation test.

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