An electrostatic discharge immunity automated test management method and system

Abstract

The present invention discloses an electrostatic discharge immunity automated test management method and system. The method of the present invention comprises: real-time collection of real-time environmental status data in an experimental environment, followed by redundancy mechanism and verification algorithm processing, and real-time calculation and adjustment of the discharge parameters of an electrostatic generator using a multi-dimensional nonlinear adaptive control algorithm; electrostatic discharge is performed based on the adjusted electrostatic generator discharge parameters to generate electrostatic interference of specified parameters, while simulating the actual scene of electrostatic discharge, and further the generated electrostatic discharge interference signal is transmitted to the test equipment and a corresponding response signal is generated; after pre-processing the response signal and the interference signal, a deep spectrum analysis is performed to extract key features, and the immunity of the test equipment is intelligently evaluated through a neural network model. The present invention not only improves the control accuracy of the electrostatic generator, but also enables it to dynamically adapt to changes in the experimental environment, ensuring that the discharge conditions are always in the optimal state.

Description

Technical Field

[0001] The invention belongs to the technical field of automated test management, and in particular to an automated test management method and system for electrostatic discharge immunity. Background Art

[0002] In the development and application of modern electronic equipment and systems, electrostatic discharge (ESD) immunity testing is a key link. Electrostatic discharge is a common form of electromagnetic interference, which may cause electronic equipment to malfunction, malfunction or even permanent damage. Therefore, electrostatic discharge immunity testing has become an important means to evaluate the stability and reliability of electronic equipment in complex electromagnetic environments.

[0003] Traditional ESD immunity testing mainly relies on manual setting and manual adjustment of ESD parameters, such as discharge voltage, frequency, and duration. However, due to the complexity and diversity of the test environment and equipment conditions, manual adjustment methods are difficult to accurately control the test conditions, often resulting in insufficient accuracy and repeatability of test results. In addition, the effect of ESD is affected by a variety of environmental factors, such as temperature, humidity, and electrostatic voltage. Traditional methods are usually difficult to respond to and adjust discharge parameters in real time to adapt to these environmental changes, further reducing the reliability of the test. Summary of the invention

[0004] The present invention provides an electrostatic discharge immunity automatic test management method and system to solve the technical problems of poor accuracy and adaptability of existing electrostatic discharge parameter control and poor dynamic optimization and adaptive adjustment effect of test flow.

[0005] To this end, the present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides an electrostatic discharge immunity automated test management method, which comprises the steps of:

[0007] S1. Real-time acquisition of real-time environmental status data in the experimental environment, the real-time environmental status data is processed by a redundancy mechanism and a verification algorithm, and the processed real-time environmental status data is calculated and adjusted in real time using a multidimensional nonlinear adaptive control algorithm for the discharge parameters of the electrostatic generator;

[0008] S2. Execute electrostatic discharge based on the adjusted discharge parameters of the electrostatic generator to generate electrostatic interference of specified parameters, and simulate the actual scene of electrostatic discharge to provide an interference source for the immunity of the test equipment. The electrostatic discharge interference signal generated is further transmitted to the test equipment and a corresponding response signal is generated;

[0009] S3. The response signal and the interference signal are preprocessed, and then deep spectrum analysis is performed. The key features are extracted based on the spectrum comparison results of the interference signal and the response signal. Based on the key features, the anti-interference performance of the test equipment is intelligently evaluated through the neural network model to generate the anti-interference evaluation results. At the same time, based on the feedback of the anti-interference evaluation results, the test process is dynamically optimized through the adaptive fuzzy inference control algorithm.

[0010] Furthermore, in S1, the multidimensional nonlinear adaptive control algorithm obtains the discharge parameters of the electrostatic generator by constructing a multidimensional nonlinear coupling function, recursively solving the environmental influence matrix, and implementing a multi-step feedback adjustment mechanism.

[0011] Furthermore, in S1, the specific content of the multi-dimensional nonlinear adaptive control algorithm is as follows:

[0012] In the initial processing stage, a multidimensional nonlinear coupling function is used to transform the interaction relationship between environmental parameters into a mathematical model, thereby capturing the complex effects of different environmental factors on electrostatic discharge.

[0013] The results of the coupling function are used to construct an environmental influence matrix, which is used to quantitatively describe the magnitude of the forces of different environmental parameters. The calculation of any element in the environmental influence matrix is ​​achieved by time integration of the coupling function results, thereby capturing the long-term impact of the dynamic changes of environmental factors on the electrostatic generator.

[0014] Through the recursive solution method, the discharge parameters of the electrostatic generator are optimized in multiple steps according to the results of the environmental influence matrix.

[0015] In order to ensure the ability to adapt to environmental changes in practical applications, a multi-step feedback adjustment mechanism is introduced. This mechanism continuously corrects the control coefficients in the algorithm based on the feedback data during the test to adapt to the new experimental environment.

[0016] Furthermore, in S3, a nonlinear multiple spectrum transformation algorithm is used to perform deep spectrum analysis on the preprocessed interference signal and response signal to achieve in-depth analysis of the signal spectrum.

[0017] Furthermore, in S3, multiple nonlinear modulation terms are introduced in the implementation process of the nonlinear multiple spectrum transformation algorithm, so that the signal in the conversion process not only depends on the linear change in the time domain, but is also controlled by the influence of nonlinear functions.

[0018] Furthermore, in S3, a deep spectrum analysis is performed after the preprocessing, and key features are extracted based on the spectrum comparison results of the interference signal and the response signal. Specifically, deep spectrum analysis is performed to obtain the spectrum representation of the interference signal and the response signal respectively, and the spectrum difference between the interference signal and the response signal at the same frequency is calculated, and the spectrum difference reflects the response characteristics of the device to the interference signal at the same frequency; after extracting the spectrum difference features, the harmonic components in the signal are further analyzed to extract the harmonic features.

[0019] Furthermore, in S3, after extracting the spectrum difference features and harmonic features, the spectrum representation of the interference signal, the spectrum representation of the response signal, the spectrum difference features and the harmonic features are combined into a high-dimensional feature vector through a nonlinear mapping function for subsequent intelligent evaluation of anti-interference performance.

[0020] Furthermore, the adaptive fuzzy inference control algorithm utilizes fuzzy logic and adaptive control theory to optimize the efficiency and accuracy of the test by dynamically adjusting key parameters during the test.

[0021] Furthermore, the specific implementation steps of the adaptive fuzzy reasoning control algorithm are as follows: the first step, fuzzification processing; the second step, fuzzy rule setting; the third step, fuzzy reasoning; the fourth step, defuzzification processing; the fifth step, dynamic adjustment of the test process.

[0022] In a second aspect, the present invention provides an electrostatic discharge immunity automated test management system, including an environmental data acquisition module, a central control module, an electrostatic generator module, a data test analysis module, and a data storage module;

[0023] The environmental data acquisition module collects real-time environmental status data in the experimental environment through the sensor network, and transmits the real-time environmental status data to the central control module after processing through a redundancy mechanism and a verification algorithm;

[0024] The central control module uses the real-time environmental status data to calculate and adjust the discharge parameters of the electrostatic generator in real time using a multi-dimensional nonlinear adaptive control algorithm, and dynamically optimizes the test process based on the evaluation results obtained by the data test analysis module through an adaptive fuzzy inference control algorithm, and transmits the adjusted electrostatic generator discharge parameters to the electrostatic generator module;

[0025] The electrostatic generator module performs electrostatic discharge based on the adjusted electrostatic generator discharge parameters to generate electrostatic interference of specified parameters, and simulates the actual scene of electrostatic discharge to provide an interference source for the anti-interference of the test equipment. The electrostatic discharge interference signal generated is further transmitted to the test equipment and generates a corresponding response signal;

[0026] The data test and analysis module pre-processes the response signal and the interference signal, and then performs deep spectrum analysis, extracts key features based on the spectrum comparison results of the interference signal and the response signal, and intelligently evaluates the anti-interference performance of the test equipment through a neural network model based on the key features, generates an anti-interference evaluation result, and feeds back the evaluation result to the central control module;

[0027] The data storage module stores the test data of electrostatic discharge immunity in a distributed manner through blockchain technology, providing a traceable storage chain for subsequent test data calls. The test data includes real-time environmental status data, adjusted electrostatic generator discharge parameters, electrostatic discharge interference signals, and immunity evaluation results.

[0028] The beneficial effects of the present invention are as follows: 1. The introduction of a multidimensional nonlinear adaptive control algorithm enables the present invention to adjust the discharge parameters of the electrostatic generator in real time. This algorithm accurately captures the influence of environmental parameter variables on discharge parameters through the recursive solution of complex multidimensional nonlinear coupling functions and environmental influence matrices, thereby accurately regulating key parameters such as voltage, frequency and duration of electrostatic discharge. This not only improves the control accuracy of the electrostatic generator, but also enables it to dynamically adapt to changes in the experimental environment to ensure that the discharge conditions are always in the optimal state. 2. Through the intelligent evaluation of the anti-interference ability through the neural network model, the present invention can obtain the anti-interference ability score of the equipment in real time. Based on this score, using the adaptive fuzzy reasoning control algorithm, the present invention can dynamically adjust the key parameters in the test process. The algorithm combines the flexibility of fuzzy logic and the accuracy of adaptive control, so that the test process can be dynamically optimized according to the real-time device response, further improving the efficiency and accuracy of the test, and ensuring the reliability and consistency of the test results.

[0029] In addition, the present invention uses a nonlinear multiple spectrum transformation algorithm to perform deep spectrum analysis on the response signal and the interference signal, so that the system can extract more detailed and rich signal features. Through in-depth analysis of the signal spectrum, the present invention can not only identify the anti-interference characteristics of the device at different frequencies, but also comprehensively evaluate the anti-interference ability of the device through high-dimensional feature vectors such as harmonic features and spectrum differences. This process provides more informative input data for the neural network model, thereby greatly improving the accuracy of the anti-interference assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Figure 1 A flowchart of an electrostatic discharge immunity automated test management method of the present invention;

[0032] Figure 2 This is a structural diagram of an electrostatic discharge immunity automatic test management system of the present invention. DETAILED DESCRIPTION

[0033] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0035] The specific scheme of an electrostatic discharge immunity automatic test management method and system provided by the present invention is described in detail below in conjunction with the accompanying drawings of the specification.

[0036] See attached Figure 1 , which shows a flow chart of an electrostatic discharge immunity automated test management method provided by an embodiment of the present invention, the method comprising the following steps:

[0037] S1. Real-time collection of real-time environmental status data in the experimental environment, the real-time environmental status data is processed by a redundancy mechanism and a verification algorithm, and the processed real-time environmental status data is used to calculate and adjust the discharge parameters of the electrostatic generator in real time using a multi-dimensional nonlinear adaptive control algorithm.

[0038] The real-time environmental status data in the experimental environment, such as temperature and humidity, are collected in real time through the sensor network deployed in the experimental environment; the real-time environmental status data is processed by a redundancy mechanism and a verification algorithm. The implementation of the redundancy mechanism collects the same type of environmental data through multiple sensors, such as temperature data can be collected by multiple sensors, and then these redundant data are compared and analyzed; if the data of a certain sensor is abnormal (such as obviously deviating from the measured values ​​of other sensors), the abnormal data will be automatically identified and discarded. The redundancy mechanism can also further improve the accuracy of the data by calculating the average or weighted average of the redundant data. The verification algorithm is used to detect errors in the data transmission process, including parity check, cyclic redundancy check (CRC), etc. The specific verification algorithm is selected according to the specific implementation scenario and is not limited here. When the data is transmitted to the central control module, a verification code is automatically generated and verified when the data is received to ensure that the data has not been tampered with or damaged. If the verification fails, the data will be requested to be resent to ensure the integrity of the data.

[0039] Furthermore, the real-time environmental status data processed by the redundancy mechanism and the verification algorithm is used to calculate and adjust the discharge parameters of the electrostatic generator in real time using a multidimensional nonlinear adaptive control algorithm; the multidimensional nonlinear adaptive control algorithm obtains the discharge parameters of the electrostatic generator by constructing a multidimensional nonlinear coupling function, recursively solving the environmental influence matrix, and implementing a multi-step feedback adjustment mechanism. The specific implementation process is as follows:

[0040] Use multidimensional nonlinear coupling functions in the initial processing stage It is used to convert the interaction between environmental parameters into a mathematical model, thereby capturing the complex effects of different environmental factors on electrostatic discharge. The formula is as follows:

[0041]

[0042] in, It is Temperature data at each moment; It is Humidity data at each moment; It is Electrostatic voltage data at each moment; , , It is a unitless variable after dimensionless processing, which is achieved by dividing the variable by its corresponding characteristic quantity (typical value or nominal value in the experiment); is the first control parameter, which is used to adjust the influence weight of the temperature term in the coupling function; It is the coupling strength parameter between temperature and humidity, which affects the nonlinear characteristics of the temperature term; is the second control parameter, which is used to adjust the influence weight of the humidity term in the coupling function; It is the coupling strength parameter between humidity and voltage, which affects the nonlinear characteristics of the humidity term; is the third control parameter, which is used to adjust the influence weight of the voltage term in the coupling function; It is the temperature correction parameter in the voltage term, which is used to avoid the voltage being divided by zero and to adjust the relationship between voltage and temperature; is a small constant in the humidity term, which is used to avoid the situation where the divisor is zero. The above function describes the nonlinear relationship between temperature, humidity and voltage, reflects the comprehensive influence of the environmental state through a mathematical model, and provides basic data for the subsequent environmental influence matrix.

[0043] Furthermore, the results of the coupling function are used to construct an environmental influence matrix. The environmental influence matrix is ​​used to quantitatively describe the magnitude of the force of different environmental parameters (such as temperature and humidity) on the discharge parameters (such as voltage, frequency, and duration) of the electrostatic generator. The calculation of any element in the environmental influence matrix is ​​achieved by integrating the results of the coupling function over time, thereby capturing the long-term impact of the dynamic changes of environmental factors on the electrostatic generator. The calculation formula for any element in the environmental influence matrix is:

[0044]

[0045] in, The environmental impact matrix The elements in represent the The environmental parameters (such as temperature and humidity) have an impact on the The cumulative effect of the discharge parameters (such as voltage, frequency, and duration) of the electrostatic generator represents the The environmental parameters The combined effect of the discharge parameters of the electrostatic generator; It is a multidimensional nonlinear coupling function that represents the combined effects of temperature, humidity, and voltage. It reflects the complex interactions among environmental factors through nonlinear operations and serves as the basis of the environmental influence matrix. is the upper limit of the integral, indicating the termination time point of the experiment, and determines the period of cumulative influence of environmental parameters on the discharge parameters of the electrostatic generator; is the frequency-dependent coefficient, which is used to adjust the periodic effect of environmental parameters on the discharge parameters of the electrostatic generator; is the attenuation coefficient, which is used to describe the gradual weakening of the influence of environmental factors on the discharge parameters of the electrostatic generator over time, reflecting the attenuation characteristics of the influence of environmental parameters during the experimental time; is the time variable, representing the process over time during the integration process; is the attenuation factor, which represents the exponential attenuation of environmental influence over time. It simulates the reality that environmental influence gradually weakens over time. It means The environmental parameters The periodic influence of the discharge parameters of an electrostatic generator is measured, and the cosine function is used to simulate the periodic fluctuation effect of environmental parameters on the discharge parameters of the electrostatic generator.

[0046] Furthermore, the discharge parameters of the electrostatic generator are optimized in multiple steps according to the results of the environmental influence matrix through a recursive solution method.

[0047] First, the discharge voltage of the electrostatic generator is initialized. This initial value is calculated by the environmental influence matrix The combined relationship between the first row elements of and temperature, humidity, and voltage is calculated.

[0048] Initial discharge voltage of electrostatic generator The formula is:

[0049]

[0050] in, It is the first row and first column element in the environmental influence matrix, indicating the initial effect of temperature on the discharge voltage; is the temperature data after coupling function correction, , is the initial temperature; is the humidity data after coupling function correction; is the fourth control parameter, used to adjust the environmental influence matrix element The degree of influence on the initial discharge voltage; It is the fifth control parameter, which appears in the denominator together with the square term of temperature, and is used to adjust the degree of influence of temperature on the initial discharge voltage, in order to control the sensitivity of temperature to voltage changes; is the sixth control parameter, used to adjust the periodic effect of temperature on voltage, and is related to the cosine function Combined, it reflects how temperature fluctuations affect the initial discharge voltage; is the modulation frequency; the above formula combines the environmental influence of the environmental influence matrix and converts the nonlinear effects of temperature, humidity and voltage into the initial discharge voltage setting value.

[0051] Furthermore, the initial discharge voltage is iterated multiple times through a recursive formula to optimize the voltage value so that it can better adapt to the changes in the actual environment. The recursive process is as follows:

[0052]

[0053] in, It is The discharge voltage after the recursive optimization; It is The discharge voltage after the recursive optimization; It is the phase offset, which controls the phase adjustment in the recursive optimization process. Through different combinations of nonlinear sinusoidal functions, the voltage adjustment of each step can subtly reflect the current environment's demand for electrostatic discharge. The purpose of recursion is to continuously adjust the voltage value. , gradually approaching the optimal discharge voltage, thereby ensuring the accuracy and stability of the test results.

[0054] Similarly, the calculation of discharge frequency and discharge duration is also processed in a similar recursive manner. and initial discharge duration The calculations are as follows:

[0055]

[0056]

[0057] in, , , All are control parameters used to adjust the effect of discharge voltage and humidity on the initial discharge frequency; , , They are also control parameters used to adjust the effect of discharge voltage and humidity on the initial discharge duration; , are adjustment parameters, which are used to adjust the exponential and sine functions in the discharge frequency and discharge duration formulas respectively; , , They are the electrostatic voltage, temperature and humidity data after coupling function correction.

[0058] The recursive optimization formula for discharge frequency and discharge duration is as follows:

[0059]

[0060]

[0061] in, It is The discharge frequency after recursive optimization; It is The discharge frequency after recursive optimization; It is Discharge duration after recursive optimization; It is The discharge duration after recursive optimization; the above formula gradually adjusts the frequency and time by recursive accumulation, so that they are highly consistent with the results of the environmental influence matrix, and finally achieves the effect of optimal control.

[0062] Finally, in order to ensure that the system can adapt to environmental changes in actual applications, a multi-step feedback adjustment mechanism is introduced. This mechanism continuously corrects the control coefficients in the algorithm based on the feedback data during the test to adapt to the new experimental environment. The feedback adjustment process is based on the error term of the calculated discharge parameters. , the calculation formula is:

[0063]

[0064] in, is the target discharge parameter, is the actual discharge parameter currently calculated. Through feedback adjustment, the formula for updating the control coefficient is as follows:

[0065]

[0066]

[0067]

[0068] In the formula, , Represents the feedback gain coefficient, which is used to adjust the speed of updating the control coefficient;

[0069] By continuously adjusting the control coefficient, it can adapt to different environmental changes and gradually optimize the discharge parameters of the electrostatic generator so that it always matches the current environmental conditions, ensuring high accuracy and consistency of the test.

[0070] S2. Execute electrostatic discharge based on the adjusted discharge parameters of the electrostatic generator to generate electrostatic interference of specified parameters, and simulate the actual scenario of electrostatic discharge to provide an interference source for the anti-interference of the test equipment. The electrostatic discharge interference signal generated further is transmitted to the test equipment and generates a corresponding response signal.

[0071] The adjusted electrostatic generator discharge parameters that determine the specific characteristics of electrostatic discharge, including key indicators such as discharge voltage, frequency, and duration, are used as initialization settings to ensure that subsequent discharge operations are performed in full accordance with the predetermined parameters without any deviation or accident.

[0072] Before performing electrostatic discharge, the electrostatic generator will perform a series of self-inspections and preparations. The primary task of the self-inspection is to ensure that all components of the electrostatic generator are in normal working condition, including the high-voltage generator, discharge needle, insulator, and internal circuit. Only after confirming that all components are fault-free can the next step be entered. In addition, the electrostatic generator will review the status of the experimental environment to ensure that the current temperature, humidity, and electrostatic voltage are within a safe range to prevent accidents during the experiment. Finally, after ensuring that the equipment and environment meet the requirements, the electrostatic generator will initiate necessary safety protection measures, such as setting up protective covers to prevent accidental contact and electric shock, and turning on leakage current protection devices to avoid potential harm to the laboratory environment or operators caused by high-voltage discharge.

[0073] After the inspection and safety protection measures are in place, the electrostatic generator will officially perform the electrostatic discharge operation. First, high-voltage static electricity is generated through the internal high-voltage generator, and the voltage, frequency and waveform of the static electricity are strictly executed in accordance with the parameters set by the central control module. The polarity and frequency of the discharge are adjusted in real time to ensure that the voltage output by the electrostatic generator is both stable and meets the predetermined parameter settings. In terms of discharge path selection, the electrostatic generator will accurately guide the static electricity through the discharge needle to the preset discharge point according to the layout of the test equipment and experimental requirements. These discharge points are usually selected in sensitive parts of the test equipment, such as power input ports, communication interfaces or other key parts that are susceptible to interference, to simulate electrostatic interference that may be encountered in real application environments. During the discharge process, the electrostatic generator strictly controls the duration of the discharge, and generates pulses or continuous discharges at the set frequency and waveform to ensure the stability of the entire discharge process and avoid the test results being affected by voltage fluctuations or other uncertain factors.

[0074] During the discharge process, the electrostatic generator will generate an electrostatic interference signal, which will directly act on the test equipment through the discharge needle. The main forms of electrostatic interference signals include electric field interference, transient current interference and high-frequency noise interference. Electric field interference mainly forms an electromagnetic field around the test equipment through the high-voltage electric field generated by discharge. This electromagnetic field may induce interference voltage or current inside the equipment, thereby affecting the normal function of the equipment. Transient current interference is a high-voltage instantaneous current generated by electrostatic discharge. It is transmitted through the conductive path of the equipment and may cause transient overvoltage or overcurrent in the circuit, resulting in temporary functional abnormality or damage to the equipment. High-frequency noise interference is a high-frequency electromagnetic wave generated during the electrostatic discharge process. These high-frequency noises are transmitted to the equipment circuit through spatial coupling or direct coupling, causing high-frequency interference of the equipment.

[0075] When the electrostatic interference signal is applied to the test equipment, the internal circuit of the equipment will respond to the interference and generate a response signal. The response signal can be manifested as voltage or current fluctuations. This is because the transient current or electric field interference directly acts on the circuit of the equipment, causing the voltage or current to change instantaneously. In addition, the equipment may have short-term functional abnormalities, such as communication interruption, system reset or even equipment failure, which will also be recorded as response signals.

[0076] S3. The response signal and the interference signal are preprocessed, and then deep spectrum analysis is performed. The key features are extracted based on the spectrum comparison results of the interference signal and the response signal. Based on the key features, the anti-interference performance of the test equipment is intelligently evaluated through the neural network model to generate the anti-interference evaluation results. At the same time, based on the feedback of the anti-interference evaluation results, the test process is dynamically optimized through the adaptive fuzzy inference control algorithm.

[0077] The response signal and the interference signal are preprocessed by filtering and denoising. The filtering removes high-frequency noise and low-frequency drift in the signal. A bandpass filter can be used to retain the signal components within the frequency range of interest. The denoising uses a noise suppression algorithm, such as wavelet transform or adaptive filtering, to further remove the interference of environmental noise on the signal. The denoised response signal and the interference signal are amplitude normalized to ensure that the signal amplitude is within a uniform range to facilitate subsequent analysis and comparison.

[0078] The pre-processed interference signal and response signal are subjected to deep spectrum analysis using a nonlinear multiple spectrum transformation algorithm to achieve in-depth analysis of the signal spectrum. The implementation process of the nonlinear multiple spectrum transformation algorithm includes multi-level mathematical transformation to achieve spectrum analysis. The specific implementation process is as follows:

[0079] The preprocessed interference signal and response signal are transformed into a high-dimensional nonlinear spectrum to capture deeper signal characteristics. To achieve this nonlinear spectrum transformation, the preprocessed interference signal and response signal are first converted to the frequency domain. In order to increase the depth of analysis, a multiple nonlinear modulation term is introduced so that the signal not only depends on the linear change in the time domain during the conversion process, but is also controlled by the influence of nonlinear functions.

[0080] Interference signal The spectrum representation of It can be calculated by the following integral formula:

[0081]

[0082] in, is the frequency The spectrum representation of the interference signal below; It's time Interference signal under is the amplitude modulation factor, which controls the intensity of the nonlinear modulation added to the signal during the transformation process; It is the time scale factor, which affects the frequency of sinusoidal modulation and reflects the time characteristics of the signal; is the nonlinear intensity factor, which controls the degree of nonlinear mapping of the signal; is the phase modulation factor, which adjusts the phase response characteristics of the signal; is an imaginary unit, which represents the imaginary part in the complex domain. By introducing nonlinear modulation terms, the interference signal can be In the process of spectrum conversion, nonlinear responses are generated to different frequency components, thus enriching the content of spectrum analysis. By applying complex nonlinear modulation and phase modulation to the signal, the frequency components of the spectrum can reflect more signal characteristics and implicit information.

[0083] Similarly, the response signal Perform the same processing to obtain its spectrum representation :

[0084]

[0085] The nonlinear modulation function here is similar to the interference signal The slight difference, mainly reflected in the interchange of sine and cosine functions, is to introduce different nonlinear characteristics in the spectrum analysis so that the relationship between interference and response can be better identified when comparing spectra.

[0086] Furthermore, by comparing and The spectrum of the interference signal and the response signal at the same frequency are first calculated. The spectrum difference reflects the response characteristics of the device to the interference signal at the same frequency. Spectrum difference feature The calculation formula is as follows:

[0087]

[0088] in, is the spectrum difference feature, indicating that The difference between the interference signal and the response signal reflects the immunity of the equipment and is used for subsequent immunity evaluation. The above formula not only considers the direct difference between the interference signal and the response signal, but also introduces more complex signal change characteristics through nonlinear terms, so that the spectrum difference can reflect deeper response characteristics.

[0089] After extracting the spectrum difference feature, the harmonic components in the signal are further analyzed. Harmonic Features The calculation formula is as follows:

[0090]

[0091] in, is the harmonic feature, indicating that the signal is The spectrum difference of subharmonic components reflects the response characteristics of the equipment under high-frequency interference; is the harmonic order, starting from 2 to the highest harmonic order ; is the highest harmonic order; , The interference signal and the response signal are Frequency The spectrum value under As an attenuation factor of the harmonic order, the contribution of higher-order harmonics to the overall harmonic characteristics is gradually reduced. Through the above formula, the response characteristics of the test equipment at different frequencies and their multiples can be captured, especially the performance of the equipment under high-frequency interference.

[0092] After extracting the spectral difference feature Harmonic characteristics After that, through the nonlinear mapping function Combine these features into a high-dimensional feature vector , which is used for subsequent intelligent evaluation of noise immunity. The purpose of nonlinear mapping is to further enhance the correlation between features and capture potential nonlinear relationships. The process of nonlinear mapping is as follows:

[0093]

[0094] in, It is the final feature vector, which contains multiple spectral feature components after nonlinear transformation and is used for subsequent anti-interference evaluation; nonlinear mapping function The representation ability of features is further improved by performing logarithmic transformation, square transformation, ratio calculation and sine transformation on features; Used to reduce the impact of large spectrum differences; The role of harmonic features in evaluation is emphasized; It directly reflects the ratio characteristics of the interference signal and the response signal; This further introduces nonlinear phase characteristics.

[0095] The final feature vector It is passed as input to a pre-trained neural network model selected according to the specific situation for noise immunity assessment. The neural network model has been trained with a large amount of historical data and can perform complex nonlinear processing on the input feature vector to output an noise immunity score. The output formula of the neural network can be expressed as:

[0096]

[0097] in, is the immunity score output by the neural network, which indicates the device's immunity to interference under specific conditions; It is a pre-trained neural network model, and the input is the feature vector , the output is the immunity score; are the weights of the neural network; is the activation function (such as ReLU or Sigmoid); is the bias term; is the dimension of the feature vector. The neural network model extracts the final immunity evaluation result from the feature vector through multi-layer nonlinear mapping.

[0098] In particular, the test process is dynamically optimized through the adaptive fuzzy inference control algorithm based on the feedback of the evaluation results. The adaptive fuzzy inference control algorithm uses fuzzy logic and adaptive control theory to dynamically adjust the key parameters in the test process to optimize the efficiency and accuracy of the test. The specific implementation steps are as follows:

[0099] The first step is fuzzification processing; according to the immunity evaluation results (immunity scores) output by the neural network model, the continuous immunity scores are mapped to fuzzy sets. The fuzzy sets reflect the immunity levels of the test equipment, such as "low immunity", "medium immunity" and "high immunity"; and the membership function is used to describe the degree to which the immunity scores belong to different fuzzy sets, as follows:

[0100]

[0101]

[0102]

[0103] in, , , Respectively represent the immunity rating The membership corresponding to the fuzzy sets of “low immunity”, “medium immunity” and “high immunity”; , , It is the adjustment factor of the fuzzification function, which controls the steepness of the membership function; , , It is the central value of the fuzzification function and determines the displacement of the membership function.

[0104] The second step is to set fuzzy rules. According to the pre-set fuzzy rule base and the result of fuzzification of the immunity score, the corresponding control strategy is determined. The fuzzy rule base contains adjustment strategies for test process parameters under different immunity levels, such as "increasing electrostatic discharge intensity" and "extending test time". For example:

[0105] if Belong to "low immunity", then "increase the electrostatic discharge voltage" and "extend the test time"; if Belong to "medium immunity", then "moderately increase the electrostatic discharge voltage" and "keep the test time unchanged"; if If it belongs to “high immunity”, then “reduce electrostatic discharge voltage” and “shorten test time”.

[0106] The third step is fuzzy reasoning: according to the fuzzy rule base and the results of fuzzification processing, the test process parameters that need to be adjusted are calculated through the reasoning mechanism. The reasoning process involves the combination and calculation of multiple fuzzy rules, and finally obtains the accurate adjustment value. The specific formula is as follows:

[0107]

[0108] in, It is the increment of the test parameter that needs to be adjusted, such as the increment of the electrostatic discharge voltage or the increment of the test time ; Is the immunity rating In the The membership degree under fuzzy rules; It is a fuzzy rule The corresponding parameter adjustment amount; is the number of fuzzy rules corresponding to the test parameters that need to be adjusted.

[0109] The fourth step is defuzzification processing; the result of fuzzy reasoning is converted into specific test parameter adjustment values ​​through defuzzification processing, such as the increment of electrostatic discharge voltage or the extension of test time. The defuzzification process converts the data into specific values ​​for actual test process adjustment. The defuzzification process uses the centroid method, which is to calculate the weighted average of the membership function.

[0110] The fifth step is to dynamically adjust the test process; based on the defuzzification results, dynamically adjust the parameters of the current test process to ensure that the test can be carried out under optimal conditions.

[0111] In particular, the test data and evaluation results of electrostatic discharge immunity are distributedly stored through blockchain technology, providing a traceable storage chain for subsequent test data calls.

[0112] See attached Figure 2 , which shows a structural diagram of an electrostatic discharge immunity automated test management system provided by an embodiment of the present invention, which is used to implement the above-mentioned electrostatic discharge immunity automated test management method, and the system includes the following parts:

[0113] Environmental data acquisition module, central control module, electrostatic generator module, data test and analysis module and data storage module.

[0114] The environmental data acquisition module collects real-time environmental status data (such as temperature, humidity, and electrostatic voltage) in the experimental environment through a sensor network, and transmits the real-time environmental status data to the central control module after processing through a redundancy mechanism and a verification algorithm.

[0115] The central control module uses the multi-dimensional nonlinear adaptive control algorithm to calculate and adjust the discharge parameters (such as voltage, frequency, duration, etc.) of the electrostatic generator in real time based on the real-time environmental status data processed by the redundant mechanism and verification algorithm. At the same time, based on the evaluation results obtained by the data test analysis module, the test process is dynamically optimized through the adaptive fuzzy inference control algorithm, and the adjusted electrostatic generator discharge parameters are transmitted to the electrostatic generator module.

[0116] The electrostatic generator module performs electrostatic discharge based on the adjusted electrostatic generator discharge parameters to generate electrostatic interference of specified parameters. At the same time, it simulates the actual scenario of electrostatic discharge to provide an interference source for the anti-interference of the test equipment. The electrostatic discharge interference signal generated further is transmitted to the test equipment and generates a corresponding response signal.

[0117] The data test analysis module performs deep spectrum analysis after pre-processing the response signal and the interference signal such as filtering and denoising. It extracts key features (such as signal amplitude, frequency response, phase difference, etc.) based on the spectrum comparison results of the interference signal and the response signal. Based on the key features, it uses a neural network model to intelligently evaluate the anti-interference performance of the test equipment, generate anti-interference evaluation results, and feed back the evaluation results to the central control module.

[0118] The data storage module stores the test data and evaluation results of electrostatic discharge immunity in a distributed manner through blockchain technology, providing a traceable storage chain for subsequent test data calls.

[0119] The sequence of the embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0121] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An electrostatic discharge immunity automated test management method, characterized in that: Includes steps: S1. Real-time acquisition of real-time environmental status data in the experimental environment, the real-time environmental status data is processed by a redundancy mechanism and a verification algorithm, and the processed real-time environmental status data is calculated and adjusted in real time using a multidimensional nonlinear adaptive control algorithm for the discharge parameters of the electrostatic generator; S2. Execute electrostatic discharge based on the adjusted discharge parameters of the electrostatic generator to generate electrostatic interference of specified parameters, and simulate the actual scene of electrostatic discharge to provide an interference source for the immunity of the test equipment. The electrostatic discharge interference signal generated is further transmitted to the test equipment and a corresponding response signal is generated; S3. Preprocess the response signal and the interference signal, perform deep spectrum analysis after preprocessing, extract key features based on the spectrum comparison results of the interference signal and the response signal, and intelligently evaluate the immunity of the test equipment through the neural network model based on the key features to generate the immunity evaluation results. At the same time, based on the feedback of the immunity evaluation results, dynamically optimize the test process through the adaptive fuzzy inference control algorithm; In S1, the multidimensional nonlinear adaptive control algorithm obtains the discharge parameters of the electrostatic generator by constructing a multidimensional nonlinear coupling function, recursively solving the environmental influence matrix, and implementing a multi-step feedback adjustment mechanism; The specific contents of the multidimensional nonlinear adaptive control algorithm are as follows: In the initial processing stage, a multidimensional nonlinear coupling function is used to convert the interaction relationship between environmental parameters into a mathematical model, thereby capturing the complex effects of different environmental factors on electrostatic discharge. The formula of the multidimensional nonlinear coupling function is as follows: ， in, represents the multidimensional nonlinear coupling function, It is Temperature data at each moment; It is Humidity data at each moment; It is Electrostatic voltage data at each moment; , , It is a unitless variable after dimensionless processing, which is achieved by dividing the variable by its corresponding characteristic quantity; is the first control parameter, which is used to adjust the influence weight of the temperature term in the coupling function; It is the coupling strength parameter between temperature and humidity, which affects the nonlinear characteristics of the temperature term; is the second control parameter, which is used to adjust the influence weight of the humidity term in the coupling function; is the coupling strength parameter between humidity and voltage, which affects the nonlinear characteristics of the humidity term; is the third control parameter, which is used to adjust the influence weight of the voltage term in the coupling function; It is the temperature correction parameter in the voltage term, which is used to avoid the voltage being divided by zero and to adjust the relationship between voltage and temperature; is a small constant in the humidity term used to avoid division by zero; The results of the coupling function are used to construct an environmental influence matrix, which is used to quantitatively describe the magnitude of the forces of different environmental parameters. The calculation of any element in the environmental influence matrix is ​​achieved by time integration of the coupling function results, thereby capturing the long-term impact of the dynamic changes of environmental factors on the electrostatic generator. Through the recursive solution method, the discharge parameters of the electrostatic generator are optimized in multiple steps according to the results of the environmental influence matrix. In order to ensure the ability to adapt to environmental changes in practical applications, a multi-step feedback adjustment mechanism is introduced. This mechanism continuously corrects the control coefficients in the algorithm based on the feedback data during the test to adapt to the new experimental environment.

2. The electrostatic discharge immunity automated test management method according to claim 1, characterized in that: In S3, a nonlinear multiple spectrum transformation algorithm is used to perform deep spectrum analysis on the preprocessed interference signal and response signal to achieve in-depth analysis of the signal spectrum.

3. The electrostatic discharge immunity automated test management method according to claim 2, characterized in that: In S3, multiple nonlinear modulation terms are introduced during the implementation of the nonlinear multiple spectrum conversion algorithm, so that the signal in the conversion process not only depends on the linear change in the time domain, but is also controlled by the influence of nonlinear functions.

4. The electrostatic discharge immunity automated test management method according to claim 2, characterized in that: In S3, a deep spectrum analysis is performed after the preprocessing, and key features are extracted based on the spectrum comparison results of the interference signal and the response signal. Specifically, deep spectrum analysis is performed to obtain the spectrum representation of the interference signal and the response signal respectively, and the spectrum difference between the interference signal and the response signal at the same frequency is calculated, and the spectrum difference reflects the response characteristics of the device to the interference signal at the same frequency; after extracting the spectrum difference features, the harmonic components in the signal are further analyzed to extract the harmonic features.

5. The electrostatic discharge immunity automated test management method according to claim 4, characterized in that: In S3, after extracting the spectrum difference feature and the harmonic feature, the spectrum representation of the interference signal, the spectrum representation of the response signal, the spectrum difference feature and the harmonic feature are combined into a high-dimensional feature vector through a nonlinear mapping function for subsequent intelligent evaluation of anti-interference performance.

6. The electrostatic discharge immunity automated test management method according to claim 2, characterized in that: The adaptive fuzzy inference control algorithm utilizes fuzzy logic and adaptive control theory to optimize the efficiency and accuracy of the test by dynamically adjusting key parameters during the test.

7. The electrostatic discharge immunity automated test management method according to claim 6, characterized in that: The specific implementation steps of the adaptive fuzzy reasoning control algorithm are as follows: the first step, fuzzification processing; the second step, fuzzy rule setting; the third step, fuzzy reasoning; the fourth step, defuzzification processing; the fifth step, dynamic adjustment of the test process.

8. An electrostatic discharge immunity automated test management system, used to implement the electrostatic discharge immunity automated test management method according to any one of claims 1 to 7, characterized in that: It includes an environmental data acquisition module, a central control module, an electrostatic generator module, a data test and analysis module, and a data storage module; The environmental data acquisition module collects real-time environmental status data in the experimental environment through the sensor network, and transmits the real-time environmental status data to the central control module after processing through a redundancy mechanism and a verification algorithm; The central control module uses the real-time environmental status data to calculate and adjust the discharge parameters of the electrostatic generator in real time using a multi-dimensional nonlinear adaptive control algorithm, and dynamically optimizes the test process based on the evaluation results obtained by the data test analysis module through an adaptive fuzzy inference control algorithm, and transmits the adjusted electrostatic generator discharge parameters to the electrostatic generator module; The electrostatic generator module performs electrostatic discharge based on the adjusted electrostatic generator discharge parameters to generate electrostatic interference of specified parameters, and simulates the actual scene of electrostatic discharge to provide an interference source for the anti-interference of the test equipment. The electrostatic discharge interference signal generated is further transmitted to the test equipment and generates a corresponding response signal; The data test and analysis module pre-processes the response signal and the interference signal, and then performs deep spectrum analysis, extracts key features based on the spectrum comparison results of the interference signal and the response signal, and intelligently evaluates the anti-interference performance of the test equipment through a neural network model based on the key features, generates an anti-interference evaluation result, and feeds back the evaluation result to the central control module; The data storage module stores the test data of electrostatic discharge immunity in a distributed manner through blockchain technology, providing a traceable storage chain for subsequent test data calls.

Images