Visual automotive electromagnetic compatibility radiation immunity test method, device and equipment

Abstract

The present application discloses a visual automobile electromagnetic compatibility radiation immunity test method, device and equipment, which relate to the field of vehicle testing technology. The method comprises: collecting radiation immunity test data of automobile electronic equipment from multiple test devices in real time through a data acquisition layer, and transmitting the data to a data processing layer; cleaning, analyzing and feature extracting the data in the data processing layer to obtain key indicators, which include maximum radiation value and frequency of exceeding the standard; inputting the key indicators into a visual display layer, and displaying the distribution, ranking and trend of test results of different automobile manufacturers and models in the form of maps, bar charts and line charts; monitoring the key indicators in real time based on preset thresholds, triggering an alarm signal when the key indicators exceed the preset thresholds, and generating early warning prompt information; optimizing the test plan according to the alarm signal and early warning prompt information, and dynamically adjusting the test parameters of the data acquisition layer.

Description

Technical Field

[0001] The present application relates to the field of vehicle testing technology, and in particular to a method, device and equipment for visualizing automobile electromagnetic compatibility radiation immunity testing. Background Art

[0002] With the rapid development of automotive electronics, modern vehicles integrate a large number of electronic devices, such as engine control units, advanced driver assistance systems, and in-vehicle infotainment systems. These devices operate in complex electromagnetic environments and may be interfered with by external electromagnetic radiation, resulting in performance degradation or even failure. Therefore, electromagnetic compatibility testing, especially radiated immunity testing, has become a critical step in ensuring the reliability of automotive electronic equipment.

[0003] Radiated immunity testing simulates a realistic electromagnetic environment to assess the stability of electronic equipment under strong electromagnetic field interference. The testing process involves high-frequency signal transmission, field strength measurement, and device response analysis, generating massive amounts of data. Traditional data processing methods rely on manual analysis, which is inefficient and unable to cope with the high complexity of modern automotive electronics testing. Summary of the Invention

[0004] The purpose of this application is to provide a visual automotive electromagnetic compatibility radiation immunity test method, device and equipment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for visualizing automobile electromagnetic compatibility radiation immunity testing, including:

[0007] Establish an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, which includes a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer;

[0008] Collecting radiation immunity test data of automotive electronic equipment from multiple test devices in real time through the data acquisition layer, and transmitting the data to the data processing layer;

[0009] Cleaning, analyzing and feature extracting the data in the data processing layer to obtain key indicators, including the maximum radiation value and the number of frequencies exceeding the standard;

[0010] Input the key indicators into the visualization layer to display the distribution, ranking and trend of test results of different car manufacturers and models in the form of maps, bar charts and line charts;

[0011] The key indicator is monitored in real time based on a preset threshold value, and an alarm signal is triggered when the key indicator exceeds the preset threshold value, and an early warning prompt information is generated;

[0012] The test plan is optimized according to the alarm signal and early warning prompt information, and the test parameters of the data acquisition layer are dynamically adjusted.

[0013] Optionally, the step of collecting radiation immunity test data of automotive electronic equipment from multiple test devices in real time through the data collection layer includes:

[0014] deploying a signal source to generate an electromagnetic radiation signal covering a preset frequency range;

[0015] Amplifying the electromagnetic radiation signal by a power amplifier to ensure that the output power meets the preset field strength range;

[0016] Using a field strength probe to measure the electric field strength in the test environment and record the frequency domain distribution of the electric field strength;

[0017] radiating the signal output by the power amplifier to the automotive electronic device under test in the form of electromagnetic waves through a transmitting antenna;

[0018] Using a receiver to capture a response signal of the tested automotive electronic device under radiation interference, and transmitting the response signal to a big data platform;

[0019] A high-speed network is used to synchronize the data of the signal source, power amplifier, field strength probe, transmitting antenna and receiver to the data processing layer in real time.

[0020] Optionally, the step of cleaning, analyzing and extracting features from the data in the data processing layer includes:

[0021] Using filtering algorithms to remove noise and outliers from the data and calibrate data deviations between different devices;

[0022] Use the time series analysis module to predict trends in the cleaned data and combine it with the preset algorithm to generate simulation results for future test scenarios;

[0023] extracting frequency, amplitude, and phase parameters from the data, and identifying a response mode of the tested automotive electronic device based on a machine learning algorithm;

[0024] Integrate vehicle sensor data with electromagnetic compatibility test data to build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results;

[0025] Through deep learning models, the electromagnetic response characteristics in historical data are dynamically learned to predict the risk of excessive radiation at specific frequencies.

[0026] Optionally, the step of displaying the distribution, ranking, and trend of test results of different automakers and models in the form of maps, bar charts, and line charts includes:

[0027] The geographic location of each automaker is marked in the map module, and the density of radiation exceeding the standard in different areas is displayed through color gradients;

[0028] In the bar chart module, the ranking of the maximum radiation values ​​is displayed by vehicle type, and the difference in the frequency of exceeding the standard among various car manufacturers is compared;

[0029] Draw a curve of the change of the frequency of exceeding the standard over time in the line graph module, and superimpose the frequency domain distribution trend of the radiation value;

[0030] Generate 3D electromagnetic field heat maps based on WebGL technology, supporting interactive operations to locate high radiation interference sources;

[0031] Customize the display component combination through dynamic dashboards and integrate the timeline function to compare test results in different time periods.

[0032] Optionally, the step of monitoring the key indicator in real time based on a preset threshold includes:

[0033] Setting an upper threshold of the maximum radiation value and a cumulative threshold of the number of frequencies exceeding the standard according to preset standards;

[0034] Dynamically adjust the upper threshold and the cumulative threshold through the threshold setting module to adapt to different test scenarios and vehicle manufacturer needs;

[0035] When the maximum radiation value exceeds the upper limit threshold, an audible and visual alarm is triggered and a text message is sent to the engineer terminal;

[0036] When the frequency of exceeding the standard reaches a preset percentage of the cumulative threshold, an early warning report is generated and a solution for optimizing the test parameters is recommended;

[0037] Record historical data of all alarm and warning events, and generate statistical analysis reports to support decision optimization.

[0038] Optionally, the step of dynamically adjusting the test parameters of the data acquisition layer includes:

[0039] Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal;

[0040] reconfiguring the position of the antenna and the output power level of the power amplifier based on the warning prompt information;

[0041] Sending updated test parameters to the signal source, power amplifier and transmitting antenna through the system;

[0042] Verify in real time whether the adjusted test parameters are within the safety range specified by the preset standards;

[0043] The parameter adjustment record is synchronized to the data processing layer, and the display content of the visual display layer is updated.

[0044] Optionally, the method further includes:

[0045] Realize parallel collection and real-time synchronization of multi-device data in the data collection layer;

[0046] Complete the fully automated process of data cleaning, feature extraction and pattern recognition in the data processing layer;

[0047] Provide multi-dimensional interactive charts in the visual display layer to support engineers in making quick decisions;

[0048] Establish intelligent feedback mechanisms in the alarm and early warning layer to optimize the testing process;

[0049] Through closed-loop control, the optimized test parameters are input back to the data acquisition layer to form a continuously iterative test system.

[0050] In a second aspect, the present application provides a visual automotive electromagnetic compatibility radiation immunity test device, comprising:

[0051] A construction module for establishing an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, the system comprising a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer;

[0052] a processing module, configured to collect radiation immunity test data of automotive electronic equipment from a plurality of test devices in real time through the data acquisition layer, and transmit the data to the data processing layer;

[0053] Cleaning, analyzing and feature extracting the data in the data processing layer to obtain key indicators, including the maximum radiation value and the number of frequencies exceeding the standard;

[0054] Input the key indicators into the visualization layer to display the distribution, ranking and trend of test results of different car manufacturers and models in the form of maps, bar charts and line charts;

[0055] The key indicator is monitored in real time based on a preset threshold value, and an alarm signal is triggered when the key indicator exceeds the preset threshold value, and an early warning prompt information is generated;

[0056] The test plan is optimized according to the alarm signal and early warning prompt information, and the test parameters of the data acquisition layer are dynamically adjusted.

[0057] Optionally, the processing module is further configured to:

[0058] deploying a signal source to generate an electromagnetic radiation signal covering a preset frequency range;

[0059] Amplifying the electromagnetic radiation signal by a power amplifier to ensure that the output power meets the preset field strength range;

[0060] Using a field strength probe to measure the electric field strength in the test environment and record the frequency domain distribution of the electric field strength;

[0061] radiating the signal output by the power amplifier to the automotive electronic device under test in the form of electromagnetic waves through a transmitting antenna;

[0062] Using a receiver to capture a response signal of the tested automotive electronic device under radiation interference, and transmitting the response signal to a big data platform;

[0063] A high-speed network is used to synchronize the data of the signal source, power amplifier, field strength probe, transmitting antenna and receiver to the data processing layer in real time.

[0064] Optionally, the processing module is further configured to:

[0065] Using filtering algorithms to remove noise and outliers from the data and calibrate data deviations between different devices;

[0066] Use the time series analysis module to predict trends in the cleaned data and combine it with the preset algorithm to generate simulation results for future test scenarios;

[0067] extracting frequency, amplitude, and phase parameters from the data, and identifying a response mode of the tested automotive electronic device based on a machine learning algorithm;

[0068] Integrate vehicle sensor data with electromagnetic compatibility test data to build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results;

[0069] Through deep learning models, the electromagnetic response characteristics in historical data are dynamically learned to predict the risk of excessive radiation at specific frequencies.

[0070] Optionally, the processing module is further configured to:

[0071] The geographic location of each automaker is marked in the map module, and the density of radiation exceeding the standard in different areas is displayed through color gradients;

[0072] In the bar chart module, the ranking of the maximum radiation values ​​is displayed by vehicle type, and the difference in the frequency of exceeding the standard among various car manufacturers is compared;

[0073] Draw a curve of the change of the frequency of exceeding the standard over time in the line graph module, and superimpose the frequency domain distribution trend of the radiation value;

[0074] Generate 3D electromagnetic field heat maps based on WebGL technology, supporting interactive operations to locate high radiation interference sources;

[0075] Customize the display component combination through dynamic dashboards and integrate the timeline function to compare test results in different time periods.

[0076] Optionally, the processing module is further configured to: set an upper threshold of the maximum radiation value and a cumulative threshold of the number of frequencies exceeding the standard according to a preset standard;

[0077] Dynamically adjust the upper threshold and the cumulative threshold through the threshold setting module to adapt to different test scenarios and vehicle manufacturer needs;

[0078] When the maximum radiation value exceeds the upper limit threshold, an audible and visual alarm is triggered and a text message is sent to the engineer terminal;

[0079] When the frequency of exceeding the standard reaches a preset percentage of the cumulative threshold, an early warning report is generated and a solution for optimizing the test parameters is recommended;

[0080] Record historical data of all alarm and warning events, and generate statistical analysis reports to support decision optimization.

[0081] Optionally, the processing module is further configured to:

[0082] Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal;

[0083] reconfiguring the position of the antenna and the output power level of the power amplifier based on the warning prompt information;

[0084] Sending updated test parameters to the signal source, power amplifier and transmitting antenna through the system;

[0085] Verify in real time whether the adjusted test parameters are within the safety range specified by the preset standards;

[0086] The parameter adjustment record is synchronized to the data processing layer, and the display content of the visual display layer is updated.

[0087] Optionally, the processing module is further configured to:

[0088] Realize parallel collection and real-time synchronization of multi-device data in the data collection layer;

[0089] Complete the fully automated process of data cleaning, feature extraction and pattern recognition in the data processing layer;

[0090] Provide multi-dimensional interactive charts in the visual display layer to support engineers in making quick decisions;

[0091] Establish intelligent feedback mechanisms in the alarm and early warning layer to optimize the testing process;

[0092] Through closed-loop control, the optimized test parameters are input back to the data acquisition layer to form a continuously iterative test system.

[0093] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-described visual automotive electromagnetic compatibility radiation immunity testing methods.

[0094] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the above-mentioned visual automotive electromagnetic compatibility radiation immunity testing methods.

[0095] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned visual automotive electromagnetic compatibility radiation immunity testing methods.

[0096] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0097] This application provides a visual automotive electromagnetic compatibility (EMC) radiation immunity test method, device, and equipment. This method automates the entire process, from data acquisition and processing to visual display and alarm warning, significantly improving test efficiency and data processing capabilities. Through real-time monitoring and dynamic optimization, the system can quickly identify issues and guide improvements, providing an efficient and accurate solution for EMC testing of automotive electronic equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0099] Figure 1 A flow chart of a visual automotive electromagnetic compatibility radiation immunity test method provided in one embodiment of the present application;

[0100] Figure 2 A connection diagram of a visual automotive electromagnetic compatibility radiation immunity test device provided in one embodiment of the present application;

[0101] Figure 3 This is one of the schematic diagrams showing the effects of a visual automotive electromagnetic compatibility radiation immunity test method provided in one embodiment of the present application;

[0102] Figure 4 This is a second schematic diagram of the effect of a visual automotive electromagnetic compatibility radiation immunity test method provided by an embodiment of the present application;

[0103] Figure 5 A schematic diagram of the functional modules of a visual automotive electromagnetic compatibility radiation immunity test device provided in one embodiment of the present application;

[0104] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0105] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0106] like Figure 1 As shown, some embodiments of the present application provide a visual automobile electromagnetic compatibility radiation immunity test method, which includes the following steps 101 to 106. Among them:

[0107] Step 101: Establish an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, wherein the system includes a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer.

[0108] In this embodiment, a distributed architecture is one that distributes system functionality across multiple independent nodes, improving system processing power and scalability. The data acquisition layer collects raw data from test equipment; the data processing layer cleans, analyzes, and extracts features from the data; the visualization layer presents the processed data in graphical form; and the alarm and warning layer monitors data anomalies and triggers alarms.

[0109] The system is designed based on a distributed architecture, with functional modules divided into a data acquisition layer, a data processing layer, a visualization layer, and an alarm and warning layer. The data acquisition layer connects to multiple test devices via a high-speed network to ensure real-time data acquisition. The data processing layer utilizes distributed computing technology for parallel data processing. The visualization layer uses graphical tools to generate intuitive charts. The alarm and warning layer monitors data anomalies using pre-set rules. Each layer communicates through standardized interfaces, ensuring efficient system operation and data consistency.

[0110] Step 102 : collecting radiation immunity test data of the automotive electronic equipment from a plurality of test devices in real time through the data collection layer, and transmitting the data to the data processing layer.

[0111] In the embodiment of the present application, the test equipment includes a signal source, a power amplifier, a field strength probe, a transmitting antenna, and a receiver, etc., which are used to generate and measure electromagnetic signals. Real-time acquisition means that the system continuously obtains data from the test equipment.

[0112] For example, refer to Figure 2 The hardware connection diagram of the output test equipment, in which the connection of the immunity test equipment mainly relies on the data acquisition module, which collects data from multiple test equipment in the radiation immunity test of automotive electronic equipment, including information such as electric field strength, frequency range, and device response, and stores the data in the big data platform. The specific collection method is as follows:

[0113] Signal Source: Used to generate electromagnetic radiation signals of specific frequency and intensity, covering a frequency range of 10kHz to 10GHz, ensuring comprehensive testing. The signal source can generate unmodulated sine waves, 1kHz 80% modulated AM signals, and PM signals of specific periods to meet diverse testing requirements.

[0114] Power amplifier: Amplifies the signal generated by the signal source to meet the radiation intensity requirements of the test, with a maximum output power of 100W. The power amplifier ensures that the signal is not distorted during transmission while providing sufficient power to cover the required field strength range for the test.

[0115] Field strength probes: Used to measure the electric field strength in the test environment, ensuring that test conditions meet standard requirements. Field strength probes offer high sensitivity and wideband measurement capabilities, accurately capturing electric field strength at different frequencies.

[0116] Transmitting Antenna: This antenna transmits the amplifier's output signal as electromagnetic waves, allowing for radiated interference testing of the device under test. The transmitting antenna has excellent directivity and radiation characteristics, ensuring uniform signal coverage across the test area.

[0117] Receiver: This device receives the response signals of automotive electronic devices during radiated immunity testing, including parameters such as signal distortion and bit error rate. It supports multi-channel parallel reception. The receiver features high sensitivity and wideband reception capabilities, enabling it to accurately capture the device's response signals at different frequencies.

[0118] Data transmission network: The collected data is transmitted to the big data platform via a high-speed network to ensure the real-time and integrity of the data. The data transmission network uses a reliable communication protocol to ensure the stability and security of the data during transmission.

[0119] The data acquisition layer connects to test equipment via a high-speed network, acquiring real-time radiated immunity test data from automotive electronic equipment. The signal source generates an electromagnetic signal of a specific frequency, the power amplifier amplifies the signal, the field strength probe measures the electric field strength, the transmitting antenna transmits the signal, and the receiver captures the device's response data. The acquisition layer packages the data and transmits it to the data processing layer, ensuring data integrity and real-time performance.

[0120] Step 103: Clean, analyze and extract features of the data in the data processing layer to obtain key indicators, which include the maximum radiation value and the number of frequencies exceeding the standard.

[0121] In the embodiments of the present application, data cleaning is the process of removing invalid or abnormal data; data analysis is the process of extracting useful information through algorithms; feature extraction is the identification of key parameters from the data; the maximum radiation value is the highest radiation level of the device during testing; and the number of exceeding standards is the number of frequencies whose radiation values ​​exceed the preset standard.

[0122] The data processing layer first cleans the raw data, using filtering algorithms to remove noise and outliers. Statistical analysis then extracts signal characteristics, such as frequency, amplitude, and phase. Machine learning algorithms are used to identify the device's response pattern. Finally, the system calculates key metrics such as maximum radiation values ​​and the number of frequencies exceeding the standard, storing the results for future use.

[0123] Step 104: input the key indicators into the visualization display layer, and display the distribution, ranking and trend of test results of different car manufacturers and models in the form of maps, bar charts and line charts.

[0124] In this embodiment, the visualization layer receives key indicator data and generates interactive charts. A map module displays the distribution of test results by automaker, a bar chart displays the ranking of maximum radiation values, and a line chart shows the changing trend of the frequency of exceeding the standard. Users can customize the view through the interface, and the system supports dynamic data updates to ensure the real-time and accurate display of the content.

[0125] For example, the test results of different automakers can be plotted on a map, including information such as the number of qualified and unqualified companies, the number of models, and the number of vehicles. This map display can visually illustrate the distribution of test results by automaker, helping engineers quickly understand the overall industry situation. The distribution trends of test results in different regions can be displayed through color depth or icon size. Geographic trend analysis can reveal differences in electromagnetic compatibility levels in different regions, providing a reference for regional testing and improvement.

[0126] Step 105 : monitoring the key indicator in real time based on a preset threshold value, triggering an alarm signal when the key indicator exceeds the preset threshold value, and generating early warning prompt information.

[0127] In the embodiment of the present application, the preset threshold is a critical value set according to industry standards; the alarm signal is an abnormal notification triggered by the system; and the early warning prompt information is a message reminding the user to pay attention to potential problems.

[0128] The alarm and early warning layer continuously monitors key indicators and compares them against pre-set thresholds. When data exceeds a threshold, the system triggers an alarm signal through sound, text message, or pop-up window on the interface. It also generates detailed early warning information, including the exceeded value, timestamp, and recommended actions. This warning information is recorded and pushed to relevant terminals.

[0129] Step 106: Optimize the test plan according to the alarm signal and the early warning prompt information, and dynamically adjust the test parameters of the data acquisition layer.

[0130] In the embodiment of the present application, optimizing the test plan is to improve the test process according to the alarm information; dynamic adjustment is to modify the test parameters in real time to meet the needs.

[0131] The system analyzes alarm signals and warning information and automatically generates optimization suggestions, such as adjusting the test frequency range or antenna position. The data acquisition layer dynamically updates test parameters based on the optimization plan, ensuring the accuracy and efficiency of subsequent tests. The adjusted parameters are distributed to the test equipment through the configuration interface, achieving closed-loop control.

[0132] This embodiment of the application automates the entire process, from data collection and processing to visualization and alarm warning, significantly improving test efficiency and data processing capabilities. Through real-time monitoring and dynamic optimization, the system can quickly identify problems and guide improvements, providing an efficient and accurate solution for electromagnetic compatibility testing of automotive electronic equipment.

[0133] Optionally, step 102 includes:

[0134] Step 1021: deploy a signal source to generate an electromagnetic radiation signal covering a preset frequency range.

[0135] In the embodiment of the present application, the signal source is a test device capable of generating an electromagnetic signal of a specific frequency and modulation mode. The preset frequency range is a frequency interval of the electromagnetic signal pre-set according to the test standard, for example, 20MHz-2000MHz.

[0136] The system is configured with a signal source, with start, end, and step frequency parameters set according to the test standard. The signal source generates electromagnetic signals with various modulation methods, including an unmodulated sine wave, a 1kHz 80% modulated AM signal, or a PM signal with a specific period. The system precisely adjusts the signal source's output frequency and modulation parameters via a control interface, ensuring that the generated signal fully covers the required test frequency range.

[0137] Step 1022: Amplify the electromagnetic radiation signal by a power amplifier to ensure that the output power meets a preset field strength range.

[0138] In the embodiment of the present application, the power amplifier is short for a power amplifier, which is used to amplify the power of an input signal to a desired level. The preset field strength range is a standard value range of electromagnetic field strength set according to test requirements.

[0139] The system feeds the low-power signal from the signal source into a power amplifier. The amplifier linearly amplifies the signal according to preset gain parameters, achieving a maximum output power of 100W. The system monitors the amplifier's output power in real time and, through feedback adjustment, ensures that the amplified signal power precisely meets the required field strength range for testing while avoiding signal distortion.

[0140] Step 1023: Use a field strength probe to measure the electric field strength in the test environment, and record the frequency domain distribution of the electric field strength.

[0141] In the embodiment of the present application, the field intensity probe is a sensor device used to measure the electromagnetic field strength. Frequency domain distribution refers to the intensity distribution characteristics of the electric field strength at different frequencies.

[0142] The system controls the electric field strength probes to perform multi-point measurements across the test area. The probes convert the detected analog electric field strength signals into digital signals, and the system records the field strength values ​​at each frequency point. By scanning the entire test frequency band, the system establishes a complete frequency-domain distribution map of the electric field strength, providing benchmark reference data for subsequent tests.

[0143] Step 1024 : radiate the signal output by the power amplifier to the automotive electronic device under test in the form of electromagnetic waves through a transmitting antenna.

[0144] In the embodiment of the present application, the transmitting antenna is a transducer device that converts electrical signals into electromagnetic waves and radiates them. Electromagnetic waves are energy of the electromagnetic field that propagates in space in the form of waves.

[0145] The system feeds the electrical signal output by the power amplifier into the transmitting antenna. Based on its radiation characteristics, the transmitting antenna converts the electrical signal into spatial electromagnetic waves, which then cover the device under test in a predefined polarization direction and radiation pattern. The system controls the antenna's position and angle to ensure uniform electromagnetic field exposure to the device under test.

[0146] Step 1025 : Using a receiver to capture a response signal of the tested automotive electronic device under radiation interference, and transmitting the response signal to a big data platform.

[0147] In the embodiment of the present application, the receiver is a test device for receiving and analyzing electromagnetic signals. The response signal is various abnormal responses generated by the device under test under electromagnetic interference.

[0148] The system uses multiple receiver channels to receive the output signals of the device under test in parallel. The receiver demodulates, filters, and digitizes the signals, detecting changes in parameters such as signal distortion and bit error rate. The system packages the collected response signal data and uploads it in real time to the big data platform's storage cluster via a data transmission interface.

[0149] Step 1026: Use a high-speed network to synchronize the data of the signal source, power amplifier, field strength probe, transmitting antenna and receiver to the data processing layer in real time.

[0150] In the embodiments of the present application, a high-speed network refers to a data transmission network with high bandwidth and low latency. Real-time synchronization means that the data of each device is transmitted to the processing center immediately after collection.

[0151] The system connects all test equipment via Gigabit Ethernet or a dedicated bus network. Each device's built-in data acquisition module encapsulates test parameters and measurement results into standardized data packets. Network switches prioritize and schedule data to ensure real-time transmission of critical test data. Upon receiving data, the data processing layer immediately aligns timestamps and standardizes its format.

[0152] This embodiment of the application implements fully automated testing, from signal generation, power amplification, and field strength measurement to interference application and device response. Precise instrument control and real-time data synchronization ensure test repeatability and data reliability. High-speed network transmission and a big data platform provide a complete and accurate raw data foundation for subsequent data processing and analysis.

[0153] Optionally, step 103 includes:

[0154] Step 1031 : Remove noise and outliers from the data through a filtering algorithm, and calibrate data deviations between different devices.

[0155] In the embodiments of the present application, a filtering algorithm is a mathematical method used for signal processing that can separate valid signals from noise from raw data. Data deviation refers to the systematic differences between data collected by different test equipment.

[0156] The system first preprocesses the raw test data using digital filters, employing low-pass filtering to eliminate high-frequency noise and median filtering to remove pulse interference. For outlier detection, the system establishes a statistically based threshold determination mechanism to automatically identify and remove data points that fall outside a reasonable range. The system then performs inter-device data calibration, comparing the output of each device under standard test conditions and calculating and applying calibration coefficients to ensure data comparability across all test devices.

[0157] Step 1032: Use the time series analysis module to perform trend prediction on the cleaned data and generate simulation results of future test scenarios in combination with a preset algorithm.

[0158] In the embodiment of the present application, the time series analysis module is a software component that specializes in processing time series data. Preset algorithms include time series prediction algorithms such as ARIMA (Autoregressive Integrated Moving Average) and Prophet.

[0159] The system feeds the cleaned test data into the time series analysis module in chronological order. This module first performs a stationarity test and seasonality decomposition, then automatically selects the optimal prediction algorithm based on the data characteristics. The system trains the prediction model using a sliding window technique to continuously update the training dataset. Once the model is complete, the system infers the EMC performance curve for a specific future time period based on current test data trends and outputs the simulated test scenarios in a visual format.

[0160] Step 1033 : extract frequency, amplitude, and phase parameters from the data, and identify a response mode of the tested automotive electronic device based on a machine learning algorithm.

[0161] In the embodiments of the present application, frequency is the rate at which an electromagnetic signal changes periodically, amplitude is the signal strength, and phase is the position of the waveform on the time axis. Machine learning algorithms include classification algorithms such as support vector machines (SVMs) and random forests.

[0162] The system extracts frequency components from the time-domain signal using a fast Fourier transform (FFT), employs a peak detection algorithm to obtain the amplitude at each frequency point, and calculates phase information using a Hilbert transform. These characteristic parameters are formatted and fed into a machine learning model. The system uses a pre-labeled training dataset for supervised learning, establishing a mapping between characteristic parameters and device response types (e.g., normal operation, minor interference, or severe failure). Once deployed, the model can identify the operating status of the device under test in real time under the current electromagnetic environment.

[0163] Step 1034 , integrating the vehicle sensor data and the electromagnetic compatibility test data, and constructing a multi-dimensional analysis model to evaluate the impact of environmental factors on the test results.

[0164] In the embodiment of the present application, the vehicle sensor data includes vehicle operating environment parameters such as temperature, humidity, vibration, etc. The multidimensional analysis model is a mathematical model that can handle the correlation analysis of multiple data types.

[0165] The system collects environmental sensor data from the CAN bus via the vehicle bus interface and establishes a time-synchronized correlation with the electromagnetic test data. During the data fusion phase, the system uses Kalman filtering to eliminate sensor noise and construct a multidimensional feature matrix that includes electromagnetic parameters, environmental parameters, and device status. The system then uses principal component analysis (PCA) to reduce data dimensionality and trains a regression analysis model to quantitatively assess the impact of factors such as temperature changes on the device's electromagnetic compatibility performance.

[0166] Step 1035: Dynamically learn the electromagnetic response characteristics in the historical data through the deep learning model to predict the risk of excessive radiation at a specific frequency.

[0167] In the embodiments of this application, a deep learning model refers to a machine learning model with a multi-layer neural network structure, such as a CNN (convolutional neural network) and an LSTM (long short-term memory network). The risk of excessive radiation refers to the probability that the device under test will exceed the electromagnetic compatibility standard at a specific frequency.

[0168] The system utilizes a hybrid neural network architecture consisting of convolutional and recurrent layers. The convolutional layers process frequency-domain features, while the LSTM layers learn time series dependencies. Model training utilizes historical test data sets, with inputs including frequency-amplitude spectrograms and time series data, and outputs the probability of exceeding the standard at each frequency point. The system incorporates an online learning mechanism to continuously update model parameters with new test data. During the prediction phase, the model analyzes current test data and outputs frequency points and risk levels that are likely to exceed the standard in the future, providing a basis for optimizing the test plan.

[0169] The embodiments of this application achieve the transformation from raw signals to intelligent analysis through a multi-level data processing flow. Noise filtering and data calibration ensure data quality, time series prediction provides a forward-looking perspective, feature extraction and pattern recognition reveal device operating characteristics, multidimensional analysis quantifies environmental influencing factors, and deep learning enables risk warning. The combined application of these technologies significantly enhances the intelligence level of electromagnetic compatibility testing, transforming test result analysis from passive detection to active prediction, and providing data support and decision-making basis for the electromagnetic compatibility design and optimization of automotive electronic equipment.

[0170] Optionally, step 104 includes:

[0171] Step 1041: Mark the geographical location of each car company in the map module, and use color gradients to display the radiation value exceeding the standard density in different areas.

[0172] In the embodiment of the present application, the map module is a software component for visualizing geographic information, which can display data in association with geographic locations. A color gradient is a method of using colors of different shades or hues to represent continuous changes in numerical values.

[0173] The system invokes a geographic information system (GIS) engine to load a digital map containing automaker location information. Based on the test data reported by each automaker, the system calculates the density of radiation exceeding standards for each geographic area. The data processing unit maps the density values ​​to a preset color gradient range and generates visual rendering instructions. The graphics processing unit performs the rendering, displaying color block markers at corresponding locations on the map. The color depth is positively correlated with the density of exceeding standards. The system also generates a legend to ensure intuitive and accurate data presentation.

[0174] Step 1042: Display the ranking of the maximum radiation values ​​by vehicle type in a bar chart module, and compare the differences in the frequency of exceeding the standard for each vehicle manufacturer.

[0175] In this embodiment of the present application, the histogram module is a chart generation component used for comparing categorized data. The maximum radiation value refers to the highest electromagnetic radiation level recorded during the test. The number of frequencies exceeding the standard limit is the number of frequency points where the radiation intensity exceeds the standard limit.

[0176] The system extracts test statistics for each vehicle model from the database and sorts them in descending order by maximum radiation value. The visualization engine creates a dual-axis bar chart, with the primary axis displaying the maximum radiation value for each vehicle model and the secondary axis plotting the frequency of exceeding the standard for each vehicle model. The chart automatically adds axis labels, data labels, and discrepancy indicators. The system also includes an interactive feature, so when a user hovers over a specific bar, detailed test parameters and comparative analysis data for that vehicle model are displayed.

[0177] For example, refer to Figure 3 , using a bar chart to display the maximum radiation value ranking of car manufacturers and models, helping engineers quickly identify high-risk objects. The bar chart display can intuitively present the maximum radiation values ​​of different car manufacturers and models, providing engineers with clear improvement directions. Figure 4 By comparing the frequency of exceeding the standard across different automakers and models using a bar chart, the stability of the equipment can be analyzed. This comparison of frequency exceeding the standard can reveal differences in electromagnetic compatibility between different automakers and models, providing a basis for subsequent optimization and improvement.

[0178] Step 1043: draw a curve of the variation of the frequency number exceeding the standard over time in a line graph module, and superimpose the frequency domain distribution trend of the radiation value.

[0179] In the embodiment of the present application, the line chart module is a chart tool that connects data points with line segments to show trend changes. Frequency domain distribution refers to the distribution characteristics of electromagnetic signal energy at different frequencies.

[0180] The system establishes a time series database index and aggregates frequency data for exceeded standards by test time point. The graphing engine creates a dual Y-axis coordinate system, with the primary axis plotting the time-varying frequency of exceeded standards and the secondary axis displaying the frequency domain distribution spectrum of a typical test point. The system uses different line types and colors to distinguish data series and adds dynamic annotation points to mark key events. Spectral data is calculated in real time using Fast Fourier Transform (FFT) to ensure timely trend analysis. Charts support zooming and panning, allowing for detailed analysis of data characteristics for specific time periods.

[0181] For example, a line graph can be used to display the changing trend of the frequency of exceeding the standard over time, allowing analysis of fluctuations during the test process. This line graph can visually demonstrate the changing trend of the frequency of exceeding the standard, helping engineers understand fluctuations during the test and promptly identify and resolve problems. Displaying the changing trend of the device's radiation values ​​at different frequencies helps engineers understand the device's frequency domain characteristics. Radiation value trend analysis can reveal the device's radiation characteristics at different frequencies, providing engineers with valuable information to guide subsequent testing and optimization work.

[0182] Step 1044: Generate a three-dimensional electromagnetic field heat map based on WebGL technology, supporting interactive operations to locate high radiation interference sources.

[0183] In the embodiment of the present application, WebGL is a JavaScript API for rendering 3D graphics on web pages. A three-dimensional electromagnetic field heat map is a visualization form that uses color gradients to represent the distribution of electromagnetic field intensity in three-dimensional space.

[0184] The system constructs a 3D scene using WebGL shader programming, converting the test area's coordinate data into a vertex buffer. Based on the data collected by the field intensity probes, the system calculates the color attributes of each spatial point and establishes a mapping from numerical values ​​to colors. The graphics pipeline performs real-time rendering, generating a 3D heat map with depth perception. The interactive module enables mouse-controlled rotation, zooming, and panning, while a collision detection algorithm ensures accurate responsiveness. The system also incorporates a threshold screening function to automatically highlight radiation hotspots exceeding safety standards.

[0185] Step 1045 , customize the display component combination through the dynamic dashboard, and integrate the timeline function to compare the test results of different time periods.

[0186] In the embodiment of the present application, the dynamic dashboard is a configurable data visualization interface that allows users to freely arrange chart components. The timeline is an interactive slider control for controlling time range selection.

[0187] The system provides a library of visualization components, including maps, bar charts, line charts, and other chart types. Users arrange components on the canvas by dragging and dropping, and the system automatically binds them to data sources. A timeline control is linked to all charts, so when the user adjusts the time range, each component synchronously updates and displays data for the corresponding time period. The system saves dashboard configuration templates, allowing for quick switching between different analytical perspectives. A data caching mechanism ensures smooth transitions and fast rendering of charts as time ranges change.

[0188] This application example uses geospatial display to reveal regional distribution characteristics, uses ranking comparison to highlight critical problem models, uses temporal trend analysis to identify performance variation patterns, employs 3D visualization to locate interference sources, and finally integrates various analytical perspectives through a customizable dashboard. The combined application of these visualization technologies transforms complex EMC test data into intuitive graphical information, significantly improving data interpretation efficiency.

[0189] Optionally, step 105 includes:

[0190] Step 1051 : setting an upper threshold of the maximum radiation value and a cumulative threshold of the frequency exceeding the standard according to a preset standard.

[0191] In the embodiment of the present application, the preset standard is an electromagnetic compatibility test specification document developed by an industry or enterprise. The upper threshold is the maximum allowable radiation intensity limit, and the cumulative threshold is the total number of exceeding standards frequencies allowed within a certain test cycle.

[0192] The system accesses the standards and specifications database and extracts threshold parameters applicable to the current test item. For maximum radiation values, the system sets different thresholds based on the test frequency range. For frequencies exceeding the standard, the system calculates the cumulative allowable value based on the test duration and the number of sampling points. The threshold parameters are stored in the configuration database and are available to the real-time monitoring module. The system performs parameter verification to ensure that the set thresholds meet the safety requirements of the test standards.

[0193] Step 1052: Dynamically adjust the upper threshold and the cumulative threshold through a threshold setting module to adapt to different test scenarios and vehicle manufacturer requirements.

[0194] In the embodiment of the present application, the threshold setting module is a software component responsible for managing and adjusting the alarm threshold. Test scenarios include different test types such as vehicle testing and component testing.

[0195] The system provides a graphical threshold configuration interface, supporting the selection of preset templates based on test scenarios. When switching between test items, the system automatically loads the corresponding threshold configuration. Engineers can adjust threshold parameters for specific frequency bands using sliders or numeric input to meet specific needs. The system performs compliance checks before implementing adjustments to prevent unsafe threshold settings. Adjusted parameters take effect immediately and are synchronized to all monitoring nodes, ensuring threshold consistency across the entire test system.

[0196] Step 1053: When the maximum radiation value exceeds the upper limit threshold, an audible and visual alarm is triggered and a text message is pushed to the engineer terminal.

[0197] In the embodiment of the present application, the sound and light alarm is an emergency alarm issued by sound and light signals. The engineer terminal refers to a mobile device or computer installed with a monitoring application.

[0198] The real-time monitoring module continuously compares test data with threshold parameters. When a radiation value exceeding the specified threshold is detected, the system immediately activates the alarm protocol: the alarm emits a specific frequency of beeps and a flashing red warning light. Simultaneously, an alarm message containing the test number, the exceeded value, and a timestamp is sent via SMS to a pre-set list of contacts. The system records complete alarm event data, including the test parameters and environmental conditions at the time of the trigger, providing a basis for subsequent analysis.

[0199] Step 1054: When the frequency of exceeding the standard reaches a preset percentage of the cumulative threshold, an early warning report is generated and a solution for optimizing the test parameters is recommended.

[0200] In the embodiment of the present application, the early warning report is a problem prediction document automatically generated by the system. The optimization test parameter solution includes improvement suggestions such as adjusting the test frequency and field strength.

[0201] The system sets a preset percentage of the cumulative threshold (for example, 80%). When the frequency of violations reaches 80% of the cumulative threshold, the analysis module initiates the early warning process: first, all relevant data from the current test is collected. Then, the solution recommendation algorithm is invoked to generate improvement suggestions based on historical optimization cases and an expert knowledge base. The report generation engine integrates this information into a structured document containing a current status analysis, risk prediction, and specific optimization measures. The system pushes the early warning report to the responsible engineer via the message center and highlights the warning status in the visual interface.

[0202] Step 1055: record the historical data of all alarm and warning events, and generate statistical analysis reports to support decision optimization.

[0203] In the embodiment of the present application, historical data refers to the past alarm and warning records stored by the system. Statistical analysis reports are regularly generated trend analysis and problem summary documents.

[0204] The system establishes a dedicated event database, storing structured data for each alarm / warning event, including test parameters, environmental conditions, treatment measures, and result verification. The report generation module regularly performs data analysis tasks, such as calculating event rates, classifying and statistically analyzing problem types, and identifying high-frequency anomaly bands. The system automatically generates PDF reports containing charts and text analysis, allowing for filtering by time range, test item, and other dimensions. Data mining techniques are used to identify underlying patterns in the report data, providing data support for the revision of test standards and equipment improvements.

[0205] The embodiments of this application achieve standard adaptation through intelligent threshold management, employ a hierarchical alarm mechanism to ensure timely response to issues, combine early warning analysis to provide forward-looking optimization suggestions, and finally support continuous improvement through systematic data accumulation. These functions work together to significantly improve the standardization and reliability of electromagnetic compatibility testing, effectively preventing test errors and equipment damage. Real-time alarms reduce problem response time, intelligent early warnings reduce the risk of test failure, and historical analysis provides a data foundation for long-term quality improvement, ultimately achieving the simultaneous optimization of test efficiency and quality.

[0206] Optionally, step 106 includes:

[0207] Step 1061: Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal.

[0208] In this embodiment of the present application, an alarm signal refers to a warning signal generated by the system when it detects that test data exceeds a preset threshold, which is used to trigger subsequent automatic adjustment operations. A frequency range refers to the frequency range covered by the signal source during electromagnetic compatibility radiation immunity testing, for example, 20MHz-2000MHz. A signal modulation mode refers to the type of waveform modulation output by the signal source, such as amplitude modulation (AM), frequency modulation (FM), or an unmodulated sine wave.

[0209] Upon receiving an alarm signal, the system automatically analyzes the frequency range and signal modulation method of the current test task to see if they conflict with pre-set standards. If any data exceeds the standard, the system uses a built-in algorithm to recalculate the appropriate frequency range and adjust the signal modulation method (for example, switching from AM to FM). These adjusted parameters are directly applied to the test task configuration, ensuring that subsequent tests meet safety requirements.

[0210] Step 1062: reconfigure the position of the antenna and the output power level of the power amplifier based on the warning prompt information.

[0211] In the present application, the warning prompt information refers to the predictive prompt generated by the system for data approaching the threshold, which is used to optimize the test conditions in advance. The antenna position refers to the physical placement coordinates of the transmitting antenna in the test environment, which affects the electromagnetic field coverage. The output power level of the power amplifier refers to the signal strength level of the power amplifier output, which determines the field strength of the radiated immunity test.

[0212] Based on the warning information, the system invokes a pre-set antenna position optimization model to calculate more optimal placement coordinates (such as moving away from interference sources or adjusting the height). Simultaneously, the system dynamically adjusts the amplifier's output power level through the power control module to ensure it remains within the maximum safety range permitted by pre-set standards. These adjusted parameters are then sent to the hardware via commands for execution.

[0213] Step 1063: Send the updated test parameters to the signal source, power amplifier, and transmitting antenna through the system.

[0214] In this embodiment of the present application, the system packages the updated parameters generated in steps 1061 and 1062 into control commands via a high-speed communication protocol (such as TCP / IP) and sends them to the signal source, power amplifier, and transmitting antenna. The signal source receives the new frequency and modulation parameters and resets its output waveform; the power amplifier adjusts its amplification factor based on the new power level; and the transmitting antenna simultaneously updates its physical location coordinates. All devices then send confirmation signals back to the system to ensure the parameters take effect.

[0215] Step 1064 , verifying in real time whether the adjusted test parameters comply with the safety range specified by the preset standard.

[0216] In the embodiments of the present application, the preset standard refers to the electromagnetic compatibility test safety threshold specified by the industry or enterprise, such as the upper limit of the radiation value. The safety range refers to the legal range allowed by the test parameters, such as the frequency range of 10kHz-10GHz.

[0217] After parameter adjustments are completed, the system immediately activates the real-time monitoring module, collecting data on the signal source output frequency, amplifier power, and antenna position, and comparing each parameter against pre-set standards. If any parameters are found to be outside the safe range, the system triggers a secondary adjustment process. If verification passes, the status is recorded and the next step is entered. Verification results are synchronized to the alarm and early warning layer.

[0218] Step 1065: Synchronize the parameter adjustment record to the data processing layer, and update the display content of the visualization display layer.

[0219] In this embodiment, the system encapsulates the adjusted parameters, verification results, and timestamps into log files and transmits them to the data processing layer for structured storage. Simultaneously, the visualization layer uses APIs to obtain the latest data and dynamically updates maps, bar charts, line graphs, and other content. For example, a bar chart displays the latest power level rankings, while a line chart reflects frequency range adjustment trends.

[0220] This embodiment of the application automatically adjusts the frequency, modulation mode, antenna position, and power level based on alarm or warning signals, verifying compliance in real time and simultaneously updating the data and visualization interface. This process significantly improves testing efficiency, ensures the accuracy and safety of electromagnetic compatibility and radiation immunity testing, and facilitates rapid decision-making by engineers through data transparency.

[0221] Optionally, the method further includes:

[0222] Step 201: Implement parallel collection and real-time synchronization of multi-device data in the data collection layer.

[0223] In this embodiment of the present application, the data acquisition layer refers to the module in the system responsible for collecting raw data from multiple test devices (such as signal sources, power amplifiers, and field strength probes) and transmitting this data to a big data platform via a high-speed network. Parallel acquisition refers to the system independently collecting data from multiple test devices simultaneously to improve data acquisition efficiency. Real-time synchronization refers to the system using timestamps or synchronization protocols to ensure that data collected by all devices is aligned on the timeline to avoid data delays or misalignment.

[0224] The system deploys multiple test devices (such as signal sources, power amplifiers, and field strength probes) at the data acquisition layer. Each device operates independently and collects specific parameters (such as electric field strength and frequency range). The system uses a high-speed network (such as Gigabit Ethernet or a dedicated communication protocol) to transmit data from each device in real time to a central big data platform. During transmission, the system timestamps the data to ensure strict temporal synchronization of data collected by different devices. For example, the field strength probe's measurement data and the receiver's response signal must match at the same time to accurately reflect the impact of the electromagnetic environment on the device.

[0225] Step 202: Complete the fully automated process of data cleaning, feature extraction, and pattern recognition in the data processing layer.

[0226] In the embodiment of the present application, the data processing layer refers to the module in the system that cleans, analyzes and converts the original data to provide structured data for subsequent visualization and early warning.

[0227] At the data processing layer, the system first invokes the data cleaning module, which uses threshold filtering and calibration algorithms to remove outliers caused by sensor failures or environmental interference. The data analysis module then performs feature extraction on the cleaned data, calculating, for example, the signal's maximum radiation value or the number of frequencies exceeding the standard. The pattern recognition module, based on pre-trained deep learning models (such as CNN or LSTM), analyzes the device's electromagnetic response pattern and determines whether it is in an abnormal state. This entire process requires no human intervention; the system automatically converts raw data into structured results.

[0228] Step 203: Provide multi-dimensional interactive charts in the visual display layer to support engineers in making quick decisions.

[0229] In this application, the visualization layer refers to the module in the system that presents processed data in a graphical form (such as a map or bar chart). Interactive charts refer to visualization tools that allow users to dynamically adjust the displayed content through operations (such as zooming and filtering).

[0230] The system generates a variety of charts in the visualization layer: a map module displays the distribution of automaker test results; a bar chart module displays the ranking of maximum radiation values; and a line chart module shows the trend of the frequency of exceeding standards. The system allows engineers to customize views through interactive operations (such as clicking and dragging the timeline), for example, by overlaying data for comparisons of different models. Charts utilize WebGL or VR technology for highly dynamic rendering, ensuring real-time and intuitive data presentation.

[0231] Step 204: Establish an intelligent feedback mechanism in the alarm and warning layer to optimize the test process.

[0232] In the present application, the alarm and warning layer refers to the module in the system that monitors whether the data exceeds the threshold and triggers notification. The intelligent feedback mechanism refers to the function of the system to automatically adjust the test parameters or warning rules based on historical data or real-time analysis.

[0233] The system's alarm and warning layers include preset thresholds for radiation levels and frequency violations. When real-time data exceeds these thresholds, the alarm trigger module notifies engineers via text message or sound. The warning module uses time series analysis (such as the ARIMA algorithm) to predict potential risks, such as frequencies that may exceed standards in future tests. The system automatically records alarm events and generates optimization suggestions (such as adjusting antenna position or test frequency) that are fed back into the testing process.

[0234] In step 205 , the optimized test parameters are input back to the data acquisition layer through closed-loop control to form a continuously iterative test system.

[0235] In this embodiment of the present application, the system automatically generates optimized test parameters (such as adjusting signal source frequency or amplifier power) based on the analysis results of the alarm and early warning layer. These parameters are transmitted to the data acquisition layer via a closed-loop control circuit, directly configuring the operating status of the test equipment. For example, if the system detects that the radiation value in a certain frequency band continuously exceeds the standard, it automatically reduces the test intensity in that frequency band. This process is repeated repeatedly, allowing the test system to be continuously iterated and optimized.

[0236] This embodiment of the application builds an efficient and intelligent automotive electromagnetic compatibility testing system through the coordinated work of data acquisition, processing, visualization, early warning, and closed-loop control. The system enables real-time collection and precise analysis of test data, reducing the need for manual intervention. Multi-dimensional interactive charts improve decision-making efficiency. Intelligent early warning and closed-loop optimization significantly shorten testing cycles and improve the reliability of test results.

[0237] Based on the same inventive concept, embodiments of the present application also provide a visual automotive EMC radiation immunity test device for implementing the aforementioned visual automotive EMC radiation immunity test method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the visual automotive EMC radiation immunity test device provided below can be found in the aforementioned limitations of the visual automotive EMC radiation immunity test method and will not be further elaborated here.

[0238] In an exemplary embodiment, Figure 5 As shown, a visual automobile electromagnetic compatibility radiation immunity test device 30 is provided, comprising:

[0239] Construction module 301 is used to establish an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, the system comprising a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer;

[0240] The processing module 302 is configured to collect radiation immunity test data of the automotive electronic device from a plurality of test devices in real time through the data acquisition layer, and transmit the data to the data processing layer;

[0241] Cleaning, analyzing and feature extracting the data in the data processing layer to obtain key indicators, including the maximum radiation value and the number of frequencies exceeding the standard;

[0242] Input the key indicators into the visualization layer to display the distribution, ranking and trend of test results of different car manufacturers and models in the form of maps, bar charts and line charts;

[0243] The key indicator is monitored in real time based on a preset threshold value, and an alarm signal is triggered when the key indicator exceeds the preset threshold value, and an early warning prompt information is generated;

[0244] The test plan is optimized according to the alarm signal and early warning prompt information, and the test parameters of the data acquisition layer are dynamically adjusted.

[0245] Optionally, the processing module 302 is further configured to:

[0246] deploying a signal source to generate an electromagnetic radiation signal covering a preset frequency range;

[0247] Amplifying the electromagnetic radiation signal by a power amplifier to ensure that the output power meets the preset field strength range;

[0248] Using a field strength probe to measure the electric field strength in the test environment and record the frequency domain distribution of the electric field strength;

[0249] radiating the signal output by the power amplifier to the automotive electronic device under test in the form of electromagnetic waves through a transmitting antenna;

[0250] Using a receiver to capture a response signal of the tested automotive electronic device under radiation interference, and transmitting the response signal to a big data platform;

[0251] A high-speed network is used to synchronize the data of the signal source, power amplifier, field strength probe, transmitting antenna and receiver to the data processing layer in real time.

[0252] Optionally, the processing module 302 is further configured to:

[0253] Using filtering algorithms to remove noise and outliers from the data and calibrate data deviations between different devices;

[0254] Use the time series analysis module to predict trends in the cleaned data and combine it with the preset algorithm to generate simulation results for future test scenarios;

[0255] extracting frequency, amplitude, and phase parameters from the data, and identifying a response mode of the tested automotive electronic device based on a machine learning algorithm;

[0256] Integrate vehicle sensor data with electromagnetic compatibility test data to build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results;

[0257] Through deep learning models, the electromagnetic response characteristics in historical data are dynamically learned to predict the risk of excessive radiation at specific frequencies.

[0258] Optionally, the processing module 302 is further configured to:

[0259] The geographic location of each automaker is marked in the map module, and the density of radiation exceeding the standard in different areas is displayed through color gradients;

[0260] In the bar chart module, the ranking of the maximum radiation values ​​is displayed by vehicle type, and the difference in the frequency of exceeding the standard among various car manufacturers is compared;

[0261] Draw a curve of the change of the frequency of exceeding the standard over time in the line graph module, and superimpose the frequency domain distribution trend of the radiation value;

[0262] Generate 3D electromagnetic field heat maps based on WebGL technology, supporting interactive operations to locate high radiation interference sources;

[0263] Customize the display component combination through dynamic dashboards and integrate the timeline function to compare test results in different time periods.

[0264] Optionally, the processing module 302 is further configured to: set an upper threshold of the maximum radiation value and a cumulative threshold of the number of frequencies exceeding the standard according to a preset standard;

[0265] Dynamically adjust the upper threshold and the cumulative threshold through the threshold setting module to adapt to different test scenarios and vehicle manufacturer needs;

[0266] When the maximum radiation value exceeds the upper limit threshold, an audible and visual alarm is triggered and a text message is sent to the engineer terminal;

[0267] When the frequency of exceeding the standard reaches a preset percentage of the cumulative threshold, an early warning report is generated and a solution for optimizing the test parameters is recommended;

[0268] Record historical data of all alarm and warning events, and generate statistical analysis reports to support decision optimization.

[0269] Optionally, the processing module 302 is further configured to:

[0270] Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal;

[0271] reconfiguring the position of the antenna and the output power level of the power amplifier based on the warning prompt information;

[0272] Sending updated test parameters to the signal source, power amplifier and transmitting antenna through the system;

[0273] Verify in real time whether the adjusted test parameters are within the safety range specified by the preset standards;

[0274] The parameter adjustment record is synchronized to the data processing layer, and the display content of the visual display layer is updated.

[0275] Optionally, the processing module 302 is further configured to:

[0276] Realize parallel collection and real-time synchronization of multi-device data in the data collection layer;

[0277] Complete the fully automated process of data cleaning, feature extraction and pattern recognition in the data processing layer;

[0278] Provide multi-dimensional interactive charts in the visual display layer to support engineers in making quick decisions;

[0279] Establish intelligent feedback mechanisms in the alarm and early warning layer to optimize the testing process;

[0280] Through closed-loop control, the optimized test parameters are input back to the data acquisition layer to form a continuously iterative test system.

[0281] This embodiment of the application automates the entire process, from data collection and processing to visualization and alarm warning, significantly improving test efficiency and data processing capabilities. Through real-time monitoring and dynamic optimization, the system can quickly identify problems and guide improvements, providing an efficient and accurate solution for electromagnetic compatibility testing of automotive electronic equipment.

[0282] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store visual automobile electromagnetic compatibility radiation immunity test data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a visual automobile electromagnetic compatibility radiation immunity test method is implemented.

[0283] Those skilled in the art will understand that Figure 6The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0284] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0285] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0286] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0287] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0288] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0289] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0290] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0291] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A visual automobile electromagnetic compatibility radiation immunity test method, characterized in that: The visual automobile electromagnetic compatibility radiation immunity test method includes: Establish an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, which includes a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer; Collecting radiation immunity test data of automotive electronic equipment from multiple test devices in real time through the data acquisition layer, and transmitting the data to the data processing layer; Cleaning, analyzing and feature extracting the data in the data processing layer to obtain key indicators, including the maximum radiation value and the number of frequencies exceeding the standard; Input the key indicators into the visualization layer to display the distribution, ranking and trend of test results of different car manufacturers and models in the form of maps, bar charts and line charts; The key indicator is monitored in real time based on a preset threshold value, and an alarm signal is triggered when the key indicator exceeds the preset threshold value, and an early warning prompt information is generated; Optimize the test plan according to the alarm signal and early warning prompt information, and dynamically adjust the test parameters of the data acquisition layer; The steps of displaying the distribution, ranking, and trends of test results of different automakers and models in the form of maps, bar charts, and line charts include: The geographic location of each automaker is marked in the map module, and the density of radiation exceeding the standard in different areas is displayed through color gradients; In the bar chart module, the ranking of the maximum radiation values ​​is displayed by vehicle type, and the difference in the frequency of exceeding the standard among various car manufacturers is compared; Draw a curve of the change of the frequency of exceeding the standard over time in the line graph module, and superimpose the frequency domain distribution trend of the radiation value; Generate 3D electromagnetic field heat maps based on WebGL technology, supporting interactive operations to locate high radiation interference sources; Customize the display component combination through dynamic dashboards and integrate the timeline function to compare test results in different time periods.

2. The visual automobile electromagnetic compatibility radiation immunity test method according to claim 1 is characterized in that: The step of collecting radiation immunity test data of automotive electronic equipment from multiple test devices in real time through the data collection layer includes: deploying a signal source to generate an electromagnetic radiation signal covering a preset frequency range; Amplifying the electromagnetic radiation signal by a power amplifier to ensure that the output power meets the preset field strength range; Using a field strength probe to measure the electric field strength in the test environment and record the frequency domain distribution of the electric field strength; radiating the signal output by the power amplifier to the automotive electronic device under test in the form of electromagnetic waves through a transmitting antenna; Using a receiver to capture a response signal of the tested automotive electronic device under radiation interference, and transmitting the response signal to a big data platform; A high-speed network is used to synchronize the data of the signal source, power amplifier, field strength probe, transmitting antenna and receiver to the data processing layer in real time.

3. The visual automobile electromagnetic compatibility radiation immunity test method according to claim 2 is characterized in that: The steps of cleaning, analyzing and feature extracting the data in the data processing layer include: Using filtering algorithms to remove noise and outliers from the data and calibrate data deviations between different devices; Use the time series analysis module to predict trends in the cleaned data and combine it with the preset algorithm to generate simulation results for future test scenarios; extracting frequency, amplitude, and phase parameters from the data, and identifying a response mode of the tested automotive electronic device based on a machine learning algorithm; Integrate vehicle sensor data with electromagnetic compatibility test data to build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results; Through deep learning models, the electromagnetic response characteristics in historical data are dynamically learned to predict the risk of excessive radiation at specific frequencies.

4. The visual automobile electromagnetic compatibility radiation immunity test method according to claim 1 is characterized in that: The step of monitoring the key indicators in real time based on a preset threshold value includes: Setting an upper threshold of the maximum radiation value and a cumulative threshold of the number of frequencies exceeding the standard according to preset standards; Dynamically adjust the upper threshold and the cumulative threshold through the threshold setting module to adapt to different test scenarios and vehicle manufacturer needs; When the maximum radiation value exceeds the upper limit threshold, an audible and visual alarm is triggered and a text message is sent to the engineer terminal; When the frequency of exceeding the standard reaches a preset percentage of the cumulative threshold, an early warning report is generated and a solution for optimizing the test parameters is recommended; Record historical data of all alarm and warning events, and generate statistical analysis reports to support decision optimization.

5. The visual automobile electromagnetic compatibility radiation immunity test method according to claim 2 is characterized in that: The step of dynamically adjusting the test parameters of the data acquisition layer includes: Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal; reconfiguring the position of the antenna and the output power level of the power amplifier based on the warning prompt information; Sending updated test parameters to the signal source, power amplifier and transmitting antenna through the system; Verify in real time whether the adjusted test parameters are within the safety range specified by the preset standards; The parameter adjustment record is synchronized to the data processing layer, and the display content of the visual display layer is updated.

6. The visual automobile electromagnetic compatibility radiation immunity test method according to claim 1 is characterized in that: The method further comprises: Realize parallel collection and real-time synchronization of multi-device data in the data collection layer; Complete the fully automated process of data cleaning, feature extraction and pattern recognition in the data processing layer; Provide multi-dimensional interactive charts in the visual display layer to support engineers in making quick decisions; Establish intelligent feedback mechanisms in the alarm and early warning layer to optimize the testing process; Through closed-loop control, the optimized test parameters are input back to the data acquisition layer to form a continuously iterative test system.

7. A visual automobile electromagnetic compatibility radiation immunity test device, characterized in that: The visual automobile electromagnetic compatibility radiation immunity test device includes: A construction module for establishing an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, the system comprising a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer; a processing module, configured to collect radiation immunity test data of automotive electronic equipment from a plurality of test devices in real time through the data acquisition layer, and transmit the data to the data processing layer; Cleaning, analyzing and feature extracting the data in the data processing layer to obtain key indicators, including the maximum radiation value and the number of frequencies exceeding the standard; Input the key indicators into the visualization layer to display the distribution, ranking and trend of test results of different car manufacturers and models in the form of maps, bar charts and line charts; The key indicator is monitored in real time based on a preset threshold value, and an alarm signal is triggered when the key indicator exceeds the preset threshold value, and an early warning prompt information is generated; Optimize the test plan according to the alarm signal and early warning prompt information, and dynamically adjust the test parameters of the data acquisition layer; The steps of displaying the distribution, ranking, and trends of test results of different automakers and models in the form of maps, bar charts, and line charts include: The geographic location of each automaker is marked in the map module, and the density of radiation exceeding the standard in different areas is displayed through color gradients; In the bar chart module, the ranking of the maximum radiation values ​​is displayed by vehicle type, and the difference in the frequency of exceeding the standard among various car manufacturers is compared; Draw a curve of the change of the frequency of exceeding the standard over time in the line graph module, and superimpose the frequency domain distribution trend of the radiation value; Generate 3D electromagnetic field heat maps based on WebGL technology, supporting interactive operations to locate high radiation interference sources; Customize the display component combination through dynamic dashboards and integrate the timeline function to compare test results in different time periods.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the visual automobile electromagnetic compatibility radiation immunity test method according to any one of claims 1 to 6.

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