Performance simulation optimization method and system of high-speed connector

Abstract

The invention relates to a performance simulation optimization method and system for a high-speed connector, and the method comprises the following steps: carrying out the grid processing of a physical structure of the high-speed connector, and obtaining a three-dimensional structure discrete grid model; based on the three-dimensional structure discrete grid model and a time domain finite element, electromagnetic field distribution of the high-speed connector is solved, and a multi-order electromagnetic field distribution matrix is obtained; based on the multi-order electromagnetic field distribution matrix, performing spectral analysis on the output signal of the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector; when the performance score is lower than a preset performance threshold value, analyzing a key performance influence factor set of the high-speed connector based on a performance analysis report; and the high-speed connector is simulated and optimized based on the key performance influence factor set, so that the performance score of the high-speed connector is higher than a preset performance threshold, and the problem that the electromagnetic property of the high-speed connector and the influence of the electromagnetic property on the signal integrity are difficult to accurately predict by a traditional method is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of connectors, and in particular to a performance simulation optimization method and system for high-speed connectors. Background Art

[0002] With the rapid development of information technology, the demand for faster data transmission continues to grow, making high-speed connectors increasingly important as key components in various electronic devices. High-speed connectors must not only ensure signal integrity but also meet the requirements of low loss, high bandwidth, and interference resistance. However, in practical applications, factors such as poor design, inappropriate material selection, or manufacturing process limitations often prevent high-speed connectors from fully meeting these requirements, thus affecting overall system performance. Therefore, effectively improving the performance of high-speed connectors has become a common concern in academia and industry.

[0003] Currently, the design and optimization of high-speed connectors primarily rely on rules of thumb and traditional trial-and-error methods. These methods are inefficient and costly, especially when faced with complex electromagnetic environments and the demands of high-speed data transmission. Furthermore, the lack of systematic simulation tools to accurately predict the electromagnetic characteristics of high-speed connectors and their impact on signal integrity further limits their performance. These issues urgently require a new solution that can effectively simulate and optimize the performance of high-speed connectors to meet the demands of modern electronic technology.

[0004] Against this backdrop, utilizing advanced computational electromagnetics methods, such as the finite element method, to simulate and optimize the performance of high-speed connectors has become a highly promising area. This approach not only enables detailed analysis of the complex electromagnetic field distribution within the connector but also allows for rapid evaluation of the effectiveness of different design solutions by varying design parameters, significantly improving design efficiency and accuracy. However, achieving this requires integrating knowledge from multiple disciplines, including electromagnetics, materials science, and computer science, posing significant challenges for researchers. Furthermore, accurately identifying the key factors influencing high-speed connector performance and effectively optimizing them remains a crucial challenge in this field. Summary of the Invention

[0005] The main purpose of the present invention is to provide a performance simulation optimization method and system for a high-speed connector, which solves the problem that traditional methods are difficult to accurately predict the electromagnetic characteristics of a high-speed connector and its impact on signal integrity.

[0006] To achieve the above object, the present invention provides a performance simulation and optimization method for a high-speed connector, comprising the following steps: Performing grid processing on the physical structure of the high-speed connector to obtain a three-dimensional discrete grid model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating medium layer; Based on the three-dimensional structure discrete grid model, the time domain finite element method is used to solve the electromagnetic field distribution of the high-speed connector to obtain a multi-order electromagnetic field distribution matrix; Based on the multi-order electromagnetic field distribution matrix, a spectrum analysis is performed on the output signal of the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector; When the performance score is lower than a preset performance threshold, analyzing a set of key performance influencing factors of the high-speed connector based on the performance analysis report; The high-speed connector is simulated and optimized based on the set of key performance influencing factors to achieve a performance score of the high-speed connector that is higher than a preset performance threshold.

[0007] Furthermore, the physical structure of the high-speed connector is gridded to obtain a three-dimensional discrete grid model, including: Performing a geometric structural analysis on the physical structure of the high-speed connector to obtain geometric characteristic parameters; and performing adaptive meshing on the physical structure of the high-speed connector based on the geometric characteristic parameters to obtain an initial mesh model; Boundary layer mesh generation is performed on the physical structure of the high-speed connector based on the initial mesh model to obtain a boundary layer mesh model; and merging processing is performed on the initial mesh model based on the initial mesh model to obtain a three-dimensional structure discrete mesh model.

[0008] Furthermore, the time-domain finite element method for solving the multi-order electromagnetic field distribution matrix of the high-speed connector based on the three-dimensional structural discrete grid model includes: Performing electromagnetic boundary condition configuration and material parameter assignment on the three-dimensional structure discrete grid model to obtain a grid unit electromagnetic characteristic distribution set; Performing time-domain electromagnetic field discretization processing on the grid unit electromagnetic characteristic distribution set to obtain a time-domain discrete electromagnetic field equation group; Based on the time-domain discrete electromagnetic field equations, a high-order time integration method is used to solve the electromagnetic field of the high-speed connector in the time domain to obtain time-domain electromagnetic field distribution data; wherein the high-order time integration method includes a Runge-Kutta method or a Newmark-Beta method; Performing frequency domain transformation processing on the time domain electromagnetic field distribution data to obtain frequency domain electromagnetic field distribution data; Based on the frequency domain electromagnetic field distribution data, a multi-order modal analysis is performed on the electromagnetic field of the high-speed connector to obtain a multi-order electromagnetic field distribution matrix; wherein the multi-order electromagnetic field distribution matrix includes a magnetic field intensity distribution tensor and an electromagnetic energy density distribution tensor.

[0009] Furthermore, the output signal spectrum analysis of the high-speed connector is performed based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector, including: Performing multi-port scattering parameter extraction on the multi-order electromagnetic field distribution matrix to obtain a frequency domain transmission characteristic data set; wherein the frequency domain transmission characteristic data set includes an insertion loss curve, a return loss curve, and a crosstalk parameter distribution; Performing time domain reflection calculation on the high-speed connector based on the frequency domain transmission characteristic data set to obtain an impedance matching characteristic curve, and performing differential loop analysis on the impedance matching characteristic curve to obtain an impedance discontinuity point distribution diagram; Performing an eye diagram simulation on the output signal of the high-speed connector based on the frequency domain transmission characteristic data set and the impedance discontinuity point distribution diagram to obtain a time domain eye diagram; When the jitter peak-to-peak value of the time domain eye diagram exceeds the tolerance range, a bit error rate prediction is performed on the high-speed connector based on the time domain eye diagram to obtain a bit error rate contour curve, and a statistical distribution characteristic analysis is performed on the bit error rate contour curve to obtain a connector reliability quantitative parameter; The connector reliability quantification parameters, the frequency domain transmission characteristic dataset, and the time domain eye diagram are multi-dimensionally integrated using an adaptive weighted regularization method to obtain a connector performance comprehensive scoring matrix, and the principal component contribution rate of the connector performance comprehensive scoring matrix is calculated to obtain performance bottleneck positioning data; Based on the comprehensive performance scoring matrix and the performance bottleneck location data, parameter sensitivity mapping is performed on the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector.

[0010] Furthermore, analyzing the set of key performance influencing factors of the high-speed connector based on the performance analysis report includes: Performing multi-level pedigree decomposition on the performance analysis report to obtain a performance correlation matrix, and performing singular value decomposition on the performance correlation matrix to obtain a feature contribution weight vector; wherein the feature contribution weight vector includes a geometric dimension contribution component, a material property contribution component, and an interface transition zone contribution component; Performing nonlinear transfer function mapping on the performance bottleneck positioning data based on the feature contribution weight vector to obtain a parameter sensitivity gradient field, and performing topological partitioning and clustering on the parameter sensitivity gradient field to obtain a key performance impact area distribution map; Performing frequency domain feature extraction on the key performance impact area distribution map through adaptive multi-scale wavelet transform to obtain a structural defect characteristic spectrum, and performing spatial correlation analysis on the impedance discontinuity point distribution map based on the structural defect characteristic spectrum to obtain a structure-performance coupling relationship spectrum; Based on the structure-performance coupling relationship spectrum, the geometric structure and material parameters of the high-speed connector are orthogonally sorted by sensitivity to obtain a priority list of performance influencing factors, and the priority list of performance influencing factors is screened by Pareto optimization to obtain a set of key performance influencing factors.

[0011] Furthermore, the nonlinear transfer function mapping is performed on the performance bottleneck positioning data based on the feature contribution weight vector to obtain a parameter sensitivity gradient field, including: Performing a multi-dimensional tensor expansion on the characteristic contribution weight vector to obtain a weight component mapping matrix, and performing a spectral domain transformation on the weight component mapping matrix to obtain a frequency domain characteristic response spectrum; wherein the frequency domain characteristic response spectrum includes a geometric characteristic spectrum component, a material characteristic spectrum component, and an interface characteristic spectrum component; Performing a nonlinear kernel function transformation on the performance bottleneck location data based on the frequency domain characteristic response spectrum to obtain a high-dimensional feature mapping space, and performing manifold dimensionality reduction processing on the high-dimensional feature mapping space to obtain a parameter-performance mapping relationship diagram; wherein the parameter-performance mapping relationship diagram includes a structural parameter mapping layer, a material parameter mapping layer, and a boundary condition mapping layer; Performing multi-scale gradient calculation on the parameter-performance mapping relationship diagram to obtain a local sensitivity distribution field, and topologically reconstructing the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain a global sensitivity field; wherein the global sensitivity field includes a structural sensitivity component, a material sensitivity component, and a boundary sensitivity component; Based on the global sensitivity field, a nonlinear response analysis is performed on the parameter-performance mapping relationship diagram to obtain a parameter coupling matrix, and the parameter coupling matrix is subjected to eigenvalue decomposition to obtain a parameter sensitivity gradient field; wherein the parameter sensitivity gradient field includes a first-order sensitivity gradient, a cross-coupling gradient, and a high-order nonlinear gradient.

[0012] Furthermore, the topological reconstruction of the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain a global sensitivity field includes: Performing multi-resolution spectral decomposition on the local sensitivity distribution field to obtain a hierarchical sensitivity feature tensor, and performing singular value screening on the hierarchical sensitivity feature tensor to obtain a dominant sensitivity mode set; wherein the dominant sensitivity mode set includes a primary axis sensitivity component, a secondary axis sensitivity component, and a cross-coupling sensitivity component; Based on the dominant sensitivity mode set, the high-dimensional feature mapping space is geometrically topologically partitioned to obtain a multi-level sensitivity boundary surface, and the multi-level sensitivity boundary surface is subjected to differential morphological transformation to obtain a sensitivity critical point distribution map; wherein the sensitivity critical point distribution map includes a maximum point set, a saddle point set, and a transition point set; A connected domain analysis is performed on the sensitivity critical point distribution map using a tensor field decomposition method to obtain a sensitivity manifold structure, and based on the sensitivity manifold structure, isoparametric remapping is performed on the high-dimensional feature mapping space to obtain a normalized sensitivity distribution field; wherein the normalized sensitivity distribution field includes a linear sensitivity region, a weak nonlinear sensitivity region, and a strong nonlinear sensitivity region; Based on the normalized sensitivity distribution field, curvature flow evolution calculation is performed on the multi-level sensitivity boundary surface to obtain sensitivity propagation dynamic characteristic data, and based on the sensitivity propagation dynamic characteristic data, global topological integration is performed on the sensitivity manifold structure to obtain a global sensitivity field; wherein, the global sensitivity field includes a structural sensitivity component, a material sensitivity component and a boundary sensitivity component.

[0013] The present invention also provides a performance simulation and optimization system for a high-speed connector, comprising: Constructing a model for meshing the physical structure of the high-speed connector to obtain a three-dimensional discrete mesh model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating dielectric layer; A solution model is used to solve the electromagnetic field distribution of the high-speed connector using a time-domain finite element method based on the three-dimensional structural discrete grid model to obtain a multi-order electromagnetic field distribution matrix; A first analysis model is used to perform spectrum analysis on the output signal of the high-speed connector based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector; a second analysis model, configured to analyze a set of key performance influencing factors of the high-speed connector based on the performance analysis report when the performance score is lower than a preset performance threshold; An optimization model is used to simulate and optimize the high-speed connector based on the set of key performance influencing factors to achieve a performance score of the high-speed connector that is higher than a preset performance threshold.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0016] The present invention provides a performance simulation and optimization method for a high-speed connector, comprising the following steps: meshing the physical structure of the high-speed connector to obtain a three-dimensional discrete grid model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating dielectric layer; based on the three-dimensional discrete grid model, solving the electromagnetic field distribution of the high-speed connector using a time-domain finite element method to obtain a multi-order electromagnetic field distribution matrix; based on the multi-order electromagnetic field distribution matrix, performing a spectrum analysis on the output signal of the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector; when the performance score is lower than a preset performance threshold, analyzing a set of key performance influencing factors of the high-speed connector based on the performance analysis report; and simulating and optimizing the high-speed connector based on the set of key performance influencing factors to achieve a performance score higher than the preset performance threshold. This method solves the problem that traditional methods are difficult to accurately predict the electromagnetic characteristics of high-speed connectors and their impact on signal integrity, and implements spectrum analysis of the output signal of the high-speed connector based on the multi-order electromagnetic field distribution matrix, thereby obtaining a performance score of the connector and a detailed performance analysis report. This method enables designers to fully understand the performance of the connector at different frequencies, thereby achieving targeted improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a schematic diagram of the steps of a method for simulating and optimizing the performance of a high-speed connector according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a performance simulation and optimization system for a high-speed connector according to an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 This is a schematic diagram of the steps of a performance simulation optimization method for a high-speed connector in one embodiment of the present invention; In one embodiment of the present invention, a method for simulating and optimizing the performance of a high-speed connector is provided, comprising the following steps: Step S1 , meshing the physical structure of the high-speed connector to obtain a three-dimensional discrete mesh model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating medium layer.

[0021] Specifically, meshing the physical structure of a high-speed connector to create a three-dimensional discrete mesh model requires first accurately capturing the specific morphology and positional relationships of the signal transmission structure, electromagnetic shielding structure, and insulating dielectric layer. This process is typically performed using computer-aided design (CAD) software. The actual physical structure is converted into a digital model and then meticulously segmented, known as meshing. Specifically, each component, such as the signal transmission path, is broken down into numerous small units based on its geometry and size. These units together form a discretized mesh model. This approach allows subsequent simulations to more accurately simulate the propagation characteristics of electromagnetic fields between different materials, as each mesh unit can be used to calculate the behavior of electromagnetic waves based on the actual material properties it represents (such as conductivity and dielectric constant). For example, in designing a connector for high-speed data transmission between servers in a data center, the signal transmission structure may include multiple parallel conductors, while the electromagnetic shielding structure consists of a metal shell surrounding the outer shell, and the insulating dielectric layer is a non-conductive material filling the gap between the two. In this application scenario, by meshing these three key components, we can create a detailed three-dimensional model that not only reflects the physical dimensions of each component but also takes into account how they interact with each other. Based on this model, we can further analyze the transmission efficiency and interference of signals at different frequencies, providing a basis for optimizing connector performance. This allows designers to adjust the design plan based on the problems discovered, such as changing the layout of the signal transmission structure or enhancing the electromagnetic shielding effect, thereby improving the performance of the entire system. Throughout the process, maintaining consistency in terminology and relevance to the application scenario is crucial to ensuring the accuracy of the design direction.

[0022] Step S2: Based on the three-dimensional structure discrete grid model, a time-domain finite element method is used to solve the electromagnetic field distribution of the high-speed connector to obtain a multi-order electromagnetic field distribution matrix.

[0023] Specifically, using the time-domain finite element method (TFE) to solve the electromagnetic field distribution of a high-speed connector based on the three-dimensional discrete mesh model to obtain a multi-order electromagnetic field distribution matrix is a key step in accurately analyzing the performance of high-speed connectors. This process begins by importing the previously constructed three-dimensional discrete mesh model into specialized electromagnetic simulation software. This software applies Maxwell's equations to simulate the propagation of electromagnetic waves within and around the connector based on the physical properties and positional relationships of each mesh element. Specifically, the TFE method uses a stepwise iterative calculation within the time domain to track the temporal evolution of the electromagnetic field, thereby capturing the electromagnetic response at different frequencies. This approach not only accounts for the effects of static electric and magnetic fields but also effectively analyzes reflection, refraction, and interference during high-frequency signal transmission. For example, when designing connectors for high-speed data exchange between servers in a data center, applying this method allows for detailed analysis of the interactions between the connector's internal signal transmission path, electromagnetic shielding layer, and insulating dielectric layer. When signals are transmitted through conductors, the electromagnetic shielding layer effectively reduces the impact of external electromagnetic interference on signal quality; the insulating dielectric layer, on the other hand, determines the degree of signal transmission loss. Using the time-domain finite element method, we can accurately simulate how these factors interact to influence signal transmission. By observing the resulting multi-order electromagnetic field distribution matrix, we can evaluate the connector's overall performance. This provides an important basis for subsequent optimization, such as adjusting conductor layout or improving shielding material selection, to further enhance the connector's data transmission efficiency and stability. Maintaining consistent terminology throughout the entire process is crucial to ensuring the accuracy and validity of the analysis results.

[0024] Step S3: performing spectrum analysis on the output signal of the high-speed connector based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector.

[0025] Specifically, performing spectrum analysis on the output signal of a high-speed connector based on the multi-order electromagnetic field distribution matrix to generate a connector performance score and performance analysis report is a key step in evaluating connector performance. This process first involves converting the multi-order electromagnetic field distribution matrix calculated using the time-domain finite element method into frequency-domain information, enabling a more intuitive observation of signal transmission characteristics at different frequencies. Specifically, by performing a Fourier transform on the electromagnetic field distribution matrix, the original time series data is converted into frequency components, clearly identifying which frequency components are affected during signal transmission. Next, based on these frequency components and combined with pre-defined criteria and parameters (such as insertion loss and reflection coefficient), a comprehensive performance score is calculated to quantify the connector's overall performance. A detailed performance analysis report is also generated, which includes not only the performance score but also detailed analysis of signal integrity, interference immunity, and other aspects. For example, when designing a connector for high-speed data exchange between servers in a data center, the above method can be used to deeply analyze the connector's performance under actual operating conditions. Suppose, after simulation, significant signal attenuation or interference is observed within specific frequency ranges. Spectral analysis can pinpoint the location and cause of these issues. Furthermore, based on the resulting performance scores and analysis reports, designers can identify key factors in connector performance degradation and take targeted measures to improve them, such as optimizing signal transmission path design or selecting more suitable insulation materials. This not only improves the connector's data transmission efficiency and reliability, but also ensures its stable operation in complex electromagnetic environments. The entire process emphasizes an integrated solution from electromagnetic field distribution to spectrum analysis to performance evaluation, which is of great significance for promoting the development of high-speed connector technology.

[0026] Step S4: When the performance score is lower than a preset performance threshold, analyzing a set of key performance influencing factors of the high-speed connector based on the performance analysis report.

[0027] Specifically, when the performance score falls below a preset threshold, the high-speed connector's key performance-influencing factors are analyzed based on the performance analysis report. This process aims to identify the root cause of the substandard performance through detailed analysis. This process relies primarily on a detailed performance analysis report, which not only includes the overall performance score but also details various aspects of the signal transmission characteristics, electromagnetic interference, and material properties at different frequencies. By thoroughly analyzing this data, it is possible to identify factors negatively impacting connector performance. For example, if insertion loss exceeds expectations, this may indicate a need for optimization of the signal transmission path design; while an abnormal reflection coefficient may be due to improper impedance matching. Furthermore, consideration must be given to the extent to which external electromagnetic interference affects signal quality, as well as whether the appropriate material for the insulating dielectric layer is selected to minimize signal attenuation. Continuing with the example of the connector used for high-speed data exchange between servers in a data center, suppose spectrum analysis reveals that its performance in the high-frequency band fails to meet the preset standard. The performance analysis report is then used to determine the specific cause. If the report indicates that the key issue lies in design flaws within the signal transmission structure, such as increased crosstalk caused by close spacing between conductors, the next step is to adjust the design to address this issue. Similarly, if poor electromagnetic shielding is found to be the cause of a low performance score, this can be improved by increasing the material or thickness of the shielding layer. In this way, each key factor affecting performance can be accurately located and an effective improvement strategy can be developed accordingly, ensuring that the final product can meet performance requirements while also having good reliability and stability. The entire process emphasizes a series of steps from problem diagnosis to solution development, which is crucial for improving the overall performance of high-speed connectors.

[0028] Step S5: simulating and optimizing the high-speed connector based on the set of key performance influencing factors to achieve a performance score of the high-speed connector that is higher than a preset performance threshold.

[0029] Specifically, optimizing the high-speed connector through simulation based on the set of key performance influencing factors to achieve a performance score above a preset threshold is a crucial step in the design process. This process relies on a previously identified set of key performance influencing factors, which may include aspects such as signal transmission structure design, electromagnetic shielding effectiveness, and dielectric selection. By thoroughly analyzing the specific impact of these factors on connector performance, engineers can make targeted design adjustments and verify the effectiveness of these improvements in a simulation environment. For example, if it is determined that certain parts of the signal transmission path are causing high insertion loss due to improper design, the conductor layout or shape can be adjusted to optimize signal transmission efficiency. Similarly, if electromagnetic interference is found to be a significant factor affecting performance, increasing the thickness of the shielding layer or selecting a more efficient shielding material can be considered. Continuing with the example of the connector used for high-speed data exchange between servers in a data center, assume that analysis determines that an excessively high reflection coefficient is one of the main reasons for a performance score below the preset threshold, which typically indicates an impedance mismatch. To address this issue, designers can simulate different design options in simulation software, such as changing the material properties of the dielectric layer or adjusting the distance between the signal line and the ground plane, until they find the optimal configuration that reduces the reflection coefficient to an acceptable level. In addition, in response to the severe signal attenuation at specific frequencies discovered in spectrum analysis, the signal transmission quality can be improved by optimizing the internal structure of the connector or adopting new materials. Throughout the entire process, the simulation needs to be rerun after each design adjustment to evaluate whether the new design solution can improve the performance score. Through this iterative optimization method, the performance score of the high-speed connector can eventually exceed the preset performance threshold, ensuring that the product not only meets the requirements of high-speed data transmission, but also maintains stable and reliable performance in complex working environments. This method emphasizes a complete closed-loop process from problem diagnosis to solution implementation to effect verification, which is of great significance for promoting the advancement of high-speed connector technology.

[0030] In a specific embodiment, the meshing process of the physical structure of the high-speed connector to obtain a three-dimensional structure discrete mesh model includes: Performing a geometric structural analysis on the physical structure of the high-speed connector to obtain geometric characteristic parameters; and performing adaptive meshing on the physical structure of the high-speed connector based on the geometric characteristic parameters to obtain an initial mesh model; Boundary layer mesh generation is performed on the physical structure of the high-speed connector based on the initial mesh model to obtain a boundary layer mesh model; and merging processing is performed on the initial mesh model based on the initial mesh model to obtain a three-dimensional structure discrete mesh model.

[0031] Specifically, when meshing the physical structure of a high-speed connector to obtain a three-dimensional discrete mesh model, a geometric analysis is first performed to obtain geometric characteristic parameters. Based on these parameters, the physical structure of the high-speed connector is adaptively meshed to form an initial mesh model. This step is fundamental to the entire process, aiming to accurately capture the complex geometry of the connector's internal components. Specifically, using computer-aided design (CAD) tools or specialized geometric analysis software, detailed geometric characteristic parameters such as the specific dimensions and relative positions of the signal transmission structure, electromagnetic shielding structure, and insulating dielectric layer can be measured and recorded. For example, in a connector used for high-speed data exchange between servers in a data center, the signal transmission path may include multiple parallel conductors, while the electromagnetic shielding structure consists of an outer metal shell with an insulating dielectric layer between them. Based on the specific shapes and dimensions of these components, adaptive meshing technology can automatically adjust the mesh density according to the importance and complexity of different areas, ensuring accurate representation of details in critical areas such as joints or bends. Next, based on the generated initial mesh model, boundary layer meshing is performed on the physical structure of the high-speed connector to obtain a boundary layer mesh model. This process focuses particularly on boundary regions that significantly influence the electromagnetic field distribution, such as the interface between the conductor surface and the surrounding medium. Boundary layer meshes are typically finer to improve computational accuracy, as the electric and magnetic fields often vary most dramatically in these regions. For example, in the aforementioned application scenario, considering the potential reflection and refraction during signal transmission, particularly at conductor edges or corners, a fine boundary layer mesh can better capture these localized effects. The boundary layer mesh model is then merged with the initial mesh model to produce a complete 3D structural discrete mesh model. This merging step requires a smooth transition between the two meshes to avoid discontinuities or errors, ensuring the accuracy of subsequent simulation results. Every step in the entire process, from geometric analysis to adaptive meshing, to boundary layer mesh generation and its merging with the initial mesh model, is crucial. Together, they ensure that the resulting 3D structural discrete mesh model not only faithfully reflects the complex internal structure of the high-speed connector but also provides a solid data foundation for subsequent electromagnetic field distribution solutions using the time-domain finite element method. For example, when optimizing a high-speed connector for use in a data center environment, an accurate mesh model can help designers gain a deeper understanding of potential issues in the signal transmission path, such as signal crosstalk or reflection loss. Using such a model, engineers can run simulation tests by changing the conductor layout or selecting different insulation materials to observe how these changes affect the overall performance score.By continuously iteratively optimizing the design until it meets a preset performance threshold, this approach significantly improves R&D efficiency and reduces development costs, enabling high-speed connectors to remain competitive in the face of growing data transmission demands. In short, this systematic grid-based approach provides strong support for the design and optimization of high-speed connectors, helping to advance the development of related technologies.

[0032] In a specific embodiment, the time-domain finite element method for solving the multi-order electromagnetic field distribution matrix of the high-speed connector based on the three-dimensional structural discrete grid model includes: Performing electromagnetic boundary condition configuration and material parameter assignment on the three-dimensional structure discrete grid model to obtain a grid unit electromagnetic characteristic distribution set; Performing time-domain electromagnetic field discretization processing on the grid unit electromagnetic characteristic distribution set to obtain a time-domain discrete electromagnetic field equation group; Based on the time-domain discrete electromagnetic field equations, a high-order time integration method is used to solve the electromagnetic field of the high-speed connector in the time domain to obtain time-domain electromagnetic field distribution data; wherein the high-order time integration method includes a Runge-Kutta method or a Newmark-Beta method; Performing frequency domain transformation processing on the time domain electromagnetic field distribution data to obtain frequency domain electromagnetic field distribution data; Based on the frequency domain electromagnetic field distribution data, a multi-order modal analysis is performed on the electromagnetic field of the high-speed connector to obtain a multi-order electromagnetic field distribution matrix; wherein the multi-order electromagnetic field distribution matrix includes a magnetic field intensity distribution tensor and an electromagnetic energy density distribution tensor.

[0033] Specifically, in the process of solving the multi-order electromagnetic field distribution matrix of a high-speed connector using the time-domain finite element method based on the three-dimensional structural discrete grid model, it is first necessary to configure the electromagnetic boundary conditions and assign material parameters to the three-dimensional structural discrete grid model to obtain the electromagnetic characteristic distribution set of the grid cells. This process is the foundation of the entire simulation process. By accurately setting the physical properties of each grid cell, such as conductivity and dielectric constant, as well as the influence of the external electromagnetic environment, such as grounding or free space conditions, the accuracy of subsequent calculations can be ensured. For example, when designing a connector for high-speed data exchange between data center servers, considering the conductors in the signal transmission path and the surrounding insulating dielectric layer and electromagnetic shielding structure, corresponding parameters such as conductivity and relative dielectric constant need to be set for these components. Appropriate boundary conditions should be configured according to the actual application scenario, such as grounding points at both ends of the connector as zero potential reference points. Next, the electromagnetic characteristic distribution set of the grid cells with configured electromagnetic boundary conditions and material parameters is discretized in the time domain to generate the time-domain discrete electromagnetic field equations. This step involves converting the continuous Maxwell equations into a discrete form suitable for numerical calculation. Specifically, by performing a piecewise approximation of the electromagnetic field variations in the time domain and combining the electromagnetic properties of each grid cell, a mathematical model describing the time evolution of the electromagnetic field is established. In this process, selecting an appropriate difference scheme is crucial to ensuring the stability and accuracy of the computational results. Continuing with the aforementioned data center application, when considering high-frequency signals propagating within a connector, accurately simulating the signal's behavior at the interfaces between different materials becomes crucial. This requires that the discretization process not only consider the material properties themselves but also carefully characterize the variations at their boundaries. Subsequently, based on the resulting time-domain discretized electromagnetic field equations, high-order time integration methods such as the Runge-Kutta method or the Newmark-Beta method are used to solve the electromagnetic field of the high-speed connector in the time domain, generating time-domain electromagnetic field distribution data. Both methods offer high numerical stability and accuracy, providing reliable solutions while ensuring computational efficiency. The Runge-Kutta method, for example, progressively approaches the true solution through multiple stages of prediction and correction, making it particularly well-suited for solving nonlinear problems. In the aforementioned application scenarios, this means more accurately capturing the propagation behavior of high-speed signals in complex geometric structures, including reflection, refraction, and interference phenomena. Furthermore, due to the large number of electronic devices in data centers, electromagnetic interference is also a significant factor. This method effectively analyzes the impact of external electromagnetic fields on signal quality. The resulting time-domain electromagnetic field distribution data is then transformed into the frequency domain to obtain frequency-domain electromagnetic field distribution data. This transformation is typically achieved using the Fourier transform, allowing signal characteristics that are difficult to intuitively understand in the time domain to be clearly displayed in the frequency domain.This is particularly important for evaluating connector performance at different frequencies, as many key metrics such as insertion loss and reflection coefficient are defined in the frequency domain. For example, in data center applications, understanding the connector's response within the operating frequency band can help optimize the design and reduce unnecessary signal loss and interference. Finally, based on the frequency-domain electromagnetic field distribution data, a multi-order modal analysis of the high-speed connector's electromagnetic field is performed, resulting in a multi-order electromagnetic field distribution matrix containing a magnetic field intensity distribution tensor and an electromagnetic energy density distribution tensor. Multi-order modal analysis aims to reveal the natural vibration modes of the electromagnetic field in the system and their corresponding frequencies, which is very useful for gaining a deeper understanding of the dynamic characteristics of the electromagnetic field within the connector. In real-world data center applications, this analysis not only identifies potential design flaws, such as resonance effects at specific frequencies that may cause signal distortion, but also guides engineers in taking targeted improvement measures, such as adjusting conductor spacing or enhancing shielding effectiveness. Together, these steps form a complete simulation optimization process that not only helps designers quickly identify and resolve issues but also significantly improves the overall performance of high-speed connectors to meet the growing demands of modern information technology. Throughout the entire process, from the configuration of electromagnetic boundary conditions to the final multi-order modal analysis, each link is closely linked and indispensable. They work together to ensure the authenticity and reliability of the simulation results.

[0034] In a specific embodiment, the output signal spectrum analysis of the high-speed connector is performed based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector, including: Performing multi-port scattering parameter extraction on the multi-order electromagnetic field distribution matrix to obtain a frequency domain transmission characteristic data set; wherein the frequency domain transmission characteristic data set includes an insertion loss curve, a return loss curve, and a crosstalk parameter distribution; Performing time domain reflection calculation on the high-speed connector based on the frequency domain transmission characteristic data set to obtain an impedance matching characteristic curve, and performing differential loop analysis on the impedance matching characteristic curve to obtain an impedance discontinuity point distribution diagram; Performing an eye diagram simulation on the output signal of the high-speed connector based on the frequency domain transmission characteristic data set and the impedance discontinuity point distribution diagram to obtain a time domain eye diagram; When the jitter peak-to-peak value of the time domain eye diagram exceeds the tolerance range, a bit error rate prediction is performed on the high-speed connector based on the time domain eye diagram to obtain a bit error rate contour curve, and a statistical distribution characteristic analysis is performed on the bit error rate contour curve to obtain a connector reliability quantitative parameter; The connector reliability quantification parameters, the frequency domain transmission characteristic dataset, and the time domain eye diagram are multi-dimensionally integrated using an adaptive weighted regularization method to obtain a connector performance comprehensive scoring matrix, and the principal component contribution rate of the connector performance comprehensive scoring matrix is calculated to obtain performance bottleneck positioning data; Based on the comprehensive performance scoring matrix and the performance bottleneck location data, parameter sensitivity mapping is performed on the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector.

[0035] Specifically, when analyzing the output signal spectrum of a high-speed connector based on the multi-order electromagnetic field distribution matrix to obtain a connector performance score and performance analysis report, the multi-port scattering parameter extraction process is first performed on the multi-order electromagnetic field distribution matrix to generate a frequency-domain transmission characteristic dataset, including an insertion loss curve, a return loss curve, and a crosstalk parameter distribution. This process converts the complex electromagnetic field distribution into easily understood and analyzed electrical parameters, allowing designers to intuitively evaluate the connector's transmission performance at different frequencies. For example, when designing a connector for high-speed data exchange between data center servers, by analyzing the electromagnetic field distribution within the connector structure and extracting scattering parameters (S parameters), it is possible to accurately measure the attenuation (insertion loss) and reflection (return loss) of signals passing through the connector at different frequencies. Furthermore, crosstalk caused by mutual interference between adjacent signal paths can be identified. Next, based on the obtained frequency-domain transmission characteristic dataset, time-domain reflectometry calculations are performed on the high-speed connector to generate an impedance matching characteristic curve. This curve is then subjected to differential loop analysis to ultimately generate an impedance discontinuity distribution map. Time-domain reflectometry is an effective tool for detecting impedance mismatches within connectors, which often lead to signal reflections and distortion. Specifically, in the aforementioned application scenario, when significant reflection peaks are observed at specific frequencies, these locations can be precisely located using time-domain reflectometry calculations, and specific impedance discontinuities can be identified through differential loop analysis. This step is crucial for optimizing connector design, as it directly impacts signal integrity and transmission efficiency. Subsequently, based on the frequency-domain transmission characteristic dataset and the impedance discontinuity distribution plot, an eye diagram simulation is performed on the high-speed connector's output signal to generate a time-domain eye diagram. As a graphical representation, the eye diagram provides a visual representation of signal quality, particularly regarding jitter and noise. In data center applications, eye diagrams can help designers quickly determine whether signals meet regulatory requirements, such as whether the eye height and width are sufficiently large to ensure reliable data transmission. If the time-domain eye diagram indicates that the peak-to-peak jitter value exceeds the tolerance range, a bit error rate prediction is performed on the high-speed connector based on the eye diagram to generate bit error rate contour curves. These curves are then statistically analyzed for their distribution characteristics, thereby deriving quantitative connector reliability parameters. This step aims to quantify the connector's stability and reliability under actual operating conditions, ensuring its long-term stable operation in complex electromagnetic environments. Subsequently, an adaptive weighted regularization method is used to perform a multi-dimensional fusion of connector reliability parameters, frequency-domain transmission characteristic datasets, and time-domain eye diagrams to construct a comprehensive connector performance score matrix. The principal component contribution rates of this matrix are then calculated to identify performance bottlenecks.This process comprehensively considers multiple influencing factors, including but not limited to signal integrity and electromagnetic compatibility, to comprehensively evaluate the connector's overall performance. In the context of data center applications, this approach helps identify key bottlenecks limiting connector performance, such as improper material selection or inappropriate design. Finally, based on the comprehensive performance scoring matrix and performance bottleneck location data, parameter sensitivity mapping is performed on the high-speed connector to generate a connector performance score and a detailed performance analysis report. This step not only provides quantitative performance metrics but also deeply explores the impact of individual design parameters on overall performance. For example, in the aforementioned application scenario, designers can adjust conductor layout, improve shielding effectiveness, or optimize the choice of insulation medium based on the recommendations in the performance analysis report, effectively improving the connector's performance score. The entire process emphasizes an integrated solution from electromagnetic field distribution to spectrum analysis to performance evaluation, providing strong support for the design and optimization of high-speed connectors, ensuring they meet high-speed data transmission requirements while maintaining excellent reliability and stability. This approach not only enhances product market competitiveness but also promotes the development and advancement of related technologies.

[0036] In a specific embodiment, analyzing the set of key performance influencing factors of the high-speed connector based on the performance analysis report includes: Performing multi-level pedigree decomposition on the performance analysis report to obtain a performance correlation matrix, and performing singular value decomposition on the performance correlation matrix to obtain a feature contribution weight vector; wherein the feature contribution weight vector includes a geometric dimension contribution component, a material property contribution component, and an interface transition zone contribution component; Performing nonlinear transfer function mapping on the performance bottleneck positioning data based on the feature contribution weight vector to obtain a parameter sensitivity gradient field, and performing topological partitioning and clustering on the parameter sensitivity gradient field to obtain a key performance impact area distribution map; Performing frequency domain feature extraction on the key performance impact area distribution map through adaptive multi-scale wavelet transform to obtain a structural defect characteristic spectrum, and performing spatial correlation analysis on the impedance discontinuity point distribution map based on the structural defect characteristic spectrum to obtain a structure-performance coupling relationship spectrum; Based on the structure-performance coupling relationship spectrum, the geometric structure and material parameters of the high-speed connector are orthogonally sorted by sensitivity to obtain a priority list of performance influencing factors, and the priority list of performance influencing factors is screened by Pareto optimization to obtain a set of key performance influencing factors.

[0037] Specifically, the process of analyzing the set of key performance-influencing factors of a high-speed connector based on the performance analysis report first requires performing a multi-level spectral decomposition of the performance analysis report to obtain a performance correlation matrix, and then performing singular value decomposition on this matrix to extract a characteristic contribution weight vector. This process aims to mathematically quantify the degree of influence of different factors on connector performance, where the characteristic contribution weight vector includes geometric dimension contribution components, material property contribution components, and interface transition region contribution components. For example, when designing a connector for high-speed data exchange between servers in a data center, a deep analysis of the previously obtained performance analysis report can identify which design parameters, such as conductor width, shield thickness, or insulation medium selection, have the greatest impact on signal transmission quality. This information not only helps designers understand the interactions between various components but also provides a theoretical basis for subsequent optimization. Next, based on the characteristic contribution weight vector, a nonlinear transfer function mapping is performed on the performance bottleneck location data to generate a parameter sensitivity gradient field. This gradient field is then topologically partitioned and clustered, ultimately forming a distribution map of key performance impact areas. This step utilizes nonlinear mapping techniques to explore the impact of design parameter changes on overall performance, while topological partitioning and clustering help divide the complex parameter space into manageable regions, each containing a similar set of design variables. In the aforementioned application scenario, this means accurately identifying localized areas critical to signal integrity, such as connectors or the interface between electromagnetic shielding and conductors. This detailed partitioning allows designers to precisely target specific areas without impacting other areas. Subsequently, an adaptive multiscale wavelet transform is used to extract frequency domain features from the distribution map of critical performance-impacting regions to obtain a structural defect signature spectrum. Based on this signature spectrum, spatial correlation analysis is performed on the impedance discontinuity distribution map to derive a structure-performance coupling relationship spectrum. As a powerful signal processing tool, the wavelet transform is particularly well-suited for analyzing multi-scale datasets, enabling us to observe and understand how structural defects affect electromagnetic performance at varying levels of resolution. Continuing with the aforementioned data center application example, when significant impedance mismatches are detected at specific frequencies, the wavelet transform can be used to pinpoint the specific structural defects causing these issues and further explore the inherent relationship between these defects and overall performance. Finally, based on the structure-performance coupling spectrum, the high-speed connector's geometric and material parameters were orthogonally ranked for sensitivity to obtain a prioritized list of performance-influencing factors. This list was then subjected to Pareto optimization screening to ultimately determine a set of key performance-influencing factors. Orthogonal sensitivity ranking is a systematic approach that allows us to consider multiple independent design variables while assessing their respective importance to the target performance indicator.Pareto optimization screening further refines the selection criteria based on this, ensuring that the selected key influencing factors not only have the highest influence, but also meet the operability and economic requirements of actual projects. For example, in the context of data center applications, if analysis confirms that conductor spacing is one of the main factors affecting signal crosstalk, then this parameter can be adjusted as a priority in subsequent designs; at the same time, if it is found that a new type of insulation material can significantly improve signal transmission quality but is too expensive, it may be necessary to weigh the pros and cons before deciding whether to adopt it. Through a series of complex data processing and technical means, the entire process achieves comprehensive coverage from macro-performance evaluation to micro-structure optimization, providing a scientific and systematic solution for the design of high-speed connectors, ensuring that they meet high performance requirements while maintaining good reliability and economy. This approach not only enhances the market competitiveness of products, but also lays a solid foundation for promoting the development of high-speed connector technology.

[0038] In a specific embodiment, performing nonlinear transfer function mapping on the performance bottleneck positioning data based on the feature contribution weight vector to obtain a parameter sensitivity gradient field includes: Performing a multi-dimensional tensor expansion on the characteristic contribution weight vector to obtain a weight component mapping matrix, and performing a spectral domain transformation on the weight component mapping matrix to obtain a frequency domain characteristic response spectrum; wherein the frequency domain characteristic response spectrum includes a geometric characteristic spectrum component, a material characteristic spectrum component, and an interface characteristic spectrum component; Performing a nonlinear kernel function transformation on the performance bottleneck location data based on the frequency domain characteristic response spectrum to obtain a high-dimensional feature mapping space, and performing manifold dimensionality reduction processing on the high-dimensional feature mapping space to obtain a parameter-performance mapping relationship diagram; wherein the parameter-performance mapping relationship diagram includes a structural parameter mapping layer, a material parameter mapping layer, and a boundary condition mapping layer; Performing multi-scale gradient calculation on the parameter-performance mapping relationship diagram to obtain a local sensitivity distribution field, and topologically reconstructing the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain a global sensitivity field; wherein the global sensitivity field includes a structural sensitivity component, a material sensitivity component, and a boundary sensitivity component; Based on the global sensitivity field, a nonlinear response analysis is performed on the parameter-performance mapping relationship diagram to obtain a parameter coupling matrix, and the parameter coupling matrix is subjected to eigenvalue decomposition to obtain a parameter sensitivity gradient field; wherein the parameter sensitivity gradient field includes a first-order sensitivity gradient, a cross-coupling gradient, and a high-order nonlinear gradient.

[0039] Specifically, the process of applying a nonlinear transfer function mapping to the performance bottleneck location data based on the characteristic contribution weight vector to obtain the parameter sensitivity gradient field first requires a multidimensional tensor expansion of the characteristic contribution weight vector to generate a weight component mapping matrix. This matrix is then transformed into a spectral domain to obtain a frequency domain characteristic response spectrum. This process mathematically transforms complex physical structure information into an easily analyzable frequency domain characteristic response spectrum, which contains geometric characteristic spectral components, material characteristic spectral components, and interface characteristic spectral components. For example, when designing a connector for high-speed data exchange between data center servers, by performing a multidimensional tensor expansion on the key performance influencing factors identified in the early stages, a detailed analysis of the frequency domain representation of different design parameters such as conductor width, insulation thickness, and electromagnetic shielding layer material can be achieved. These frequency domain features not only reveal the behavioral patterns of each component at different frequencies but also provide a scientific basis for subsequent optimization. Next, based on the frequency domain characteristic response spectrum, a nonlinear kernel function transformation is applied to the performance bottleneck location data to generate a high-dimensional feature mapping space. This space is then subjected to manifold dimensionality reduction, ultimately forming a parameter-performance mapping relationship diagram. Nonlinear kernel transformations are powerful tools that can map low-dimensional data into a higher-dimensional space while preserving the complexity of the original data, thereby better capturing the inherent connections between the data. Manifold dimensionality reduction helps simplify this high-dimensional space, making it easier to understand and manipulate. In the aforementioned application scenario, this means accurately depicting the relationship between different design parameters and the overall performance of the connector, such as how conductor spacing affects signal crosstalk or how electromagnetic shielding effectiveness varies with material selection. This mapping provides designers with an intuitive perspective, helping them quickly identify and resolve potential design bottlenecks. Multiscale gradient calculations are then performed on the parameter-performance mapping to generate a local sensitivity distribution field. Based on this field, the high-dimensional feature map space is topologically reconstructed to generate a global sensitivity field. Multiscale gradient calculations aim to assess the performance impact of design parameter changes at multiple resolution levels, while topological reconstruction allows for a holistic perspective on how these local changes contribute to the overall system performance. Continuing with the aforementioned data center application example, if significant performance fluctuations are observed in specific areas, such as connectors or electromagnetic shielding boundaries, multiscale gradient calculations can pinpoint these locations and further explore their role in the overall system. This approach not only improves the accuracy of problem location but also enhances the effectiveness and specificity of the optimization solution. Finally, a nonlinear response analysis is performed on the parameter-performance mapping diagram based on the global sensitivity field to obtain a parameter coupling matrix. This matrix is then subjected to eigenvalue decomposition to generate a parameter sensitivity gradient field. Nonlinear response analysis reveals the complex dynamic mechanisms within the system by exploring the interactions between different design parameters and their impact on performance.Eigenvalue decomposition, as an effective mathematical tool, can help us extract the most influential parameter combinations, thereby guiding subsequent design optimization. In the aforementioned application scenario, if analysis confirms that conductor spacing and insulation thickness are the two key factors affecting signal integrity, these two parameters can be prioritized in subsequent design adjustments. At the same time, if a new material is found to significantly improve performance but is prohibitively expensive, a weighing of the pros and cons may be necessary to determine its adoption. This entire process, through a series of complex data processing and technical means, comprehensively addresses everything from micro-parameter optimization to macro-performance enhancement, providing a scientific and systematic solution for high-speed connector design, ensuring that it meets high-performance requirements while maintaining excellent reliability and cost-effectiveness. This approach not only enhances product competitiveness but also lays a solid foundation for the advancement of high-speed connector technology. Through this meticulous analysis, designers can more accurately identify performance bottlenecks and implement effective measures to address them, ensuring stable operation of the connector in complex operating environments and meeting the high-speed data transmission demands of modern information technology.

[0040] In a specific embodiment, topologically reconstructing the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain a global sensitivity field includes: Performing multi-resolution spectral decomposition on the local sensitivity distribution field to obtain a hierarchical sensitivity feature tensor, and performing singular value screening on the hierarchical sensitivity feature tensor to obtain a dominant sensitivity mode set; wherein the dominant sensitivity mode set includes a primary axis sensitivity component, a secondary axis sensitivity component, and a cross-coupling sensitivity component; Based on the dominant sensitivity mode set, the high-dimensional feature mapping space is geometrically topologically partitioned to obtain a multi-level sensitivity boundary surface, and the multi-level sensitivity boundary surface is subjected to differential morphological transformation to obtain a sensitivity critical point distribution map; wherein the sensitivity critical point distribution map includes a maximum point set, a saddle point set, and a transition point set; A connected domain analysis is performed on the sensitivity critical point distribution map using a tensor field decomposition method to obtain a sensitivity manifold structure, and based on the sensitivity manifold structure, isoparametric remapping is performed on the high-dimensional feature mapping space to obtain a normalized sensitivity distribution field; wherein the normalized sensitivity distribution field includes a linear sensitivity region, a weak nonlinear sensitivity region, and a strong nonlinear sensitivity region; Based on the normalized sensitivity distribution field, a curvature flow evolution calculation is performed on the multi-level sensitivity boundary surface to obtain sensitivity propagation dynamics characteristic data. Based on the sensitivity propagation dynamics characteristic data, a global topological integration is performed on the sensitivity manifold structure to obtain a global sensitivity field. The global sensitivity field includes structural sensitivity components, material sensitivity components, and boundary sensitivity components. Specifically, the process of topologically reconstructing the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain the global sensitivity field first requires multi-resolution spectral decomposition of the local sensitivity distribution field to generate a hierarchical sensitivity feature tensor, which is then subjected to singular value screening to ultimately extract the dominant sensitivity mode set. This process mathematically transforms complex sensitivity information into an easily analyzable hierarchical structure, where the dominant sensitivity mode set includes primary axis sensitivity components, secondary axis sensitivity components, and cross-coupling sensitivity components. For example, when designing connectors for high-speed data exchange between servers in a data center, multi-resolution spectral decomposition of the previously identified local sensitivity distribution field allows for detailed analysis of the scale-dependent behavior of various design parameters, such as conductor spacing, insulation thickness, and electromagnetic shielding material. These hierarchical sensitivity features not only reveal the behavioral patterns of each component at different scales but also provide a scientific basis for subsequent optimization. Next, geometric topological partitioning is performed on the high-dimensional feature map space based on the dominant sensitivity mode set to generate a multi-level sensitivity boundary surface. This surface is then subjected to a differential morphological transformation, ultimately forming a distribution map of sensitivity critical points. Geometric topological partitioning aims to partition the high-dimensional feature map space from multiple perspectives, ensuring that each subregion exhibits similar sensitivity characteristics. The differential morphological transformation allows further refinement of these subregions to identify the key locations that most significantly impact overall performance. Continuing with the aforementioned application scenario, this means accurately characterizing the relationship between different design parameters and the overall performance of the connector, such as how conductor spacing affects signal crosstalk or how electromagnetic shielding effectiveness varies with material selection. This sensitivity critical point distribution map provides designers with an intuitive perspective, helping them quickly locate and resolve potential design bottlenecks. Subsequently, the sensitivity critical point distribution map is analyzed for connected domains using the tensor field decomposition method to obtain the sensitivity manifold structure, and based on this structure, the high-dimensional feature mapping space is subjected to isoparametric remapping to generate a normalized sensitivity distribution field. Tensor field decomposition is a powerful tool that can reveal the connectivity and structural characteristics within the data while maintaining the complexity of the original data. Isoparametric remapping allows us to transform these complex structural features into a form that is easier to understand and operate. In the above application scenarios, when significant performance fluctuations are found in certain specific areas such as joints or electromagnetic shielding layer boundaries, these locations can be accurately located through tensor field decomposition, and their roles in the entire system can be further explored.This approach not only improves the accuracy of problem location but also enhances the effectiveness and targeted nature of the optimization solution. Finally, based on the normalized sensitivity distribution field, curvature flow evolution calculations are performed on the multi-level sensitivity boundary surfaces to obtain sensitivity propagation dynamics data. Based on this data, a global topological integration of the sensitivity manifold structure is performed to generate a global sensitivity field. Curvature flow evolution calculations simulate the dynamic propagation of sensitivity in space, revealing the complex dynamic mechanisms within the system. Global topological integration integrates these local dynamic characteristics to form a comprehensive view. In the aforementioned application scenario, if analysis confirms that conductor spacing and insulation thickness are two key factors affecting signal integrity, these two parameters can be prioritized in subsequent design adjustments. Alternatively, if a new material is found to significantly improve performance but is prohibitively expensive, a trade-off may be necessary before deciding whether to adopt it. This entire process, through a series of complex data processing and technical approaches, achieves comprehensive coverage from micro-parameter optimization to macro-performance improvement, providing a scientific and systematic solution for high-speed connector design, ensuring that they meet high performance requirements while maintaining excellent reliability and cost-effectiveness. This approach not only enhances product competitiveness but also lays a solid foundation for the development of high-speed connector technology. Through this meticulous analysis, designers can more accurately identify performance bottlenecks and implement effective measures to improve them, enabling connectors to operate stably in complex environments and meet the high-speed data transmission demands of modern information technology. Furthermore, this systematic analysis approach helps engineers better understand the interactions between various design variables, enabling a more efficient design optimization process.

[0041] The above describes the performance simulation optimization method of the high-speed connector in the embodiment of the present invention. The following describes the performance simulation optimization system of the high-speed connector in the embodiment of the present invention. Figure 2 An embodiment of a high-speed connector performance simulation and optimization system according to an embodiment of the present invention includes: Constructing a model 21 for performing grid processing on the physical structure of the high-speed connector to obtain a three-dimensional discrete grid model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating dielectric layer; A solution model 22 is used to solve the electromagnetic field distribution of the high-speed connector using a time-domain finite element method based on the three-dimensional structure discrete grid model to obtain a multi-order electromagnetic field distribution matrix; A first analysis model 23 is used to perform spectrum analysis on the output signal of the high-speed connector based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector; A second analysis model 24 is configured to analyze a set of key performance influencing factors of the high-speed connector based on the performance analysis report when the performance score is lower than a preset performance threshold; The optimization model 25 is used to simulate and optimize the high-speed connector based on the set of key performance influencing factors to achieve a performance score of the high-speed connector that is higher than a preset performance threshold.

[0042] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0043] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0044] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0045] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0046] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0047] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0048] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A performance simulation and optimization method for a high-speed connector, characterized in that: The following steps are involved: Performing grid processing on the physical structure of the high-speed connector to obtain a three-dimensional discrete grid model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating medium layer; Based on the three-dimensional structure discrete grid model, the time domain finite element method is used to solve the electromagnetic field distribution of the high-speed connector to obtain a multi-order electromagnetic field distribution matrix; Based on the multi-order electromagnetic field distribution matrix, a spectrum analysis is performed on the output signal of the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector; When the performance score is lower than a preset performance threshold, analyzing a set of key performance influencing factors of the high-speed connector based on the performance analysis report; The high-speed connector is simulated and optimized based on the set of key performance influencing factors to achieve a performance score of the high-speed connector that is higher than a preset performance threshold.

2. The performance simulation optimization method of a high-speed connector according to claim 1, characterized in that: The meshing process of the physical structure of the high-speed connector to obtain a three-dimensional structure discrete mesh model includes: Performing a geometric structural analysis on the physical structure of the high-speed connector to obtain geometric characteristic parameters; and performing adaptive meshing on the physical structure of the high-speed connector based on the geometric characteristic parameters to obtain an initial mesh model; Boundary layer mesh generation is performed on the physical structure of the high-speed connector based on the initial mesh model to obtain a boundary layer mesh model; and merging processing is performed on the initial mesh model based on the initial mesh model to obtain a three-dimensional structure discrete mesh model.

3. The performance simulation optimization method of a high-speed connector according to claim 1, characterized in that: The method of solving the multi-order electromagnetic field distribution matrix of the high-speed connector using a time-domain finite element method based on the three-dimensional structural discrete grid model includes: Performing electromagnetic boundary condition configuration and material parameter assignment on the three-dimensional structure discrete grid model to obtain a grid unit electromagnetic characteristic distribution set; Performing time-domain electromagnetic field discretization processing on the grid unit electromagnetic characteristic distribution set to obtain a time-domain discrete electromagnetic field equation group; Based on the time-domain discrete electromagnetic field equations, a high-order time integration method is used to solve the electromagnetic field of the high-speed connector in the time domain to obtain time-domain electromagnetic field distribution data; wherein the high-order time integration method includes a Runge-Kutta method or a Newmark-Beta method; Performing frequency domain transformation processing on the time domain electromagnetic field distribution data to obtain frequency domain electromagnetic field distribution data; Based on the frequency domain electromagnetic field distribution data, a multi-order modal analysis is performed on the electromagnetic field of the high-speed connector to obtain a multi-order electromagnetic field distribution matrix; wherein the multi-order electromagnetic field distribution matrix includes a magnetic field intensity distribution tensor and an electromagnetic energy density distribution tensor.

4. The performance simulation and optimization method of a high-speed connector according to claim 1, characterized in that: The output signal spectrum analysis of the high-speed connector is performed based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector, including: Performing multi-port scattering parameter extraction on the multi-order electromagnetic field distribution matrix to obtain a frequency domain transmission characteristic data set; wherein the frequency domain transmission characteristic data set includes an insertion loss curve, a return loss curve, and a crosstalk parameter distribution; Performing time domain reflection calculation on the high-speed connector based on the frequency domain transmission characteristic data set to obtain an impedance matching characteristic curve, and performing differential loop analysis on the impedance matching characteristic curve to obtain an impedance discontinuity point distribution diagram; Performing an eye diagram simulation on the output signal of the high-speed connector based on the frequency domain transmission characteristic data set and the impedance discontinuity point distribution diagram to obtain a time domain eye diagram; When the jitter peak-to-peak value of the time domain eye diagram exceeds the tolerance range, a bit error rate prediction is performed on the high-speed connector based on the time domain eye diagram to obtain a bit error rate contour curve, and a statistical distribution characteristic analysis is performed on the bit error rate contour curve to obtain a connector reliability quantitative parameter; The connector reliability quantification parameters, the frequency domain transmission characteristic dataset, and the time domain eye diagram are multi-dimensionally integrated using an adaptive weighted regularization method to obtain a connector performance comprehensive scoring matrix, and the principal component contribution rate of the connector performance comprehensive scoring matrix is calculated to obtain performance bottleneck positioning data; Based on the comprehensive performance scoring matrix and the performance bottleneck location data, parameter sensitivity mapping is performed on the high-speed connector to obtain a performance score of the connector and a performance analysis report of the connector.

5. The performance simulation and optimization method of a high-speed connector according to claim 4, characterized in that: Analyzing a set of key performance influencing factors of the high-speed connector based on the performance analysis report includes: Performing multi-level pedigree decomposition on the performance analysis report to obtain a performance correlation matrix, and performing singular value decomposition on the performance correlation matrix to obtain a feature contribution weight vector; wherein the feature contribution weight vector includes a geometric dimension contribution component, a material property contribution component, and an interface transition zone contribution component; Performing nonlinear transfer function mapping on the performance bottleneck positioning data based on the feature contribution weight vector to obtain a parameter sensitivity gradient field, and performing topological partitioning and clustering on the parameter sensitivity gradient field to obtain a key performance impact area distribution map; Performing frequency domain feature extraction on the key performance impact area distribution map through adaptive multi-scale wavelet transform to obtain a structural defect characteristic spectrum, and performing spatial correlation analysis on the impedance discontinuity point distribution map based on the structural defect characteristic spectrum to obtain a structure-performance coupling relationship spectrum; Based on the structure-performance coupling relationship spectrum, the geometric structure and material parameters of the high-speed connector are orthogonally sorted by sensitivity to obtain a priority list of performance influencing factors, and the priority list of performance influencing factors is screened by Pareto optimization to obtain a set of key performance influencing factors.

6. The performance simulation and optimization method of a high-speed connector according to claim 5, characterized in that: The performing of nonlinear transfer function mapping on the performance bottleneck positioning data based on the feature contribution weight vector to obtain a parameter sensitivity gradient field includes: Performing a multi-dimensional tensor expansion on the characteristic contribution weight vector to obtain a weight component mapping matrix, and performing a spectral domain transformation on the weight component mapping matrix to obtain a frequency domain characteristic response spectrum; wherein the frequency domain characteristic response spectrum includes a geometric characteristic spectrum component, a material characteristic spectrum component, and an interface characteristic spectrum component; Performing a nonlinear kernel function transformation on the performance bottleneck location data based on the frequency domain characteristic response spectrum to obtain a high-dimensional feature mapping space, and performing manifold dimensionality reduction processing on the high-dimensional feature mapping space to obtain a parameter-performance mapping relationship diagram; wherein the parameter-performance mapping relationship diagram includes a structural parameter mapping layer, a material parameter mapping layer, and a boundary condition mapping layer; Performing multi-scale gradient calculation on the parameter-performance mapping relationship diagram to obtain a local sensitivity distribution field, and topologically reconstructing the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain a global sensitivity field; wherein the global sensitivity field includes a structural sensitivity component, a material sensitivity component, and a boundary sensitivity component; Based on the global sensitivity field, a nonlinear response analysis is performed on the parameter-performance mapping relationship diagram to obtain a parameter coupling matrix, and the parameter coupling matrix is subjected to eigenvalue decomposition to obtain a parameter sensitivity gradient field; wherein the parameter sensitivity gradient field includes a first-order sensitivity gradient, a cross-coupling gradient, and a high-order nonlinear gradient.

7. The performance simulation and optimization method of a high-speed connector according to claim 6, characterized in that: The topological reconstruction of the high-dimensional feature mapping space based on the local sensitivity distribution field to obtain a global sensitivity field includes: Performing multi-resolution spectral decomposition on the local sensitivity distribution field to obtain a hierarchical sensitivity feature tensor, and performing singular value screening on the hierarchical sensitivity feature tensor to obtain a dominant sensitivity mode set; wherein the dominant sensitivity mode set includes a primary axis sensitivity component, a secondary axis sensitivity component, and a cross-coupling sensitivity component; Based on the dominant sensitivity mode set, the high-dimensional feature mapping space is geometrically topologically partitioned to obtain a multi-level sensitivity boundary surface, and the multi-level sensitivity boundary surface is subjected to differential morphological transformation to obtain a sensitivity critical point distribution map; wherein the sensitivity critical point distribution map includes a maximum point set, a saddle point set, and a transition point set; A connected domain analysis is performed on the sensitivity critical point distribution map using a tensor field decomposition method to obtain a sensitivity manifold structure, and based on the sensitivity manifold structure, isoparametric remapping is performed on the high-dimensional feature mapping space to obtain a normalized sensitivity distribution field; wherein the normalized sensitivity distribution field includes a linear sensitivity region, a weak nonlinear sensitivity region, and a strong nonlinear sensitivity region; Based on the normalized sensitivity distribution field, curvature flow evolution calculation is performed on the multi-level sensitivity boundary surface to obtain sensitivity propagation dynamic characteristic data, and based on the sensitivity propagation dynamic characteristic data, global topological integration is performed on the sensitivity manifold structure to obtain a global sensitivity field; wherein, the global sensitivity field includes a structural sensitivity component, a material sensitivity component and a boundary sensitivity component.

8. A performance simulation and optimization system for a high-speed connector, characterized in that: include: Constructing a model for meshing the physical structure of the high-speed connector to obtain a three-dimensional discrete mesh model; wherein the physical structure includes a signal transmission structure, an electromagnetic shielding structure, and an insulating dielectric layer; A solution model is used to solve the electromagnetic field distribution of the high-speed connector using a time-domain finite element method based on the three-dimensional structural discrete grid model to obtain a multi-order electromagnetic field distribution matrix; A first analysis model is used to perform spectrum analysis on the output signal of the high-speed connector based on the multi-order electromagnetic field distribution matrix to obtain a performance score of the connector and a performance analysis report of the connector; a second analysis model, configured to analyze a set of key performance influencing factors of the high-speed connector based on the performance analysis report when the performance score is lower than a preset performance threshold; An optimization model is used to simulate and optimize the high-speed connector based on the set of key performance influencing factors to achieve a performance score of the high-speed connector that is higher than a preset performance threshold.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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