A vehicle-mounted antenna simulation method and device based on twin modeling and a medium

Abstract

The application discloses a kind of based on twin modeling's vehicle antenna simulation method, equipment and medium, it is related to digital twinborn technical field, including: acquisition vehicle state variable, and mapping and encapsulation, generate simulation data package;Vehicle antenna digital twinborn is divided into three linkage levels, and according to simulation data package is according to interlayer mapping rule for three linkage levels configuration solution strategy, generate multi-fidelity solution rule set;Real-time acquisition vehicle operating state parameter, and vehicle operating state parameter is working condition parameter mapping, obtain current working condition data, according to multi-fidelity solution rule set is to current working condition data online simulation calculation, generate initial online simulation data.The application provides efficient, accurate simulation calculation under the dynamic change of vehicle working condition, not only improves the precision of vehicle antenna system design, but also enhances the adaptability to complex environment, provides more reliable technical support for the performance optimization of vehicle antenna.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, and in particular to a method, device and medium for simulating vehicle-mounted antennas based on twin modeling. Background Technology

[0002] With the development of intelligent connected vehicles, vehicle antennas, as a core component of vehicle communication systems, directly affect vehicle communication quality, navigation accuracy, and safety. In recent years, digital twin technology has been gradually introduced into the design and simulation process of vehicle antennas. Twin-based modeling methods construct virtual mappings of physical antennas and utilize real-time acquired vehicle data for driving, enabling the simulation of the dynamic behavior of physical antennas in virtual space. This provides a new technical approach for online evaluation and optimization of antenna performance.

[0003] Existing twin-modeling-based vehicle antenna simulation methods typically suffer from high computational complexity, poor real-time performance, and insufficient adaptability to various operating conditions. Most simulation methods rely on a single solution strategy, which cannot accurately model and adjust the complex vehicle antenna environment in real time, resulting in ineffective simulation calculations under changing operating conditions. Furthermore, existing technologies often lack the ability to dynamically adapt to and promptly correct for changes in vehicle state, making it difficult to meet the requirements of high accuracy and real-time performance in practical applications. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a vehicle-mounted antenna simulation method based on twin modeling to solve the problems of poor real-time performance and insufficient adaptability to operating conditions in vehicle-mounted antenna simulation calculations.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: Firstly, this invention provides a vehicle-mounted antenna simulation method based on twin modeling, comprising: collecting vehicle state variables, mapping and encapsulating them to generate simulation data packets; dividing the vehicle-mounted antenna digital twin into three linkage levels, configuring solution strategies for the three linkage levels according to the simulation data packets and inter-layer mapping rules, and generating a multi-fidelity solution rule set; collecting vehicle operating state parameters in real time, mapping the vehicle operating state parameters to operating condition parameters, obtaining current operating condition data, performing online simulation calculations on the current operating condition data according to the multi-fidelity solution rule set, and generating initial online simulation data; defining state change events, and determining whether a state change event is triggered based on the initial online simulation data; directly outputting the initial online simulation data when not triggered, and performing local recalculation correction when triggered to generate real-time simulation data; and organizing and storing the real-time simulation data to generate a simulation result report.

[0007] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the specific steps of collecting vehicle state variables, mapping and encapsulating them, and generating simulation data packets are as follows: Vehicle state variables are collected uniformly, and their validity is verified, boundaries are corrected, and normalization is performed to form unified state data. Based on the coupling relationship between unified state data, the unified state data is fused and outlier correction is performed to generate comprehensive state mapping data. The comprehensive state mapping data is then fixedly encapsulated to generate simulation data packets.

[0008] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the process of dividing the vehicle-mounted antenna digital twin into three interconnected levels and configuring solution strategies for the three interconnected levels according to the simulation data packets and inter-layer mapping rules to generate a multi-fidelity solution rule set is as follows: Collect vehicle-mounted antenna digital twin data, preprocess the vehicle-mounted antenna digital twin data, extract parameters and perform unified mapping to obtain a standardized parameter dataset, perform hierarchical association and experimental calibration, and construct a vehicle-mounted antenna digital twin; The vehicle-mounted antenna digital twin is fixedly divided into a mechanical boundary layer, a structural coupling layer, and an electromagnetic radiation layer according to the transmission path; Based on the simulation data package, solution strategies are configured for the mechanical boundary layer, structural coupling layer, and electromagnetic radiation layer respectively. The solution output of the previous layer is used as the solution input of the next layer according to the inter-layer mapping rules, and then unified, integrated and stored to generate a multi-fidelity solution rule set.

[0009] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the specific steps for real-time acquisition of vehicle operating status parameters and mapping of these parameters to operating condition parameters to obtain current operating condition data are as follows: Real-time acquisition of vehicle operating status parameters; comprehensive mapping of vehicle operating status parameters based on operating condition characterization quantities to determine vehicle operating condition category; and calculation and output of vehicle operating condition parameters in combination with vehicle operating status parameters. Vehicle operating condition categories are identified and encapsulated, and then aligned and uniformly encapsulated in conjunction with vehicle operating condition parameters and corresponding vehicle operating status parameters to generate current operating condition data.

[0010] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the specific steps for generating initial online simulation data by performing online simulation calculations on the current operating condition data according to a multi-fidelity solution rule set are as follows: Substitute the current operating condition data into the multi-fidelity solution rule set, and calculate the installation boundary response data according to the mechanical boundary layer solution strategy; According to the inter-layer mapping rules, the installation boundary response data is sequentially passed to the structural coupling layer and the electromagnetic radiation layer for recursive solution layer by layer to obtain the structural electromagnetic coupling data. The data is then combined with the installation boundary response data to generate the initial online simulation data.

[0011] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the specific steps of defining state change events and determining whether to trigger state change events based on initial online simulation data are as follows: Within the digital twin of the vehicle-mounted antenna, by comparing the initial online simulation data of the current cycle with that of the previous cycle, events that synchronously change across levels are identified and defined as state change events. The initial online simulation data of the current cycle and the previous cycle are compared by difference, smoothed and normalized to generate state change characterization parameters; The state change characterization parameter is compared with the preset trigger threshold. When the state change characterization parameter reaches the preset trigger threshold, the current period is marked as a triggered state change event, and a trigger identifier is generated. When the state change representation parameter is lower than the preset trigger threshold, the current period is marked as an untriggered state change event, and an untriggered flag is generated.

[0012] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the steps of directly outputting initial online simulation data when no trigger is performed, and performing local recalculation correction to generate real-time simulation data when triggered, are as follows: In the current cycle, first read the judgment result of the state change event. If the judgment result is that it has not been triggered, do not start the recalculation process, and directly output the initial online simulation data of the current cycle. When the determination result is triggered, the installation boundary response data and structural electromagnetic coupling data are extracted, differential processing and local recursive recalculation are performed to obtain local recalculation correction data, and the local recalculation correction data is replaced, written back and uniformly packaged to generate real-time simulation data.

[0013] As a preferred embodiment of the vehicle-mounted antenna simulation method based on twin modeling described in this invention, the specific steps for organizing and storing real-time simulation data and generating a simulation result report are as follows: The real-time simulation data is extracted, sorted, classified and merged, and stored in chronological order according to a preset time window. The statistical information is then merged and summarized to obtain the simulation summary information. The simulation summary information is written into the report header, report body and report footer in the order of preset fields and then encapsulated to generate a simulation result report.

[0014] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the vehicle-mounted antenna simulation method based on twin modeling as described in the first aspect of the present invention.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements any step of the twin-modeling-based vehicle antenna simulation method described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By defining a multi-level linkage and multi-fidelity solution rule set based on twin modeling, online simulation calculations are performed on the basis of real-time acquisition of vehicle operating state parameters. Simulation calculations are quickly performed and preliminary results are generated according to different operating condition data. By judging state change events and performing local recalculation corrections, the simulation results can be further optimized, ensuring the accuracy and reliability of real-time simulation data. It provides efficient and accurate simulation calculations under dynamically changing vehicle operating conditions, which not only improves the accuracy of vehicle antenna system design, but also enhances the adaptability to complex environments, providing more reliable technical support for the performance optimization of vehicle antennas. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a vehicle-mounted antenna simulation method based on twin modeling.

[0019] Figure 2 A flowchart for generating simulation data packages.

[0020] Figure 3 A flowchart for generating current operating condition data.

[0021] Figure 4 A flowchart for generating the untriggered flag.

[0022] Figure 5 This is a CAD structural diagram of a vehicle-mounted antenna. Detailed Implementation

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0024] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0025] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0026] Reference Figures 1-5 As one embodiment of the present invention, this embodiment provides a vehicle-mounted antenna simulation method based on twin modeling, including the following steps: S1. Collect vehicle state variables, map and encapsulate them, and generate simulation data packets.

[0027] S1.1 Collect vehicle state variables in a unified manner, and perform validity verification, boundary correction and normalization processing on the vehicle state variables to form unified state data.

[0028] Furthermore, vehicle state variables are collected uniformly, and their validity is verified. By setting reasonable boundary conditions, the data is checked to see if it is within the expected range. When anomalies are found, boundary correction is performed to adjust data that exceeds the range to a reasonable interval and to supplement missing data. Min-max normalization is used to map each vehicle state variable to the interval [0, 1], transforming different state variables into a unified dimension to ensure that all variables are compared on the same scale, forming unified state data that has undergone validity verification, boundary correction, and normalization.

[0029] It should be noted that the expected range is determined by combining the measurement range of physical sensors, the normal operating range defined in the vehicle design specifications, and the statistical analysis results of historical operating data.

[0030] S1.2 Based on the coupling relationship between the unified state data, the unified state data is fused and outlier correction is performed to generate comprehensive state mapping data. The comprehensive state mapping data is then fixedly encapsulated to generate simulation data packets.

[0031] Furthermore, the coupling relationship between unified state data is quantified by calculating the correlation coefficient between them. Based on this coupling relationship, the unified state data is fused using a weighted average method. The weights are generated based on the correlation coefficients between the unified state data, negative correlation coefficients are truncated to zero, and only positive correlation coefficients are retained. The retained positive correlation coefficients are then normalized by summing them to obtain the weighted fusion weights for each unified state data point. Outliers are identified by calculating the standard score (Z-score) of each unified state data point. When the absolute value of the standard score of a unified state data point exceeds a preset outlier threshold, that unified state data point is identified as an outlier, and the outlier is replaced and corrected using the mean of the corresponding data within a preset sliding window or the interpolation result based on adjacent valid data. After fusion and outlier correction, comprehensive state mapping data is generated. The comprehensive state mapping data is written into a binary file according to a predefined data format (such as double-precision floating-point or integer) to generate a simulation data package.

[0032] It should be noted that the comprehensive state mapping data is an optimized state set generated by the fusion and outlier correction of multi-source data coupling relationships. Specifically, it includes: core state variables with high confidence (such as fused position, velocity, and attitude), state confidence and error assessment, coupling relationship identifiers on which data fusion is based, and standardized metadata such as unified time base and units.

[0033] S2. Divide the vehicle-mounted antenna digital twin into three linkage levels, and configure the solution strategy for the three linkage levels according to the simulation data packet and the inter-layer mapping rules to generate a multi-fidelity solution rule set.

[0034] S2.1 Collect vehicle-mounted antenna digital twin data, preprocess the vehicle-mounted antenna digital twin data, extract parameters and perform unified mapping to obtain a standardized parameter dataset, perform hierarchical association and actual measurement calibration, and construct a vehicle-mounted antenna digital twin.

[0035] Furthermore, the digital twin data of the vehicle-mounted antenna is divided into time-series response data and structural parameter data. The time-series response data is preprocessed using a moving average filter to remove noise, and frequency domain feature parameters are extracted using a fast Fourier transform. The structural parameter data undergoes format verification, missing data completion, and dimensional unification, and geometric dimension parameters, material property parameters, and connection relationship parameters are extracted. The frequency domain feature parameters, geometric dimension parameters, material property parameters, and connection relationship parameters are then uniformly mapped using minimum-maximum normalization to generate a standardized parameter dataset. The standardized parameter dataset is hierarchically associated according to the physical composition hierarchy of the vehicle-mounted antenna to establish a correspondence index between parameters and antenna elements, feed networks, and radiating structures. Measured data is then used to perform experimental calibration using the least squares method to fit correction coefficients and adjust the hierarchically associated standardized parameter dataset, thus constructing a digital twin of the vehicle-mounted antenna.

[0036] It should be noted that the correction coefficient refers to the calibration parameter obtained by fitting the deviation between the measured data and the simulation data. It is used to compensate and correct the key parameters in the standardized parameter dataset, so that the digital twin model is closer to the real vehicle antenna state. It may specifically include one or more of the following: amplitude correction coefficient, frequency offset correction coefficient, phase correction coefficient, stiffness correction coefficient, damping correction coefficient, etc.

[0037] S2.2 The vehicle-mounted antenna digital twin is fixedly divided into a mechanical boundary layer, a structural coupling layer, and an electromagnetic radiation layer according to the transmission path.

[0038] Furthermore, the fixed division is based on the energy and signal transmission path of the physical vehicle antenna described by the vehicle antenna digital twin during operation. The mechanical boundary layer corresponds to the mounting contact surface between the physical vehicle antenna and the vehicle body, and is responsible for describing the boundary conditions of fixation and support. The structural coupling layer corresponds to the mechanical structural components of the physical vehicle antenna, and is responsible for describing the transmission relationship of vibration and stress. The electromagnetic radiation layer corresponds to the radiating element of the physical vehicle antenna, and is responsible for describing the transmission and reception characteristics of electromagnetic waves.

[0039] It should be noted that this solution can be specifically implemented by combining the example roof-mounted shark fin vehicle-mounted antenna. A roof-mounted shark fin vehicle-mounted antenna typically consists of a housing, an external antenna interface, an internal antenna, an electronic control unit (transceiver, tuner), a power / CAN and computer common channel interface, an Ethernet / multimedia directional transmission system interface, and a Bluetooth antenna, and is connected and fixed to the vehicle roof via a bottom mounting structure. During vehicle operation, this device is simultaneously affected by multiple factors such as vehicle body mounting boundary constraints, structural vibration transmission, and changes in electromagnetic radiation performance. Therefore, it has a high degree of compatibility with the layered modeling and joint analysis process of this solution.

[0040] This solution integrates the geometric dimensions, material properties, component connections, installation status, and operational response data of roof-mounted shark fin vehicle-mounted antennas into a unified processing flow, establishing a hierarchical relationship between the roof mounting interface, antenna housing and internal components, and communication and radiation units. Specifically, the mechanical boundary layer corresponds to the mounting contact area between the antenna's bottom mounting base and the vehicle body sheet metal; the structural coupling layer corresponds to the housing, external antenna interface, internal antenna, electronic control unit, and various connection interfaces and transmission components; and the electromagnetic radiation layer corresponds to the internal antenna, Bluetooth antenna, and radiation units related to communication functions. Based on this application, this solution enables the linked analysis of structural response and electromagnetic performance, thereby characterizing the antenna's deformation, frequency shift, gain variation, and overall radiation performance changes under different vehicle operating conditions.

[0041] The roof-mounted shark fin vehicle-mounted combination antenna can be specifically a multi-frequency combination antenna that integrates a cellular communication antenna, a GNSS navigation antenna, and a WiFi / Bluetooth antenna, or an integrated vehicle-mounted antenna that integrates 5G communication, V2X communication, satellite positioning, and short-range wireless communication functions.

[0042] S2.3. Based on the simulation data package, configure the solution strategies for the mechanical boundary layer, structural coupling layer and electromagnetic radiation layer respectively. Use the solution output of the previous layer as the solution input of the next layer according to the inter-layer mapping rules, and integrate and store them in a unified manner to generate a multi-fidelity solution rule set.

[0043] Furthermore, based on the simulation data package and the vehicle-mounted antenna digital twin, solution strategies are configured for the mechanical boundary layer, structural coupling layer, and electromagnetic radiation layer, respectively. When configuring the solution strategies, the boundary condition parameters of the mechanical boundary layer are extracted from the simulation data package to set the finite element solver configuration for the mechanical boundary layer; the material properties and connection relationship parameters of the structural coupling layer are extracted from the vehicle-mounted antenna digital twin to set the modal superposition method solver configuration for the structural coupling layer; and the geometric and material parameters of the radiating elements of the electromagnetic radiation layer are extracted from the vehicle-mounted antenna digital twin to set the method of moments solver configuration for the electromagnetic radiation layer. The solution output of the previous layer is used as the solution input of the next layer according to the inter-layer mapping rules, which specify the mechanical boundary layer... The displacement and stress distribution in the solution output of the layer are mapped to the load conditions in the solution input of the structural coupling layer through interpolation. The structural deformation in the solution output of the structural coupling layer is mapped to the geometric deformation parameters in the solution input of the electromagnetic radiation layer through data format conversion. The solution strategy configurations of the mechanical boundary layer, the structural coupling layer, and the electromagnetic radiation layer are integrated with the interlayer mapping rules from the mechanical boundary layer to the structural coupling layer and from the structural coupling layer to the electromagnetic radiation layer to form a structured dataset containing solver parameters, data transfer relationships, and fidelity switching rules. The structured dataset is then stored as a binary file to generate a multi-fidelity solution rule set.

[0044] It should be noted that the fidelity switching rule is used to select the corresponding solution path based on the vehicle operating condition, the intensity of state change, or the current computing resource usage. Specifically, when the vehicle operating condition is relatively stable, the intensity of state change is low, or computing resources are limited, a low-fidelity solution path is selected. When the vehicle operating condition is complex, the intensity of state change is high, or computing resources are sufficient, a high-fidelity solution path is selected. The interlayer mapping rule uses displacement coordination and force equivalence principle to map the displacement and stress of the mechanical boundary layer to the load of the structural coupling layer through shape function interpolation, and uses the influence mechanism of geometric deformation on electromagnetic field to map the deformation of the structural coupling layer to the geometric deformation parameter setting of the electromagnetic radiation layer through nodal coordinate reconstruction. The multi-fidelity solution rule set, by solidifying and encapsulating the solution configuration and inter-layer mapping rules of multiple physical layers, constructs a standardized simulation pipeline, fundamentally solving the problems of inconsistent data formats and chaotic coupled logic, realizing high-precision automated collaborative solution, and significantly improving simulation efficiency and reproducibility.

[0045] S3. Real-time acquisition of vehicle operating status parameters, mapping of vehicle operating status parameters to operating condition parameters, acquisition of current operating condition data, online simulation calculation of current operating condition data based on multi-fidelity solution rule set, and generation of initial online simulation data.

[0046] S3.1 Real-time acquisition of vehicle operating status parameters, comprehensive mapping of vehicle operating status parameters based on operating condition characterization quantities to determine vehicle operating condition category, and calculation and output of vehicle operating condition parameters in combination with vehicle operating status parameters.

[0047] Furthermore, the vehicle operating status parameters (including vehicle speed, engine speed, and throttle opening) are collected in real time. Based on the operating condition characterization quantities, the vehicle operating status parameters are comprehensively mapped to determine the vehicle operating condition category. The comprehensive mapping is achieved by comparing the vehicle operating status parameters with predefined operating condition characterization quantities. Combined with the calculation of vehicle operating status parameters, specific statistical quantities are extracted from the vehicle operating status parameters according to the determined vehicle operating condition category. For example, after determining that the vehicle operating condition category is the uniform speed operating condition category, the average value and standard deviation of the vehicle speed in the vehicle operating status parameters within a 10-second time window of the example value are calculated. The formula for calculating average vehicle speed is: ; in, This represents the average vehicle speed within a 10-second time window for the example value. This represents the total number of vehicle speed sampling points collected within the 10-second time window of the example value; This indicates the sequence number of the vehicle speed sampling point, with a value ranging from 1 to... ; Indicates the first The instantaneous vehicle speed value corresponding to each sampling point.

[0048] The expression for calculating the standard deviation of vehicle speed is: ; in, This represents the standard deviation of vehicle speed within a 10-second time window for the example value; Indicates the first The deviation between the instantaneous vehicle speed value and the average vehicle speed at each sampling point.

[0049] Output vehicle operating condition parameters, which are statistical results of the calculated vehicle operating state parameters.

[0050] It should be noted that the operating condition characterization quantity is a combination of numerical ranges for vehicle speed, engine speed, and throttle opening, determined jointly by the clustering results of historical operating data and the boundaries of the vehicle calibration operating conditions. Specifically, cluster analysis is first performed on the historical operating data to obtain the parameter distribution ranges corresponding to each typical operating condition. Then, the boundary thresholds of each operating condition category are determined by combining the vehicle calibration data to form the operating condition characterization quantity used for vehicle operating condition classification. For example, vehicle operating state parameters with vehicle speed in the example range of 0 to 30 km / h, engine speed in the example range of 0 to 1500 rpm, and throttle opening in the example range of 0% to 10% are mapped to the idle operating condition category.

[0051] S3.2 Identify and encapsulate the vehicle operating condition category, and align and uniformly encapsulate it with the vehicle operating condition parameters and the corresponding vehicle operating status parameters to generate the current operating condition data.

[0052] Furthermore, vehicle operating condition categories are identified and encapsulated. Identification is achieved by adding a label field describing the operating condition name to the vehicle operating condition category. Encapsulation involves writing the identified vehicle operating condition category into the data header in a key-value pair format. Alignment processing is performed by combining the vehicle operating condition parameters and the corresponding vehicle operating status parameters. The alignment process uses a timestamp field shared by the vehicle operating condition parameters and the corresponding vehicle operating status parameters to match data rows, ensuring that the vehicle operating condition parameters and vehicle operating status parameters at the same time are in the same data record. The encapsulated vehicle operating condition categories, aligned vehicle operating condition parameters, and corresponding vehicle operating status parameters are then uniformly encapsulated. This uniform encapsulation follows a predefined data frame format, sequentially writing the vehicle operating condition category, vehicle operating condition parameters, and corresponding vehicle operating status parameters into a binary data block to generate the current operating condition data.

[0053] S3.3 Substitute the current operating condition data into the multi-fidelity solution rule set and calculate the installation boundary response data according to the mechanical boundary layer solution strategy.

[0054] Furthermore, a mechanical boundary layer solution strategy is extracted from the multi-fidelity solution rule set. This strategy includes the configuration parameters of the finite element solver. Current operating data is substituted into the mechanical boundary layer solution strategy, along with a pre-established calibration mapping model between vehicle operating state parameters and vibration loads on the vehicle body mounting surface. The vehicle speed and engine speed in the current operating data are converted into boundary excitation parameters required by the mechanical boundary layer finite element solver. These parameters include excitation frequency, load amplitude, and load direction, and are further generated as one or more boundary condition load inputs, such as equivalent nodal forces, equivalent nodal moments, displacement boundary conditions, or vibration acceleration boundary conditions. Based on the finite element solver configuration in the mechanical boundary layer solution strategy, the finite element solver is invoked to solve the finite element model of the mechanical boundary layer, calculating the displacement and stress field distributions of the mechanical boundary layer under the input loads. The calculated displacement and stress field distributions are then output as the installation boundary response data.

[0055] It should be noted that the mechanical boundary layer solution strategy is a set of mechanical solution rules pre-stored in a multi-fidelity solution rule set, including the mapping rules from working conditions to boundary loads / constraints and the solver configuration parameters, which are generated in advance by combining digital twin modeling with offline simulation and field calibration. The installation boundary response data is the output of the mechanical layer, specifically including displacement, rotation angle, stress / strain, reaction force, and vibration acceleration.

[0056] S3.4. According to the inter-layer mapping rules, the installation boundary response data is sequentially transferred to the structural coupling layer and the electromagnetic radiation layer for layer-by-layer recursive solution to obtain the structural electromagnetic coupling data. Combined with the installation boundary response data, the data is organized to generate the initial online simulation data.

[0057] Furthermore, the interlayer mapping rules from the mechanical boundary layer to the structural coupling layer, the structural coupling layer solution strategy, the interlayer mapping rules from the structural coupling layer to the electromagnetic radiation layer, and the electromagnetic radiation layer solution strategy are extracted from the multi-fidelity solution rule set. According to the interlayer mapping rules from the mechanical boundary layer to the structural coupling layer, the displacement field in the installation boundary response data is converted into load boundary conditions for the structural coupling layer solution. Based on the structural coupling layer solution strategy, the modal superposition method solver is invoked to obtain structural deformation and stress distribution data. According to the interlayer mapping rules from the structural coupling layer to the electromagnetic radiation layer, the structural deformation data is converted into geometric deformation parameters for the electromagnetic radiation layer solution. Based on the electromagnetic radiation layer solution strategy, the method of moments solver is invoked to obtain radiation pattern and gain data. The structural deformation and stress distribution data, as well as the radiation pattern and gain data, are collectively referred to as structural electromagnetic coupling data. The installation boundary response data and the structural electromagnetic coupling data are merged to generate initial online simulation data.

[0058] It should be noted that the modal superposition method solver is a structural dynamics solution tool. By decomposing the complex vibration response into a linear combination of several modes, it transforms the dynamic equations in physical coordinates to modal coordinates for decoupling and solving, thereby efficiently obtaining the deformation and stress distribution data of the structure under load. The method of moments (MoM) solver is a numerical solution tool for electromagnetic fields. It discretizes the continuous current distribution on the antenna surface into a combination of basis functions, transforms the integral equation into a matrix equation for solution, and then accurately calculates the radiation pattern and gain data of the deformed antenna structure.

[0059] S4. Define state change events and determine whether to trigger them based on the initial online simulation data. If not triggered, directly output the initial online simulation data. If triggered, perform local recalculation correction to generate real-time simulation data.

[0060] S4.1 Within the digital twin of the vehicle-mounted antenna, by comparing the initial online simulation data of the current cycle with that of the previous cycle, events that change synchronously across levels are identified and defined as state change events.

[0061] Furthermore, within the digital twin of the vehicle-mounted antenna, the initial online simulation data of the current cycle is compared with the initial online simulation data of the previous cycle. The changes in the installation boundary response data in the mechanical boundary layer, the changes in the structural deformation and stress distribution data in the structural coupling layer, and the changes in the radiation pattern and gain data in the electromagnetic radiation layer are calculated. When the changes in the installation boundary response data, the changes in the structural deformation and stress distribution data, and the changes in the radiation pattern and gain data all exceed the preset level state change threshold, the identified cross-level synchronous change event is marked as a state change candidate event, and the state change candidate event is sent to the subsequent triggering and judgment process.

[0062] It should be noted that the threshold for hierarchical state change is determined by analyzing the statistical distribution of changes in the mechanical boundary layer displacement field, structural coupling layer deformation field, and electromagnetic radiation layer gain in historical simulation data. The initial value is the sum of the mean and three times the standard deviation. After engineering verification by combining the mechanical fatigue limit of the physical antenna, the material yield stress margin, and the communication link gain fluctuation tolerance, it is solidified in the multi-fidelity solution rule set.

[0063] S4.2 Perform differential comparison, smoothing and normalization on the initial online simulation data of the current cycle and the previous cycle to generate state change characterization parameters.

[0064] Furthermore, the initial online simulation data of the current period is compared with the initial online simulation data of the previous period by differential comparison. The differential comparison is achieved by calculating the difference between corresponding data points in the initial online simulation data of the two periods. The difference data obtained after differential comparison is smoothed by using the moving average filtering method, which removes short-term fluctuations by calculating the average value of the difference data within a specified window. The smoothed data is then normalized by using the min-max normalization method, and the normalized data is used as a parameter to represent state changes.

[0065] S4.3. Compare the state change characterization parameter with the preset trigger threshold. When the state change characterization parameter reaches the preset trigger threshold, mark the current period as a triggered state change event and generate a trigger identifier.

[0066] Furthermore, the comparison operation involves checking whether each value in the state change characterization parameter is greater than or equal to the corresponding threshold component in the preset trigger threshold. When the value in the state change characterization parameter is greater than or equal to the corresponding threshold component in the preset trigger threshold, the timestamp of the current period is added to the state change event record. An identifier field is added to the state change event record for the current period to generate a trigger identifier.

[0067] It should be noted that the trigger threshold is determined by statistically analyzing the offline simulation results and measured calibration data, extracting the fluctuation range of the state change characterization parameters under normal operating conditions, and combining the change amplitude when recalculation is required to determine the preset boundary value.

[0068] S4.4 When the state change characterization parameter is lower than the preset trigger threshold, the current period is marked as an untriggered state change event, and an untriggered flag is generated.

[0069] Furthermore, the state change characterization parameters are compared with preset trigger thresholds. The comparison operation involves reading each value in the state change characterization parameters one by one and comparing each value with the corresponding threshold in the preset trigger thresholds. When all values ​​in the state change characterization parameters are less than the corresponding threshold in the preset trigger thresholds, a non-triggered flag is generated in the record line corresponding to the timestamp of the current period in the state change event record.

[0070] It should be noted that the trigger threshold specifically refers to the set of thresholds that correspond one-to-one with each component of the state change characterization parameter. Specifically, these include the installation point displacement change threshold, the installation surface rotation angle change threshold, the structural stress / strain change threshold, the vibration acceleration change threshold, and the electromagnetic performance change threshold. The threshold for installation point displacement variation is set based on the displacement fluctuation range of the installation point under normal operating conditions, combined with the minimum displacement variation that triggers recalculation; the threshold for installation surface rotation angle variation is set based on the fluctuation range of the installation surface attitude angle under normal operating conditions, combined with the minimum rotation angle variation that triggers recalculation; the threshold for structural stress / strain variation is set based on the fluctuation range of structural stress or strain under normal operating conditions, combined with the allowable stress, allowable strain, and safety margin of the material; the threshold for vibration acceleration variation is set based on the fluctuation range of vibration acceleration under normal operating conditions, combined with the minimum vibration response amplitude that can characterize a significant change in state; the threshold for electromagnetic performance variation is set based on the fluctuation range of electromagnetic performance parameters under normal operating conditions, combined with the minimum performance variation that triggers recalculation.

[0071] S4.5. In the current cycle, first read the judgment result of the state change event. When the judgment result is not triggered, do not start the recalculation process, and directly output the initial online simulation data of the current cycle.

[0072] Furthermore, the system reads the judgment result of the current period's corresponding state change event from the state change event record. The reading operation involves retrieving the identifier field of the corresponding row in the state change event record based on the timestamp of the current period and obtaining the value of the identifier field. The value of the identifier field is then judged by comparing it with a preset non-triggered identifier value. When the value of the identifier field matches the preset non-triggered identifier value, the judgment result is that it has not been triggered. The recalculation process is not initiated, and the initial online simulation data of the current period is directly copied from memory to the output buffer, completing the direct output of the initial online simulation data of the current period.

[0073] It should be noted that the non-triggered flag value is set through a preset flag field encoding rule. This rule defines the flag field as a binary enumeration type in the multi-fidelity solution rule set, and explicitly specifies the example value 0 as the non-triggered flag value.

[0074] S4.6 When the judgment result is triggered, extract the installation boundary response data and structural electromagnetic coupling data, perform differential processing and local recursive recalculation, obtain local recalculation correction data, replace and write back the local recalculation correction data and encapsulate it uniformly to generate real-time simulation data.

[0075] Furthermore, when the determination result is triggered, the installation boundary response data and structural electromagnetic coupling data for the current period are extracted from the vehicle-mounted antenna digital twin. Differential processing is performed on the installation boundary response data and structural electromagnetic coupling data to obtain the difference between the installation boundary response data and the structural electromagnetic coupling data. Based on the difference between the installation boundary response data, local recursive recalculation is performed according to the inter-layer mapping rules from the mechanical boundary layer to the structural coupling layer and the structural coupling layer solution strategy to obtain updated structural coupling layer data. Based on the updated structural coupling layer data and the difference between the structural electromagnetic coupling data, local recursive recalculation is performed according to the inter-layer mapping rules from the structural coupling layer to the electromagnetic radiation layer and the electromagnetic radiation layer solution strategy to obtain updated electromagnetic radiation layer data. The updated structural coupling layer data and the updated electromagnetic radiation layer data are integrated into locally recalculated correction data. The locally recalculated correction data is then written back to the initial online simulation data for the current period, and the replaced data is uniformly packaged to generate real-time simulation data.

[0076] S5. Organize and store the real-time simulation data, and generate a simulation result report.

[0077] S5.1 Extract fields from real-time simulation data according to a preset time window, organize them in order, classify and merge them, and store them in time sequence. Then, merge and summarize various statistical information to obtain simulation summary information.

[0078] Furthermore, based on a preset time window, real-time simulation data is read from the output buffer or the current cycle cache. The displacement field values ​​of the installation boundary response data, the structural deformation and stress distribution values ​​of the structural coupling layer data, and the radiation pattern and gain values ​​of the electromagnetic radiation layer data are extracted. The extracted field values ​​are sorted by timestamp and categorized into mechanical boundary layer, structural coupling layer, and electromagnetic radiation layer categories. The data are written to the database in chronological order, and the average displacement field value of the mechanical boundary layer, the maximum and minimum structural deformation values ​​of the structural coupling layer, and the gain variance of the electromagnetic radiation layer are calculated. The simulation summary information containing statistical information of the mechanical boundary layer, structural coupling layer, and electromagnetic radiation layer is then generated.

[0079] It should be noted that the time window is set by analyzing the matching relationship between the minimum time scale of the vehicle antenna dynamic response and the data sampling frequency. The minimum integral duration that can capture the main frequency of mechanical vibration is determined based on the transient change characteristics of the installation boundary response data. The minimum statistical unit that can characterize the change of operating conditions is determined based on the update rate of vehicle operating parameters. At the same time, the write throughput of the storage medium and the computational load constraints of the embedded processor are combined, and multiple constraints are comprehensively weighed and solidified into the preset parameters of the simulation summary process.

[0080] S5.2 Write the simulation summary information into the report header, report body and report footer in the order of preset fields and encapsulate it to generate a simulation result report.

[0081] Furthermore, the metadata from the simulation summary information, such as timestamps and simulation identifiers, is sequentially written into the corresponding field positions of the report header according to a preset field order (set according to the predefined data frame structure format in the data exchange protocol of the simulation summary information), thus completing the report header filling. The mechanical boundary layer statistics, structural coupling layer statistics, and electromagnetic radiation layer statistics from the simulation summary information are sequentially written into the corresponding field positions of the report body according to a preset field order, thus completing the report body filling. The checksum and end flag from the simulation summary information are sequentially written into the corresponding field positions of the report tail according to a preset field order, thus completing the report tail filling. The filled report header, report body, and report tail are combined into a complete data block in the order of report header, report body, and report tail, and the complete data block is compressed and packaged to generate a simulation result report.

[0082] This embodiment also provides a computer device applicable to the vehicle-mounted antenna simulation method based on twin modeling, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the vehicle-mounted antenna simulation method based on twin modeling as proposed in the above embodiment.

[0083] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0084] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the vehicle-mounted antenna simulation method based on twin modeling as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0085] In summary, this invention achieves the following: by defining a multi-level linkage and multi-fidelity solution rule set based on twin modeling, it performs online simulation calculations based on real-time acquisition of vehicle operating state parameters. It rapidly performs simulation calculations and generates preliminary results according to different operating conditions. Through the judgment of state change events and local recalculation correction, it can further optimize simulation results, ensuring the accuracy and reliability of real-time simulation data. It provides efficient and accurate simulation calculations under dynamically changing vehicle operating conditions, not only improving the accuracy of vehicle-mounted antenna system design but also enhancing its adaptability to complex environments, providing more reliable technical support for the performance optimization of vehicle-mounted antennas.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A simulation method for vehicle-mounted antennas based on twin modeling, characterized in that, include: Collect vehicle state variables, map and encapsulate them, and generate simulation data packets; The vehicle-mounted antenna digital twin is divided into three linkage levels, and a solution strategy is configured for the three linkage levels according to the inter-layer mapping rules based on the simulation data packet, generating a multi-fidelity solution rule set; Real-time acquisition of vehicle operating status parameters, mapping of vehicle operating status parameters to operating condition parameters, acquisition of current operating condition data, online simulation calculation of current operating condition data based on multi-fidelity solution rule set, and generation of initial online simulation data; Define state change events and determine whether to trigger them based on the initial online simulation data. If no state change event is triggered, directly output the initial online simulation data. If a state change event is triggered, perform local recalculation correction and generate real-time simulation data. The real-time simulation data is organized and stored to generate simulation result reports.

2. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 1, characterized in that, The specific steps for collecting vehicle state variables, mapping and encapsulating them, and generating simulation data packets are as follows: Vehicle state variables are collected uniformly, and their validity is verified, boundaries are corrected, and normalization is performed to form unified state data. Based on the coupling relationship between unified state data, the unified state data is fused and outlier correction is performed to generate comprehensive state mapping data. The comprehensive state mapping data is then fixedly encapsulated to generate simulation data packets.

3. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 2, characterized in that, The process involves dividing the vehicle-mounted antenna digital twin into three interconnected levels and configuring solution strategies for the three interconnected levels according to the simulation data packets and inter-layer mapping rules, thereby generating a multi-fidelity solution rule set. The specific steps are as follows: Collect vehicle-mounted antenna digital twin data, preprocess the vehicle-mounted antenna digital twin data, extract parameters and perform unified mapping to obtain a standardized parameter dataset, perform hierarchical association and experimental calibration, and construct a vehicle-mounted antenna digital twin; The vehicle-mounted antenna digital twin is fixedly divided into a mechanical boundary layer, a structural coupling layer, and an electromagnetic radiation layer according to the transmission path; Based on the simulation data package, solution strategies are configured for the mechanical boundary layer, structural coupling layer, and electromagnetic radiation layer respectively. The solution output of the previous layer is used as the solution input of the next layer according to the inter-layer mapping rules, and then unified, integrated and stored to generate a multi-fidelity solution rule set.

4. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 1, characterized in that, The real-time acquisition of vehicle operating status parameters, and the mapping of these parameters to operating condition parameters to obtain current operating condition data, are carried out through the following steps: Real-time acquisition of vehicle operating status parameters; comprehensive mapping of vehicle operating status parameters based on operating condition characterization quantities to determine vehicle operating condition category; and calculation and output of vehicle operating condition parameters in combination with vehicle operating status parameters. Vehicle operating condition categories are identified and encapsulated, and then aligned and uniformly encapsulated in conjunction with vehicle operating condition parameters and corresponding vehicle operating status parameters to generate current operating condition data.

5. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 3, characterized in that, The steps for generating initial online simulation data by performing online simulation calculations on the current operating condition data based on a multi-fidelity solution rule set are as follows: Substitute the current operating condition data into the multi-fidelity solution rule set, and calculate the installation boundary response data according to the mechanical boundary layer solution strategy; According to the inter-layer mapping rules, the installation boundary response data is sequentially passed to the structural coupling layer and the electromagnetic radiation layer for recursive solution layer by layer to obtain the structural electromagnetic coupling data. The data is then combined with the installation boundary response data to generate the initial online simulation data.

6. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 5, characterized in that, The process of defining state change events and determining whether to trigger them based on initial online simulation data is as follows: Within the digital twin of the vehicle-mounted antenna, by comparing the initial online simulation data of the current cycle with that of the previous cycle, events that synchronously change across levels are identified and defined as state change events. The initial online simulation data of the current cycle and the previous cycle are compared by difference, smoothed and normalized to generate state change characterization parameters; The state change characterization parameter is compared with the preset trigger threshold. When the state change characterization parameter reaches the preset trigger threshold, the current period is marked as a triggered state change event, and a trigger identifier is generated. When the state change representation parameter is lower than the preset trigger threshold, the current period is marked as an untriggered state change event, and an untriggered flag is generated.

7. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 5, characterized in that, The process involves directly outputting initial online simulation data when no trigger is applied, and performing local recalculation and correction to generate real-time simulation data when a trigger is applied. The specific steps are as follows: In the current cycle, first read the judgment result of the state change event. If the judgment result is that it has not been triggered, do not start the recalculation process, and directly output the initial online simulation data of the current cycle. When the determination result is triggered, the installation boundary response data and structural electromagnetic coupling data are extracted, differential processing and local recursive recalculation are performed to obtain local recalculation correction data, and the local recalculation correction data is replaced, written back and uniformly packaged to generate real-time simulation data.

8. The vehicle-mounted antenna simulation method based on twin modeling as described in claim 7, characterized in that, The specific steps for organizing and storing real-time simulation data and generating simulation result reports are as follows: The real-time simulation data is extracted, sorted, classified and merged, and stored in chronological order according to a preset time window. The statistical information is then merged and summarized to obtain the simulation summary information. The simulation summary information is written into the report header, report body and report footer in the order of preset fields and then encapsulated to generate a simulation result report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the vehicle-mounted antenna simulation method based on twin modeling as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the vehicle-mounted antenna simulation method based on twin modeling as described in any one of claims 1 to 8.

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