Visual automobile electromagnetic compatibility radiation anti-interference test method, device and equipment

Abstract

The invention discloses a visual automobile electromagnetic compatibility radiation anti-interference test method, device and equipment, and relates to the technical field of vehicle testing, and the method comprises the steps: collecting the radiation anti-interference test data of automobile electronic equipment from a plurality of test equipment in real time through a data collection layer, and transmitting the data to a data processing layer; cleaning, analysis and feature extraction are carried out on the data in the data processing layer to obtain key indexes, and the key indexes comprise the maximum radiation value and the standard exceeding frequency number; inputting the key indexes into a visual display layer, and displaying the distribution, ranking and trend of test results of different vehicle enterprises and vehicle types in the forms of maps, histograms and line diagrams; the key index is monitored in real time based on a preset threshold value, an alarm signal is triggered when the key index exceeds the preset threshold value, and early warning prompt information is generated; and a test scheme is optimized according to the alarm signal and the early warning prompt information, and test parameters of the data acquisition layer are dynamically adjusted.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle testing, and particularly to a visualization method, device and equipment for automotive electromagnetic compatibility radiation immunity testing. Background Art

[0002] With the rapid development of automotive electronic technology, modern vehicles integrate a large number of electronic devices, such as engine control units, advanced driver assistance systems, in-vehicle infotainment systems, etc. These devices operate in a complex electromagnetic environment and may be interfered by external electromagnetic radiation, resulting in performance degradation or even failure. Therefore, electromagnetic compatibility testing, especially radiation immunity testing, has become a key link to ensure the reliability of automotive electronic devices.

[0003] Radiation immunity testing evaluates the stability of electronic devices under strong electromagnetic field interference by simulating a real electromagnetic environment. The testing process involves steps such as high-frequency signal emission, field strength measurement, and device response analysis, generating a large amount of data. Traditional data processing methods rely on manual analysis, with low efficiency and difficulty in meeting the high complexity requirements of modern automotive electronic testing. Summary of the Invention

[0004] The purpose of the present application is to provide a visualization method, device and equipment for automotive electromagnetic compatibility radiation immunity testing.

[0005] To achieve the above purpose, the present application provides the following solutions: In a first aspect, the present application provides a visualization method for automotive electromagnetic compatibility radiation immunity testing, including: Establish an innovative application system for electromagnetic compatibility radiation immunity test data based on a distributed architecture, the system including a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer; Real-time collect the radiation immunity test data of automotive electronic devices from multiple test devices through the data acquisition layer, and transmit the data to the data processing layer; Clean, analyze and extract features from the data in the data processing layer to obtain key indicators, the key indicators including the maximum radiation value and the number of over-standard frequencies; Input the key indicators into the visualization display layer, and display the test result distribution, ranking and trend of different vehicle manufacturers and models in the form of maps, bar charts and line charts; Real-time monitor the key indicators based on a preset threshold, trigger an alarm signal when the key indicators exceed the preset threshold, and generate an early warning prompt message; Optimize the test plan according to the alarm signal and the early warning prompt message, and dynamically adjust the test parameters of the data acquisition layer.

[0006] Optionally, the step of collecting the radiation immunity test data of automotive electronic devices from multiple test devices in real time through the data acquisition layer includes: Deploy a signal source to generate an electromagnetic radiation signal covering a preset frequency range; Amplify the power of the electromagnetic radiation signal through a power amplifier to ensure that the output power meets the preset field strength range; 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; 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; Use a receiver to capture the response signal of the automotive electronic device under test under radiation interference and transmit the response signal to the big data platform; Adopt 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.

[0007] Optionally, the step of cleaning, analyzing and extracting features from the data in the data processing layer includes: Remove the noise and outliers in the data through a filtering algorithm and calibrate the data deviation between different devices; Use a time series analysis module to predict the trend of the cleaned data and generate a simulation result of a future test scenario in combination with a preset algorithm; Extract the frequency, amplitude and phase parameters in the data and identify the response mode of the automotive electronic device under test based on a machine learning algorithm; Integrate the in-vehicle sensor data and electromagnetic compatibility test data to construct a multi-dimensional analysis model to evaluate the impact of environmental factors on the test results; Dynamically learn the electromagnetic response characteristics in historical data through a deep learning model to predict the risk of radiation exceeding the standard at specific frequencies.

[0008] Optionally, the step of displaying the test result distribution, ranking and trend of different vehicle manufacturers and models in the form of maps, bar charts and line charts includes: Mark the geographical locations of each vehicle manufacturer in the map module and display the density of radiation value exceeding the standard in different regions through color gradients; Display the ranking of the maximum radiation value by vehicle model category in the bar chart module and compare the differences in the number of times of exceeding the standard frequency of each vehicle manufacturer; Draw a curve of the change of the number of times of exceeding the standard frequency over time in the line chart module and superimpose the frequency domain distribution trend of the radiation value; Generate a three-dimensional electromagnetic field heat map based on WebGL technology to support interactive operations to locate high radiation interference sources; Customize the display component combination through a dynamic dashboard, and integrate the timeline function to compare the test results in different time periods.

[0009] Optionally, the step of performing real-time monitoring on the key metrics based on a preset threshold includes: Set the upper limit threshold of the maximum radiation value and the cumulative threshold of the over-standard frequency number according to the preset standard regulations; Dynamically adjust the upper limit threshold and the cumulative threshold through a threshold setting module to adapt to different test scenarios and the requirements of vehicle manufacturers; When the maximum radiation value exceeds the upper limit threshold, trigger an audible and visual alarm and push a text message to the engineer's terminal; When the over-standard frequency number reaches a preset percentage of the cumulative threshold, generate a warning report and recommend a solution to optimize the test parameters; Record the historical data of all alarm and warning events, and generate a statistical analysis report to support decision-making optimization.

[0010] Optionally, 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; Reconfigure the position of the antenna and the output power level of the power amplifier based on the warning prompt information; Send the updated test parameters to the signal source, power amplifier, and transmitting antenna through the system; Real-time verify whether the adjusted test parameters meet the safety range specified by the preset standard regulations; Synchronize the parameter adjustment record to the data processing layer and update the display content of the visualization display layer.

[0011] Optionally, the method further includes: Implement parallel acquisition and real-time synchronization of multi-device data in the data acquisition layer; Complete the fully automated processes of data cleaning, feature extraction, and pattern recognition in the data processing layer; Provide multi-dimensional interactive charts in the visualization display layer to support engineers in making quick decisions; Establish an intelligent feedback mechanism in the alarm and warning layer to optimize the test process; Through closed-loop control, reverse input the optimized test parameters to the data acquisition layer to form a continuously iterative test system.

[0012] In a second aspect, the present application provides a visualization automotive electromagnetic compatibility radiation immunity test device, including: A building module for establishing an innovative application system for electromagnetic compatibility radiated 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 for real-time collecting radiated immunity test data of automotive electronic devices from multiple test devices 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, the key indicators including the maximum radiation value and the number of non-compliance frequencies; Inputting the key indicators into the visualization display layer to display the test result distribution, ranking, and trend of different automotive manufacturers and models in the forms of maps, bar charts, and line charts; Real-time monitoring the key indicators based on a preset threshold, triggering an alarm signal when the key indicators exceed the preset threshold, and generating an early warning prompt message; Optimizing the test plan according to the alarm signal and the early warning prompt message, and dynamically adjusting the test parameters of the data acquisition layer.

[0013] Optionally, the processing module is further configured to: Deploy a signal source to generate an electromagnetic radiation signal covering a preset frequency range; Amplify the power of the electromagnetic radiation signal through a power amplifier to ensure that the output power meets the preset field strength range; 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; 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; Use a receiver to capture the response signal of the automotive electronic device under test under radiation interference and transmit the response signal to a big data platform; 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.

[0014] Optionally, the processing module is further configured to: Remove noise and outliers in the data through a filtering algorithm and calibrate the data deviation between different devices; Use a time series analysis module to perform trend prediction on the cleaned data and generate a simulation result of a future test scenario in combination with a preset algorithm; Extract frequency, amplitude, and phase parameters in the data and identify the response mode of the automotive electronic device under test based on a machine learning algorithm; Integrate in-vehicle sensor data and electromagnetic compatibility test data to build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results; Dynamically learn the electromagnetic response characteristics in historical data through a deep learning model to predict the risk of radiation exceeding the standard at specific frequencies.

[0015] Optionally, the processing module is further configured to: Mark the geographical locations of each automobile enterprise in the map module, and display the density of radiation value exceeding the standard in different regions through color gradients; In the bar chart module, display the ranking of the maximum radiation values by vehicle type, and compare the differences in the number of times of exceeding the standard for each automobile enterprise; In the line chart module, draw the change curve of the number of times of exceeding the standard over time, and superimpose the frequency domain distribution trend of the radiation value; Generate a three-dimensional electromagnetic field heat map based on WebGL technology, supporting interactive operations to locate high-radiation interference sources; Customize the display component combination through a dynamic dashboard, and integrate a timeline function to compare the test results in different time periods.

[0016] Optionally, the processing module is further configured to: set the upper threshold of the maximum radiation value and the cumulative threshold of the number of times of exceeding the standard according to the preset standard regulations; Dynamically adjust the upper threshold and the cumulative threshold through a threshold setting module to adapt to different test scenarios and the requirements of automobile enterprises; When the maximum radiation value exceeds the upper threshold, trigger an audible and visual alarm and push a short message to the engineer terminal; When the number of times of exceeding the standard reaches a preset percentage of the cumulative threshold, generate a warning report and recommend a solution to optimize the test parameters; Record the historical data of all alarm and warning events, and generate a statistical analysis report to support decision-making optimization.

[0017] Optionally, the processing module is further configured to: Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal; Reconfigure the position of the antenna and the output power level of the power amplifier based on the warning prompt information; Send the updated test parameters to the signal source, power amplifier, and transmitting antenna through the system; Real-time verify whether the adjusted test parameters meet the safety range specified by the preset standard regulations; Synchronize the parameter adjustment record to the data processing layer and update the display content of the visualization display layer.

[0018] Optionally, the processing module is further configured to: Implement parallel acquisition and real-time synchronization of multi-device data in the data acquisition 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 visualization display layer to support engineers in making quick decisions; Establish an intelligent feedback mechanism in the alarm and early warning layer to optimize the test process; Through closed-loop control, reverse the optimized test parameters to the data acquisition layer to form a continuously iterative test system.

[0019] Thirdly, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the steps of the visualization method for automotive electromagnetic compatibility radiated immunity testing described in any one of the above.

[0020] Fourthly, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the visualization method for automotive electromagnetic compatibility radiated immunity testing described in any one of the above.

[0021] Fifthly, the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the visualization method for automotive electromagnetic compatibility radiated immunity testing described in any one of the above.

[0022] According to the specific embodiments provided by the present application, the following technical effects are disclosed: The present application provides a visualization method, device, and equipment for automotive electromagnetic compatibility radiated immunity testing, realizing the full-process automation from data acquisition, processing to visualization display and alarm and early warning, significantly improving the test efficiency and data processing ability. Through real-time monitoring and dynamic optimization, the system can quickly identify problems and guide improvements, providing an efficient and accurate solution for the electromagnetic compatibility testing of automotive electronic devices. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0024] Figure 1 It is a schematic flowchart of a visualization method for automotive electromagnetic compatibility radiated immunity testing provided by an embodiment of the present application; Figure 2 It is a schematic connection diagram of a visualization device for automotive electromagnetic compatibility radiated immunity testing provided by an embodiment of the present application; Figure 3 One of the effect schematic diagrams of a visual automotive electromagnetic compatibility radiated immunity test method provided by an embodiment of the present application; Figure 4 Another effect schematic diagram of a visual automotive electromagnetic compatibility radiated immunity test method provided by an embodiment of the present application; Figure 5 Functional module schematic diagram of a visual automotive electromagnetic compatibility radiated immunity test device provided by an embodiment of the present application; Figure 6 Structural schematic diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0025] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] As Figure 1 shown, some embodiments of the present application provide a visual automotive electromagnetic compatibility radiated immunity test method. In the embodiments of the present application, the following steps 101 to 106 are included. Among them: Step 101, establish an electromagnetic compatibility radiated immunity test data innovation application system based on a distributed architecture, and the system includes a data acquisition layer, a data processing layer, a visual display layer, and an alarm and early warning layer.

[0027] In the embodiments of the present application, the distributed architecture is an architecture mode in which the system functions are dispersed and run on multiple independent nodes, which can improve the processing ability and scalability of the system. The data acquisition layer is responsible for collecting raw data from the test equipment; the data processing layer cleans, analyzes, and extracts features from the data; the visual display layer presents the processed data in a graphical form; the alarm and early warning layer is used to monitor data anomalies and trigger alarms.

[0028] The system is designed based on a distributed architecture, and the functional modules are divided into a data acquisition layer, a data processing layer, a visual display layer, and an alarm and early warning layer. The data acquisition layer connects multiple test equipment through a high-speed network to ensure real-time data acquisition; the data processing layer uses distributed computing technology to process data in parallel; the visual display layer uses graphical tools to generate intuitive charts; the alarm and early warning layer monitors data anomalies through preset rules. Communication between layers is through standardized interfaces to ensure the efficient operation of the system and data consistency.

[0029] Step 102: Real-time collect the radiation immunity test data of automotive electronic devices from multiple test devices through the data acquisition layer, and transmit the data to the data processing layer.

[0030] In the embodiments of the present application, the test devices include a signal source, a power amplifier, a field strength probe, a transmitting antenna, a receiver, etc., which are used to generate and measure electromagnetic signals. Real-time collection means that the system continuously obtains data from the test devices.

[0031] Exemplarily, refer to Figure 2 Output the schematic diagram of the hardware connection of the test devices. Among them, the connection of the immunity test devices mainly relies on the data acquisition module to collect data of automotive electronic devices in the radiation immunity test from multiple test devices, including information such as electric field strength, frequency range, and device response, and store the data in the big data platform. The specific collection method is as follows: Signal source: Used to generate electromagnetic radiation signals with specific frequencies and intensities, covering a frequency range of 10 kHz to 10 GHz to ensure the comprehensiveness of the test. The signal source can generate unmodulated sine wave signals, AM signals modulated at 1 kHz 80%, and PM signals with specific periods to meet different test requirements.

[0032] Power amplifier: Amplify the signals generated by the signal source to meet the radiation intensity requirements for testing, with a maximum output power of up to 100 W. The power amplifier can ensure that the signals are not distorted during transmission and at the same time provide sufficient power to cover the field strength range required for testing.

[0033] Field strength probe: Used to measure the electric field strength in the test environment to ensure that the test conditions meet the standard requirements. The field strength probe has high sensitivity and broadband measurement capabilities and can accurately capture the electric field strength at different frequencies.

[0034] Transmitting antenna: Used to emit the signals output by the power amplifier in the form of electromagnetic waves to conduct radiation interference tests on the device under test. The transmitting antenna has good directivity and radiation characteristics and can ensure that the signals evenly cover the test area.

[0035] Receiver: Receive the response signals of automotive electronic devices in the radiation immunity test, including parameters such as signal distortion and bit error rate, and support multi-channel parallel reception. The receiver has high sensitivity and broadband reception capabilities and can accurately capture the response signals of the device at different frequencies.

[0036] Data transmission network: Transmit the collected data to the big data platform through a high-speed network to ensure the real-time and integrity of the data. The data transmission network adopts a reliable communication protocol to ensure the stability and security of the data during transmission.

[0037] The data acquisition layer is connected to the test equipment through a high-speed network to obtain the radiation immunity test data of automotive electronic devices in real time. The signal source generates electromagnetic signals of specific frequencies, the power amplifier amplifies the signal intensity, the field strength probe measures the electric field intensity, the transmitting antenna emits the signals, and the receiver captures the device response data. The acquisition layer packs the data and transmits it to the data processing layer to ensure the integrity and real-time nature of the data.

[0038] Step 103: Clean, analyze, and extract features from the data in the data processing layer to obtain key indicators, where the key indicators include the maximum radiation value and the number of frequencies exceeding the standard.

[0039] In the embodiments of the present application, data cleaning is the process of removing invalid or abnormal data; data analysis is the extraction of 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 the test; and the number of frequencies exceeding the standard is the number of frequencies at which the radiation value exceeds the preset standard.

[0040] The data processing layer first cleans the original data, using filtering algorithms to remove noise and outliers. Subsequently, signal features such as frequency, amplitude, and phase are extracted through statistical analysis. Machine learning algorithms are used to identify the response patterns of the device. Finally, the system calculates key indicators such as the maximum radiation value and the number of frequencies exceeding the standard, and stores the results for subsequent use.

[0041] Step 104: Input the key indicators into the visualization display layer, and display the test result distribution, ranking, and trends of different vehicle manufacturers and models in the forms of maps, bar charts, and line charts.

[0042] In the embodiments of the present application, the visualization display layer receives the key indicator data and generates interactive charts. The map module marks the test result distribution of vehicle manufacturers, the bar chart shows the ranking of the maximum radiation value, and the line chart displays the change trend of the number of frequencies exceeding the standard. Users can customize the view through the interface, and the system supports dynamic data updates to ensure the real-time nature and accuracy of the displayed content.

[0043] Exemplarily, the test results of different vehicle manufacturers can be marked on the map, including information such as the number of qualified and unqualified enterprises, the number of vehicle models, and the number of vehicles. The map display can intuitively present the test result distribution of different vehicle manufacturers, helping engineers quickly understand the overall situation of the industry. The distribution trend of test results in different regions is shown through the depth of color or the size of icons. Geographic trend analysis can reveal the differences in electromagnetic compatibility levels in different regions, providing a reference for regional testing and improvement.

[0044] Step 105: Monitor the key indicators in real time based on a preset threshold. When the key indicators exceed the preset threshold, trigger an alarm signal and generate a warning prompt message.

[0045] In the embodiments 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; the early warning prompt message is a message to remind the user to pay attention to potential problems.

[0046] The alarm and early warning layer continuously monitors key indicators and compares them with the preset threshold. When the data exceeds the threshold, the system triggers an alarm signal through sound, text message or interface pop-up window, and at the same time generates a detailed early warning prompt message, including the exceeded value, timestamp and recommended measures. The early warning information is recorded and pushed to relevant terminals.

[0047] Step 106, optimize the test plan according to the alarm signal and the early warning prompt message, and dynamically adjust the test parameters of the data acquisition layer.

[0048] In the embodiments 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 requirements.

[0049] The system analyzes the alarm signal and the early warning information, and automatically generates optimization suggestions, such as adjusting the test frequency range or the antenna position. The data acquisition layer dynamically updates the test parameters according to the optimization plan to ensure the accuracy and efficiency of subsequent tests. The adjusted parameters are sent to the test equipment through the configuration interface to achieve closed-loop control.

[0050] The embodiments of the present application realize the full-process automation from data acquisition, processing to visualization display and alarm and early warning, significantly improving the test efficiency and data processing ability. Through real-time monitoring and dynamic optimization, the system can quickly identify problems and guide improvements, providing an efficient and accurate solution for the electromagnetic compatibility test of automotive electronic devices.

[0051] Optionally, step 102 includes: Step 1021, deploy a signal source to generate an electromagnetic radiation signal covering a preset frequency range.

[0052] In the embodiments of the present application, the signal source is a test device capable of generating electromagnetic signals with specific frequencies and modulation methods. The preset frequency range is an electromagnetic signal frequency interval preset according to the test standard, such as 20 MHz - 2000 MHz.

[0053] The system configures the signal source device and sets the start frequency, stop frequency and step frequency parameters according to the test standard. The signal source generates electromagnetic signals with different modulation methods such as unmodulated sine waves, AM signals modulated by 1 kHz 80%, or PM signals with specific periods. The system precisely adjusts the output frequency and modulation parameters of the signal source through the control interface to ensure that the generated signals fully cover the frequency range required for testing.

[0054] Step 1022: Amplify the electromagnetic radiation signal through a power amplifier to ensure that the output power meets the preset field strength range.

[0055] In the embodiments of the present application, the power amplifier is short for a power amplification device, which is used to amplify the power of the input signal to the required level. The preset field strength range is the standard value range of the electromagnetic field strength set according to the test requirements.

[0056] The system inputs the low-power signal output by the signal source into the power amplifier device. The power amplifier linearly amplifies the signal according to the preset gain parameters, and the maximum output power can reach 100W. The system monitors the output power of the power amplifier in real time, and through feedback adjustment, ensures that the power of the amplified signal precisely meets the field strength range requirements for testing, while avoiding signal distortion.

[0057] 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.

[0058] In the embodiments of the present application, the field strength probe is a sensor device for measuring the electromagnetic field strength. The frequency-domain distribution refers to the intensity distribution characteristics of the electric field strength at different frequencies.

[0059] The system controls the field strength probe to perform multi-point measurements in the test area. The probe converts the detected analog signal of the electric field strength into a digital signal, 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 reference data for subsequent tests.

[0060] 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.

[0061] In the embodiments of the present application, the transmitting antenna is a transducer device that converts an electrical signal into electromagnetic waves for radiation. Electromagnetic waves are the energy of the electromagnetic field propagating in space in the form of waves.

[0062] The system feeds the electrical signal output by the power amplifier into the transmitting antenna. The transmitting antenna converts the electrical signal into space electromagnetic waves according to its radiation characteristics, covering the device under test according to the set polarization direction and radiation pattern. The system ensures that the device under test is irradiated by a uniform electromagnetic field by controlling the position and angle of the antenna.

[0063] Step 1025: Use a receiver to capture the response signal of the automotive electronic device under test under radiation interference and transmit the response signal to the big data platform.

[0064] In the embodiments 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.

[0065] The system receives the output signals of the device under test through multiple channels of the receiver in parallel. The receiver demodulates, filters, and digitizes the signals, and detects parameter changes such as signal distortion and bit error rate. The system packs the collected response signal data and uploads it to the storage cluster of the big data platform in real time through the data transmission interface.

[0066] 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.

[0067] In the embodiments of the present application, the high-speed network refers to a data transmission network with high bandwidth and low latency characteristics. Real-time synchronization means that the data of each device is transmitted to the processing center immediately after being collected.

[0068] The system connects all test devices through Gigabit Ethernet or a dedicated bus network. The built-in data acquisition module of each device encapsulates the test parameters and measurement results into standardized data packets. The network switch ensures the real-time transmission of key test data according to the priority scheduling strategy. The data processing layer immediately performs timestamp alignment and format unification processing after receiving the data.

[0069] The embodiments of the present application realize the full-process automated test from signal generation, power amplification, field strength measurement to interference application and device response. Through precise instrument control and real-time data synchronization, the repeatability of the test process and the reliability of the data are ensured. The high-speed network transmission and the support of the big data platform provide a complete and accurate raw data basis for subsequent data processing and analysis.

[0070] Optionally, step 103 includes: Step 1031, remove the noise and outliers in the data through a filtering algorithm, and calibrate the data deviation between different devices.

[0071] In the embodiments of the present application, the filtering algorithm is a mathematical method for signal processing, which can separate the effective signal from the noise in the raw data. The data deviation refers to the systematic difference between the data collected by different test devices.

[0072] The system first preprocesses the original test data using a digital filter, uses low-pass filtering to eliminate high-frequency noise, and uses median filtering to remove pulse interference. For outlier detection, the system establishes a threshold determination mechanism based on statistics to automatically identify and remove data points that exceed the reasonable range. Subsequently, the system performs data calibration between devices. By comparing the output results of each device under standard test conditions, the calibration coefficient is calculated and applied to ensure the comparability of the data of all test devices.

[0073] Step 1032, use the time series analysis module to predict the trend of the cleaned data, and generate the simulation results of future test scenarios in combination with the preset algorithm.

[0074] In the embodiment of the present application, the time series analysis module is a software component dedicated to processing time series data. The preset algorithms include time series prediction algorithms such as ARIMA (Autoregressive Integrated Moving Average Model) and Prophet.

[0075] The system inputs the cleaned test data into the time series analysis module in chronological order. This module first conducts stationarity tests and seasonal decomposition, and then automatically selects the optimal prediction algorithm according to the data characteristics. When the system trains the prediction model, it uses the sliding window technique to continuously update the training data set. After completing the modeling, the system deduces the electromagnetic compatibility performance change curve for a specific future time period based on the current test data trend and outputs the simulated test scenario in a visual form.

[0076] Step 1033: Extract the frequency, amplitude, and phase parameters from the data, and identify the response mode of the tested automotive electronic device based on machine learning algorithms.

[0077] In the embodiment of the present application, the frequency is the rate of periodic change of an electromagnetic signal, the amplitude is the magnitude of the signal strength, and the phase is the position of the waveform on the time axis. Machine learning algorithms include classification algorithms such as Support Vector Machine (SVM) and Random Forest.

[0078] The system extracts the frequency components from the time-domain signal through the Fast Fourier Transform (FFT), uses the peak detection algorithm to obtain the amplitude values at each frequency point, and calculates the phase information using the Hilbert transform. These characteristic parameters are formatted and input into the machine learning model. The system uses a pre-labeled training data set to perform supervised learning on the model and establish a mapping relationship between the characteristic parameters and the device response types (such as normal operation, minor interference, severe fault). After the model is deployed, it can identify the working state of the tested device in the current electromagnetic environment in real time.

[0079] Step 1034: Integrate the in-vehicle sensor data and the electromagnetic compatibility test data to construct a multi-dimensional analysis model to evaluate the influence of environmental factors on the test results.

[0080] In the embodiment of the present application, the in-vehicle sensor data includes vehicle operating environment parameters such as temperature, humidity, and vibration. The multi-dimensional analysis model is a mathematical model capable of processing the correlation analysis of multiple data types.

[0081] The system collects the environmental sensor data on the CAN bus through the in-vehicle bus interface and establishes a time synchronization association with the electromagnetic test data. In the data fusion stage, the system uses the Kalman filter to eliminate sensor noise and constructs a multi-dimensional feature matrix containing electromagnetic parameters, environmental parameters, and device status. The system applies Principal Component Analysis (PCA) to reduce the data dimension, and then trains a regression analysis model to quantitatively evaluate the specific influence degree of factors such as temperature change on the electromagnetic compatibility performance of the device.

[0082] Step 1035: Dynamically learn the electromagnetic response characteristics in historical data through a deep learning model to predict the risk of radiation exceeding the standard at a specific frequency.

[0083] In the embodiments of the present application, the deep learning model refers to a machine learning model with a multi-layer neural network structure, such as CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory Network). The risk of radiation exceeding the standard refers to the probability that the device under test exceeds the electromagnetic compatibility standard at a specific frequency.

[0084] The system constructs a hybrid neural network architecture that includes a convolutional layer and a recurrent layer. The convolutional layer processes frequency-domain features, and the LSTM layer learns time series dependencies. The model is trained using a historical test data set, with the input being the frequency-amplitude spectrogram and time series data, and the output being the probability of exceeding the standard at each frequency point. The system sets up an online learning mechanism to continuously update the model parameters with new test data. In the prediction phase, the model analyzes the current test data and outputs the frequency points that may exceed the standard in the future and the risk level, providing a basis for optimizing the test plan.

[0085] The embodiments of the present application realize the transformation from the original signal 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 the working characteristics of the device, multi-dimensional analysis quantifies environmental impact factors, and deep learning realizes risk warning. The comprehensive application of these technologies significantly improves the intelligence level of electromagnetic compatibility testing, transforms the analysis of test results from passive detection to active prediction, and provides data support and decision-making basis for the electromagnetic compatibility design and optimization of automotive electronic devices.

[0086] Optionally, step 104 includes: Step 1041: Mark the geographical locations of each automobile enterprise in the map module and display the density of radiation value exceeding the standard in different regions through a color gradient.

[0087] In the embodiments of the present application, the map module is a software component for geographical information visualization, which can associate and display data with geographical locations. The color gradient uses colors with different shades or hues to represent the continuous change of numerical values.

[0088] The system calls the Geographic Information System (GIS) engine to load a digital map containing the location information of automobile enterprises. According to the test data reported by each automobile enterprise, the system calculates the density index of radiation value exceeding the standard for each geographical region. The data processing unit maps the density value to a preset color gradient range to generate a visualization rendering instruction. The graphics processing unit executes the rendering and displays color block marks at the corresponding positions on the map, with the color shade being positively correlated with the density of exceeding the standard. The system also generates a legend to ensure the intuitiveness and accuracy of data presentation.

[0089] Step 1042: Display the ranking of the maximum radiation values by vehicle type in the bar chart module, and compare the differences in the over-standard frequency numbers of each vehicle manufacturer.

[0090] In the embodiment of the present application, the bar chart module is a chart generation component for classifying data comparison. The maximum radiation value refers to the highest electromagnetic radiation level recorded during the test. The over-standard frequency number is the number of frequency points where the radiation intensity exceeds the standard limit value.

[0091] The system extracts the test statistical indicators of each vehicle type from the database and sorts them in descending order according to the maximum radiation value. The visualization engine creates a dual-axis bar chart. The main coordinate axis shows the bar of the maximum radiation value of each vehicle type, and the secondary coordinate axis plots the broken line of the over-standard frequency number corresponding to the vehicle type. The chart automatically adds axis labels, data labels, and difference indication marks. The system sets an interactive function. When the user hovers over a specific bar, the detailed test parameters and comparative analysis data of the vehicle type are displayed.

[0092] Exemplarily, referring to Figure 3 , use a bar chart to display the ranking of the maximum radiation values of vehicle manufacturers and vehicle types to help engineers quickly identify high-risk objects. The bar chart display can intuitively present the maximum radiation values of different vehicle manufacturers and vehicle types, providing a clear improvement direction for engineers. Referring to Figure 4 , compare the over-standard frequency numbers of different vehicle manufacturers and vehicle types through a bar chart to analyze the stability of the equipment. The comparison of over-standard frequency numbers can reveal the differences in electromagnetic compatibility among different vehicle manufacturers and vehicle types, providing a basis for subsequent optimization and improvement.

[0093] Step 1043: Plot the curve of the over-standard frequency number changing with time in the line chart module, and superimpose the frequency domain distribution trend of the radiation value.

[0094] 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. The frequency domain distribution refers to the distribution characteristics of electromagnetic signal energy at different frequencies.

[0095] The system establishes a time series database index and aggregates the over-standard frequency number data according to the test time points. The plotting engine creates a dual-Y axis coordinate system. The main coordinate axis plots the time change curve of the over-standard frequency number, and the secondary coordinate axis shows the frequency domain distribution spectrum line of typical test points. The system uses different line types and colors to distinguish data series and adds dynamic annotation points to mark key events. The spectrum data is calculated in real time through fast Fourier transform to ensure the timeliness of trend analysis. The chart supports zooming and panning operations for easy detailed viewing of data characteristics in a specific time period.

[0096] Exemplarily, a line chart is used to display the changing trend of the number of non-compliance frequencies over time, and analyze the fluctuations during the test process. The line chart display can visually present the changing trend of the number of non-compliance frequencies, helping engineers understand the fluctuations during the test process and promptly discover and solve problems. It also shows the changing trend of the radiation values of the device at different frequencies, helping engineers understand the frequency domain characteristics of the device. The analysis of the changing trend of radiation values can reveal the radiation characteristics of the device at different frequencies, providing valuable information for engineers and guiding subsequent testing and optimization work.

[0097] Step 1044: Generate a three-dimensional electromagnetic field heat map based on WebGL technology, supporting interactive operations to locate high-radiation interference sources.

[0098] In the embodiment of the present application, WebGL is a JavaScript API for web 3D graphics rendering. The three-dimensional electromagnetic field heat map is a visualization form that represents the electromagnetic field intensity distribution in three-dimensional space using a color gradient.

[0099] The system constructs a three-dimensional scene through WebGL shader programming, converting the coordinate data of the test area into a vertex buffer. According to the data values collected by the field strength probe, the system calculates the color attributes of each spatial point, establishing a mapping relationship from numerical values to colors. The graphics pipeline performs real-time rendering to generate a stereoscopic heat map with depth perception. The interaction module realizes functions such as mouse-controlled perspective rotation, zooming, and panning, and the collision detection algorithm ensures the accuracy of operation responses. The system sets a threshold screening function to automatically highlight the radiation hot spot areas that exceed the safety standard.

[0100] Step 1045: Customize the display component combination through a dynamic dashboard and integrate a timeline function to compare the test results in different time periods.

[0101] 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 the time range selection.

[0102] The system provides a visualization component library, including various chart types such as maps, bar charts, and line charts. Users arrange components on the canvas by dragging, and the system automatically establishes the binding relationship between the components and the data source. The timeline control establishes a linkage relationship with all charts. When the user adjusts the time range, each component synchronously updates to display the data for the corresponding period. The system saves the dashboard configuration template, supporting quick switching between different analysis perspectives. The data caching mechanism ensures that the charts can smoothly transition and be quickly rendered when the time range changes.

[0103] In the embodiments of the present application, the regional distribution characteristics are revealed through geospatial display, the key problem models are highlighted by sorting and comparison, the performance change rules are discovered by time trend analysis, the interference source positions are located by three-dimensional visualization, and finally various analysis perspectives are integrated through a customizable dashboard. The comprehensive application of these visualization technologies transforms complex electromagnetic compatibility test data into intuitive graphical information, significantly improving the data interpretation efficiency.

[0104] Optionally, step 105 includes: Step 1051, set the upper threshold of the maximum radiation value and the cumulative threshold of the number of exceeding-standard frequencies according to the preset standard regulations.

[0105] In the embodiments of the present application, the preset standard regulations are electromagnetic compatibility test specification documents formulated within the industry or enterprise. The upper threshold is the limit value of the maximum allowable radiation intensity, and the cumulative threshold is the total number of allowable exceeding-standard frequencies within a certain test period.

[0106] The system reads the standard specification database and extracts the threshold parameters applicable to the current test project. For the maximum radiation value, the system sets different thresholds in segments according to the test frequency range; for the number of exceeding-standard frequencies, 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 for the real-time monitoring module to call. The system performs parameter verification to ensure that the set thresholds meet the safety requirements of the test standard.

[0107] Step 1052, dynamically adjust the upper threshold and the cumulative threshold through the threshold setting module to adapt to different test scenarios and the requirements of vehicle manufacturers.

[0108] In the embodiments of the present application, the threshold setting module is a software component responsible for managing and adjusting the alarm threshold. The test scenarios include different test types such as vehicle-level testing and component testing.

[0109] The system provides a graphical threshold configuration interface and supports the selection of preset templates based on the test scenario. When the test project is switched, the system automatically loads the corresponding threshold configuration. For special requirements, engineers can adjust the threshold parameters of specific frequency bands through a slider or numerical input. The system performs a compliance check before implementing the adjustment to prevent setting unsafe thresholds. The adjusted parameters take effect immediately and are synchronized to all monitoring nodes to ensure the threshold consistency of the entire test system.

[0110] Step 1053, when the maximum radiation value exceeds the upper threshold, trigger an audible and visual alarm and send a text message to the engineer's terminal.

[0111] In the embodiments of the present application, the audible and visual alarm is an emergency alarm issued through sound and light signals. The engineer's terminal refers to a mobile device or computer installed with a monitoring application.

[0112] The real-time monitoring module continuously compares the test data with the threshold parameters. When it detects that the radiation value exceeds the standard, the system immediately activates the alarm protocol: controls the alarm to emit a beeping sound at a specific frequency and flash a red warning light; at the same time, sends an alarm message containing the test number, the exceeded value, and the timestamp to the preset contact list through the SMS gateway. The system records the complete alarm event data, including the test parameters and environmental conditions at the time of triggering, providing a basis for subsequent analysis.

[0113] Step 1054, when the number of exceeded frequencies reaches a preset percentage of the cumulative threshold, generate a warning report and recommend a plan to optimize the test parameters.

[0114] In the embodiment of the present application, the warning report is a problem prediction document automatically generated by the system. The plan to optimize the test parameters includes improvement suggestions such as adjusting the test frequency and field strength.

[0115] The system sets a preset percentage of the cumulative threshold (for example, the preset percentage is 80%). When the number of exceeded frequencies reaches 80% of the cumulative threshold, the analysis module starts the warning process: first collects all relevant data of the current test, and then calls the plan recommendation algorithm to generate improvement suggestions based on historical optimization cases and the expert knowledge base. The report generation engine integrates this information into a structured document, including the current situation analysis, risk prediction, and specific optimization measures. The system pushes the warning report to the responsible engineer through the message center and highlights the warning status on the visualization interface.

[0116] Step 1055, record the historical data of all alarm and warning events, and generate a statistical analysis report to support decision-making optimization.

[0117] In the embodiment of the present application, the historical data refers to the past alarm and warning records stored by the system. The statistical analysis report is a trend analysis and problem summary document generated regularly.

[0118] The system establishes a dedicated event database to structurally store the complete context data of each alarm / warning event, including test parameters, environmental conditions, handling measures, and result verification. The report generation module regularly performs data analysis tasks: calculates the event incidence rate, classifies and counts the problem types, identifies the high-frequency abnormal frequency bands, etc. The system automatically generates a PDF report containing charts and text analysis, supporting filtering and viewing by dimensions such as time range and test items. The report data identifies potential patterns through data mining technology, providing data support for revising the test standards and improving the equipment.

[0119] The embodiment of the present application achieves standard adaptation through intelligent threshold management, adopts a hierarchical alarm mechanism to ensure timely response to problems, provides forward-looking optimization suggestions in combination with early warning analysis, and finally supports continuous improvement through systematic data accumulation. These functions work together to significantly improve the standardization and reliability of electromagnetic compatibility testing, and effectively prevent test errors and equipment damage. Real-time alarms reduce problem response time, intelligent early warnings reduce the risk of test failures, and historical analysis provides a data basis for long-term quality improvement, ultimately achieving simultaneous optimization of test efficiency and quality.

[0120] Optionally, the step 106 includes: Step 1061, automatically modifying the frequency range and signal modulation mode of the test task according to the alarm signal.

[0121] In the embodiment of the present application, the alarm signal refers to the warning signal generated by the system when it detects that the test data exceeds the preset threshold, which is used to trigger the subsequent automatic adjustment operation. The frequency range refers to the frequency range covered by the signal source in the electromagnetic compatibility radiation immunity test, such as 20MHz-2000MHz. The signal modulation mode refers to the waveform modulation type output by the signal source, such as amplitude modulation (AM), frequency modulation (FM) or unmodulated sine wave.

[0122] After receiving the alarm signal, the system automatically analyzes whether the frequency range and signal modulation method of the current test task conflict with the preset standard. If the data exceeds the standard, the system recalculates the appropriate frequency range through the built-in algorithm and adjusts the signal modulation method (for example, switching from AM to FM). The adjusted parameters are directly applied to the test task configuration to ensure that subsequent tests meet safety requirements.

[0123] Step 1062: reconfigure the position of the antenna and the output power level of the power amplifier based on the warning prompt information.

[0124] In the embodiments of the present application, the early warning prompt information refers to the predictive prompt generated by the system for data close to 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 coverage of the electromagnetic field. 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 radiation immunity test.

[0125] According to the early warning information, the system calls the preset antenna position optimization model to calculate more reasonable placement coordinates (such as moving away from interference sources or adjusting the height). At the same time, the system dynamically adjusts the output power level of the power amplifier through the power control module to ensure that it is within the maximum safety range allowed by the preset standard. The adjusted parameters are sent to the hardware device for execution through instructions.

[0126] Step 1063: The updated test parameters are sent to the signal source, power amplifier, and transmitting antenna through the system.

[0127] In the embodiment of the present application, the system packages the updated parameters generated in Steps 1061 and 1062 into control instructions through a high-speed communication protocol (such as TCP / IP) and sends them to the signal source, power amplifier, and transmitting antenna respectively. The signal source receives the new frequency and modulation mode parameters and resets the output waveform; the power amplifier adjusts the amplification factor according to the new power level; the transmitting antenna synchronously updates its physical position coordinates. All devices feedback confirmation signals to the system to ensure that the parameters take effect.

[0128] Step 1064: Real-time verify whether the adjusted test parameters meet the safety range specified by the preset standard.

[0129] In the embodiment 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 interval allowed for the test parameters, for example, the frequency range of 10 kHz - 10 GHz.

[0130] After the parameter adjustment is completed, the system immediately starts the real-time monitoring module, collects the output frequency of the signal source, the power of the power amplifier, and the antenna position data, and compares them item by item with the preset standard. If it is found that the parameters exceed the safety range, the system triggers a secondary adjustment process; if the verification passes, the status is recorded and the next link is entered. The verification result is synchronized to the alarm and early warning layer.

[0131] Step 1065: Synchronize the parameter adjustment record to the data processing layer and update the display content of the visualization display layer.

[0132] In the embodiment of the present application, the system encapsulates the adjusted parameters, verification results, and timestamps into a log file and transmits it to the data processing layer for structured storage. At the same time, the visualization display layer calls the API interface to obtain the latest data and dynamically updates the content such as maps, bar charts, and line charts. For example, the bar chart shows the latest power level ranking, and the line chart reflects the adjustment trend of the frequency range.

[0133] In the embodiment of the present application, the frequency, modulation mode, antenna position, and power level are automatically adjusted according to the alarm or early warning signal, the compliance is verified in real time, and the data and visualization interface are synchronously updated. This process significantly improves the test efficiency, ensures the accuracy and safety of the electromagnetic compatibility radiation immunity test, and at the same time assists engineers in making quick decisions through data transparency.

[0134] Optionally, the method further includes: Step 201: Implement parallel acquisition and real-time synchronization of multi-device data in the data acquisition layer.

[0135] In the embodiments 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, field strength probes, etc.), and transmitting the data to the big data platform through a high-speed network. Parallel acquisition means that the system independently acquires data from multiple test devices simultaneously to improve the data acquisition efficiency. Real-time synchronization means that the system ensures that the data collected by all devices is aligned on the time axis through timestamps or synchronization protocols, avoiding data delay or misalignment.

[0136] The system deploys multiple test devices (such as signal sources, power amplifiers, field strength probes, etc.) through the data acquisition layer, and each device operates independently and acquires specific parameters (such as electric field strength, frequency range). The system uses a high-speed network (such as Gigabit Ethernet or a dedicated communication protocol) to transmit the data of each device to the central big data platform in real time. During the transmission process, the system marks the data with timestamps to ensure that the data collected by different devices is strictly synchronized in time. For example, the measurement data of the field strength probe and the response signal of the receiver need to match at the same time point to accurately reflect the impact of the electromagnetic environment on the device.

[0137] Step 202, complete the fully automated process of data cleaning, feature extraction, and pattern recognition in the data processing layer.

[0138] In the embodiments of the present application, the data processing layer refers to the module in the system that cleans, analyzes, and transforms the raw data, providing structured data for subsequent visualization and early warning.

[0139] The system first calls the data cleaning module in the data processing layer, and removes the outliers caused by sensor failures or environmental interferences through threshold filtering and calibration algorithms. Subsequently, the data analysis module performs feature extraction on the cleaned data, such as calculating the maximum radiation value or the number of over-standard frequencies of the signal. The pattern recognition module analyzes the electromagnetic response pattern of the device based on a pre-trained deep learning model (such as CNN or LSTM) to determine whether it is in an abnormal state. The entire process requires no manual intervention, and the system automatically completes the conversion from raw data to structured results.

[0140] Step 203, provide multi-dimensional interactive charts in the visualization display layer to support engineers in making quick decisions.

[0141] In the embodiments of the present application, the visualization display layer refers to the module in the system that presents the processed data in a graphical form (such as maps, bar charts). An interactive chart is a visualization tool that allows users to dynamically adjust the display content through operations (such as zooming, filtering).

[0142] The system generates various charts at the visualization layer: the map module marks the distribution of the test results of automobile enterprises; the bar chart module shows the ranking of the maximum radiation values; the line chart module displays the trend change of the over-standard frequency numbers. The system supports engineers to customize views through interactive operations (such as clicking and dragging the timeline), for example, overlaying data comparison of different vehicle models. The charts are based on WebGL or VR technology to achieve high-dynamic rendering, ensuring the real-time and intuitive display of data.

[0143] Step 204, establish an intelligent feedback mechanism in the alarm and early warning layer to optimize the test process.

[0144] In the embodiment of the present application, the alarm and early warning layer refers to the module in the system that monitors whether the data exceeds the threshold and triggers notifications. The intelligent feedback mechanism refers to the function of the system to automatically adjust test parameters or early warning rules based on historical data or real-time analysis.

[0145] The system preset thresholds for radiation values and over-standard frequency numbers in the alarm and early warning layer. When the real-time data exceeds the threshold, the alarm trigger module notifies the engineer via text message or sound. The early warning module predicts potential risks based on time series analysis (such as the ARIMA algorithm), for example, the frequency points that may exceed the standard in future tests. The system automatically records alarm events and generates optimization suggestions (such as adjusting the antenna position or test frequency), and feeds them back to the test process.

[0146] Step 205, reverse input the optimized test parameters to the data acquisition layer through closed-loop control to form a continuously iterative test system.

[0147] In the embodiment of the present application, the system automatically generates optimized test parameters (such as adjusting the signal source frequency or power amplifier power) according to the analysis results of the alarm and early warning layer. These parameters are transmitted to the data acquisition layer through a closed-loop control loop to directly configure the operating state of the test equipment. For example, when 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 executed cyclically to continuously iterate and optimize the test system.

[0148] The embodiment of the present application constructs a set of efficient and intelligent automotive electromagnetic compatibility test system through the collaborative work of data acquisition, processing, visualization, early warning, and closed-loop control. The system realizes the real-time acquisition and accurate analysis of test data, reduces the need for manual intervention; the multi-dimensional interactive charts improve the decision-making efficiency; the intelligent early warning and closed-loop optimization significantly shorten the test cycle and improve the reliability of test results.

[0149] Based on the same inventive concept, an embodiment of the present application further provides a visualization automotive electromagnetic compatibility radiation immunity test device for implementing the visualization automotive electromagnetic compatibility radiation immunity test method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the visualization automotive electromagnetic compatibility radiation immunity test device provided below can refer to the limitations on the visualization automotive electromagnetic compatibility radiation immunity test method in the above text, and will not be repeated here.

[0150] In an exemplary embodiment, as Figure 5 shown, a visualization automotive electromagnetic compatibility radiation immunity test device 30 is provided, including: A construction module 301, configured to establish an electromagnetic compatibility radiation immunity test data innovation application system based on a distributed architecture, and the system includes a data acquisition layer, a data processing layer, a visualization display layer, and an alarm and early warning layer; A processing module 302, configured to collect radiation immunity test data of automotive electronic devices from multiple test devices in real time through the data acquisition layer, and transmit the data to the data processing layer; Clean, analyze, and extract features from the data in the data processing layer to obtain key indicators, where the key indicators include the maximum radiation value and the number of non-compliance frequencies; Input the key indicators into the visualization display layer, and display the test result distribution, ranking, and trend of different vehicle manufacturers and models in the form of maps, bar charts, and line charts; Based on a preset threshold, monitor the key indicators in real time. When the key indicators exceed the preset threshold, trigger an alarm signal and generate a warning prompt message; Optimize the test plan according to the alarm signal and the warning prompt message, and dynamically adjust the test parameters of the data acquisition layer.

[0151] Optionally, the processing module 302 is further configured to: Deploy a signal source to generate an electromagnetic radiation signal covering a preset frequency range; Amplify the power of the electromagnetic radiation signal through a power amplifier to ensure that the output power meets the preset field strength range; 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; 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; Use a receiver to capture the response signal of the automotive electronic device under test under radiation interference, and transmit the response signal to the big data platform; 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.

[0152] Optionally, the processing module 302 is further configured to: Remove noise and outliers from the data through a filtering algorithm and calibrate the data deviation between different devices; Use a timing analysis module to perform trend prediction on the cleaned data and generate a simulation result of a future test scenario in combination with a preset algorithm; Extract frequency, amplitude, and phase parameters from the data and identify the response pattern of the automotive electronic device under test based on a machine learning algorithm; Integrate in-vehicle sensor data and electromagnetic compatibility test data to build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results; Dynamically learn the electromagnetic response characteristics in historical data through a deep learning model to predict the risk of radiation exceeding the standard at specific frequencies.

[0153] Optionally, the processing module 302 is further configured to: Mark the geographical locations of each automobile enterprise in the map module and display the density of radiation value exceeding the standard in different regions through color gradients; Display the ranking of the maximum radiation value by vehicle type in the bar chart module and compare the differences in the number of times of exceeding the standard among different automobile enterprises; Draw a curve of the number of times of exceeding the standard changing with time in the line chart module and superimpose the frequency domain distribution trend of the radiation value; Generate a three-dimensional electromagnetic field heat map based on WebGL technology to support interactive operations to locate high-radiation interference sources; Customize the display component combination through a dynamic dashboard and integrate a timeline function to compare test results in different time periods.

[0154] Optionally, the processing module 302 is further configured to: set the upper threshold of the maximum radiation value and the cumulative threshold of the number of times of exceeding the standard according to preset standards; Dynamically adjust the upper threshold and the cumulative threshold through a threshold setting module to adapt to different test scenarios and the needs of automobile enterprises; When the maximum radiation value exceeds the upper threshold, trigger an audible and visual alarm and push a short message to the engineer terminal; When the number of times of exceeding the standard reaches a preset percentage of the cumulative threshold, generate a warning report and recommend a solution to optimize test parameters; Record the historical data of all alarm and warning events and generate a statistical analysis report to support decision-making optimization.

[0155] Optionally, the processing module 302 is further configured to: Automatically modify the frequency range and signal modulation mode of the test task according to the alarm signal; Reconfigure the position of the antenna and the output power level of the power amplifier based on the warning prompt information; Send the updated test parameters to the signal source, power amplifier, and transmitting antenna through the system; Verify in real time whether the adjusted test parameters meet the safety range specified by the preset standard; Synchronize the parameter adjustment record to the data processing layer and update the display content of the visualization display layer.

[0156] Optionally, the processing module 302 is further configured to: Implement parallel acquisition and real-time synchronization of multi-device data in the data acquisition layer; Complete the full-automation process of data cleaning, feature extraction, and pattern recognition in the data processing layer; Provide multi-dimensional interactive charts in the visualization display layer to support engineers in making quick decisions; Establish an intelligent feedback mechanism in the alarm and warning layer to optimize the test process; Through closed-loop control, input the optimized test parameters back to the data acquisition layer to form a continuously iterative test system.

[0157] The embodiment of the present application realizes the full-process automation from data acquisition, processing to visualization display and alarm warning, significantly improving the test efficiency and data processing ability. Through real-time monitoring and dynamic optimization, the system can quickly identify problems and guide improvements, providing an efficient and accurate solution for the electromagnetic compatibility test of automotive electronic devices.

[0158] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as shown in Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the computer program in the non-volatile storage medium. The database of the computer device is used to store visual automotive electromagnetic compatibility radiation immunity test data. The input / output interface of the computer device is used to exchange information between the processor and external devices. 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, it implements a method for visual automotive electromagnetic compatibility radiation immunity testing.

[0159] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0160] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0161] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0162] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0163] 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 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 need to comply with relevant regulations.

[0164] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing 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 embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0165] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0166] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0167] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A visual automobile electromagnetic compatibility radiation immunity test method, characterized in that: The visual automobile electromagnetic compatibility radiation immunity test method comprises: 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; Collect radiation immunity test data of automotive electronic equipment from multiple 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 maximum radiation value and frequency of exceeding the standard; Input the key indicators into the visualization layer, and display the distribution, ranking and trend of test results of different car companies 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; 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.

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 a preset field strength range; Using a field strength probe to measure the electric field strength in the test environment, and recording 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 step of cleaning, analyzing and feature extracting the data in the data processing layer includes: Remove noise and outliers from the data through filtering algorithms, and calibrate data deviations between different devices; Use the time series analysis module to predict the trend of the cleaned data and combine it with the preset algorithm to generate simulation results of 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 and build a multi-dimensional analysis model to evaluate the impact of environmental factors on test results; The deep learning model dynamically learns the electromagnetic response characteristics in historical data and predicts 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 steps of 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 include: The geographical 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 of each vehicle manufacturer is compared; 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; 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.

5. The visual automobile electromagnetic compatibility radiation immunity test method according to claim 1 is characterized in that: The step of real-time monitoring of the key indicators based on the preset thresholds includes: According to the preset standard, the upper limit threshold of the maximum radiation value and the cumulative threshold of the number of exceeding the standard frequency are set; The upper threshold and the cumulative threshold are dynamically adjusted through a threshold setting module to adapt to different test scenarios and vehicle company requirements; 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; 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.

6. 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 meet 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 visualization display layer is updated.

7. 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 mechanism in the alarm and early warning layer to optimize the test process; Through closed-loop control, the optimized test parameters are input back to the data acquisition layer to form a continuously iterative test system.

8. A visual automobile electromagnetic compatibility radiation immunity test device, characterized in that: The visual automobile electromagnetic compatibility radiation immunity test device comprises: A construction module is used to 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; A processing module, used to collect radiation immunity test data of automotive electronic equipment from multiple test devices in real time through the data collection 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 maximum radiation value and frequency of exceeding the standard; Input the key indicators into the visualization layer, and display the distribution, ranking and trend of test results of different car companies 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; 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.

9. 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 described in any one of claims 1 to 7.

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