A method, apparatus, program product, and medium for intelligent testing of a circuit board

Abstract

A method, device, program product, and medium for intelligent testing of circuit boards are disclosed, relating to the field of automated circuit board testing technology. The method involves acquiring response waveform data of the target circuit board; performing frequency domain decomposition processing on the response waveform data to obtain a frequency domain fingerprint spectrum; obtaining modulation influence coefficients based on the frequency domain fingerprint spectrum; when any result exceeds a preset reasonable range, obtaining reversible modulation results and irreversible damage results based on the target frequency domain fingerprint spectrum corresponding to the target test item and each target modulation influence coefficient; for reversible modulation results, obtaining a standard environment equivalent spectrum based on the target modulation influence coefficients, and recalculating the result of the target test item based on the standard environment equivalent spectrum; for irreversible damage results, extracting damage frequency domain features from the target frequency domain fingerprint spectrum, and obtaining the defect type and corresponding defect location information of the target circuit board based on the damage frequency domain features. This improves the overall efficiency of circuit board testing.

Description

Technical Field

[0001] This application relates to the field of automated testing technology for circuit boards, specifically to an intelligent testing method, equipment, program product, and medium for circuit boards. Background Technology

[0002] In the electronics manufacturing industry, circuit boards are core components of electronic devices, and their quality and reliability directly affect the overall performance of the product. For electronic products with high reliability requirements, such as aerospace equipment, medical devices, and military electronic equipment, circuit board testing not only needs to verify their functional correctness, but also needs to ensure the consistency and stability of test data through repeated testing to meet stringent quality control standards.

[0003] A widely adopted circuit board quality assurance technology is the repeated testing method. This technology involves performing the same test procedure multiple times (usually 7 times) on the same circuit board, with each test lasting 30-40 minutes. Multiple sets of test data are collected, statistically analyzed, and statistical indicators such as average, variance, and pass rate are calculated to generate a comprehensive test report. This repeated testing mechanism effectively identifies performance fluctuations in the circuit board, improves the reliability of test results, and ensures that product quality meets high reliability requirements.

[0004] However, the aforementioned repeated testing techniques suffer from low testing efficiency. In actual testing, human error (such as incorrect test lead connections or parameter settings) or unexpected equipment malfunctions can lead to abnormal or invalid test data. When this occurs, existing testing systems typically deem the entire test cycle invalid and require re-execution of all seven test cycles, a process that takes 3-6 hours. This full retesting mechanism results in significant time and resource waste, increases equipment usage time and labor costs, and ultimately impacts the overall efficiency of circuit board testing. Summary of the Invention

[0005] This application provides an intelligent testing method, device, program product, and medium for circuit boards, which improves the overall efficiency of circuit board testing.

[0006] The first aspect of this application provides an intelligent testing method for a circuit board, specifically including:

[0007] While performing standard electrical tests on the target circuit board, a preset detection signal is injected into the target circuit board, and the response waveform data of the target circuit board is collected. The standard electrical tests include multiple test items.

[0008] The response waveform data is subjected to frequency domain decomposition to obtain a frequency domain fingerprint spectrum, which includes characteristic parameters of multiple frequency components;

[0009] Environmental modulation analysis is performed on the frequency domain fingerprint spectrum to obtain the modulation influence coefficient of each frequency component;

[0010] When performing standard electrical tests, if the result of any target test item on the target circuit board exceeds the preset reasonable range, a correlation analysis is performed on the target frequency domain fingerprint spectrum and each target modulation influence coefficient corresponding to the target test item to obtain abnormal results. The abnormal results include reversible modulation results and irreversible damage results.

[0011] For the reversible modulation result, the target frequency domain fingerprint spectrum is subjected to environmental frequency domain inverse transformation processing using the target modulation influence coefficients corresponding to each frequency component to obtain a standard environmental equivalent spectrum, and the result of the target test item is recalculated based on the standard environmental equivalent spectrum;

[0012] For the irreversible damage result, damage frequency domain features are extracted from the target frequency domain fingerprint spectrum, and the damage frequency domain features are matched with a preset defect feature library to obtain the defect type and corresponding defect location information of the target circuit board.

[0013] By adopting the above technical solution, a preset detection signal is injected and the response waveform data of the target circuit board is collected while performing standard electrical tests, realizing comprehensive monitoring of the circuit board's working status. Frequency domain decomposition processing of the response waveform data yields a frequency domain fingerprint spectrum, obtaining characteristic parameters of multiple frequency components of the circuit board and establishing a foundation for circuit board feature identification. Environmental modulation analysis processing of the frequency domain fingerprint spectrum yields the modulation influence coefficients of each frequency component, clarifying the degree of influence of environmental factors on the test results. When the target test result exceeds the preset reasonable range, correlation analysis of the target frequency domain fingerprint spectrum and the target modulation influence coefficient distinguishes between reversible modulation results caused by environmental factors and irreversible damage results caused by circuit damage, improving the accuracy of anomaly cause judgment. For reversible modulation results, the target modulation influence coefficients corresponding to the frequency components are used to perform inverse environmental frequency domain transformation processing to obtain a standard environmental equivalent spectrum and recalculate the test results, eliminating the interference of environmental factors on the test results. For irreversible damage results, by extracting damage frequency domain features and matching them with a preset defect feature library, the defect type and defect location information of the target circuit board are accurately identified. Because circuit board anomaly detection, environmental impact elimination, and defect location are completed in the same testing process, repeated testing is avoided. At the same time, by accurately distinguishing between environmental factors and actual damage, rework caused by misjudgment is reduced, thereby improving the overall efficiency of circuit board testing.

[0014] Optionally, injecting a preset detection signal into the target circuit board and acquiring the response waveform data of the target circuit board specifically includes:

[0015] Obtain the test sequence of each test item in the standard electrical test. The test sequence includes multiple test steps, and each test step includes an excitation application phase, a stabilization waiting phase, and a measurement phase.

[0016] Based on the circuit topology of the target circuit board and the electrical test type of the standard electrical test, a matching detection signal is selected from a preset multi-band detection signal library;

[0017] During the excitation application phase, the excitation signal status of the standard electrical test and the operating parameters of the target circuit board are monitored in real time.

[0018] When the excitation signal is detected to be stable and the operating parameters of the target circuit board are within a stable range, it is determined to enter the stable waiting stage. The detection signal is injected into the key nodes of the target circuit board in a pulse manner through the test probe. Within a preset time window after the detection signal is injected, the response signals of multiple monitoring points of the target circuit board are collected to obtain the initial response waveform data. The key nodes are the power input terminal, the signal input and output terminal and the connection nodes of each functional module in the target circuit board.

[0019] During the measurement phase, the initial response waveform data is subjected to noise reduction filtering and amplitude normalization processing to form the response waveform data of each monitoring point.

[0020] By adopting the above technical solution and obtaining the test sequence of each test item in standard electrical testing, the test steps, including the excitation application stage, the stabilization waiting stage, and the measurement stage, were clarified. Matching probe signals were selected based on the circuit topology of the target circuit board and the type of electrical test, ensuring the compatibility of the probe signals with the circuit under test. During the excitation application stage, the excitation signal status and circuit board operating parameters were monitored in real time, ensuring the stability of the test conditions. When the excitation signal was stable and the operating parameters were within a stable range, the probe signal was injected into key nodes of the circuit board in a pulse manner using test probes, and response signals from multiple monitoring points were collected, achieving comprehensive monitoring of the connection nodes of each functional module of the circuit board. During the measurement stage, the initial response waveform data was denoised, filtered, and normalized, improving the signal-to-noise ratio and comparability of the response waveform data. Because the precise injection of probe signals and the high-quality acquisition of response signals were achieved during the test, a reliable data foundation was provided for subsequent frequency domain analysis, thus ensuring the accuracy of the circuit board test results.

[0021] Optionally, the step of performing frequency domain decomposition processing on the response waveform data to obtain a frequency domain fingerprint spectrum specifically includes:

[0022] Based on the electrical connection path length and impedance characteristics between each monitoring point and the key node, the propagation parameters of each monitoring point are calculated, including the signal propagation delay value and the signal attenuation coefficient value.

[0023] Based on the propagation parameters of each monitoring point, time offset compensation and amplitude attenuation compensation are performed on the response waveform data of each monitoring point to obtain the compensated response data corresponding to each monitoring point.

[0024] Calculate the waveform energy change rate of the compensation response data of each monitoring point, set the time period when the waveform energy change rate exceeds the preset energy change rate threshold as the fine analysis window, and set the time period when the waveform energy change rate does not exceed the preset energy change rate threshold as the regular analysis window;

[0025] Fourier transform is applied to the compensation response data within the fine analysis window and the regular analysis window to obtain window frequency domain transform data;

[0026] Based on the propagation parameters of each monitoring point, the data reliability weight corresponding to each monitoring point is determined;

[0027] The window frequency domain transformation data is weighted and fused based on the data reliability weight of each monitoring point to obtain fused frequency domain data, and a frequency domain fingerprint spectrum is constructed based on the fused frequency domain data.

[0028] By adopting the above technical solution, propagation parameters were calculated based on the electrical connection path length and impedance characteristics between monitoring points and key nodes, thus determining the signal propagation delay and signal attenuation coefficient values. Time offset compensation and amplitude attenuation compensation were performed based on the propagation parameters of each monitoring point, eliminating distortion during signal transmission. By calculating the waveform energy change rate of the compensation response data, the test data was divided into a fine analysis window and a regular analysis window, achieving a reasonable allocation of test resources. Fourier transform was applied to the compensation response data within different analysis windows to obtain the window frequency domain transform data, acquiring the frequency domain characteristics of the signal. Data reliability weights were determined based on the propagation parameters of each monitoring point, ensuring the accuracy of data fusion. Based on the data reliability weights, the window frequency domain transform data was weighted and fused to construct a frequency domain fingerprint spectrum, forming a reliable frequency domain characteristic representation of the circuit board. Because the signal transmission characteristics were fully considered and a differentiated analysis strategy was adopted during the frequency domain decomposition process, the representation accuracy of the frequency domain fingerprint spectrum was improved.

[0029] Optionally, the step of performing environmental modulation analysis on the frequency domain fingerprint spectrum to obtain the modulation influence coefficients of each frequency component specifically includes:

[0030] The current environmental parameters during the testing process of the target circuit board are obtained, including test temperature, test humidity, power supply voltage fluctuation value and electromagnetic interference intensity value.

[0031] Extract the time-series variation data of the same frequency component in the frequency domain fingerprint spectrum at different test times, wherein the time-series variation data includes amplitude values ​​and phase values;

[0032] The current environmental parameters and the time-series change data are aligned and matched according to the test time to obtain time-synchronized environmental frequency data pairs;

[0033] Calculate the correlation between each environmental parameter and each frequency component value in the environmental frequency data pair; construct an environmental frequency correlation matrix based on the correlations, where the rows of the environmental frequency correlation matrix represent the environmental parameter type, the columns represent the frequency component type, and the elements are the correlation values.

[0034] The proportion of numerical difference between each frequency component under standard environmental parameters and current environmental parameters is calculated based on the environmental frequency correlation matrix, and the proportion of numerical difference is determined as the modulation influence coefficient of the corresponding frequency component.

[0035] By employing the above technical solution, the current environmental parameters during the target circuit board testing process, including test temperature, test humidity, power supply voltage fluctuations, and electromagnetic interference intensity, are obtained, comprehensively recording the test environment status. The temporal variation data of amplitude and phase values ​​of the same frequency components at different test times are extracted from the frequency domain fingerprint spectrum, revealing the dynamic changes in frequency domain characteristics. The current environmental parameters and temporal variation data are aligned and matched according to the test time, establishing a correspondence between environmental changes and frequency domain characteristic changes. The correlation between each environmental parameter and each frequency component value in the environmental frequency data pair is calculated, and an environmental frequency correlation matrix is ​​constructed, quantifying the degree of influence of environmental factors on frequency domain characteristics. Based on the environmental frequency correlation matrix, the numerical difference ratio of each frequency component under standard environmental parameters and current environmental parameters is calculated as the modulation influence coefficient, achieving precise quantification of environmental impact. Because a quantitative relationship between environmental parameters and frequency domain characteristics is established, the impact of environmental factors on test results can be accurately assessed and compensated, thereby improving the consistency of circuit board test results under different environmental conditions.

[0036] Optionally, the correlation analysis of the target frequency domain fingerprint spectrum and the modulation influence coefficients of each target corresponding to the target test item is performed to obtain abnormal results. The abnormal results include reversible modulation results and irreversible damage results, specifically including:

[0037] Identify abnormal frequency components whose amplitude or phase values ​​exceed a preset reasonable range from the target frequency domain fingerprint spectrum;

[0038] Obtain the target modulation influence coefficient corresponding to each of the abnormal frequency components;

[0039] Calculate the actual deviation of each abnormal frequency component, where the actual deviation is the difference between the current value of the abnormal frequency component and the value within a preset reasonable range.

[0040] The actual deviation of each abnormal frequency component is compared with the corresponding preset theoretical modulation amount.

[0041] When the difference between the actual deviation of the abnormal frequency component and the theoretical modulation amount is less than a preset difference threshold, it is determined that the deviation of the abnormal frequency component is caused by environmental modulation, and the abnormal frequency component is marked as a reversible modulation result.

[0042] When the difference between the actual deviation of the abnormal frequency component and the theoretical modulation value is greater than a preset difference threshold, it is determined that the deviation of the abnormal frequency component is caused by damage to the circuit board, and the abnormal frequency component is marked as an irreversible damage result.

[0043] By employing the above technical solution, abnormal frequency components whose amplitude or phase values ​​exceed a preset reasonable range are identified from the target frequency domain fingerprint spectrum, achieving preliminary detection of abnormal states. The target modulation influence coefficients corresponding to each abnormal frequency component are obtained, establishing a correlation between the abnormal phenomenon and environmental influences. The difference between the current value of the abnormal frequency component and its value within a preset reasonable range is calculated as the actual deviation, quantifying the degree of abnormality. The actual deviation of the abnormal frequency component is compared with a preset theoretical modulation amount, providing a basis for determining the cause of the abnormality. By comparing the numerical difference with a preset difference threshold, the abnormal frequency components are distinguished into reversible modulation results caused by environmental modulation and irreversible damage results caused by circuit board damage, accurately identifying the root cause of the abnormality. Because an anomaly classification method based on frequency domain characteristics and environmental influences is established, it avoids misjudging temporary anomalies caused by environmental factors as circuit board damage, and also prevents mistaking actual damage for environmental influences, thus improving the accuracy of circuit board anomaly detection.

[0044] Optionally, the step of performing an inverse environmental frequency domain transformation on the target frequency domain fingerprint using the target modulation influence coefficients corresponding to each of the frequency components to obtain a standard environmental equivalent map specifically includes:

[0045] Calculate the current environmental deviation between the current environmental parameters and the standard environmental parameters, and determine the influence value of each frequency component due to the environmental deviation based on the current environmental deviation and the modulation influence coefficient corresponding to each frequency component.

[0046] The amplitude values ​​of each frequency component in the target frequency domain fingerprint spectrum are subtracted from the corresponding influence values ​​to obtain the amplitude values ​​after environmental compensation.

[0047] Subtract the corresponding influence value from the phase value of each frequency component in the target frequency domain fingerprint spectrum to obtain the phase value after environmental compensation.

[0048] The amplitude and phase values ​​after environmental compensation are rearranged in frequency order to form a standard environmental equivalent spectrum.

[0049] By employing the above technical solution, the current environmental deviation between the current environmental parameters and the standard environmental parameters is calculated. The influence value of the environmental deviation is determined by combining the modulation influence coefficients corresponding to each frequency component, quantifying the specific impact of environmental changes on the test results. The amplitude values ​​of each frequency component in the target frequency domain fingerprint spectrum are subtracted from their corresponding influence values, eliminating the modulation effect of environmental factors on the signal amplitude. Similarly, the phase values ​​of each frequency component are subtracted from their corresponding influence values, eliminating the modulation effect of environmental factors on the signal phase. The environmentally compensated amplitude and phase values ​​are then rearranged in frequency order to form an equivalent spectrum for the standard environment, achieving an equivalent conversion of the test data to standard environmental conditions. Because environmental compensation processing is applied to the frequency domain characteristics, test results obtained under different environmental conditions can be restored to the baseline state under the standard environment.

[0050] Optionally, the step of extracting damage frequency domain features from the target frequency domain fingerprint spectrum and matching the damage frequency domain features with a preset defect feature library to obtain the defect type and corresponding defect location information of the target circuit board specifically includes:

[0051] Obtain the abnormal frequency components corresponding to the marked irreversible damage results;

[0052] The amplitude, phase, and frequency location information of the abnormal frequency components are extracted from the target frequency domain fingerprint spectrum to form the damaged frequency domain data;

[0053] Calculate the degree of numerical difference between each abnormal frequency component in the damage frequency domain data and the frequency components in the frequency domain fingerprint spectrum that are within a preset reasonable range;

[0054] The distribution characteristics of the abnormal frequency components in the frequency domain are statistically analyzed, and the degree of numerical difference and distribution characteristics are combined to form the damage frequency domain characteristics;

[0055] Calculate the feature similarity value between the damage frequency domain features and each standard defect feature in the preset defect feature library;

[0056] Select the standard defect feature with the highest feature similarity value as the matching result, and obtain the defect type and corresponding defect location information corresponding to the matching result.

[0057] By employing the above technical solution, abnormal frequency components corresponding to irreversible damage results are obtained, thus determining the frequency domain features required for defect analysis. Amplitude, phase, and frequency location information of the abnormal frequency components are extracted from the target frequency domain fingerprint spectrum to form damage frequency domain data, establishing a complete damage feature description. The degree of numerical difference between each abnormal frequency component in the damage frequency domain data and frequency components within a preset reasonable range is calculated, quantifying the degree of frequency domain feature deviation caused by the damage. The distribution characteristics of abnormal frequency components within the frequency domain are statistically analyzed, and the degree of numerical difference and distribution characteristics are combined to form damage frequency domain features, resulting in a comprehensive damage characterization. The feature similarity values ​​between the damage frequency domain features and each standard defect feature in the preset defect feature library are calculated, establishing a quantitative basis for damage type identification. The standard defect feature with the highest feature similarity value is selected as the matching result, and the corresponding defect type and location information are obtained, achieving accurate identification and location of circuit board defects. Because a defect identification method based on frequency domain features has been established, the diagnosis process of circuit board defects is more objective and accurate, providing a reliable basis for subsequent repair and quality improvement.

[0058] In a second aspect, this application provides an electronic device for intelligent testing of a circuit board, the electronic device comprising: one or more processors and a memory; the memory being coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors calling the computer instructions to cause the electronic device for intelligent testing of the circuit board to perform the method described in the first aspect and any possible implementation thereof.

[0059] Thirdly, this application provides a computer program product containing instructions that, when run on an electronic device for intelligent testing of a circuit board, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0060] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a device for intelligent testing of a circuit board, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the architecture of an intelligent testing system for a circuit board provided in an embodiment of this application;

[0062] Figure 2 This is a flowchart illustrating an intelligent testing method for a circuit board provided in an embodiment of this application;

[0063] Figure 3This is a schematic diagram of the structure of a test sequence provided in an embodiment of this application;

[0064] Figure 4 This is an exemplary hardware structure diagram of an electronic device for intelligent testing of a circuit board provided in an embodiment of this application. Detailed Implementation

[0065] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0066] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0067] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0068] Figure 1 An exemplary system architecture for an intelligent testing system for a circuit board is shown.

[0069] like Figure 1 As shown, the system architecture may include electronic device 11, network 12, and server 13. Network 12 serves as the medium for providing a communication link between electronic device 11 and server 13. Network 12 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0070] Operators can use electronic device 11 to interact with server 13 via network 12 to upload and store test data. Various test applications, such as circuit board testing programs and data analysis programs, can be installed on electronic device 11.

[0071] Electronic device 11 is hardware and can be a test device with test probes and data analysis functions, including but not limited to circuit board testers, signal analyzers, and data acquisition devices. Electronic device 11 performs intelligent testing of circuit boards, including core functions such as injecting probe signals, acquiring response waveforms, performing frequency domain analysis, environmental modulation analysis, and anomaly detection.

[0072] Server 13 is used to store test data uploaded by electronic device 11, including historical data such as frequency domain fingerprint spectrum and modulation influence coefficient, as well as a preset defect feature library. Server 13 can back up and manage the data, and provide the required historical data according to the request of electronic device 11.

[0073] The following detailed explanation uses the electronic device side as an example.

[0074] This embodiment provides an intelligent testing method for circuit boards. Figure 2 This is a flowchart illustrating an intelligent testing method for a circuit board provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes steps S101 to S106:

[0075] S101: While performing standard electrical tests on the target circuit board, a preset detection signal is injected into the target circuit board, and the response waveform data of the target circuit board is collected. The standard electrical tests include multiple test items.

[0076] In this application embodiment, the target circuit board refers to the circuit board product to be tested that needs to be inspected for quality and functional verification. Standard electrical testing refers to the routine test process for inspecting the various electrical performances of the circuit board according to predetermined specifications. Response waveform data refers to the voltage or current change waveform record generated by the target circuit board after receiving the probe signal.

[0077] Specifically, after the electronic device initiates the standard electrical testing procedure for the target circuit board, the testing system will sequentially perform multiple test items according to the preset test sequence. Each test item will measure and evaluate specific electrical parameters of the target circuit board. During the standard electrical test, the testing equipment will simultaneously inject preset detection signals into designated node positions of the target circuit board through test probes. The injected detection signals will propagate inside the target circuit board and interact with the circuit structure. After receiving the detection signals, the target circuit board will generate corresponding electrical responses. The testing equipment will collect these electrical response signals in real time through monitoring probes placed at key positions on the target circuit board. After analog-to-digital conversion and data processing, the collected signals will form digital response waveform data. The response waveform data completely records the dynamic response characteristics of the target circuit board under the action of the detection signals.

[0078] For example, when a test device performs standard electrical tests on a power amplifier circuit board, the test system will sequentially perform multiple tests, such as power supply voltage test, signal gain test, and frequency response test. When performing the power supply voltage test, the test device will simultaneously inject a preset multi-band sine wave detection signal into the power input terminal of the power amplifier circuit board. During the propagation of the multi-band sine wave detection signal inside the power amplifier circuit board, it will interact with functional modules such as amplification circuits and filtering circuits. After receiving the multi-band sine wave detection signal, the power amplifier circuit board will generate corresponding voltage responses at the output terminal and the connection points of each stage of the amplification circuit. The test device will collect these voltage response signals in real time through monitoring probes connected to the output terminal and the connection points of each stage of the amplification circuit. The collected voltage response signals will be converted into digital response waveform data after analog-to-digital conversion and data processing.

[0079] Based on the above embodiments, as an optional embodiment, the step of injecting a preset detection signal into the target circuit board and acquiring the response waveform data of the target circuit board includes steps S201 to S205:

[0080] S201: Obtain the test sequence of each multi-test item in the standard electrical test. The test sequence includes multiple test steps, and each test step includes an excitation application phase, a stabilization waiting phase, and a measurement phase.

[0081] In this embodiment, a test sequence refers to a pre-planned, ordered test operation process to complete a certain test item. A test step represents the basic execution unit in the test sequence, and each test step includes an excitation application phase, a stabilization waiting phase, and a measurement phase. The excitation application phase represents the time period during which a test signal or test voltage is applied to the target circuit board; the stabilization waiting phase is a buffer period during which the circuit board's response stabilizes; and the measurement phase represents the time period during which data is collected and recorded from the circuit board's response parameters.

[0082] Specifically, the electronic device reads all test item information involved in standard electrical testing from the preset test configuration file. The test system parses the test sequence structure corresponding to each test item. The test sequence consists of multiple test steps arranged in chronological order. Each test step follows the same three-stage execution mode. In the excitation application stage, the test system applies a preset excitation signal or excitation voltage to the designated test point of the target circuit board. After the excitation signal is applied, the test system enters the stabilization waiting stage. In the stabilization waiting stage, the test equipment monitors the working status of the target circuit board until the circuit response reaches a stable state. After the stabilization waiting stage ends, the test system begins to perform the measurement stage. In the measurement stage, the test equipment collects the voltage, current, or other electrical parameter values ​​of each key node of the target circuit board. The collected parameter values ​​are recorded in the test database for subsequent analysis.

[0083] To more clearly reveal the internal logic structure of the test sequence described in S201, please refer to [link / reference needed]. Figure 3 This diagram illustrates the complete hierarchical relationship from macro-level testing tasks to micro-level execution phases in a modular manner.

[0084] like Figure 3 As shown, a complete standard electrical test 300 consists of one or more test items 310 (test item 1 (310a), test item 2 (310b) to test item N (310n) in the figure). The information for these test items is usually read from a preset test configuration file.

[0085] For any one of the test items (taking test item 1 (310a) as an example), the system will parse its corresponding test sequence 320. This test sequence defines a series of operations that need to be performed to complete the test item.

[0086] Test sequence 320 consists of multiple test steps 330 arranged in chronological order (test step 1 (330a), test step 2 (330b) to test step M (330m) in the figure). These steps are the basic execution units for completing the test sequence. Each test step 330 follows a standardized three-phase execution pattern, as shown in the figure as an expansion of test step 1 (330a):

[0087] Excitation application stage 331: The test system applies a preset excitation signal or excitation voltage to the designated test point on the target circuit board.

[0088] Stable waiting phase 332: After the excitation is applied, the system enters a waiting period to monitor the working status of the circuit board until its response reaches stability.

[0089] Measurement Phase 333: After the response stabilizes, the system collects electrical parameters such as voltage and current of key nodes and records them in the test database for subsequent analysis.

[0090] pass Figure 3 As demonstrated, those skilled in the art can clearly understand that the test sequence obtained by this invention is a structured, modular, and standardized data structure. This structure provides a clear time window and operational anchor point for subsequently injecting probe signals and collecting data synchronously at specific stages (such as the stabilization waiting stage or the measurement stage) (as described in Method 1).

[0091] S202: Select a matching probe signal from a preset multi-band probe signal library based on the circuit topology of the target circuit board and the electrical test type of the standard electrical test.

[0092] In the embodiments of this application, circuit topology refers to the connection relationship and circuit layout between various electronic components on the target circuit board.

[0093] Specifically, the electronic device reads the circuit topology information from the target circuit board's design file. This information includes the connection relationships of various functional modules on the circuit board, the parameter specifications of each electronic component, and the signal transmission paths between modules. The testing system also identifies the electrical test type of the current standard electrical test. The electrical test type determines the location and frequency characteristics of the circuit nodes that need to be excited and monitored during the test. The electronic device inputs the circuit topology information and electrical test type information as matching conditions into the signal selection algorithm. The signal selection algorithm traverses all candidate probe signals in the multi-band probe signal library. This library stores probe signals of various waveform types, such as sine waves, square waves, pulse signals, and swept-frequency signals. Each probe signal has different frequency ranges, amplitude characteristics, and phase characteristics. The signal selection algorithm analyzes the degree of fit between each candidate probe signal and the frequency response characteristics of the target circuit board. The fit evaluation includes frequency coverage matching, signal dynamic range matching, and waveform distortion matching. The candidate probe signal with the highest matching degree is determined as the matched probe signal.

[0094] S203: During the excitation application phase, monitor the excitation signal status of the standard electrical test and the operating parameters of the target circuit board in real time.

[0095] Specifically, during the excitation application phase, the electronic device activates a signal generator to input an excitation signal to a designated test point on the target circuit board. The signal generator continuously outputs a matching probe signal to the input port of the target circuit board. Simultaneously, the test system activates a multi-channel data acquisition module to monitor the excitation signal status in real time. Excitation signal status monitoring includes continuous tracking of the amplitude stability, frequency accuracy, waveform integrity, and phase consistency of the excitation signal. The data acquisition module samples the voltage amplitude, current amplitude, and power level of the excitation signal at millisecond levels using a high-speed analog-to-digital converter. The test system simultaneously deploys sensor arrays at multiple key nodes on the target circuit board to monitor operating parameters in real time. Operating parameter monitoring covers the dynamic changes in the voltage response, current response, power consumption, temperature distribution, and frequency response of the target circuit board. The sensor array transmits the collected operating parameter data to the data processing unit via a high-speed communication interface. The data processing unit timestamps and stores the excitation signal status and operating parameter data in real time. The test system continuously compares the changing trends of the excitation signal status and operating parameters to evaluate the electrical performance stability of the target circuit board.

[0096] S204: When the excitation signal is detected to be stable and the operating parameters of the target circuit board are within a stable range, the system enters the stabilization waiting phase. The probe is used to inject the detection signal into the key nodes of the target circuit board in a pulse manner. Within the preset time window after the detection signal is injected, the response signals of multiple monitoring points of the target circuit board are collected to obtain the initial response waveform data. The key nodes are the power input terminal, the signal input and output terminal, and the connection nodes of each functional module in the target circuit board.

[0097] Specifically, the testing system continuously monitors the amplitude fluctuations, frequency shifts, and phase jitter of the excitation signal to determine whether the excitation signal has reached a stable state. A stable excitation signal requires the amplitude of signal parameter changes to remain within a preset threshold range. Simultaneously, the testing system monitors the voltage, current, and power consumption values ​​at various monitoring points on the target circuit board to determine whether the operating parameters are within a stable range. The stable range of operating parameters is jointly determined by the design specifications and testing standards of the target circuit board. The electronic device generates a stable state confirmation signal when it detects that the excitation signal is stable and the operating parameters are within a stable range. This stable state confirmation signal triggers the testing system to enter a stable waiting phase. During this phase, the testing system initiates test probes to probe key components of the target circuit board. The test probe injects a detection signal and uses a precision positioning mechanism to accurately deliver the detection signal to key nodes in a pulse manner. Pulse injection can provide sufficient signal energy in a short time while avoiding damage to the target circuit board. Key nodes include the power input terminal, signal input and output terminals, and connection nodes of various functional modules. The test system will immediately start the high-speed data acquisition system after the detection signal injection is completed. The high-speed data acquisition system will synchronously acquire the voltage response, current response, and frequency response of multiple monitoring points on the target circuit board within a preset time window. The monitoring points cover the main signal transmission path and key circuit nodes of the target circuit board. The data acquisition system will convert the acquired response signals into digital initial response waveform data.

[0098] S205: During the measurement phase, the initial response waveform data is subjected to noise reduction filtering and amplitude normalization to form the response waveform data of each monitoring point.

[0099] Specifically, during the measurement phase, the electronic device initiates a digital signal processing module to analyze and process the initial response waveform data. The digital signal processing module first performs denoising filtering on the initial response waveform data. This denoising filtering includes high-frequency noise removal, power frequency interference suppression, and random noise reduction. The filtering algorithm identifies the effective signal frequency components and noise frequency components in the initial response waveform data. The filter retains the effective signal frequency components while removing the noise frequency components. After denoising filtering, purified waveform data is generated. The digital signal processing module then performs amplitude normalization processing on the purified waveform data. Amplitude normalization processing normalizes the waveforms at different monitoring points. The waveform data amplitude is uniformly mapped to a standard amplitude range. The normalization algorithm calculates the maximum and minimum amplitude values ​​of the waveform data at each monitoring point. The normalization process linearly transforms the amplitude range of the waveform data to a unified standard interval. Amplitude normalization can eliminate amplitude differences between different monitoring points and facilitate subsequent waveform comparison and analysis. The digital signal processing module organizes the data after noise reduction filtering and amplitude normalization into response waveform data for each monitoring point. The response waveform data includes the time-domain waveform characteristics, frequency-domain spectrum characteristics, and phase response characteristics of each monitoring point. The test system stores the response waveform data in the waveform database for subsequent analysis.

[0100] S102: Perform frequency domain decomposition on the response waveform data to obtain a frequency domain fingerprint spectrum, which includes characteristic parameters of multiple frequency components.

[0101] In this embodiment, the frequency domain fingerprint is a unique identifier image formed by extracting the frequency characteristics of the circuit response signal through frequency domain decomposition technology, which can reflect the electrical response characteristics of the target circuit board at different frequencies.

[0102] Specifically, the electronic device performs frequency domain decomposition on the response waveform data. The electronic device uses the Fast Fourier Transform (FFT) algorithm to convert the time-domain response waveform data into a frequency-domain signal representation. The FFT algorithm decomposes the response waveform data into multiple sinusoidal components of different frequencies. The decomposition process extracts the amplitude, phase, and power information of each frequency component. The electronic device then arranges the extracted frequency component information in frequency order to form a frequency domain fingerprint spectrum.

[0103] Based on the above embodiments, as an optional embodiment, the step of performing frequency domain decomposition processing on the response waveform data to obtain the frequency domain fingerprint spectrum includes steps S301 to S306:

[0104] S301: Calculate the propagation parameters of each monitoring point based on the electrical connection path length and impedance characteristics between each monitoring point and the key node. The propagation parameters include the signal propagation delay value and the signal attenuation coefficient value.

[0105] In this embodiment of the application, propagation parameters refer to a set of physical quantities used to describe the transmission characteristics of electrical signals in a circuit path. Propagation parameters include two core parameters: signal propagation delay value and signal attenuation coefficient value. The signal propagation delay value represents the amount of time required for an electrical signal to propagate from the starting point to the target point, and the signal attenuation coefficient value represents a quantitative index of the degree of amplitude attenuation of the electrical signal during propagation.

[0106] Specifically, the electronic device first acquires the spatial location information between each monitoring point and key node. After acquiring the spatial location information, it identifies and tracks the electrical connection path. The electrical connection path tracking process measures the complete conductive path formed by wires, vias, pads, and component pins. The measurement process records the total length data of the electrical connection path, which includes the sum of the lengths of horizontal wires, vertical vias, and internal connections of components. After the length measurement is completed, impedance characteristic analysis is performed. The impedance characteristic analysis measures the characteristic impedance value of each conductor segment on the electrical connection path. The characteristic impedance value calculation takes into account conductor width, dielectric thickness, and phase. After impedance characteristic analysis of physical parameters such as dielectric constant and loss tangent, propagation delay is calculated. The propagation delay calculation divides the electrical connection path length by the electromagnetic wave propagation speed to obtain the signal propagation delay value. The electromagnetic wave propagation speed calculation is corrected by combining the relative permittivity and relative permeability of the propagation medium. After the signal propagation delay value is calculated, signal attenuation analysis is performed. The signal attenuation analysis calculates the influence of conductor loss, dielectric loss, and radiation loss on the signal amplitude. The attenuation degree is quantified by using an exponential attenuation function to obtain the signal attenuation coefficient value. The signal attenuation coefficient value and the signal propagation delay value are combined to form the complete propagation parameters for each monitoring point.

[0107] S302: Based on the propagation parameters of each monitoring point, perform time offset compensation and amplitude attenuation compensation on the response waveform data of each monitoring point to obtain the compensated response data corresponding to each monitoring point.

[0108] In this embodiment, the compensated response data refers to the response waveform data after time offset compensation and amplitude attenuation compensation. The compensated response data eliminates the effects of time delay and amplitude loss during signal propagation and can accurately reflect the true response characteristics of the monitoring point under ideal transmission conditions.

[0109] Specifically, the electronic device uses the propagation parameters of each monitoring point to perform dual compensation processing on the response waveform data. The compensation processing first performs a time offset compensation operation, which extracts the signal propagation delay value from the propagation parameters and moves each sampling point of the response waveform data forward by the corresponding amount of time to eliminate the influence of propagation delay on the time axis. After the time offset compensation is completed, an amplitude attenuation compensation operation is performed, which extracts the signal attenuation coefficient value from the propagation parameters and multiplies each amplitude value of the time-compensated response waveform data by the reciprocal of the attenuation coefficient to restore the original amplitude level before signal propagation. After the dual compensation processing is completed, the compensated response data corresponding to each monitoring point is generated.

[0110] S303: Calculate the waveform energy change rate of the compensation response data at each monitoring point, set the time period when the waveform energy change rate exceeds the preset energy change rate threshold as the fine analysis window, and set the time period when the waveform energy change rate does not exceed the preset energy change rate threshold as the regular analysis window.

[0111] Specifically, the electronic device calculates the waveform energy change rate of the compensation response data at each monitoring point. The waveform energy change rate calculation process involves time window sliding processing of the compensation response data. The time window sliding processing calculates the signal energy value within each time window. After the signal energy value is calculated, the energy difference between adjacent time windows is calculated. The result of the energy difference calculation is divided by the time interval to obtain the waveform energy change rate. After the waveform energy change rate is calculated, a threshold comparison operation is performed. The threshold comparison operation compares the waveform energy change rate with a preset energy change rate threshold. The numerical comparison result is used to determine the analysis window type. The time period when the waveform energy change rate exceeds the preset energy change rate threshold is set as the fine analysis window, and the time period when the waveform energy change rate does not exceed the preset energy change rate threshold is set as the regular analysis window.

[0112] S304: Apply Fourier transform to the compensation response data within the fine analysis window and the regular analysis window to obtain the window frequency domain transform data.

[0113] In this embodiment, window frequency domain transform data refers to a data set in which time-domain compensation response data is converted into frequency domain representation through Fourier transform, and is used to represent the frequency distribution characteristics and frequency component information of signals within different analysis windows.

[0114] Specifically, the electronic device performs Fourier transform processing on the compensation response data in the fine analysis window and the regular analysis window respectively. The Fourier transform processing extracts the compensation response data in the fine analysis window and performs a mathematical transformation from the time domain to the frequency domain. The mathematical transformation from the time domain to the frequency domain decomposes the time series signal into complex representations of different frequency components. The complex representations contain the amplitude and phase information of each frequency component. After the Fourier transform of the fine analysis window is completed, the same Fourier transform processing is performed on the compensation response data in the regular analysis window. The Fourier transform of the regular analysis window also generates complex representations of the frequency components. After the Fourier transform processing of the two analysis windows is completed, the corresponding window frequency domain transform data is generated.

[0115] S305: Determine the data reliability weight for each monitoring point based on the propagation parameters of each monitoring point.

[0116] Specifically, the electronic equipment calculates and determines the data reliability weight based on the propagation parameters of each monitoring point. The data reliability weight calculation extracts the signal propagation delay value and signal attenuation coefficient value from the propagation parameters. The signal propagation delay value and signal attenuation coefficient value undergo reliability assessment processing. The reliability assessment processing calculates the variance of the signal propagation delay value and compares it with a preset delay stability threshold to generate a delay stability index. The delay stability index is converted into a delay reliability score through a linear mapping function. The signal attenuation coefficient value is calculated to deviate from the theoretical attenuation coefficient value and compared with a preset attenuation deviation threshold to generate an attenuation accuracy index. The attenuation accuracy index is converted into an attenuation reliability score through a linear mapping function. The delay reliability score and the attenuation reliability score undergo comprehensive weight calculation. The comprehensive weight calculation sums the two scores according to a preset ratio. The weighted sum is normalized to generate the data reliability weight corresponding to each monitoring point.

[0117] S306: Based on the data credibility weight of each monitoring point, perform weighted fusion calculation on the window frequency domain transformation data to obtain fused frequency domain data, and construct a frequency domain fingerprint spectrum based on the fused frequency domain data.

[0118] Specifically, the electronic device performs weighted fusion calculations on the window frequency domain transformation data based on the data credibility weights of each monitoring point. The weighted fusion calculation multiplies the window frequency domain transformation data of each monitoring point with the corresponding data credibility weight point by point. The point-by-point multiplication operation multiplies the frequency domain amplitude of the monitoring point with the data credibility weight at each frequency point. The weighted frequency domain data of each monitoring point are then summed to generate fused frequency domain data. The fused frequency domain data contains the weighted cumulative amplitude information of all monitoring points at each frequency point. The electronic device constructs a frequency domain fingerprint spectrum based on the fused frequency domain data. The frequency domain fingerprint spectrum construction arranges the fused frequency domain data in a two-dimensional arrangement according to the frequency axis and the time axis. The two-dimensional arrangement forms a frequency domain fingerprint spectrum matrix with frequency as the horizontal axis and time as the vertical axis. Each element in the frequency domain fingerprint spectrum matrix corresponds to the fused amplitude at a specific time and frequency point. The frequency domain fingerprint spectrum can intuitively display the characteristic distribution pattern of the test target in the time and frequency domain.

[0119] S103: Perform environmental modulation analysis on the frequency domain fingerprint spectrum to obtain the modulation influence coefficients of each frequency component.

[0120] In this embodiment, the modulation influence coefficient is used to represent a quantitative index of the intensity of the modulation effect of environmental factors on a specific frequency component.

[0121] Specifically, the electronic device performs environmental modulation analysis on the frequency domain fingerprint spectrum. This analysis divides the frequency domain fingerprint spectrum into segments based on frequency, dividing it into multiple frequency sub-bands and extracting the time-domain variation sequences of each sub-band. Modulation features are then extracted from these time-domain variation sequences. The extraction of these features calculates the envelope fluctuation amplitude and periodic variation characteristics of each frequency sub-band's time-domain variation sequence. The envelope fluctuation amplitude is obtained through standard deviation calculation, and the periodic variation characteristics are obtained through autocorrelation function analysis. Modulation intensity is then assessed using the envelope fluctuation amplitude and periodic variation characteristics. This assessment calculates the fluctuation modulation factor by comparing the envelope fluctuation amplitude with a preset fluctuation threshold, and the periodic variation characteristics are calculated by comparing them with a preset periodic threshold. Finally, a weighted combination of the fluctuation modulation factor and the periodic modulation factor is used to generate the modulation influence coefficient corresponding to each frequency component.

[0122] Based on the above embodiments, as an optional embodiment, the step of performing environmental modulation analysis on the frequency domain fingerprint spectrum to obtain the modulation influence coefficients of each frequency component includes steps S401 to S405:

[0123] S401: Obtain the current environmental parameters during the target circuit board testing process. The current environmental parameters include test temperature, test humidity, power supply voltage fluctuation value, and electromagnetic interference intensity value.

[0124] Specifically, the electronic device acquires the current environmental parameters during the target circuit board testing process. This acquisition is achieved through real-time data collection by a test environment monitoring system, which includes a temperature sensor, a humidity sensor, a voltage monitoring module, and an electromagnetic field strength detection module. The temperature sensor collects real-time temperature values ​​of the test environment to generate the test temperature, which is continuously monitored at a preset temperature sampling frequency. The humidity sensor collects real-time humidity values ​​of the test environment to generate the test humidity, which is continuously monitored at a preset humidity sampling frequency. The voltage monitoring module collects voltage changes in the power supply system, which are monitored in real-time by a voltage sampling circuit, and the voltage standard deviation is calculated to generate power supply voltage fluctuation values. The electromagnetic field strength detection module collects the electromagnetic field distribution of the test environment, which is scanned across a wide frequency band by a spectrum analyzer, and the electromagnetic field energy density is calculated to generate electromagnetic interference intensity values.

[0125] S402: Extract the time-series variation data of the same frequency component in the frequency domain fingerprint spectrum at different test times. The time-series variation data includes amplitude values ​​and phase values.

[0126] Specifically, the electronic device extracts the time-series variation data of the same frequency component in the frequency domain fingerprint spectrum at different test times. The time-series variation data extraction involves performing time-series extraction with a fixed frequency dimension on the frequency domain fingerprint spectrum. The fixed frequency dimension involves selecting a specific frequency point as the extraction target and extracting the data sequence along the time axis. The data sequence extraction obtains the complex frequency domain values ​​of that frequency point at all test times. The complex frequency domain values ​​include real and imaginary components. The real and imaginary components are then subjected to amplitude and phase calculations. The amplitude calculation is performed by taking the square root of the sum of the squares of the real and imaginary components. The phase calculation is performed by calculating the ratio of the imaginary component to the real component using the arctangent function to obtain the phase value. The amplitude and phase values ​​are arranged in chronological order to generate time-series variation data. The time-series variation data is then used to extract and process all frequency components in the frequency domain fingerprint spectrum one by one.

[0127] S403: Align and match the current environmental parameters with the time-series change data according to the test time to obtain time-synchronized environmental frequency data pairs.

[0128] Specifically, the electronic device aligns and matches the current environmental parameters with the time-series change data according to the test time. The alignment and matching process performs time base unification processing on the timestamps of the current environmental parameters and the time-series change data. The time base unification processing converts the acquisition timestamp of the current environmental parameters and the test timestamp of the time-series change data into a unified time base. The unified time base uses the system standard time as a reference and performs time offset correction. The time offset correction calculates the time difference between each data source and the system standard time and performs compensation and adjustment. After compensation and adjustment, time window matching is performed. The time window matching process sets a preset time window range and pairs the current environmental parameters and time-series change data with timestamp differences within this range. The pairing process matches the current environmental parameters within the corresponding time window for each time-series change data test moment. The matching result generates environmental frequency data pairs containing environmental parameters and frequency domain characteristics.

[0129] S404: Calculate the correlation between the values ​​of each environmental parameter and each frequency component in the environmental frequency data pair; construct an environmental frequency correlation matrix based on the correlations, where the rows of the environmental frequency correlation matrix represent the types of environmental parameters, the columns represent the types of frequency components, and the elements are the correlation values.

[0130] In this embodiment of the application, correlation refers to a quantitative indicator of the degree of statistical association between environmental parameters and frequency component values. Correlation is used to measure the intensity of the influence of environmental factors on frequency domain feature changes. The environmental frequency correlation matrix is ​​a two-dimensional data matrix with environmental parameter types as rows, frequency component types as columns, and correlation values ​​as elements. The environmental frequency correlation matrix is ​​used to describe the global association between environmental factors and frequency domain features.

[0131] Specifically, the electronic device calculates the correlation between the values ​​of each environmental parameter and each frequency component in the environmental frequency data pair. The correlation calculation involves performing Pearson correlation coefficient calculation on the environmental parameter and frequency component values ​​in the environmental frequency data pair. The Pearson correlation coefficient calculation extracts the numerical sequence of each type of environmental parameter and each type of frequency component. The numerical sequences are paired in chronological order and the covariance and standard deviation are calculated. The covariance calculation involves averaging the product of the environmental parameter value and the frequency component value and subtracting the product of their respective means. The standard deviation calculation involves taking the square root of the variance of each environmental parameter value and the frequency component value. The correlation value is obtained by dividing the covariance by the product of the two standard deviations. The correlation value ranges from -1 to +1, with positive values ​​indicating a positive correlation and negative values ​​indicating a negative correlation. The absolute value indicates the strength of the correlation. An environmental frequency correlation matrix is ​​constructed based on the correlations. The construction of the environmental frequency correlation matrix uses the test temperature, test humidity, power supply voltage fluctuation value and electromagnetic interference intensity value as matrix rows, each frequency component as matrix columns, and the corresponding correlation values ​​as matrix elements. The matrix elements are arranged according to the row and column indices to generate the environmental frequency correlation matrix.

[0132] S405: Calculate the proportion of numerical difference between each frequency component under standard environmental parameters and current environmental parameters based on the environmental frequency correlation matrix, and determine the proportion of numerical difference as the modulation influence coefficient of the corresponding frequency component.

[0133] Specifically, the electronic device calculates the proportion of numerical difference between each frequency component under standard environmental parameters and the current environmental parameters based on the environmental frequency correlation matrix. The calculation of the numerical difference proportion extracts the correlation values ​​between each environmental parameter type and each frequency component type in the environmental frequency correlation matrix. The correlation value is then multiplied by the difference between the standard environmental parameter and the current environmental parameter. The product operation multiplies the difference between the standard value and the current value of each environmental parameter by the corresponding correlation value. The product result is then summed for all environmental parameter types. The summation yields the total influence of environmental factors on each frequency component. The total influence is then divided by the baseline value under the standard environmental parameters for the corresponding frequency component. The division result yields the proportion of numerical difference for that frequency component. The proportion of numerical difference is directly determined as the modulation influence coefficient of the corresponding frequency component. The modulation influence coefficient is expressed as a percentage to represent the intensity of the influence of environmental modulation on the frequency component.

[0134] S104: When performing standard electrical tests, if the result of any target test item on the target circuit board exceeds the preset reasonable range, a correlation analysis is performed on the target frequency domain fingerprint spectrum and the modulation influence coefficient of each target test item to obtain abnormal results. Abnormal results include reversible modulation results and irreversible damage results.

[0135] In this application embodiment, abnormal results refer to the analysis of test results that exceed the preset reasonable range in standard electrical testing of the target circuit board. Abnormal results are used to distinguish between reversible effects caused by environmental factors and irreversible damage to the circuit board itself. Reversible modulation results refer to temporary test deviations caused by changes in environmental factors, while irreversible damage results refer to permanent test abnormalities caused by physical defects in the circuit board or damage to components.

[0136] Specifically, during standard electrical testing of electronic equipment, when the result of any target test item on the target circuit board exceeds the preset reasonable range, correlation analysis is performed on the target frequency domain fingerprint spectrum and each target modulation influence coefficient corresponding to the target test item. The correlation analysis extracts the target frequency domain fingerprint spectrum data corresponding to the abnormal moment of the target test item. The target frequency domain fingerprint spectrum data is then compared with the preset standard frequency domain fingerprint spectrum for deviation calculation. The deviation calculation calculates the amplitude deviation and phase deviation of each frequency component. The deviation data is then matched with each target modulation influence coefficient for matching analysis. The matching analysis calculates the correlation degree between the deviation data and the target modulation influence coefficient. The correlation degree is used to determine the abnormality type through the correlation coefficient threshold. When the correlation coefficient exceeds the preset reversible threshold, it is judged as a reversible modulation result. When the correlation coefficient is lower than the preset reversible threshold and the deviation degree exceeds the preset damage threshold, it is judged as an irreversible damage result. A reversible modulation result indicates that the test abnormality is caused by environmental factors and can be restored to normal through environmental control. An irreversible damage result indicates that the test abnormality is caused by internal defects of the circuit board and cannot be repaired by environmental adjustment.

[0137] Based on the above embodiments, as an optional embodiment, correlation analysis is performed on the target frequency domain fingerprint spectrum corresponding to the target test item and the modulation influence coefficient of each target to obtain abnormal results. The abnormal results include reversible modulation results and irreversible damage results. This step includes steps S501 to S505:

[0138] S501: Identify abnormal frequency components whose amplitude or phase values ​​exceed a preset reasonable range from the target frequency domain fingerprint spectrum.

[0139] Specifically, the electronic device identifies abnormal frequency components whose amplitude or phase values ​​exceed a preset reasonable range from the target frequency domain fingerprint spectrum. Abnormal frequency component identification traverses all frequency components in the target frequency domain fingerprint spectrum. Frequency component traversal extracts the amplitude and phase values ​​corresponding to each frequency point in sequence. The amplitude value is compared with a preset reasonable amplitude range. The reasonable amplitude range comparison checks whether the amplitude value exceeds a preset upper amplitude threshold or falls below a preset lower amplitude threshold. The phase value is compared with a preset reasonable phase range. The reasonable phase range comparison checks whether the phase value exceeds a preset upper phase threshold or falls below a preset lower phase threshold. When the amplitude value or phase value exceeds the reasonable amplitude range or phase range, the corresponding frequency component is marked as an abnormal frequency component. The abnormal frequency component records its frequency position, abnormality type, and deviation degree. Abnormality types include amplitude abnormality, phase abnormality, and simultaneous amplitude and phase abnormality. The deviation degree is calculated by the ratio of the difference between the abnormal value and the reasonable range boundary value.

[0140] S502: Obtain the target modulation influence coefficient corresponding to each abnormal frequency component; calculate the actual deviation of each abnormal frequency component, where the actual deviation is the difference between the current value of the abnormal frequency component and the value within a preset reasonable range.

[0141] In the embodiments of this application, the target modulation influence coefficient refers to the intensity coefficient of the abnormal frequency component under the current environmental conditions due to the modulation effect of environmental factors. The target modulation influence coefficient is used to measure the degree of contribution of environmental changes to the deviation of the abnormal frequency component value. The actual deviation is the absolute difference between the current value of the abnormal frequency component and its normal range reference value. The actual deviation is used to quantify the degree of deviation of the abnormal frequency component.

[0142] Specifically, the electronic device acquires the target modulation influence coefficient corresponding to each abnormal frequency component. The acquisition of the target modulation influence coefficient involves searching for the corresponding coefficient value from the modulation influence coefficient set based on the frequency position of the abnormal frequency component. The modulation influence coefficient set contains the modulation influence coefficients of each frequency component under the current environmental parameters. The coefficient search is performed by precisely matching the frequency identifier of the frequency component. The matching result returns the target modulation influence coefficient of the corresponding frequency component. The target modulation influence coefficient records the modulation intensity of the frequency component by environmental factors. The electronic device calculates the actual deviation of each abnormal frequency component. The actual deviation calculation extracts the current amplitude and phase values ​​of the abnormal frequency component. The current value is then compared with the corresponding reference value within a preset reasonable range using a difference operation. The reference value within the preset reasonable range is usually selected as the center value or standard reference value of the reasonable range. The difference operation calculates the amplitude deviation and phase deviation separately. The amplitude deviation is equal to the absolute value of the current amplitude value minus the reference amplitude value, and the phase deviation is equal to the absolute value of the current phase value minus the reference phase value. The actual deviation is then combined with the amplitude deviation and phase deviation to form a composite deviation index.

[0143] S503: Compare the actual deviation of each abnormal frequency component with the corresponding preset theoretical modulation amount.

[0144] Specifically, the electronic equipment compares the actual deviation of each abnormal frequency component with the corresponding preset theoretical modulation amount. The theoretical modulation amount is calculated by multiplying the target modulation influence coefficient by the difference between the current environmental parameters and the standard environmental parameters. The environmental parameter difference includes the changes in multiple environmental factors such as temperature difference, humidity difference, and electromagnetic interference intensity difference. The target modulation influence coefficient performs a weighted calculation on the differences of each environmental factor, and the weighted calculation result yields the theoretical modulation amount of the abnormal frequency component. The numerical comparison calculates the ratio between the actual deviation and the theoretical modulation amount. The ratio calculation divides the actual deviation by the theoretical modulation amount to obtain the deviation ratio coefficient. The deviation ratio coefficient reflects the degree of conformity between the actual deviation and the theoretical expectation. The numerical comparison also calculates the difference between the actual deviation and the theoretical modulation amount. The difference calculation subtracts the theoretical modulation amount from the actual deviation to obtain the deviation margin. The deviation margin represents the abnormal deviation that exceeds the environmental modulation expectation. The numerical comparison result includes two key indicators: the deviation ratio coefficient and the deviation margin.

[0145] S504: When the difference between the actual deviation of the abnormal frequency component and the theoretical modulation is less than the preset difference threshold, the deviation of the abnormal frequency component is determined to be caused by environmental modulation, and the abnormal frequency component is marked as a reversible modulation result.

[0146] Specifically, when the actual deviation of an abnormal frequency component differs from the theoretical modulation value by less than a preset difference threshold, the electronic device determines that the deviation of the abnormal frequency component is caused by environmental modulation. The numerical difference is calculated by subtracting the theoretical modulation value from the actual deviation and taking the absolute value. The numerical difference value is then compared with the preset difference threshold, which is set according to the frequency component type and environmental modulation accuracy requirements. When the numerical difference value is less than the preset difference threshold, the environmental modulation judgment logic is triggered. The environmental modulation judgment confirms that the deviation of the abnormal frequency component is mainly due to the modulation effect of environmental factors. The judgment result classifies the abnormal frequency component as a deviation caused by environmental modulation. The electronic device marks the abnormal frequency component as a reversible modulation result. The reversible modulation result mark adds a reversible modulation identifier to the attribute information of the abnormal frequency component. The reversible modulation identifier includes information such as environmental modulation type, modulation intensity level, and recovery difficulty. The marking process also records the main environmental factors causing the modulation and the modulation contribution ratio. The reversible modulation result is distinguished from the irreversible anomaly caused by device defects.

[0147] S505: When the difference between the actual deviation of the abnormal frequency component and the theoretical modulation value is greater than the preset difference threshold, it is determined that the deviation of the abnormal frequency component is caused by damage to the circuit board, and the abnormal frequency component is marked as an irreversible damage result.

[0148] In this embodiment, a numerical difference greater than a preset difference threshold indicates that the actual deviation of the abnormal frequency component is significantly different from the expected value of the environmental modulation theory. The significant difference indicates that the deviation of the abnormal frequency component exceeds the range of environmental modulation capability. Circuit board damage refers to irreversible damage or aging of the physical structure of the circuit board components, connecting lines or solder joints. Circuit board damage will cause the frequency component to produce abnormal deviations unrelated to environmental modulation. Irreversible damage result refers to abnormal frequency components caused by physical damage to the circuit board that cannot be restored to normal by adjusting environmental parameters. Irreversible damage result indicates that the abnormal frequency component needs to be repaired or replaced by hardware.

[0149] Specifically, when the actual deviation of an abnormal frequency component differs from the theoretical modulation value by more than a preset difference threshold, the electronic device determines that the deviation of the abnormal frequency component is caused by circuit board damage. The numerical difference determination compares the calculated numerical difference value with the preset difference threshold. When the numerical difference value exceeds the preset difference threshold, the circuit board damage determination logic is triggered. The circuit board damage determination analyzes the deviation mode and deviation amplitude characteristics of the abnormal frequency component. The deviation mode analysis checks whether the abnormal frequency component exhibits damage characteristics such as continuous deviation, nonlinear deviation, or abrupt deviation. The deviation amplitude analysis confirms that the degree of abnormal deviation exceeds the maximum influence range of environmental modulation. The determination result classifies the abnormal frequency component as an anomaly caused by circuit board damage. The electronic device marks the abnormal frequency component as an irreversible damage result. The irreversible damage result marking adds an irreversible damage identifier to the attribute information of the abnormal frequency component. The irreversible damage identifier includes information such as damage type, damage severity, and recommended repair measures. The marking process also records the possible causes of the damage and the estimated repair cost. The irreversible damage result is clearly distinguished from the reversible modulation result, which facilitates the adoption of different processing strategies in the future.

[0150] S105: For the reversible modulation results, the target frequency domain fingerprint spectrum is processed by environmental frequency domain inverse transformation using the target modulation influence coefficients corresponding to each frequency component to obtain the standard environmental equivalent spectrum, and the results of the target test items are recalculated based on the standard environmental equivalent spectrum.

[0151] Specifically, the electronic device performs an environmental frequency domain inverse transform on the target frequency domain fingerprint spectrum using the target modulation influence coefficients corresponding to each frequency component for the reversible modulation results. This environmental frequency domain inverse transform performs inverse compensation calculations on the abnormal frequency components marked as reversible modulation results. The inverse compensation calculation uses the reciprocal of the target modulation influence coefficient as the inverse transform coefficient, which is then divided by the actual measured value of the abnormal frequency component. The division result yields the theoretical value of the frequency component after eliminating the environmental modulation influence. The environmental frequency domain inverse transform replaces the frequency components of all reversible modulation results with their corresponding theoretical values. The inverse transform also maintains the frequency component values ​​of irreversible damage results unchanged. The processing result yields a standard environmental equivalent spectrum, which contains complete frequency domain distribution information after eliminating the environmental modulation influence. The electronic device recalculates the results of the target test items based on the standard environmental equivalent spectrum. This recalculation uses the frequency component values ​​in the standard environmental equivalent spectrum to re-evaluate various performance indicators. The performance indicator evaluation includes multiple dimensions such as power spectrum analysis, signal-to-noise ratio calculation, and frequency stability testing. The recalculation results reflect the true performance level of the circuit board under standard environmental conditions.

[0152] Based on the above embodiments, as an optional embodiment, the step of performing environmental frequency domain inverse transform processing on the target frequency domain fingerprint spectrum using the target modulation influence coefficients corresponding to each frequency component to obtain the standard environmental equivalent spectrum includes steps S601 to S604:

[0153] S601: Calculate the current environmental deviation between the current environmental parameters and the standard environmental parameters, and determine the influence value of each frequency component due to the environmental deviation based on the current environmental deviation and the modulation influence coefficient corresponding to each frequency component.

[0154] Specifically, the electronic device calculates the current environmental deviation between the current environmental parameters and the standard environmental parameters. The current environmental deviation calculation obtains the environmental parameter values ​​such as temperature, humidity, and electromagnetic interference intensity during the actual test. The environmental parameter acquisition is achieved through devices such as temperature sensors, humidity sensors, and electromagnetic field strength detectors. The current environmental deviation calculation subtracts the corresponding standard environmental parameters from each current environmental parameter to obtain each environmental deviation component. The environmental deviation component includes specific values ​​such as temperature deviation, humidity deviation, and electromagnetic interference deviation. The electronic device determines the influence value of each frequency component due to the environmental deviation based on the current environmental deviation and the modulation influence coefficient corresponding to each frequency component. The influence value calculation multiplies each environmental deviation component with the modulation influence coefficient of the corresponding frequency component. The multiplication operation obtains the individual influence of each environmental factor on the frequency component. The individual influence values ​​are synthesized into the total influence value of the frequency component through linear superposition or weighted superposition. The total influence value reflects the degree of deviation of the frequency component relative to the standard environment under the current environmental conditions. The influence value determination establishes a quantitative relationship between environmental influence and frequency deviation for each frequency component.

[0155] S602: Subtract the corresponding influence value from the amplitude value of each frequency component in the target frequency domain fingerprint spectrum to obtain the amplitude value after environmental compensation.

[0156] Specifically, the electronic device subtracts the corresponding influence value from the amplitude value of each frequency component in the target frequency domain fingerprint spectrum. The subtraction operation establishes a one-to-one correspondence between the frequency component and the influence value, ensuring that the amplitude value of each frequency component is subtracted from the influence value specific to that frequency component. The subtraction operation is performed sequentially according to the order of the frequency components, with the original amplitude value as the minuend and the corresponding influence value as the subtrahend. The subtraction result is the amplitude value after eliminating the environmental influence. The electronic device then obtains the amplitude value after environmental compensation. The amplitude value after environmental compensation replaces the original amplitude value in the target frequency domain fingerprint spectrum. The replaced frequency domain fingerprint spectrum will exhibit frequency domain distribution characteristics equivalent to those under standard environmental conditions. The amplitude value after environmental compensation eliminates the modulation influence of environmental factors such as temperature, humidity, and electromagnetic interference.

[0157] S603: Subtract the corresponding influence value from the phase value of each frequency component in the target frequency domain fingerprint spectrum to obtain the phase value after environmental compensation.

[0158] Specifically, the electronic device subtracts the corresponding influence value from the phase value of each frequency component in the target frequency domain fingerprint spectrum. The phase subtraction operation establishes a one-to-one correspondence between the frequency component and the phase influence value, ensuring that the phase value of each frequency component is subtracted from the phase influence value specific to that frequency component. The phase subtraction operation is performed sequentially according to the order of the frequency components, using the original phase value as the minuend and the corresponding phase influence value as the subtrahend, to obtain the phase value after eliminating environmental influences. The electronic device obtains the environmentally compensated phase value, which replaces the original phase value in the target frequency domain fingerprint spectrum. The replaced frequency domain fingerprint spectrum will exhibit phase distribution characteristics equivalent to those under standard environmental conditions. The environmentally compensated phase value eliminates the modulation influence of environmental factors such as temperature, humidity, and electromagnetic interference on the phase.

[0159] S604: Reorganize and arrange the amplitude and phase values ​​after environmental compensation in frequency order to form a standard environmental equivalent spectrum.

[0160] Specifically, the electronic device reorganizes and arranges the environmentally compensated amplitude and phase values ​​according to frequency order. This reorganization establishes a pairing relationship between frequency components and their corresponding amplitude and phase values. This pairing relationship ensures that each frequency component contains both the environmentally compensated amplitude and phase values. The electronic device sorts the frequency components from low to high according to their numerical values. This sorting process maintains the binding relationship between amplitude and phase values ​​and frequency components. The reorganization constructs a complete frequency domain data structure, which contains the compensated amplitude and phase information of all frequency components, forming a standard environmental equivalent map. This standard environmental equivalent map presents a complete frequency domain feature distribution equivalent to that under standard environmental conditions. The standard environmental equivalent map provides environmentally independent benchmark data for subsequent fingerprint matching and recognition.

[0161] S106: For irreversible damage results, extract damage frequency domain features from the target frequency domain fingerprint spectrum, match the damage frequency domain features with the preset defect feature library, and obtain the defect type and corresponding defect location information of the target circuit board.

[0162] Specifically, the electronic device extracts damage frequency domain features from the target frequency domain fingerprint spectrum for irreversible damage results. Damage frequency domain feature extraction identifies abnormal frequency components and amplitude variation patterns in the frequency domain spectrum. The extraction process analyzes damage feature parameters such as peak shift, harmonic distortion, and abnormal frequency response in the frequency domain spectrum. The electronic device matches the damage frequency domain features with a preset defect feature library. The matching process calculates the similarity values ​​between the damage frequency domain features and various defect patterns in the defect feature library. The similarity calculation uses Euclidean distance or cosine similarity algorithms of frequency domain feature vectors. The electronic device determines the defect type of the target circuit board based on the highest similarity matching result. The defect type includes specific categories such as short circuit, open circuit, component damage, and poor soldering. The electronic device combines the position mapping information in the defect feature library to obtain the corresponding defect location information. The defect location information indicates the specific coordinate area of ​​the defect on the circuit board and the related circuit modules.

[0163] Based on the above embodiments, as an optional embodiment, the step of extracting damage frequency domain features from the target frequency domain fingerprint spectrum and matching the damage frequency domain features with a preset defect feature library to obtain the defect type and corresponding defect location information of the target circuit board includes steps S701 to S705:

[0164] S701: Obtain the abnormal frequency components corresponding to the irreversible damage results. Extract the amplitude, phase, and frequency position information of the abnormal frequency components from the target frequency domain fingerprint spectrum to form damage frequency domain data.

[0165] Specifically, the electronic device acquires the abnormal frequency components corresponding to the irreversible damage results. The acquisition of abnormal frequency components identifies all frequency components marked as abnormal based on the damage assessment results. The electronic device extracts the amplitude value, phase value, and frequency position information of the abnormal frequency components from the target frequency domain fingerprint spectrum. The amplitude value extraction obtains the signal amplitude intensity corresponding to the abnormal frequency component, the phase value extraction obtains the phase angle value corresponding to the abnormal frequency component, and the frequency position information extraction obtains the specific frequency value and position index of the abnormal frequency component in the frequency domain. The electronic device organizes the extracted amplitude value, phase value, and frequency position information into structured data. The structured data maintains the correspondence between the abnormal frequency components and their characteristic parameters. The electronic device forms damage frequency domain data, which contains complete frequency domain feature information of all abnormal frequency components.

[0166] S702: Calculate the degree of numerical difference between each abnormal frequency component in the damage frequency domain data and the frequency components in the frequency domain fingerprint spectrum that are within a preset reasonable range.

[0167] Specifically, the electronic device calculates the degree of numerical difference between each abnormal frequency component in the damaged frequency domain data and the frequency components within a preset reasonable range in the frequency domain fingerprint spectrum. The numerical difference calculation compares the amplitude, phase, and frequency position information of the abnormal frequency components and normal frequency components. The amplitude difference calculation obtains the absolute or relative difference between the amplitude of the abnormal frequency component and the amplitude within the normal range. The phase difference calculation obtains the angular difference between the phase of the abnormal frequency component and the phase within the normal range. The frequency position difference calculation obtains the offset between the frequency position of the abnormal frequency component and the frequency position within the normal range. The electronic device comprehensively evaluates all the difference values. The comprehensive evaluation uses a weighted average or Euclidean distance calculation method to obtain the overall degree of numerical difference. The electronic device generates a corresponding numerical difference index for each abnormal frequency component, which reflects the severity of the abnormal frequency component's deviation from the normal state.

[0168] S703: Statistically analyze the distribution characteristics of abnormal frequency components in the frequency domain, and combine the degree of numerical difference and distribution characteristics to form the damage frequency domain characteristics.

[0169] In this embodiment, distribution characteristics refer to the distribution patterns and clustering patterns of abnormal frequency components in the frequency domain. Damage frequency domain characteristics are comprehensive feature descriptors that integrate numerical differences and distribution characteristics. Distribution characteristics can reflect the spatial distribution patterns of circuit board damage, and damage frequency domain characteristics provide comprehensive feature information for defect location and type identification.

[0170] Specifically, the electronic device statistically analyzes the distribution characteristics of abnormal frequency components within the frequency domain. Distribution characteristic statistics analyze the positional distribution, density distribution, and clustering degree of abnormal frequency components in the frequency domain coordinate system. Positional distribution statistics calculate the frequency position dispersion and coverage of abnormal frequency components, density distribution statistics calculate the concentration density of abnormal frequency components in different frequency domain intervals, and clustering degree statistics analyze the spatial clustering characteristics and distribution patterns of abnormal frequency components. The electronic device then fuses the numerical difference degree and distribution characteristics, establishing a correlation mapping relationship between the numerical difference degree and distribution characteristics. This results in damage frequency domain features of the electronic device, which contain information on the difference degree and spatial distribution information of abnormal frequency components. These damage frequency domain features form a comprehensive feature vector describing the damage state of the circuit board.

[0171] S704: Calculate the feature similarity value between the damage frequency domain features and each standard defect feature in the preset defect feature library.

[0172] Specifically, the electronic device calculates the feature similarity value between the damage frequency domain features and each standard defect feature in the preset defect feature library. The feature similarity calculation compares and analyzes the damage frequency domain features with each standard defect feature one by one. The comparison analysis uses algorithms such as cosine similarity, Euclidean distance, or Pearson correlation coefficient to calculate the similarity between feature vectors. The electronic device calculates the similarity between the numerical difference part of the damage frequency domain features and the corresponding part of the standard defect features, and at the same time calculates the similarity between the distribution feature part and the corresponding part of the standard defect features. The electronic device performs weighted fusion of the similarities of each part to obtain a comprehensive feature similarity value. The weighted fusion assigns corresponding weight coefficients according to the importance of different feature parts. The electronic device generates a corresponding feature similarity value for each standard defect feature. The feature similarity value reflects the degree of matching between the damage frequency domain features and the standard defect feature.

[0173] S705: Select the standard defect feature with the highest feature similarity value as the matching result, and obtain the defect type and corresponding defect location information corresponding to the matching result.

[0174] Specifically, the electronic device selects the standard defect feature with the highest feature similarity value as the matching result. The matching result selection sorts and compares all feature similarity values, and finds the standard defect feature corresponding to the feature with the highest similarity value. The electronic device verifies whether the maximum feature similarity value exceeds a preset matching threshold. If it exceeds the preset matching threshold, the matching result is confirmed to be valid. The electronic device obtains the defect type corresponding to the matching result. The defect type acquisition extracts the defect classification information associated with the matching result from the preset defect feature library. The electronic device obtains the defect location information corresponding to the matching result. The defect location information acquisition extracts the spatial coordinates, area range, and location description associated with the matching result from the preset defect feature library. The electronic device outputs the defect type and defect location information as the final identification result of the circuit board defect detection.

[0175] The following describes an electronic device for intelligent testing of an exemplary circuit board provided in an embodiment of this application. Figure 4 This is an exemplary hardware structure diagram of an electronic device for intelligent testing of a circuit board provided in an embodiment of this application.

[0176] In some embodiments, the electronic device for intelligent testing of the circuit board is a computer device, or the electronic device for intelligent testing of the circuit board includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0177] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0178] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0179] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0180] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A smart testing method for circuit boards, characterized in that, The method includes: while performing standard electrical tests on the target circuit board, acquiring the test sequence of each multi-test item in the standard electrical tests, the test sequence including multiple test steps, each test step including an excitation application stage, a stabilization waiting stage and a measurement stage; Based on the circuit topology of the target circuit board and the electrical test type of the standard electrical test, a matching detection signal is selected from a preset multi-band detection signal library; During the excitation application phase, the excitation signal status of the standard electrical test and the operating parameters of the target circuit board are monitored in real time. When the excitation signal is detected to be stable and the operating parameters of the target circuit board are within a stable range, it is determined to enter the stable waiting stage. The detection signal is injected into the key nodes of the target circuit board in a pulse manner through the test probe. Within a preset time window after the detection signal is injected, the response signals of multiple monitoring points of the target circuit board are collected to obtain the initial response waveform data. The key nodes are the power input terminal, the signal input and output terminal and the connection nodes of each functional module in the target circuit board. During the measurement phase, the initial response waveform data is denoised, filtered, and normalized to form response waveform data for each monitoring point. The standard electrical test includes multiple test items. The response waveform data is then subjected to frequency domain decomposition to obtain a frequency domain fingerprint spectrum, which includes characteristic parameters of multiple frequency components. Environmental modulation analysis is performed on the frequency domain fingerprint spectrum to obtain the modulation influence coefficients of each frequency component. During the standard electrical test, if the result of any target test item on the target circuit board exceeds a preset reasonable range, the target frequency domain fingerprint spectrum corresponding to the target test item and each target modulation... Correlation analysis is performed on the influence coefficients to obtain abnormal results, including reversible modulation results and irreversible damage results. For the reversible modulation results, the environmental frequency domain inverse transform is performed on the target frequency domain fingerprint spectrum using the target modulation influence coefficients corresponding to each frequency component to obtain a standard environmental equivalent spectrum, and the results of the target test items are recalculated based on the standard environmental equivalent spectrum. For the irreversible damage results, damage frequency domain features are extracted from the target frequency domain fingerprint spectrum, and the damage frequency domain features are matched with a preset defect feature library to obtain the defect type and corresponding defect location information of the target circuit board.

2. The intelligent testing method for circuit boards according to claim 1, characterized in that, The step of performing frequency domain decomposition processing on the response waveform data to obtain a frequency domain fingerprint spectrum specifically includes: calculating the propagation parameters of each monitoring point based on the electrical connection path length and impedance characteristics between each monitoring point and the key node, wherein the propagation parameters include signal propagation delay and signal attenuation coefficient; performing time offset compensation and amplitude attenuation compensation on the response waveform data of each monitoring point based on the propagation parameters of each monitoring point to obtain the compensated response data corresponding to each monitoring point; calculating the waveform energy change rate of the compensated response data of each monitoring point, setting the time period in which the waveform energy change rate exceeds a preset energy change rate threshold as a fine analysis window, and setting the time period in which the waveform energy change rate does not exceed the preset energy change rate threshold as a regular analysis window; performing Fourier transform on the compensated response data in the fine analysis window and the regular analysis window to obtain window frequency domain transform data; determining the data confidence weight corresponding to each monitoring point based on the propagation parameters of each monitoring point; performing weighted fusion calculation on the window frequency domain transform data based on the data confidence weight of each monitoring point to obtain fused frequency domain data, and constructing a frequency domain fingerprint spectrum based on the fused frequency domain data.

3. The intelligent testing method for circuit boards according to claim 1, characterized in that, The environmental modulation analysis of the frequency domain fingerprint spectrum to obtain the modulation influence coefficient of each frequency component specifically includes: acquiring the current environmental parameters during the target circuit board testing process, including test temperature, test humidity, power supply voltage fluctuation value, and electromagnetic interference intensity value; extracting the time-series variation data of the same frequency component in the frequency domain fingerprint spectrum at different test times, the time-series variation data including amplitude and phase values; aligning and matching the current environmental parameters and the time-series variation data according to the test time to obtain time-synchronized environmental frequency data pairs; calculating the correlation between each environmental parameter and each frequency component value in the environmental frequency data pairs; constructing an environmental frequency correlation matrix based on the correlations, where the rows of the environmental frequency correlation matrix represent environmental parameter types, the columns represent frequency component types, and the elements are correlation values; calculating the proportion of numerical difference between each frequency component under standard environmental parameters and current environmental parameters according to the environmental frequency correlation matrix, and determining the proportion of numerical difference as the modulation influence coefficient of the corresponding frequency component.

4. The intelligent testing method for circuit boards according to claim 1, characterized in that, The correlation analysis of the target frequency domain fingerprint spectrum and each target modulation influence coefficient corresponding to the target test item is performed to obtain abnormal results. The abnormal results include reversible modulation results and irreversible damage results. Specifically, it includes: identifying abnormal frequency components whose amplitude or phase values ​​exceed a preset reasonable range from the target frequency domain fingerprint spectrum; obtaining the target modulation influence coefficient corresponding to each abnormal frequency component; calculating the actual deviation of each abnormal frequency component, where the actual deviation is the difference between the current value of the abnormal frequency component and the value within a preset reasonable range; comparing the actual deviation of each abnormal frequency component with the corresponding preset theoretical modulation amount; when the difference between the actual deviation of the abnormal frequency component and the theoretical modulation amount is less than a preset difference threshold, it is determined that the deviation of the abnormal frequency component is caused by environmental modulation, and the abnormal frequency component is marked as a reversible modulation result; when the difference between the actual deviation of the abnormal frequency component and the theoretical modulation amount is greater than a preset difference threshold, it is determined that the deviation of the abnormal frequency component is caused by circuit board damage, and the abnormal frequency component is marked as an irreversible damage result.

5. The intelligent testing method for circuit boards according to claim 4, characterized in that, The step of performing an inverse environmental frequency domain transformation on the target frequency domain fingerprint spectrum using the target modulation influence coefficients corresponding to each frequency component to obtain a standard environmental equivalent spectrum specifically includes: calculating the current environmental deviation between the current environmental parameters and the standard environmental parameters; determining the influence value of each frequency component due to the environmental deviation based on the current environmental deviation and the modulation influence coefficients corresponding to each frequency component; subtracting the corresponding influence value from the amplitude value of each frequency component in the target frequency domain fingerprint spectrum to obtain the environmentally compensated amplitude value; subtracting the corresponding influence value from the phase value of each frequency component in the target frequency domain fingerprint spectrum to obtain the environmentally compensated phase value; and rearranging the environmentally compensated amplitude and phase values ​​in frequency order to form a standard environmental equivalent spectrum.

6. The intelligent testing method for circuit boards according to claim 4, characterized in that, The step of extracting damage frequency domain features from the target frequency domain fingerprint spectrum and matching the damage frequency domain features with a preset defect feature library to obtain the defect type and corresponding defect location information of the target circuit board specifically includes: obtaining abnormal frequency components marked as irreversible damage results; extracting the amplitude, phase, and frequency location information of the abnormal frequency components from the target frequency domain fingerprint spectrum to form damage frequency domain data; calculating the degree of numerical difference between each abnormal frequency component in the damage frequency domain data and the frequency components in the frequency domain fingerprint spectrum that are within a preset reasonable range; statistically analyzing the distribution characteristics of the abnormal frequency components within the frequency domain range, and combining the degree of numerical difference and the distribution characteristics to constitute the damage frequency domain features; calculating the feature similarity value between the damage frequency domain features and each standard defect feature in the preset defect feature library; selecting the standard defect feature with the largest feature similarity value as the matching result, and obtaining the defect type and corresponding defect location information corresponding to the matching result.

7. An electronic device for intelligent testing of circuit boards, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-6.

8. A computer program product containing instructions, characterized in that, When the computer program product is run on an electronic device for intelligent testing of a circuit board, the electronic device causes the electronic device to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device for intelligent testing of a circuit board, the electronic device causes the electronic device to perform the method as described in any one of claims 1-6.

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