Humanoid robot flexible power cable fatigue resistance design simulation system and method

Abstract

The application discloses a humanoid robot flexible power cable fatigue resistance design simulation system and method, and relates to the technical field of robots.The humanoid robot flexible power cable fatigue resistance design simulation system and method accurately predict the fatigue damage of the cable under a complex working environment through multi-field coupling simulation, cross-scale damage modeling, multi-objective optimization and virtual verification, and provide an efficient simulation platform for the fatigue resistance design of the cable.The specific embodiments include a multi-field coupling and critical positioning module, a cross-scale damage modeling module, a multi-objective optimization module and a virtual verification and iteration module.The multi-physical field coupling simulation dynamically generates multi-modal data of cable deformation, friction heat generation and resistivity change, analyzes the evolution process of cable damage, and optimizes the design parameters to improve the fatigue durability of the cable.

Description

Technical Field

[0001] This invention relates to the field of robotics, specifically to a simulation system and method for fatigue-resistant design of flexible power cables for humanoid robots. Background Technology

[0002] With the rapid development of humanoid robot technology, its applications in service, medical, and industrial collaboration fields are constantly deepening. Humanoid robots need to simulate the flexible movement of human joints, and their power systems rely on flexible cables for energy transmission and signal control. However, frequent joint bending, torsion, and dynamic loads in complex environments cause cables to be subjected to alternating stress for extended periods, making them prone to fatigue fracture, insulation wear, and other failures, directly affecting the robot's reliability, safety, and service life. In high-intensity application scenarios such as industrial automation and emergency rescue, the fatigue resistance of cables has become one of the key bottlenecks restricting the implementation of humanoid robot technology, and the industry urgently needs high-precision, high-efficiency cable design simulation methods.

[0003] Current simulation methods for fatigue resistance design of flexible cables mainly rely on simplified mechanical models, static load analysis, or single-physics field simulation, which struggle to realistically reproduce the multi-physics coupling effects under complex motion conditions. While traditional empirical formulas and physical experiments can partially verify designs, they suffer from problems such as long cycles, high costs, and poor parameter generalization. For example, ignoring factors such as cable material nonlinearity, interlayer contact friction, and dynamic curvature changes leads to significant errors in fatigue life prediction. Furthermore, existing simulation tools lack the ability to simulate large deformations and high-frequency cyclic loads on flexible bodies, making it difficult to accurately capture the initiation and propagation behavior of microcracks. These problems severely restrict the iterative efficiency of lightweight and high-durability cable designs, failing to meet the stringent requirements for high reliability in the power systems of humanoid robots. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a simulation system and method for fatigue-resistant design of flexible power cables for humanoid robots, thus solving the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a simulation method for fatigue-resistant design of flexible power cables for humanoid robots, comprising the following steps: S1. Acquiring time-series data of joint motion of the humanoid robot, constructing a dynamic coupling simulation model of the cable, dynamically generating a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation, and locating the coordinates of the critical energy accumulation region at the cable stranding gap and sheath bend; S2. Inputting the multimodal correlation dataset and critical region coordinates into a pre-trained cross-scale damage analysis model, analyzing the macroscopic strain energy density gradient distribution and microcrack propagation in the critical region of the cable. S3. Based on the spatiotemporal mapping relationship of the cable damage characteristics, a cable damage weight distribution map is generated; S4. Based on the cable damage weight distribution map, a multi-objective dynamic optimization algorithm is used to perform multi-scale collaborative optimization of the cable stranding pitch gradient distribution, the non-uniform design of the sheath thickness at the bend, and the interlayer friction coefficient, outputting the optimal set of cable structural parameters for fatigue resistance; S5. The optimal set of cable structural parameters for fatigue resistance is input into the cable dynamic coupling simulation model for virtual accelerated life testing to evaluate the cable fatigue index. If the cable fatigue index is greater than or equal to a preset threshold, the parameters of the cross-scale damage analysis model are corrected and iterated again until the cable fatigue index is less than the preset threshold.

[0006] Furthermore, the specific process of acquiring the time-series data of the humanoid robot's joint motion and constructing a dynamic coupling simulation model of the cable is as follows: The angular displacement, angular velocity, and time-series load spectrum of the motion trajectory are collected in real time through the humanoid robot's joint sensors, and the joint motion mode is established based on time-series analysis; Based on the cable's geometric topology and material nonlinear properties, a dynamic coupling simulation model of the cable is constructed by combining the cable assembly path and motion degrees of freedom to simulate the mechanical response of the cable under highly dynamic joint conditions; Variable load boundary conditions are applied to the simulation model to simulate the influence of different working conditions on cable deformation and stress distribution, defining the interlayer dynamic contact boundary conditions and the frictional heat generation coupling equation, and outputting a preliminary simulation dataset.

[0007] Furthermore, the specific process of dynamically generating a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation is as follows: Based on the preliminary simulation dataset, a nonlinear mechanical model of the cable structure is established, considering the material properties of the sheath, insulation layer, and conductor stranding layer; through an adaptive mesh refinement strategy, high-precision finite element simulations are performed on the cable deformation field, frictional heat generation field, and conductor resistivity change field; based on the distribution characteristics of stress, temperature, and resistivity, highly correlated feature parameters of cable fatigue damage are extracted to construct a multimodal correlation dataset.

[0008] Furthermore, the specific process of analyzing the spatiotemporal mapping relationship between the macroscopic strain energy density gradient distribution and the microcrack propagation characteristics in the critical region of the cable is as follows: a crystal plasticity sub-model is embedded in the finite element model to simulate the dislocation motion of copper conductor grains and the initiation of microcracks in the critical region; the macroscopic strain energy density gradient field is extracted, and a nonlinear mapping of the microcrack propagation rate is established through a transfer learning algorithm; a spatiotemporal correlation weight function is defined to quantify the contribution of micro-defects to the accumulation of macroscopic damage.

[0009] Furthermore, the specific process for generating the cable damage weight distribution map is as follows: calculate the damage weight value of the critical area based on the spatiotemporal correlation weight function; combine the crack priority propagation path prediction algorithm to generate the cable damage level distribution map for the entire area; mark the stranding gap and sheath bend as the highest risk areas, and output the damage weight heat map.

[0010] Furthermore, the specific process of multi-scale collaborative optimization of cable stranding pitch gradient distribution, non-uniform thickness design at sheath bends, and interlayer friction coefficient using a multi-objective dynamic optimization algorithm is as follows: A multi-objective game-theoretic optimization framework for fatigue resistance, lightweighting, and electrical stability is constructed; a Pareto optimal solution is searched in the space of stranding pitch gradient, sheath thickness distribution, and friction coefficient using an adaptive non-dominated sorting genetic algorithm; and local inferior solutions are avoided through dynamic constraints to generate a balanced set of design parameters that satisfy multiple objectives.

[0011] Furthermore, the optimal set of cable structural parameters for fatigue resistance includes: gradient distribution parameters of stranding pitch from the inner layer to the outer layer, non-uniform design parameters of sheath thickness at bends, and dynamic friction coefficient matrix between the shielding layer and the conductor.

[0012] Furthermore, the specific process for evaluating the cable fatigue resistance index is as follows: acquire virtual accelerated life test data, construct a cable damage evolution time series, and extract the damage evolution sequence of the cable critical region; extract the damage accumulation curve of the cable critical region through the damage evolution time series, and calculate the fatigue life decay rate in combination with the material fatigue characteristics, construct a fatigue cumulative damage prediction matrix, and quantify the damage propagation trend; calculate the cable fatigue life consumption ratio based on the fatigue cumulative damage prediction matrix, and normalize it to generate the cable fatigue resistance index.

[0013] Furthermore, the specific process of triggering parameter correction and re-iteration of the cross-scale damage analysis model is as follows: Based on the cable damage weight distribution map, compare the damage accumulation trend predicted by simulation with the target life requirement, and extract the damage evolution error; based on the damage evolution error, correct the material constitutive relation in the cross-scale damage analysis model, including the strain energy density threshold and fatigue crack propagation coefficient; combine the macroscopic stress distribution and microscopic crack propagation trend, and adjust the weight parameters of the fatigue damage accumulation model.

[0014] A simulation system for fatigue-resistant design of flexible power cables for humanoid robots includes the following modules: a multi-field coupling and critical positioning module, a cross-scale damage modeling module, a multi-objective optimization module, and a virtual verification and iteration module. The multi-field coupling and critical positioning module acquires the time-series data of the humanoid robot's joint motion, constructs a dynamic coupling simulation model of the cable, and dynamically generates a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation. It also locates the coordinates of the critical energy accumulation region at the cable stranding gap and sheath bend. The cross-scale damage modeling module inputs the multimodal correlation dataset and critical region coordinates into a pre-trained cross-scale damage analysis model to analyze the macroscopic strain energy density of the cable's critical region. The spatiotemporal mapping relationship between gradient distribution and microcrack propagation characteristics generates a cable damage weight distribution map. The multi-objective optimization module is used to perform multi-scale collaborative optimization of the cable stranding pitch gradient distribution, non-uniform thickness design at sheath bends, and interlayer friction coefficient based on the cable damage weight distribution map using a multi-objective dynamic optimization algorithm, and outputs a set of optimal cable structure parameters for fatigue resistance. The virtual verification and iteration module is used to input the set of optimal cable structure parameters for fatigue resistance into the cable dynamic coupling simulation model for virtual accelerated life testing, evaluate the cable fatigue index, and if the cable fatigue index is greater than or equal to a preset threshold, trigger the parameter correction of the cross-scale damage analysis model and re-iterate until the cable fatigue index is less than the preset threshold.

[0015] The present invention has the following beneficial effects:

[0016] (1) A simulation method for fatigue-resistant design of flexible power cables for humanoid robots: By acquiring the timing data of joint motion of humanoid robots and constructing a dynamic coupling simulation model of the cable, this invention can accurately simulate the deformation of the cable under different motion states, and dynamically generate multimodal correlation datasets of cable deformation field, frictional heat generation field, and conductor resistivity change field. These datasets can help accurately locate the energy accumulation areas at the cable stranding gap and sheath bend, help identify potential fatigue failure risk areas, provide a scientific basis for subsequent optimization, and thus improve the design accuracy and service life of the cable.

[0017] (2) The humanoid robot flexible power cable fatigue resistance design simulation system combines cable damage weight distribution map with multi-objective dynamic optimization algorithm to achieve multi-scale collaborative optimization of cable structural parameters (such as stranding pitch, sheath thickness, friction coefficient, etc.). This optimization method can effectively improve the cable's fatigue resistance and extend its service life. During virtual accelerated life testing, the cable's fatigue index can be evaluated in real time, and the design parameters can be optimized and adjusted according to the test results to ensure that the cable performance reaches the best state and avoid premature damage and failure.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] Figure 1 This is a flowchart of the fatigue resistance design simulation method for flexible power cables of humanoid robots according to the present invention.

[0020] Figure 2 This is a flowchart of the fatigue resistance design simulation system for the flexible power cable of the humanoid robot according to the present invention. Detailed Implementation

[0021] This application's embodiments utilize a humanoid robot flexible power cable fatigue resistance design simulation system and method to solve the fatigue damage problem caused by frequent bending, friction, and deformation of cables during complex movements. Furthermore, through multimodal data correlation and cross-scale optimization analysis, the optimal design of cable structural parameters is achieved, thereby improving the cable's fatigue resistance and service life.

[0022] The overall concept of the solution in this application embodiment is as follows:

[0023] Acquire the joint motion timing data of the humanoid robot, construct a dynamic coupling simulation model of the cable, and dynamically generate a multimodal correlation dataset of cable deformation field, frictional heat generation field and conductor resistivity change field through finite element simulation, and locate the coordinates of the critical region of energy accumulation at the cable stranding gap and sheath bend.

[0024] By inputting the multimodal association dataset and the coordinates of the critical region into a pre-trained cross-scale damage analysis model, the spatiotemporal mapping relationship between the macroscopic strain energy density gradient distribution and the microscopic crack propagation characteristics of the cable critical region is analyzed, and a cable damage weight distribution map is generated.

[0025] Based on the cable damage weight distribution map, a multi-objective dynamic optimization algorithm is used to perform multi-scale collaborative optimization of the cable stranding pitch gradient distribution, the non-uniform design of the sheath thickness at the bend, and the interlayer friction coefficient, and output the optimal set of cable structural parameters for fatigue resistance.

[0026] The optimal set of cable structural parameters for fatigue resistance is input into the dynamic coupling simulation model of the cable for virtual accelerated life testing to evaluate the cable fatigue index. If the cable fatigue index is greater than or equal to a preset threshold, the parameters of the cross-scale damage analysis model are corrected and iterated again until the cable fatigue index is less than the preset threshold.

[0027] Please see Figure 1This invention provides a technical solution: a fatigue-resistant design simulation method for flexible power cables for humanoid robots, comprising the following steps: S1. Acquiring the joint motion timing data of the humanoid robot, constructing a dynamic coupling simulation model of the cable, dynamically generating a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation, and locating the coordinates of the energy accumulation critical region at the cable stranding gap and sheath bend; S2. Inputting the multimodal correlation dataset and critical region coordinates into a pre-trained cross-scale damage analysis model, analyzing the macroscopic strain energy density gradient distribution and microscopic crack propagation characteristics of the cable critical region. S3. Based on the spatiotemporal mapping relationship, a cable damage weight distribution map is generated; S4. Based on the cable damage weight distribution map, a multi-objective dynamic optimization algorithm is used to perform multi-scale collaborative optimization of the cable stranding pitch gradient distribution, the non-uniform design of the sheath bending thickness, and the interlayer friction coefficient, outputting the optimal set of cable structural parameters for fatigue resistance; S5. The optimal set of cable structural parameters for fatigue resistance is input into the cable dynamic coupling simulation model for virtual accelerated life testing to evaluate the cable fatigue index. If the cable fatigue index is greater than or equal to a preset threshold, the parameters of the cross-scale damage analysis model are corrected and iterated again until the cable fatigue index is less than the preset threshold.

[0028] In this implementation plan, step S1: Acquiring the timing data of humanoid robot joint motion: The core of this step is to acquire data on the robot joints during motion (such as timing data on joint angles, velocities, and accelerations). This data will serve as the basis for subsequent simulation and analysis. Joint motion data can accurately simulate the physical effects such as stress, deformation, and friction experienced by the cable during the robot's motion. Constructing a dynamic coupling simulation model for the cable: Based on the timing data of joint motion, a coupling model is constructed to simulate the interaction between the cable and the robot. The dynamic response of the cable is closely related to the joint motion of the robot, and the simulation model can show how the cable deforms and is subjected to frictional forces during motion. Finite element simulation: The finite element method is used to simulate the physical behavior of the cable under different motion states. This is done by dividing the cable into small units (finite elements) and calculating the stress, strain, and other changes of each unit during motion, thereby obtaining the overall response of the cable. Cable deformation field, frictional heat generation field, and conductor resistivity change field: In the simulation, the cable deformation, the heat generated by friction, and the resistivity change of the cable conductor are important physical fields. The deformation field illustrates the shape changes of the cable during movement; the frictional heat field reflects the thermal load of friction on the cable; and the resistivity change field describes the changes in the cable conductor resistance due to deformation and friction. Multimodal correlation dataset: During simulation, the interrelationships between different physical fields (such as deformation, friction, conductivity, etc.) are comprehensively considered to form a multimodal dataset, which will be used for subsequent damage analysis and optimization design. Coordinate location of energy accumulation critical regions at cable stranding gaps and sheath bends: The cable stranding structure and sheath bends are usually the most vulnerable parts of the cable. By analyzing the dataset, energy accumulation zones at these locations are identified, and the coordinates of these critical regions are marked, providing important data for subsequent damage analysis. Step S2: Cross-scale damage analysis model: This model comprehensively considers damage mechanisms at different scales, analyzing cable damage behavior from macroscopic to microscopic levels. The macroscopic level focuses on the overall strain energy distribution of the cable, while the microscopic level focuses on crack propagation and local characteristics of fatigue damage. Spatiotemporal mapping relationship between macroscopic strain energy density gradient distribution and microscopic crack propagation characteristics: This analysis uses a cross-scale model to map the strain and crack propagation of the cable at different scales, obtaining the strain energy density gradient and crack propagation characteristics of the cable at various locations, and thus deriving the spatiotemporal evolution law of cable damage. Cable damage weight distribution map: Based on the above analysis, a cable damage map is generated, showing the degree of damage at different locations. This map can help designers identify weak areas of the cable and provide a basis for subsequent optimization design. Step S3: Multi-objective dynamic optimization algorithm: This algorithm aims to simultaneously optimize multiple design parameters of the cable, such as stranding pitch, sheath bending thickness, and interlayer friction coefficient. Optimization of these parameters can effectively reduce cable fatigue damage and improve the cable's fatigue resistance.Optimization of cable stranding pitch gradient distribution, non-uniform sheath thickness design at bends, and interlayer friction coefficient: By adjusting these structural and material parameters, the cable design can be optimized to make it more durable during robot movement and reduce fatigue damage caused by bending and friction. Output of the optimal set of fatigue-resistant cable structural parameters: Through optimization algorithms, a set of optimal cable structural parameters with the best performance in fatigue resistance is obtained. Step S4: Virtual Accelerated Life Test: This step involves inputting the optimized cable parameters into a simulation model to conduct a virtual accelerated life test. During the test, the cable's fatigue resistance in a real working environment is evaluated by accelerating the simulation of long-term cable use. Cable Fatigue Index: This is a quantitative indicator used to measure the cable's fatigue resistance performance. It is calculated based on the cable's damage process, life test results, etc. Parameter Correction and Iteration of the Cross-Scale Damage Analysis Model: If the test results show that the cable's fatigue index does not meet the preset standard value, the parameters of the cross-scale damage analysis model will be corrected and iteratively optimized until the cable's fatigue resistance performance meets the requirements. By acquiring joint motion data from a humanoid robot and combining finite element simulation and multimodal data analysis, cross-scale damage analysis and optimization design were implemented. Through a multi-objective optimization algorithm, a set of optimal cable structure parameters was obtained, improving the cable's fatigue resistance under complex motion. The effectiveness was verified through virtual accelerated life testing. This method not only improves cable performance but also optimizes its design process and reduces the risk of cable damage.

[0029] Specifically, the process of acquiring the time-series data of humanoid robot joint motion and constructing a dynamic coupling simulation model of the cable is as follows: The angular displacement, angular velocity, and time-series load spectrum of the motion trajectory are collected in real time through the joint sensors of the humanoid robot, and the joint motion mode is established based on time series analysis; A dynamic coupling simulation model of the cable is constructed based on the cable geometry and topology and the nonlinear properties of the material, combined with the cable assembly path and motion degrees of freedom, to simulate the mechanical response of the cable under highly dynamic joint conditions; Variable load boundary conditions are applied to the simulation model to simulate the influence of different working conditions on cable deformation and stress distribution, the interlayer dynamic contact boundary conditions and frictional heat generation coupling equations are defined, and a preliminary simulation dataset is output.

[0030] In this implementation scheme, angular displacement, angular velocity, and time-series load spectrum of the motion trajectory are collected through joint sensors. The purpose of this step is to acquire real-time motion data of the robot joints through joint sensors. Specifically: angular displacement represents the rotation angle of the joint. The angular displacement data of the motion trajectory is obtained by measuring the joint position at each moment through sensors. Angular velocity represents the rotation rate of the joint, that is, the rate of change of angular displacement over time. Angular velocity data reflects the speed of the robot's movement. The time-series load spectrum refers to the change of the load applied to each joint over time during the robot's joint movement. The load spectrum helps to further analyze the stress on the cable. Based on these time-series data, the motion mode of the joint is established, the working state of the robot joint (such as acceleration, deceleration, or uniform motion) is identified, and necessary data support is provided for subsequent simulation analysis. The core of this step is to construct a dynamic coupling simulation model of the cable. This includes: cable geometry topology: the geometry of the cable (such as length, diameter, number of layers, etc.) and its assembly method (how it is arranged around the joint). The cable geometry directly affects its deformation and stress distribution during joint movement. Nonlinear Material Properties: Cable materials exhibit nonlinear mechanical properties. Simulations must consider how the cable material responds to different stresses. For example, cable materials may yield under high stress, or their properties may be affected by temperature increases due to frictional heat. Cable Assembly Path and Degrees of Freedom: How the cable is arranged inside the robot (e.g., transmission paths) and the degrees of freedom of the robot joints (e.g., joint rotation or extension) are crucial factors. These factors determine how the cable deforms with joint movement. By inputting these factors into the simulation model, the mechanical response of the cable during joint movement can be simulated, such as bending, stretching, and twisting. Applying variable load boundary conditions in the simulation model is key to simulating the stress and deformation of the cable under different operating conditions. Variable Load Boundary Conditions: In actual use, the cable is not subjected to constant stress; rather, it experiences forces of different amplitudes and directions depending on the different stages of robot movement. By setting different load conditions, the deformation and stress of the cable under various dynamic conditions can be simulated. For example, the tension in the cable may increase when the robot joint accelerates, and decrease when the joint decelerates. These boundary conditions allow for the analysis of cable deformation, stress distribution, and potential damage modes under different operating conditions. Interlayer dynamic contact boundary conditions: Relative motion occurs between different layers of the cable, especially under dynamic conditions. This relative motion generates contact forces, which affect the cable's deformation and stress distribution. Friction-heat coupling equation: Friction between the cable layers generates heat, thus affecting the cable's temperature and mechanical properties.

[0031] Specifically, the process of dynamically generating a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation is as follows: Based on the preliminary simulation dataset, a nonlinear mechanical model of the cable structure is established, considering the material properties of the sheath, insulation layer, and conductor stranding layer; high-precision finite element simulation of cable deformation field, frictional heat generation field, and conductor resistivity change field is performed through an adaptive mesh refinement strategy; based on the distribution characteristics of stress, temperature, and resistivity, highly correlated feature parameters of cable fatigue damage are extracted to construct a multimodal correlation dataset.

[0032] In this implementation plan, a nonlinear mechanical model of the cable structure is established: Based on the preliminary simulation dataset, a nonlinear mechanical model of the cable is established, mainly considering the material properties of the cable sheath, insulation layer, and conductor stranding layer. Different materials exhibit different mechanical behaviors under stress, especially in high-dynamic environments, where cable materials will produce nonlinear responses. Adaptive mesh refinement strategy: An adaptive mesh refinement strategy is adopted during finite element simulation. This means that in areas where the cable stress varies greatly, a finer mesh is used to obtain higher computational accuracy; while in areas where the stress is small, a coarser mesh is used to improve computational efficiency. Through this strategy, the behavior of the cable under different operating conditions can be accurately simulated. High-precision finite element simulation analysis: Using the finite element simulation method, the deformation field, frictional heat generation field, and conductor resistivity change field of the cable under motion or external force are obtained. Specifically, the deformation field describes the geometric changes of the cable after being stressed, the frictional heat generation field reflects the temperature distribution caused by friction between the cable layers, and the resistivity change field considers the resistance changes of the cable conductor due to temperature changes. Extraction of highly correlated feature parameters: Through the analysis of the above simulation results, feature parameters highly correlated with cable fatigue damage are extracted. The analysis primarily focuses on the distribution of stress, temperature, and resistivity. For example, high-stress areas can lead to cable damage, while changes in temperature and resistivity are related to frictional heat generation and the electrical properties of the conductor. A multimodal correlation dataset is constructed: combining feature parameters extracted from simulations, a multimodal correlation dataset is built. This dataset contains physical quantities such as cable deformation, temperature, stress, and resistivity under different operating conditions. In this way, the fatigue damage behavior and performance degradation of cables under various conditions can be analyzed more comprehensively.

[0033] Specifically, the process of analyzing the spatiotemporal mapping relationship between the macroscopic strain energy density gradient distribution and the microcrack propagation characteristics in the critical region of the cable is as follows: a crystal plasticity sub-model is embedded in the finite element model to simulate the dislocation motion of copper conductor grains and the initiation of microcracks in the critical region; the macroscopic strain energy density gradient field is extracted, and a nonlinear mapping of the microcrack propagation rate is established through a transfer learning algorithm; a spatiotemporal correlation weight function is defined to quantify the contribution of micro-defects to the accumulation of macroscopic damage.

[0034] In this implementation scheme, a crystal plasticity sub-model is embedded in the finite element model to simulate the grain dislocation movement and microcrack initiation in the critical region of the copper conductor. The finite element model and the crystal plasticity sub-model: A crystal plasticity sub-model is added to the finite element analysis model. This model describes the deformation behavior of metallic materials (such as copper conductors) at the microscale. By considering the grain structure, dislocation movement, and inter-grain interactions, this model can simulate the plastic deformation process of the material under loading conditions. Grain dislocation movement and microcrack initiation: In the critical region of the cable, the application of external loads or currents generates localized stress concentrations. This stress concentration leads to dislocation slip within the copper conductor grains, further initiating the initiation of microcracks. These microcracks may propagate with changes in loading conditions, thus affecting the mechanical properties of the cable. Formula: Where μ is the shear modulus of the material, b is the Bertolt factor of the dislocation, and r is the source radius of the dislocation. This formula describes the stress conditions for dislocation movement within the grain. Macroscopic strain energy density gradient field: Strain energy density is the energy stored per unit volume of a material under stress, reflecting the energy density stored during deformation. The macroscopic strain energy density gradient field represents the strain energy distribution at various locations in the material and is usually closely related to stress concentration regions. Strain energy density formula: ;in, For a certain position strain energy density, The stress at that location, The strain at this location. Transfer learning algorithm: This step uses transfer learning techniques to establish a nonlinear mapping relationship between the macroscopic strain density gradient and the microscopic crack propagation rate. Transfer learning is a machine learning method that utilizes existing data or models applied to new tasks, adjusting existing knowledge to adapt to new data, thereby effectively predicting the microcrack propagation rate. Nonlinear mapping: The relationship between the strain energy density gradient and the crack propagation layer is nonlinear, therefore, machine learning methods are needed to identify its complex mapping relationship. Transfer learning can train a model to predict the crack propagation rate under new working conditions using existing microcrack propagation data of wire materials. Formula: Crack propagation rate (ae): ;in, This represents the crack propagation rate. and These coefficients are obtained through transfer learning. The formula represents the macroscopic strain energy density. It expresses the nonlinear relationship between the crack propagation rate and the strain energy density gradient. A spatiotemporal correlation weighting function is defined to quantify the contribution of microscopic defects to the accumulation of macroscopic damage. This function quantitatively describes the contribution of microscopic defect (such as crack) propagation to the accumulation of macroscopic damage. It considers the evolution of microcracks in time and space, effectively correlating microscopic behavior with macroscopic damage. The spatiotemporal correlation weighting function helps reveal the interaction of crack propagation in different regions and time scales of the cable. The weighting function is defined as follows: ;in, It is a spatiotemporal correlation weight function. For the stress of the cable, This is a location function for the microcrack. It is a constant used to adjust the weight function.

[0035] Specifically, the process of generating a cable damage weight distribution map is as follows: calculate the damage weight value of the critical area based on the spatiotemporal correlation weight function; combine the crack priority propagation path prediction algorithm to generate a damage level distribution map of the entire cable area; mark the stranding gap and sheath bend as the highest risk areas, and output the damage weight heat map.

[0036] In this implementation scheme, the damage accumulation rate varies significantly across different parts of the cable during its long-term service life. Therefore, it is necessary to quantify the damage degree of each region using a spatiotemporal correlation weighting function. The spatiotemporal correlation weighting function is calculated as follows: Let Ω represent the cable material domain, x∈Ω represent the spatial location, and t represent time. The damage weight value D(x,t) is calculated based on the local stress field, strain energy density, and crack propagation rate. Where: D(x,t) represents the damage weight value at position x; γ represents the normalization coefficient to keep the damage weight value within a reasonable range; Φ(x,τ) represents the strain energy density gradient at position x. Let x represent the crack propagation rate at location x, t represent the time variable, and represent the damage accumulation process. This formula indicates that, over the entire service life of the cable, the microcrack propagation rate... Integrating the data while considering the strain energy density gradient Φ(x,τ), the damage weight values ​​at each location on the cable are calculated. Under stress or environmental influences, crack propagation in cables exhibits path selectivity; therefore, a crack-preferred propagation path prediction algorithm is needed to identify the most vulnerable areas and generate a damage level distribution map. The crack-preferred propagation path prediction uses an energy release rate-driven model to predict the crack propagation path. Let the crack front location be... The crack propagation direction is The energy driving factor G for crack propagation is defined as: ;in: Indicates the current position Along direction The crack driving force, Indicates the crack propagation sensitivity coefficient This represents the strain energy density at the current position x. Indicates the direction of crack propagation The strain energy gradient; crack propagation path prediction step, calculating the driving force for all possible crack propagation directions. The direction with the largest G value is selected as the optimal path for crack propagation. Iterative calculations are performed to simulate the crack propagation process in the global domain of the power grid, generating an integral distribution map of damage.

[0037] Specifically, the process of multi-scale collaborative optimization of cable stranding pitch gradient distribution, non-uniform thickness design at sheath bends, and interlayer friction coefficient using a multi-objective dynamic optimization algorithm is as follows: A multi-objective game-theoretic optimization framework for fatigue resistance, lightweighting, and electrical stability is constructed; a Pareto optimal solution is searched in the space of stranding pitch gradient, sheath thickness distribution, and friction coefficient using an adaptive non-dominated sorting genetic algorithm; and local inferior solutions are avoided through dynamic constraints to generate a balanced set of design parameters that satisfy multiple objectives.

[0038] In this implementation plan, the optimization process aims to achieve multi-scale collaborative optimization of cable stranding pitch gradient distribution, non-uniform sheath thickness design at bends, and interlayer friction coefficient. Based on balancing fatigue resistance, lightweighting, and electrical stability, an adaptive non-dominated sorting genetic algorithm (NSGA-II) is used to search for Pareto optimal solutions. Locally inferior solutions are avoided through dynamic constraints, ultimately obtaining an optimized set of design parameters. A multi-objective game-theoretic optimization framework is constructed to collaboratively optimize the cable's fatigue resistance (F1), lightweighting (F2), and electrical stability (F3). Mathematical expression: Define the multi-objective optimization problem: Parameter description: x: Design variable vector, including the twist pitch gradient Sheath thickness distribution interlayer friction coefficient ; Fatigue damage factor, calculated from stress amplitude and number of cycles; Contact resistance fluctuation; Sheath material density, : Sheath cross-sectional area; Var : Variance of resistivity as a function of temperature and strain; , Weighting coefficients are used to balance fatigue and electrical stability objectives. Design spatial constraints. The purpose of dynamic constraint design is to prevent the algorithm from getting trapped in local solutions and to guide the search direction. Constraint strategy: Objective-related constraints: Dynamically adjust constraint priority based on the relevance of the objective function. and If there is a strong negative correlation, relax the restrictions first. Constraints. Penalty function: A secondary penalty function is applied to individuals who violate the constraints. ;in As constraints, The penalty coefficient. Pareto front: a set of non-dominated solutions representing the trade-offs between different objectives; Equilibrium solution selection: determining the final design scheme through fuzzy decision-making methods. Parameter description: :Target Membership functions (such as linear normalization); User-defined target weights (e.g.) =0.4, =0.3, =0.3).

[0039] Specifically, the optimal set of cable structural parameters for fatigue resistance includes: gradient distribution parameters of stranding pitch from the inner layer to the outer layer, non-uniform design parameters of sheath thickness at bends, and dynamic friction coefficient matrix between the shielding layer and the conductor.

[0040] In this implementation scheme, the stranding pitch is gradient-distributed from the inner to the outer layer: by adjusting the pitch of different stranded wires to create a gradient from the inside to the outside, the internal stress distribution is optimized, fatigue damage is reduced, and bending resistance is improved. The sheath bending thickness is designed to be non-uniformly: a variable thickness design is used in the vulnerable bending areas of the sheath to strengthen the fatigue resistance of key parts while avoiding excessive rigidity that could lead to stress concentration. The dynamic friction coefficient matrix between the shielding layer and the core is controlled: by controlling the dynamic friction characteristics between the shielding layer and the core, it can adapt to deformation during bending or stretching, reducing wear and fatigue damage and improving service life.

[0041] Specifically, the process for evaluating the cable fatigue resistance index is as follows: acquire virtual accelerated life test data, construct a cable damage evolution time series, and extract the damage evolution sequence of the cable critical region; extract the damage accumulation curve of the cable critical region through the damage evolution time series, and calculate the fatigue life decay rate in combination with the material fatigue characteristics, construct a fatigue accumulation damage prediction matrix, and quantify the damage propagation trend; calculate the cable fatigue life consumption ratio based on the fatigue accumulation damage prediction matrix, normalize it, and generate the cable fatigue resistance index.

[0042] In this implementation plan, the cable fatigue index is used to quantify the degree of fatigue damage to cables during long-term use and assess their remaining lifespan. The specific calculation steps are as follows: 1. Obtain virtual accelerated life test data: Use finite element simulation or experimental accelerated life testing to obtain damage evolution data of the cable under different stress cycle conditions. 2. Construct a cable damage evolution time series: Through time synchronization analysis, extract the damage evolution curves of the cable's critical regions (such as stranding gaps and sheath folds) and establish a damage accumulation model. 3. Calculate the fatigue life decay rate: Based on the material's fatigue characteristics and combined with the damage accumulation curve, calculate the fatigue life decay rate to form a fatigue accumulation damage prediction matrix to quantify the damage accumulation trend. 4. Calculate and normalize the fatigue life consumption ratio: Calculate the fatigue life consumption ratio using the cumulative damage prediction matrix and perform normalization to ensure that the fatigue index of different cables is evaluated on a uniform scale. Cable fatigue index calculation formula. ;in: Cable fatigue index (range 0-1, the higher the value, the stronger the fatigue resistance). : The cumulative damage at the i-th time step; N: The total number of time steps; Fatigue damage threshold (maximum allowable damage value) of cable material.

[0043] Specifically, the process of triggering parameter correction and re-iteration of the cross-scale damage analysis model is as follows: Based on the cable damage weight distribution map, the damage accumulation trend predicted by simulation is compared with the target life requirement, and the damage evolution error is extracted; based on the damage evolution error, the material constitutive relation in the cross-scale damage analysis model is corrected, including the strain energy density threshold and fatigue crack propagation coefficient; combined with the macroscopic stress distribution and microscopic crack propagation trend, the weight parameters of the fatigue damage accumulation model are adjusted.

[0044] In this implementation plan, based on the cable damage weight distribution map, the damage accumulation trend predicted by simulation is compared with the target life requirement to calculate the damage evolution error. The damage evolution error represents the difference between the cumulative damage value predicted by simulation and the target life requirement. The formula is as follows: ;in: :time Damage evolution error; : The cumulative damage predicted by simulation; The cumulative damage amount under the target lifetime requirement. The material constitutive relation in the multi-scale damage analysis model is adjusted based on the damage evolution error. The main material parameters corrected include: strain energy density threshold (…). ): Defines the critical strain energy density at which a material enters a state of fatigue damage; fatigue crack propagation coefficient ( ): Represents the crack propagation rate under different load levels. Formula: ; ;in: and This is an adjustment coefficient, dynamically adjusted according to the magnitude of the damage error; and These are the corrected material parameters. The weighting coefficients of the fatigue damage accumulation model are adjusted, dynamically modifying the weighting parameters by considering both macroscopic stress distribution and microscopic crack propagation trends. These weighting parameters quantify the impact of different damage characteristics on the total damage accumulation. The formula is as follows: ;in: Original weight parameters; Weighting adjustment coefficient. The corrected material parameters and weighting parameters are input into the scale-shifting damage analysis model for a new iterative simulation. This iteration continues until the damage evolution error converges to a preset threshold, achieving the target lifespan requirement.

[0045] A simulation system for fatigue-resistant design of flexible power cables for humanoid robots includes the following modules: a multi-field coupling and critical positioning module, a cross-scale damage modeling module, a multi-objective optimization module, and a virtual verification and iteration module. The multi-field coupling and critical positioning module acquires the time-series data of the humanoid robot's joint motion, constructs a dynamic coupling simulation model of the cable, and dynamically generates a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation. It also locates the coordinates of the critical energy accumulation region at the cable stranding gap and sheath bend. The cross-scale damage modeling module inputs the multimodal correlation dataset and critical region coordinates into a pre-trained cross-scale damage analysis model to analyze the macroscopic strain energy density of the cable's critical region. The spatiotemporal mapping relationship between gradient distribution and microcrack propagation characteristics generates a cable damage weight distribution map. The multi-objective optimization module is used to perform multi-scale collaborative optimization of the cable stranding pitch gradient distribution, non-uniform thickness design at sheath bends, and interlayer friction coefficient based on the cable damage weight distribution map using a multi-objective dynamic optimization algorithm, and outputs a set of optimal cable structure parameters for fatigue resistance. The virtual verification and iteration module is used to input the set of optimal cable structure parameters for fatigue resistance into the cable dynamic coupling simulation model for virtual accelerated life testing, evaluate the cable fatigue index, and if the cable fatigue index is greater than or equal to a preset threshold, trigger the parameter correction of the cross-scale damage analysis model and re-iterate until the cable fatigue index is less than the preset threshold.

[0046] In this implementation plan, the multi-field coupling and critical location module accurately identifies key damage areas: based on finite element simulation, it comprehensively analyzes cable deformation, frictional heat generation, and conductor resistivity changes to accurately identify high-stress and high-temperature areas. Multi-modal data fusion integrates data from different physical fields to improve the accuracy of cable fatigue damage modeling, providing high-precision input data for subsequent optimization. A cross-scale damage modeling module combines macroscopic and microscopic damage characteristics: using a pre-trained model to establish a spatiotemporal mapping relationship between strain energy density and crack propagation, taking into account the damage evolution of the material's microstructure and improving the reliability of damage prediction. It efficiently generates damage distribution maps: based on multi-modal data analysis of key area damage levels, it provides quantitative references for the optimization module. A multi-objective optimization module globally optimizes cable structure design: through multi-objective optimization algorithms, it comprehensively considers stranding pitch, sheath thickness, and friction coefficient to maximize fatigue resistance. Multi-scale collaborative optimization combines damage distribution data to optimize structural parameters at different scales, improving overall fatigue life. A virtual verification and iteration module implements a closed-loop optimization design process: based on virtual accelerated life testing, it evaluates the fatigue resistance index to ensure the design meets expected life requirements. Adaptive parameter adjustment: If the test results are not up to standard, the key parameters of the cross-scale damage model are automatically corrected and continuously optimized until the performance requirements are met, thereby improving design efficiency and reliability.

[0047] In summary, this application achieves at least the following effects: A simulation system and method for fatigue-resistant design of flexible power cables using humanoid robots, through multi-objective dynamic optimization, achieves optimal design of stranding pitch, sheath thickness, and interlayer friction coefficient, significantly improving the cable's fatigue resistance. Based on finite element simulation analysis, it dynamically generates multi-modal data on cable deformation, frictional heat generation, and conductor resistivity changes, and accurately identifies key damage areas such as stranding gaps and sheath bends. It establishes a spatiotemporal mapping relationship between macroscopic strain energy density gradient and microscopic crack propagation characteristics, quantifying the impact of microscopic cracks on macroscopic damage accumulation and improving the accuracy of cable fatigue damage prediction. Combined with virtual accelerated life testing, it constructs a fatigue resistance index evaluation system and triggers parameter correction and optimization iterations of the cross-scale damage analysis model when the index fails to meet the standards, ensuring that the final optimization result meets the expected life requirements. Through adaptive mesh refinement strategies and transfer learning algorithms, it improves the accuracy of finite element simulation, reduces computational burden, and makes the optimized design more practical for engineering applications.

[0048] 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.

[0049] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, 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 processor, 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 and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0050] 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.

[0051] 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.

[0052] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0053] 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 the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A simulation method for fatigue resistance design of flexible power cables for humanoid robots, characterized in that, Includes the following steps: S1. Obtain the joint motion timing data of the humanoid robot, construct a dynamic coupling simulation model of the cable, dynamically generate a multimodal correlation dataset of cable deformation field, frictional heat generation field and conductor resistivity change field through finite element simulation, and locate the coordinates of the critical region of energy accumulation at the cable stranding gap and sheath bend. S2. Input the multimodal association dataset and the coordinates of the critical region into the pre-trained cross-scale damage analysis model, analyze the spatiotemporal mapping relationship between the macroscopic strain energy density gradient distribution and the microscopic crack propagation characteristics of the cable critical region, and generate a cable damage weight distribution map. S3. Based on the cable damage weight distribution map, a multi-objective dynamic optimization algorithm is used to perform multi-scale collaborative optimization of the cable stranding pitch gradient distribution, the non-uniform design of the sheath thickness at the bend, and the interlayer friction coefficient, and outputs the set of optimal cable structural parameters for fatigue resistance. S4. Input the set of optimal cable structure parameters for fatigue resistance into the cable dynamic coupling simulation model to conduct virtual accelerated life test and evaluate the cable fatigue resistance index. If the cable fatigue resistance index is greater than or equal to the preset threshold, trigger the parameter correction of the cross-scale damage analysis model and iterate again until the cable fatigue resistance index is less than the preset threshold. The specific process of obtaining the joint motion timing data of the humanoid robot and constructing a dynamic coupling simulation model of the cable is as follows: The angular displacement, angular velocity, and time-series load spectrum of the motion trajectory are collected in real time by the joint sensors of the humanoid robot, and the joint motion pattern is established based on the time series analysis. Based on the cable geometry and nonlinear material properties, a dynamic coupling simulation model of the cable is constructed by combining the cable assembly path and motion degrees of freedom to simulate the mechanical response of the cable under high dynamic conditions at the joint. Variable load boundary conditions are applied to the simulation model to simulate the effects of different working conditions on cable deformation and stress distribution. Interlayer dynamic contact boundary conditions and frictional heat generation coupling equations are defined, and preliminary simulation datasets are output. The specific process of analyzing the spatiotemporal mapping relationship between the macroscopic strain energy density gradient distribution and the microscopic crack propagation characteristics in the critical region of the cable is as follows: A crystal plasticity sub-model was embedded in the finite element model to simulate the grain dislocation motion and microcrack initiation of copper conductors in the critical region. The macroscopic strain energy density gradient field is extracted, and a nonlinear mapping of the microcrack propagation rate is established through a transfer learning algorithm. Define a spatiotemporal correlation weighting function to quantify the contribution of microscopic defects to the accumulation of macroscopic damage; The specific process of multi-scale collaborative optimization of cable stranding pitch gradient distribution, non-uniform thickness design at sheath bends, and interlayer friction coefficient using a multi-objective dynamic optimization algorithm is as follows: A multi-objective game-theoretic optimization framework is constructed to consider fatigue resistance, lightweight design, and electrical stability. The Pareto optimal solution is searched in the space of the twist pitch gradient, sheath thickness distribution and friction coefficient using an adaptive non-dominated sorting genetic algorithm. By using dynamic constraints, local suboptimal solutions are avoided, and a set of design parameters that are balanced and satisfy multiple objectives is generated.

2. The fatigue resistance design simulation method for flexible power cables of humanoid robots according to claim 1, characterized in that: The specific process of dynamically generating a multimodal correlation dataset of cable deformation field, frictional heat generation field, and conductor resistivity change field through finite element simulation is as follows: Based on the preliminary simulation dataset, a nonlinear mechanical model of the cable structure is established, taking into account the material properties of the sheath, insulation layer and conductor stranding layer; High-precision finite element simulations of cable deformation field, frictional heat generation field and conductor resistivity change field are performed using an adaptive mesh refinement strategy. Based on the distribution characteristics of stress, temperature, and resistivity, highly correlated feature parameters of cable fatigue damage are extracted to construct a multimodal association dataset.

3. The fatigue resistance design simulation method for flexible power cables of humanoid robots according to claim 1, characterized in that: The specific process for generating the cable damage weight distribution map is as follows: Calculate the critical region damage weight value based on the spatiotemporal correlation weight function; By combining the crack priority propagation path prediction algorithm, a damage level distribution map of the entire cable area is generated; Mark the twisting gap and the sheath bend as the highest risk areas, and output a damage weight heatmap.

4. The fatigue resistance design simulation method for flexible power cables of humanoid robots according to claim 1, characterized in that: The optimal set of cable structural parameters for fatigue resistance includes: gradient distribution parameters of stranding pitch from the inner layer to the outer layer, non-uniform design parameters of sheath thickness at bends, and dynamic friction coefficient matrix between the shield and the conductor.

5. The fatigue resistance design simulation method for flexible power cables of humanoid robots according to claim 1, characterized in that: The specific process for evaluating the fatigue index of cables is as follows: Acquire virtual accelerated life test data, construct cable damage evolution time series, and extract the damage evolution sequence of the cable critical region; By using the damage evolution time series, the damage accumulation curve of the critical region of the cable is extracted, and combined with the material fatigue characteristics, the fatigue life decay rate is calculated, a fatigue accumulation damage prediction matrix is ​​constructed, and the damage propagation trend is quantified. Based on the fatigue cumulative damage prediction matrix, the cable fatigue life consumption ratio is calculated and normalized to generate the cable fatigue resistance index.

6. The fatigue resistance design simulation method for flexible power cables of humanoid robots according to claim 1, characterized in that: The specific process of triggering parameter correction and re-iteration of the cross-scale damage analysis model is as follows: Based on the cable damage weight distribution map, the damage evolution error is extracted by comparing the damage accumulation trend predicted by simulation with the target life requirement. Based on the damage evolution error, the material constitutive relations in the multi-scale damage analysis model are corrected, including the strain energy density threshold and the fatigue crack propagation coefficient. By combining macroscopic stress distribution and microscopic crack propagation trends, the weight parameters of the fatigue damage accumulation model are adjusted.

7. A fatigue design simulation system for flexible power cables of humanoid robots, applied to the fatigue design simulation method for flexible power cables of humanoid robots as described in any one of claims 1-6, characterized in that, It includes the following modules: multi-field coupling and critical location module, cross-scale damage modeling module, multi-objective optimization module, and virtual verification and iteration module; The multi-field coupling and critical positioning module is used to acquire the joint motion timing data of the humanoid robot, construct a dynamic coupling simulation model of the cable, dynamically generate a multi-modal correlation dataset of cable deformation field, frictional heat generation field and conductor resistivity change field through finite element simulation, and locate the coordinates of the critical region of energy accumulation at the cable stranding gap and sheath bend. The cross-scale damage modeling module is used to input the multimodal associated dataset and the coordinates of the critical region into the pre-trained cross-scale damage analysis model, analyze the spatiotemporal mapping relationship between the macroscopic strain energy density gradient distribution and the microscopic crack propagation characteristics of the cable critical region, and generate a cable damage weight distribution map. The multi-objective optimization module is used to perform multi-scale collaborative optimization of cable stranding pitch gradient distribution, non-uniform design of sheath thickness at bending points and interlayer friction coefficient based on cable damage weight distribution map and multi-objective dynamic optimization algorithm, and outputs the set of optimal cable structure parameters for fatigue resistance. The virtual verification and iteration module is used to input the set of optimal cable structure parameters for fatigue resistance into the cable dynamic coupling simulation model for virtual accelerated life testing, evaluate the cable fatigue resistance index, and if the cable fatigue resistance index is greater than or equal to a preset threshold, the parameters of the cross-scale damage analysis model are corrected and iterated again until the cable fatigue resistance index is less than the preset threshold.

Images