Method for optimizing high-frequency signal transmission of radio frequency coaxial connector

Abstract

The application relates to the technical field of high-frequency signal transmission, and discloses a high-frequency signal transmission optimization method for a radio frequency coaxial connector. The method collects original electromagnetic parameter sequences of high-frequency signals transmitted in a target working frequency band in real time, and the original electromagnetic parameter sequences include impedance change values, dielectric loss factors and conductor skin depth measurement values; hierarchical feature analysis is performed on the original electromagnetic parameter sequences, basic transmission layer, interface reflection layer and dielectric coupling layer features are separated, and a hierarchical electromagnetic feature set is generated; a key performance influence section is identified according to the set, a non-uniform frequency domain analysis grid is constructed, fine frequency band division is used in a reflection sensitive area, and wide frequency band division is used in a transmission stable area; time-frequency joint simulation is carried out based on the grid, a full-frequency dynamic transmission response spectrum is extracted; a structure parameter configuration scheme is optimized in combination with a material electromagnetic characteristic database, an optimization instruction set is generated, and the optimization instruction set is loaded into a machining control system to execute a physical structure dynamic adjustment operation.

Description

Technical Field

[0001] The present invention relates to the technical field of high-frequency signal transmission, and in particular to a method for optimizing high-frequency signal transmission of a radio frequency coaxial connector. Background Art

[0002] In modern communications, radar, aerospace, and other fields, stable transmission of high-frequency signals is a prerequisite for the normal operation of equipment. As a key component connecting high-frequency signal transmission lines, the performance of RF coaxial connectors is directly related to the signal transmission quality of the entire system. As operating frequencies continue to increase, traditional RF coaxial connectors have exposed numerous problems during signal transmission.

[0003] Currently, the design and optimization of most RF coaxial connectors still rely on empirical parameter adjustments, lacking a precise understanding of the dynamic changes in electromagnetic characteristics during signal transmission. At high frequencies, impedance mismatch, dielectric loss, and interface reflections become increasingly pronounced. These factors interact to increase signal transmission attenuation and distortion. For example, as signal frequency increases, the conductor's skin effect causes uneven current distribution, increasing transmission loss. Reflection and refraction are more likely to occur at the interface of different dielectric materials, compromising signal integrity. Furthermore, slight deviations in the connector structure are amplified at high frequencies, further impacting signal transmission stability.

[0004] Existing technologies often use uniform frequency domain grids for performance analysis of RF coaxial connectors, making it difficult to account for the differences in characteristics across different frequency segments. In reflection-sensitive areas, coarse frequency band divisions lead to insufficient simulation accuracy, making it impossible to accurately capture sudden changes in the signal. In stable transmission areas, overly detailed divisions increase the computational workload and reduce analysis efficiency. Furthermore, traditional methods often consider single parameters such as impedance and loss in isolation, ignoring the physical correlations between these parameters. This results in a lack of systematic optimization solutions, making it difficult to fundamentally address the bottlenecks of high-frequency signal transmission.

[0005] Process control systems often adjust connector structures based on fixed parameter configurations, failing to dynamically respond to real-time electromagnetic parameters. This makes it difficult for connectors to maintain optimal transmission in complex high-frequency environments. These issues limit the further application of RF coaxial connectors in high-frequency applications, necessitating a new optimization method to improve their high-frequency signal transmission performance. Summary of the Invention

[0006] The object of the present invention is to provide a method for optimizing high-frequency signal transmission of a radio frequency coaxial connector to solve the problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides a method for optimizing high-frequency signal transmission of a radio frequency coaxial connector, the method comprising:

[0008] Real-time acquisition of the original electromagnetic parameter sequence of the RF coaxial connector when transmitting high-frequency signals within the target operating frequency band, wherein the original electromagnetic parameter sequence includes impedance change value, dielectric loss factor and conductor skin depth measurement value;

[0009] Performing hierarchical feature analysis on the original electromagnetic parameter sequence to separate basic transmission layer features, interface reflection layer features, and dielectric coupling layer features, and generating a hierarchical electromagnetic feature set based on the physical correlation of the features of each layer;

[0010] Based on the hierarchical electromagnetic feature set, key performance-impacting sections on the high-frequency signal transmission path are identified, and a non-uniform frequency domain analysis grid is constructed based on the spatial resolution requirements of different sections. The non-uniform frequency domain analysis grid uses fine frequency band division in reflection-sensitive areas and wide frequency band division in transmission-stable areas;

[0011] Based on the non-uniform frequency domain analysis grid, a time-frequency joint simulation is performed on the high-frequency signal transmission process to extract the full-band dynamic transmission response spectrum;

[0012] Combining the full-band dynamic transmission response spectrum with a preset material electromagnetic property database, optimizing the structural parameter configuration scheme of the RF coaxial connector, and generating a high-frequency signal transmission parameter optimization instruction set;

[0013] The high-frequency signal transmission parameter optimization instruction set is loaded into the processing control system of the radio frequency coaxial connector to perform a physical structure dynamic adjustment operation.

[0014] Preferably, performing hierarchical feature analysis on the original electromagnetic parameter sequence includes:

[0015] The impedance change value in the original electromagnetic parameter sequence is extracted to form the basic transmission layer feature set, the dielectric loss factor is extracted to form the dielectric coupling layer feature set, and the conductor skin depth measurement value is extracted to form the interface reflection layer feature set;

[0016] Establishing an energy attenuation correlation model between the basic transmission layer feature set and the dielectric coupling layer feature set, and simultaneously establishing a phase offset mapping model between the interface reflection layer feature set and the basic transmission layer feature set;

[0017] The outputs of the energy attenuation correlation model and the phase shift mapping model are fused to generate a hierarchical electromagnetic feature set including a transmission loss weight factor and a reflection suppression coefficient.

[0018] Preferably, the constructing of a non-uniform frequency domain analysis grid includes:

[0019] parsing the transmission loss weight factor in the hierarchical electromagnetic feature set, and marking the section where the transmission loss exceeds the threshold as a reflection sensitive area;

[0020] Analyze the reflection suppression coefficient and mark the section where the coefficient is lower than a preset standard as a transmission stable region;

[0021] A fine-grained frequency band segmentation strategy is used for reflection-sensitive areas to generate high-density frequency domain grid cells;

[0022] A frequency band aggregation strategy is adopted for transmission stable areas to generate wide frequency domain grid cells;

[0023] High-density frequency domain grid cells and wide-frequency domain grid cells are integrated to form a non-uniform frequency domain analysis grid covering the entire transmission path.

[0024] Preferably, the performing time-frequency joint simulation includes:

[0025] Importing the non-uniform frequency domain analysis grid into an electromagnetic field finite element solver, and loading the transmission loss weight factor in the hierarchical electromagnetic feature set as a boundary condition;

[0026] Inject multi-frequency test signals into the high-density frequency domain grid cells in the reflection-sensitive area to collect phase interference data between the interface reflection wave and the transmission signal;

[0027] Apply a swept frequency excitation signal to the wide-band grid cells in the transmission stability region and record the signal attenuation distribution within the wide frequency band.

[0028] By integrating phase interference data and signal attenuation distribution, a full-band dynamic transmission response spectrum containing a three-dimensional frequency-amplitude-phase relationship matrix is ​​constructed.

[0029] Preferably, the optimized structural parameter configuration scheme includes:

[0030] Inputting the full-band dynamic transmission response spectrum into a material electromagnetic property database to match candidate material combinations that meet frequency-amplitude-phase constraints;

[0031] Calculate the dielectric constant tolerance range of the candidate material combination in the corresponding frequency band based on the phase interference data of the reflection sensitive area;

[0032] According to the signal attenuation distribution in the transmission stable region, the conductor conductivity optimization threshold of the candidate material combination is calculated;

[0033] The optimal material combination is screened based on the dielectric constant tolerance range and the conductor conductivity optimization threshold, and a structural parameter configuration scheme including the insulator dielectric parameters, conductor coating thickness and contact surface roughness is generated.

[0034] Preferably, the generating of the high-frequency signal transmission parameter optimization instruction set includes:

[0035] parsing dielectric parameters of the insulator in the structural parameter configuration scheme to generate a dielectric filling density adjustment instruction;

[0036] Analyze conductor coating thickness data and generate surface deposition process control instructions;

[0037] Analyze the contact surface roughness index and generate mechanical grinding precision calibration instructions;

[0038] The dielectric filling density adjustment instructions, surface deposition process control instructions and mechanical polishing precision calibration instructions are integrated into a high-frequency signal transmission parameter optimization instruction set according to the process timing.

[0039] Preferably, the method for updating the hierarchical electromagnetic feature set includes:

[0040] After the RF coaxial connector performs a physical structure dynamic adjustment operation, an updated electromagnetic parameter sequence is re-collected;

[0041] Compare the maximum impedance deviation values ​​of the basic transmission layer feature set before and after the update. If the deviation exceeds the tolerance, re-trigger the hierarchical feature analysis;

[0042] The skin depth change rate of the interface reflection layer feature set is monitored, and when the change rate exceeds the preset window, the division strategy of the non-uniform frequency domain analysis grid is updated.

[0043] Preferably, the verification method of the full-band dynamic transmission response spectrum includes:

[0044] Load the full-band dynamic transmission response spectrum generated by simulation into the network analyzer;

[0045] Apply a swept frequency test signal identical to the simulated one to the actual port of the RF coaxial connector;

[0046] Collect the actual transmission response curve and compare it with the response spectrum prediction value frequency by frequency point;

[0047] When the error of the key frequency point exceeds the threshold, it is fed back to the hierarchical electromagnetic feature set for feature weight recalibration.

[0048] Preferably, the execution logic of the physical structure dynamic adjustment operation includes:

[0049] Receive the medium filling density adjustment instruction in the high-frequency signal transmission parameter optimization instruction set, and control the injection molding machine to adjust the insulating medium injection pressure according to the gradient;

[0050] Respond to surface deposition process control instructions and adjust the ion sputtering time of the vacuum coating machine;

[0051] Execute mechanical grinding precision calibration instructions and drive the CNC grinder to adopt a spiral progressive grinding path;

[0052] The execution data of each process is synchronized in real time to the full-band dynamic transmission response spectrum verification system.

[0053] Preferably, the method for synchronizing process execution data includes:

[0054] Real-time acquisition of injection molding machine pressure curve, coating machine sputtering rate and grinder grinding trajectory data;

[0055] Map the pressure curve to the predicted value of the dielectric constant of the medium, convert the sputtering rate into the conductor surface impedance value, and analyze the grinding track data into the contact surface roughness index;

[0056] The predicted dielectric constant value, conductor surface impedance value and roughness index are input into the time-frequency joint simulation module to generate a real-time optimization evaluation report of the structural parameters;

[0057] When the evaluation report shows that the key parameters deviate from the optimization target, the iterative generation of the high-frequency signal transmission parameter optimization instruction set is triggered.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] By collecting raw electromagnetic parameter sequences in real time, the dynamic changes in impedance, dielectric loss factor, and conductor skin depth during signal transmission can be captured promptly, providing authentic and comprehensive basic data for subsequent feature analysis. This real-time data collection allows for more targeted research into the electromagnetic characteristics of connectors under actual operating conditions, avoiding the biases associated with static parameter analysis.

[0060] By performing a hierarchical feature analysis on the original electromagnetic parameter sequence, the characteristics of the basic transmission layer, the interface reflection layer, and the dielectric coupling layer are separated, and a hierarchical electromagnetic feature set is generated, which can deeply explore the physical connections between the characteristics of each layer. This hierarchical analysis method breaks the limitations of traditional methods that analyze electromagnetic parameters in isolation. It can more clearly present the influencing mechanisms at different levels during signal transmission, making the subsequent identification of performance-impacting sections more scientific.

[0061] By identifying key performance-impacting sections based on a hierarchical set of electromagnetic signatures and constructing a non-uniform frequency domain analysis grid, the team achieved differentiated processing for each section. Fine-grained frequency divisions were employed in reflection-sensitive areas to accurately capture subtle signal variations, while wide-band divisions were employed in transmission-stable areas to effectively reduce computational effort and improve analysis efficiency. This analytical approach, which balances accuracy and efficiency, provides a sound grid foundation for subsequent time-frequency joint simulations.

[0062] Based on a non-uniform frequency domain analysis grid, time-frequency co-simulation is performed to extract the full-band dynamic transmission response spectrum, which comprehensively reflects the signal transmission characteristics at different frequencies. By combining the preset material electromagnetic properties database to optimize the structural parameter configuration scheme, the generated optimization instruction set is more closely aligned with actual application requirements, making the adjustment of structural parameters more targeted.

[0063] Loading the optimized instruction set into the machining control system, dynamic adjustments to the physical structure are performed, achieving closed-loop control from parameter analysis to structural optimization. This dynamic adjustment mechanism enables RF coaxial connectors to adapt to real-time changes in electromagnetic characteristics in high-frequency operating environments, better adapting to complex signal transmission requirements and improving the stability and integrity of signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a working principle diagram of the method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to the present invention;

[0065] Figure 2 Flowchart for grid construction for non-uniform frequency domain analysis;

[0066] Figure 3 This is the flow chart of time-frequency joint simulation;

[0067] Figure 4 Flowchart generated for the instruction set to optimize high-frequency signal transmission parameters;

[0068] Figure 5 Flowchart of the dynamic adjustment operation for the physical structure. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] See also Figure 1 The present invention provides a method for optimizing high-frequency signal transmission of a radio frequency coaxial connector, the method comprising:

[0071] The original electromagnetic parameter sequence of the RF coaxial connector when transmitting high-frequency signals within the target working frequency band is collected in real time. The original electromagnetic parameter sequence includes impedance change value, dielectric loss factor and conductor skin depth measurement value.

[0072] A hierarchical feature analysis is performed on the original electromagnetic parameter sequence to separate the basic transmission layer features, the interface reflection layer features and the dielectric coupling layer features, and a hierarchical electromagnetic feature set is generated based on the physical correlation of the features of each layer.

[0073] Based on the hierarchical electromagnetic feature set, key performance-impacting sections on the high-frequency signal transmission path are identified, and a non-uniform frequency domain analysis grid is constructed based on the spatial resolution requirements of different sections. The non-uniform frequency domain analysis grid uses fine frequency band division in the reflection-sensitive area and wide frequency band division in the transmission stable area.

[0074] Based on the non-uniform frequency domain analysis grid, a time-frequency joint simulation is performed on the high-frequency signal transmission process to extract the full-band dynamic transmission response spectrum.

[0075] Combining the full-band dynamic transmission response spectrum with a preset material electromagnetic property database, the structural parameter configuration scheme of the RF coaxial connector is optimized, and a high-frequency signal transmission parameter optimization instruction set is generated.

[0076] The high-frequency signal transmission parameter optimization instruction set is loaded into the processing control system of the radio frequency coaxial connector to perform a physical structure dynamic adjustment operation.

[0077] Example 1: See Figure 2 When the hierarchical feature analysis operation of the high-frequency signal transmission optimization method of the RF coaxial connector is implemented, three types of basic physical quantities are extracted from the original electromagnetic parameter sequence collected in real time: the impedance change value constitutes the basic transmission layer feature set, the dielectric loss factor constitutes the dielectric coupling layer feature set, and the conductor skin depth measurement value constitutes the interface reflection layer feature set.

[0078] The impedance change value sequence is acquired by sampling at equal intervals within the target frequency band using an impedance analyzer. The dielectric loss factor is measured synchronously at the same frequency using a dielectric spectrometer. The conductor skin depth is measured using a four-probe method combined with high-frequency eddy current testing to generate discrete frequency point measurements. The base transmission layer feature set focuses on the intrinsic characteristics of signal transmission in the conductor, the dielectric coupling layer feature set reflects the absorption and dissipation behavior of the insulating material with respect to the electromagnetic field, and the interface reflection layer feature set characterizes the electromagnetic reflection characteristics of the conductor-dielectric interface. An energy attenuation correlation model for the base transmission layer and the dielectric coupling layer is established. This model is constructed as a multivariate regression equation. The impedance change value and the dielectric loss factor are input, and the energy transfer coefficient matrix is ​​solved using the partial least squares method. The output is a weighting factor representing the transmission loss per unit length. A phase offset mapping model for the interface reflection layer and the base transmission layer is established. Based on the covariance analysis of the skin depth and impedance change values, the phase lag is calculated using the cross-correlation function, and a dynamic matrix of the reflection suppression coefficient is output. By integrating the results of the energy attenuation correlation model and the phase shift mapping model, a feature tensor superposition algorithm is used to construct a hierarchical electromagnetic feature set, with transmission loss weight factors as row vectors and reflection suppression coefficients as column vectors. This set contains frequency-domain index labels, each of which corresponds to a pair of loss weight and suppression coefficient at a specific frequency.

[0079] The operation to construct a non-uniform frequency domain analysis grid uses a hierarchical set of electromagnetic features as input. The transmission loss weight factor is normalized and then fed into a threshold discrimination module. A dynamic floating threshold range is set between 1.2 and 3.0 times the baseline loss value. Transmission paths with weight factors outside this range are marked as reflection-sensitive regions. The reflection suppression coefficient is processed through a low-pass filter to eliminate high-frequency perturbations and noise before being input into a comparator. Regions with coefficient values ​​below 0.35 are identified as transmission-stable regions. A fine-grained segmentation strategy is implemented for frequency band division in the reflection-sensitive region: a fixed bandwidth of 100 MHz is used for the 5-6 GHz band, an adaptive bandwidth of 50 MHz is used for the 6-8 GHz band, and a variable bandwidth of 30 MHz is used for bands above 8 GHz. Each segmentation unit generates an independent, high-density frequency domain grid cell, with at least three sampling frequencies (center frequency and ±1 / 4 bandwidth points) set within the cell. A frequency band aggregation strategy is implemented in the transmission stability zone: 3-5 adjacent 200MHz fundamental frequency bands are merged into wide-frequency domain grid cells, resulting in a cell bandwidth range of 400-800MHz. A 5% overlap buffer is set at the grid cell boundaries to eliminate frequency truncation effects. High-density frequency domain grid cells and wide-frequency domain grid cells are integrated in the spatial coordinate system using a coordinate mapping method: an axial coordinate system is established along the signal transmission path, with start and end coordinate values ​​assigned to each grid cell. The coordinate domain is partitioned with an accuracy of 0.1mm in the reflection-sensitive zone and 1.0mm in the transmission stability zone. Cubic spline interpolation is implemented in the data interface between grid cells to ensure cross-cell parameter continuity. The non-uniform frequency domain analysis grid is ultimately output as a structured data set with topological identifiers, containing three types of labels: spatial coordinates, frequency band range, and grid attributes. The grid attribute labels define the sampling density level: high density corresponds to grid cells in the reflection-sensitive zone, and low density corresponds to grid cells in the transmission stability zone. This process forms an analysis framework for the coordinated optimization of spatial and frequency resolutions, laying the foundation for differentiated calculations in subsequent simulations.

[0080] During the update of the hierarchical electromagnetic feature set, the transmission loss weighting factor is recalibrated using an incremental update mechanism. When the impedance deviation of the base transmission layer feature set exceeds a 10% tolerance, the regression coefficients of the energy attenuation correlation model are recalculated, and the loss weighting factor matrix is ​​updated. The reflection suppression coefficient is corrected based on skin depth change rate monitoring. When the skin depth measurement rate of change exceeds ±5% / GHz, the phase offset mapping model is reconstructed, and the sensitive frequency band data in the coefficient matrix is ​​updated. The non-uniform frequency domain analysis grid is updated in real time using dynamic mesh reorganization technology. The meshing strategy adjustment includes three steps: extracting the updated frequency domain coordinates of the reflection-sensitive area and recalculating the fine-grained segmentation parameters; scanning the transmission stability region boundary and adjusting the frequency band aggregation range based on the new feature data; and reconstructing the mesh topology mapping while retaining the original unit numbering system and modifying only the attribute parameters of the affected units. The mesh update process is executed asynchronously in the background, without affecting the main process. This implementation maintains dynamic coordination between feature analysis and mesh construction, adapting to changing connector operating conditions.

[0081] An exception mechanism is set for the processing of special frequency bands. Near the dielectric resonance frequency point (marked as the dielectric loss factor peak point), the corresponding grid unit is forced to upgrade to a high-density level, even if it is located in the original transmission stability zone. When the conductor material suddenly changes and causes the skin depth to be abnormal, a temporary high-density grid unit is inserted within 2mm around the coordinate position. These mechanisms compensate for the lack of local adaptability of the standard algorithm. The spatial coordinate data of all grid cells is stored as a historical version with a timestamp to support parameter traceability analysis. The entire implementation process is executed by an embedded controller, and the feature analysis cycle is synchronized 1:1 with the electromagnetic parameter acquisition cycle. The response time of the grid construction process is controlled within 50ms.

[0082] Example 2: See Figure 3 , based on the time-frequency joint simulation implementation process of the non-uniform frequency domain analysis grid, the grid structure data is loaded into the electromagnetic field finite element solver environment. The solver presets the Maxwell control equation solution mode and constructs a three-dimensional grid topology model in the memory. The transmission loss weight factor in the hierarchical electromagnetic feature set is transmitted to the solver through the data interface and configured as a spatial boundary condition distribution parameter. Each grid cell is associated with a specific loss weight value. In the high-density frequency domain grid cell, this weight value participates in the near-field radiation calculation and acts on the far-field propagation model in the wide-frequency domain grid cell. The boundary condition is imposed using the gradient attenuation constraint rule, and the continuous transition of parameters is achieved through weight interpolation at the grid cell boundary. After the solver is initialized, preprocessing operations are performed to perform quality detection and singular point elimination on the grid structure.

[0083] Multi-frequency test signal injection is performed within the high-density frequency domain grid cells corresponding to the reflection-sensitive area. The signal source module automatically configures the center frequency, boundary frequency, and transition frequency test points according to the frequency band range of the grid cell, and injects no less than three discrete frequency signals into each cell. The signal injection port is set to the cross-section of the conductor inside the connector, and the excitation signal is in the form of a transverse electromagnetic wave mode. A virtual probe array is used to collect interface reflection waves, and one sampling point is arranged every millimeter in the normal direction of the medium-conductor interface. The transmission signal is captured at the grid cell exit along the conductor axis. Phase interference data processing uses an orthogonal decomposition algorithm to convert the reflected wave and the transmitted signal into in-phase component and orthogonal component data groups, and record the amplitude ratio and phase difference two-dimensional array. Each high-density grid cell generates an independent interference data set, which contains three-dimensional data: frequency coordinates, position coordinates, and interference phase angle.

[0084] A swept frequency excitation operation is implemented within the wide-band grid cells of the transmission stability zone. The signal source generates a continuous swept frequency waveform, with the start and end frequencies aligned with the grid cell band boundaries. The sweep signal step interval is dynamically adjusted based on the cell bandwidth, with a 10 MHz step for bandwidths less than 500 MHz and a 25 MHz step for bandwidths greater than 800 MHz. The excitation signal is introduced through a wide-band transmission port, with the receiver placed at the end of the transmission path. Signal attenuation distribution monitoring uses a multi-point sampling scheme, recording local field strength values ​​at 10 mm intervals on the conductor surface. The attenuation data is stored in decibels, forming a two-dimensional frequency-attenuation matrix with position markers. The frequency resolution of data acquisition in the transmission stability zone adapts to the cell aggregation strategy, and data reconstruction across cell boundaries is not performed.

[0085] The integration of phase interferometry data and signal attenuation distribution is implemented in the data fusion engine. During engine initialization, a three-dimensional coordinate system is established, with frequency as the primary index and spatial location as the secondary index. For high-density grid cell areas, an interferometry phase angle data layer is inserted; for wide-band grid cell areas, a signal attenuation data layer is written. The fusion process performs grid cell alignment checks to eliminate data overlap or gaps between cells. A hierarchical assignment method is used to construct the three-dimensional frequency-amplitude-phase relationship matrix: the amplitude dimension incorporates signal attenuation and reflected wave amplitude ratio data; the phase dimension incorporates interferometry phase angle information. When multiple frequency points exist at the same spatial location, frequency domain interpolation is performed to generate continuous spectral lines. The final output data structure for the full-band dynamic transmission response spectrum consists of four core components: a spatial coordinate index segment, a frequency scale segment, an amplitude spectrum matrix, and a phase spectrum matrix. This data structure uses a hierarchical storage format, supporting random access by spatial location or frequency band range.

[0086] After the optimization process for the structural parameter configuration scheme is initiated, the full-band dynamic transmission response spectrum is input into the material electromagnetic properties database through a data pipeline. The database search criteria are set based on key feature points in the three-dimensional spectrum: attenuation extreme points are screened in the amplitude spectrum, and sudden change points are marked in the phase spectrum. A multi-feature constraint algorithm is used to match candidate material combinations, comparing the theoretical transmission response curve of the material with the measured spectrum for each target frequency point. The feature point matching tolerance is set to ±0.5dB for amplitude and ±5 degrees for phase. Successfully matched material combinations are classified and stored by frequency band distribution, forming a list of candidate materials with weighted scores. The search process for the material electromagnetic properties database covers 62 standard materials in three categories: insulating dielectric materials, conductive metal materials, and surface-treated materials.

[0087] The phase interference data of the reflection sensitive area is processed through feature extraction to separate the sequence of phase mutation points. The tolerance range of the material dielectric constant is calculated for each mutation point: the phase gradient of the three frequency points before and after the point is extracted to establish the phase change rate curve; the phase sensitive bandwidth is determined by the second-order derivative of the curve, and the dielectric constant fluctuation tolerance is inverted within this bandwidth. The calculated result of the tolerance range is expressed as a percentage, such as (2.8±0.15) means that the dielectric constant is allowed to fluctuate within the range of 2.65 to 2.95. The parameter consistency of the candidate material combination within this range is included in the material scoring system.

[0088] Signal attenuation distribution data within the transmission stability zone is used to calculate the conductor conductivity optimization threshold. The first-order derivative extreme points of the distribution curve are extracted, corresponding to the locations of maximum attenuation change; the second-order derivative zero points are extracted to identify attenuation plateaus. The average attenuation in these plateaus is calculated, and the minimum conductivity requirement is inverted using a skin effect model. The conductivity optimization threshold is output as a discrete value list by frequency band, with the threshold automatically increased in high-frequency bands. This threshold serves as a hard indicator to screen out candidate materials that do not meet the requirements.

[0089] The final structural parameter configuration scheme is generated through multiple rounds of screening: the first round eliminates materials with incompatible parameters based on the dielectric constant tolerance range; the second round applies the conductivity optimization threshold to filter conductor materials; and the final round performs a comprehensive scoring and ranking. The scoring model assigns a 60% weight to dielectric parameters and a 40% weight to conductivity parameters. Once the optimal material combination is determined, the structural parameter configuration scheme automatically generates parameter definitions containing three levels: the dielectric parameters of the insulator determine the dielectric material type and molding density; the conductor coating thickness specifies the metal substrate type and the surface treatment layer thickness tolerance range; and the contact surface roughness index sets the profile arithmetic mean and maximum peak-to-valley height parameters. The scheme output is a structured parameter table, with each parameter item annotated with the applicable frequency band range and spatial position mark, providing a complete parameter basis for physical processing. The entire optimization process is embedded in an automated decision-making system, and the parameter generation cycle is controlled to be completed within 15 milliseconds.

[0090] Example 3: See Figure 4 This embodiment focuses on the process of generating an instruction set for optimizing high-frequency signal transmission parameters, derived from the input data of a structural parameter configuration scheme. The structural parameter configuration scheme, as the output of the pre-optimization module, contains three key parameters: dielectric parameters of the insulator, conductor coating thickness, and contact surface roughness. These parameters are transmitted to the instruction generation module via a system interface, where they are parsed and converted item by item, ultimately forming a control instruction set capable of driving the processing equipment. The entire process is based on a modular design, with the instruction generation unit embedded in an industrial controller environment and the processing logic adopting a data-driven approach. The parsing operation begins with the dielectric parameters of the insulator. These parameters are typically stored in the data table of the structural parameter configuration scheme as numerical values ​​of dielectric constants, recorded as floating-point numbers (e.g., 2.8 or 3.2). Each value is associated with a specific frequency band and spatial location label. The parsing engine retrieves these parameter values ​​and applies a mapping algorithm to convert them into the physical quantity of dielectric fill density, using a pre-set physical relationship model. Controlling the density of the insulating material directly affects the dielectric properties, so fill density adjustment instructions are generated accordingly. The instruction format is defined as machine-executable code (e.g., a G-code sequence) and includes multiple sets of pressure setpoints and time gradient parameters. Specifically, the command specifies the injection molding machine's pressure regulation curve. For example, when the dielectric constant is 2.8, it maps to a density target of 1200 kg / m³. The command sets an initial pressure of 10 MPa, increasing by 0.5 MPa per millisecond to a target pressure of 40 MPa, and maintaining the pressure for 500 milliseconds. The pressure gradient is calculated based on the correlation between the dielectric constant and density to avoid nonlinear errors. The command generation module is equipped with an error-checking unit to initiate a remapping routine if the parameter is out of range in the material database.

[0091] Next, the conductor coating thickness data is parsed. The structural parameter configuration scheme stores this data as micron-level values ​​(e.g., 0.5μm or 1.2μm) with tolerance requirements (e.g., ±0.05μm). This thickness data corresponds to the physical coating deposition volume, and the parsing process decomposes it into control variables for the vacuum coating process. Surface deposition process control instructions are generated from this data. The instruction structure includes timing triggers and feedback loops. Implementation involves calculating the ion sputtering duration: the target thickness is divided by the preset deposition rate (0.1μm / ms) to determine the sputtering duration instruction parameter. For example, for a target thickness of 1.0μm, the instruction configures the sputtering source power to 500W and the duration to 10ms. Closed-loop monitoring logic is also embedded, collecting coating thickness sensor data in real time. If the deviation from the target exceeds 0.01μm, the instruction automatically adds a compensation period. The instruction format uses industrial bus protocols (e.g., Profinet messages) and is encapsulated into device-specific control blocks. Each block contains a device ID, an operation code, and a parameter set. Key implementation details include dynamic rate calibration: Coater environmental parameters (such as vacuum and temperature) are input in real time, and an interpolation algorithm adjusts the deposition rate to ensure precise thickness matching. Coating thickness tolerances are enforced within the instructions, and if the calculated value exceeds the equipment's capabilities, the system switches to searching a database of alternative coating solutions.

[0092] The analysis of the contact surface roughness index is relatively complex. This index is usually stored in the form of the arithmetic mean of the surface profile (Ra value, in μm), such as Ra = 0.2μm or Ra = 0.1μm, in the form field of the structural parameter configuration scheme. The analysis goal is to convert the Ra value into the precision parameter of the grinding operation. The mechanical grinding precision calibration instructions are then generated. The roughness index is imported into the path planning algorithm, which introduces a single formula to convert the Ra value into the grinding path parameter:

[0093]

[0094] In this formula, p represents the grinding precision parameter (unit: mm), indicating the step resolution of the grinding tool; Ra represents the raw roughness index (unit: microns); and k represents the material-dependent coefficient (dimensionless), ranging from 0.8 to 1.2, set using a lookup table based on the hardness of the conductor material. For example, when Ra = 0.2 μm and k = 1.0 (applicable to copper alloys), p = 5.0 mm, indicating a 5.0 mm step interval for the grinder. The formula is implemented in the controller firmware, using floating-point arithmetic to avoid path deviations caused by rounding errors. The command drives the CNC grinder using a spiral progressive path mode: the path start and end points are set based on the contact surface coordinates, and the radius increment decreases according to the value of p. For example, when p = 5.0 mm, the command specifies an internal spiral path with a radius of 2.5 mm and a reduction of 0.5 mm per revolution. The grinding depth is set to 0.1 times Ra (e.g., 0.02 mm for a Ra of 0.2 μm). The grinding trajectory parameterization process also includes vibration suppression logic, limiting the grinder's acceleration to 5 m / s² to prevent excessive stress. If roughness indicators are abnormal (e.g., Ra exceeds the limit), the command generation module activates a safety protocol, suspends output, and issues an alarm. This process emphasizes real-time data feedback. Actual roughness is sampled immediately after the grinding trajectory is executed and compared with the target Ra value via a comparator. If the deviation exceeds 2%, an iterative command is generated.

[0095] The final stage involves the integration of instructions to optimize high-frequency signal transmission parameters. Previously generated individual instructions—dielectric fill density adjustment instructions, surface deposition process control instructions, and mechanical lapping precision calibration instructions—are imported into the integration engine. Integration is based on process timing rules, strictly defining the execution sequence: dielectric fill is performed first to stabilize the insulator foundation, followed by conductor plating deposition, and finally mechanical lapping to refine the contact surfaces. Timing management utilizes a first-in-first-out queue with a fixed length of three instruction slots. In implementation, the engine assigns timestamps and dependency tags to each instruction. For example, the dielectric fill instruction is marked with a start time of t=0, the surface deposition instruction is set to a delayed start (t=fill duration + 50 milliseconds), and the lapping instruction is configured to trigger upon completion of deposition. The instruction set integration algorithm uses a tree-like data structure to store instruction relationships. The root node is the global controller instruction, and the child nodes correspond to individual device instruction blocks. The output instruction set is a binary file format (e.g., XML or binary stream) containing header information (version, checksum) and multiple instruction fragments. Each fragment is accompanied by a device address code and timeout settings, such as a 200 millisecond timeout threshold for the lapping instruction. The background monitoring unit runs parallel threads, checking the execution status of instructions in real time. If a process stalls and times out, the instruction set automatically injects abort or restart subcommand. Ultimately, the instruction set is loaded into the execution queue of the machining control system via a communication interface (such as Ethernet or RS-485), directly driving the physical adjustment equipment. The entire process is coordinated by a central controller, with the instruction generation cycle controlled within 20 milliseconds and system resource allocation optimized to avoid congestion. The integrated logic emphasizes data integrity verification, ensuring lossless instruction transmission through a cyclic redundancy check. This implementation completes the end-control chain for optimized signal transmission, seamlessly connecting simulation optimization with physical manufacturing.

[0096] Example 4: This example involves an update mechanism for a hierarchical electromagnetic feature set and a verification process for a full-band dynamic transmission response spectrum. This implementation process is initiated after the dynamic adjustment operation of the physical structure is completed, forming a closed-loop feedback system. When the connector completes the adjustment of processing parameters, the system automatically triggers the re-collection process of the electromagnetic parameters, using the same sensor configuration and sampling frequency as the initial collection. The impedance analyzer sets five equidistant monitoring points along the transmission path, and each monitoring point collects impedance values ​​at 100 discrete frequency points within the target frequency band; the dielectric spectrometer installs three detection heads at key positions of the dielectric layer to synchronously record the dielectric loss factor; the four-probe detection device scans along the surface of the conductor in a spiral path to record the skin depth distribution. These data constitute the updated electromagnetic parameter sequence, which is stored in the database with a timestamp.

[0097] The basic transmission layer feature set before and after the update is compared using a differential analysis method. The system extracts impedance changes at the same spatial location and frequency point and calculates the relative deviation percentage. The comparison results are recorded in a dynamic monitoring table, which continuously tracks the changing trends of key parameters. When the impedance deviation in a specific frequency band exceeds a preset tolerance (e.g., 10%), the system automatically marks the area as requiring re-analysis. The hierarchical feature analysis module initiates a local recalculation of the marked area, retaining the original feature data in areas that do not exceed the tolerance. This selective update strategy reduces the computational load while ensuring parameter accuracy in critical areas. The interface reflection layer feature set update monitors the rate of change of the skin depth. The system establishes a moving time window statistical model and calculates the slope of the change over the last three measurements. When the slope exceeds a threshold, the non-uniform frequency domain analysis grid division strategy is adjusted. The grid update algorithm prioritizes frequency bands with high rates of change and reassesses the reflection sensitivity level in these areas, potentially upgrading a previously stable transmission zone to a reflection-sensitive zone, or vice versa.

[0098] Verification of the full-band dynamic transmission response spectrum utilizes a comparison of physical test data with simulation data. The network analyzer is configured with the same frequency spacing and sweep speed as the simulation environment, and a standard frequency sweep signal is applied to the actual connector port. The test signal power level is set to -10dBm, the same as the simulation excitation, to avoid interference from nonlinear effects. During the acquisition of the actual transmission response curve, the system samples three times at each frequency point and takes the average value to eliminate random noise. The comparison of verification data with simulation predictions utilizes a frequency alignment strategy. The following table shows example comparison data for typical frequency bands:

[0099] Table 1: Comparison of full-band dynamic transmission response spectrum simulation and measured data

[0100] Frequency (GHz) Simulation amplitude (dB) Measured amplitude (dB) Amplitude deviation Simulation phase (°) Measured phase (°) Phase deviation 5.20 -1.25 -1.32 +0.07 12.8 13.1 -0.3 5.85 -2.10 -2.35 +0.25 24.5 23.8 +0.7 6.40 -1.68 -1.70 +0.02 18.2 18.5 -0.3 7.10 -3.05 -3.22 +0.17 32.1 31.5 +0.6 8.30 -4.20 -4.45 +0.25 45.6 44.9 +0.7

[0101] The comparison process sets dynamic error thresholds, and frequencies with amplitude deviations exceeding 0.2dB or phase deviations exceeding 1 degree are marked as critical differences. The system initiates a feature weight recalibration process for these frequencies, adjusting the transmission loss weighting factors and reflection suppression coefficients for the corresponding frequency bands in the hierarchical electromagnetic feature set. The calibration algorithm automatically determines the adjustment range based on the direction of the deviation: when the measured loss exceeds the simulation prediction, the transmission loss weight is increased; when the phase lag exceeds expectations, the reflection suppression coefficient is increased. A new version of the calibrated feature set is generated and archived together with the historical version, forming a traceable parameter evolution chain.

[0102] The data synchronization mechanism, which dynamically adjusts the physical structure, enables tight coupling between processing and verification. The injection molding machine's pressure curve data is recorded at a 100Hz sampling rate and transmitted to a central database via an industrial IoT interface. The system extracts the mean value of the pressure plateau segment and maps it to a predicted dielectric constant value for the dielectric, using a material property curve library. The coating machine's sputtering rate data is derived from real-time feedback from the plasma monitor. The system calculates the average rate per minute and converts it into an estimated conductor surface impedance. The grinding machine's grinding path data, including position coordinates and contact pressure, is converted into a roughness index using a surface topography reconstruction algorithm. These processing parameters are input into the time-frequency co-simulation module, generating a real-time structural parameter optimization evaluation report. The report compares the achieved values ​​of key parameters with the target values, along with a predicted change in electromagnetic performance. If the evaluation indicates a dielectric constant deviation exceeding 2% or a surface impedance deviation exceeding 5%, the system automatically initiates a request to generate a new round of parameter optimization instruction sets. This mechanism ensures that the physical parameters achieved during processing remain on track for optimization, forming a complete closed-loop process from design to manufacturing.

[0103] This embodiment emphasizes data-driven dynamic adjustment, maintaining optimal high-frequency transmission performance through a multi-level verification and feedback mechanism. During system operation, all operations and parameter changes are logged in detail, enabling status tracing and analysis at any point in time.

[0104] Example 5: See Figure 5 This embodiment focuses on the specific execution logic and process data synchronization mechanism for dynamic physical structure adjustment operations. The process begins by loading a high-frequency signal transmission parameter optimization instruction set into the central processing unit of the machining control system. The system analyzes the instruction set structure and identifies three independent instruction segments: instructions for adjusting the medium filling density, controlling the surface deposition process, and calibrating the mechanical grinding accuracy. Each instruction segment is encoded using a device-specific protocol and carries time stamps and execution priority information. The controller distributes instructions according to a preset process sequence, with the medium filling adjustment instruction first activating the injection molding machine control unit.

[0105] After the injection molding machine receives the medium filling density adjustment instruction, its internal pressure control system starts the gradient adjustment mode. The instruction parameters are decoded to obtain the target pressure value sequence and the corresponding time node, and the control unit drives the hydraulic servo valve to perform segmented pressure adjustment. The initial pressure is set to the baseline value, and then gradually increased according to the increment defined by the instruction. The pressure sensor samples the actual pressure value at a frequency of milliseconds, and compares the real-time feedback with the instruction target value. When it is detected that the instantaneous pressure deviation exceeds the set tolerance range, the control unit injects a compensation pulse signal to dynamically correct the flow valve opening. The filling process is carried out in a closed mold, and the molten insulating medium completes the filling and solidification under the pressure gradient. The injection cycle end signal triggers the data recorder to save the complete pressure-time curve and key node status marks. The curve data is uploaded to the central database via the industrial Ethernet interface and formatted as a data stream with a timestamp.

[0106] When the surface deposition process control instructions are distributed to the vacuum coating equipment, the coating machine main control system parses the ion sputtering duration parameters and power constraints in the instructions. After the vacuum cabin door is closed and the basic vacuum is started, the sputtering source power supply receives the target power setting value and reaches the target power level within the specified millisecond window. The target ion sputtering process is controlled by a timer, and the start time is precisely synchronized to the system clock. A plasma emission spectrometer is configured in the coating chamber to monitor the film deposition rate in real time; when the monitoring value deviates from the preset rate curve, the system automatically generates a compensation period expansion factor to extend or shorten the actual sputtering time. After sputtering is completed, the contact film thickness gauge performs three-point measurement to collect coating thickness distribution data. All process parameters, including the sputtering power curve, actual duration, and deposition rate distribution, are encapsulated as process record files and pushed to the data bus via the OPCUA protocol.

[0107] The mechanical grinding precision calibration instruction triggers the action sequence of the CNC grinder. The instruction data is parsed to obtain the contact surface roughness target value and the spiral progressive path parameters. The grinder spindle starts at the preset coordinate position and executes the spiral trajectory motion: the motion trajectory radius decreases from the initial value in an arithmetic sequence, the axial feed amount is calculated according to the roughness target, and a fixed micron-level displacement is fed for each rotation. The diamond grinding wheel performs high-speed fine grinding on the contact surface, and the vibration accelerometer continuously monitors the mechanical vibration spectrum. If an abnormal resonance frequency point is detected, the feed rate is automatically reduced to protect the workpiece. After grinding is completed, the white light interferometer scans the contact surface morphology and outputs the surface contour point cloud data and the arithmetic average roughness value. The actual grinding path coordinates, time-position data, and final roughness index are packaged into a data structure package, stored in the local cache, and uploaded synchronously.

[0108] Real-time synchronization of execution data for each process is achieved by a dedicated data acquisition agent. The pressure curve data of the injection molding machine is formatted as an array of time-pressure value pairs, marked with the equipment number and process batch number. The sputtering rate data of the coating machine contains a triplet of sputtering power, timestamp and rate value, with additional ambient vacuum sampling points. The grinding trajectory data of the grinder is stored as a discrete spatial coordinate sequence, including XYZ position and instantaneous feed speed. The synchronization engine registers different parsers according to the data source type: the pressure curve parser extracts key feature points and maps them into predicted values ​​of the dielectric constant of the medium; the sputtering rate parser applies the rate-impedance conversion model to generate the surface impedance value of the conductor; the grinding trajectory parser imports the surface reconstruction algorithm to derive the contact surface roughness index. After data conversion, a metadata package with a unified interface is generated, which contains the predicted parameter values ​​and conversion confidence indicators.

[0109] The metadata package is input into the backend service interface of the time-frequency co-simulation module in real time. The simulation module loads the current structural parameter configuration baseline and replaces the corresponding parameter items with the predicted values ​​from the metadata package. The electromagnetic field solver performs lightweight and fast simulations, calculating the transmission response changes at key frequency points. The simulation output is summarized as a real-time structural parameter optimization evaluation report. The report body consists of three parts: a parameter deviation analysis list, a transmission response drift warning, and process correction suggestions. The report detects deviation status at a specific frequency: when the absolute deviation between the predicted dielectric constant and the target value exceeds a set percentage range, or the conductor surface impedance value deviates from the optimization threshold, or the roughness index exceeds the tolerance boundary, the report marks a significant deviation flag. This flag triggers the instruction set iteration mechanism: the optimization instruction generation module wakes up from its dormant state and initiates the parameter re-optimization process. The iterative process is executed asynchronously in the background, generating a new version of the optimization instruction set. This version of the instruction set contains correction information and can be immediately loaded into the processing control system to initiate readjustment.

[0110] The entire execution and synchronization process forms a closed-loop control loop, ensuring that process data flows are dynamically coordinated with performance simulation. The controller strictly monitors time windows within the processing cycle and enforces timeout thresholds to forcefully interrupt low-priority tasks, ensuring critical process timing constraints. All raw data and intermediate processing results are written to a distributed storage system, supporting data traceability throughout its lifecycle.

[0111] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0112] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing high-frequency signal transmission of a radio frequency coaxial connector, characterized in that: The method comprises: Real-time acquisition of the original electromagnetic parameter sequence of the RF coaxial connector when transmitting high-frequency signals within the target operating frequency band, wherein the original electromagnetic parameter sequence includes impedance change value, dielectric loss factor and conductor skin depth measurement value; Performing hierarchical feature analysis on the original electromagnetic parameter sequence to separate basic transmission layer features, interface reflection layer features, and dielectric coupling layer features, and generating a hierarchical electromagnetic feature set based on the physical correlation of the features of each layer; Based on the hierarchical electromagnetic feature set, key performance-impacting sections on the high-frequency signal transmission path are identified, and a non-uniform frequency domain analysis grid is constructed based on the spatial resolution requirements of different sections. The non-uniform frequency domain analysis grid uses fine frequency band division in reflection-sensitive areas and wide frequency band division in transmission-stable areas; Based on the non-uniform frequency domain analysis grid, a time-frequency joint simulation is performed on the high-frequency signal transmission process to extract the full-band dynamic transmission response spectrum; Combining the full-band dynamic transmission response spectrum with a preset material electromagnetic property database, optimizing the structural parameter configuration scheme of the RF coaxial connector, and generating a high-frequency signal transmission parameter optimization instruction set; Loading the high-frequency signal transmission parameter optimization instruction set into a processing control system of a radio frequency coaxial connector to perform a physical structure dynamic adjustment operation; The constructing of the non-uniform frequency domain analysis grid includes: parsing the transmission loss weight factor in the hierarchical electromagnetic feature set, and marking the section where the transmission loss exceeds the threshold as a reflection sensitive area; Analyze the reflection suppression coefficient and mark the section where the coefficient is lower than the preset standard as the transmission stable area; A fine-grained frequency band segmentation strategy is used for reflection-sensitive areas to generate high-density frequency domain grid cells; A frequency band aggregation strategy is adopted for transmission stable areas to generate wide frequency domain grid cells; Integrate high-density frequency domain grid cells and wide frequency domain grid cells to form a non-uniform frequency domain analysis grid covering the entire transmission path; The time-frequency joint simulation includes: Importing the non-uniform frequency domain analysis grid into an electromagnetic field finite element solver, and loading the transmission loss weight factor in the hierarchical electromagnetic feature set as a boundary condition; Inject multi-frequency test signals into the high-density frequency domain grid cells in the reflection-sensitive area to collect phase interference data between the interface reflection wave and the transmission signal; Apply a swept frequency excitation signal to the wide-band grid cells in the transmission stability region and record the signal attenuation distribution within the wide frequency band. By integrating phase interference data and signal attenuation distribution, a full-band dynamic transmission response spectrum containing a three-dimensional frequency-amplitude-phase relationship matrix is ​​constructed.

2. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 1, wherein: The step of performing hierarchical feature analysis on the original electromagnetic parameter sequence includes: The impedance change value in the original electromagnetic parameter sequence is extracted to form the basic transmission layer feature set, the dielectric loss factor is extracted to form the dielectric coupling layer feature set, and the conductor skin depth measurement value is extracted to form the interface reflection layer feature set; Establishing an energy attenuation correlation model between the basic transmission layer feature set and the dielectric coupling layer feature set, and simultaneously establishing a phase offset mapping model between the interface reflection layer feature set and the basic transmission layer feature set; The outputs of the energy attenuation correlation model and the phase shift mapping model are fused to generate a hierarchical electromagnetic feature set including a transmission loss weight factor and a reflection suppression coefficient.

3. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 1, wherein: Optimize the structural parameter configuration scheme, including: Inputting the full-band dynamic transmission response spectrum into a material electromagnetic property database to match candidate material combinations that meet frequency-amplitude-phase constraints; Calculate the dielectric constant tolerance range of the candidate material combination in the corresponding frequency band based on the phase interference data of the reflection sensitive area; According to the signal attenuation distribution in the transmission stable region, the conductor conductivity optimization threshold of the candidate material combination is calculated; The optimal material combination is screened based on the dielectric constant tolerance range and the conductor conductivity optimization threshold, and a structural parameter configuration scheme including the insulator dielectric parameters, conductor coating thickness and contact surface roughness is generated.

4. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 3, wherein: The generating of the high-frequency signal transmission parameter optimization instruction set comprises: parsing dielectric parameters of the insulator in the structural parameter configuration scheme to generate a dielectric filling density adjustment instruction; Analyze conductor coating thickness data and generate surface deposition process control instructions; Analyze the contact surface roughness index and generate mechanical grinding precision calibration instructions; The dielectric filling density adjustment instructions, surface deposition process control instructions and mechanical polishing precision calibration instructions are integrated into a high-frequency signal transmission parameter optimization instruction set according to the process timing.

5. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 1, wherein: The method for updating the hierarchical electromagnetic feature set includes: After the RF coaxial connector performs a physical structure dynamic adjustment operation, an updated electromagnetic parameter sequence is re-collected; Compare the maximum impedance deviation values ​​of the basic transmission layer feature set before and after the update. If the deviation exceeds the tolerance, re-trigger the hierarchical feature analysis; The skin depth change rate of the interface reflection layer feature set is monitored, and when the change rate exceeds the preset window, the division strategy of the non-uniform frequency domain analysis grid is updated.

6. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 1, wherein: The verification method of the full-band dynamic transmission response spectrum includes: Load the full-band dynamic transmission response spectrum generated by simulation into the network analyzer; Apply a swept frequency test signal identical to the simulated one to the actual port of the RF coaxial connector; Collect the actual transmission response curve and compare it with the response spectrum prediction value frequency by frequency point; When the error of the key frequency point exceeds the threshold, it is fed back to the hierarchical electromagnetic feature set for feature weight recalibration.

7. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 4, wherein: The execution logic of the physical structure dynamic adjustment operation includes: Receive the medium filling density adjustment instruction in the high-frequency signal transmission parameter optimization instruction set, and control the injection molding machine to adjust the insulating medium injection pressure according to the gradient; Respond to surface deposition process control instructions and adjust the ion sputtering time of the vacuum coating machine; Execute mechanical grinding precision calibration instructions and drive the CNC grinder to adopt a spiral progressive grinding path; The execution data of each process is synchronized in real time to the full-band dynamic transmission response spectrum verification system.

8. The method for optimizing high-frequency signal transmission of a radio frequency coaxial connector according to claim 7, wherein: The method for synchronizing process execution data includes: Real-time acquisition of injection molding machine pressure curve, coating machine sputtering rate and grinder grinding trajectory data; Map the pressure curve to the predicted value of the dielectric constant of the medium, convert the sputtering rate into the conductor surface impedance value, and analyze the grinding track data into the contact surface roughness index; The predicted dielectric constant value, conductor surface impedance value and roughness index are input into the time-frequency joint simulation module to generate a real-time optimization evaluation report of the structural parameters; When the evaluation report shows that the key parameters deviate from the optimization target, the iterative generation of the high-frequency signal transmission parameter optimization instruction set is triggered.

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