Cable swing test evaluation method and system based on digital twinning

Abstract

The invention provides a cable swing test evaluation method and system based on digital twinning, and belongs to the field of power cable test. The method comprises the following steps: establishing a cable digital twinning model for a target cable, and verifying and optimizing the cable digital twinning model based on historical swing test data; based on the optimized cable digital twinning model, carrying out twinning simulation in combination with a predefined real working condition demand and a to-be-evaluated swing test scheme, and generating a corresponding reference damage response mode set and a comparison damage response mode set; and carrying out comparative analysis on the reference damage response mode set and the contrast damage response mode set, and evaluating the simulation effectiveness of the swing test scheme on the real working condition according to the comparative analysis result. The technical problem that a cable swing test scheme in the prior art cannot accurately evaluate the simulation effectiveness of the cable swing test scheme on real working conditions is solved, and the technical effect of accurately evaluating the simulation effectiveness of the swing test scheme through digital twinborn simulation comparative analysis is achieved.

Description

Technical Field

[0001] This invention relates to the field of power cable testing, and more particularly to a method and system for cable sway testing and evaluation based on digital twins. Background Technology

[0002] Cables used in marine engineering, wind power generation, overhead power transmission and other applications are subjected to swaying stress caused by external factors such as wind, water flow and mechanical vibration. Long-term swaying motion may lead to fatigue damage to the cables.

[0003] To assess the reliability of cables under swaying conditions, existing technologies employ laboratory sway testing methods, which simulate swaying tests on cables using specific testing equipment and parameters. However, laboratory testing environments differ from real-world operating conditions, and test parameter settings are based on experience or simplified theoretical models, making it difficult to accurately reflect the stress state and damage evolution process of cables under actual operating conditions.

[0004] In the existing technology, there is a lack of effective methods to evaluate the accuracy of the swing test scheme in simulating real working conditions. As a result, it is impossible to quantitatively determine whether the test scheme can reflect the swing response characteristics of the cable in the actual application environment, leading to insufficient reliability of the test results. Summary of the Invention

[0005] This invention addresses the technical problem that existing cable sway testing schemes cannot accurately assess their effectiveness in simulating real-world operating conditions, and provides a cable sway testing and evaluation method and system based on digital twins to solve this problem.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a cable swing test evaluation method based on digital twins, comprising: establishing a cable digital twin model for a target cable, and verifying and optimizing the cable digital twin model based on historical swing test data; performing twin simulation based on the optimized cable digital twin model, combined with predefined real working condition requirements and the swing test scheme to be evaluated, to generate a corresponding reference damage response mode set and a comparative damage response mode set; comparing and analyzing the reference damage response mode set and the comparative damage response mode set, and evaluating the effectiveness of the swing test scheme in simulating real working conditions based on the comparison and analysis results.

[0007] Secondly, this invention provides a cable swing test evaluation system based on digital twins, comprising: a twin modeling and verification module, used to establish a cable digital twin model for the target cable, and to verify and optimize the cable digital twin model based on historical swing test data; a simulation damage analysis module, used to perform twin simulation based on the optimized cable digital twin model, combined with predefined real working condition requirements and the swing test scheme to be evaluated, to generate corresponding reference damage response mode sets and comparative damage response mode sets; and a scheme effectiveness evaluation module, used to compare and analyze the reference damage response mode sets and the comparative damage response mode sets, and to evaluate the simulation effectiveness of the swing test scheme for real working conditions based on the comparison and analysis results.

[0008] The beneficial effects of this invention are: A digital twin model of the target cable was established, and the model was validated and optimized based on historical swing test data. A virtual model highly consistent with the real cable in terms of geometry, material properties, and boundary conditions was constructed through digital modeling. Historical test data was used to correct model parameters, ensuring that the digital twin model accurately reflects the dynamic response characteristics of the real cable, providing a reliable digital foundation for subsequent simulation analysis. Based on the optimized cable digital twin model, twin simulations were performed using predefined real-world operating conditions and the swing test scheme to be evaluated, generating corresponding reference and contrast damage response pattern sets. By simulating both the real-world and test scheme environments on the same digital twin platform, complete characteristic data of cable damage evolution under both conditions were obtained, providing a data basis for comparative analysis for effectiveness evaluation. The reference and contrast damage response pattern sets were compared and analyzed. Based on the results, the effectiveness of the swing test scheme in simulating real-world conditions was evaluated. By quantitatively comparing the differences between the two sets of damage response patterns, the rationality and accuracy of the test scheme were determined.

[0009] The above technical solution solves the technical problem that the existing cable swing test scheme cannot accurately evaluate its effectiveness in simulating real working conditions, and achieves the technical effect of accurately evaluating the simulation effectiveness of the swing test scheme through digital twin simulation comparison analysis. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the cable swing test and evaluation method based on digital twin provided by this invention; Figure 2 This is a schematic diagram of the structure of the cable swing test and evaluation system based on digital twin provided by the present invention.

[0011] In the attached diagram, the components represented by each number are as follows: Twin modeling verification module 11, simulation damage analysis module 12, scheme effectiveness evaluation module 13. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0015] Example 1, as Figure 1 As shown, this embodiment of the invention provides a cable swing test and evaluation method based on digital twins, including: S1. Establish a digital twin model of the target cable and verify and optimize the digital twin model of the cable based on historical swing test data.

[0016] Specifically, firstly, a digital twin model of the target cable is established. This involves constructing a geometric model of the target cable to accurately describe its spatial morphology and geometric characteristics. The finite element method is used to discretize the cable geometry into multiple beam elements, each with three translational degrees of freedom and three rotational degrees of freedom at its nodes. Geometric parameters for each element are defined, including element length, cross-sectional area, and moment of inertia, as well as material parameters such as elastic modulus, Poisson's ratio, density, and damping coefficient. The stiffness matrix, mass matrix, and damping matrix of each element are derived based on beam element theory. The element matrices are then assembled according to node connections to form the overall system matrix of the cable. Boundary conditions are applied, including fixed-end constraints and load boundary conditions, establishing the cable's dynamic equations. Through this modeling process, a digital twin model of the cable capable of describing its dynamic characteristics is established. This model can accept external excitation input, calculate and output the cable's dynamic response, providing a numerical simulation platform for swing test evaluation.

[0017] Then, the cable digital twin model was validated and optimized based on historical swing test data. Input excitation data from historical swing tests were collected, including motion parameters such as swing angle time history, swing frequency, and swing amplitude, as well as corresponding output response data, including measured data on displacement response, velocity response, acceleration response, and stress response at various cable measuring points. The input excitation conditions from historical tests were applied as boundary conditions to the cable digital twin model, and the dynamic equations were solved through numerical integration to obtain the cable response data calculated by the model simulation. An objective function was established, and the error between the simulated response data and the historical measured response data was calculated. Subsequently, key parameters in the model, such as the material elastic modulus, damping coefficient, and boundary stiffness, were set as variables to be optimized, and reasonable value ranges for each parameter were defined. Iterative optimization was performed using a parameter identification algorithm, automatically adjusting parameter values ​​to gradually reduce the objective function value. Convergence conditions were monitored during the optimization process; the optimization process was terminated when the error change in consecutive iterations was less than a set threshold or the maximum number of iterations was reached. Independent validation data was used to verify the accuracy of the optimized model, ensuring that the model has good predictive capabilities under different operating conditions.

[0018] Through the above verification and optimization process, the parameters of the cable digital twin model are calibrated, enabling the model to accurately reflect the real physical characteristics and dynamic response features of the target cable, and providing a reliable numerical simulation basis for subsequent swing test evaluation and analysis.

[0019] S2. Based on the optimized cable digital twin model, and combined with the predefined real working condition requirements and the swing test scheme to be evaluated, twin simulation is performed to generate the corresponding reference damage response mode set and comparison damage response mode set.

[0020] Specifically, based on predefined real-world operating conditions, a minimum operating cycle for the target cable is defined. This minimum operating cycle includes typical load variation patterns of the cable in actual service environments. Based on a swing test scheme, a minimum test cycle for the target cable is extracted, where the ratio of the cycle length of the minimum operating cycle to the cycle length of the minimum test cycle is no greater than n and no less than the reciprocal of n, where n is greater than 1.

[0021] The minimum operating cycle is input into the cable digital twin model for twin simulation. The dynamic response of the cable under real operating conditions is obtained through numerical calculation, and a reference response mode set is extracted based on the twin simulation results. The minimum test cycle is also input into the cable digital twin model for twin simulation to obtain the cable's dynamic response under swing test conditions. A comparative response mode set is extracted based on the twin simulation results.

[0022] The reference damage response model set and the comparative damage response model set include at least damage accumulation models and loss distribution models. The damage accumulation models reflect the cumulative change of damage over time, while the loss distribution models reflect the distribution characteristics of damage in the spatial location of the cable, providing a data basis for comparative analysis for subsequent simulation effectiveness evaluation.

[0023] S3. Compare and analyze the reference damage response pattern set with the comparative damage response pattern set, and evaluate the effectiveness of the swing test scheme in simulating real working conditions based on the comparison and analysis results.

[0024] Specifically, the dynamic time warping distance between the reference damage response pattern set and the comparison damage response pattern set in the dimension of damage accumulation class patterns is analyzed and calculated to obtain a measure of temporal shape difference. The dynamic time warping algorithm can handle the nonlinear correspondence between two time series on the time axis. By finding the optimal time alignment path, it calculates the similarity distance between the damage accumulation time series curves, reflecting the degree of difference between the swing test and the actual working condition in the damage accumulation process.

[0025] The image similarity and statistical feature distance between the reference damage response pattern set and the contrast damage response pattern set in terms of loss distribution class pattern dimension are analyzed and calculated. The weighted output is a measure of spatial distribution difference. By calculating the structural similarity index of damage distribution patterns, peak signal-to-noise ratio and other image similarity indices, as well as the Euclidean distance of statistical feature parameters such as mean, variance, skewness, and kurtosis, the similarity of damage spatial distribution under the two working conditions is comprehensively evaluated.

[0026] Using temporal shape difference measure and spatial distribution difference measure as comparative analysis results, a weighted calculation method was employed to obtain the simulation effectiveness evaluation index. When both temporal shape difference measure and spatial distribution difference measure are small, it indicates that the swing test scheme can better simulate the damage characteristics of real working conditions; conversely, it indicates that the simulation effectiveness of the test scheme is poor, and further optimization of test parameters is needed.

[0027] The above technical solution effectively evaluates the simulation effectiveness of swing test schemes for real-world operating conditions. This method establishes an accurate digital twin model of the cable, verifies and optimizes the model using historical data to ensure its accuracy, and compares and analyzes the damage response characteristics under real and test conditions on a unified simulation platform. This provides a scientific basis for the design and optimization of swing test schemes, thus solving the technical problem of existing swing test evaluation methods lacking quantitative analysis tools and avoiding the subjectivity and uncertainty of traditional empirical evaluation methods. The high-precision simulation capability of the digital twin model allows for the pre-assessment of the effectiveness of the test scheme before testing, reducing experimental costs and risks. Simultaneously, multi-dimensional comparative analysis based on damage accumulation and loss distribution patterns improves the reliability and accuracy of the evaluation results, providing an effective technical means for cable fatigue performance testing and life prediction.

[0028] Furthermore, a digital twin model of the cable for the target cable is established, and the digital twin model is verified and optimized based on historical swing test data. Prior to this, the following steps are included: S41. Obtain the historical swing test data; S42. Calculate and determine the time proximity based on the interval between the generation time of the historical swing test data and the current time; S43. Calculate and determine the degree of difference between the historical test scheme corresponding to the historical swing test data and the swing test scheme. S44. Calculate the selection priority of each of the historical swing test data based on the time proximity and the scheme difference, wherein the selection priority is negatively correlated with the time proximity and positively correlated with the scheme difference; S45. Based on the selected priority, sort each historical swing test data item in descending order, and divide the sorted sequence into a priori dataset and a verification dataset according to a preset ratio threshold. The prior dataset is used to optimize the parameters of the cable digital twin model, and the verification dataset is used to verify the optimized cable digital twin model.

[0029] In a preferred embodiment, firstly, historical swing test data is acquired. Specifically, historical swing test data for the target cable and similar cables are collected from a test database. The historical swing test data includes two parts: historical test plans and historical test results. The historical test plans record the test conditions used in historical tests, including swing motion parameters such as swing angle, swing frequency, swing duration, and swing waveform type, as well as test environment parameters such as temperature and humidity. The historical test results include cable dynamic response data obtained under the corresponding test conditions, such as displacement, stress, and strain time history curves at each monitoring point, as well as damage accumulation and fatigue characteristics data.

[0030] Then, based on the interval between the generation time of historical swing test data and the current time, the time proximity is calculated. For each set of historical data, the time interval between its generation time and the current time is calculated; the smaller the time interval, the greater the time proximity. The time proximity is calculated using a normalization method, with the maximum acceptable time interval set as the benchmark value. The ratio of the difference between the current time interval and the benchmark value to the benchmark value is the time proximity. Historical data exceeding the maximum acceptable time interval has a time proximity of zero.

[0031] Subsequently, based on the differences between historical test schemes and the current swing test scheme corresponding to historical swing test data, the scheme difference degree is calculated and determined. Differences in key parameters between historical and current swing test schemes are compared, including swing frequency, swing amplitude, and test duration. The differences in each parameter are expressed as relative deviations, and a weighted combination is used to obtain the comprehensive scheme difference degree. The model performs well on historical test schemes with greater scheme difference degrees, indicating stronger applicability of the model.

[0032] Next, the selection priority of each historical swing test data point is calculated based on temporal proximity and scheme difference. Selection priority is negatively correlated with temporal proximity (the older the data, the lower the priority); and positively correlated with scheme difference (the greater the scheme difference, the higher the priority). By setting weighting coefficients for temporal proximity and scheme difference, a weighted combination method is used to calculate the selection priority value for each group of historical data.

[0033] Next, the historical swing test data items are sorted in descending order based on selection priority, and the sorted sequence is divided into a priori and validation datasets according to a preset proportion threshold. All historical data are sorted from highest to lowest selection priority. A proportion threshold is set for the priori dataset, with higher-priority data designated as the priori dataset for subsequent parameter optimization of the cable digital twin model; the remaining data is designated as the validation dataset for validating the optimized model, ensuring its prediction accuracy and generalization ability. For example, when the proportion threshold is set to 0.7, the first 70% of the sorted sequence is used as the priori dataset, and the last 30% as the validation dataset. The selection of the proportion threshold should balance the sufficiency of the priori dataset and the representativeness of the validation dataset; typically, the proportion of the priori dataset is set between 60% and 80%.

[0034] The above steps completed the screening, evaluation, and grouping of historical swing test data. This process fully considered the timeliness and applicability of historical data, and ensured that the selected historical data had high reference value through a quantitative priority calculation method. The division of the prior dataset and the verification dataset provided a reliable data foundation for the subsequent construction, optimization, and verification of the cable digital twin model, improving the model's accuracy and reliability, and ensuring the accuracy of the evaluation results.

[0035] Furthermore, a digital twin model of the cable is established for the target cable, and the digital twin model is verified and optimized based on historical swing test data, including: S11. Construct a parameterized digital twin model of the target cable, wherein the key physical properties and boundary conditions of the digital twin model are defined as adjustable model parameters. S12. Based on the historical swing test data, extract the prior input conditions and prior output response data, and apply the prior input conditions to the cable digital twin model to perform twin simulation to obtain prior simulation response data. S13. With the goal of minimizing the difference between the prior simulation response data and the prior output response data, the model parameters are iteratively optimized. S14. Based on the historical swing test data, extract the verification input conditions and verification output response data, verify the cable digital twin model, and output the verification results.

[0036] In a preferred embodiment, firstly, a parametric digital twin model of the target cable is constructed. During the construction of the cable digital twin model, key physical properties and boundary conditions of the cable are defined as adjustable model parameters, enabling parametric design of the model. Key physical properties include material properties such as elastic modulus, Poisson's ratio, density, and damping coefficient, as well as geometric properties such as cross-sectional area, moment of inertia, and length of the cable. Boundary conditions include the constraint stiffness of the fixed end, the coupling stiffness of the connection end, and the application method of external loads. By setting these parameters as adjustable variables, the digital twin model possesses parameter self-adaptation capabilities, providing a foundation for subsequent parameter optimization.

[0037] Then, based on historical swing test data, prior input conditions and prior output response data are extracted. Input conditions from historical tests are extracted from the prior dataset, including excitation parameters such as swing angle time history, swing frequency, and swing amplitude, as prior input conditions. Corresponding test output results are extracted, including displacement response, stress response, and fatigue damage data at various monitoring points of the cable, as prior output response data. The prior input conditions are applied to a parameterized cable digital twin model, and twin simulation calculations are performed. The model's predicted prior simulation response data is obtained through numerical solution. Next, the model parameters are iteratively optimized to minimize the difference between the prior simulation response data and the prior output response data. An objective function is established, and error indices between the prior simulation response data and the prior output response data are calculated, including evaluation indicators such as root mean square error and correlation coefficient. Optimization algorithms, such as gradient descent, genetic algorithms, or particle swarm optimization, are used to iteratively adjust adjustable model parameters, gradually reducing the objective function value. In each iteration, the simulation is recalculated based on the new parameter values ​​until the objective function converges or meets the preset convergence conditions, thus completing the optimization process of the model parameters.

[0038] Subsequently, based on historical swing test data, verification input conditions and verification output response data are extracted to verify the cable digital twin model. Input conditions from historical tests are extracted from the verification dataset as verification input conditions, and corresponding test output results are extracted as verification output response data. The verification input conditions are applied to the optimized cable digital twin model for simulation to obtain verification simulation response data. The consistency between the verification simulation response data and the verification output response data is compared, and the model's prediction accuracy and error distribution are calculated. The reliability of the model is evaluated based on the verification results. If the verification accuracy meets the preset requirements, the optimized cable digital twin model is output; otherwise, the process returns to step S13 to continue optimizing the model parameters.

[0039] Through the aforementioned modeling, verification, and optimization process, a digital twin model of the cable with high-precision predictive capabilities was established. This model, optimized using historical data and independently verified, accurately reflects the dynamic response characteristics and damage evolution patterns of the target cable under different load conditions. The optimized model parameters have clear physical meaning and a reliable numerical range, ensuring the reliability of the simulation results.

[0040] Furthermore, based on the optimized cable digital twin model, and combined with predefined real-world operating conditions and the swing test scheme to be evaluated, twin simulation is performed to generate corresponding reference damage response mode sets and comparison damage response mode sets, including: S21. Based on the actual working conditions, define the minimum working condition cycle for the target cable; S22. Based on the swing test scheme, extract the minimum test cycle of the target cable, wherein the ratio of the cycle length of the minimum working condition cycle to the cycle length of the minimum test cycle is not greater than n and not less than the reciprocal of n, and n is greater than 1. S23. Input the minimum operating condition cycle into the cable digital twin model for twin simulation, and extract the reference response mode set based on the twin simulation results; S24. Input the minimum test cycle into the cable digital twin model for twin simulation, and extract the comparison response pattern set based on the twin simulation results; The reference damage response pattern set and the comparative damage response pattern set include at least damage accumulation patterns and loss distribution patterns.

[0041] In a preferred embodiment, firstly, the minimum operating cycle of the target cable is defined based on actual operating conditions. By analyzing the load characteristics of the cable in its actual service environment, representative load variation patterns are identified, and the minimum operating cycle is extracted. The minimum operating cycle refers to the smallest unit of time that reflects the typical load variation pattern of the cable under real operating conditions, including key elements such as load amplitude variation, frequency characteristics, and duration. For example, for offshore platform cables, the minimum operating cycle includes the load variation process within a complete wave cycle, covering the load states at wave crests, troughs, and transition phases.

[0042] Then, based on the swing test scheme, the minimum test cycle of the target cable is extracted. The swing motion pattern with periodic characteristics is extracted from the swing test scheme to determine the time length and load characteristics of the minimum test cycle. The minimum test cycle should be representative of the typical load variation process experienced by the cable during the swing test. To ensure the rationality of the comparative analysis, the ratio of the cycle length of the minimum operating cycle to the minimum test cycle should be controlled within a reasonable range, i.e., the ratio should not be greater than n and not less than the reciprocal of n, where n is greater than 1. This constraint ensures the comparability of the two cycles on the time scale and avoids affecting the accuracy of the comparative analysis due to excessive differences in time length.

[0043] Next, the minimum operating cycle is input into the cable digital twin model for twin simulation, and a reference response mode set is extracted based on the simulation results. The load time history of the minimum operating cycle is applied as a boundary condition to the optimized cable digital twin model for dynamic simulation calculation. The dynamic response data of the cable under the minimum operating cycle is obtained through simulation, including the time history changes of physical quantities such as displacement, velocity, acceleration, and stress. Fatigue damage is calculated based on the stress time history data, and response modes such as damage accumulation law and damage distribution characteristics are extracted to form a reference response mode set.

[0044] Subsequently, the minimum test cycle is input into the cable digital twin model for twin simulation, and a set of comparative response patterns is extracted based on the twin simulation results. Using the same simulation method and data processing flow as in step S23, the minimum test cycle is used as input for twin simulation to obtain the dynamic response data of the cable under swing test conditions. Response pattern features are extracted using the same damage analysis method to form a set of comparative response patterns. Both the reference damage response pattern set and the comparative damage response pattern set include at least damage accumulation patterns and loss distribution patterns. Damage accumulation patterns describe the cumulative change trend of damage over time, including temporal characteristics such as accumulation rate and accumulation curve shape; loss distribution patterns describe the distribution pattern of damage in the cable's spatial location, including spatial characteristics such as damage peak location, distribution range, and distribution shape. These two types of patterns provide a comprehensive comparative analysis basis for subsequent evaluation of the simulation effectiveness of the swing test scheme.

[0045] Furthermore, a comparative analysis is performed between the reference damage response pattern set and the contrast damage response pattern set, and based on the comparison analysis results, the effectiveness of the swing test scheme in simulating real working conditions is evaluated, including: S31. Analyze and calculate the dynamic time warping distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of the damage accumulation class pattern to obtain a temporal shape difference metric. S32. Analyze and calculate the image similarity and statistical feature distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of the loss distribution class pattern, and output the weighted output as a spatial distribution difference measure. S33. Using the temporal shape difference measure and the spatial distribution difference measure as the comparison analysis results, the simulation validity is obtained by weighted calculation.

[0046] In a preferred embodiment, firstly, the dynamic time warping distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of damage accumulation class pattern is analyzed and calculated to obtain a temporal shape difference metric. Specifically, the damage accumulation time history curves in the reference damage response pattern set are extracted as the reference damage accumulation curves, and the damage accumulation time history curves in the contrast damage response pattern set are extracted as the contrast damage accumulation curves. A dynamic time warping algorithm is used to perform temporal alignment and similarity analysis on the reference and contrast damage accumulation curves. The dynamic time warping algorithm addresses the nonlinear matching problem on the time axis between the reference and contrast damage accumulation curves by finding the optimal temporal correspondence, and calculates the degree of difference in shape features of the damage accumulation curves. Specifically, firstly, a distance matrix between the reference and contrast damage accumulation curves is constructed, and the Euclidean distance between any pair of time points in the two curves is calculated as the local distance. Then, a dynamic programming method is used to search for the optimal time alignment path from the starting point to the ending point. This path represents the best temporal correspondence between the two curves, minimizing the cumulative distance along the path. During path search, continuity and monotonicity constraints are set to ensure the physical rationality of the time correspondence. Continuity constraints limit the range of change in the correspondence between adjacent moments, preventing unreasonable jumps in the time mapping; monotonicity constraints ensure that the time correspondence always develops in the forward direction, avoiding time reversal. These constraints effectively address the differences in cycle length between the minimum operating cycle and the minimum test cycle, as well as the time scale changes caused by different load change rates. The system automatically identifies and matches similar change stages in the reference and comparison damage accumulation curves, even if these stages differ in position and duration on the time axis. Finally, a standardized temporal shape difference metric is output, comprehensively reflecting the similarity between the two damage accumulation curves in terms of overall shape, change trend, key inflection points, and other temporal characteristics.

[0047] Then, the image similarity and statistical feature distance between the reference damage response pattern set and the contrast damage response pattern set in terms of loss distribution class pattern dimension are analyzed and calculated, and the weighted output is a spatial distribution difference measure. Specifically, loss distribution class patterns in the reference damage response pattern set are extracted as reference loss distribution patterns, and loss distribution class patterns in the contrast damage response pattern set are extracted as contrast loss distribution patterns. The reference loss distribution patterns and contrast loss distribution patterns are converted into damage distribution images, and image processing methods are used to calculate image similarity indices. Image similarity calculation includes multiple similarity metrics such as structural similarity index, peak signal-to-noise ratio, and normalized cross-correlation coefficient. The structural similarity index comprehensively evaluates the overall similarity of images by comparing the brightness, contrast, and structural information of two images; the peak signal-to-noise ratio reflects the degree of image distortion by calculating the mean square error between images; and the normalized cross-correlation coefficient measures the degree of matching of image patterns by calculating the correlation between images. At the same time, statistical feature parameters of the reference loss distribution pattern and the contrast loss distribution pattern are extracted, including features such as mean, variance, skewness, kurtosis, maximum value position, distribution range, and distribution shape factor. The distances between the reference loss distribution pattern and the contrast loss distribution pattern for each statistical characteristic parameter are calculated. Euclidean distance or Manhattan distance and other distance measurement methods are used to quantify the degree of difference between the two distribution patterns in terms of statistical characteristics and obtain the statistical characteristic distance.

[0048] Subsequently, by setting weight coefficients for image similarity indices and statistical feature distances, a weighted combination method is used to calculate the spatial distribution difference measure. The allocation of weight coefficients should be adjusted according to the specific application scenario's emphasis on both overall image features and local statistical characteristics, ensuring that the spatial distribution difference measure comprehensively reflects the similarity between the reference loss distribution pattern and the contrast loss distribution pattern in terms of spatial distribution features. For example, when focusing more on the overall visual features of the damage distribution, the weight coefficient for the image similarity index can be set to 0.7, and the weight coefficient for the statistical feature distance to 0.3; when focusing more on the numerical statistical characteristics of the damage distribution, the weight coefficient for the image similarity index can be set to 0.4, and the weight coefficient for the statistical feature distance to 0.6. By adjusting the weight ratio of these two types of indices, the calculation results of the spatial distribution difference measure can be optimized for different evaluation needs. Finally, the weighted results of the image similarity index and the weighted results of the statistical feature distance are combined to output a comprehensive spatial distribution difference measure.

[0049] Through the above comparative analysis, a quantitative evaluation system for the simulation effectiveness of swing test schemes was established. This evaluation method comprehensively analyzes the differences in damage response patterns from both temporal and spatial dimensions, overcoming the subjectivity and inaccuracy of traditional qualitative evaluation methods. The application of the dynamic time warping algorithm effectively solves the problem of comparing damage accumulation processes at different time scales. The combined analysis of image similarity and statistical feature distance accurately reflects the similarity of damage spatial distribution, thereby achieving objectivity and precision in the effectiveness evaluation of swing test schemes and providing quantitative indicators for the optimized design of test schemes.

[0050] Furthermore, based on the optimized cable digital twin model, and combined with predefined real-world operating conditions and the swing test scheme to be evaluated, twin simulation is performed to generate corresponding reference damage response mode sets and comparison damage response mode sets. Previously, this also included: S51. Obtain the actual working condition requirements and parse to obtain the corresponding typical working condition load module set; S52. Taking the actual working condition requirements as the scheme constraint, randomly combine the typical working condition load modules according to the typical working condition load module set to obtain multiple differentiated working condition load schemes. S53. Extract load module combination information of multiple load conditions schemes, and calculate the information entropy of multiple load conditions schemes based on the load module combination information; S54. If the information entropy meets the preset entropy threshold, then output multiple working condition load schemes as a set of differentiated working condition test schemes.

[0051] In a preferred embodiment, firstly, actual operating condition requirements are obtained, and the corresponding typical operating condition load module set is obtained through parsing. Actual operating condition requirements include various load conditions that the cable may encounter in its actual service environment, covering different sea state levels, different wind conditions, and different platform motion states. By analyzing actual operating condition requirements, typical load characteristics are identified, and the complex operating conditions are decomposed into several relatively independent load modules, forming a typical operating condition load module set. For example, the typical operating condition load module set includes basic load units such as wave load modules, ocean current load modules, wind load modules, and platform motion load modules. Each load module has clearly defined parameters such as load amplitude, frequency characteristics, and direction of action.

[0052] Then, using real-world operating conditions as constraints, typical load modules are randomly combined based on a set of typical operating condition load modules to obtain multiple differentiated load schemes. Under the premise of meeting the basic conditions of real-world operating conditions, a random combination method is used to combine different typical load modules according to different combination methods, weight ratios, and temporal relationships. By changing the combination parameters of the load modules, such as load amplitude ratio, phase relationship, and duration, multiple differentiated load schemes are generated. These schemes, while maintaining realism, cover different variations that may occur in real-world operating conditions, providing a more comprehensive reference benchmark for subsequent comparative analysis.

[0053] Subsequently, load module combination information for multiple load condition schemes is extracted, and the information entropy of these schemes is calculated based on this information. The load module combination information includes the type, quantity, combination method, and parameter settings of the load modules in each load condition scheme. Entropy calculation methods from information theory are used to calculate the information entropy based on the diversity and distribution characteristics of the load module combinations. Information entropy reflects the degree of differentiation in the set of load condition schemes; a higher entropy value indicates more significant differences between schemes and a more comprehensive coverage of the variation range of real-world load conditions.

[0054] If the information entropy meets the preset entropy threshold, multiple load scenarios are output as a differentiated load test scenario set. The preset entropy threshold is set as the criterion for evaluating the degree of differentiation of the scenario set. When the calculated information entropy is greater than or equal to the preset entropy threshold, the current load scenario set is considered to have sufficient differentiation to effectively represent the diverse changes in real-world operating conditions. In this case, the load scenario set is output as a differentiated load test scenario set for subsequent twin simulations and comparative analysis. If the information entropy does not reach the preset threshold, the combination strategy of the load module needs to be adjusted or the number of scenarios increased until the differentiation requirements are met.

[0055] The process of constructing the differentiated test scheme set described above effectively addresses the limitations and randomness that may exist in evaluation results under single operating conditions, generating representative and diverse operating condition schemes and ensuring the comprehensiveness and reliability of the reference damage response model set. The introduction of information entropy provides a quantitative evaluation standard for the degree of differentiation in the scheme set, avoiding subjectivity in scheme selection.

[0056] Furthermore, based on the optimized cable digital twin model, and combined with predefined real-world operating conditions and the swing test scheme to be evaluated, twin simulation is performed to generate corresponding reference damage response mode sets and comparison damage response mode sets, which also include: S311. Based on the cable digital twin model, perform twin simulation analysis by traversing the set of differentiated chemical condition test schemes. S312. Based on the twin simulation analysis results corresponding to multiple load conditions, calculate and obtain the reference damage response mode set, wherein the reference damage response mode set includes several subsets of reference damage response modes. S313. Perform a twin simulation based on the cable digital twin model and the swing test scheme to obtain the set of comparative damage response modes.

[0057] In a preferred embodiment, firstly, based on the cable digital twin model, a set of differentiated operating condition test schemes is traversed for twin simulation analysis. Each load scheme in the set of differentiated operating condition test schemes is sequentially used as input conditions and applied to the optimized cable digital twin model for twin simulation calculation. For each load scheme, its constituent load modules are converted into boundary conditions and excitation inputs of the model according to a set combination method and timing relationship. The dynamic equations are solved by numerical integration to obtain the dynamic response data of the cable under that specific operating condition. The traversal process ensures that all schemes in the set of differentiated operating condition test schemes are fully simulated and analyzed, providing a complete data foundation for subsequent statistical analysis and pattern extraction.

[0058] Then, based on the twin simulation analysis results corresponding to multiple load conditions, a reference damage response pattern set is calculated and obtained. Damage-related response data, including stress time history, fatigue damage accumulation process, and damage spatial distribution, are extracted from the simulation results of each load condition. Statistical analysis and pattern recognition are performed on the damage response data of multiple load conditions to extract common and dissimilar features, forming several reference damage response pattern subsets. Each reference damage response pattern subset corresponds to a typical damage response characteristic, including damage accumulation patterns and loss distribution patterns. Through comprehensive analysis of multiple load conditions, the reference damage response pattern set can more comprehensively reflect the damage response variation patterns that cables may exhibit within the actual operating conditions.

[0059] Next, a twin simulation is performed based on the cable digital twin model and the swing test scheme to obtain a set of comparative damage response patterns. The motion parameters and test conditions of the swing test scheme to be evaluated are applied as boundary conditions to the cable digital twin model for twin simulation calculation, obtaining the dynamic response data of the cable under swing test conditions. Using the same damage analysis and feature extraction methods as in step S312, damage accumulation patterns and loss distribution patterns are extracted from the swing test simulation results to form a set of comparative damage response patterns. The set of comparative damage response patterns maintains consistency with the reference damage response pattern set in terms of data structure and feature dimensions, providing a standardized data interface for subsequent differential comparative analysis.

[0060] Through the above multi-condition simulation and comparative analysis process, a swing test evaluation system based on differentiated working conditions was established. By traversing multiple working condition load schemes, a statistically representative set of reference damage response modes was obtained, effectively avoiding the one-sidedness and randomness of evaluation results under a single working condition.

[0061] Furthermore, a comparative analysis is performed between the reference damage response pattern set and the contrast damage response pattern set, including: S331. Based on the reference damage response mode set, extract multiple sets of reference damage accumulation patterns and reference loss distribution patterns corresponding to multiple load conditions. S332. Based on the aforementioned set of contrasting damage response patterns, extract contrasting damage accumulation patterns and contrasting loss distribution patterns; S333. Traverse multiple sets of reference damage accumulation patterns and reference loss distribution patterns, and calculate the binary difference metric between the reference damage accumulation patterns and the reference loss distribution patterns, including: S3331. Calculate the dynamic time warping distance between the reference damage accumulation pattern and the contrast damage accumulation pattern as a measure of temporal shape difference. S3332. Calculate the image similarity and statistical feature distance between the reference loss distribution class pattern and the contrast loss distribution class pattern, and output the weighted output as a spatial distribution difference measure. S3333. Based on multiple sets of the binary difference measures, calculate the mean and variance of the difference measures, and output the mean and variance of the difference measures as the comparison analysis results.

[0062] In a preferred embodiment, firstly, based on the reference damage response pattern set, multiple sets of reference damage accumulation patterns and reference loss distribution patterns corresponding to multiple load conditions are extracted. Specifically, from several subsets of the reference damage response patterns in the reference damage response pattern set, damage response features corresponding to each load condition are extracted one by one. For each load condition, its damage accumulation time history curve is extracted as the reference damage accumulation pattern, and its damage spatial distribution data is extracted as the reference loss distribution pattern. Since the reference damage response pattern set contains simulation results of multiple load conditions, multiple sets of reference damage accumulation patterns and multiple sets of reference loss distribution patterns are finally obtained, providing sufficient reference data for subsequent differential comparative analysis.

[0063] Simultaneously, comparative damage accumulation patterns and comparative loss distribution patterns are extracted from the comparative damage response pattern set. Specifically, damage accumulation time history curves are extracted from the swing test simulation results as comparative damage accumulation patterns, and damage spatial distribution data are extracted as comparative loss distribution patterns. Unlike the reference damage response pattern set, the comparative damage response pattern set corresponds to only a single swing test scheme; therefore, a set of comparative damage accumulation patterns and a set of comparative loss distribution patterns are extracted.

[0064] Next, multiple sets of reference damage accumulation patterns and reference loss distribution patterns are traversed, and binary difference metrics are calculated between them and the contrast damage accumulation patterns and contrast loss distribution patterns, respectively. Specifically, a double-loop traversal mechanism is established. The outer loop traverses each load case scheme in the reference damage response pattern set, while the inner loop processes the damage accumulation pattern and loss distribution pattern for each load case scheme. For the i-th load case scheme, its corresponding reference damage accumulation pattern and reference loss distribution pattern are extracted and compared with the contrast damage accumulation pattern and contrast loss distribution pattern, respectively, forming the binary difference metric calculation task for the i-th group. Specifically, for each binary difference metric calculation task, firstly, the dynamic time warping distance between the reference damage accumulation pattern and the contrast damage accumulation pattern is calculated as a time series shape difference metric. For each set of reference damage accumulation patterns, data preprocessing is first performed, including time series normalization, noise filtering, and missing data imputation. Then, a local distance matrix is ​​constructed between the reference damage accumulation curve and the contrast damage accumulation curve, and a dynamic time warping algorithm is used to search for the optimal time alignment path. During the path search process, step size constraints and window constraints are set to prevent excessive distortion of the time mapping relationship. The cumulative distance along the optimal path is calculated, and the path length is normalized to obtain the temporal shape difference metric value for this combination. This process is repeated until the temporal difference metric calculation for all working conditions is completed. Simultaneously, the image similarity and statistical feature distance between the reference loss distribution class pattern and the contrast loss distribution class pattern are calculated, and the weighted output is the spatial distribution difference metric. For each group of reference loss distribution class patterns, it is first converted into a standardized damage distribution image to ensure that the image size, resolution, and numerical range are consistent with the contrast loss distribution image. Various image similarity indices are calculated, including structural similarity index, peak signal-to-noise ratio, normalized cross-correlation coefficient, etc., each index reflecting image similarity features at different levels. At the same time, statistical feature parameters of the damage distribution are extracted, and statistical feature distances such as mean difference, variance difference, skewness difference, and kurtosis difference are calculated. Image similarity indices and statistical feature distances are weighted and combined using preset weighting coefficients to obtain spatial distribution difference metrics. Subsequently, based on multiple sets of binary difference metrics, the mean and variance of the difference metrics are calculated, and these metrics are output as the comparison analysis results. Temporal shape difference metrics corresponding to all working condition schemes are collected, and their arithmetic mean is calculated as the temporal shape difference metric mean, and their sample variance is calculated as the temporal shape difference metric variance. Similarly, spatial distribution difference metrics corresponding to all working condition schemes are collected, and their mean and variance are calculated. Finally, four statistical parameters are output: temporal shape difference metric mean, temporal shape difference metric variance, spatial distribution difference metric mean, and spatial distribution difference metric variance, constituting a complete comparison analysis result.These statistical parameters not only reflect the average simulation effect of the swing test scheme, but more importantly, quantify its performance stability under different real working conditions.

[0065] Through the above multi-condition differential comparison analysis process, by calculating the mean and variance of the difference measure, we can evaluate the average degree of simulation of the swing test scheme to the real working conditions, and at the same time quantify its stability and robustness under different working condition changes.

[0066] Furthermore, a comparative analysis is performed between the reference damage response pattern set and the contrast damage response pattern set, and based on the comparison analysis results, the effectiveness of the swing test scheme in simulating real working conditions is evaluated. This process also includes: S61. Iterative analysis is performed to obtain the simulation effectiveness of multiple swing test schemes; S62. Combine the preset cost evaluation function to calculate the simulated cost factor of each swing test scheme, wherein the simulated cost factor includes a time cost item and a resource cost item. S63. Based on the simulation effectiveness and the simulation cost factor, and combined with the dimensionless weighting method, calculate and obtain the recommendation degree of multiple swing test schemes, and provide feedback on the recommendation degree of multiple schemes.

[0067] In a preferred embodiment, firstly, the simulation effectiveness of multiple swing test schemes is obtained through iterative analysis. Specifically, for the multiple swing test schemes to be evaluated, the aforementioned twin simulation, damage response pattern extraction, and comparative analysis processes are repeatedly executed. For each swing test scheme, the mean and variance of its difference measure with the reference damage response pattern set are calculated, and the same weighted calculation method is used to obtain the simulation effectiveness of the scheme. Through iterative analysis, simulation effectiveness values ​​corresponding to multiple swing test schemes are obtained, providing a quantitative performance evaluation basis for subsequent scheme comparison and optimization.

[0068] Then, using a pre-defined cost evaluation function, the simulated cost factor for each swing test plan is calculated. The simulated cost factor comprehensively considers the implementation cost of the swing test plan, including two main components: time cost and resource cost. The time cost reflects time-related cost elements such as the duration of the test plan, test frequency, and data acquisition cycle; the resource cost reflects resource-related cost elements such as equipment investment, personnel allocation, material consumption, and site occupation. Through the pre-defined cost evaluation function, each cost element is quantitatively calculated and weighted to obtain the comprehensive simulated cost factor for each swing test plan. The parameter settings of the cost evaluation function should be adjusted according to specific test conditions, equipment capabilities, and economic constraints. Specifically, the weighting of the time cost and resource cost items should consider the importance that specific projects place on time efficiency and resource investment. For example, when the project cycle is tight, the weight of the time cost item can be set to 0.6, and the weight of the resource cost item to 0.4; when budget constraints are strict, the weight of the time cost item can be set to 0.3, and the weight of the resource cost item to 0.7. The simulated cost factor is calculated using a weighted summation method, which means that the simulated cost factor equals the time cost item multiplied by its weight coefficient plus the resource cost item multiplied by its weight coefficient.

[0069] Subsequently, based on simulation effectiveness and simulation cost factors, and using a dimensionless weighted method, the recommendation scores of multiple swing test schemes were calculated and fed back. Since simulation effectiveness and simulation cost factors have different dimensions and numerical ranges, these two indicators were first dimensionlessized, and normalization or standardization methods were used to convert them into the same numerical range. Then, weight coefficients were set for simulation effectiveness and simulation cost factors, and a comprehensive evaluation index, i.e., the recommendation score, was calculated for each swing test scheme through weighted combination. A higher recommendation score indicates a better overall performance of the test scheme in terms of simulation effectiveness and cost-effectiveness. Finally, the ranking results of the recommendation scores of multiple swing test schemes were output, providing a basis and technical support for the selection and decision-making of test schemes.

[0070] Example 2, as Figure 2 As shown, based on the same inventive concept as the cable sway test and evaluation method based on digital twin provided in Embodiment 1, this embodiment of the invention also provides a cable sway test and evaluation system based on digital twin, including: The twin modeling verification module 11 is used to establish a cable digital twin model for the target cable and to verify and optimize the cable digital twin model based on historical swing test data. The simulation damage analysis module 12 is used to perform twin simulation based on the optimized cable digital twin model, combined with predefined real working condition requirements and the swing test scheme to be evaluated, and generate corresponding reference damage response mode set and comparison damage response mode set. The scheme effectiveness evaluation module 13 is used to compare and analyze the reference damage response pattern set and the comparative damage response pattern set, and evaluate the simulation effectiveness of the swing test scheme for real working conditions based on the comparison and analysis results.

[0071] Furthermore, this embodiment also includes a historical data filtering and grouping module, which is used for: Obtain the historical swing test data; The time proximity is calculated and determined based on the interval between the generation time of the historical swing test data and the current time. Based on the differences between the historical test schemes corresponding to the historical swing test data and the swing test scheme, the scheme difference degree is calculated and determined. Based on the time proximity and the scheme difference, the selection priority of each of the historical swing test data is calculated, wherein the selection priority is negatively correlated with the time proximity and positively correlated with the scheme difference; Based on the selected priority, each historical swing test data item is sorted in descending order, and according to the preset ratio threshold, the sorted sequence is divided into a priori dataset and a verification dataset. The prior dataset is used to optimize the parameters of the cable digital twin model, and the verification dataset is used to verify the optimized cable digital twin model.

[0072] Furthermore, the twin modeling verification module 11 is also used for: A parameterized digital twin model of the target cable is constructed, wherein the key physical properties and boundary conditions of the digital twin model are defined as adjustable model parameters. Based on the historical swing test data, the prior input conditions and prior output response data are extracted, and the prior input conditions are applied to the cable digital twin model to perform twin simulation to obtain prior simulation response data. The model parameters are iteratively optimized with the goal of minimizing the difference between the prior simulation response data and the prior output response data; Based on the historical swing test data, the verification input conditions and verification output response data are extracted to verify the cable digital twin model, and the verification results are output.

[0073] Furthermore, the simulation damage analysis module 12 is also used for: Based on the actual operating conditions, define the minimum operating cycle for the target cable; Based on the swing test scheme, the minimum test cycle of the target cable is extracted, wherein the ratio of the cycle length of the minimum operating cycle to the cycle length of the minimum test cycle is not greater than n and not less than the reciprocal of n, where n is greater than 1. The minimum operating condition cycle is input into the cable digital twin model for twin simulation, and a reference response mode set is extracted based on the twin simulation results; The minimum test cycle is input into the cable digital twin model for twin simulation, and a set of comparative response patterns is extracted based on the twin simulation results. The reference damage response pattern set and the comparative damage response pattern set include at least damage accumulation patterns and loss distribution patterns.

[0074] Furthermore, the solution effectiveness evaluation module 13 is also used for: The dynamic time warping distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of the damage accumulation class pattern is analyzed and calculated to obtain a temporal shape difference metric. The image similarity and statistical feature distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of the loss distribution class pattern are analyzed and calculated, and the weighted output is a measure of spatial distribution difference. The simulation validity is obtained by weighting the comparison analysis results using the temporal shape difference measure and the spatial distribution difference measure.

[0075] Furthermore, embodiments of this application also include a differential working condition scheme generation module, which is used for: Obtain the actual working condition requirements and parse to obtain the corresponding typical working condition load module set; Using the actual working conditions as constraints, and based on the typical working condition load module set, a random combination of typical working condition load modules is performed to obtain multiple differentiated working condition load schemes. Extract load module combination information of multiple load conditions and load schemes, and calculate the information entropy of multiple load conditions and load schemes based on the load module combination information; If the information entropy meets the preset entropy threshold, then multiple operating condition load schemes are output as a set of differentiated operating condition test schemes.

[0076] Furthermore, the solution effectiveness evaluation module 13 is also used for: Based on the cable digital twin model, a twin simulation analysis is performed by traversing the set of differentiated chemical condition test schemes. Based on the twin simulation analysis results corresponding to multiple load conditions, the reference damage response mode set is calculated and obtained, wherein the reference damage response mode set includes several subsets of reference damage response modes. Based on the cable digital twin model and the swing test scheme, a twin simulation is performed to obtain the set of comparative damage response modes.

[0077] Furthermore, the solution effectiveness evaluation module 13 is also used for: Based on the reference damage response pattern set, extract multiple sets of reference damage accumulation patterns and reference loss distribution patterns corresponding to multiple load conditions; Compared with the aforementioned set of damage response patterns, comparative damage accumulation patterns and comparative loss distribution patterns are extracted; Iterate through multiple sets of reference damage accumulation patterns and reference loss distribution patterns, and calculate the binary difference metric between them and the comparative damage accumulation patterns and the comparative loss distribution patterns, including: Calculate the dynamic temporal warping distance between the reference damage accumulation pattern and the contrast damage accumulation pattern as a measure of temporal shape difference; Calculate the image similarity and statistical feature distance between the reference loss distribution class pattern and the contrast loss distribution class pattern, and output the weighted result as a spatial distribution difference measure; Based on multiple sets of the binary difference measures, the mean and variance of the difference measures are calculated, and the mean and variance of the difference measures are output as the comparison analysis results.

[0078] Furthermore, the solution effectiveness evaluation module 13 is also used for: Iterative analysis was conducted to obtain the simulation effectiveness of multiple swing test schemes; Based on a preset cost evaluation function, the simulated cost factor for each swing test scheme is calculated, wherein the simulated cost factor includes a time cost item and a resource cost item. Based on the simulation effectiveness and the simulation cost factor, and combined with the dimensionless weighting method, the recommendation degree of multiple swing test schemes is calculated and obtained, and the recommendation degree of multiple schemes is fed back.

[0079] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0080] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0084] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0085] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A cable sway test and evaluation method based on digital twins, characterized in that, include: A digital twin model of the cable for the target cable is established, and the digital twin model of the cable is verified and optimized based on historical swing test data; Based on the optimized cable digital twin model, twin simulation is performed in combination with predefined real working condition requirements and the swing test scheme to be evaluated to generate the corresponding reference damage response mode set and comparison damage response mode set. The reference damage response pattern set and the comparative damage response pattern set are compared and analyzed, and the effectiveness of the swing test scheme in simulating real working conditions is evaluated based on the comparison and analysis results.

2. The cable sway test and evaluation method based on digital twin as described in claim 1, characterized in that, Establish a digital twin model of the target cable and validate and optimize the model based on historical swing test data. Prior to this, the following steps are included: Obtain the historical swing test data; The time proximity is calculated and determined based on the interval between the generation time of the historical swing test data and the current time. Based on the differences between the historical test schemes corresponding to the historical swing test data and the swing test scheme, the scheme difference degree is calculated and determined. Based on the time proximity and the scheme difference, the selection priority of each of the historical swing test data is calculated, wherein the selection priority is negatively correlated with the time proximity and positively correlated with the scheme difference; Based on the selected priority, each historical swing test data item is sorted in descending order, and according to the preset ratio threshold, the sorted sequence is divided into a priori dataset and a verification dataset. The prior dataset is used to optimize the parameters of the cable digital twin model, and the verification dataset is used to verify the optimized cable digital twin model.

3. The cable sway test and evaluation method based on digital twin as described in claim 1, characterized in that, Establish a digital twin model of the target cable and validate and optimize the model based on historical swing test data, including: A parameterized digital twin model of the target cable is constructed, wherein the key physical properties and boundary conditions of the digital twin model are defined as adjustable model parameters. Based on the historical swing test data, the prior input conditions and prior output response data are extracted, and the prior input conditions are applied to the cable digital twin model to perform twin simulation to obtain prior simulation response data. The model parameters are iteratively optimized with the goal of minimizing the difference between the prior simulation response data and the prior output response data; Based on the historical swing test data, the verification input conditions and verification output response data are extracted to verify the cable digital twin model, and the verification results are output.

4. The cable sway test and evaluation method based on digital twin as described in claim 1, characterized in that, Based on the optimized cable digital twin model, and combined with predefined real-world operating conditions and the swing test scheme to be evaluated, twin simulation is performed to generate corresponding reference damage response mode sets and comparison damage response mode sets, including: Based on the actual operating conditions, define the minimum operating cycle for the target cable; Based on the swing test scheme, the minimum test cycle of the target cable is extracted, wherein the ratio of the cycle length of the minimum working condition cycle to the cycle length of the minimum test cycle is not greater than n and not less than the reciprocal of n, where n is greater than 1. The minimum operating condition cycle is input into the cable digital twin model for twin simulation, and a reference response mode set is extracted based on the twin simulation results; The minimum test cycle is input into the cable digital twin model for twin simulation, and a set of comparative response patterns is extracted based on the twin simulation results. The reference damage response pattern set and the comparative damage response pattern set include at least damage accumulation patterns and loss distribution patterns.

5. The cable sway test and evaluation method based on digital twin as described in claim 4, characterized in that, The reference damage response pattern set and the comparative damage response pattern set are compared and analyzed, and based on the comparison and analysis results, the effectiveness of the swing test scheme in simulating real working conditions is evaluated, including: The dynamic time warping distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of the damage accumulation class pattern is analyzed and calculated to obtain a temporal shape difference metric. The image similarity and statistical feature distance between the reference damage response pattern set and the contrast damage response pattern set in the dimension of the loss distribution class pattern are analyzed and calculated, and the weighted output is a measure of spatial distribution difference. The simulation validity is obtained by weighting the comparison analysis results using the temporal shape difference measure and the spatial distribution difference measure.

6. The cable sway test and evaluation method based on digital twin as described in claim 1, characterized in that, Based on the optimized cable digital twin model, and combined with predefined real-world operating conditions and the swing test scheme to be evaluated, twin simulation is performed to generate corresponding reference damage response mode sets and comparison damage response mode sets. Prior to this, it also includes: Obtain the actual working condition requirements and parse to obtain the corresponding typical working condition load module set; Using the actual working conditions as constraints, and based on the typical working condition load module set, a random combination of typical working condition load modules is performed to obtain multiple differentiated working condition load schemes. Extract load module combination information of multiple load conditions and load schemes, and calculate the information entropy of multiple load conditions and load schemes based on the load module combination information; If the information entropy meets the preset entropy threshold, then multiple operating condition load schemes are output as a set of differentiated operating condition test schemes.

7. The cable sway test and evaluation method based on digital twin as described in claim 6, characterized in that, Based on the optimized cable digital twin model, and combined with predefined real-world operating conditions and the swing test scheme to be evaluated, a twin simulation is performed to generate a corresponding reference damage response mode set and a comparison damage response mode set, which also includes: Based on the cable digital twin model, a twin simulation analysis is performed by traversing the set of differentiated chemical condition test schemes. Based on the twin simulation analysis results corresponding to multiple load conditions, the reference damage response mode set is calculated and obtained, wherein the reference damage response mode set includes several subsets of reference damage response modes. Based on the cable digital twin model and the swing test scheme, a twin simulation is performed to obtain the set of comparative damage response modes.

8. The cable sway test and evaluation method based on digital twin as described in claim 7, characterized in that, The comparison analysis between the reference damage response pattern set and the contrast damage response pattern set includes: Based on the reference damage response pattern set, extract multiple sets of reference damage accumulation patterns and reference loss distribution patterns corresponding to multiple load conditions; Compared with the aforementioned set of damage response patterns, comparative damage accumulation patterns and comparative loss distribution patterns are extracted; Iterate through multiple sets of reference damage accumulation patterns and reference loss distribution patterns, and calculate the binary difference metric between them and the comparative damage accumulation patterns and the comparative loss distribution patterns, including: Calculate the dynamic temporal warping distance between the reference damage accumulation pattern and the contrast damage accumulation pattern as a measure of temporal shape difference; Calculate the image similarity and statistical feature distance between the reference loss distribution class pattern and the contrast loss distribution class pattern, and output the weighted result as a spatial distribution difference measure; Based on multiple sets of the binary difference measures, the mean and variance of the difference measures are calculated, and the mean and variance of the difference measures are output as the comparison analysis results.

9. The cable sway test and evaluation method based on digital twin as described in claim 1, characterized in that, The reference damage response pattern set and the comparative damage response pattern set are compared and analyzed. Based on the comparison and analysis results, the effectiveness of the swing test scheme in simulating real working conditions is evaluated. The process then includes: Iterative analysis was conducted to obtain the simulation effectiveness of multiple swing test schemes; Based on a preset cost evaluation function, the simulated cost factor for each swing test scheme is calculated, wherein the simulated cost factor includes a time cost item and a resource cost item. Based on the simulation effectiveness and the simulation cost factor, and combined with the dimensionless weighting method, the recommendation degree of multiple swing test schemes is calculated and obtained, and the recommendation degree of multiple schemes is fed back.

10. A cable sway test and evaluation system based on digital twins, characterized in that, A method for implementing a cable swing test evaluation based on digital twins as described in any one of claims 1 to 9, comprising: The twin modeling and verification module is used to establish a cable digital twin model for the target cable and to verify and optimize the cable digital twin model based on historical swing test data. The simulation damage analysis module is used to perform twin simulation based on the optimized cable digital twin model, combined with predefined real working condition requirements and the swing test scheme to be evaluated, and generate corresponding reference damage response mode set and comparison damage response mode set. The scheme effectiveness evaluation module is used to compare and analyze the reference damage response pattern set and the comparison damage response pattern set, and evaluate the simulation effectiveness of the swing test scheme for real working conditions based on the comparison and analysis results.

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