A performance testing device for central control screens in new energy vehicles

Abstract

This invention belongs to the field of central control screen performance testing technology, specifically a performance testing device for central control screens in new energy vehicles. It includes a dynamic extreme environment reproduction chamber, a bionic human-computer interaction simulator, a multimodal parameter synchronous acquisition array, an intelligent decision-level analysis and evaluation engine, and a holographic visualization decision support terminal. The invention provides physical environment support through the dynamic extreme environment reproduction chamber, whose environmental parameters are synchronized in real-time to the multimodal parameter synchronous acquisition array via a CAN bus. The operation data generated by the bionic human-computer interaction simulator and the performance indicators acquired by the multimodal parameter synchronous acquisition array are preprocessed by edge computing nodes and then transmitted to the intelligent decision-level analysis and evaluation engine via gigabit Ethernet. The evaluation report generated by the intelligent decision-level analysis and evaluation engine drives the holographic visualization decision support terminal to display the results, thus realizing the construction of a closed-loop testing system. This significantly improves testing efficiency and performance evaluation accuracy, and demonstrates a high level of automation and intelligence.

Description

Technical Field

[0001] This invention relates to the field of central control screen performance testing technology, specifically a central control screen performance testing device for new energy vehicles. Background Technology

[0002] The central control screen of a new energy vehicle refers to an integrated display and interaction device installed inside the new energy vehicle and located in the central control area in front of the driver and passenger. As the core carrier of vehicle intelligence and connectivity, it undertakes key tasks such as information display, function control, and human-machine interaction, and is the main interaction interface between the driver and the vehicle system.

[0003] As the intelligence level of new energy vehicles increases, the performance of the central control screen directly affects driving safety and user experience. Therefore, it is necessary to conduct performance testing on the central control screen of new energy vehicles. Traditional testing methods mostly adopt single-environment simulation or manual operation evaluation, which cannot build a closed-loop testing system to achieve full-process quantitative evaluation from extreme environment adaptability to human-computer interaction efficiency. The test scenarios are not fully covered, subjective factors have great interference, multi-parameter coupling analysis is insufficient, and the degree of adverse effects on the testing process cannot be accurately analyzed and timely warnings are given, which is not conducive to ensuring test stability and test result accuracy.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a performance testing device for the central control screen of new energy vehicles, which solves the problems of existing technologies that cannot build a closed-loop testing system to achieve full-process quantitative evaluation from extreme environment adaptability to human-computer interaction efficiency, and cannot accurately analyze and provide timely warnings on the degree of adverse effects on the testing process, which is not conducive to ensuring the stability of the test and the accuracy of the test results.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A performance testing device for a central control screen in a new energy vehicle includes a dynamic extreme environment reproduction chamber, a bionic human-computer interaction simulator, a multimodal parameter synchronous acquisition array, an intelligent decision-level analysis and evaluation engine, and a holographic visualization decision support terminal; the dynamic extreme environment reproduction chamber serves as a scenario construction base and immediately starts the environment modeling program after receiving the test task instruction;

[0008] The bionic human-computer interaction simulator is equipped with a multi-degree-of-freedom robotic arm and a pressure sensing platform. It reconstructs the driver's operation model through inverse kinematics algorithm. The end of the multi-degree-of-freedom robotic arm integrates a capacitive touch head and a voice playback module to simulate typical touch operations, including finger swiping, clicking and long pressing. The pressure platform achieves adjustment of pressing force from 0.5N to 20N through pneumatic control.

[0009] The multimodal parameter synchronous acquisition array enters the data capture mode at the same time as the behavior simulation starts, acquires relevant test data, and uses an edge computing architecture to compress and encode the acquired data in real time. The data is then transmitted to the intelligent decision-level analysis and evaluation engine via the TDMA time division multiplexing protocol. After receiving the acquired data, the intelligent decision-level analysis and evaluation engine analyzes and evaluates the performance of the central control screen of the new energy vehicle. The evaluation results are output in the form of a structured report to the holographic visualization decision support terminal for display.

[0010] Furthermore, the dynamic extreme environment reproduction chamber integrates a temperature and humidity controller, an electromagnetic interference generator, a light simulation device, and a vibration table to reproduce temperature ranges from -40℃ to 85℃, humidity ranges from 5%RH to 95%RH, electromagnetic field strengths from 0-40V / m, and multi-frequency vibration conditions.

[0011] Furthermore, the multimodal parameter synchronous acquisition array adopts a fiber optic distributed architecture, integrating acquisition terminals including high-speed cameras, infrared thermal imagers, and touch force sensors. It achieves nanosecond-level data alignment through TDC time synchronization technology, synchronously acquiring key indicators including central control screen display brightness, touch response latency, and interface switching smoothness. It also uses structured light 3D reconstruction technology to capture the geometric distortion of the screen display content, and combines colorimeter data to capture color reproduction information.

[0012] Furthermore, the intelligent decision-level analysis and evaluation engine adopts a dual-channel analysis architecture. The basic analysis layer uses a rule engine to perform compliance checks on the collected data, including automatic judgment of EMC immunity and touch accuracy. The advanced analysis layer builds a deep learning model, establishes a performance degradation curve for the central control screen based on historical test data, predicts the service life under extreme working conditions through an LSTM network, and designs an interaction efficiency evaluation model. It weights and fuses parameters including operation completion time, false touch rate, and interface layer depth to generate a human-computer interaction efficiency index. The evaluation results are output in the form of a structured report, which includes a performance trend chart, an abnormal event timeline, and a list of improvement suggestions.

[0013] Furthermore, the holographic visualization decision support terminal uses coherent light modulation technology to generate a 1280×720 resolution holographic projection, simultaneously displaying a three-dimensional visualization model of environmental parameters, operational behaviors, and performance indicators. It also features a multi-dimensional correlation analysis interface, allowing testers to view temperature curves, touch trajectories, and performance indicators in conjunction with gestures.

[0014] When abnormal data is detected, the holographic visualization decision support terminal automatically triggers local magnification and parameter backtracking functions, identifies areas of performance degradation through color mapping, and has a built-in expert knowledge base to automatically recommend optimization solutions based on test results.

[0015] Furthermore, the holographic visualization decision support terminal communication connection performance test monitoring unit monitors the performance test process of the central control screen of the new energy vehicle, analyzes the data to determine whether a monitoring abnormal signal is generated, and sends it to the holographic visualization decision support terminal for display and warning when a monitoring abnormal signal is generated.

[0016] Furthermore, the specific analysis process of the performance test monitoring unit is as follows:

[0017] Obtain the standard data values ​​of various environmental parameters that need to be simulated during the test, collect the real-time data values ​​of the corresponding environmental parameters, calculate the difference between the real-time data values ​​and the corresponding standard data values ​​and take the absolute value to obtain the simulated abnormal value, compare the simulated abnormal value with the corresponding preset simulated abnormal threshold, and if the simulated abnormal value exceeds the preset simulated abnormal threshold, it is determined that the corresponding environmental parameter is in a simulated unstable state.

[0018] The detection period is divided into several sub-periods. If there are environmental parameters in a simulated unstable state within the corresponding sub-period, the corresponding sub-period is marked as an abnormal period. The number of abnormal periods within the detection period is obtained and the ratio of it to the total number of sub-periods is calculated to obtain the abnormal period feature value. The abnormal period feature value is compared with the preset abnormal period feature threshold. If the abnormal period feature value exceeds the preset abnormal period feature threshold, a monitoring anomaly signal is generated.

[0019] Furthermore, if the time-varying feature value does not exceed the preset time-varying feature threshold, the total duration of the corresponding environmental parameter in the simulated unstable state during the detection period is obtained and marked as the simulated unstable time measurement value. The average value of all simulated abnormal values ​​corresponding to the corresponding environmental parameter during the detection period is calculated to obtain the simulated abnormal value, and the maximum value of the simulated abnormal value corresponding to the corresponding environmental parameter during the detection period is marked as the simulated abnormal amplitude value.

[0020] The parameter simulation analysis value is obtained by weighted summation of simulated unstable time measurement values, simulated abnormal table values, and simulated abnormal amplitude values. The parameter simulation analysis value is then compared with a preset parameter simulation analysis threshold. If the parameter simulation analysis value exceeds the preset parameter simulation analysis threshold, the corresponding environmental parameter is marked as a simulated characteristic parameter. If a simulated characteristic parameter exists within the detection period, a monitoring anomaly signal is generated.

[0021] If no simulated feature parameters are available during the detection period, all interactive test operations performed by the bionic human-computer interaction simulator during the detection period are acquired. If the corresponding interactive test operation is not successfully completed within the specified time requirement, the corresponding interactive test operation is marked as an abnormal operation. The number of abnormal operations during the detection period is counted and the ratio is calculated with the total number of interactive test operations during the detection period to obtain the abnormal operation detection value. The abnormal operation detection value is compared with the preset abnormal operation detection threshold. If the abnormal operation detection value exceeds the preset abnormal operation detection threshold, a monitoring anomaly signal is generated.

[0022] Furthermore, the holographic visualization decision support terminal communication connection test requirement judgment unit, before testing the central control screen of the new energy vehicle, determines whether unnecessary signals are generated through test requirement judgment. If unnecessary signals are generated, they are sent to the holographic visualization decision support terminal for display and warning.

[0023] Furthermore, the specific analysis process for determining test requirements is as follows:

[0024] The system acquires various dimensional information of the central control screen of the new energy vehicle. If there are any dimensional inspection items in the central control screen that do not meet the corresponding requirements, an unnecessary signal is generated. If all dimensional inspection items of the central control screen meet the corresponding requirements, the surface image of the central control screen is collected, and surface defects on the central control screen are identified. If there are surface defects on the central control screen that exceed the corresponding allowable specifications, an unnecessary signal is generated.

[0025] If there are no surface defects exceeding the corresponding allowable specifications on the central control screen of the new energy vehicle, then several detection areas are marked on the central control screen of the new energy vehicle. If there are surface defects in the corresponding detection areas, then the corresponding detection areas are marked as feature areas. The number of feature areas on the central control screen of the new energy vehicle is obtained and marked as feature area condition values. The feature area condition values ​​are compared with the preset feature area condition threshold. If the feature area condition value exceeds the preset feature area condition threshold, then unnecessary signals are generated.

[0026] If the feature area condition value does not exceed the preset feature area condition threshold, the parts where the feature area is clustered are obtained, the number of feature areas in the corresponding parts is marked as the feature cluster value, the number of parts on the new energy vehicle central control screen where the feature cluster value exceeds the preset feature cluster threshold is marked as the cluster risk value, and the feature cluster value with the largest value is marked as the feature cluster amplitude value.

[0027] The characteristic performance value is calculated by weighting and summing the characteristic area condition value, cluster risk frequency value, and characteristic cluster amplitude value. The characteristic performance value is then compared with a preset characteristic performance threshold. If the characteristic performance value exceeds the preset characteristic performance threshold, an unnecessary signal is generated.

[0028] Compared with the prior art, the beneficial effects of the present invention are:

[0029] 1. In this invention, a dynamic extreme environment reproduction chamber provides physical environment support, and environmental parameters are synchronized in real time to a multimodal parameter synchronous acquisition array. The operation data generated by the bionic human-computer interaction simulator and the performance indicators obtained by the multimodal parameter synchronous acquisition array are preprocessed by edge computing nodes and then transmitted to the intelligent decision-level analysis and evaluation engine via gigabit Ethernet. The evaluation report generated by the intelligent decision-level analysis and evaluation engine drives the holographic visualization decision support terminal to display the results, thereby realizing the construction of a closed-loop testing system, significantly improving testing efficiency and performance evaluation accuracy, and achieving a high level of automation and intelligence.

[0030] 2. In this invention, the performance test monitoring unit analyzes the data to determine whether an abnormal monitoring signal is generated. When an abnormal monitoring signal is generated, the cause is investigated and analyzed, and reasonable improvement measures are taken to improve the accuracy and stability of the test results of the central control screen of new energy vehicles, reduce the difficulty of test supervision, and the test requirement judgment unit analyzes the data before testing the central control screen of new energy vehicles to determine whether an unnecessary signal is generated. When an unnecessary signal is generated, the corresponding central control screen of new energy vehicles is not subjected to performance testing, thus avoiding unnecessary testing that leads to wasted time and test costs. Attached Figure Description

[0031] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0032] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0033] Figure 2 This is a system block diagram of Embodiments 2 and 3 of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Example 1: As Figure 1 As shown, the present invention proposes a performance testing device for the central control screen of a new energy vehicle, which includes a dynamic extreme environment reproduction chamber, a bionic human-computer interaction behavior simulator, a multimodal parameter synchronous acquisition array, an intelligent decision-level analysis and evaluation engine, and a holographic visualization decision support terminal.

[0036] The dynamic extreme environment reproduction chamber serves as the foundation for scenario construction, immediately initiating the environmental modeling process upon receiving test mission instructions. This chamber integrates a temperature and humidity controller, an electromagnetic interference generator, a lighting simulation device, and a vibration table (a six-degree-of-freedom vibration platform employing an acceleration integral algorithm to accurately reproduce the vibration spectrum specified in the ISO 16750 standard). It is used to reproduce temperature ranges from -40℃ to 85℃, humidity ranges from 5%RH to 95%RH, electromagnetic field strengths from 0-40V / m, and multi-frequency vibration conditions. Furthermore, environmental parameter adjustment utilizes a predictive control algorithm, automatically generating environmental time-series curves based on test cases. This ensures that parameters such as the rate of change of temperature gradient and vibration frequency achieve a mapping accuracy of over 98% with actual vehicle operating conditions. Through multi-physics coupling simulation, it covers extreme conditions such as high temperatures, low temperatures, and strong vibrations, achieving a 300% improvement in scenario coverage compared to traditional testing methods.

[0037] The bionic human-computer interaction simulator is equipped with a multi-degree-of-freedom robotic arm and a pressure-sensing platform. It reconstructs the driver's operation model using inverse kinematics algorithms. The end effector of the multi-degree-of-freedom robotic arm integrates a capacitive touch sensor and a voice playback module to simulate typical touch operations such as finger swiping, clicking, and long-pressing. The pressure platform uses pneumatic control to adjust the pressing force from 0.5N to 20N. The simulator's built-in operation library includes over 200 typical in-vehicle interaction tasks such as navigation settings, air conditioning adjustment, and multimedia control, and supports custom operation sequence editing. By using a bionic robotic arm to replace manual operation, individual differences are eliminated, and the touch position repeatability accuracy reaches 0.1mm, significantly improving the objectivity of the assessment.

[0038] The multimodal parameter synchronous acquisition array enters the data acquisition mode at the same time as the behavior simulation starts, acquires relevant test data, and uses an edge computing architecture to compress and encode the acquired data in real time. It is then transmitted to the intelligent decision-level analysis and evaluation engine through the TDMA time division multiplexing protocol. It should be noted that the multimodal parameter synchronous acquisition array adopts a fiber optic distributed architecture and integrates acquisition terminals including high-speed cameras, infrared thermal imagers and touch force sensors.

[0039] By using TDC time synchronization technology to achieve nanosecond-level data alignment, key indicators such as central control screen display brightness, touch response latency, and interface switching smoothness can be acquired synchronously. A specially designed visual fidelity evaluation module is also available, which uses structured light 3D reconstruction technology to capture the geometric distortion of the screen display content and combines colorimeter data to capture color reproduction information.

[0040] After receiving the collected data, the intelligent decision-level analysis and evaluation engine analyzes and evaluates the performance of the central control screen of the new energy vehicle. The evaluation results are output to the holographic visualization decision support terminal for display in the form of a structured report. It should be noted that the intelligent decision-level analysis and evaluation engine adopts a dual-channel analysis architecture. The basic analysis layer uses a rule engine to perform compliance checks on the collected data, including automatic interpretation of EMC immunity and touch accuracy.

[0041] The advanced analysis layer constructs a deep learning model, establishes a performance degradation curve for the central control screen based on historical test data, predicts the lifespan under extreme working conditions through an LSTM network, and designs an interaction efficiency evaluation model. It weights and fuses parameters including operation completion time, accidental touch rate, and interface hierarchy depth to generate a human-computer interaction efficiency index (HMI-EfficiencyIndex). The evaluation results are output in the form of a structured report, which includes performance trend charts, timelines of abnormal events, and a list of improvement suggestions.

[0042] Furthermore, the holographic visualization decision support terminal uses coherent light modulation technology to generate a 1280×720 resolution holographic projection, which can simultaneously display a three-dimensional visualization model of environmental parameters, operational behaviors, and performance indicators. It also features a multi-dimensional correlation analysis interface, allowing testers to view temperature curves, touch trajectories, and performance indicators in conjunction with gestures.

[0043] When abnormal data is detected, the holographic visualization decision support terminal automatically triggers local magnification and parameter backtracking functions, identifies areas of performance degradation through color mapping, and has a built-in expert knowledge base that automatically recommends optimization solutions based on test results, such as suggesting adjustments to the screen backlight strategy or optimization of touch algorithm parameters.

[0044] Example 2: Figure 2 As shown, the difference between this embodiment and Embodiment 1 lies in the communication connection between the holographic visualization decision support terminal and the performance test monitoring unit. The performance test monitoring unit monitors the performance testing process of the new energy vehicle's central control screen and analyzes the data to determine if any abnormal monitoring signals are generated. When an abnormal monitoring signal is generated, it is sent to the holographic visualization decision support terminal for display and warning. This allows for a progressive and precise assessment of the test impact during the testing process, enabling test management personnel to promptly investigate and analyze the causes and implement reasonable improvement measures. This significantly improves the accuracy and stability of the test results for the new energy vehicle's central control screen and reduces the difficulty of test supervision. The specific analysis process of the performance test monitoring unit is as follows:

[0045] Obtain standard data values ​​of various environmental parameters (such as temperature and humidity) that need to be simulated during the test, collect real-time data values ​​of the corresponding environmental parameters, calculate the difference between the real-time data values ​​and the corresponding standard data values ​​and take the absolute value to obtain the simulated abnormal value, compare the simulated abnormal value with the corresponding preset simulated abnormal threshold, and if the simulated abnormal value exceeds the preset simulated abnormal threshold, it is determined that the corresponding environmental parameter is in a simulated unstable state.

[0046] The detection period is divided into several sub-periods. If environmental parameters in a simulated unstable state exist within a corresponding sub-period, the corresponding sub-period is marked as an abnormal period. The number of abnormal periods within the detection period is obtained and the ratio of it to the total number of sub-periods is calculated to obtain the abnormal period feature value. The abnormal period feature value is compared with a preset abnormal period feature threshold. If the abnormal period feature value exceeds the preset abnormal period feature threshold, it indicates that the environmental control of the testing process within the detection period is poor, which is not conducive to ensuring test stability and test result accuracy. In this case, a monitoring anomaly signal is generated.

[0047] Furthermore, if the time-varying feature value does not exceed the preset time-varying feature threshold, the total duration of the corresponding environmental parameter in the simulated unstable state during the detection period is obtained and marked as the simulated unstable time measurement value. The average of all simulated abnormal values ​​corresponding to the corresponding environmental parameter during the detection period is calculated to obtain the simulated abnormal value, and the maximum value of the simulated abnormal value corresponding to the corresponding environmental parameter during the detection period is marked as the simulated abnormal amplitude value.

[0048] The parameter simulation analysis value is obtained by weighted summation of simulated non-stationary time measurements, simulated out-of-state values, and simulated out-of-state amplitude values. Specifically, each of the simulated non-stationary time measurements, simulated out-of-state values, and simulated out-of-state amplitude values ​​is assigned a corresponding preset weight coefficient, and then each of these values ​​is multiplied by its respective preset weight coefficient. The sum of these three products is then marked as the parameter simulation analysis value. It should be noted that the larger the value of the parameter simulation analysis value, the worse the overall performance of the control over the corresponding environmental parameters during the test.

[0049] The simulated parameter analysis value is compared with the preset simulated parameter analysis threshold. If the simulated parameter analysis value exceeds the preset simulated parameter analysis threshold, it indicates that the overall control performance of the corresponding environmental parameter during the test is poor, and the corresponding environmental parameter is marked as a simulated feature parameter. If a simulated feature parameter exists within the detection period, it indicates that the environmental control situation within the detection period is not conducive to ensuring test stability and test result accuracy, and a monitoring anomaly signal is generated.

[0050] If no simulated feature parameters are available during the detection period, all interactive test operations performed by the bionic human-computer interaction simulator during the detection period are acquired. If the corresponding interactive test operation is not successfully completed within the specified time requirement, the corresponding interactive test operation is marked as an abnormal operation. The number of abnormal operations during the detection period is counted and the ratio is calculated with the total number of interactive test operations during the detection period to obtain the abnormal operation detection value. The abnormal operation detection value is compared with the preset abnormal operation detection threshold. If the abnormal operation detection value exceeds the preset abnormal operation detection threshold, it indicates that the interactive test execution during the detection period is not good, and a monitoring abnormal signal is generated.

[0051] Example 3: Figure 2 As shown, the difference between this embodiment and Embodiments 1 and 2 lies in the fact that the holographic visualization decision support terminal communication connection test requirement judgment unit, before testing the central control screen of the new energy vehicle, determines whether unnecessary signals are generated through test requirement judgment. When unnecessary signals are generated, they are sent to the holographic visualization decision support terminal for display and warning. This allows for accurate assessment of the necessity of testing before performance testing, avoiding unnecessary testing that leads to wasted time and testing costs. The specific analysis process of test requirement judgment is as follows:

[0052] If the dimensions of the central control screen of a new energy vehicle are obtained, and there are any dimension testing items in the dimensions of the central control screen that do not meet the corresponding requirements, it indicates that the dimensions of the central control screen of the new energy vehicle are non-compliant and cannot meet the factory quality. Therefore, it is meaningless to perform performance testing on it, and an unnecessary signal is generated.

[0053] If all the size inspection items of the new energy vehicle's central control screen meet the corresponding requirements, the surface image of the new energy vehicle's central control screen will be collected, and the surface defects on the new energy vehicle's central control screen will be identified. If there are surface defects on the new energy vehicle's central control screen that exceed the corresponding allowable specifications, an unnecessary signal will be generated.

[0054] If there are no surface defects exceeding the corresponding allowable specifications on the central control screen of the new energy vehicle, several detection areas are marked on the central control screen. If surface defects exist in the corresponding detection area, the corresponding detection area is marked as a feature area. The number of feature areas on the central control screen of the new energy vehicle is obtained and marked as feature area condition values. The feature area condition values ​​are compared with preset feature area condition thresholds. If the feature area condition values ​​exceed the preset feature area condition thresholds, it indicates that the central control screen of the new energy vehicle is difficult to meet the factory quality and performance testing is meaningless. Therefore, an unnecessary signal is generated.

[0055] If the feature area condition value does not exceed the preset feature area condition threshold, the parts where the feature areas are clustered are obtained, the number of feature areas in the corresponding parts is marked as the feature cluster value, the number of parts on the new energy vehicle central control screen where the feature cluster value exceeds the preset feature cluster threshold is marked as the cluster risk value, and the feature cluster value with the largest value is marked as the feature cluster amplitude value.

[0056] The characteristic performance value is obtained by weighted summation of characteristic area condition value, cluster risk frequency value and characteristic cluster amplitude value. That is, the characteristic area condition value, cluster risk frequency value and characteristic cluster amplitude value are assigned corresponding preset weight coefficients, and the characteristic area condition value, cluster risk frequency value and characteristic cluster amplitude value are multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the characteristic performance value.

[0057] It should be noted that the larger the value of the feature performance, the more serious the overall defect condition on the central control screen of the new energy vehicle. The feature performance value is compared with the preset feature performance threshold. If the feature performance value exceeds the preset feature performance threshold, it indicates that the overall defect condition on the central control screen of the new energy vehicle is relatively serious and it is difficult to meet the factory quality. Therefore, it is meaningless to perform performance testing on it, and an unnecessary signal is generated.

[0058] The working principle of this invention is as follows: During use, a dynamic extreme environment reproduction chamber provides physical environment support for the bionic human-computer interaction simulator. Its environmental parameters are synchronized in real time to the multimodal parameter synchronous acquisition array via a CAN bus. The operation data generated by the bionic human-computer interaction simulator and the performance indicators acquired by the multimodal parameter synchronous acquisition array are preprocessed by edge computing nodes and then transmitted to the intelligent decision-level analysis and evaluation engine via gigabit Ethernet. The evaluation report generated by the intelligent decision-level analysis and evaluation engine drives the holographic visualization decision support terminal to display the results, thereby realizing the construction of a closed-loop testing system. It automates the multi-dimensional quantitative evaluation of the performance of the central control screen of new energy vehicles, provides data support for product iteration, significantly improves testing efficiency and evaluation accuracy, and has a high level of intelligence.

[0059] The threshold, preset value, preset range, etc. in the technical solution of this invention are set for result comparison and analysis in order to determine whether it is good or bad. The value of the threshold is set and stored based on a combination of large model analysis of sample data and human experience. It can also be appropriately adjusted by seasonal or common sense influence conditions.

[0060] Furthermore, the settings for preset weight coefficients, influence factors, etc., are based on the magnitude of each parameter's influence on the results, and the specific values ​​are allocated to ultimately reflect the impact on the results. The settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can be appropriately adjusted based on seasonal or rational influence conditions.

[0061] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A performance testing device for a central control screen in a new energy vehicle, characterized in that, It includes a dynamic extreme environment reproduction chamber, a bionic human-computer interaction simulator, a multimodal parameter synchronous acquisition array, an intelligent decision-level analysis and evaluation engine, and a holographic visualization decision support terminal. The dynamic extreme environment reproduction chamber serves as a scenario construction base and immediately starts the environment modeling program after receiving the test task instruction. The bionic human-computer interaction simulator is equipped with a multi-degree-of-freedom robotic arm and a pressure sensing platform. It reconstructs the driver's operation model through inverse kinematics algorithms. The end effector of the multi-degree-of-freedom robotic arm integrates a capacitive touch head and a voice playback module. The multimodal parameter synchronous acquisition array enters the data acquisition mode at the same time as the behavior simulation starts. It uses an edge computing architecture to compress and encode the acquired data in real time and transmits it to the intelligent decision-level analysis and evaluation engine through the TDMA time division multiplexing protocol. After receiving the acquired data, the intelligent decision-level analysis and evaluation engine analyzes and evaluates the performance of the new energy vehicle's central control screen. The evaluation results are output to the holographic visualization decision support terminal for display in the form of a structured report. The holographic visualization decision support terminal communicates with the performance test monitoring unit. The performance test monitoring unit monitors the performance test process of the central control screen of the new energy vehicle and analyzes the data to determine whether an abnormal monitoring signal is generated. When an abnormal monitoring signal is generated, it is sent to the holographic visualization decision support terminal for display and warning. The specific analysis process of the performance test monitoring unit is as follows: Obtain the standard data values ​​of various environmental parameters that need to be simulated during the test, collect the real-time data values ​​of the corresponding environmental parameters, calculate the difference between the real-time data values ​​and the corresponding standard data values ​​and take the absolute value to obtain the simulated abnormal value, compare the simulated abnormal value with the corresponding preset simulated abnormal threshold, and if the simulated abnormal value exceeds the preset simulated abnormal threshold, it is determined that the corresponding environmental parameter is in a simulated unstable state. The detection period is divided into several sub-periods. If environmental parameters in a simulated unstable state exist within a corresponding sub-period, the corresponding sub-period is marked as an out-of-period period. The number of time periods within the detection period is obtained and the ratio of this number to the total number of sub-time periods is calculated to obtain the time period feature value. The time period feature value is then compared with a preset time period feature threshold. If the time period feature value exceeds the preset time period feature threshold, a monitoring anomaly signal is generated.

2. The performance testing device for a central control screen in a new energy vehicle according to claim 1, characterized in that, The dynamic extreme environment reproduction chamber integrates a temperature and humidity controller, an electromagnetic interference generator, a light simulation device, and a vibration table to reproduce temperature ranges from -40℃ to 85℃, humidity ranges from 5%RH to 95%RH, electromagnetic field strengths from 0-40V / m, and multi-frequency vibration conditions.

3. The performance testing device for a central control screen in a new energy vehicle according to claim 1, characterized in that, The multimodal parameter synchronous acquisition array adopts a fiber optic distributed architecture, integrating acquisition terminals including high-speed cameras, infrared thermal imagers, and touch force sensors. It achieves nanosecond-level data alignment through TDC time synchronization technology, synchronously acquiring key indicators including central control screen display brightness, touch response latency, and interface switching smoothness. It also uses structured light 3D reconstruction technology to capture the geometric distortion of the screen display content and combines colorimeter data to capture color reproduction information.

4. The performance testing device for a central control screen in a new energy vehicle according to claim 1, characterized in that, The intelligent decision-making-level analysis and evaluation engine adopts a dual-channel analysis architecture. The basic analysis layer performs compliance checks on the collected data through a rule engine, while the advanced analysis layer builds a deep learning model, establishes a performance degradation curve for the central control screen based on historical test data, predicts the lifespan under extreme working conditions through an LSTM network, and designs an interaction efficiency evaluation model. This model weights and fuses parameters including operation completion time, accidental touch rate, and interface layer depth to generate a human-computer interaction efficiency index. The evaluation results are output in the form of a structured report, which includes a performance trend chart, an abnormal event timeline, and a list of improvement suggestions.

5. The performance testing device for a central control screen in a new energy vehicle according to claim 1, characterized in that, The holographic visualization decision support terminal uses coherent light modulation technology to generate a holographic projection with a resolution of 1280×720, and is designed with a multi-dimensional correlation analysis interface, allowing testers to view temperature curves, touch trajectories, and performance indicators in conjunction with gestures. When abnormal data is detected, the holographic visualization decision support terminal automatically triggers local magnification and parameter backtracking functions, identifies areas of performance degradation through color mapping, and has a built-in expert knowledge base to automatically recommend optimization solutions based on test results.

6. The performance testing device for a central control screen in a new energy vehicle according to claim 1, characterized in that, If the time-varying characteristic value does not exceed the preset time-varying characteristic threshold, the parameter simulation analysis value is obtained by weighted summation of the simulated non-steady-time measurement value, simulated out-of-time value, and simulated out-of-time value. If the parameter simulation analysis value exceeds the preset parameter simulation analysis threshold, the corresponding environmental parameter is marked as the simulation characteristic parameter. If simulated characteristic parameters exist within the detection period, a monitoring anomaly signal is generated; If no simulated feature parameters are available during the detection period, the number of abnormal table operations during the detection period is counted and the ratio of this number to the total number of interactive test operations during the detection period is calculated to obtain the abnormal operation detection value. If the abnormal operation detection value exceeds the preset abnormal operation detection threshold, a monitoring anomaly signal is generated.

7. The performance testing device for a central control screen in a new energy vehicle according to claim 5, characterized in that, Before testing the central control screen of a new energy vehicle, the holographic visualization decision support terminal communication connection test requirement judgment unit determines whether unnecessary signals are generated. If unnecessary signals are generated, they are sent to the holographic visualization decision support terminal.

8. The performance testing device for a central control screen in a new energy vehicle according to claim 7, characterized in that, The specific analysis process for determining test requirements is as follows: If there are any size inspection items in the size information of the central control screen of the new energy vehicle that do not meet the corresponding requirements, an unnecessary signal will be generated; if all size inspection items of the central control screen of the new energy vehicle meet the corresponding requirements, the surface image of the central control screen of the new energy vehicle will be collected; if there are surface defects on the central control screen of the new energy vehicle that exceed the corresponding allowable specifications, an unnecessary signal will be generated. If there are no surface defects on the central control screen of the new energy vehicle that exceed the corresponding allowable specifications, the feature condition value is compared with the preset feature condition threshold. If the feature condition value exceeds the preset feature condition threshold, an unnecessary signal is generated. If the feature condition value does not exceed the preset feature condition threshold, the feature performance value is obtained by weighted summation of the feature condition value, cluster risk frequency value, and feature cluster amplitude value. If the feature performance value exceeds the preset feature performance threshold, an unnecessary signal is generated.

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