High-precision EMI diagnosis and pretest system based on vector signal analyzer

Abstract

The invention provides a high-precision EMI diagnosis and pretest system based on a vector signal analyzer, and belongs to the technical field of data processing, and the system specifically comprises the steps: according to EMI interference signal source data in an interference signal source combination in different superposition risk types, determining an interference signal in a darkroom when it is determined that an environment interference signal in the darkroom needs to be considered; the method comprises the following steps: acquiring a deviation condition of vector signal characteristics of interference signal source combinations between different test targets, determining an identification strategy for identifying interference signals in environment interference signals according to the deviation condition, and determining the identification strategy for identifying the interference signals in the environment interference signals according to different identification interference signals. According to the invention, the method determines the diagnosis test method of the test target according to the coincidence condition between the superposed interference signals of the test target and other test targets in combination with the recognition strategy for recognizing the interference signals, and improves the reliability of test processing.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a high-precision EMI diagnosis and pre-testing system based on a vector signal analyzer. Background Technology

[0002] During the measurement of electromagnetic interference (EMI) of electronic equipment in a dark room, abnormal electromagnetic interference signals occur frequently. Traditional electromagnetic compatibility pretests mainly rely on spectrum analyzers, which can only provide frequency domain amplitude information of the interference signal and cannot obtain deep vector information such as the phase and modulation characteristics of the signal. A similar technical solution is given in the invention patent application CN202511222214.8 "An Electromagnetic Interference Prediction Method Based on Statistical Analysis of Electromagnetic Environment Monitoring Data".

[0003] Vector signal analyzers can identify vector information, but for a given test target, there are often multiple sources of electromagnetic interference signals. When multiple sources simultaneously generate electromagnetic interference signals, it may be impossible to accurately determine the identification result of the electromagnetic interference signal. Therefore, how to consider environmental interference signals and distinguish them from the aforementioned abnormal states, and then construct a differentiated diagnostic model to ensure the reliability of the diagnostic results, has become an urgent technical problem to be solved.

[0004] Therefore, there is an urgent need for a high-precision EMI diagnosis and pre-test system based on a vector signal analyzer. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer, which includes: S1 uses the analysis results of the EMI signal in the vector signal analyzer to determine the EMI interference signal source in the test target. Based on the correlation between the vector signal characteristics of the EMI interference signal sources in different test targets, the superposition risk type and the combination of interference signal sources of the test target are determined. Based on the EMI interference signal source data in the combination of interference signal sources in different superposition risk types, when it is determined that the environmental interference signal in the anechoic chamber needs to be considered, proceed to the next step. S2 acquires the deviation of vector signal characteristics of different test target interference signal source combinations, and uses the deviation to determine the identification strategy for the interference signal in the environmental interference signal. S3 determines the diagnostic test method for the test target based on the overlap between the superimposed interference signals of the test target and other test targets in different interference signals, and in combination with the identification strategy of the interference signals.

[0006] The beneficial effects of this invention are as follows: Based on the deviation of vector signal characteristics of interference signal source combinations between different test targets, an identification strategy for interference signals in environmental interference signals is determined. This enables the evaluation and analysis of the dispersion of vector signal characteristics from the deviation of vector signal characteristics of electromagnetic interference signals between different test targets. Thus, from the perspective of the dispersion of vector signal characteristics, the identification strategy for interference signals is determined. This ensures the comprehensiveness and reliability of the evaluation and analysis of the impact of environmental interference signals even when the distribution is relatively discrete, i.e., it is easily affected by electromagnetic interference signals from the external environment.

[0007] Based on the overlap between the test target and the superimposed interference signals of other test targets in different interference signals, and the identification strategy for interference signals, the diagnostic test method for the test target is determined. This method considers the impact of the overlap between the test target and the superimposed interference signals of other test targets on the identification accuracy of the fault diagnosis model of other test targets. Furthermore, by combining the identification strategy for interference signals, a comprehensive consideration of the update identification efficiency of interference signals is achieved. Thus, for test targets with similar superimposed interference signals, when the update identification efficiency of interference signals is high, the signal identification model in the test process is constructed separately, thereby improving the reliability of signal identification processing.

[0008] Furthermore, the analysis results include the phase characteristics and frequency domain characteristics of the EMI signal.

[0009] Furthermore, the EMI interference signal source is a signal source in the test target that has historically exhibited EMI signals.

[0010] Furthermore, the method for determining the superimposed risk type of the test target is as follows: Based on the correlation between the vector signal characteristics of the EMI interference signal sources in the test target, the similarity of the phase characteristics and frequency domain characteristics of different EMI interference signal sources is determined; Based on the aforementioned similarities, the test targets were identified as having EMI interference signal sources with a risk of superposition. Based on the EMI interference signal sources in the test target that pose a superposition risk, determine the superposition risk type of the test target.

[0011] Furthermore, the method for determining the diagnostic testing method for the test target is as follows: Based on the overlap between the superimposed interference signals of the test target and other test targets, superimposed interference signals whose phase and frequency characteristics are similar to the superimposed interference signals of the test target are greater than a preset similarity coefficient threshold are considered as similar superimposed signals. The superimposed interference signal with similar superimposed signals in the test target is used as the influencing signal. The number of similar superimposed signals in other test targets is determined by the distribution data of similar superimposed signals in other test targets. Based on the influencing signals in the test target, the number of similar superimposed signals in other test targets, and the identification strategy for interference signals, a diagnostic test method for the test target is determined.

[0012] Secondly, this invention provides a high-precision EMI diagnosis and pre-testing system based on a vector signal analyzer, employing the aforementioned high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer, specifically including: Risk type identification module, identification strategy determination module, and test method determination module; The risk type identification module is responsible for determining the superimposed risk type of the test target and the combination of interference signal sources; The identification strategy determination module is responsible for determining the identification strategy for the identification interference signals in the environmental interference signals. The test method determination module is responsible for determining the diagnostic test method for the test target.

[0013] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer; Figure 2 This is a flowchart illustrating the method for determining the superimposed risk type of the test target; Figure 3 This is a flowchart for determining which environmental interference signals in the anechoic chamber need to be considered; Figure 4This is a flowchart of a method for determining the identification strategy for environmental interference signals. Detailed Implementation

[0017] 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 specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0018] Example 1 like Figure 1 As shown, this application provides a high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer, specifically including: S1 uses the analysis results of the EMI signal in the vector signal analyzer to determine the EMI interference signal source in the test target. Based on the correlation between the vector signal characteristics of the EMI interference signal sources in different test targets, the superposition risk type and the combination of interference signal sources of the test target are determined. Based on the EMI interference signal source data in the combination of interference signal sources in different superposition risk types, when it is determined that the environmental interference signal in the anechoic chamber needs to be considered, proceed to the next step. Furthermore, the analysis results include the phase characteristics and frequency domain characteristics of the EMI signal.

[0019] Furthermore, the EMI interference signal source is a signal source in the test target that has historically exhibited EMI signals.

[0020] Specifically, such as Figure 2 As shown, the method for determining the superimposed risk type of the test target is as follows: In an anechoic chamber, a standard radiated emissions test was conducted on a car navigation system (the test target). The system's mainboard integrates multiple clock sources and digital circuits. The test environment was a fully anechoic chamber with fixed receiving antenna and test target positions. The test method involved scanning the 30MHz - 1GHz frequency band according to standard procedures at multiple antenna heights and polarizations. S11 determines the similarity of phase and frequency domain characteristics of different EMI interference signal sources by considering the correlation between the vector signal characteristics of the EMI interference signal sources in the test target. Test engineers observed multiple out-of-range frequencies near 125MHz harmonics (such as 250MHz, 375MHz, and 500MHz). The peak values ​​at these frequencies were extremely high, exceeding regulatory limits.

[0021] When attempting to pinpoint the location, engineers encountered a thorny issue: near the main processor, graphics processor, and high-speed memory chips on the motherboard, the probe could capture interference signals of similar intensity and identical frequency characteristics at 125MHz and its multiples.

[0022] Feature analysis and similarity analysis: Analysis results: In-depth analysis of EMI signals captured from these three regions was performed to extract their phase and frequency domain features.

[0023] Frequency domain characteristics: The center frequency, harmonic spacing, and sideband characteristics of the three signals are highly consistent because they all originate from the same 125MHz reference clock.

[0024] Phase characteristics: Because these chips share a clock or need to work synchronously, there is a fixed phase relationship between their signals, which causes the EMI signals they emit to also exhibit a high degree of correlation / similarity in phase.

[0025] Calculate similarity coefficients: Construct multidimensional vector signal features from the phase and frequency domain features of each source, calculate the Euclidean distance between each pair, and convert it into similarity coefficients.

[0026] Example Results: The similarity coefficient between the main processor and the image processor is 0.92, the similarity coefficient between the main processor and the high-speed memory is 0.89, and the similarity coefficient between the image processor and the high-speed memory is 0.91. The preset similarity coefficient threshold is set to 0.85. All calculated results are greater than this threshold.

[0027] S12 determines, based on the aforementioned similarities, the EMI interference signal sources in the test target that pose a risk of superposition; Specifically, based on the S11 analysis, the main processor, image processor, and high-speed memory were identified as "EMI interference signal sources with a risk of superposition".

[0028] The difficulty in accurately locating "which chip" is at fault using a near-field probe stems from the highly similar signal characteristics of these three sources. The electromagnetic waves they generate in the spatial radiation field are coherent, and their signals undergo vector superposition at the receiving antenna in the anechoic chamber. In some directions, this superposition can be constructive, leading to abnormally high far-field measurement results.

[0029] S13 determines the superimposed risk type of the test target based on the EMI interference signal sources that pose a superimposed risk in the test target.

[0030] Specifically, the superimposed risk type of the test target is determined. In this embodiment, the number of signal sources with superimposed risks is 3, and the threshold is set to 2 (that is, as long as there is a pair of risk sources, it is considered high risk).

[0031] Risk type determination: Since 3 > 2, the superimposed risk type of this car navigation system is determined to be a type 1 risk.

[0032] It is understood that the EMI interference signal source with superposition risk in the test target is the EMI interference signal source whose phase characteristics and frequency domain characteristics are similar to other EMI interference signal sources in the test target with a similarity coefficient greater than a preset similarity coefficient threshold.

[0033] It should be noted that the similarity coefficient is determined based on the Euclidean distance coefficient of the phase characteristics and frequency domain characteristics.

[0034] Specifically, based on the EMI interference signal sources in the test targets that pose a risk of superposition, the type of superposition risk of the test targets is determined, including: Determine whether the number of EMI interference signal sources with superimposed risks in the test target is greater than a preset threshold for the number of signal sources. If yes, the superimposed risk type of the test target is determined to be a type I risk; otherwise, the superimposed risk type of the test target is determined to be a type II risk.

[0035] Specifically, the interference signal source combination consists of EMI interference signal sources whose phase characteristics and frequency domain characteristics are all greater than a preset similarity coefficient threshold.

[0036] Specifically, such as Figure 3 As shown, it is determined that environmental interference signals in the anechoic chamber need to be considered, specifically including: Specifically, when the interference signal generated by the test target itself is too complex (manifested as a combination of high-risk types and multiple risk sources), its signal may be indistinguishably coupled or superimposed with the inherent weak environmental interference signals in the anechoic chamber, thus these environmental interferences must be taken into consideration.

[0037] Based on the superimposed risk types of the test objectives, determine the test objective data for one type of risk and the test objective data for two types of risk. In one possible embodiment, the test target classification data is as follows: a risk type of target (high risk, >1 pair of risk sources): a total of 4, T1: car navigation system, T2: server motherboard, T3: 5G base station module, T4: industrial controller.

[0038] Category II risk type targets (low risk, but with at least 2 sources of disturbance / 1 pair of risk sources): 6 in total.

[0039] T5: High-end router (with 2 internal risk sources), T6: In-vehicle entertainment host (with 2 internal risk sources), T7: Industrial switch (with 2 internal risk sources), T8: Graphics processing card (with 2 internal risk sources), T9: Medical monitoring equipment (with 2 internal risk sources), T10: Drone flight controller (with 2 internal risk sources).

[0040] The preset threshold for the number of test targets (N_target) is 3. The preset threshold for the risk factor (R_threshold) is 0.35. The preset threshold for the number of interference signal source combinations (N_source_threshold) is 10. The preset number of interference signal sources is 2. The preset preset number of target combinations (N_combo_target) is 2.

[0041] By using the test target data of the first type of risk and the test target data of the second type of risk, and the number of EMI interference signal sources in the combination of interference signal sources in different test targets, it is determined whether environmental interference signals in the anechoic chamber need to be considered.

[0042] It is understandable that, by utilizing the test target data of the first and second risk types, and the number of EMI interference signal sources in different combinations of interference signal sources within the test targets, it is determined whether environmental interference signals in the anechoic chamber need to be considered. Specifically, this includes: Obtain the number of test targets of the aforementioned risk type, and determine whether the number of test targets of the aforementioned risk type is greater than a preset threshold for the number of test targets. If yes, it is determined that environmental interference signals in the darkroom need to be considered; otherwise, proceed to the next step. In the above steps, based on the number of risk targets of a certain type, the following is obtained and judged: the number of test targets of a certain type of risk = 4, decision: because 4 > N_target (3), the condition is met, final conclusion: directly determine "the environmental interference signal in the dark room needs to be considered".

[0043] Based on the number of test targets of the first type of risk and the number of test targets of the second type of risk, a superimposed risk coefficient is determined. It is then determined whether the superimposed risk coefficient is greater than a preset risk coefficient threshold. If so, proceed to the next step; otherwise, it is determined that environmental interference signals in the darkroom do not need to be considered. In another embodiment, if the number of risk targets of a certain type is small (hypothetical extrapolation), in order to fully demonstrate the robustness of the method, a different scenario is assumed: there are only 2 risk targets of a certain type (T1, T2), and there are 8 risk targets of a certain type (T3-T10).

[0044] Step 1: Number of risk targets in category 1 = 2, no more than 3. Proceed to Step 2.

[0045] Step 2: Based on the superimposed risk coefficient, the risk coefficient = number of risk targets of one type / total number of targets = 2 / 10 = 0.2. The judgment is: 0.2 < R_threshold (0.35). The condition is not met. Intermediate conclusion: It is determined that "environmental interference signals in the anechoic chamber do not need to be considered".

[0046] In this hypothetical scenario, because the proportion of high-risk devices is low, the system determines that the background interference in the current anechoic chamber environment can be ignored. This demonstrates the dynamic judgment capability of this method: it is not static, but makes flexible decisions based on the statistical characteristics of historical test data.

[0047] Based on the data of interference signal source combinations in different test targets, determine the total number of interference signal source combinations, and determine whether the total number of interference signal source combinations is greater than a preset threshold for the number of interference signal source combinations. If yes, it is determined that environmental interference signals in the dark room need to be considered; otherwise, proceed to the next step. Based on the number of EMI interference sources in different interference source combinations, interference source combinations with a number of EMI interference sources greater than a preset value are identified and used as target combinations. Based on the test target data containing the target combinations, it is determined whether environmental interference signals in the anechoic chamber need to be considered.

[0048] Furthermore, if the number of test targets including the target combination is greater than a preset target number, then it is determined that environmental interference signals in the darkroom need to be considered; otherwise, it is determined that environmental interference signals in the darkroom do not need to be considered.

[0049] S2 acquires the deviation of vector signal characteristics of different test target interference signal source combinations, and uses the deviation to determine the identification strategy for the interference signal in the environmental interference signal. Specifically, such as Figure 4 As shown, the method for determining the identification strategy for the environmental interference signal is as follows: The ultimate goal of this application is to determine the appropriate "rigor" for identifying and monitoring environmental interference signals in an anechoic chamber. This involves deciding whether to monitor all environmental signals (pre-defined identification strategy) or only those similar to known interference sources (second pre-defined identification strategy), thereby ensuring the reliability of identification processing under environmental interference conditions while maintaining a relatively discrete distribution of interference signal characteristics.

[0050] Based on the deviation of vector signal characteristics of interference signal source combinations between different test targets, determine the vector signal characteristics whose similarity coefficient between the interference signal source combination and other interference signal source combinations is not greater than a preset similarity coefficient threshold, and treat them as isolated signal combinations; Based on the EMI interference signal source data of the isolated signal combination in the test target, determine the number of EMI interference signal sources in the isolated signal combination; Based on the isolated signal combination data in the test target and the number of EMI interference signal sources in different isolated signal combinations, a strategy for identifying interference signals in the environmental interference signals is determined.

[0051] In one possible specific embodiment, 1. Preset threshold setting: The preset similarity coefficient threshold (SimTh) is 0.8, the preset isolated signal combination number threshold (IsoComboTh) is 2, the preset group number threshold (GroupTh) is 3, the preset discrete coefficient threshold (SpreadTh) is 0.5, and the preset discrete coefficient value (CompositeTh) is 0.2.

[0052] 2. Strategy Definition: Preset identification strategy (global monitoring strategy): Electromagnetic interference signals from all sources of disturbance in the environment are distributed processing targets. That is, regardless of the signal, as long as it appears in the environmental scan, it needs to be recorded, tracked, and identified. (High stringency).

[0053] The second pre-defined identification strategy (targeted monitoring strategy): Only environmental interference sources with characteristics similar to the EMI signal in the test target (similarity coefficient ≥ 0.8) need to be identified as interference signals. (Low stringency, focused on the target) 3. Historical test target and interference source combinations (using the previous example and supplementing features): We have 10 test targets (T1-T10), and the known combinations of interference sources within them are C1 to C6. Now, we define a representative vector signal feature for each combination (e.g., a vector signal feature with parameters including phase characteristics and frequency domain characteristics).

[0054] It is understood that the similarity coefficient of the vector signal characteristics between the interference signal source combination and other interference signal source combinations is determined based on the maximum value of the similarity coefficient of the vector signal characteristics between the interference signal source combination and the EMI interference signal source in the other interference signal source combinations.

[0055] Calculate the similarity coefficient between combinations: Calculate the similarity coefficient of vector signal features between each pair of all combinations (C1-C6), and determine the specific similarity coefficient based on the analytical result of the Euclidean distance function between vector signal features.

[0056] Rule: For a combination, if the maximum value of its similarity coefficient with all other combinations is not greater than SimTh (0.8), it is determined to be an "isolated signal combination".

[0057] Analysis: C5: Its similarity coefficient with C1~C4 is the highest at 0.45 (with C4), and its similarity coefficient with C6 is also assumed to be very low. Its maximum similarity coefficient 0.45 < 0.8. -> Isolated combination. C6: Similarly, its maximum similarity coefficient 0.25 < 0.8. -> Isolated combination. Conclusion: The identified isolated signal combinations are C5 and C6.

[0058] The number of isolated signal combinations = 2, the number of interference sources for isolated combination C5 = 2, and the number of interference sources for isolated combination C6 = 2.

[0059] Furthermore, based on the isolated signal combination data in the test target and the number of EMI interference signal sources in different isolated signal combinations, a strategy for identifying interference signals in the environmental interference signals is determined, specifically including: The number of isolated signal combinations is obtained, and it is determined whether the number of isolated signal combinations is greater than a preset threshold for the number of isolated signal combinations. If so, the identification strategy for identifying interference signals in the environmental interference signals is determined to be the preset identification strategy. If not, proceed to the next step. In the above steps, it is determined that the number of isolated signal combinations (2) is not greater than IsoComboTh (2). If the condition is not met, proceed to the next step.

[0060] Interference signal sources whose vector signal features have a similarity coefficient greater than a preset similarity coefficient threshold are grouped into the same group. It is determined whether the number of groups in all test targets is less than a preset group number threshold. If so, the identification strategy for the interference signal is determined to be the second preset identification strategy. If not, proceed to the next step. Further, grouping: group the combinations with a similarity coefficient greater than 0.8 into the same group, count the number of groups: a total of 4 groups, judge: the number of groups (4) is not less than GroupTh (3), decision: the condition is not met, proceed to the next step.

[0061] Based on the ratio of the isolated signal combination to the number of groups, the distribution dispersion coefficient of the vector signal feature is determined, and it is determined whether the distribution dispersion coefficient of the vector signal feature is greater than a preset dispersion coefficient threshold. If so, the identification strategy for identifying the interference signal in the interference signal is determined to be the preset identification strategy; otherwise, proceed to the next step. In the above steps, calculate the distribution dispersion coefficient: Formula: Distribution dispersion coefficient = Number of isolated signal combinations / Number of groups, Calculation: 2 / 4 = 0.5, Judgment: Distribution dispersion coefficient (0.5) is not greater than SpreadTh (0.5), Decision: Condition is not met, proceed to the next step.

[0062] The test target with isolated signal combinations is obtained, and the comprehensive dispersion coefficient is determined in combination with the distribution dispersion coefficient. Based on the comprehensive dispersion coefficient, the identification strategy for identifying interference signals in the environmental interference signal is determined.

[0063] It is understood that when the overall discrete coefficient is greater than the preset value of the discrete coefficient, if so, the identification strategy for identifying the interference signal in the interference signal is determined to be the preset identification strategy; otherwise, the identification strategy for identifying the interference signal in the interference signal is determined to be the second preset identification strategy.

[0064] In the above steps, the test targets with isolated signal combinations are identified: target T8 contains isolated combination C5, and target T10 contains isolated combination C6. Therefore, the set of test targets with isolated combinations is {T8, T10}, with a quantity of 2. The overall dispersion coefficient is calculated using the formula: Overall Dispersion Coefficient = Distribution Dispersion Coefficient * (Number of Targets with Isolated Combinations / Total Number of Targets). The calculation is: 0.5 * (2 / 10) = 0.1 Final judgment: The composite discrete coefficient (0.1) is not greater than CompositeTh (0.2), the condition is not met. Therefore, the identification strategy for the identification interference signal in the environmental interference signal is determined to be the second preset identification strategy.

[0065] It should be noted that the preset identification strategy treats all electromagnetic interference signals from disturbance sources in the environment as distributed processing targets, while the second preset identification test strategy requires that electromagnetic interference signals from disturbance sources in the environment whose phase and frequency characteristics are not less than a preset similarity threshold with the electromagnetic interference signals in the test target be used as identification interference signals.

[0066] S3 determines the diagnostic test method for the test target based on the overlap between the superimposed interference signals of the test target and other test targets in different interference signals, and in combination with the identification strategy of the interference signals.

[0067] Specifically, the superimposed interference signal is the electromagnetic interference signal formed by superimposing the electromagnetic interference signal of the EMI interference signal source in the test target with a certain identification interference signal. Different EMI interference signal sources correspond to different superimposed interference signals. That is, the electromagnetic interference signal of one EMI interference signal source will form a superimposed interference signal after being superimposed with a certain identification interference signal.

[0068] Specifically, the method for determining the diagnostic testing method for the test target is as follows: The ultimate goal of this application is to determine whether a new test target should be "tailor-made" with a diagnostic model or allowed to use a "general-purpose" diagnostic model. The core basis for this decision is whether the interference signal characteristics of the target are widespread and whether they will "contaminate" the general-purpose model.

[0069] Based on the overlap between the superimposed interference signals of the test target and other test targets, superimposed interference signals whose phase and frequency characteristics are similar to the superimposed interference signals of the test target are greater than a preset similarity coefficient threshold are considered as similar superimposed signals. In one possible specific embodiment, the scenario setup and initial data, and the historical database store the "superimposed interference signal" characteristics of multiple historical test targets (T1, T2, T3...), i.e., the total signal characteristics measured in an anechoic chamber that already include environmental interference. The task is to determine whether Tx should use a unified model or a separate model for diagnosis. The identification strategy is a second preset identification strategy (directional monitoring). The decision-making process is implemented (based on the correct definition), and each target has its known "superimposed interference signal" vector signal characteristics (including phase and frequency domain characteristics).

[0070] Identification strategy: Following the conclusion of the previous embodiment, the identification strategy adopted by the current darkroom is the second preset identification strategy (directional monitoring strategy).

[0071] Preset thresholds: Preset target quantity threshold (N_total_th) = 10, preset similarity coefficient threshold (SimTh) = 0.8, preset proportion threshold (RatioTh) = 0.4, preset impact coefficient threshold (OverlapTh) = 1.5, preset impact signal quantity threshold (N_impact_th) = 3.

[0072] The superimposed interference signal with similar superimposed signals in the test target is used as the influencing signal. The number of similar superimposed signals in other test targets is determined by the distribution data of similar superimposed signals in other test targets. Specifically, conceptual preparation involves identifying "similar superimposed signals" and "influencing signals." Similar superimposed signals refer to the highly similar phase and frequency characteristics of the total electromagnetic signals (i.e., the target's own EMI + environmental interference) measured in an anechoic chamber for two different test targets (such as Tx and Ty). The key point is that the reasons for the similarity in the total signals of Tx and Ty may be completely different. Tx might be a very strong signal S1 superimposed with a weak environmental noise E1. Ty might be a weak signal S2 superimposed with a very strong environmental noise E2 that has similar characteristics to S1. The final result is: S1 + E1 ≈ S2 + E2, which may lead to poor reliability of the general recognition model.

[0073] The superimposed signal of Tx was measured: Tx was placed in a dark room and scanned to obtain a total interference signal S_Tx after superposition of one (or more) signals.

[0074] Find similar superimposed signals: Compare S_Tx with the "superimposed interference signal" of each historical target in the historical database.

[0075] Suppose that the similarity coefficient between S_Tx and a superimposed signal S_T5 of historical target T5 is 0.92 (>0.8), and the similarity coefficient between S_Tx and a superimposed signal S_T8 of historical target T8 is 0.87 (>0.8).

[0076] Conclusion: S_T5 and S_T8 are identified as similar superimposed signals of Tx.

[0077] Determine the influencing signal: Since the measured signal S_Tx of Tx is similar to S_T5 and S_T8, S_Tx itself is marked as the influencing signal of Tx, and the number of influencing signals of Tx = 1 (i.e., S_Tx itself).

[0078] Based on the influencing signals in the test target, the number of similar superimposed signals in other test targets, and the identification strategy for interference signals, a diagnostic test method for the test target is determined.

[0079] Furthermore, based on the influencing signals in the test target, the number of similar superimposed signals in other test targets, and the updated identification data for identifying interference signals, a diagnostic test method for the test target is determined, specifically including: Determine whether the total number of test targets is greater than a preset target number threshold. If yes, proceed to the next step. If no, determine that the diagnostic test method for the test targets is to use a unified signal recognition model for all test targets for fault identification processing. In the above steps, perform the first level judgment: the total number of test targets. If the total number of historical targets is 20, which is greater than the threshold of 10, proceed to the next step.

[0080] Based on the number of similar superimposed signals in other test targets, determine the number of other test targets with similar superimposed signals. Based on the proportion of the number of other test targets with similar superimposed signals in the other test targets, determine the influence proportion. Determine whether the influence proportion is greater than a preset proportion threshold. If so, determine the diagnostic test method for the test target and build a separate signal recognition model for it, so as to avoid the influence of similar superimposed signals on the accuracy of the diagnostic test results of other test targets. If not, proceed to the next step. Level 2 Judgment: Based on the influence ratio, calculate the number of other test targets with similar superimposed signals: that is, the number of historical targets similar to the Tx signal. In this example, there are 2 (T5 and T8), and the influence ratio = 2 / 20 = 0.1. Judgment: 0.1 < preset ratio threshold (0.4). The condition is not met. Decision: Proceed to the next step.

[0081] Based on the influence ratio and the average number of similar superimposed signals in other test targets, the overlap influence coefficient of the test target is determined, and it is determined whether the overlap influence coefficient is greater than the preset influence coefficient threshold. If yes, proceed to the next step; if no, determine that the diagnostic test method for the test target is to use a unified signal recognition model for all test targets. In the above steps, the third level of judgment is performed: Based on the overlap influence coefficient, the average number of similar superimposed signals in other test targets: T5 and T8 each have only 1 signal similar to Tx, so the average number = 2 / 20 = 0.1. At this time, the overlap influence coefficient = (influence ratio + average number) / 2 = 0.1. Judgment: 0.1 < preset influence coefficient threshold (0.6). The condition is not met, proceed to the next step.

[0082] Determine whether the number of influencing signals in the test target is greater than a preset threshold for the number of influencing signals. If so, determine the diagnostic test method for the test target and construct a separate signal recognition model for it, thereby avoiding the influence of similar superimposed signals on the accuracy of the diagnostic test results of other test targets. If not, proceed to the next step. Level 4 Judgment: Based on the number of influencing signals, the judgment is: the number of influencing signals of Tx (1) < the preset threshold for the number of influencing signals (3), and the decision is: proceed to the last step.

[0083] Based on the identification strategy for interference signals, a diagnostic testing method for the test target is determined.

[0084] Furthermore, based on the identification strategy for interference signals, a diagnostic testing method for the test target is determined, specifically including: Case 1: If the identification strategy is a preset identification strategy, the update efficiency of the interference signal is faster. Therefore, the diagnostic test method for the test target is determined to construct a separate signal identification model for it, thereby avoiding the influence of similar superimposed signals on the accuracy of the diagnostic test results of other test targets.

[0085] Case 2: If the identification strategy is the second preset identification strategy, the update efficiency of the identification interference signal is relatively slow. Therefore, the diagnostic test method for the test target is to use a unified signal identification model for all test targets. That is, a unified signal identification model is used to perform diagnostic processing of electromagnetic interference signals during the testing process of the test target in the electromagnetic interference signal test.

[0086] Fifth layer judgment: Based on the identification strategy, the current strategy is the second preset identification strategy (targeted monitoring, slow update efficiency). According to situation 2: the diagnostic test method of Tx is determined to be the unified signal identification model of all test targets.

[0087] Although the measured signal of Tx closely resembles the historical signals of T5 and T8, this situation is not widespread (affecting only 10%). More importantly, under the "targeted monitoring" strategy, the system assumes that environmental interference is relatively stable and known. Therefore, the system can reasonably infer that: "The signal of Tx is similar to that of T5 and T8. The more likely reason is that the three of them have similar EMI source characteristics and naturally produce similar overall signals under the same environmental interference (those that are of concern to directional monitoring). This is a 'normal' common phenomenon and there is no need to overreact." Conversely, suppose Tx is a very special device whose measured signal is similar to more than 40% of the targets in the historical database (i.e., influence ratio > 0.4). This would trigger a second-level judgment, leading to the construction of a separate model for it.

[0088] The logic here is: "It is highly abnormal that Tx's signal is similar to so many different types of targets! It is very likely that a unique signal of Tx itself is superimposed on a common but not fully identified (or newly emerging) environmental interference E_new, thus 'disguising' itself as many historical targets. If Tx is treated with a uniform model, it will severely pollute the model, causing the model to consider this 'Tx + E_new' mixed feature as a common feature, thereby seriously affecting the diagnostic accuracy of other targets. Therefore, Tx must be isolated and processed with a separate model." Example 2 Secondly, this invention provides a high-precision EMI diagnosis and pre-testing system based on a vector signal analyzer, employing the aforementioned high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer, specifically including: Risk type identification module, identification strategy determination module, and test method determination module; The risk type identification module is responsible for determining the superimposed risk type of the test target and the combination of interference signal sources; The identification strategy determination module is responsible for determining the identification strategy for the identification interference signals in the environmental interference signals. The test method determination module is responsible for determining the diagnostic test method for the test target.

[0089] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0090] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0091] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer, characterized in that, Specifically, it includes: Based on the analysis results of the EMI signal in the vector signal analyzer, the EMI interference signal source in the test target is determined. According to the correlation between the vector signal characteristics of the EMI interference signal sources in different test targets, the superposition risk type and the combination of interference signal sources of the test target are determined. According to the EMI interference signal source data in the combination of interference signal sources in different superposition risk types, when it is determined that the environmental interference signal in the anechoic chamber needs to be considered, proceed to the next step. The deviation of vector signal characteristics of different test targets and their combinations of interference signal sources is obtained, and the deviation is used to determine the identification strategy for the interference signal in the environmental interference signal. Based on the overlap between the test target and the superimposed interference signals of other test targets in different interference signals, and in combination with the identification strategy of interference signals, the diagnostic test method for the test target is determined.

2. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 1, characterized in that, The analysis results include the phase characteristics and frequency domain characteristics of the EMI signal.

3. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 1, characterized in that, The EMI interference signal source is a signal source in the test target that has historically exhibited EMI signals.

4. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 1, characterized in that, The method for determining the superimposed risk type of the test target is as follows: Based on the correlation between the vector signal characteristics of the EMI interference signal sources in the test target, the similarity of the phase characteristics and frequency domain characteristics of different EMI interference signal sources is determined; Based on the aforementioned similarities, the test targets were identified as having EMI interference signal sources with a risk of superposition. Based on the EMI interference signal sources in the test target that pose a superposition risk, determine the superposition risk type of the test target.

5. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 4, characterized in that, The EMI interference signal source with superposition risk in the test target is the EMI interference signal source whose phase characteristics and frequency domain characteristics are similar to other EMI interference signal sources in the test target with a similarity coefficient greater than a preset similarity coefficient threshold.

6. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 5, characterized in that, Based on the EMI interference signal sources in the test targets that pose a risk of superposition, determine the type of superposition risk for the test targets, specifically including: Determine whether the number of EMI interference signal sources with superimposed risks in the test target is greater than a preset threshold for the number of signal sources. If yes, the superimposed risk type of the test target is determined to be a type I risk. If no, the superimposed risk type of the test target is determined to be a type II risk. Specifically, the interference signal source combination consists of EMI interference signal sources whose phase characteristics and frequency domain characteristics are all greater than a preset similarity coefficient threshold.

7. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 1, characterized in that, The method for determining the identification strategy for the environmental interference signal is as follows: Based on the deviation of vector signal characteristics of interference signal source combinations between different test targets, determine the vector signal characteristics whose similarity coefficient between the interference signal source combination and other interference signal source combinations is not greater than a preset similarity coefficient threshold, and treat them as isolated signal combinations. Based on the EMI interference signal source data of the isolated signal combination in the test target, determine the number of EMI interference signal sources in the isolated signal combination; Based on the isolated signal combination data in the test target and the number of EMI interference signal sources in different isolated signal combinations, determine the identification strategy for the identification interference signal in the environmental interference signal; It is understood that the similarity coefficient of the vector signal characteristics between the interference signal source combination and other interference signal source combinations is determined based on the maximum value of the similarity coefficient of the vector signal characteristics between the interference signal source combination and the EMI interference signal source in the other interference signal source combinations.

8. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 7, characterized in that, Based on the isolated signal combination data in the test target, and the number of EMI interference signal sources in different isolated signal combinations, a strategy for identifying interference signals in the environmental interference signals is determined, specifically including: If the number of isolated signal combinations is obtained and it is determined that the number of isolated signal combinations is greater than a preset threshold for the number of isolated signal combinations, then the identification strategy for identifying interference signals in the environmental interference signals is determined to be the preset identification strategy.

9. The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer as described in claim 8, characterized in that, The preset identification strategy treats electromagnetic interference signals from all sources of disturbance in the environment as distributed processing targets.

10. A high-precision EMI diagnosis and pre-test system based on a vector signal analyzer, characterized in that, The high-precision EMI diagnosis and pre-testing method based on a vector signal analyzer, as described in any one of claims 1-9, specifically includes: Risk type identification module, identification strategy determination module, and test method determination module; The risk type identification module is responsible for determining the superimposed risk type of the test target and the combination of interference signal sources; The identification strategy determination module is responsible for determining the identification strategy for the identification interference signals in the environmental interference signals. The test method determination module is responsible for determining the diagnostic test method for the test target.

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