Capacitance detection method, system and device and storage medium

Abstract

The invention provides a capacitance detection method, system and device and a storage medium, and relates to the technical field of capacitance detection. The method comprises the following steps: acquiring a capacitance type and a parameter range of a measured capacitor, and matching with a preset resonance library according to the capacitance type and the parameter range to obtain a target resonance inductance parameter; constructing a target LC resonance test circuit based on the target resonance inductance parameter, the capacitor type and the parameter range, and measuring the tested capacitor based on the target LC resonance test circuit to obtain resonance response data; and analyzing the resonance response data to obtain a capacitance performance index. Through the scheme, the capacitor testing efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of capacitance detection technology, and in particular to a capacitance detection method, system, device and storage medium. Background Art

[0002] Capacitor is one of the key components in electronic circuits, and its performance directly affects the reliability and stability of the circuit. Traditional capacitor detection methods mainly include DC resistance test, insulation resistance test, capacity test, dielectric loss test, etc. Although these methods can obtain certain characteristic parameters of capacitors, they have certain limitations. Among them, frequency response analysis (Frequency Response Analysis, FRA) is a relatively advanced capacitor detection method. This method applies excitation signals of different frequencies to the capacitor, measures its response characteristics, and then analyzes the performance of the capacitor. However, the existing frequency response analysis method still has shortcomings in practical applications, mainly reflected in the low test efficiency. Summary of the invention

[0003] The present application provides a capacitance detection method, system, device and storage medium, which can improve the efficiency of capacitance testing.

[0004] In a first aspect, the present application provides a capacitance detection method, the method comprising: Acquire the capacitance type and parameter range of the capacitor under test, and match the capacitance type and parameter range with a preset resonance library to obtain a target resonant inductance parameter; Constructing a target LC resonance test circuit based on the target resonant inductance parameter, the capacitor type and the parameter range, and measuring the measured capacitor based on the target LC resonance test circuit to obtain resonance response data; The resonance response data is analyzed to obtain a capacitance performance index.

[0005] By adopting the above technical scheme, the capacitance detection method proposed in the present application obtains the capacitance type and parameter range of the capacitor to be measured, and matches the capacitance type and parameter range with a preset resonance library to obtain the target resonant inductance parameter, and then constructs a target LC resonance test circuit based on the target resonant inductance parameter, capacitance type and parameter range, and measures the capacitance to be measured based on the target LC resonance test circuit to obtain resonance response data, and finally analyzes the resonance response data to obtain the capacitance performance index, which has significant technical effects and application value.

[0006] First, by building a preset resonance library and matching it according to the capacitance type and parameter range of the capacitor being tested, the target resonant inductor parameters that best match the capacitor being tested can be quickly and accurately obtained. This method avoids the inefficiency and uncertainty of manual selection or blind trial of resonant inductors, ensures the optimal design of the resonant test circuit, and lays a good foundation for subsequent measurements.

[0007] Secondly, the target LC resonance test circuit is constructed based on the target resonant inductance parameters, capacitor type and parameter range, making full use of the amplification characteristics of the resonant circuit. In the resonant state, the impedance characteristics of the capacitor are amplified, and its quality factor, dielectric loss and other characteristics are more obvious, which is conducive to the comprehensive and detailed collection of capacitor response data. At the same time, exclusive test circuits are constructed for capacitors of different types and different parameter ranges to improve the adaptability and accuracy of the measurement.

[0008] Thirdly, by analyzing the resonant response data of the capacitor under test in the target LC resonant test circuit, a quantitative index that fully reflects the performance of the capacitor can be obtained. The resonant response data contains rich information in the frequency domain, time domain, and energy domain, and contains the key characteristics of the capacitor. Through data mining and information fusion technology, the characteristic parameters of each domain are extracted, and a comprehensive evaluation is performed using methods such as principal component analysis, and finally an intuitive and reliable capacitor performance index is obtained. This index condenses complex multi-parameter and multi-domain data into a single quantitative value, greatly simplifying the evaluation criteria for capacitor performance, allowing non-professionals to quickly judge and screen capacitor quality.

[0009] In a second aspect of the present application, a capacitance detection method system is provided, comprising: A data acquisition module, used to acquire the capacitance type and parameter range of the capacitor under test, and match the capacitance type and parameter range with a preset resonance library to obtain a target resonant inductance parameter; A measurement module, configured to construct a target LC resonance test circuit based on the target resonant inductance parameter, the capacitor type and the parameter range, and measure the measured capacitor based on the target LC resonance test circuit to obtain resonance response data; The performance evaluation module is used to analyze the resonance response data to obtain a capacitance performance index.

[0010] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the above method steps.

[0011] In the fourth aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the above method.

[0012] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application can quickly and accurately obtain the target resonant inductance parameters that best match the measured capacitor by constructing a preset resonant library and matching it according to the capacitance type and parameter range of the measured capacitor. This method avoids the inefficiency and uncertainty of manual selection or blind trial of resonant inductance, ensures the optimized design of the resonant test circuit, and lays a good foundation for subsequent measurements.

[0013] 2. This application constructs a target LC resonant test circuit based on the target resonant inductance parameters, capacitor type and parameter range, making full use of the amplification characteristics of the resonant circuit. In the resonant state, the impedance characteristics of the capacitor are amplified, and its quality factor, dielectric loss and other characteristics are more obvious, which is conducive to the comprehensive and detailed collection of the capacitor's response data. At the same time, exclusive test circuits are constructed for capacitors of different types and different parameter ranges, which improves the adaptability and accuracy of the measurement.

[0014] 3. This paper analyzes the resonant response data of the measured capacitor in the target LC resonant test circuit to obtain a quantitative index that fully reflects the performance of the capacitor. The resonant response data contains rich information in the frequency domain, time domain, and energy domain, and contains various key characteristics of the capacitor. Through data mining and information fusion technology, the characteristic parameters of each domain are extracted, and a comprehensive evaluation is performed using methods such as principal component analysis, and finally an intuitive and reliable capacitor performance index is obtained. This index condenses complex multi-parameter and multi-domain data into a single quantitative value, greatly simplifying the evaluation criteria for capacitor performance, allowing non-professionals to quickly judge and screen capacitor quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a capacitance detection method provided in an embodiment of the present application; Figure 2 An architectural diagram of a capacitance detection system provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0016] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0017] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0018] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0019] In order to facilitate understanding of the method and system provided by the embodiments of the present application, before introducing the embodiments of the present application, the background of the embodiments of the present application is first introduced.

[0020] As one of the key components in electronic circuits, the performance of capacitors is directly related to the reliability and stability of the circuit. For a long time, the industry has mainly used traditional methods such as DC resistance test, insulation resistance test, capacity test, and dielectric loss test to detect capacitors. Although these methods can obtain certain characteristic parameters of capacitors, they still have certain limitations and it is difficult to fully evaluate the performance of capacitors.

[0021] With the continuous development of electronic technology, frequency response analysis (FRA) as a relatively advanced capacitor detection method has attracted more and more attention. The basic principle of this method is to apply excitation signals of different frequencies to the capacitor under test, measure the response characteristics of the capacitor at each frequency, and then analyze and evaluate the comprehensive performance of the capacitor. Compared with traditional methods, frequency response analysis is more comprehensive and sensitive, and can capture the dynamic characteristics of the capacitor under different working conditions, providing richer data support for the performance evaluation of the capacitor. However, the existing frequency response analysis method still has certain shortcomings in practical applications, among which the most prominent problem is the low test efficiency. In order to obtain comprehensive and accurate frequency response characteristics, it is necessary to perform frequency sweep test on the capacitor within a wide frequency range. This process is often time-consuming and difficult to meet the needs of large-scale and rapid screening. At the same time, during the test process, it is necessary to select appropriate excitation signals and test conditions according to the type and parameter range of the capacitor, which puts higher requirements on the design of the test system and the technical level of the operator, further increasing the complexity and cost of the test.

[0022] Therefore, how to improve the test efficiency of the frequency response analysis method and simplify the test process while ensuring the test accuracy and reliability has become a key technical problem that needs to be solved in the field of capacitor detection. This requires not only innovation in test principles and methods, but also the use of advanced data processing and analysis technology to realize the intelligence and automation of the test process, and ultimately achieve the goal of evaluating capacitor performance quickly, efficiently and at low cost, providing strong technical support for capacitor quality control, reliability analysis, life prediction, etc.

[0023] After the background introduction of the above content, those skilled in the art can understand the problems existing in the prior art. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0024] Based on the above background technology, please refer to Figure 1 , Figure 1 A flow chart of a capacitance detection method provided in an embodiment of the present application. The system can be implemented by a computer program or can be run as an independent tool application. Specifically, in an embodiment of the present application, the method can be applied on a server, but can also be applied to electronic devices such as servers. A capacitance detection method includes the following steps: S101, obtaining a capacitance type and a parameter range of a capacitor under test, and matching the capacitance type and the parameter range with a preset resonance library to obtain a target resonant inductance parameter; Specifically, when using the capacitance detection method, it is first necessary to obtain the capacitance type and parameter range of the capacitor being tested. The purpose of this step is to clarify the basic characteristics of the test object and provide necessary input information for subsequent resonance matching and test circuit construction. Specifically, the type of capacitor (such as ceramic capacitor, film capacitor, electrolytic capacitor, etc.) and parameter range (such as rated voltage, rated capacity, frequency range, temperature range, etc.) can be obtained through manual input, barcode scanning, chip reading, etc. This information is usually printed on the casing or instruction manual of the capacitor, and can also be obtained from the technical specification document of the capacitor.

[0025] After obtaining the capacitance type and parameter range of the measured capacitor, the next step is to match this information with the preset resonance library to obtain the target resonant inductance parameters. The resonance library is a key feature of this application. It is similar to a parameter query table that pre-stores the optimal resonant inductance parameters corresponding to capacitors of various types and different parameter ranges. These parameters are obtained through a lot of theoretical analysis and experimental accumulation, and represent the optimal design of the resonant circuit under different working conditions.

[0026] Based on the above embodiment, as an optional embodiment, matching the capacitor type and the parameter range with a preset resonance library to obtain a target resonant inductance parameter includes: S201, generating a structured query condition according to the capacitor type and the parameter range; Specifically, the method for generating structured query conditions is as follows: First, the obtained capacitor type and parameter range are split and classified according to the field definition of the resonance library. For example, the capacitor type corresponds to the "cap_type" field in the resonance library, the rated voltage corresponds to the "rated_voltage" field, the rated capacity corresponds to the "rated_capacitance" field, the frequency range corresponds to the "frequency_range" field, and so on. Then, according to the field type and value rules of the resonance library, the classified capacitor parameters are converted into corresponding query condition expressions. For fields of enumeration type, such as capacitor type, its value can be directly used as an equivalent query condition, such as "cap_type='ceramic'". For fields of numeric type, such as rated voltage and rated capacity, range query conditions can be generated based on their values, such as "rated_voltage BETWEEN 10 AND 50". For fields of string type, such as frequency range, fuzzy matching query conditions can be generated based on their values, such as "frequency_rangeLIKE'100kHz%'". Finally, the query conditions of each field are combined through logical operators to form a complete structured query condition, such as "cap_type='ceramic'AND rated_voltage BETWEEN 10 AND 50 ANDrated_capacitance BETWEEN 1 AND 100 AND frequency_range LIKE'100kHz%'".

[0027] By generating structured query conditions based on capacitor type and parameter range, the scattered and heterogeneous capacitor parameter information can be converted into a standard form that can be understood and processed by the resonance library, laying the foundation for subsequent resonant inductor parameter matching.

[0028] S202: Perform conditional search on the preset resonance library based on the query condition to obtain a candidate matching set, and use the top N resonance inductance parameters with the highest matching values ​​in the candidate matching set as the target resonance inductance parameters.

[0029] Specifically, the process of implementing the above steps is as follows: First, use the generated structured query conditions as filter expressions to perform conditional retrieval on the resonance library. Since the query conditions have been structured and fully match the field definitions of the resonance library, they can be directly retrieved efficiently with the help of the database query engine to quickly obtain the candidate matching set. The candidate matching set contains all the inductance parameter records in the resonance library that meet the query conditions, but the matching degree of these records may vary and need further identification and sorting.

[0030] In order to evaluate the degree of match between each record in the candidate matching set and the query condition, the present application introduces the concept of matching value. The matching value is a real number between 0 and 1, which indicates the degree of closeness between the candidate record and the query condition in each attribute. The larger the matching value, the closer the candidate record is to the type and parameter range of the capacitor to be measured, and the higher the matching degree. The calculation of the matching value adopts the weighted average method, scores each attribute of the candidate record separately, and then assigns weights according to the importance of the attribute, and finally adds up the weighted scores of each attribute to obtain the total matching value. For example, the attributes of "cap_type", "rated_voltage", "rated_capacitance", "frequency_range" and other attributes of the candidate record can be scored separately according to factors such as whether the capacitor type is completely consistent, whether the rated voltage and rated capacity fall within the query range, and the overlap of the frequency range, and then assign weights according to the degree of influence of these attributes on the selection of inductor parameters, such as 0.4, 0.3, 0.2, 0.1, and finally add the weighted scores of each attribute to obtain the matching value of the candidate record.

[0031] After calculating the matching values ​​of all records in the candidate matching set, the application sorts the candidate records in descending order of matching values, and then selects the top N (such as the top 3) inductance parameters with the highest matching values ​​as the target resonant inductance parameters. The N value here can be configured according to actual needs, usually between 3 and 5. The purpose of selecting the top N matches rather than the highest match is to provide a certain amount of selection space for the subsequent construction and optimization of the resonant test circuit while ensuring the matching quality, so as to avoid being too rigid in sticking to a single optimal solution and ignoring other possible high-quality solutions.

[0032] S102, constructing a target LC resonance test circuit based on the target resonant inductance parameter, the capacitor type and the parameter range, and measuring the measured capacitor based on the target LC resonance test circuit to obtain resonance response data; Specifically, the process of constructing the target LC resonance test circuit is as follows: First, select a suitable inductor component according to the target resonant inductor parameters, and its rated current, saturation current, quality factor and other parameters must meet the test requirements. Then, connect the selected inductor in series with the capacitor to be measured to form a basic LC resonant circuit. In order to achieve the resonant state, it is also necessary to configure a suitable excitation source and load resistor. The excitation source can be a sine wave signal generator, and its output frequency, amplitude, DC bias and other parameters need to be adjusted according to the frequency range and withstand voltage characteristics of the capacitor to be measured. The resistance value of the load resistor needs to match the impedance characteristics of the capacitor to be measured to achieve maximum power transmission and avoid resonance point offset. In addition, in order to achieve coupling and isolation of the test signal, coupling capacitors and DC blocking capacitors need to be introduced into the test circuit.

[0033] After building the target LC resonance test circuit, the circuit can be used to perform resonance measurement on the capacitor under test. The measurement process is usually divided into two stages: coarse scanning and fine scanning. In the coarse scanning stage, the test system quickly scans the entire frequency range of the capacitor under test, records the amplitude-frequency response and phase-frequency response at each frequency point, and determines the change trend of the capacitor impedance to preliminarily determine the approximate location of the resonance point. In the fine scanning stage, the test system subdivides the frequency range near the resonance point determined by the coarse scanning, and repeatedly measures each subdivided frequency point multiple times, calculates the mean and standard deviation of the impedance at each frequency point, and obtains high-precision key parameters such as resonance frequency, quality factor, and impedance modulus. At the same time, the system will also record the phase mutation, amplitude peak and other characteristics at the resonance point to provide richer information for subsequent data analysis. The entire measurement process can be achieved through automatic control and data acquisition software, and the measurement results are stored in the form of digital resonance response data for subsequent reading and processing.

[0034] Based on the above embodiment, as an optional embodiment, the target LC resonance test circuit is constructed based on the target resonant inductance parameter, the capacitor type and the parameter range, including: S301, determining a resonant inductor element according to the target resonant inductor parameter; Specifically, the process of determining the resonant inductor component is as follows: First, extract the key indicators of the inductor value, quality factor, frequency range, saturation current, etc. from the target resonant inductor parameters. The inductance value reflects the inductance of the inductor and is the main factor determining the resonant frequency; the quality factor reflects the loss characteristics of the inductor, affecting the selectivity and sensitivity of the resonant circuit; the frequency range reflects the applicable frequency range of the inductor, which needs to cover the working frequency band of the measured capacitor; the saturation current reflects the current tolerance of the inductor, which needs to meet the excitation current requirements of the resonant circuit. Based on these indicators, find the inductor model specifications that meet the conditions in the selection manual or device library of the inductor device.

[0035] After determining the inductor model and specifications, it is also necessary to consider the inductor's packaging form, pin arrangement, installation method and other manufacturability factors. In order to facilitate circuit integration and mass production, priority should be given to inductor components with SMD packaging, such as 0603, 0805, 1206 and other specifications; the pin arrangement should match the PCB layout, such as direct plug-in type, SMD type, etc.; the installation method should consider heat dissipation and anti-interference requirements, and a magnetic shielding cover should be provided when necessary. When the packaging and pins meet the requirements, the advantages and disadvantages of different types of inductors in terms of cost performance, delivery cycle, substitutability, etc. should be further compared, and finally an inductor component with excellent performance, economic benefits and stable delivery period should be selected as the resonant inductor.

[0036] The resonant inductor element determined by the above process can highly match the target resonant inductor parameters in key parameters such as inductance and quality factor, and meet the test requirements of the capacitor under test in terms of performance indicators such as frequency range and withstand current.

[0037] S302, constructing a preliminary LC resonance test circuit based on the resonant inductor element according to a conventional series resonance circuit topology; Specifically, the process of constructing a preliminary LC resonance test circuit is as follows: First, according to the pin arrangement and package size of the selected inductor component, draw the PCBFootprint of the inductor package in the PCB Layout software (such as Altium Designer, OrCAD, etc.). Then, place the inductor symbol in the PCB schematic editor and assign the corresponding package Footprint to it. Next, place the measured capacitor symbol at one end of the inductor symbol and connect the signal source symbol (such as a signal generator, oscilloscope, etc.) at the other end. The signal source provides an excitation signal, and the measured capacitor and the resonant inductor form a series resonant circuit. At the connection node between the inductor and the capacitor, place a test probe symbol (such as a coaxial connector, a microstrip probe, etc.) for connecting the test instrument. At the input and output ports of the circuit, place an impedance matching resistor symbol (such as a 50-ohm terminal resistor) to eliminate the influence of signal reflection and standing waves. Finally, layout the entire circuit and generate a PCB production file.

[0038] After completing the PCB design, this application uses conventional PCB processing and assembly technology to produce a physical prototype of the preliminary LC resonance test circuit. PCB processing uses mature photolithography, etching, drilling, surface treatment and other process flows to ensure the circuit board's conductivity, insulation, dimensional accuracy and other performance indicators. PCB assembly uses a combination of conventional SMT patch and manual welding. First, SMT equipment is used to complete the mounting of passive components such as inductors, capacitors, and resistors, and then active components and connectors such as signal sources and test probes are manually welded. The entire production process is very simple. S303, calculating a theoretical resonant frequency and a theoretical characteristic impedance of a resonant test circuit based on the target resonant inductance parameter, the capacitor type, and the parameter range; Specifically, the process of calculating the theoretical resonant frequency and theoretical characteristic impedance is as follows: First, extract the inductance value L and quality factor Q from the target resonant inductor parameters, and extract the capacitance value C and dielectric loss tangent tanδ from the capacitor type and parameter range. Then, according to the frequency characteristic equation of the series resonant circuit, i.e., f=1 / [2π√(LC)], substitute the values ​​of L and C to calculate the theoretical resonant frequency f. It should be noted that since the inductance value L and the capacitance value C are affected by the production process and environmental factors, there are certain deviations. Therefore, the tolerance range of the inductance and capacitance needs to be considered in the calculation to obtain the upper and lower limits of the theoretical resonant frequency.

[0039] After deriving the theoretical resonant frequency, the present application continues to calculate the theoretical characteristic impedance at the resonance point according to the impedance characteristic equation of the series resonant circuit. For an ideal lossless resonant circuit, the characteristic impedance Zc=√(L / C), but in the actual circuit, due to the loss of inductance and capacitance, it is necessary to introduce the quality factor Q of the inductor and the dielectric loss tangent tanδ of the capacitor as correction factors on the basis of the ideal characteristic impedance, and obtain Zc=√(L / C)·(1 / Q+tanδ). Substituting the values ​​of L, C, Q, and tanδ, the theoretical characteristic impedance Zc can be calculated. Similarly, due to the discreteness and uncertainty of the inductor quality factor and the capacitor loss angle, the fluctuation range of the Q value and the tanδ value also needs to be considered when calculating Zc to derive the upper and lower limits of the theoretical characteristic impedance.

[0040] S304, performing a frequency scanning test on the preliminary LC resonance test circuit by means of a vector network analyzer, obtaining an impedance characteristic curve and a resonance point position of the preliminary LC resonance test circuit at different frequencies, and optimizing the preliminary LC resonance test circuit based on the impedance characteristic curve, the resonance point position, the theoretical resonance frequency and the theoretical characteristic impedance to obtain the target LC resonance test circuit.

[0041] Specifically, the process of frequency scanning test and circuit optimization using vector network analyzer is as follows: First, connect the preliminary LC resonance test circuit to the vector network analyzer. The vector network analyzer usually has two test ports, which can measure the dual-port parameters of the circuit. Connect the input end of the resonant circuit to Port 1 of the vector network analyzer, and the output end to Port 2, and connect a 50 ohm matching load in parallel to the input and output ports. Then, set the test parameters of the vector network analyzer, including the frequency range of the scan, the number of scan points, the input power, the intermediate frequency bandwidth, the scanning mode (such as linear scanning, logarithmic scanning), etc. The frequency range should cover the theoretical resonant frequency and extend appropriately at its upper and lower limits to observe the impedance change trend near the resonance point. The more scan points, the more precise the test results, but the longer the test time. The input power should be controlled below the maximum withstand power of the circuit components to avoid damaging the circuit. The intermediate frequency bandwidth affects the test speed and dynamic range, which needs to be weighed according to the specific situation. The scanning mode usually adopts logarithmic scanning, with fewer points in the low frequency band and more points in the high frequency band, which is more consistent with the change law of circuit impedance.

[0042] After the settings are completed, start the vector network analyzer for frequency sweep testing. The instrument will measure the impedance, phase and other parameters of the circuit point by point within the set frequency range, and draw the impedance characteristic curve in real time. By observing the impedance characteristic curve, you can intuitively judge the resonance characteristics of the circuit and find out the key parameters such as the position of the resonance point, peak value, bandwidth, etc. Ideally, the impedance characteristic curve should have a sharp peak at the resonance point. The peak height corresponds to the theoretical characteristic impedance, the peak position corresponds to the theoretical resonant frequency, and the curves on both sides of the peak are symmetrical and steep, indicating high resonance sharpness and good frequency selectivity. However, in actual testing, due to the influence of factors such as circuit component loss, circuit board parasitic effects, and instrument system errors, the impedance characteristic curve often deviates from the ideal situation, which is manifested as the position of the resonance point offset, the peak height reduction, and the curve asymmetry.

[0043] Specifically, the process of generating the target excitation signal and conducting the resonant response test is as follows: First, determine the basic characteristics of the target excitation signal according to the capacitance type (such as ceramic capacitors, film capacitors, mica capacitors, etc.) and parameter range (such as capacitance range, loss range, withstand voltage range, etc.) of the capacitor being tested. For high-frequency, low-loss ceramic capacitors, it is advisable to use a sine wave signal with a higher frequency (such as above 100kHz) and a smaller amplitude (such as below 1V). For medium-low frequency, high-stability film capacitors, it is advisable to use a square wave signal with a moderate frequency (such as around 10kHz) and a suitable amplitude (such as around 5V). For mica capacitors with high withstand voltage and low-frequency loss, it is advisable to use a triangle wave signal with a lower frequency (such as below 1kHz) and a larger amplitude (such as above 10V). Of course, the specific signal parameters need to be further adjusted and optimized according to the capacitance value and loss level of the capacitor.

[0044] After determining the basic characteristics of the target excitation signal, use a signal generator (such as a function signal generator, arbitrary waveform generator, etc.) to generate the corresponding signal waveform. Most modern signal generators have a wealth of signal output functions. The various parameters of the signal can be set through the front panel buttons or computer software, and the output waveform can be monitored in real time. In order to improve the purity and stability of the signal, it is advisable to connect a low-pass filter in series at the output port of the signal generator to filter out high-order harmonics and noise interference. At the same time, in order to isolate the signal generator from the circuit under test and prevent mutual influence, it is advisable to install a high-impedance signal isolator (such as a voltage follower, buffer, etc.) between the signal generator and the LC resonance test circuit.

[0045] The generated target excitation signal is applied to the input of the LC resonance test circuit through an isolator, and an oscilloscope is connected in parallel to the input to monitor the actual input signal waveform and amplitude. Another oscilloscope is connected in parallel to the output of the circuit to observe the circuit's response waveform, i.e., the resonance response data. The oscilloscope should be a high-bandwidth, high-sampling-rate digital oscilloscope with waveform storage, data analysis and other functions. In order to avoid the load effect of the oscilloscope on the test circuit, a high-impedance probe should be installed between the oscilloscope and the circuit.

[0046] Gradually change the frequency of the target excitation signal, and on the basis of covering the parameter range of the measured capacitor, appropriately encrypt the frequency points near the resonant frequency to obtain a series of resonant response data at different frequency points. Since the LC series resonant circuit has a frequency selection characteristic, when approaching the resonant frequency, the amplitude of the circuit output signal will increase sharply and the phase will change suddenly. By observing the change law of the output signal, it can be judged whether the circuit enters the resonant state. The resonant response data such as the frequency domain waveform, time domain waveform, amplitude-frequency curve, phase-frequency curve of the output signal are saved in the oscilloscope, or transmitted to the computer through the oscilloscope bus interface for subsequent data processing and parameter extraction.

[0047] Depending on the type of capacitor, the number of measurements, measurement time, frequency step, etc. of the resonance response data are also different. For capacitors with high stability and high consistency, the number of measurements can be appropriately reduced to speed up the test; for capacitors with high temperature coefficients and obvious aging, it is necessary to repeat the test under different environmental conditions to evaluate the stability of the capacitor performance. The size of the frequency step determines the precision of the test. Under the premise of ensuring efficiency, the smaller the step, the more accurate the test results.

[0048] Based on the above embodiment, as an optional embodiment, the measuring the measured capacitance based on the target LC resonance test circuit to obtain the resonance response data includes: A target excitation signal is generated based on the capacitor type and the parameter range, and the target excitation signal is applied to the LC resonance test circuit to obtain the resonance response data.

[0049] Specifically, the process of generating the target excitation signal and conducting the resonance response test is as follows: First, determine the basic characteristics of the target excitation signal according to the capacitance type (such as ceramic capacitors, film capacitors, mica capacitors, etc.) and parameter range (such as capacitance range, loss range, withstand voltage range, etc.) of the capacitor being tested. For high-frequency, low-loss ceramic capacitors, it is advisable to use a sine wave signal with a higher frequency (such as above 100kHz) and a smaller amplitude (such as below 1V). For medium-low frequency, high-stability film capacitors, it is advisable to use a square wave signal with a moderate frequency (such as around 10kHz) and a suitable amplitude (such as around 5V). For mica capacitors with high withstand voltage and low-frequency loss, it is advisable to use a triangle wave signal with a lower frequency (such as below 1kHz) and a larger amplitude (such as above 10V). Of course, the specific signal parameters need to be further adjusted and optimized according to the capacitance value and loss level of the capacitor.

[0050] After determining the basic characteristics of the target excitation signal, use a signal generator (such as a function signal generator, arbitrary waveform generator, etc.) to generate the corresponding signal waveform. Most modern signal generators have a wealth of signal output functions. The various parameters of the signal can be set through the front panel buttons or computer software, and the output waveform can be monitored in real time. In order to improve the purity and stability of the signal, it is advisable to connect a low-pass filter in series at the output port of the signal generator to filter out high-order harmonics and noise interference. At the same time, in order to isolate the signal generator from the circuit under test and prevent mutual influence, it is advisable to install a high-impedance signal isolator (such as a voltage follower, buffer, etc.) between the signal generator and the LC resonance test circuit.

[0051] The generated target excitation signal is applied to the input of the LC resonance test circuit through an isolator, and an oscilloscope is connected in parallel to the input to monitor the actual input signal waveform and amplitude. Another oscilloscope is connected in parallel to the output of the circuit to observe the circuit's response waveform, i.e., the resonance response data. The oscilloscope should be a high-bandwidth, high-sampling-rate digital oscilloscope with waveform storage, data analysis and other functions. In order to avoid the load effect of the oscilloscope on the test circuit, a high-impedance probe should be installed between the oscilloscope and the circuit.

[0052] Gradually change the frequency of the target excitation signal, and on the basis of covering the parameter range of the measured capacitor, appropriately encrypt the frequency points near the resonant frequency to obtain a series of resonant response data at different frequency points. Since the LC series resonant circuit has a frequency selection characteristic, when approaching the resonant frequency, the amplitude of the circuit output signal will increase sharply and the phase will change suddenly. By observing the change law of the output signal, it can be judged whether the circuit enters the resonant state. The resonant response data such as the frequency domain waveform, time domain waveform, amplitude-frequency curve, phase-frequency curve of the output signal are saved in the oscilloscope, or transmitted to the computer through the oscilloscope bus interface for subsequent data processing and parameter extraction.

[0053] Depending on the type of capacitor, the number of measurements, measurement time, frequency step, etc. of the resonance response data are also different. For capacitors with high stability and high consistency, the number of measurements can be appropriately reduced to speed up the test; for capacitors with high temperature coefficients and obvious aging, it is necessary to repeat the test under different environmental conditions to evaluate the stability of the capacitor performance. The size of the frequency step determines the precision of the test. Under the premise of ensuring efficiency, the smaller the step, the more accurate the test results.

[0054] Based on the above embodiment, as an optional embodiment, generating a target excitation signal based on the capacitor type and the parameter range includes: S401, determining an excitation signal type according to the capacitor type; Specifically, the method for determining the type of excitation signal according to the capacitor type is as follows: For high-frequency, low-loss, and temperature-stable ceramic capacitors, it is advisable to use an excitation signal with a higher frequency (such as above 100kHz), a smaller amplitude (such as below 1V), and a sinusoidal waveform. This is because ceramic capacitors use high-purity ceramic materials and metal electrodes, and have the characteristics of high dielectric constant, low loss tangent, high insulation resistance, and excellent frequency characteristics, which are suitable for working under high-frequency conditions. Using a high-frequency, small-amplitude sinusoidal wave excitation signal that matches the ceramic capacitor can fully stimulate its resonant characteristics, reduce losses and harmonic interference, and obtain true and reliable high-frequency impedance characteristic data.

[0055] For medium-low frequency, high stability, and high-precision film capacitors, it is advisable to use an excitation signal with a moderate frequency (such as around 10kHz), a suitable amplitude (such as around 5V), and a square wave waveform. This is because film capacitors are made of organic polymer materials such as polypropylene, polyester, and polyphenylene ether, and metallized films, and have the characteristics of high capacitance accuracy, flat frequency characteristics, low temperature coefficient, and good reliability, making them suitable for use in audio, communications, and other fields. Using a medium-frequency, amplitude-appropriate square wave excitation signal that matches the film capacitor, its capacitance and loss parameters can be accurately measured, its stability and reliability can be evaluated, and the requirements of precision circuits can be met.

[0056] For mica capacitors with high withstand voltage, high insulation, and low frequency loss, it is advisable to use excitation signals with low frequency (such as below 1kHz), large amplitude (such as above 10V), and triangular waveform. This is because mica capacitors are made of natural mica and metal foil laminated, with high withstand voltage, high insulation resistance, stable capacitance, low loss tangent, etc., suitable for use in high voltage pulse, radio frequency and other occasions. Using a low-frequency, large-amplitude triangular wave excitation signal that matches the mica capacitor, its high voltage breakdown characteristics and low-frequency loss characteristics can be fully evaluated, and its reliability and stability under harsh working conditions can be tested.

[0057] For large-capacity, low-frequency filtering electrolytic capacitors, it is advisable to use an excitation signal with a very low frequency (such as below 50Hz), a large amplitude (such as tens of volts), and a waveform of DC superimposed on AC. This is because electrolytic capacitors are made of electrolytes and metal foils, and use the chemical reaction of electrolytes to form an oxide film. They have the characteristics of large capacity, small size, narrow frequency range, and large leakage current. They are mainly used in low-frequency occasions such as filtering and coupling. Using ultra-low-frequency and large-amplitude mixed signal excitation that matches the electrolytic capacitor can simulate its actual working state, evaluate its key parameters such as capacity, equivalent series resistance, leakage current, and optimize its application in power supply filter circuits.

[0058] For special capacitors in special fields, such as varistors and adjustable capacitors, it is necessary to customize special excitation signal types according to their unique material properties and device structures. For example, for varistors with fast response speed and voltage sensitivity, it is advisable to use spike pulse excitation with fast rising edge and narrow pulse width to evaluate its response time and clamping characteristics. For adjustable capacitors with adjustable voltage and frequency sensitivity, such as silicon piezoresistors and pn junction capacitors, it is advisable to use slow linear scanning signal excitation to evaluate its voltage tuning degree, quality factor and other indicators.

[0059] By specifically selecting an excitation signal that matches the capacitor type, the compatibility of the test signal and the capacitor under test can be guaranteed at the source, signal distortion and power loss can be reduced, and the test signal-to-noise ratio and dynamic range can be improved. A well-matched excitation signal can maximize the inherent resonance characteristics of the capacitor, presenting a sharp, symmetrical, and repeatable resonance peak, making it easier to extract the key parameter indicators of the capacitor.

[0060] S402, determining an excitation signal parameter range according to the parameter range; Specifically, the method for determining the excitation signal parameter range based on the parameter range is as follows: First, obtain the nominal value, minimum value, maximum value, deviation and other information of its key parameters based on the capacitor device's specification sheet, data sheet and other technical documents. For capacitors without a clear parameter range, you can refer to the general indicators of similar products, or estimate the approximate range of its parameters through pre-testing of a small number of samples.

[0061] Taking capacitance parameters as an example, the nominal capacitance of capacitors of different types and specifications ranges from a few pF to tens of thousands of μF, while the capacitance deviation ranges from ±1% to ±20%. In order to cover the capacitance parameter range of the capacitor, the frequency range of the excitation signal should match the impedance range of the capacitor. According to the calculation formula of capacitor impedance, the impedance modulus of the capacitor is inversely proportional to the frequency and inversely proportional to the capacitance. Therefore, for high-frequency capacitors with small capacitance (such as pF level), a higher frequency range (such as MHz level) should be selected; while for low-frequency capacitors with large capacitance (such as mF level), a lower frequency range (such as Hz level) should be selected. On the basis of determining the frequency range, the frequency step and the number of frequency points should also be considered to take into account the test accuracy and efficiency. Generally speaking, near the resonant frequency, the frequency step should be appropriately reduced (such as within 1% of the resonant frequency), and the number of frequency points should be appropriately increased (such as more than 10 points per level) to improve the capture probability and fitting accuracy of the resonant point. Taking the withstand voltage parameter as an example, the rated working voltage of capacitors of different types and packages ranges from a few volts to several thousand volts, and the breakdown voltage is generally more than twice the rated voltage. In order to evaluate the withstand voltage parameter range of the capacitor, the amplitude range of the excitation signal should cover the interval from the rated voltage to the breakdown voltage. For miniaturized and low-voltage capacitors (such as chip ceramic capacitors), a smaller amplitude range (such as less than 1V) should be used to avoid damage to the capacitor due to excessive voltage. For large and high-voltage capacitors (such as high-voltage shunt capacitors), a larger amplitude range (such as thousands of volts) is required, and the voltage rise rate and duration are strictly controlled to conduct reliable withstand voltage testing. Within the amplitude range, a geometric increase method is generally used, and a test point is set every certain voltage (such as 10% or 20% of the rated voltage) until the withstand voltage limit is reached.

[0062] Taking into account the range requirements of various key parameters of the capacitor and the output capacity of the excitation signal source, this application determines the frequency range, amplitude range, and waveform parameter range of the excitation signal to form a multi-dimensional parameter space. In this parameter space, according to certain rules and strategies (such as orthogonal experimental design, intelligent optimization algorithm, etc.), the optimal parameter combination is selected as the specific value of the excitation signal parameter range to guide and constrain the generation of the target excitation signal.

[0063] S403: Generate the target excitation signal based on the excitation signal type and the excitation signal parameter range.

[0064] Specifically, the method for generating the target excitation signal is as follows: First, according to the determined excitation signal type, such as sine wave, square wave, etc., the corresponding signal template is selected in the signal generator or function signal source. Generally, direct digital synthesis (DDS) or arbitrary waveform generation (AWG) and other technologies are used, and digital signal processing chips and digital-to-analog conversion devices are used to flexibly generate various common waveforms. For complex combined waveforms, waveform editing software can be used to superimpose or modulate multiple basic waveforms according to a certain rule to synthesize the target waveform. Then, according to the determined excitation signal parameter range, the value of the signal parameter is set and adjusted on the basis of the signal template. For the frequency parameter, by controlling the frequency word or clock division coefficient of the signal source, the frequency value is changed in sequence within the specified frequency range, and the capacitance response data corresponding to each frequency point is recorded. The setting of the frequency step and the number of frequency points must take into account both resolution and efficiency, and the number of frequency points should be minimized while covering the resonant frequency of the measured capacitor. For the amplitude parameter, by controlling the amplitude word or attenuation resistance of the signal source, the amplitude value is changed in sequence within the specified amplitude range to measure the capacitance characteristic parameters under different excitation voltages. Similarly, the amplitude step and the number of voltage points should be set to minimize the test time and power consumption while ensuring that the test voltage covers the working range of the capacitor under test.

[0065] After correctly setting the excitation signal parameters, the target excitation signal that fully matches the required type and parameters is generated through DDS or AWG technology according to the digital code sequence and timing control rules. To ensure the quality of the excitation signal and power supply capacity, the signal source generally uses a high-stability crystal oscillator and clock circuit, as well as a large dynamic range and low-noise amplifier circuit. At the same time, in order to facilitate the connection and impedance matching with the subsequent circuit, the signal source should be equipped with a complete output interface and protection device, and as close as possible to the capacitor to be measured to reduce transmission loss and signal attenuation. If necessary, an impedance conversion circuit or isolation buffer can be added between the signal source and the capacitor to improve the signal's driving ability and anti-interference.

[0066] By generating a target excitation signal that matches the capacitor characteristics with high fidelity, the intrinsic physical characteristics of the capacitor under test can be stimulated to the greatest extent, and accurate and detailed frequency domain response and impedance spectrum data can be obtained. High-quality excitation signals can not only minimize the nonlinear distortion and noise interference caused by the signal source, but also reflect the true dynamic characteristics of the capacitor under test under different excitation conditions, and comprehensively evaluate its performance parameters and service life. Especially for the needs of wide-band and high-precision capacitor testing, high-quality and pure excitation signals are crucial, which directly determines the credibility and consistency of the test data.

[0067] S103, analyzing the resonance response data to obtain a capacitance performance index.

[0068] Specifically, the process of realizing resonant response data analysis and capacitor performance index calculation is as follows: First, the collected resonant response data is preprocessed, including outlier removal, data smoothing, signal segmentation, etc., to eliminate the influence of measurement errors and random noise. Then, the key characteristic parameters of the resonant response data, such as resonant frequency, quality factor, impedance modulus, phase mutation point, etc., are extracted, and various indicators reflecting capacitor performance, such as dielectric loss factor, insulation resistance, equivalent series resistance, resonant frequency offset, etc., are calculated based on these parameters. In the process of extracting characteristic parameters, it is necessary to consider the correlation between each parameter and capacitor performance, and to take into account the real-time and robustness of the algorithm, and try to select indicators with clear physical meaning, high calculation efficiency, and insensitivity to noise.

[0069] After obtaining a series of indicators reflecting capacitor performance, the next step is to combine these indicators into a quantitative capacitor performance index. Taking into account that different indicators have different weights on capacitor performance, and the performance evaluation standards of capacitors of different types and parameter ranges may also be different, this application uses a method combining weighted average and segmented mapping to calculate the capacitor performance index. Specifically, each performance indicator is first normalized and mapped to the interval of [0, 1] to eliminate the differences in dimension and numerical range. Then, according to the type and parameter range of the capacitor, the weight coefficients of each indicator are queried from the preset weight matrix, and the weighted average is calculated. Finally, according to the position of the weighted average in the piecewise function, it is mapped to the corresponding capacitor performance index, whose numerical range is [0, 100]. The higher the score, the better the capacitor performance.

[0070] Based on the above embodiment, as an optional embodiment, the resonance response data includes frequency domain response data, time domain response data and energy response data; the analysis of the resonance response data to obtain the capacitance performance index includes: S501, extracting features from the frequency domain response data to obtain frequency domain feature data; Specifically, the method for extracting features from frequency domain response data is as follows: First, the collected frequency domain response data is preprocessed, including data format conversion, data alignment, DC removal, etc., to convert it into a standardized form suitable for feature extraction. Then, based on prior knowledge of capacitor frequency domain characteristics and engineering experience, the feature type and feature quantity to be extracted are determined.

[0071] For frequency characteristics, it is usually necessary to extract features such as resonant frequency, half-power bandwidth, and quality factor. Among them, the resonant frequency corresponds to the minimum or maximum point on the impedance spectrum, which characterizes the natural oscillation frequency of the capacitor; the half-power bandwidth refers to the frequency range on the impedance curve that is greater than 2 times the minimum impedance value, reflecting the frequency selection characteristics of the capacitor; the quality factor is defined as the ratio of the resonant frequency to the half-power bandwidth, which measures the loss of the capacitor resonant circuit. The above characteristics can be obtained by searching for extreme points on the impedance spectrum curve, calculating the frequency interval between extreme points, and performing numerical comparisons.

[0072] For impedance characteristics, it is usually necessary to extract the minimum impedance, maximum impedance, impedance phase angle, reactance-resistance ratio and other characteristics of the capacitor. Among them, the minimum impedance and maximum impedance correspond to the impedance modulus at the resonance point and anti-resonance point respectively, reflecting the basic impedance level of the capacitor; the impedance phase angle is the phase angle of the impedance vector, which characterizes the proportional relationship between the reactance and resistance of the capacitor; the reactance-resistance ratio is the ratio of the imaginary part to the real part of the impedance, which measures the ideal degree of the capacitor. These characteristics can be directly derived from the impedance data in complex form through vector operations and trigonometric function transformations.

[0073] After extracting the key features of the frequency domain response data, the feature data needs to be post-processed, such as feature normalization, feature screening, feature dimensionality reduction, etc., to further optimize the quality and efficiency of the feature data. Normalization can eliminate the scale differences of features of different dimensions, facilitating comprehensive evaluation and comparison; feature screening can eliminate redundant or highly correlated features, reducing the complexity of subsequent analysis; feature dimensionality reduction can use methods such as principal component analysis to map multidimensional features to low-dimensional space while retaining the main feature information, and intuitively display the frequency domain characteristics of the capacitor.

[0074] By extracting features from the frequency domain response data, we can extract the key information that best reflects the performance of the capacitor from the massive frequency sampling data, and abstract the complex frequency domain response curve into a few representative characteristic indicators. These characteristic indicators can not only quantitatively describe the response law of the capacitor in different frequency bands and different impedance states, but also comprehensively examine the frequency characteristics of multiple aspects of the capacitor, which is of great significance for judging the actual working performance of the capacitor. Based on the frequency domain characteristic data, further application analysis such as capacitor quality assessment, fault diagnosis, and parameter identification can be carried out, which greatly expands the application space of frequency domain response data.

[0075] S502, extracting features from the time domain response data to obtain time domain feature data; Specifically, feature extraction is performed on the frequency domain response data to obtain frequency domain feature data. Frequency domain response data is usually presented in the form of a two-dimensional curve of impedance amplitude and phase changing with frequency, reflecting the dependence of capacitor impedance parameters on frequency. In this impedance spectrum curve, the resonant characteristics of the capacitor are manifested as local impedance mutations and phase reversals. Therefore, feature extraction of the impedance spectrum curve is mainly to find out key information such as the location, shape and change law of the resonance point.

[0076] The specific method for extracting frequency domain feature data is as follows: First, the impedance amplitude curve is smoothed and filtered to remove high-frequency noise and inconsistent fluctuations in the curve, highlighting the main trend of change. Then the extreme points on the impedance curve are calculated, including the maximum point (i.e., the parallel resonance point) and the minimum point (i.e., the series resonance point), as the reference points of the frequency domain characteristics. At the extreme points, the corresponding frequency and impedance amplitude are taken as the characteristic values ​​of the resonant frequency and resonant impedance. At the same time, the curve segment near the extreme point is locally enlarged, and the geometric parameters such as the slope, curvature and area of ​​the curve segment are calculated to characterize the steep drop speed, sharpness and bandwidth of the impedance at the resonance point.

[0077] On the basis of finding the position of the resonance point, the phase curve is further analyzed. At the resonance point, the phase curve will drop or rise rapidly, forming a phase reversal. By calculating the size and slope of the phase reversal point, important parameters such as the quality factor and ratio of the capacitor and inductor at the resonance point can be obtained. The larger the quality factor, the better the frequency selectivity and low loss characteristics of the capacitor at the resonance point. In addition, in the resonance range, the shape and symmetry of the phase curve are also important characteristic information. By Fourier transforming and mathematically fitting the phase curve, the high-order harmonic components and frequency-related coefficients reflecting the nonlinear and dispersion characteristics of the capacitor can be obtained, which are used to evaluate the complexity and scope of application of the capacitor model.

[0078] The frequency domain feature data extracted by the above method is small in quantity but rich in information, which can highly concentrate the core features of the capacitance impedance spectrum and avoid the complexity and inefficiency of processing massive spectrum data.

[0079] S503, extracting features from the energy response data to obtain energy feature data; Specifically, the specific method for feature extraction of time domain response data is as follows: First, the collected time domain response data is preprocessed, such as denoising, smoothing, segmentation, etc., to eliminate the influence of random interference and trend items. Then, according to the mechanism model of the time domain characteristics of the capacitor, determine the type of feature to be extracted. Common time domain features include: rise time, fall time, peak time, steady-state time, overshoot, undershoot, etc. Among them, the rise time is the time required for the output response to rise from a low level to a high level, which characterizes the charging speed of the capacitor; the fall time is the time required for the output response to fall from a high level to a low level, which characterizes the discharge speed of the capacitor; the peak time is the time required for the output response to reach the first maximum value, reflecting the transient response speed of the capacitor; the steady-state time is the time required for the output response to enter the steady-state interval and remain stable, reflecting the response damping characteristics of the capacitor; the overshoot and undershoot refer to the maximum positive deviation and negative deviation of the output response, respectively, and measure the dynamic response amplitude characteristics of the capacitor. The above time domain features can be obtained by detecting the key points of the time domain response curve, such as inflection points, extreme points, stable points, etc., combined with basic operations such as differentiation and integration.

[0080] The specific method for feature extraction of energy response data is as follows: First, the collected energy response data is preprocessed, such as power calculation, energy integration, temperature rise compensation, etc., to obtain a quantitative description of capacitor energy storage and energy consumption. Then, based on the physical mechanism of capacitor energy response, appropriate characteristic indicators are selected to characterize the energy storage and conversion performance of the capacitor. Common energy characteristics include: specific capacity, specific energy, charge and discharge efficiency, self-discharge rate, loss factor, etc. Among them, specific capacity refers to the amount of charge stored in a unit volume or mass capacitor, reflecting the charge storage capacity of the material; specific energy refers to the energy stored in a unit volume or mass capacitor, reflecting the energy density level of the material; charge and discharge efficiency refers to the ratio of the energy released to the stored energy in a charge and discharge cycle, measuring the energy conversion efficiency of the material; self-discharge rate refers to the ratio of the charge or energy reduced by the capacitor due to leakage loss per unit time to the initial value, reflecting the charge retention capacity of the capacitor; loss factor refers to the ratio of active power loss caused by the equivalent series resistance of the capacitor to reactive power, characterizing the energy dissipation level of the capacitor. These energy characteristics can be obtained by performing mathematical operations such as definite integral, calibration comparison, and percentage calculation on the energy response data.

[0081] By extracting features from the time domain response data and energy response data, key indicators reflecting the dynamic charging and discharging characteristics and energy storage characteristics of the capacitor can be obtained respectively, which makes up for the shortcomings of the single frequency domain features and realizes the multi-dimensional and three-dimensional characterization of the capacitor performance. The time domain feature data highlights the transient response capability of the capacitor to pulse signals, which is crucial for analyzing the dynamic performance of capacitors in applications such as switching power supplies and surge protection. The energy feature data highlights the basic properties of capacitors as energy storage elements, which is of great significance for evaluating the application potential of capacitors in new energy fields such as hybrid power and smart grids. By integrating the time domain and energy features with the aforementioned frequency domain features, a complete capacitor performance index system can be constructed to achieve quantitative evaluation and comprehensive diagnosis of various capacitor performances, and comprehensively improve the analysis and testing level of capacitor devices.

[0082] S504, summarizing and extracting the frequency domain feature data, the time domain feature data, and the energy feature data to obtain key performance indicators, and performing analysis based on the key performance indicators to obtain the capacitance performance index.

[0083] Specifically, the specific method for feature extraction of time domain response data is as follows: First, the original time domain response waveform is subjected to signal conditioning, including amplitude calibration, zero point calibration, smoothing filtering and other preprocessing to improve the purity and consistency of the signal. On this basis, according to the needs of capacitor dynamic characteristics analysis, appropriate feature extraction methods are selected, such as time domain statistical characteristics, wavelet analysis, EMD decomposition, etc., to mine the physical information contained in the time domain data.

[0084] For step response, commonly used time domain features include rise time, peak time, overshoot, steady-state error, etc. Among them, rise time refers to the time required for the response curve to rise from the initial state to 90% of the final amplitude, reflecting the charging speed; peak time is the moment when the first wave peak is reached, reflecting the rapidity of the response; overshoot indicates the degree to which the peak value exceeds the steady-state value, reflecting the damping characteristics of the response; steady-state error measures the deviation of the steady-state value from the input value, reflecting the DC accuracy. These features can be obtained by detecting the key inflection points of the waveform and calculating the change amount and rate of change of the amplitude.

[0085] For pulse response, the time domain features commonly extracted include pulse width, pulse amplitude, pulse energy, repetition frequency, etc. Among them, pulse width is the duration of a single pulse, reflecting the discharge time constant; pulse amplitude is the maximum value of the pulse, reflecting the discharge capacity; pulse energy is represented by the product of pulse amplitude and width, measuring the energy level of a single discharge; repetition frequency is the number of pulses occurring per second, reflecting the charge and discharge frequency of the capacitor. The above features can be extracted through algorithms such as detecting edge jumps of pulse waveforms, accumulating the product of pulse amplitude and width, and counting the number of pulses per unit time.

[0086] For different types of time-domain feature data, after feature extraction, feature post-processing is required, such as feature normalization and dimensionless processing, to eliminate differences in different dimensions and scales for easy comparison and comprehensive evaluation. Normalization can map the eigenvalues ​​to the range of 0 to 1, which not only retains the relative size of the original values ​​but also simplifies the value range. Dimensionless processing can express the eigenvalues ​​in physical parameters such as time constants and damping coefficients, highlighting their physical meaning and enhancing interpretability.

[0087] By extracting the features of the time domain response data, the key parameters reflecting the dynamic performance of the capacitor can be extracted from the complex time domain data, and the time domain waveform can be transformed into quantitative behavioral characteristics, which greatly condenses the information content of the original data and facilitates the characterization and evaluation of the dynamic response capability of the capacitor. Based on the time domain feature data, further control and optimization such as capacitor parameter identification, pole configuration, and state monitoring can be achieved, playing an important role in circuit simulation, state estimation and other fields. Time domain feature extraction technology can not only simplify the modeling and control process, accelerate the simulation and optimization process, but also help to evaluate the dynamic quality of the capacitor online and in real time, and warn of potential performance degradation and failure risks.

[0088] Based on the above embodiment, as an optional embodiment, the key performance indicators are analyzed to obtain the capacitance performance index, including: S601, using a principal component analysis method to perform comprehensive dimension reduction on the key performance indicators to obtain a comprehensive evaluation index of capacitor performance; Specifically, the specific method for summarizing and extracting frequency domain feature data, time domain feature data, and energy feature data is as follows: First, the feature data of each physical domain is normalized to eliminate the dimension and order of magnitude differences, and to achieve equal weight representation of different features. Then, data mining methods such as feature selection or feature dimension reduction are used to screen out the key features that contribute the most to the comprehensive performance of the capacitor from many features. Common feature selection methods include methods based on statistical indicators, such as variance analysis, correlation analysis, etc., and methods based on learning models, such as decision trees, random forests, etc. By calculating the correlation or importance of each feature with the capacitor performance, several features with the greatest impact on the performance and the least correlation with each other are selected as key performance indicators. These key indicators usually contain typical features of different physical domains, such as the resonant frequency in the frequency domain, the rise time in the time domain, the specific capacity in the energy domain, etc. Each key indicator represents a type of physical mechanism that has a significant impact on performance. Using feature selection methods to obtain a few key indicators can greatly reduce the feature dimension and simplify the complexity of subsequent performance analysis.

[0089] After obtaining the key performance indicators, this application further conducts a comprehensive analysis of the capacitor performance based on these indicators to obtain a capacitor performance index that reflects the overall performance of the capacitor. The comprehensive analysis method is used to evaluate the capacitor performance because, although the number of key performance indicators is small, they are still multivariate variables and are difficult to compare intuitively. They need to be reduced to a comprehensive evaluation index through reasonable mathematical methods. The principal component analysis method is used here to reduce the dimension of the key performance indicators and extract the principal component scores of the capacitor performance as the capacitor performance index. Principal component analysis is a commonly used unsupervised linear dimensionality reduction method that converts multiple related variables into a few unrelated principal components through orthogonal transformation. These principal components are linear combinations of the original variables, which can retain the variance information of the original data to the greatest extent. The key performance indicators are processed by principal component analysis. The specific steps are as follows: first, standardize the indicator data so that the mean of each indicator is 0 and the variance is 1, eliminating the impact of the indicator dimension; then calculate the covariance matrix between the indicators, perform eigenvalue decomposition, and obtain the eigenvalues ​​and eigenvectors of the covariance matrix; sort the eigenvalues ​​from large to small, and take the eigenvectors corresponding to the first few largest eigenvalues ​​as the coefficient vector of the principal component; use the principal component coefficients to linearly transform the original indicator data to obtain the scores of the capacitor samples on each principal component; take the first principal component score with the largest variance contribution rate as the capacitor performance index, or take the weighted sum of the first few principal component scores as the performance index. Principal component analysis makes full use of the correlation information between indicators and replaces the original high-dimensional indicators with a few principal components, which not only reduces the data dimension but also retains the information content of the indicators to the maximum extent. The capacitor performance index based on the principal component score comprehensively reflects the performance of the capacitor in key physical indicators and is a quantitative indicator for evaluating the comprehensive performance of the capacitor.

[0090] S602: Perform weighted processing on the comprehensive evaluation index to obtain the capacitance performance index.

[0091] Specifically, the specific method of summarizing and extracting key performance indicators is as follows: First, the characteristic data of different physical domains are normalized, and each characteristic data is uniformly mapped to the [0, 1] interval to eliminate the influence of dimension and order of magnitude. Then, based on the mechanism model and empirical knowledge of each physical domain, the characteristic indicators that have an important impact on the performance of the capacitor are preliminarily screened out, and the physical meaning analysis and data correlation verification are carried out to determine the candidate key performance indicators. On this basis, statistical methods such as variance analysis and independent sample t-test are used to analyze the discrimination and significant differences of different candidate indicators, and further select the optimal key performance indicator combination for distinguishing capacitors with different performance levels. Common key performance indicators include: resonant frequency, quality factor, equivalent series resistance, dielectric loss tangent, leakage current, insulation resistance, specific capacitance, energy storage density, charge and discharge efficiency, thermal impedance, etc. These indicators comprehensively reflect the frequency characteristics, loss characteristics, impedance characteristics, dielectric characteristics, charge storage characteristics and other aspects of the capacitor. Test analysis shows that the above key performance indicators can effectively distinguish capacitor devices of different types, materials and structures, and have a significant role in judging the performance level and quality grade of the capacitor.

[0092] After obtaining the key performance indicators, this application further conducts a comprehensive analysis of these indicators, and finally obtains a single numerical index that reflects the comprehensive performance of the capacitor, namely the capacitor performance index. The reason for extracting the comprehensive performance index based on the key performance indicators is that there are generally multiple key performance indicators, and the physical meaning, importance, and constraint relationship of each indicator are different. It is often difficult for people to quickly judge the comprehensive effect of the advantages and disadvantages of multiple indicators, and an authoritative and easy-to-understand single comprehensive indicator is needed to intuitively reflect the performance level of the capacitor. The introduction of the capacitor performance index can characterize the comprehensive quality of the capacitor with a quantitative and comparable dimensionless value, which not only accurately conveys product performance information, but also facilitates production control and customer selection. In addition, by analyzing the composition of the capacitor performance index, the key shortcomings of capacitor performance can also be diagnosed, the capacitor design and production process can be optimized, and the targeted improvement of capacitor performance can be achieved.

[0093] Specifically, the principal component analysis method is adopted to perform dimensionality reduction on multiple key performance indicators and extract a comprehensive evaluation index for the capacitance performance. Principal component analysis is a commonly used unsupervised linear dimensionality reduction method. It transforms the original variables into linearly independent new variables through orthogonal transformation, and selects the first few variables as the principal components according to the variance magnitudes of the new variables to retain as much information of the original data as possible. In this application, n key performance indicators are used as the original variables in the n-dimensional space. Through principal component analysis, m (m < n) mutually orthogonal principal components are obtained as the m evaluation indicators for the comprehensive capacitance performance. These m evaluation indicators can explain most of the variances of the original indicator data, centrally reflect the main information of the capacitance performance, and remove the information redundancy and related interference of the original indicators. Generally speaking, the greater the variance contribution rate of the first few principal components, the stronger the characterization ability of the corresponding comprehensive evaluation index for the capacitance performance. Through principal component analysis, not only the dimensionality reduction and compression of the key performance indicators are achieved, but also the comprehensive evaluation index has a definite statistical significance and discriminant efficacy.

[0094] After obtaining the m comprehensive evaluation indicators, this application uses the weighted average method to fuse them into the final capacitance performance index. That is, comprehensively considering the differences in the importance of the m evaluation indicators, a certain weight coefficient is assigned to each indicator, and the capacitance performance index is obtained through weighted average. Among them, the weight coefficients of the evaluation indicators can be comprehensively determined according to factors such as their variance contribution rates, physical meanings, and expert experiences. The larger the weight coefficient, the greater the influence of the evaluation indicator on the capacitance performance and the greater its component in the performance index. The weighted average of the m evaluation indicators is quantized on a percentage scale to obtain the capacitance performance index in the range of [0, 100]. The larger this index, the better the comprehensive performance of the capacitor and the higher the quality level. The capacitance performance index quantifies the performance level of the capacitor in the form of a single value, which is more intuitive, easier to understand and apply than the discrete key performance indicators.

[0095] Please refer to Figure 2 , Figure 2 which is a schematic diagram of a capacitance detection system architecture provided by an embodiment of this application. The capacitance detection system may include: A data acquisition module 1, configured to acquire the capacitance type and parameter range of the capacitor under test, and match the capacitance type and the parameter range with a preset resonance library to obtain the target resonance inductance parameter; A measurement module 2, configured to construct a target LC resonance test circuit based on the target resonance inductance parameter, the capacitance type, and the parameter range, and measure the capacitor under test based on the target LC resonance test circuit to obtain resonance response data; A performance evaluation module 3, configured to analyze the resonance response data to obtain the capacitance performance index.

[0096] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0097] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302 or end-to-end wireless communication.

[0098] The communication bus 302 is used to realize the connection and communication between these components.

[0099] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0100] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0101] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field~Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0102] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read~Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage system located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a capacitance detection method.

[0103] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program storing the capacitance detection method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application. In the above embodiments, the descriptions of each embodiment have their own emphasis, and for the parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] In the several implementations provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the system embodiments described above are only schematic, such as the division of modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, indirect coupling or communication connection of systems or modules, which can be electrical or other forms.

[0105] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] The present application also provides a computer storage medium that can store multiple instructions, which are suitable for being loaded and executed by a processor as described above. Figure 1 The capacitance detection method of the embodiment shown in the figure can be found in the specific execution process. Figure 1 The specific description of the illustrated embodiment will not be repeated here.

[0107] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0108] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0109] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0110] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A capacitance detection method, characterized in that: The method comprises: Obtaining the capacitance type and parameter range of the capacitor under test, and matching the capacitance type and parameter range with a preset resonance library to obtain a target resonant inductance parameter; Constructing a target LC resonance test circuit based on the target resonant inductance parameter, the capacitor type and the parameter range, and measuring the measured capacitor based on the target LC resonance test circuit to obtain resonance response data; The resonance response data is analyzed to obtain a capacitance performance index.

2. The method according to claim 1, characterized in that The matching of the capacitor type and the parameter range with a preset resonance library to obtain a target resonant inductance parameter includes: generating a structured query condition according to the capacitor type and the parameter range; Based on the query condition, a conditional search is performed on the preset resonance library to obtain a candidate matching set, and the top N resonance inductance parameters with the highest matching values ​​in the candidate matching set are used as the target resonance inductance parameters.

3. The method according to claim 1, characterized in that The constructing a target LC resonance test circuit based on the target resonant inductance parameter, the capacitor type and the parameter range includes: Determining a resonant inductor element according to the target resonant inductor parameter; Constructing a preliminary LC resonance test circuit based on the resonant inductor element according to a conventional series resonance circuit topology; Calculating a theoretical resonant frequency and a theoretical characteristic impedance of a resonant test circuit based on the target resonant inductance parameter, the capacitance type, and the parameter range; A frequency scanning test is performed on the preliminary LC resonance test circuit by using a vector network analyzer to obtain the impedance characteristic curve and the resonance point position of the preliminary LC resonance test circuit at different frequencies, and the preliminary LC resonance test circuit is optimized based on the impedance characteristic curve, the resonance point position, the theoretical resonance frequency and the theoretical characteristic impedance to obtain the target LC resonance test circuit.

4. The method according to claim 1, characterized in that The measuring the measured capacitance based on the target LC resonance test circuit to obtain resonance response data includes: A target excitation signal is generated based on the capacitor type and the parameter range, and the target excitation signal is applied to the LC resonance test circuit to obtain the resonance response data.

5. The method according to claim 4, characterized in that The generating a target excitation signal based on the capacitor type and the parameter range includes: Determining the type of excitation signal according to the type of capacitance; Determining an excitation signal parameter range according to the parameter range; The target excitation signal is generated based on the excitation signal type and the excitation signal parameter range.

6. The method according to claim 1, characterized in that The resonance response data includes frequency domain response data, time domain response data and energy response data; The analyzing the resonant response data to obtain a capacitance performance index includes: Performing feature extraction on the frequency domain response data to obtain frequency domain feature data; Extracting features from the time-domain response data to obtain time-domain feature data; Extracting features from the energy response data to obtain energy feature data; The frequency domain characteristic data, the time domain characteristic data and the energy characteristic data are summarized and extracted to obtain key performance indicators, and analysis is performed based on the key performance indicators to obtain the capacitance performance index.

7. The method according to claim 6, characterized in that The key performance indicators are analyzed to obtain the capacitance performance index, including: The principal component analysis method is used to perform comprehensive dimension reduction on the key performance indicators to obtain a comprehensive evaluation index of capacitor performance; The comprehensive evaluation index is weighted to obtain the capacitance performance index.

8. A capacitance detection system, characterized in that: The system comprises: A data acquisition module, used to acquire the capacitance type and parameter range of the capacitor under test, and match the capacitance type and parameter range with a preset resonance library to obtain a target resonant inductance parameter; A measurement module, configured to construct a target LC resonance test circuit based on the target resonant inductance parameter, the capacitor type and the parameter range, and measure the measured capacitor based on the target LC resonance test circuit to obtain resonance response data; The performance evaluation module is used to analyze the resonance response data to obtain a capacitance performance index.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory and a transceiver, the memory is used to store instructions, the transceiver is used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

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