Wire harness development and optimization method based on model driving

Abstract

The invention provides a wire harness development and optimization method based on model driving, and relates to the technical field of wire harness design and optimization, a digital twin model is constructed by collecting actual operation parameters, the model is subdivided into a large number of node units for accurate stress analysis, and a potential stress concentration area can be accurately identified. Therefore, the problem that the wiring harness is damaged due to stress concentration easily caused by accurate analysis of stress distribution in an actual operation state in an existing wiring scheme is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wiring harness design and optimization, and more specifically, to a model-driven wiring harness development and optimization method. Background Art

[0002] In recent years, with the rapid development of industrial fields such as automobiles and aerospace, the wiring harness, as a key component of the electrical system, has received increasing attention in terms of its design and optimization. Traditional wiring harness design mainly relies on experience and two-dimensional drawing tools. Although it can meet the requirements of some simple application scenarios, with the increasing complexity of modern industrial systems, this design method gradually exposes the problem of being difficult to cope with multi-dimensional performance requirements. To solve this problem, in recent years, the model-driven design method has gradually become a new direction for wiring harness development. The model-driven method simulates the geometric structure, physical properties, and operating status of the wiring harness by constructing a digital model, providing strong tool support for the performance analysis, optimized design, and fault prediction of the wiring harness. Among them, the introduction of digital twin technology has further promoted the innovative development of wiring harness technology. Digital twin technology uses data acquisition, physical modeling, and dynamic simulation means to map the actual operating status of the wiring harness to the virtual model, realizing the full life cycle management of the wiring harness from the design to the operation stage. This method can theoretically improve the design accuracy of the wiring harness, optimize the wiring path, and enhance the operating reliability of the wiring harness under actual working conditions.

[0003] However, the existing model-driven wiring harness development technologies still have certain deficiencies. First, most current wiring harness design methods only focus on geometric wiring and space occupancy, while ignoring key mechanical and thermal factors such as stress distribution, vibration influence, and temperature rise effect suffered by the wiring harness during actual operation, resulting in the design scheme may not meet the reliability requirements under complex working conditions. Second, the existing technologies often optimize the wiring path based on a static model, lacking stress analysis and path optimization means under dynamic working conditions, which may lead to the optimization result not matching the actual use environment. In addition, for the design of the fixed bracket position and the protection structure, most existing methods rely on human experience, lacking a systematic optimization strategy and being difficult to efficiently solve the stress concentration problem. These deficiencies not only limit the accuracy and efficiency of wiring harness design but may also cause problems such as fatigue failure, fracture, or electrical faults of the wiring harness during actual operation. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention is proposed. The present invention provides a model-driven wiring harness development and optimization method, which to a certain extent solves the problem that the existing wiring scheme is prone to stress concentration and damage to the wiring harness due to the inaccurate analysis of the stress distribution under the actual operating state.

[0005] According to one aspect of the present invention, there is provided a model-driven harness development and optimization method, which includes: Collect the actual operating parameters of the harness, and construct a harness digital twin model through the actual operating parameters; Divide the harness digital twin model into N node units, perform stress analysis on the N node units, and obtain the stress distribution data of each node unit; Based on the stress distribution data, optimize the wiring path of the harness; According to the optimization result of the wiring path, adjust the position of the fixing bracket of the harness, and add a protective structure at the node unit where the stress distribution data exceeds the preset stress threshold.

[0006] Further, the harness digital twin model associates and integrates a physical property database, a geometric property database, and a material property database through a data mapping algorithm.

[0007] Further, calculate the vibration transfer function of each section of the harness through the physical feature sub-vector in the digital feature vector. If the amplitude of the vibration transfer function of a certain section of the harness exceeds the safety limit, store the stiffness parameter of that section of the harness in the physical property database; The digital feature vector is obtained through neural network fusion processing of the actual operating parameters, and then through orthogonal decomposition, a physical feature sub-vector representing the physical properties of the harness, a geometric feature sub-vector representing the shape of the harness, and a material feature sub-vector representing the material of the harness are obtained.

[0008] Further, use the geometric feature sub-vector to construct a three-dimensional space curve equation of the harness. When the curvature of the three-dimensional space curve at any point exceeds the safety curvature, calculate the stress concentration coefficient at that point and store it in the geometric property database of the harness digital twin model.

[0009] Further, constructing the three-dimensional space curve equation includes: Perform dimensionality reduction processing on the geometric feature sub-vector, and extract the key control point coordinate sequence of the harness in three-dimensional space; When the distance between the control points is less than the preset distance d, perform control point screening; Based on the control point coordinate sequence, construct the three-dimensional space curve equation.

[0010] Further, the obtaining of the stress distribution data of each node unit includes: Based on the position information of each node unit, extract the corresponding boundary conditions and constraint conditions; After the boundary conditions are set, construct different constitutive relation equations according to the material structure of each node unit; Calculate the deformation of the nodal element based on the displacement boundary condition, and calculate the strain components in each direction according to the deformation. Substitute the strain components into the constitutive relation equation to obtain the stress components of each nodal element.

[0011] Further, when the nodal element is a composite material, establish a hierarchical constitutive relation equation set. The hierarchical constitutive relation equation set is established by determining the number of layers of the composite material and the material properties of each layer, recording the ply angles, and treating the sandwich layer as an independent layer, and establishing a local coordinate system and a global coordinate system. If the two coordinate systems do not coincide, use the coordinate transformation matrix to transform the constitutive equations of each layer from the local coordinate system to the global coordinate system; finally, based on the principle of equivalent stiffness superposition, combine the constitutive equations of each layer into a global stiffness matrix, so as to obtain a hierarchical constitutive relation equation set describing the mechanical behavior of the composite material nodal element.

[0012] Further, use the ant colony algorithm to optimize the wiring path of the wire harness. The ant colony algorithm uses the entropy weight method to dynamically adjust the stress weights of different nodal elements, and uses a pheromone decay factor to avoid the ant algorithm falling into local optimum.

[0013] Further, the dynamic adjustment of the stress weights of different nodal elements by the entropy weight method includes: Calculate the stress proportion of each nodal element, and calculate the information entropy based on these proportions. Subtract the information entropy from one to obtain the difference coefficient, and normalize the difference coefficient to obtain the initial weight. Dynamically adjust the weight according to the relationship between the stress value of the nodal element and the preset threshold.

[0014] Further, when the optimal solution remains unchanged for multiple consecutive iterations, it is determined to enter the local optimum, and by setting different pheromone decay factors, accelerate the elimination of the pheromone difference between paths, so as to jump out of the local optimum and find a better solution.

[0015] Compared with the prior art, the model-driven wire harness development and optimization method provided by the present invention constructs a digital twin model by collecting actual operation parameters, and divides the model into a large number of nodal elements for accurate stress analysis, which can accurately identify potential stress concentration areas. In this way, it solves the problem that the existing wiring scheme is prone to cause stress concentration and damage to the wire harness due to the inaccurate analysis of the stress distribution under the actual operation state. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a flowchart of a model-driven harness development and optimization method according to an embodiment of the present invention. Detailed implementation manners

[0017] Next, exemplary embodiments of the present invention will be described in detail with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0018] Figure 1 It is a flowchart of a model-driven harness development and optimization method according to an embodiment of the present invention. As Figure 1 shown, in the model-driven harness development and optimization method, it includes: S1: Collect the actual operating parameters of the harness, including the temperature distribution parameters, vibration frequency parameters, and environmental humidity parameters of the harness, and construct a harness digital twin model through the temperature distribution parameters, the vibration frequency parameters, and the environmental humidity parameters; Set temperature sensors at key node positions of the harness. The temperature sensors record the temperature distribution parameters of the harness at a preset sampling period T, where the sampling period T is 1 minute; install acceleration sensors every 20 cm along the harness. The acceleration sensors collect the vibration frequency parameters of the harness, and the acquisition range of the vibration frequency parameters is 0 - 1000 Hz; set humidity sensors at the inlet end, outlet end, and intermediate connection points of the harness. The humidity sensors collect the environmental humidity parameters; input the temperature distribution parameters, the vibration frequency parameters, and the environmental humidity parameters into a pre-trained deep neural network. The deep neural network includes three parallel LSTM layers, which respectively process the temperature distribution parameters, the vibration frequency parameters, and the environmental humidity parameters. After being fused and processed by the deep neural network, a digital feature vector of the harness is output; construct the harness digital twin model based on the digital feature vector, where the harness digital twin model includes a physical property layer, a geometric property layer, and a material property layer.

[0019] Among them, the specific steps of constructing the harness digital twin model based on the digital feature vector are as follows: Perform orthogonal decomposition on the digital feature vector to obtain a physical feature sub-vector representing the physical characteristics of the wire harness, a geometric feature sub-vector representing the shape of the wire harness, and a material feature sub-vector representing the material of the wire harness; establish a temperature field distribution using the temperature component in the physical feature sub-vector, and when the local temperature gradient in the temperature field distribution model is greater than the preset temperature threshold, mark this area as a key temperature monitoring area; calculate the vibration transfer function of each section of the wire harness based on the vibration component in the physical feature sub-vector, and if the amplitude of the vibration transfer function of a certain section of the wire harness exceeds the safety limit, store the stiffness parameter of this section of the wire harness in the physical property database of the wire harness digital twin model; construct a three-dimensional space curve equation of the wire harness using the geometric feature sub-vector, and when the curvature of the three-dimensional space curve at any point exceeds the safety curvature, calculate the stress concentration coefficient at this point and store it in the geometric property database of the wire harness digital twin model; establish a material constitutive equation according to the material feature sub-vector, update the elastic modulus and yield strength of each part of the wire harness in real time, and write the updated material parameters into the material property database of the wire harness digital twin model; associate and integrate the physical property database, the geometric property database, and the material property database through a data mapping algorithm to form a complete wire harness digital twin model.

[0020] More specifically, calculating the vibration transfer function of each section of the wire harness based on the vibration component in the physical feature sub-vector includes: Convert the vibration component in the physical feature sub-vector to the frequency domain through Fourier transform to obtain the frequency response function of each measurement point of the wire harness; when the amplitude of the frequency response function appears as a peak, record this frequency as the natural frequency of the wire harness and establish a natural frequency dataset of vibration; perform equidistant discretization processing on the wire harness, divide the wire harness into K micro-elements, and use a recursive algorithm to calculate the vibration transfer function between adjacent micro-elements. When the ratio of the vibration transfer function between the i-th and the (i + 1)-th micro-elements exceeds the preset threshold α, mark this position as a vibration weak point, where the preset threshold α is 1.5; if the number of detected vibration weak points exceeds 3, then determine that the vibration transfer characteristic of the wire harness is abnormal and trigger a vibration suppression strategy; according to the vibration transfer function of each micro-element, use the least squares method to fit the vibration transfer equation of the entire wire harness. When the goodness of fit of the vibration transfer equation is greater than 0.95, use this equation as the global vibration transfer model of the wire harness; predict the vibration response characteristics of the wire harness under different working conditions through the global vibration transfer model and feedback the prediction results to the physical property database of the wire harness digital twin model.

[0021] More detailedly, the logic of using a recursive algorithm to calculate the vibration transfer function between adjacent micro-elements is as follows: Starting from the first infinitesimal segment at the starting end of the wire harness, set it as the reference segment, and set the initial value of the vibration transfer function of the reference segment to 1; based on the data collected by the acceleration sensor of the reference segment, obtain the vibration displacement response x1 and vibration acceleration response a1 of this infinitesimal segment; for the second infinitesimal segment adjacent to the reference segment, collect its vibration displacement response x2 and vibration acceleration response a2; calculate the phase difference θ of the second infinitesimal segment relative to the reference segment, and when the phase difference θ is greater than 90 degrees, determine that there is a vibration coupling phenomenon between the two infinitesimal segments; according to the vibration displacement responses x1, x2 and the vibration acceleration responses a1, a2, establish a state space equation set, where the mass, stiffness and damping of the infinitesimal segment are used as state variables; use the fourth-order Runge-Kutta method to solve the state space equation set to obtain the vibration transfer function Hi,i+1 between adjacent infinitesimal segments; when the calculation of the vibration transfer function between the i-th segment and the (i + 1)-th segment of infinitesimals is completed, set the (i + 1)-th segment as the new reference segment, and repeat the above calculation process until all infinitesimal segments are traversed; if the calculation result of the vibration transfer function of a pair of adjacent infinitesimal segments shows a singular value, use the Kalman filter algorithm to smooth the vibration response data at this place and then recalculate.

[0022] On the other hand, the logic of using the geometric feature sub-vector to construct the three-dimensional space curve equation of the wire harness is as follows: First, perform dimensionality reduction processing on the geometric feature sub-vector to extract the key control point coordinate sequence of the wire harness in three-dimensional space. When the distance between control points is less than the preset distance d, perform control point screening to reduce the calculation complexity; based on the control point coordinate sequence, use the cubic spline interpolation method to construct the parametric equation of the wire harness center line, where the parametric equation contains three components: x(t), y(t), and z(t); when it is detected that there is a large curvature area in the wire harness, increase the control point density in this area so that the curve has a higher fitting accuracy in the large curvature area; store the constructed three-dimensional space curve equation in the geometric property database of the wire harness digital twin model.

[0023] Among them, the specific process of control point screening is as follows: Sort all the control points according to the cumulative arc length parameter along the wire harness direction to obtain the initial control point sequence P; calculate the Euclidean distance between adjacent control points. When the distance between any two adjacent control points is less than the preset threshold d, calculate the curvature values k1 and k2 at these two control points; if the curvature difference |k1 - k2| between the two control points is less than the curvature threshold ε, remove the control point with the smaller curvature from the sequence P and store the spatial position information of this point in the alternative point set Q; when it is detected that the control point density in a certain section is lower than n points per meter, select the nearest control point in the alternative point set Q to supplement this section; for the turning section of the wire harness, set the minimum control point spacing threshold dmin. When the adjacent control point spacing is less than dmin, calculate the tangent continuity of this section. If the tangent direction changes smoothly, only retain the endpoints and the midpoint as control points; while performing control point screening, ensure that the key feature points of the wire harness must be retained, including the fixed points, branch points, and contact points with other components of the wire harness; after the initial screening is completed, calculate the maximum deviation between the curve constructed based on the screened control points and the original curve. If the maximum deviation exceeds the allowable error δ, gradually add control points from the alternative point set Q until the accuracy requirement is met; if the number of screened control points is still too large, adopt the curve segment fitting method to further reduce the number of control points while ensuring geometric accuracy.

[0024] Finally, the physical property database, the geometric property database, and the material property database are associated and integrated through a data mapping algorithm as follows: First, establish a main index table based on the geometric property database, and mark each control point of the wire harness with a unique node ID; when the spacing between the control points is greater than the preset value, generate transition nodes through an interpolation algorithm to ensure the continuity of data mapping; for the temperature field data in the physical property database, establish a temperature-node mapping table to associate the temperature value of each node with the node ID; if the temperature data of a certain node is missing, calculate it by weighted averaging the temperature values of adjacent nodes; for the vibration transfer function data, establish a vibration-node mapping table to record the vibration amplitude and phase information of each node; when abnormal vibration data of a node appears, trigger a data verification process to correct the abnormal value through historical data; for the material property database, establish a material-node mapping table to store the material attribute parameters of each node; if there is a material transition zone in the wire harness, establish a material mixing rule at the corresponding node to calculate the equivalent material parameters; adopt a relational data model to establish the association relationship between the three mapping tables through the node ID; when any database is updated, automatically update the associated data through a trigger mechanism; finally, construct a unified state vector, which includes the node ID, spatial coordinates, physical parameters, and material parameters, to form the complete digital twin model data structure.

[0025] S2: Divide the wire harness digital twin model into N node units, conduct stress analysis on the N node units, and obtain the stress distribution data of each node unit, where N is an integer greater than 100; First, establish the local coordinate system of the node unit, and determine the position and direction of each node unit according to the space curve equation of the wire harness; based on the position information of each node unit, extract its corresponding boundary conditions and constraint conditions; after the boundary conditions are set, establish a stress-strain constitutive relation equation for each node unit; if the node unit is made of a single material, use Hooke's law to establish the constitutive equation; when the node unit is a composite material, establish a layered constitutive relation equation set. Then, conduct strain field calculation. First, calculate the deformation of the node unit based on the displacement boundary conditions; then calculate the strain components in each direction according to the deformation; after the strain field is determined, substitute it into the constitutive equation to calculate the stress components. Then, based on each stress component, calculate the equivalent stress using the von Mises criterion; Finally, conduct stress assessment, compare the calculated equivalent stress with the material strength; if the equivalent stress exceeds the material yield strength, mark it as a potential failure area; when a stress concentration area is found, refine the mesh of the node unit at that place and recalculate; after completing the stress analysis of all node units, store the stress distribution data in the database and mark the stress concentration area as the key monitoring object.

[0026] Among them, when the node unit is a composite material, the specific process of establishing a layered constitutive relation equation set is as follows: First, determine the number of layers of the composite material and the material properties of each layer, including the fiber direction elastic modulus E1, the transverse elastic modulus E2, the in-plane shear modulus G12, and the principal Poisson's ratio ν12; when the fiber ply direction is known, record the ply angle θ of each layer; if there is a sandwich structure, treat the sandwich layer as an independent layer; then establish a local coordinate system for each layer, define the 1-axis as the fiber direction, the 2-axis as the transverse direction perpendicular to the fiber, and the 3-axis as the laminate normal direction; when overall analysis is required, establish the overall coordinate system xyz; if the local coordinate system does not coincide with the overall coordinate system, establish a coordinate transformation matrix; then, in the local coordinate system of each layer, establish a simplified plane stress state constitutive equation; based on the coordinate transformation matrix, transform the constitutive equations of each layer to the overall coordinate system, and finally, use the equivalent stiffness superposition principle to combine the constitutive equations of each layer into an overall stiffness matrix; after the superposition, obtain a layered constitutive relation equation set that describes the mechanical behavior of the entire composite material node unit.

[0027] More specifically, the specific constitutive relation equation set is shown as follows: ; Among them, is the normal stress in the x direction, is the normal stress in the y direction, is the plane shear stress, is the element of the stiffness matrix after transformation to the global coordinate system, is the strain in the x direction, is the strain in the y direction, is the shear strain, is the coefficient of thermal expansion, is the temperature change, is the ply angle influence function of the k-th layer, is the ply angle of the k-th layer.

[0028] Among them, the ply angle influence function of the k-th layer can be expressed by the following formula: ; Among them, is the ply angle of the k-th layer (unit: radian), and its value range is [0, π]; is the elastic modulus in the fiber direction, is the transverse elastic modulus.

[0029] It should be noted that although the above description outlines the general process of establishing the hierarchical constitutive relation equations when the node element is a composite material, the specific implementation details may vary due to the material properties, laying methods, and usage environments of the automotive wire harness. For example, in the glass fiber reinforced materials commonly used in automotive wire harnesses, the elastic modulus in the fiber direction (E1) is usually high, while the transverse elastic modulus (E2) is low. This makes it necessary to pay special attention to the influence of the ply angle (θ) on the overall stiffness in the stiffness and durability analysis of the wire harness in the bending area; for complex wire harness structures (such as multi-layer shielded wire harnesses), the sandwich layer may contain a metal braided layer, and the in-plane shear modulus (G12) and Poisson's ratio (ν12) have a significant impact on the electromagnetic interference resistance performance, which requires separate modeling and is quite different from traditional composite materials.

[0030] In addition, when establishing the local coordinate system and the global coordinate system, if there is a large bending angle in the wire harness (such as the wire harness path around the motor compartment), the transformation matrix between the local coordinate system and the global coordinate system may involve complex three-dimensional rotation calculations; especially when the ply angle is an atypical angle (such as 45°), an accurate mathematical model must be introduced to describe the anisotropy of the material properties. For example, in the design of high-voltage wire harnesses in some vehicles, the local material properties need to be dynamically adjusted in combination with the temperature field and vibration environment to ensure that the deformation of the sandwich layer and the fiber layer does not exceed the yield limit of the material.

[0031] Step S3: Based on the stress distribution data, use the improved ant colony algorithm to optimize the routing path of the wire harness, where the improved ant colony algorithm introduces the entropy weight method to dynamically adjust the stress weights of different node elements; First, initialize the path search space, and construct a three-dimensional grid search space based on the starting and ending points of the wire harness. When there are obstacles, mark the obstacle areas as no-go areas. If the wire harness needs to pass through fixed points, set these points as necessary nodes. Establish an ant population based on the initial conditions, and set the population size and the maximum number of iterations.

[0032] Next, use the entropy weight method to calculate the stress weights of the node units. By calculating the information entropy of the stress data, dynamically determine the weight coefficients of the stresses of each node. Then start the path iterative search. Each ant moves in the search space based on the transition probability. When an ant passes through a high-stress area, increase the penalty factor of this path according to the stress weight. If the path passes through a low-stress area, reduce the penalty factor. Calculate the fitness value of the path based on the comprehensive evaluation of the total path length and the stress weight. Subsequently, update the pheromone concentration. Increase the pheromone concentration for the paths with better fitness values. When the path passes through the stress concentration area, appropriately reduce the pheromone increment. If a better path is found, enhance the pheromone intensity of this path. Use the pheromone decay factor to avoid the algorithm falling into local optimality. Next, perform path smoothing. Fit a curve to the obtained optimal path. When there are sharp turns in the path, use the arc transition method for smoothing. If the smoothed path interferes with the obstacles, locally adjust the radius of the transition arc. Ensure that the path after smoothing meets the requirements of the minimum bending radius of the wire harness. Finally, conduct scheme verification. Re-perform stress analysis on the optimized wiring path. When stress over-standard areas are found, increase the stress weights of these areas and re-iterate. If all constraint conditions are met, output the final wiring scheme. Provide the optimized wiring path to the downstream design link.

[0033] Among them, the process of calculating the stress weights of the node units by the entropy weight method is as follows: First, collect the stress data of all nodes and perform standardization processing to map the data to the interval from zero to one. Subsequently, calculate the stress proportion of each node, and calculate the information entropy based on these proportions. When the stress distribution of the nodes is more uniform, its information entropy is larger. Then subtract the information entropy from 1 to obtain the difference coefficient, and normalize the difference coefficient to obtain the initial weight. Finally, dynamically adjust the weight according to the relationship between the node stress value and the preset threshold. When the stress of a certain node approaches or exceeds the strength limit value, strengthen the avoidance of this area by increasing its weight coefficient, while reduce the weight coefficient for the safe area with smaller stress, and continuously update these weight coefficients during the iterative optimization process, so as to achieve adaptive path planning based on stress distribution. More specifically, the calculation of the stress weights of the node units is shown in the following formula: ; Among them, is the final weight value of the i-th node. is the information entropy of the i-th node, is the total number of nodes, is the equivalent stress value of the i-th node, is the preset stress threshold, is the maximum stress value among all nodes, is the average stress value, is the value range of the weight adjustment coefficient [0.1, 0.5], is the value range of the stress sensitivity coefficient [0.5, 1.5].

[0034] On the other hand, a pheromone decay factor is adopted to prevent the algorithm from falling into local optimality as follows: When the algorithm performs pheromone decay operation, first set the global decay factor ρ to control the retention degree of pheromone after each iteration; if the pheromone concentration of a path exceeds the threshold θ, a larger decay factor ρ1 is used to force decay on this path; when the optimal solution remains unchanged for N consecutive iterations, it is determined that the algorithm has fallen into local optimality, and the system will increase the global decay factor to ρ2 to accelerate pheromone evaporation; if the selection probability of a certain path is too high and exceeds the threshold p, an additional decay mechanism is initiated for this path, and a larger decay factor ρ3 is used to quickly eliminate its dominant position; when it is detected that the pheromone difference between multiple paths is too large, a decay enhancement operation is uniformly performed on the K paths with the highest pheromone concentration; if it is found during the iteration process that the path selection is too concentrated, the decay factor is temporarily increased to ρ4 to increase the randomness of path search; when the search falls into a repeated loop, an emergency perturbation mechanism is triggered, and the global decay factor is increased to the maximum value ρmax to forcefully break the formed fixed pattern; if the optimization objective oscillates, the decay factor is dynamically adjusted to fluctuate within the range of ρmin to ρmax to balance local exploitation and global exploration; when the algorithm rediscovers a better solution, the decay factor is gradually restored to the initial level to re-accumulate effective pheromone.

[0035] When determining the pheromone decay factor, the basic decay factor ρ is initially set to 0.5 as the standard reference value; if the path pheromone exceeds the threshold, the forced decay factor ρ1 is set to 0.7 to accelerate the volatilization of abnormal pheromones; when local optimality is detected, the global decay factor ρ2 is increased to 0.8 to disrupt the inherent path; if the selection probability of a certain path is too high, the dedicated decay factor ρ3 of this path is increased to 0.85 to quickly reduce its influence; when the path selection tends to be single, the temporary decay factor ρ4 is set to 0.9 to force the introduction of randomness; if it is necessary to ensure the convergence of the algorithm, the minimum decay factor ρmin is restricted to be above 0.3; when serious local optimality occurs, the maximum decay factor ρmax can reach 0.95 to achieve a drastic perturbation; if the optimization effect is not good, the decay factor is increased by 0.05 every certain number of iterations until ρmax is reached; when a better solution is found, the decay factor is decreased by 0.05 each time, but not lower than ρmin; if the problem scale expands, the benchmark values of all decay factors are adjusted downward accordingly to increase the search space.

[0036] For example, when planning a path in a 15x15 grid map, the system initially sets the global pheromone decay factor ρ = 0.3; if the initial pheromone concentration of each feasible path is 10, after the first round of iteration, the pheromones of all paths will decay to 7; when it is detected that the selection probability of path A is always greater than 80% in 5 consecutive rounds of iteration, it is determined to be in local optimality, and the system increases the decay factor of path A to 0.6 to quickly decay its pheromone; if the pheromone of path A was originally 50, it will drop to 20 after forced decay; when it is found that the pheromone concentration of the sub-optimal path B is 15, which is too different from path A, the global decay factor is increased to 0.5 to accelerate the elimination of the pheromone difference between paths; if after the 20th round of iteration, the pheromones of path A and B drop to 25 and 12 respectively, and at this time a new feasible path C is found; when path C shows a better path cost in subsequent iterations, its decay factor is decreased to 0.2 to slow down the pheromone volatilization; if the pheromone of path C accumulates to 40 after 10 rounds of iteration, while the pheromones of path A and B decay to 10 and 5 respectively, the algorithm successfully jumps out of local optimality and finds a better solution.

[0037] Preferably, when using the pheromone decay factor for path optimization, the effect is significantly improved compared to the traditional method; if the pheromone decay mechanism is not adopted, the path selection probability of the algorithm often concentrates on the initially discovered local optimal path after 15 iterations, resulting in too fast convergence; when the decay factor is introduced, even if the pheromone concentration of the optimal path reaches the peak, it will gradually decay over time, reserving a search space for other possible better paths; if the path length of path A discovered in the initial stage is 100, after 30 iterations, due to the effect of the decay factor, the algorithm successfully discovers path B with a path length of 85; when the iteration reaches 50 rounds, the system can still continuously search for better paths based on path B and finally discovers path C with a path length of 78; if the average path length is used as an evaluation index, the optimization result after adopting the decay factor is about 18% lower than the traditional method; when evaluated by the algorithm convergence speed, adopting the decay factor enables the algorithm to improve the convergence speed by about 25% while ensuring the global search ability.

[0038] S4: According to the optimization result of the wiring path, adjust the position of the fixing bracket of the wire harness, and add a protective structure at the node unit where the stress distribution data exceeds the preset stress threshold.

[0039] The process of adjusting the position of the wire harness fixing bracket and adding a protective structure according to the optimization result of the wiring path is as follows: First, set the fixing brackets at the path turning points and long straight line segments according to the minimum support density requirements, and appropriately increase the bracket layout in the areas with interference risks; then evaluate the stress state of the node units, and mark the areas where the stress exceeds 80% of the preset threshold as high-risk areas; for these high-risk areas, reinforce with ribs near the fixing brackets, set protective covers for the external impact areas, and add vibration damping devices for the vibration concentration areas; finally, reduce the stress concentration by adjusting the bracket position, and verify the effectiveness of the solution through finite element analysis to ensure that the stress of each node is lower than the preset threshold.

[0040] In summary, the model-driven wire harness development and optimization method based on the embodiments of the present invention is clarified. By collecting actual operation parameters to construct a digital twin model and dividing the model into a large number of node units for accurate stress analysis, it can accurately identify potential stress concentration areas. In this way, the problem that the existing wiring solution is prone to stress concentration and cause wire harness damage due to the inaccurate analysis of the stress distribution under the actual operation state is solved.

[0041] Here, those skilled in the art can understand that the specific operations of each step in the above model-driven wire harness development and optimization method have been introduced in detail in the description of the Figure 1 model-driven wire harness development and optimization method above, and therefore, the repeated description thereof will be omitted.

[0042] In summary, the model-driven harness development and optimization method based on the embodiments of the present invention is elucidated. By collecting actual operating parameters to construct a digital twin model and dividing the model into a large number of node units for accurate stress analysis, it can accurately identify potential stress concentration areas. In this way, the problem that the existing wiring scheme is prone to stress concentration and harness damage due to inaccurate analysis of the stress distribution under actual operating conditions is solved.

Claims

1. A model-driven wire harness development and optimization method, characterized in that Including: Collect the actual operating parameters of the wire harness, and construct a digital twin model of the wire harness through the actual operating parameters; Divide the digital twin model of the wire harness into N node units, perform stress analysis on the N node units, and obtain the stress distribution data of each node unit; Optimize the wiring path of the wire harness based on the stress distribution data; According to the optimization result of the wiring path, adjust the position of the fixing bracket of the wire harness, and add a protective structure at the node unit where the stress distribution data exceeds the preset stress threshold.

2. The model-driven harness development and optimization method according to claim 1, wherein The digital twin model of the wire harness associates and integrates the physical property database, geometric property database, and material property database through a data mapping algorithm.

3. The model-driven harness development and optimization method according to claim 2, wherein Calculate the vibration transfer function of each section of the wire harness through the physical feature sub-vector in the digital feature vector. If the amplitude of the vibration transfer function of a certain section of the wire harness exceeds the safety limit, store the stiffness parameter of this section of the wire harness in the physical property database; The digital feature vector is obtained through neural network fusion processing of the actual operating parameters, and then through orthogonal decomposition, a physical feature sub-vector representing the physical properties of the wire harness, a geometric feature sub-vector representing the shape of the wire harness, and a material feature sub-vector representing the material of the wire harness are obtained.

4. The method for harness development and optimization based on model-driven according to claim 2, characterized in that Use the geometric feature sub-vector to construct a three-dimensional space curve equation of the wire harness. When the curvature of the three-dimensional space curve at any point exceeds the safety curvature, calculate the stress concentration coefficient at this point and store it in the geometric property database of the digital twin model of the wire harness.

5. The model-driven harness development and optimization method according to claim 4, characterized in that Constructing the three-dimensional space curve equation includes: Perform dimensionality reduction processing on the geometric feature sub-vector, and extract the key control point coordinate sequence of the wire harness in three-dimensional space; When the distance between the control points is less than the preset distance d, perform control point screening; Based on the control point coordinate sequence, construct the three-dimensional space curve equation.

6. The model-driven harness development and optimization method according to claim 1, wherein The obtaining of the stress distribution data of each node unit includes: Based on the position information of each node unit, extract the corresponding boundary conditions and constraint conditions; After setting the boundary conditions, construct different constitutive relation equations according to the material structure of each node unit; Calculate the deformation amount of the node unit based on the displacement boundary conditions, and calculate the strain components in each direction according to the deformation amount; Substitute the strain components into the constitutive relation equation to obtain the stress components of each node unit.

7. The model-driven harness development and optimization method according to claim 5, characterized in that When the node unit is a composite material, establish a hierarchical constitutive relation equation set; The hierarchical constitutive relation equation set determines the number of layers of the composite material and the material properties of each layer, records the ply angles, and treats the sandwich layer as an independent layer, establishing a local coordinate system and a global coordinate system; If the two coordinate systems do not coincide, use the coordinate transformation matrix to transform the constitutive equations of each layer from the local coordinate system to the global coordinate system; finally, based on the principle of equivalent stiffness superposition, combine the constitutive equations of each layer into an overall stiffness matrix, so as to obtain a hierarchical constitutive relation equation set describing the mechanical behavior of the composite material node unit.

8. The model-driven harness development and optimization method according to claim 1, characterized in that The ant colony algorithm is used to optimize the wiring path of the wire harness; The ant colony algorithm uses the entropy weight method to dynamically adjust the stress weights of different node units, and uses a pheromone decay factor to avoid the ant algorithm falling into a local optimum.

9. The model-driven harness development and optimization method according to claim 8, characterized in that It is characterized in that The entropy weight method dynamically adjusts the stress weights of different node units, including: Calculating the stress proportion of each node unit and calculating the information entropy based on these proportions; Subtracting the information entropy from one to obtain a difference coefficient, and normalizing the difference coefficient to obtain an initial weight; Dynamically adjusting the weight according to the relationship between the stress value of the node unit and the preset threshold.

10. The model-driven harness development and optimization method according to claim 8, characterized in that When the optimal solution remains unchanged for multiple consecutive iterations, it is determined that the local optimum is entered. By setting different pheromone decay factors, the pheromone difference between paths is accelerated to be eliminated, so as to jump out of the local optimum and find a better solution.

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