A rapid calibration method and system for a vehicle head unit automated test equipment

Abstract

The application discloses a kind of quick calibration method and system of car machine automation test equipment, it is related to the technical field of automation calibration, including inputting preset standard calibration signal to test equipment, the response data of each test channel of acquisition equipment is used as calibration datum;Calibration datum is compared with the standard reference value built-in equipment, and the deviation data of each test channel is calculated;Based on deviation data, the calibration parameter of each channel is calculated, wherein the calibration parameter includes gain compensation coefficient, frequency compensation coefficient and phase compensation coefficient;Calibration parameter is loaded to each corresponding channel of test equipment, input preset standard calibration signal and verify test, when each channel error is less than preset residual error threshold value, then calibration is completed and calibration parameter is saved.The application establishes gain-frequency-phase joint compensation model, combines parallelization verification and incremental optimization algorithm, and realizes the high-precision quick calibration of car machine automation test equipment.

Description

Technical Field

[0001] This invention relates to the field of vehicle infotainment system automation calibration technology, and in particular to a rapid calibration method and system for vehicle infotainment system automation testing equipment. Background Technology

[0002] As a core tool for verifying the functionality and performance of in-vehicle infotainment (IVI) systems, automated testing equipment must ensure the consistency of accuracy across its test channels, and calibration technology directly determines the reliability of the test data. Currently, mainstream calibration methods are mainly based on static calibration or manual intervention, such as using standard signal sources for channel-by-channel calibration or relying on external high-precision instruments for comparative calibration. In recent years, some technologies have begun to introduce automated calibration processes, such as gain compensation based on lookup tables or phase correction at limited frequencies, but they still have limitations in multi-parameter joint calibration, dynamic error compensation, and parallel processing. Furthermore, existing calibration methods typically rely on fixed compensation models, which are difficult to adapt to the nonlinear deviation characteristics of wide-bandwidth, multi-level scenarios in IVI testing, making it difficult to balance calibration efficiency and accuracy.

[0003] The main shortcomings of existing technologies are threefold: First, traditional channel-by-channel calibration is time-consuming, especially in large-scale multi-channel test systems, where calibration time increases linearly with the number of channels, failing to meet the high-efficiency requirements of production line testing. Second, existing compensation models mostly optimize for a single error source (such as gain or phase) independently, lacking a joint compensation mechanism for gain-frequency-phase coupling effects, leading to residual error accumulation during complex signal testing. Third, the calibration verification process typically uses single-threshold judgment, without establishing a dynamic recalibration mechanism and incremental optimization algorithm, requiring repeated full-process calibration when equipment ages or the environment changes. In contrast, the multi-parameter joint fitting, parallel verification, and incremental optimization methods of this invention can significantly improve calibration speed and accuracy. For example, by constructing a gain-level composite compensation function using the least squares method, the mismatch problem of traditional piecewise linear compensation in high dynamic range testing is solved; while the phase compensation strategy based on frequency domain interpolation effectively improves the group delay characteristics under wideband signals. Summary of the Invention

[0004] In view of the problems of low efficiency, insufficient multi-parameter coupling compensation and poor dynamic adaptability of existing vehicle testing equipment calibration technology, this invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to perform multi-dimensional and rapid calibration of each channel of the test equipment in an efficient, accurate and traceable manner during the automated testing process of the vehicle system, so as to improve the consistency and reliability of the test results and meet the dual requirements of test accuracy and test efficiency in complex vehicle signal environments.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a rapid calibration method for an automated vehicle testing equipment, comprising,

[0008] Input a preset standard calibration signal into the testing equipment and collect the response data of each test channel of the equipment as calibration reference data;

[0009] The calibration benchmark data is compared with the standard reference value built into the device to calculate the deviation data of each test channel;

[0010] Based on the deviation data, the calibration parameters for each channel are calculated, wherein the calibration parameters include gain compensation coefficient, frequency compensation coefficient, and phase compensation coefficient.

[0011] The calibration parameters are loaded into the corresponding channels of the test equipment, and the preset standard calibration signal is input for verification testing. When the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved.

[0012] As a preferred embodiment of the rapid calibration method for the vehicle automation testing equipment described in this invention, the calibration parameters are loaded into each corresponding channel of the testing equipment, and the preset standard calibration signal is input for verification testing. When the error of each channel is less than a preset residual error threshold, the calibration is completed and the calibration parameters are saved, including:

[0013] Read the calibration parameter set from non-volatile memory and perform an integrity check on the calibration parameter set;

[0014] The calibration parameter group is written into the corresponding field programmable gate array register according to the channel number through the parameter configuration interface of the test equipment, and a hardware reset signal is triggered to make the configuration effective.

[0015] Regenerate the preset standard calibration signal, distribute the preset standard calibration signal to all test channels through the signal routing switch, and start the parallel acquisition mode of the test equipment.

[0016] The residual error is calculated from the data collected from the test channel, and then compared with the preset residual error threshold.

[0017] As a preferred embodiment of the rapid calibration method for the vehicle-mounted automated testing equipment described in this invention, it further includes:

[0018] If the error terms of all channels meet the following conditions: amplitude residual error is less than the amplitude threshold, frequency residual error is less than the frequency threshold, phase residual error is less than the phase threshold, and level residual error is less than the level threshold, then the calibration parameter verification is deemed successful, a version identifier is generated and written to the device's secure storage area, and the calibration time, operator ID, and key performance indicators are recorded in the system log, and the calibration flag bit in the device status register is updated.

[0019] If the calibration parameters fail, the out-of-tolerance items and deviations are recorded, the local recalibration process is automatically triggered, and the incremental parameter adjustment algorithm is used to optimize the compensation parameters. Data acquisition and error judgment are repeated until the verification is passed or the maximum number of retries is reached.

[0020] As a preferred embodiment of the rapid calibration method for the vehicle-mounted automated testing equipment of the present invention, the calibration parameters for each channel are calculated based on the deviation data, including:

[0021] The deviation dataset of the target channel is extracted from the deviation data matrix according to the test channel number, wherein the deviation dataset includes amplitude deviation sequence, frequency deviation sequence, phase deviation sequence and level deviation sequence;

[0022] The least squares method is used to jointly fit the amplitude deviation sequence and the level deviation sequence to establish a gain-level composite compensation function and generate gain compensation coefficients.

[0023] Statistical analysis is performed on the frequency deviation sequence to calculate the average frequency offset and the maximum frequency offset, and frequency compensation coefficients are generated.

[0024] A phase deviation lookup table for key frequency points is established for the phase deviation sequence, and a continuous phase compensation function is constructed using a cubic spline interpolation algorithm. The interpolation node parameters are stored as phase compensation coefficients.

[0025] The gain compensation coefficient, the frequency compensation coefficient, and the phase compensation coefficient are input into the forward verification model to calculate the residual error, wherein the residual error includes the gain residual error, the frequency residual error, and the phase residual error;

[0026] If any residual error exceeds the preset threshold, the coefficient matrix of gain compensation is optimized by iterative least squares method, a second-order correction term is introduced for frequency compensation, and the interpolation node density is increased in the phase compensation table.

[0027] The verified compensation parameters are standardized and packaged to form calibration parameter sets for each channel.

[0028] As a preferred embodiment of the rapid calibration method for the vehicle-mounted automated testing equipment of the present invention, the method includes: comparing the calibration benchmark data with the standard reference value built into the equipment, and calculating the deviation data of each test channel, including:

[0029] Read the pre-stored standard reference value from the non-volatile memory of the test equipment, and establish a data mapping relationship between the calibration benchmark data and the standard reference value according to the test channel number and signal type;

[0030] For each test channel, calculate the amplitude deviation value ΔA at frequency k. k Frequency deviation value Δf k and phase deviation value Simultaneously, for the level step l of each test channel, the level deviation value ΔP is calculated. l ;

[0031] The amplitude deviation value ΔA k Frequency deviation value Δf k and phase deviation value and the level deviation value ΔP l The data is integrated into a deviation data matrix according to the test channel number to generate structured deviation data;

[0032] Statistical analysis is performed on the deviation values ​​of each item in the deviation data matrix to calculate the distribution characteristics and trends of the deviation values ​​of each test channel, identify channels and frequency points with abnormal deviation values, and establish a deviation data quality assessment table.

[0033] Based on the deviation data quality assessment table, test channels that exceed the preset deviation range are marked as abnormal, and the effective deviation data of the deviation data matrix is ​​transmitted to the calibration parameter calculation module.

[0034] As a preferred embodiment of the rapid calibration method for the vehicle-mounted automated testing equipment described in this invention, the method includes: identifying channels and frequencies with abnormal deviation values, and establishing a deviation data quality assessment table, including:

[0035] When the system detects outliers in the deviation data matrix, it initiates the anomaly detection process and generates a deviation data quality assessment table.

[0036] If the amplitude deviation value ΔA of any channel k >First threshold or frequency deviation value Δf k If the second threshold or phase deviation value is greater than the third threshold, it is marked as a first-level anomaly. The hardware self-test process is executed and the compensation parameters are recalculated using a piecewise fitting algorithm. At the same time, the batch calibration process is paused and a red alarm is issued.

[0037] If a continuous frequency deviation is detected to show a monotonically increasing trend of more than 5% / MHz, it is marked as a level 2 anomaly, the frequency domain interpolation compensation mode is activated, compensation nodes are added on both sides of the abnormal frequency band, and the calibration level range is limited.

[0038] As a preferred embodiment of the rapid calibration method for the vehicle-mounted automated testing equipment described in this invention, the method includes: inputting a preset standard calibration signal to the testing equipment and collecting response data from each test channel of the equipment as calibration reference data, including:

[0039] Configure a preset standard calibration signal source to generate multi-frequency signals including discrete frequency points within the vehicle's test frequency band, and simultaneously generate a standard level signal;

[0040] Connect the preset standard calibration signal source to the RF input port of the test equipment via an RF coaxial cable, and set the signal acquisition parameters;

[0041] The multi-frequency signal is input to each test channel, and the first response data of each channel at k frequency points is obtained through the digital down-conversion module. The first response data includes the amplitude value A at each frequency point. k Frequency value f k and phase value

[0042] The standard level signal is synchronously input to all test channels, and the second response data of each channel at l level steps is recorded, wherein the second response data includes the level measurement value P. l and the corresponding standard level value P ref_l ;

[0043] The first response data and the second response data are preprocessed, and the preprocessed first response data and second response data are stored as a structured data matrix according to the test channel number to generate calibration reference data.

[0044] Secondly, embodiments of the present invention provide a rapid calibration system for vehicle-mounted automated testing equipment, comprising:

[0045] The standard calibration signal injection module is used to input a preset standard calibration signal into the test equipment and collect the response data of each test channel of the equipment as calibration reference data.

[0046] The response acquisition and deviation calculation module is used to compare the calibration benchmark data with the standard reference value built into the device and calculate the deviation data of each test channel.

[0047] The calibration parameter generation module calculates the calibration parameters for each channel based on the deviation data, wherein the calibration parameters include gain compensation coefficient, frequency compensation coefficient, and phase compensation coefficient.

[0048] The parameter loading and verification module is used to load the calibration parameters into the corresponding channels of the test equipment, input the preset standard calibration signal for verification testing, and when the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved.

[0049] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the rapid calibration method for the vehicle-mounted automated testing equipment as described in the first aspect of the present invention.

[0050] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the rapid calibration method for the vehicle-mounted automated testing equipment as described in the first aspect of the present invention.

[0051] Compared with existing technologies, the beneficial effects of this invention are as follows: By inputting multi-frequency signals and standard level signals covering the vehicle's test frequency band into the testing equipment, and collecting response data such as amplitude, frequency, phase, and level of each test channel as calibration benchmark data, a high-dimensional, full-band calibration foundation is constructed. This achieves comprehensive perception of the dynamic characteristics of the test channels, effectively improving the calibration coverage and representativeness, avoiding compensation failure due to a single signal sample, and thus enhancing the adaptability of the testing equipment under different operating conditions. By comparing the above calibration benchmark data with the standard reference values ​​built into the equipment, a deviation data matrix is ​​constructed, and abnormal deviation channels and frequencies are identified based on statistical analysis. This achieves structured modeling and quality assessment of error characteristics, improves the system's early perception capability of equipment performance degradation or channel drift, helps prevent test anomalies, and guides subsequent compensation. The accuracy of the compensation strategy is adjusted. Based on structured deviation data, least squares fitting, statistical inference, and interpolation reconstruction are used to generate gain, frequency, and phase compensation coefficients. Iterative optimization and node densification strategies are introduced for parameters that have not passed the initial verification, which significantly improves the accuracy and stability of parameter modeling. By establishing a composite compensation model, nonlinear error characteristics can be fully captured, making the compensation effect more consistent with the actual response characteristics of the channel. By loading the compensation parameters into the field programmable gate array register, the calibration results are judged by the error dimension according to the threshold. If they pass, the complete calibration version, operator, and index information are recorded. Otherwise, a local recalibration process is triggered and the compensation value is dynamically adjusted using an incremental algorithm. This mechanism ensures that the calibration is not only accurate but also traceable and self-correcting, which significantly improves the maintainability, data consistency, and reliability of engineering applications of automated test equipment. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0053] Figure 1 This is a flowchart of the rapid calibration method for the vehicle-mounted automated testing equipment in Example 1. Detailed Implementation

[0054] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0055] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0057] As mentioned in the background section, the main shortcomings of existing technologies are threefold: First, traditional channel-by-channel calibration methods are time-consuming, especially in large-scale multi-channel test systems, where calibration time increases linearly with the number of channels, failing to meet the high-efficiency requirements of production line testing. Second, existing compensation models mostly optimize for a single error source (such as gain or phase) independently, lacking a joint compensation mechanism for gain-frequency-phase coupling effects, leading to residual error accumulation during complex signal testing. Third, the calibration verification process typically uses single-threshold judgment, without establishing a dynamic recalibration mechanism and incremental optimization algorithm, requiring repeated full-process calibration when equipment ages or the environment changes. In contrast, the multi-parameter joint fitting, parallel verification, and incremental optimization methods of this invention can significantly improve calibration speed and accuracy. For example, by constructing a gain-level composite compensation function using the least squares method, the mismatch problem of traditional piecewise linear compensation in high dynamic range testing is solved; while the phase compensation strategy based on frequency domain interpolation effectively improves the group delay characteristics under wideband signals.

[0058] Figure 1 This is a flowchart illustrating a laboratory intelligent solution preparation control method based on the Internet of Things (IoT) according to an embodiment of the present invention. Figure 1As shown, a laboratory intelligent liquid preparation control method based on the Internet of Things includes:

[0059] S1: Input a preset standard calibration signal into the test equipment and collect the response data of each test channel of the equipment as calibration reference data.

[0060] Specifically, a preset standard calibration signal source is configured to generate multi-frequency signals including discrete frequency points within the vehicle's test frequency band, and a standard level signal is generated simultaneously; the preset standard calibration signal source is connected to the RF input port of the test equipment via an RF coaxial cable, and signal acquisition parameters are set.

[0061] It should be noted that the preset standard calibration signal includes a multi-frequency signal covering the vehicle's test frequency band and a standard level signal; the frequency interval of the multi-frequency signal is determined based on the equal division of the vehicle's test frequency band width and includes the frequency band boundary frequency points; the dynamic range of the standard level signal is -10dBm to +10dBm.

[0062] Preferably, the test equipment automatically identifies and activates all N test channels to form a parallel test path; the signal acquisition parameters are set as follows: the sampling rate of the test equipment is configured to be more than 5 times the highest frequency of the multi-frequency signal, and the quantization bit width of the analog-to-digital converter is set to 16 bits to ensure that the dynamic range coverage accuracy of the standard level signal reaches ±0.1dB.

[0063] Furthermore, the multi-frequency signal is input to each test channel, and the first response data of each channel at k frequency points is obtained through the digital down-conversion module, wherein the first response data includes the amplitude value A at each frequency point. k Frequency value f k and phase value The standard level signal is synchronously input to all test channels, and the second response data of each channel at l level steps is recorded, wherein the second response data includes the level measurement value P. l and the corresponding standard level value P ref_l .

[0064] Furthermore, the first response data and the second response data are preprocessed, and the preprocessed first response data and second response data are stored as a structured data matrix according to the test channel number to generate calibration benchmark data.

[0065] It should be noted that data preprocessing: for amplitude value A k and level measurement value P l Perform moving average filtering to eliminate random noise; for frequency value f kThe frequency resolution is corrected using the FFT interpolation algorithm to obtain accurate frequency response data; the row vectors of the data matrix correspond to the test channel numbers, and the column vectors correspond to the response data at different frequency points and levels.

[0066] S2: Compare the calibration benchmark data with the standard reference value built into the device, and calculate the deviation data of each test channel.

[0067] Specifically, the pre-stored standard reference value is read from the non-volatile memory of the test equipment, and the calibration reference data is mapped to the standard reference value according to the test channel number and signal type.

[0068] It should be noted that the standard reference value includes the multi-frequency standard amplitude value A corresponding to the calibration benchmark data. ref_k Standard frequency value f ref_k Standard phase value and standard level value P ref_l .

[0069] Furthermore, for each test channel at frequency k, the amplitude deviation value ΔA is calculated. k Frequency deviation value Δf k and phase deviation value Simultaneously, for the level step l of each test channel, the level deviation value ΔP is calculated. l .

[0070] Preferably, through amplitude value A k Compared with the standard amplitude value A ref_k The difference is used to obtain the amplitude deviation value ΔA. k And record the set of amplitude deviation values ​​for all frequency points {ΔA1, ΔA2, ..., ΔA k}; through frequency value f k Compared with the standard frequency value f ref_k The difference is used to obtain the frequency deviation value Δf. k And record the set of frequency deviation values ​​for all frequency points {Δf1, Δf2, ..., Δf k}; through phase value With the standard phase value The difference is used to obtain the phase deviation value. And record the set of phase deviation values ​​for all frequency points. Through the level measurement value P l With the standard level value P ref_l The difference is used to obtain the level deviation value ΔP. l And record the set of level deviation values ​​{ΔP1, ΔP2, ..., ΔP} for all level steps. l The row vectors of the deviation data matrix correspond to the test channel numbers, and the column vectors correspond to the deviation values ​​at different frequencies and levels.

[0071] Furthermore, statistical analysis is performed on the deviation values ​​of each item in the deviation data matrix to calculate the distribution characteristics and trends of the deviation values ​​of each test channel, identify channels and frequencies with abnormal deviation values, and establish a deviation data quality assessment table. Based on the deviation data quality assessment table, test channels that exceed the preset deviation range are marked as abnormal, and the effective deviation data of the deviation data matrix is ​​transmitted to the calibration parameter calculation module.

[0072] Preferably, as shown in Table 1, when the system detects outliers in the deviation data matrix, it initiates an anomaly detection process to generate a deviation data quality assessment table; if the amplitude deviation value ΔA of any channel... k >First threshold or frequency deviation value Δf k If the second threshold or phase deviation value is greater than the third threshold, the channel is marked as a first-level anomaly. The hardware self-test process is executed, and the compensation parameters are recalculated using a piecewise fitting algorithm. At the same time, the batch calibration process is paused, and a red alarm is issued. If a continuous frequency point deviation is detected to show a monotonically increasing trend of more than 5% / MHz, it is marked as a second-level anomaly. The frequency domain interpolation compensation mode is activated, compensation nodes are added on both sides of the abnormal frequency band, and the calibration level range is limited.

[0073] It should be noted that the first threshold is 0.3dB, which is determined based on 1.5 times the amplitude measurement accuracy of the device (±0.2dB), and is used to identify significant amplitude distortion; the second threshold is based on the crystal oscillator frequency stability (±1ppm) converted to a maximum allowable frequency deviation of 10Hz in the test frequency band; and the third threshold is a 5° phase tolerance set according to the accuracy specifications of the phase detection chip (AD8302).

[0074] Table 1. Deviation Data Quality Assessment Table

[0075]

[0076]

[0077] Specifically, test parameters are automatically adjusted according to different anomaly levels: a 4x sampling rate encrypted sampling mode is used for first-level anomaly channels; fine scanning with 0.1MHz steps is implemented for second-level anomaly channels; all channels must undergo step level excitation testing; based on the verification results, a graded response is made: when the residual error is below 50% of the threshold, the parameters are marked as the gold standard and the aging model is updated; when the error is between 30% and 100% of the threshold, gradient descent optimization is initiated; when the error exceeds the standard, a hardware diagnostic protocol including RF switch impedance, ADC clock jitter, and other items is triggered.

[0078] Furthermore, a knowledge base update operation is performed, storing the abnormal mode feature vector of this calibration into the fault knowledge graph, calculating the device health index including elements such as environmental parameter compensation curves and abnormal handling log hash values, and predicting the next calibration time based on the index calculation results.

[0079] S3: Based on the deviation data, calculate the calibration parameters for each channel, wherein the calibration parameters include the gain compensation coefficient, frequency compensation coefficient, and phase compensation coefficient.

[0080] Specifically, the deviation dataset of the target channel is extracted from the deviation data matrix according to the test channel number. The deviation dataset includes amplitude deviation sequence, frequency deviation sequence, phase deviation sequence and level deviation sequence. The least squares method is used to jointly fit the amplitude deviation sequence and the level deviation sequence to establish a gain-level composite compensation function and generate gain compensation coefficients.

[0081] Preferably, the specific formula for the gain-level composite compensation function is as follows:

[0082]

[0083] Where x is the input level value; K is the total number of frequency points; w k The normalized weight for the k-th frequency point; ΔA k denoted as , where a is the amplitude deviation at the k-th frequency point; a is the slope of the Sigmoid function; b is the center point of the Sigmoid function; c is the logarithmic compensation coefficient; d is the logarithmic scaling factor; and sgn(*) is the sign function.

[0084] It should be noted that the output of this function is the compensation amount, and the effective compensation range is when the compensation amount is within ±0.3dB.

[0085] Furthermore, the relevant formulas for the average frequency offset, maximum frequency offset, and frequency compensation coefficient are as follows:

[0086] Δf avg =(∑Δf k ) / K

[0087] Δf max =max(|Δf k |)

[0088] F comp =1 / (1+Δf) avg / f ref_center );

[0089] Where, Δf avg Δf is the average frequency offset. max For maximum frequency offset; F comp f is the frequency compensation coefficient;ref_cebter The center frequency of the test band.

[0090] Furthermore, a phase deviation lookup table for key frequency points is established for the phase deviation sequence, and a continuous phase compensation function is constructed using a cubic spline interpolation algorithm. The interpolation node parameters are stored as phase compensation coefficients. The gain compensation coefficient, the frequency compensation coefficient, and the phase compensation coefficient are input into the forward validation model to calculate the residual error, wherein the residual error includes the gain residual error, the frequency residual error, and the phase residual error.

[0091] It should be noted that the forward verification model is constructed based on the mapping relationship between compensation parameters and residual errors. By substituting the gain, frequency, and phase compensation coefficients into the signal transmission link model, the difference between the theoretical output and the measured response is calculated.

[0092] Specifically, if any residual error exceeds a preset threshold, the coefficient matrix of gain compensation is optimized using an iterative least squares method, a second-order correction term is introduced for frequency compensation, and the interpolation node density is increased in the phase compensation table until all residual errors meet the accuracy requirements of gain residual error being less than the gain residual threshold by 0.1dB, frequency residual error being less than the frequency threshold by 10Hz, and phase residual error being less than the phase threshold by 0.5°. The verified compensation parameters are standardized and packaged to form calibration parameter groups for each channel.

[0093] Preferably, the preset threshold is obtained by combining Monte Carlo simulation with the statistical analysis of historical calibration data of the equipment. The residual gain threshold of 0.1dB corresponds to a 3σ confidence interval (σ = 0.033dB), the frequency threshold of 10Hz is determined by the phase noise integration result of the phase-locked loop, and the phase threshold of 0.5° comes from the conversion requirement that the vector error (EVM) does not exceed 1%.

[0094] S4: Load the calibration parameters into each corresponding channel of the test equipment, input the preset standard calibration signal for verification testing, and when the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved.

[0095] Specifically, the calibration parameter set is read from the non-volatile memory and its integrity is verified; the calibration parameter set is written into the corresponding field-programmable gate array register according to the channel number through the parameter configuration interface of the test equipment, and a hardware reset signal is triggered to make the configuration effective.

[0096] It should be noted that the calibration parameter group includes gain compensation parameters, frequency compensation parameters, and phase compensation parameters; the programmable gate array (FPGA) registers write the gain parameters to addresses 0x8000-0x800F, the frequency parameters to addresses 0x8010-0x8017, and the phase parameters to addresses 0x8020-0x803F.

[0097] Furthermore, a preset standard calibration signal is regenerated, and the preset standard calibration signal is distributed to all test channels through a signal routing switch to start the parallel acquisition mode of the test equipment.

[0098] It should be noted that the preset standard calibration signal includes multi-frequency signals and standard level signals; activating the parallel acquisition mode of the test equipment includes:

[0099] a) Acquire the compensated amplitude value A of the multi-frequency signal. ′ k Frequency value f′ k and phase value

[0100] b) Acquire the compensated level value P of the standard level signal. ′ l ;

[0101] c) The sampling duration is set to 3 times the original calibration acquisition time.

[0102] Furthermore, the residual error is calculated from the data collected from the test channels and compared with the preset residual error threshold. If the error items of all channels meet the following conditions: amplitude residual error is less than the amplitude threshold, frequency residual error is less than the frequency threshold, phase residual error is less than the phase threshold, and level residual error is less than the level threshold, then the calibration parameter verification is deemed successful, a version identifier is generated and written to the device's secure storage area, and the calibration time, operator ID, and key performance indicators are recorded in the system log, and the calibration flag bit in the device status register is updated.

[0103] It should be noted that the collected data includes amplitude residual error, frequency residual error, phase residual error, and level residual error; the preset residual error thresholds include amplitude threshold, frequency threshold, phase threshold, and level threshold.

[0104] Specifically, if the calibration parameters fail, the out-of-tolerance items and deviations are recorded, the local recalibration process is automatically triggered, and the incremental parameter adjustment algorithm is used to optimize the compensation parameters. Data acquisition and error judgment are repeated until the verification is passed or the maximum number of retries is reached.

[0105] In summary, this invention constructs a high-dimensional, full-band calibration foundation by inputting multi-frequency signals and standard level signals covering the vehicle's test frequency band into the testing equipment. It collects amplitude, frequency, phase, and level response data from each test channel as calibration benchmark data, achieving comprehensive perception of the dynamic characteristics of the test channels. This effectively improves the calibration coverage and representativeness, avoids compensation failures caused by single signal samples, and enhances the adaptability of the testing equipment under different operating conditions. By comparing the aforementioned calibration benchmark data with the equipment's built-in standard reference values, a deviation data matrix is ​​constructed. Based on statistical analysis, abnormal channels and frequencies are identified, enabling structured modeling and quality assessment of error characteristics. This improves the system's early detection capability for equipment performance degradation or channel drift, helping to prevent test anomalies and guide subsequent compensation strategies. The system employs several techniques to improve the accuracy and stability of parameter modeling. Based on structured deviation data, it generates gain, frequency, and phase compensation coefficients using least-squares fitting, statistical inference, and interpolation reconstruction. For parameters that fail initial verification, iterative optimization and node densification strategies are introduced, significantly enhancing the accuracy and stability of parameter modeling. By establishing a composite compensation model, nonlinear error characteristics can be fully captured, making the compensation effect more closely match the actual response characteristics of the channel. By loading compensation parameters into a field-programmable gate array register, the calibration results are judged based on a threshold according to the error dimension. If the threshold is passed, the complete calibration version, operator, and indicator information are recorded; otherwise, a local recalibration process is triggered, and an incremental algorithm is used to dynamically adjust the compensation value. This mechanism ensures that calibration is not only accurate but also traceable and self-correcting, significantly improving the maintainability, data consistency, and reliability of automated testing equipment in engineering applications.

[0106] Furthermore, this embodiment also provides a rapid calibration system for in-vehicle automated testing equipment, including:

[0107] The standard calibration signal injection module is used to input a preset standard calibration signal into the test equipment and collect the response data of each test channel of the equipment as calibration reference data.

[0108] The response acquisition and deviation calculation module is used to compare the calibration benchmark data with the standard reference value built into the device and calculate the deviation data of each test channel.

[0109] The calibration parameter generation module calculates the calibration parameters for each channel based on the deviation data, wherein the calibration parameters include gain compensation coefficient, frequency compensation coefficient, and phase compensation coefficient.

[0110] The parameter loading and verification module is used to load the calibration parameters into the corresponding channels of the test equipment, input the preset standard calibration signal for verification testing, and when the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved.

[0111] This embodiment also provides a computer device applicable to the rapid calibration method for vehicle-mounted automated testing equipment, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the rapid calibration method for vehicle-mounted automated testing equipment as proposed in the above embodiment.

[0112] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A rapid calibration method for an automated vehicle testing device, characterized in that: include, Input a preset standard calibration signal into the testing equipment and collect the response data of each test channel of the equipment as calibration reference data; The calibration benchmark data is compared with the standard reference value built into the device to calculate the deviation data of each test channel; Based on the deviation data, the calibration parameters for each channel are calculated, wherein the calibration parameters include gain compensation coefficient, frequency compensation coefficient, and phase compensation coefficient. The calibration parameters are loaded into the corresponding channels of the test equipment, and the preset standard calibration signal is input for verification testing. When the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved. Read the pre-stored standard reference value from the non-volatile memory of the test equipment, and establish a data mapping relationship between the calibration benchmark data and the standard reference value according to the test channel number and signal type; For each test channel at frequency k, calculate the amplitude deviation value. Frequency deviation value and phase deviation value Simultaneously, for the level step l of each test channel, the level deviation value is calculated. ; The amplitude deviation value Frequency deviation value and phase deviation value and level deviation value The data is integrated into a deviation data matrix according to the test channel number to generate structured deviation data; Statistical analysis is performed on the deviation values ​​of each item in the deviation data matrix to calculate the distribution characteristics and trends of the deviation values ​​of each test channel, identify channels and frequency points with abnormal deviation values, and establish a deviation data quality assessment table. Based on the deviation data quality assessment table, test channels that exceed the preset deviation range are marked as abnormal, and the effective deviation data of the deviation data matrix is ​​transmitted to the calibration parameter calculation module. When the system detects outliers in the deviation data matrix, it initiates the anomaly detection process and generates a deviation data quality assessment table. If the amplitude deviation value of any channel First threshold or frequency deviation value If the second threshold or phase deviation value is greater than the third threshold, it is marked as a first-level anomaly. The hardware self-test process is executed and the compensation parameters are recalculated using a piecewise fitting algorithm. At the same time, the batch calibration process is paused and a red alarm is issued. If a continuous frequency deviation is detected to show a monotonically increasing trend of more than 5% / MHz, it is marked as a level 2 anomaly, the frequency domain interpolation compensation mode is activated, compensation nodes are added on both sides of the abnormal frequency band and the calibration level range is limited. The deviation dataset of the target channel is extracted from the deviation data matrix according to the test channel number, wherein the deviation dataset includes amplitude deviation sequence, frequency deviation sequence, phase deviation sequence and level deviation sequence; The least squares method is used to jointly fit the amplitude deviation sequence and the level deviation sequence to establish a gain-level composite compensation function and generate gain compensation coefficients. Statistical analysis is performed on the frequency deviation sequence to calculate the average frequency offset and the maximum frequency offset, and frequency compensation coefficients are generated. A phase deviation lookup table for key frequency points is established for the phase deviation sequence, and a continuous phase compensation function is constructed using a cubic spline interpolation algorithm. The interpolation node parameters are stored as phase compensation coefficients. The gain compensation coefficient, the frequency compensation coefficient, and the phase compensation coefficient are input into the forward verification model to calculate the residual error, wherein the residual error includes the gain residual error, the frequency residual error, and the phase residual error; the forward verification model is constructed based on the mapping relationship between the compensation parameters and the residual error. If any residual error exceeds the preset threshold, the coefficient matrix of gain compensation is optimized by iterative least squares method, a second-order correction term is introduced for frequency compensation, and the interpolation node density is increased in the phase compensation table. The verified compensation parameters are standardized and packaged to form calibration parameter sets for each channel.

2. The rapid calibration method for the vehicle-mounted automated testing equipment as described in claim 1, characterized in that: The calibration parameters are loaded into the corresponding channels of the test equipment, and the preset standard calibration signal is input for verification testing. When the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved, including: Read the calibration parameter set from non-volatile memory and perform an integrity check on the calibration parameter set; The calibration parameter group is written into the corresponding field programmable gate array register according to the channel number through the parameter configuration interface of the test equipment, and a hardware reset signal is triggered to make the configuration effective. Regenerate the preset standard calibration signal, distribute the preset standard calibration signal to all test channels through the signal routing switch, and start the parallel acquisition mode of the test equipment. The residual error is calculated from the data collected from the test channel, and then compared with the preset residual error threshold.

3. The rapid calibration method for the vehicle-mounted automated testing equipment as described in claim 2, characterized in that: It also includes, If the error terms of all channels meet the following conditions: amplitude residual error is less than the amplitude threshold, frequency residual error is less than the frequency threshold, phase residual error is less than the phase threshold, and level residual error is less than the level threshold, then the calibration parameter verification is deemed successful, a version identifier is generated and written to the device's secure storage area, and the calibration time, operator ID, and key performance indicators are recorded in the system log, and the calibration flag bit in the device status register is updated. If the calibration parameters fail, the out-of-tolerance items and deviations are recorded, the local recalibration process is automatically triggered, and the incremental parameter adjustment algorithm is used to optimize the compensation parameters. Data acquisition and error judgment are repeated until the verification is passed or the maximum number of retries is reached.

4. The rapid calibration method for the vehicle-mounted automated testing equipment as described in claim 3, characterized in that: Input a preset standard calibration signal into the testing equipment, and collect the response data of each test channel of the equipment as calibration reference data, including: Configure a preset standard calibration signal source to generate multi-frequency signals including discrete frequency points within the vehicle's test frequency band, and simultaneously generate a standard level signal; Connect the preset standard calibration signal source to the RF input port of the test equipment via an RF coaxial cable, and set the signal acquisition parameters; The multi-frequency signal is input to each test channel, and the first response data of each channel at k frequency points is obtained through the digital down-conversion module, wherein the first response data includes the amplitude value of each frequency point. Frequency value and phase value ; The standard level signal is synchronously input to all test channels, and the second response data of each channel at l level steps is recorded, wherein the second response data includes level measurement values. and the corresponding standard level value ; The first response data and the second response data are preprocessed, and the preprocessed first response data and second response data are stored as a structured data matrix according to the test channel number to generate calibration reference data.

5. A rapid calibration system for vehicle infotainment system automated testing equipment, based on the rapid calibration method for vehicle infotainment system automated testing equipment according to any one of claims 1 to 4, characterized in that: include, The standard calibration signal injection module is used to input a preset standard calibration signal into the test equipment and collect the response data of each test channel of the equipment as calibration reference data. The response acquisition and deviation calculation module is used to compare the calibration benchmark data with the standard reference value built into the device and calculate the deviation data of each test channel. The calibration parameter generation module calculates the calibration parameters for each channel based on the deviation data, wherein the calibration parameters include gain compensation coefficient, frequency compensation coefficient, and phase compensation coefficient. The parameter loading and verification module is used to load the calibration parameters into the corresponding channels of the test equipment, input the preset standard calibration signal for verification testing, and when the error of each channel is less than the preset residual error threshold, the calibration is completed and the calibration parameters are saved.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the rapid calibration method for the vehicle automation testing equipment according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the rapid calibration method for the vehicle automation testing equipment according to any one of claims 1 to 4.

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