Energizing control method and system for thin film capacitor, medium and program product

Abstract

The invention discloses a film capacitor enabling control method and system, a medium and a program product, and the method comprises the steps: determining current sampling data and voltage sampling data of a film capacitor in a first time period; calculating an equivalent impedance sequence within a first time length, and determining distribution characteristics of impedance components under different frequencies; dividing a plurality of frequency band intervals, and calculating an impedance change rate matrix in each frequency band interval; determining energy transmission efficiency, and determining a target frequency band interval; generating a compensation coefficient, and adjusting the amplitude and the frequency by using the compensation coefficient to obtain a target sine-wave voltage; performing energizing processing on the thin-film capacitor by adopting the target sine-wave voltage within the remaining time of the energizing duration, and calculating an instantaneous power factor in real time; and when the stability of the instantaneous power factor reaches a preset condition and the leakage current is reduced below a preset threshold value, generating an enabling completion signal. According to the invention, the adaptability between the energizing effect and the actual demand of the thin film capacitor is improved, and the energizing effect is further improved.

Description

Technical Field

[0001] This application belongs to the field of enabling technologies, and particularly relates to a method, system, medium, and program product for enabling control of thin-film capacitors. Background Art

[0002] During the use of thin-film capacitors, their capacitance values will deviate with changes in the usage environment and time. This deviation will lead to a decline in the performance of the capacitors, thereby affecting the stability and reliability of the entire circuit system. Therefore, it is necessary to perform enabling processing on thin-film capacitors to restore and stabilize their capacitance values.

[0003] In related technologies, the amplitude and frequency of a sine wave voltage can be set by a user to generate a corresponding sine wave voltage, and after expanding the voltage amplitude change range, enabling processing is performed on the thin-film capacitor. At the same time, the user is allowed to set the enabling duration to control the entire enabling process.

[0004] However, the above-mentioned related technologies use voltage amplitude, frequency, and enabling duration as basic parameters, without considering the real-time response characteristics of thin-film capacitors during the enabling process to dynamically adjust the enabling parameters, resulting in a reduction in the adaptability between the enabling effect and the actual requirements of thin-film capacitors, and a reduction in the enabling effect. Summary of the Invention

[0005] This application provides a method, system, medium, and program product for enabling control of thin-film capacitors, which is used to improve the adaptability between the enabling effect and the actual requirements of thin-film capacitors, thereby improving the enabling effect.

[0006] In a first aspect, this application provides a method for enabling control of thin-film capacitors, which receives an enabling parameter configuration instruction, and the enabling parameter configuration instruction includes the amplitude, frequency, and enabling duration of a preset sine wave voltage; Perform enabling processing on the thin-film capacitor for a first duration to obtain current sampling data and voltage sampling data of the thin-film capacitor within the first duration, where the first duration is less than the enabling duration; Calculate an equivalent impedance sequence within the first duration based on the current sampling data and voltage sampling data, and perform Fourier analysis on the equivalent impedance sequence to obtain the distribution characteristics of impedance components at different frequencies; Divide the distribution characteristics into several frequency band intervals, and calculate the impedance change trend within each frequency band interval to obtain an impedance change rate matrix; Determine the energy transfer efficiency of each frequency band interval based on the impedance change rate matrix, and determine the target frequency band interval with the maximum energy transfer efficiency; Generate a compensation coefficient according to the frequency range and impedance characteristics of the target frequency band interval, and use the compensation coefficient to adjust the amplitude and frequency of the sine wave voltage to obtain a target sine wave voltage; During the remaining time of the energization duration, the thin-film capacitor is energized with a target sine-wave voltage, and the instantaneous power factor is calculated in real time; When the stability of the instantaneous power factor reaches a preset condition and the leakage current drops below a preset threshold, an energization completion signal is generated.

[0007] By adopting the above technical solution, after obtaining the basic energization parameters by receiving the energization parameter configuration instruction, preliminary energization is performed within the first duration and current and voltage data are collected. Using these data to calculate the equivalent impedance sequence and perform Fourier analysis, the distribution characteristics of the impedance components at different frequencies can be obtained. Dividing the distribution characteristics into frequency band intervals and calculating the impedance change trend can reflect the dynamic characteristics of energy transmission within each frequency band. Based on the impedance change rate matrix, the energy transmission efficiency of each frequency band is determined, and the optimal target frequency band interval can be found. According to the characteristics of the target frequency band, a compensation coefficient is generated and the sine-wave parameters are adjusted, so that the energization process always works within the optimal frequency band range. In the remaining time, the energization is performed with the optimized target sine-wave voltage, and the energization completion status is judged by monitoring the instantaneous power factor and the leakage current, which not only improves the energization efficiency, but also avoids over-energization, improves the adaptability between the energization effect and the actual requirements of the thin-film capacitor, and further improves the energization effect.

[0008] Combined with some embodiments of the first aspect, in some embodiments, calculating the equivalent impedance sequence within the first duration based on the current sampling data and the voltage sampling data specifically includes: Calculating the instantaneous impedance value according to the current sampling data and the voltage sampling data to obtain an instantaneous impedance sequence; Performing a moving average process on the instantaneous impedance sequence to obtain a smoothed impedance sequence; Calculating the impedance phase angle based on the smoothed impedance sequence, and obtaining the equivalent impedance sequence according to the impedance phase angle and the impedance amplitude.

[0009] By adopting the above technical solution, an instantaneous impedance sequence is calculated from the current and voltage sampling data, and the moving average process is used to eliminate the influence of random fluctuations and interference signals, making the impedance data smoother. On this basis, the impedance phase angle is calculated, and the equivalent impedance sequence is obtained in combination with the impedance amplitude. This sequence comprehensively reflects the impedance characteristics of the thin-film capacitor during the energization process. This calculation method takes into account the amplitude and phase information of the impedance, reduces the limitations of calculating only using the amplitude, and improves the accuracy of impedance characteristic representation. The influence of data fluctuations on subsequent analysis is reduced through the moving average process, making the frequency band division and energy transmission efficiency calculation based on the equivalent impedance sequence more reliable, and providing a more accurate basis for determining the optimal energization parameters.

[0010] Combined with some embodiments of the first aspect, in some embodiments, dividing the distribution characteristics into several frequency band intervals specifically includes: Calculate the power spectral density of the impedance distribution characteristics to obtain a power distribution curve; Identify peak points and valley points on the power distribution curve, and divide the region between adjacent peak points and valley points into a frequency band interval; Calculate the power proportion within each frequency band interval, and merge the frequency band intervals according to the power proportion to obtain a set of target frequency band intervals.

[0011] By adopting the above technical solution, calculating the power spectral density of the impedance distribution characteristics to obtain a power distribution curve, and dividing the frequency band intervals according to the peak points and valley points on the curve, this division method fully considers the natural segmentation characteristics of the power distribution. By calculating the power proportion within each frequency band interval and performing merging, a more representative set of target frequency band intervals can be obtained. This frequency band division method based on the power distribution characteristics reduces the irrationality that may be brought about by artificially setting fixed frequency band boundaries, making the division result more in line with the actual energy transmission characteristics of the thin film capacitor. Through reasonable frequency band division and merging, both the main energy transmission channels are retained, and the subsequent analysis and calculation process is simplified, improving the efficiency and accuracy of the optimization of the energizing parameters.

[0012] Combined with some embodiments of the first aspect, in some embodiments, when the stability of the instantaneous power factor reaches a preset condition and the leakage current drops below a preset threshold, after generating an energizing completion signal, the method further includes: Calculate the instantaneous impedance change trajectory of the thin film capacitor during the energizing process; Perform wavelet decomposition on the instantaneous impedance change trajectory to obtain multi-scale characteristic coefficients; Construct a stability evaluation index based on the multi-scale characteristic coefficients, and determine the minimum observation period according to the stability evaluation index; Collect a capacitance sampling sequence and a temperature sampling sequence within the minimum observation period; Perform cross-correlation analysis on the capacitance sampling sequence and the temperature sampling sequence to obtain a temperature influence coefficient; Perform decoupling compensation on the capacitance sampling sequence according to the temperature influence coefficient to obtain a capacitance change curve under isothermal conditions; Calculate the fluctuation envelope of the capacitance change curve, and extract the characteristic parameters of the envelope; Judge whether secondary energizing processing is required based on the characteristic parameters.

[0013] By adopting the above technical solution, the instantaneous impedance change trajectory during the calculation empowerment process is calculated and wavelet decomposition is performed to obtain coefficients reflecting different scale features, and a stability evaluation index is constructed to determine the minimum observation period. Capacitance and temperature data are collected within the observation period and cross-correlation analysis is carried out to obtain the influence coefficient of temperature on capacitance. Using this coefficient to decouple and compensate the capacitance sampling sequence reduces the influence brought by temperature changes and obtains the capacitance change curve under isothermal conditions. By calculating the fluctuation envelope and extracting characteristic parameters, the stability of capacitance changes can be accurately evaluated. This monitoring and evaluation method considering temperature effects improves the accuracy of stability evaluation, reduces misjudgments caused by temperature fluctuations, makes the decision-making of secondary empowerment processing more reliable, and ensures the stability of the empowerment effect.

[0014] Combined with some embodiments of the first aspect, in some embodiments, based on the characteristic parameters, it is determined whether secondary empowerment processing is required, specifically including: Calculate the variance and mean of the fluctuation characteristic parameters; Generate a stability evaluation value according to the variance and mean; When the stability evaluation value exceeds the preset range, it is determined that secondary empowerment processing is required; When the stability evaluation value is within the preset range, it is determined that secondary empowerment processing is not required.

[0015] By adopting the above technical solution, the variance and mean of the fluctuation characteristic parameters are calculated to generate a stability evaluation value, and based on the evaluation value, it is determined whether secondary empowerment processing is required. This evaluation method based on statistical characteristics can comprehensively reflect the stable state of the capacitor during the empowerment process. The variance reflects the degree of dispersion of the fluctuation, and the mean reflects the central tendency of the fluctuation. By comparing the stability evaluation value with the preset range, the system can accurately identify whether the capacitor has reached the ideal empowerment state. This judgment mechanism avoids unnecessary secondary empowerment and can also timely detect the situation of insufficient empowerment, enabling the empowerment process to ensure sufficiency without causing over-empowerment, thereby improving the empowerment efficiency, reducing energy consumption, and prolonging the service life of the capacitor.

[0016] Combined with some embodiments of the first aspect, in some embodiments, before determining whether secondary empowerment processing is required based on the characteristic parameters, the method further includes: Perform spectral analysis on the capacitance change curve to obtain spectral distribution characteristics; Calculate the dispersion and concentration degree of the spectral distribution characteristics; Generate a frequency-domain stability index according to the dispersion and concentration degree; Perform combined calculation on the frequency-domain stability index and the characteristic parameters to obtain a comprehensive evaluation value.

[0017] By adopting the above technical solution, through spectral analysis of the capacitance change curve, calculating the dispersion and concentration of the spectral distribution characteristics to generate the frequency-domain stability index, and combining it with the characteristic parameters to obtain the comprehensive evaluation value. This evaluation method combining time-domain and frequency-domain characteristics can characterize the stability state of the capacitor from multiple dimensions. Spectral analysis can reveal the periodic fluctuation components in the capacitance change, and the dispersion and concentration reflect the distribution characteristics of these fluctuation components. By combining the frequency-domain stability index with the time-domain characteristic parameters, the system obtains a more comprehensive basis for stability evaluation, improves the accuracy and reliability of the energy enabling state assessment, and reduces the uncertainty in the assessment process.

[0018] Combined with some embodiments of the first aspect, in some embodiments, the frequency-domain stability index and the characteristic parameters are combined and calculated to obtain the comprehensive evaluation value, which specifically includes: Weight the frequency-domain stability index and the characteristic parameters according to the preset weight coefficient; Calculate the linear combination of the weighted frequency-domain stability index and the characteristic parameters; Use normalization processing to standardize the linear combination to obtain the comprehensive evaluation value.

[0019] By adopting the above technical solution, the frequency-domain stability index and the characteristic parameters are weighted by the preset weight coefficient, the weighted linear combination is calculated and normalized to obtain the comprehensive evaluation value. This evaluation method based on weighted combination enables the system to reasonably assign weights according to the importance of different indicators, avoiding the evaluation deviation that may be caused by simple averaging. Through normalization processing, indicators with different dimensions are converted to a unified standard scale for comparison, eliminating the influence of dimension differences. This standardized comprehensive evaluation mechanism improves the comparability and interpretability of the evaluation results, makes the assessment of the energy enabling state more scientific and reasonable, and provides a more reliable basis for the energy enabling control decision.

[0020] In a second aspect, an embodiment of the present application provides a thin-film capacitor energy enabling control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the system, enabling the above system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] Fourthly, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, it causes the system to execute the method described in any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a method for controlling the energization of a thin film capacitor. After obtaining the basic energization parameters by receiving an energization parameter configuration instruction, preliminary energization is performed within the first time period and current and voltage data are collected. By using these data to calculate the equivalent impedance sequence and performing Fourier analysis, the distribution characteristics of the impedance components at different frequencies can be obtained. Dividing the distribution characteristics into frequency band intervals and calculating the impedance change trend can reflect the dynamic characteristics of energy transmission in each frequency band. Based on the impedance change rate matrix, the energy transmission efficiency of each frequency band can be determined, and the optimal target frequency band interval can be found. According to the characteristics of the target frequency band, a compensation coefficient is generated and the sine wave parameters are adjusted, so that the energization process always works within the optimal frequency band range. In the remaining time, the optimized target sine wave voltage is used for energization, and the energization completion state is judged by monitoring the instantaneous power factor and leakage current. This not only improves the energization efficiency, but also avoids over-energization, improves the adaptability between the energization effect and the actual requirements of the thin film capacitor, and further improves the energization effect.

[0024] 2. The present application provides a method for controlling the energization of a thin film capacitor. Calculate the instantaneous impedance change trajectory during the energization process and perform wavelet decomposition to obtain coefficients reflecting different scale characteristics. Construct a stability evaluation index to determine the minimum observation period. Collect capacitance and temperature data within the observation period and perform cross-correlation analysis to obtain the influence coefficient of temperature on capacitance. Use this coefficient to decouple and compensate the capacitance sampling sequence, reduce the influence caused by temperature changes, and obtain the capacitance change curve under isothermal conditions. By calculating the fluctuation envelope and extracting characteristic parameters, the stability of the capacitance change can be accurately evaluated. This monitoring and evaluation method considering the influence of temperature improves the accuracy of stability evaluation, reduces misjudgment caused by temperature fluctuations, makes the decision of secondary energization processing more reliable, and ensures the stability of the energization effect.

[0025] 3. The present application provides a method for controlling the energization of a thin-film capacitor. By performing spectral analysis on the capacitance change curve, calculating the dispersion and concentration of the spectral distribution characteristics to generate a frequency-domain stability index, and combining it with characteristic parameters to obtain a comprehensive evaluation value. This evaluation method that combines time-domain and frequency-domain characteristics can characterize the stability state of the capacitor from multiple dimensions. Spectral analysis can reveal the periodic fluctuation components in the capacitance change, and the dispersion and concentration reflect the distribution characteristics of these fluctuation components. By combining the frequency-domain stability index with time-domain characteristic parameters, the system obtains a more comprehensive basis for stability evaluation, improves the accuracy and reliability of the energization state assessment, and reduces the uncertainty in the assessment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic flowchart of a method for controlling the energization of a thin-film capacitor in an embodiment of the present application.

[0027] Figure 2 is another schematic flowchart of a method for controlling the energization of a thin-film capacitor in an embodiment of the present application.

[0028] Figure 3 is a schematic structural diagram of an entity device of a thin-film capacitor energization control system provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any and all possible combinations including one or more of the listed items.

[0030] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0031] Next, an embodiment is used in combination with Figure 1 to describe a method for controlling the energization of a thin-film capacitor in an embodiment of the present application: Please refer to Figure 1 which is a schematic flowchart of a method for controlling the energization of a thin-film capacitor in an embodiment of the present application.

[0032] S101. Receive an energization parameter configuration instruction, perform an energization process on the thin-film capacitor for a first duration, and obtain current sampling data and voltage sampling data of the thin-film capacitor within the first duration; The system receives an energization parameter configuration instruction, which includes the amplitude, frequency, and energization duration of a preset sine-wave voltage, and performs an energization process on the thin-film capacitor for a first duration to obtain current sampling data and voltage sampling data of the thin-film capacitor within the first duration. The first duration is less than the energization duration.

[0033] In this step, the system first needs to receive an energization parameter configuration instruction, which contains key parameters necessary for the energization process of the thin-film capacitor, such as the amplitude, frequency of the preset sine-wave voltage, and the duration of the entire energization process. These parameters will determine the intensity and effect of the energization process and are important bases for subsequent optimization control.

[0034] After receiving the configuration instruction, the system will apply a sine-wave voltage to the target thin-film capacitor according to the specified parameters to start the energization process. During the entire energization duration, the system will select an initial stage as the first duration to focus on collecting and analyzing the electrical response data of the thin-film capacitor. Usually, the first duration is much less than the complete energization duration. Based on the analysis results at the end of the first duration, the energization strategy for the subsequent stage can be predicted and dynamically optimized.

[0035] Specifically, within the first duration, the system will, at a certain sampling frequency, continuously obtain the current and voltage data at both ends of the thin-film capacitor in real time to form a current sampling sequence and a voltage sampling sequence. The selection of the sampling frequency needs to comprehensively consider factors such as the change rate of the energization voltage, the real-time performance of data processing, and the performance limitations of hardware devices. The quality and integrity of the sampling data will directly affect the accuracy of subsequent impedance calculation and spectrum analysis. Therefore, the system also needs to perform necessary preprocessing on the sampling data, such as denoising, smoothing, interpolation, etc., to improve the signal-to-noise ratio and consistency of the data.

[0036] S102. Calculate an equivalent impedance sequence within the first duration based on the current sampling data and voltage sampling data, and perform Fourier analysis on the equivalent impedance sequence to obtain the distribution characteristics of impedance components at different frequencies; The system calculates an equivalent impedance sequence within the first duration based on the current sampling data and voltage sampling data. Specifically: calculate the instantaneous impedance value according to the current sampling data and voltage sampling data to obtain an instantaneous impedance sequence; Perform a moving average process on the instantaneous impedance sequence to obtain a smoothed impedance sequence; Calculate the impedance phase angle based on the smoothed impedance sequence, and obtain the equivalent impedance sequence according to the impedance phase angle and impedance amplitude.

[0037] After that, the system performs Fourier analysis on the equivalent impedance sequence to obtain the distribution characteristics of impedance components at different frequencies.

[0038] In this step, the system needs to use the current and voltage data collected in the previous step to calculate the change in the equivalent impedance of the thin-film capacitor within the first time period, and further analyze its frequency-domain distribution characteristics to characterize the absorption and conversion capabilities of the capacitor for different-frequency energies. Through the equivalent impedance sequence and the spectral distribution characteristics, the variation law of the capacitor impedance with time and frequency can be grasped, and the optimal frequency band interval for energy transmission can be predicted.

[0039] Specifically, to obtain the equivalent impedance sequence, the system first needs to calculate the instantaneous impedance at each sampling moment according to the instantaneous values of current and voltage using Ohm's law, forming an instantaneous impedance sequence of the same length as the sampling sequence. However, due to the possible presence of random errors such as high-frequency noise and small perturbations in the instantaneous sampling values, the instantaneous impedance data shows large short-term fluctuations. To improve the smoothness and trend of the impedance sequence, the system can perform a moving average process on the instantaneous impedance sequence on this basis. By selecting an appropriate moving window length and performing arithmetic averaging on the instantaneous impedance values within the window, a smoothed local average impedance sequence can be obtained, which better reflects the medium- and long-term variation trend of the impedance.

[0040] After obtaining the smoothed impedance sequence, to further obtain the phase information of the impedance, the system can also use the complex number form to calculate the impedance phase angle at each moment. Since there is a certain phase difference between the current and voltage across the capacitor under AC voltage, the impedance not only has an amplitude value but also includes a phase angle. Representing the smoothed impedance sequence in terms of amplitude and phase angle in complex number form, a complete time-domain equivalent impedance sequence can be obtained, which comprehensively characterizes the variation trajectory of the capacitor impedance with time.

[0041] After obtaining the equivalent impedance sequence, the system needs to further perform frequency-domain analysis on it to explore the distribution law of impedance quantities over different frequency components. The commonly used frequency-domain analysis tool is the Fourier transform. By performing a fast Fourier transform (FFT) on the time-domain impedance sequence, the spectral distribution diagram of the impedance sequence can be obtained. The abscissa of the spectral diagram is the frequency, and the ordinate is the impedance amplitude value corresponding to the frequency, intuitively showing the distribution characteristics of impedance quantities within the entire frequency range. Using the spectral diagram, key features such as the main frequency components and energy concentration intervals of impedance quantities can be clearly identified.

[0042] S103. Divide the distribution characteristics into several frequency band intervals, and calculate the impedance change trend within each frequency band interval to obtain an impedance change rate matrix; The system divides the distribution characteristics into several frequency band intervals. Specifically: calculate the power spectral density of the distribution characteristics to obtain a power distribution curve; Identify peak points and valley points on the power distribution curve, and divide the area between adjacent peak points and valley points into a frequency band interval; Calculate the power proportion within each frequency band interval, and merge the frequency band intervals according to the power proportion to obtain a set of target frequency band intervals.

[0043] After that, the system calculates the impedance change trend within each frequency band interval to obtain an impedance change rate matrix.

[0044] In this step, based on spectrum analysis, the system needs to further refine and quantify the spectrum distribution characteristics, automatically identify the impedance change rules in different frequency intervals, and form a structured change rate matrix to provide more refined data support for subsequent energy transfer efficiency analysis and frequency band selection. Through the division of frequency band intervals and the quantification of change rates, the key characteristics of the spectrum distribution can be characterized more accurately and efficiently.

[0045] Specifically, the system first needs to perform power spectral density analysis on the spectrum distribution diagram obtained in the previous step to obtain a power distribution curve of the impedance amplitude with respect to frequency. The power spectral density characterizes the average power contained in the impedance sequence within a unit frequency bandwidth and reflects the relative importance of the impedance quantity in different frequency bands. By calculating the power spectral density of each frequency point in the spectrum diagram, a continuous power distribution curve can be obtained. The peak points of the curve correspond to the main frequency bands where the impedance is most concentrated, while the valley points correspond to the transition intervals where the impedance components are relatively weak.

[0046] Based on the morphological characteristics of the power spectral density curve, the system can adopt some peak and valley detection algorithms to automatically identify the local extreme points on the curve. By setting threshold conditions, such as the peak-valley amplitude difference and the adjacent peak-valley spacing, the key inflection points of the distribution curve can be screened out, and then the entire frequency interval can be divided into several sub-frequency bands according to the inflection point positions. Each sub-frequency band corresponds to the frequency range defined by two adjacent valley points and reflects a relatively independent and stable interval of the impedance distribution.

[0047] After the preliminary division of the frequency bands, in order to further optimize the coverage range of the frequency bands, the system can also calculate the power proportion within each sub-frequency band, that is, the proportion of the impedance power within the frequency band in the total power of the entire spectrum. For the frequency bands with a relatively low power proportion, they can be considered for merging with adjacent frequency bands to reduce the total number of frequency bands and highlight the weight of the main frequency bands. For the frequency bands with a relatively high and concentrated power proportion, the original division can be maintained to retain their independence. Through the selective merging of frequency bands, a set of target frequency band intervals that can cover the main frequency components and have a high degree of integration can be obtained.

[0048] After identifying the target frequency band interval, the system needs to further analyze the variation trend of impedance with frequency in each interval and quantitatively characterize the dynamic characteristics within the frequency band. To this end, the system can locate the upper and lower boundary frequencies of each target frequency band interval in the original impedance sequence and extract the corresponding time-domain impedance subsequence within this interval. Then, perform the short-time Fourier transform on the subsequence again to obtain the amplitude distribution of the impedance at different frequency points within this frequency band interval.

[0049] Based on the impedance amplitude distribution within the frequency band, the system can fit the curve of impedance versus frequency within this interval and calculate the slope of this curve at each frequency point to obtain the rate-of-change parameter reflecting the impedance change speed within the interval. By comprehensively analyzing the magnitude and sign of the rate of change at each frequency point, it is possible to judge the overall change trend of the impedance within this interval, such as gradually increasing, gradually decreasing, or increasing first and then decreasing, etc. Arrange the calculation results of the rate of change for each target frequency band interval in order, and an impedance rate-of-change matrix with multiple rows and columns can be formed, clearly presenting the dynamic distribution law of impedance in different frequency bands.

[0050] S104. Determine the energy transfer efficiency of each frequency band interval based on the impedance rate-of-change matrix and determine the target frequency band interval with the maximum energy transfer efficiency; In this step, the system needs to use the impedance rate-of-change matrix obtained in the previous step to further analyze and evaluate the performance of each frequency band interval in terms of energy transfer efficiency, and specifically determine the target frequency band interval that is most conducive to efficient energy absorption and conversion. The level of energy transfer efficiency directly determines the loss level and energy storage capacity of the thin-film capacitor during the actual charge and discharge process. Therefore, accurately evaluating and screening the optimal frequency band is crucial for energy conservation and efficiency improvement in the energization process.

[0051] Specifically, the system first needs to establish a quantitative correlation model between the impedance change trend within the frequency band and the energy transfer efficiency. Generally, as the frequency increases, the equivalent series resistance (ESR) of the thin-film capacitor will gradually decrease, and the tangent of the dielectric loss angle (tanδ) will also gradually decrease, indicating that the energy dissipation of the capacitor is less and the transfer efficiency is higher under high-frequency conditions. However, excessively high frequencies may also cause negative effects such as resonance and thermal effects, resulting in local overheating and insulation breakdown, reducing the reliability of the device. Therefore, the optimal operating frequency needs to be balanced between energy transfer efficiency and device safety.

[0052] To quantitatively evaluate the transmission efficiency within a frequency band, the system can select key loss parameters such as ESR and tanδ as evaluation indicators, and through modeling methods such as polynomial regression and curve fitting of impedance-efficiency within the frequency band, characterize the functional relationship between the impedance change rate at each frequency point and the corresponding loss level. After obtaining the fitting function, the system can calculate the estimated loss indicators for each point in the impedance change rate matrix within this frequency band interval, forming an efficiency matrix with the same size as the change rate matrix. The elements in the efficiency matrix reflect the dissipation situation under the corresponding change rate, and the smaller the value, the higher the average transmission efficiency within the frequency band.

[0053] Then, the system can perform statistical analysis on the numerical distribution of the efficiency matrix, calculate representative indicators such as the efficiency mean, maximum value, and minimum value for each frequency band interval, and quantitatively evaluate and compare the energy transmission performance of different frequency bands. At the same time, on the basis of considering the transmission efficiency, the system also needs to perform constraint control on parameters such as the width of each frequency band and the upper and lower frequency limits. A frequency band with too narrow a width has high energy concentration, but a small frequency adaptation range and poor anti-interference ability; while a frequency band with too wide a width can cover more frequency changes, but there are large differences in the performance of frequency points within the frequency band, and the overall efficiency is unstable. Therefore, it is also necessary to set an appropriate frequency band width threshold to identify and adjust too narrow or too wide frequency bands.

[0054] S105. Generate a compensation coefficient according to the frequency range and impedance characteristics of the target frequency band interval, and use the compensation coefficient to adjust the amplitude and frequency of the sine wave voltage to obtain the target sine wave voltage; In this step, the system needs to further refine the parameter configuration of the energizing voltage according to the target frequency band interval determined in the previous step. By introducing a compensation coefficient mechanism, dynamically optimize the amplitude and frequency of the original sine wave voltage to generate a target sine wave voltage that matches the frequency band characteristics and can maximize the energy transmission efficiency. The adaptive adjustment of voltage parameters through the compensation coefficient can effectively overcome the modeling error and environmental interference in the frequency band selection process, and ensure the dynamic synchronization of the voltage excitation and the frequency band characteristics.

[0055] Specifically, the system first needs to determine the reference frequency of the optimized sine wave voltage according to the frequency range of the target frequency band interval. Usually, the center frequency of the frequency band interval can be selected as the reference, which corresponds to the average level of the impedance change rate and transmission efficiency within this interval and can better represent the overall characteristics of the frequency band. At the same time, actual engineering constraints such as the output frequency range and power bottleneck of the power supply equipment also need to be considered, and the reference frequency is corrected as necessary. If the reference frequency exceeds the controllable range of the power supply or leads to a risk of power overload, then on the premise of ensuring the frequency band coverage, the reference value should be appropriately shifted until the constraint conditions are met.

[0056] Then, the system needs to analyze the amplitude-frequency characteristics of the impedance within the target frequency band range, and study the variation laws of the impedance modulus and phase angle with respect to frequency. Through the morphological characteristics of the frequency-domain impedance curve, the phase relationship between the voltage and current of the capacitor within this frequency band can be judged, and then the compensation strategies in terms of both amplitude and phase can be determined. For example, for a frequency band where the impedance amplitude decreases monotonically with frequency, proportional compensation can be adopted, that is, the voltage amplitude is appropriately increased as the frequency increases to balance the increase in current caused by the impedance reduction and maintain a constant energy input; while for a frequency band where the impedance phase changes ahead or lags with frequency, a certain leading or lagging angle needs to be introduced in the voltage phase to form a complement with the impedance angle, ensure that the voltage and current are in the same phase, and reduce the power factor angle.

[0057] S106. During the remaining time of the energization duration, use the target sine-wave voltage to perform energization processing on the thin-film capacitor, and calculate the instantaneous power factor in real time; In this step, the system needs to use the target sine-wave voltage optimized in the previous step to implement continuous and stable energy excitation and charging control on the thin-film capacitor within the specified energization duration, and monitor the instantaneous power factor in real time to evaluate the real-time efficiency of energy transmission and determine whether the capacitor can complete full charging within the remaining time. The dynamic calculation and trend analysis of the instantaneous power factor can provide a basis for the online optimization of the energization process, and also provide a reference for the estimation of the remaining charging time and end-point control.

[0058] Specifically, the system first needs to continuously and stably apply the target sine-wave voltage signal to both ends of the thin-film capacitor to form an alternating electric field excitation. Under the action of the electric field, the polarized charges in the dielectric layer of the thin-film capacitor will move and accumulate in the direction of the electric field, showing a macroscopic charging phenomenon. As the charging progresses, the voltage across the capacitor continuously increases, and the electric field strength inside the dielectric layer also increases accordingly, and the stored electrostatic energy accumulates continuously. At the same time, due to the existence of the conductivity and polarization loss of the dielectric material itself, as well as the influence of parasitic parameters such as electrode resistance, the capacitor will also generate a certain amount of energy dissipation during the charging process, manifested as heat loss.

[0059] S107. When the stability of the instantaneous power factor reaches the preset condition and the leakage current drops below the preset threshold, generate an energization completion signal.

[0060] First, the system needs to determine whether it has reached a stable state based on the dynamic changes of the instantaneous power factor. The stability of the instantaneous power factor reflects the completion degree of the polarization process inside the capacitor and the dynamic balance level of energy loss. In the initial stage of charging, the power factor is usually at a low level with obvious fluctuations and changes, indicating that the capacitor is still in the stage of accumulating polarization charges and the charging effect is not yet complete. As the charging progresses, the power factor gradually increases and tends to be stable, indicating that the capacitor has basically established a steady electric field, the distribution of polarization charges has tended to be balanced, and the charging effect is becoming saturated.

[0061] To quantitatively judge the stability degree of the instantaneous power factor, the system can perform statistical analysis on the power factor data within a continuous number of AC cycles, calculate indicators such as its mean value and variance, and set reasonable stability criteria. For example, the standard deviation of the power factor within a certain time window can be used to measure its fluctuation level. When the standard deviation is lower than a certain threshold value, it is considered that the power factor has entered the steady-state interval. Another example is that the difference between the maximum value and the minimum value of the power factor can be used to measure its change range. When the peak value is less than a certain threshold, it can also be determined as the steady state. Of course, multiple indicators such as the mean value, standard deviation, and peak value can also be combined to form a comprehensive stability measurement criterion to improve the reliability of state judgment.

[0062] In the above embodiment, after obtaining the basic energization parameters by receiving the energization parameter configuration instruction, preliminary energization is performed within the first time period and current and voltage data are collected. By using these data to calculate the equivalent impedance sequence and perform Fourier analysis, the distribution characteristics of impedance components at different frequencies can be obtained. Dividing the distribution characteristics into frequency band intervals and calculating the impedance change trend can reflect the dynamic characteristics of energy transmission in each frequency band. Based on the impedance change rate matrix to determine the energy transmission efficiency of each frequency band, the optimal target frequency band interval can be found. Generating a compensation coefficient according to the characteristics of the target frequency band and adjusting the sine wave parameters can make the energization process always work within the best frequency band range. In the remaining time, the optimized target sine wave voltage is used for energization, and the energization completion state is judged by monitoring the instantaneous power factor and leakage current, which not only improves the energization efficiency but also avoids over-energization, improves the adaptability between the energization effect and the actual requirements of the thin-film capacitor, and further improves the energization effect.

[0063] In the first above-mentioned embodiment, the basic energization process of the thin-film capacitor is mainly introduced. Through the dynamic optimization and real-time monitoring of the energization parameters, efficient energization control is achieved. However, in actual applications, to further ensure the stability and reliability of the energization effect, it is also necessary to comprehensively evaluate the state of the energized capacitor and perform secondary energization processing when necessary. The following combines Figure 2 , and describes another thin-film capacitor energization control method in the embodiments of the present application: Please refer toFigure 2 , which is another schematic flowchart of a thin-film capacitor energization control method in an embodiment of this application.

[0064] S201. Calculate the instantaneous impedance change trajectory of the thin-film capacitor during the energization process; In this step, the system needs to track and calculate the instantaneous impedance of the capacitor in real time during the energization process to obtain a time trajectory curve reflecting the dynamic impedance change law. The instantaneous impedance is an important indicator for evaluating the charging state and energy conversion efficiency of the capacitor, and its change trajectory can reveal the evolution law of the internal physical mechanism of the capacitor during the energization process, providing a basis for subsequent state evaluation and optimal control.

[0065] Specifically, the system can adopt various impedance measurement methods, such as AC impedance spectroscopy analysis, electrochemical impedance spectroscopy analysis, etc., to obtain the voltage and current signals at both ends of the capacitor in real time, and calculate the corresponding instantaneous impedance value based on Ohm's law. During the calculation process, the system also needs to perform necessary preprocessing on the sampled signals, such as filtering, denoising, synchronous calibration, etc., to improve the signal-to-noise ratio and accuracy of the impedance calculation. At the same time, in order to comprehensively characterize the complex characteristics of the impedance, the system can also introduce a phase detection mechanism to calculate the amplitude and phase angle of the impedance to obtain a comprehensive impedance spectrum trajectory.

[0066] During the impedance measurement process, the system also needs to optimize the measurement strategy and parameter settings according to the unique structure and material characteristics of the thin-film capacitor. For example, due to the high-resistance characteristics of the dielectric layer and the polarization relaxation phenomenon of the thin-film capacitor, the amplitude of the impedance is usually very large in the low-frequency band, and the phase angle is close to -90°. In order to balance the measurement accuracy and sampling efficiency of the low-frequency impedance, the system can adopt a segmented frequency sweep strategy, appropriately reducing the sampling point density in the low-frequency band and increasing the sampling points in the high-frequency band to balance the measurement accuracy and speed. Another example is that the equivalent series resistance and inductance of the thin-film capacitor are significantly affected by frequency and temperature. In order to suppress the interference of these factors, the system can introduce a calibration compensation mechanism to correct the reference value of the impedance in real time according to the change of environmental parameters to ensure the consistency of the impedance trajectory.

[0067] S202. Perform wavelet decomposition on the instantaneous impedance change trajectory to obtain multi-scale characteristic coefficients; In this step, the system needs to perform time-frequency domain characteristic analysis on the instantaneous impedance change trajectory obtained in the previous step, and extract characteristic parameters reflecting the multi-scale characteristics of the impedance change through the wavelet decomposition method. Compared with traditional Fourier analysis, wavelet decomposition can localize and characterize the signal in both the time and frequency dimensions, and is more suitable for processing non-stationary time-varying signals such as impedance, which can clearly show the change law and energy distribution of the impedance at different time scales and frequency scales.

[0068] In specific implementation, the system first needs to select appropriate wavelet basis functions and decomposition levels according to the material properties and energization conditions of the thin-film capacitor. For example, in view of the mutation characteristics and energy concentration characteristics of the impedance signal, the system can adopt wavelets such as Haar and Daubechies with compact support, orthogonality, and vanishing moment characteristics to balance time-frequency resolution and computational efficiency. Another example is that in view of the duration and frequency change range of the energization process, the system can select a decomposition depth of 3-5 layers, which can not only fully reflect the multi-scale change characteristics of the impedance, but also avoid introducing too much redundant information.

[0069] S203. Construct a stability evaluation index based on the multi-scale characteristic coefficients, and determine the minimum observation period according to the stability evaluation index; In this step, the system needs to use the multi-scale characteristics of impedance change extracted in the previous step to construct a set of index systems for quantitatively evaluating energization stability, and determine the minimum continuous period for observing the energization state accordingly. The stability evaluation index directly reflects the consistency and reliability of the energization effect, and is the basis for optimizing charging control and predicting charging time. A reasonable minimum observation period can ensure the reliability and sufficiency of the collected data, and can also avoid the time and storage overhead caused by too long sampling.

[0070] In specific implementation, the system needs to design a set of quantitative indexes for comprehensively evaluating energization stability according to the physical meaning and statistical laws of the multi-scale characteristic coefficients. For example, the system can calculate the root mean square value of each scale coefficient to reflect the overall fluctuation level of impedance change; another example is that the system can calculate the condition number of the coefficient matrix to measure the dispersion degree of impedance characteristics in different time-frequency windows; another example is that the system can also introduce information theory indexes such as entropy value and cross-correlation coefficient to evaluate the correlation and consistency between multi-scale coefficients. Through comprehensive evaluation of multiple indexes, the stability of the energization process can be quantified from dimensions such as energy distribution and change convergence.

[0071] S204. Collect the capacitance sampling sequence and temperature sampling sequence within the minimum observation period; In this step, the system needs to sample and monitor the key state variables of the thin-film capacitor according to the minimum observation period determined in the previous step, and focus on obtaining two sampling sequences reflecting the equivalent capacitance and temperature change of the capacitor. Capacitance and temperature are important parameters for evaluating the energization effect and stability. Their dynamic change laws can reveal the internal mechanism of energy conversion and dissipation during the charging process, and are also important bases for subsequent environmental decoupling and fault diagnosis.

[0072] When collecting the capacitance sampling sequence, the system usually adopts the AC impedance method or the current integration method, applies a high-frequency AC excitation voltage, measures the voltage response across the capacitor and the current response passing through it, and calculates the equivalent capacitance based on the impedance model or differential equation model of the capacitor. Since the equivalent capacitance of the thin-film capacitor is comprehensively affected by factors such as frequency, voltage, and temperature, during the sampling process, the system needs to strictly control the stability of the excitation conditions and environmental conditions to eliminate random interference factors as much as possible. At the same time, the system also needs to optimize the frequency and amplitude parameters of the excitation signal to improve the signal-to-noise ratio and sensitivity of sampling on the premise of ensuring the safety and reliability of the capacitor.

[0073] Considering the significant impact of temperature changes on the performance of the capacitor, while collecting the capacitance sequence, the system also needs to synchronously collect the temperature sampling sequence. The arrangement of temperature sampling measurement points needs to fully consider the internal structure and heat dissipation characteristics of the thin-film capacitor and should cover the thermally sensitive areas and temperature gradient distributions of the capacitor. At the same time, to improve the timeliness and accuracy of temperature perception, the system can adopt high-precision and fast-response temperature sensors such as platinum resistors and thermocouples, and reduce the influence of temperature drift and hysteresis effects through measures such as automatic calibration and multi-point compensation.

[0074] S205. Conduct cross-correlation analysis on the capacitance sampling sequence and the temperature sampling sequence to obtain the temperature influence coefficient; In this step, the system needs to use the capacitance sequence and temperature sequence collected in the previous step to quantitatively analyze the correlation law between the two, that is, the influence mode and influence intensity of temperature changes on capacitance changes. Through cross-correlation analysis, the dynamic characteristics such as the following relationship and hysteresis effect between temperature and capacitance can be extracted from the time-domain data, and the temperature influence coefficient is used to quantitatively describe this non-linear coupling mechanism, which is the key to realizing temperature decoupling compensation and improving the accuracy of capacitance estimation.

[0075] In specific implementation, the system first needs to preprocess the synchronized capacitance sequence and temperature sequence to eliminate the trend term and DC component in them and highlight the fluctuation characteristics of the sequence. Generally, the difference method or the moving average method can be used to extract the first-order difference or zero-mean sequence of the sequence to reduce the interference of non-stationary factors. On this basis, the system can use the cross-correlation function to measure the similarity of the two sequences at different time lags, that is, calculate the cross-correlation coefficient between the capacitance sequence and the temperature sequence at different time offsets. The calculation of the cross-correlation coefficient can be based on the convolution operation of the two sequences, and the correlation intensity at different time lags is obtained by summing the products of the moving sequences.

[0076] Through cross-correlation analysis, the system can obtain a cross-correlation function curve regarding time delay, reflecting the dynamic correlation characteristics between temperature changes and capacitance changes. For example, the peak position of the cross-correlation curve represents the optimal time delay for the capacitance change to respond to the temperature change, that is, the most significant correlation time when the capacitance sequence lags behind the temperature sequence; while the peak magnitude of the cross-correlation curve reflects the influence intensity of temperature changes on capacitance changes at the optimal time delay. Using these characteristic parameters, the system can characterize the causal relationship and sensitivity between temperature and capacitance.

[0077] Furthermore, to quantitatively describe the influence law of temperature changes on capacitance changes, the system can establish a temperature influence coefficient model based on cross-correlation analysis. The influence coefficient model uses the regression analysis method to fit the functional relationship between the capacitance change amount and the temperature change amount, and introduces a time delay parameter to describe the dynamic response characteristics of capacitance changes to temperature changes. Common influence coefficient models include piecewise linear models, exponential decay models, fractional differential models, etc. The system can select an appropriate model type according to the capacitance material and structural characteristics, and use optimization methods such as the least squares method and the gradient descent method to calibrate the coefficients and time delay parameters in the model to obtain the temperature influence coefficient that best fits the sampling data.

[0078] S206. Decouple and compensate the capacitance sampling sequence according to the temperature influence coefficient to obtain the capacitance change curve under isothermal conditions; In this step, the system needs to use the temperature influence coefficient model estimated in the previous step to perform temperature decoupling processing on the capacitance sampling sequence, eliminate the influence of temperature fluctuations on capacitance estimation, and thus obtain the pure change curve of the capacitance under isothermal conditions. Through temperature decoupling compensation, the capacitance change and the temperature change can be decoupled, accurately restoring the internal change law of the capacitance during the energization process, and mastering the capacitance evolution mechanism under constant temperature conditions, which is an important prerequisite for carrying out energization state assessment and charging capacity prediction.

[0079] Specifically, when implementing, the system first needs to calculate the temperature compensation amount at each sampling moment according to the temperature influence coefficient model. The temperature compensation amount represents the capacitance estimation deviation caused by the deviation of the ambient temperature from the set reference temperature at this moment. It can estimate the equivalent change in capacitance caused by the difference between the actual temperature and the reference temperature at the sampling moment through the influence coefficient model, and its mathematical expression is: capacitance compensation amount = temperature influence coefficient × (actual temperature - reference temperature). The reference temperature usually selects the ambient normal temperature value or the designed nominal operating temperature, representing the ideal constant temperature operating point.

[0080] After obtaining the temperature compensation amount sequence at each moment, the system can subtract this compensation amount sequence from the capacitance sampling sequence to obtain the capacitance change sequence after temperature decoupling. The decoupled sequence reflects the ideal change process of the capacitance over time at the reference temperature, eliminating the influence of ambient temperature fluctuations. Further, the system can perform a smoothing filtering process on the decoupled sequence to remove the high-frequency noise and outliers therein, and extract a smooth curve reflecting the long-term trend of capacitance change, that is, the capacitance change curve under isothermal conditions. This curve intuitively presents the characteristic that the capacitance gradually increases with the charging process in an ideal constant-temperature environment, reflecting the polarization response law of the thin-film capacitor under the action of a pure electric field.

[0081] S207. Calculate the fluctuation envelope of the capacitance change curve and extract the characteristic parameters of the envelope; In this step, the system needs to perform a fluctuation analysis on the capacitance change curve under isothermal conditions obtained in the previous step. By extracting the fluctuation envelope characteristics of the curve, the phased characteristics of the energization process and the evolution law of the charging capacity are quantitatively evaluated. The fluctuation envelope reflects the local oscillation characteristics of the capacitance change curve and is an important index for characterizing the stability of the energization process and the occurrence of anomalies. Extracting and analyzing the characteristic parameters of the envelope can timely detect energization anomalies, estimate the charging end time, and provide a basis for subsequent capacity prediction and energization optimization decisions.

[0082] Specifically, the system can adopt an envelope tracking algorithm to extract the upper and lower fluctuation envelopes of the capacitance change curve in real time. Commonly used envelope extraction methods include the Hilbert transform method, peak detection method, spline interpolation method, etc. Taking the Hilbert transform method as an example, the system first performs a Hilbert transform on the capacitance change curve to obtain its analytic signal; then calculates the modulus value of the analytic signal to obtain the instantaneous amplitude of the curve; finally, fits the upper and lower envelopes of the instantaneous amplitude sequence to obtain the upper and lower envelope curves reflecting the fluctuation range of capacitance change. The upper envelope curve connects all the local maximum points of the waveform and depicts the upper limit of capacitance change; while the lower envelope curve connects all the local minimum points of the waveform and depicts the lower limit of capacitance change.

[0083] Before executing step S208, the system can also perform a spectrum analysis on the capacitance change curve to obtain the spectrum distribution characteristics; Calculate the dispersion and concentration of the spectrum distribution characteristics; Generate a frequency-domain stability index according to the dispersion and concentration; Perform a combined calculation on the frequency-domain stability index and the characteristic parameters to obtain a comprehensive evaluation value. Specifically: weight the frequency-domain stability index and the characteristic parameters according to a preset weight coefficient; Calculate the linear combination of the weighted frequency-domain stability index and the characteristic parameters; Standardize the linear combination by using normalization processing to obtain the comprehensive evaluation value.

[0084] By performing Fourier transform or wavelet transform on the curve, the energy distribution characteristics of the curve in the frequency domain can be obtained, revealing the inherent law of capacitance change from the perspective of frequency. Generally, during normal charging, the spectrum energy of the capacitance change curve is mainly concentrated in the low frequency band, corresponding to the slow and steady growth trend of capacitance; when charging is abnormal, the capacitance change frequency accelerates, and the spectrum energy gradually shifts to the high frequency band, resulting in energy dispersion. Therefore, the concentration and dispersion of the spectrum distribution can be used as another set of important characteristic indicators of abnormal capacitance change.

[0085] In order to quantify the frequency domain abnormal characteristics, the system can perform statistical analysis on the spectrum energy distribution of the capacitance change curve and extract multiple discreteness and concentration indicators. For example, the standard deviation of the frequency domain energy can measure the degree of discreteness of the spectrum energy. The larger the standard deviation, the more drastic the capacitance change. For another example, the cumulative contribution rate of the top N items after sorting the energy of different frequencies can measure the concentration of the spectrum energy. If the energy proportion of the top N items is low, the proportion of high-frequency abnormal components is high. In addition, the system can also use pattern recognition methods such as frequency domain clustering to automatically classify spectrum energy distributions of different forms, and construct frequency domain stability indicators to comprehensively characterize the frequency domain abnormality level of capacitance changes.

[0086] Furthermore, the system can perform correlation analysis and combined discrimination on the envelope characteristic parameters in the time domain and the stability indicators in the frequency domain. Using a weighted fusion strategy, the system can assign different weights to the two types of abnormal features to form a comprehensive abnormal evaluation index in the time and frequency domains. The determination of the weight can take into account factors such as the physical meaning, numerical range, and sensitivity of the indicator, and adaptively optimize the weight parameters through data-driven methods such as machine learning to obtain the optimal fusion discrimination model. The higher the comprehensive abnormal index, the greater the overall abnormal risk of capacitance changes during the empowerment process, and the system needs to give a higher abnormal level warning to prompt decision makers to take targeted regulation and optimization measures.

[0087] S208: Determine whether secondary empowerment processing is required based on the characteristic parameters.

[0088] The system determines whether secondary empowerment processing is required based on characteristic parameters, including: Calculate the variance and mean of the fluctuation characteristic parameters; Generate stability evaluation values ​​based on variance and mean; When the stability evaluation value exceeds the preset range, it is determined that a secondary energy empowerment process is required; When the stability evaluation value is within a preset range, it is determined that no secondary energizing process is required.

[0089] In this step, the system needs to comprehensively judge the completion quality and charging effect of the empowerment process, and independently decide whether it is necessary to perform secondary empowerment on the film capacitor based on the analysis results of parameters such as time domain fluctuation characteristics, frequency domain stability characteristics, and comprehensive abnormal indicators. Secondary empowerment refers to the application of empowerment voltage to the capacitor again after a short dormant period after the initial charging is completed, so that its charge is redistributed and the internal polarization state is further stabilized. Reasonable implementation of secondary empowerment can effectively improve the charging blind area, suppress the risk of local overcharging, and improve the consistency and reliability of capacitor performance. However, frequent and blind secondary empowerment will also cause energy waste and extend the production cycle. Therefore, accurately judging the necessity of secondary empowerment is crucial to balancing product quality and production efficiency.

[0090] In specific implementation, the system first needs to measure the stability level of the entire empowerment process based on the characteristic parameter sequence obtained previously. For the fluctuation characteristic parameters in the time domain, such as peak value, root mean square value, etc., the system can calculate its mean and variance and other statistics to measure the central trend and dispersion of the fluctuation level. The larger the mean, the greater the overall oscillation amplitude of the capacitance change during the empowerment process; the larger the variance, the worse the stability of the empowerment process and the more drastic the fluctuation of the capacitance change. For the stability characteristic indicators in the frequency domain, the system can analyze its numerical distribution range and set a reasonable stability threshold. When the frequency domain indicator is lower than the threshold, it means that the energy distribution of the capacitance change in the frequency domain is concentrated, and the empowerment process is relatively stable as a whole; conversely, if the frequency domain indicator exceeds the threshold, it means that the frequency domain energy of the capacitance change is relatively discrete, and there may be uneliminated high-frequency anomalies in the charging process.

[0091] In the above embodiment, the instantaneous impedance change trajectory during the empowerment process is calculated and wavelet decomposition is performed to obtain coefficients reflecting the characteristics of different scales, and a stability evaluation index is constructed to determine the minimum observation period. During the observation period, the capacitance and temperature data are collected and cross-correlation analysis is performed to obtain the influence coefficient of temperature on capacitance. The coefficient is used to decouple and compensate the capacitance sampling sequence, reduce the impact of temperature changes, and obtain the capacitance change curve under isothermal conditions. By calculating the fluctuation envelope and extracting characteristic parameters, the stability of capacitance changes can be accurately evaluated. This monitoring and evaluation method that takes into account the influence of temperature improves the accuracy of stability evaluation, reduces misjudgment caused by temperature fluctuations, makes the decision of secondary empowerment processing more reliable, and ensures the stability of the empowerment effect.

[0092] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a thin film capacitor empowerment control system provided in an embodiment of the present application.

[0093] It should be noted that Figure 3The structure of the system shown is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present invention.

[0094] As Figure 3 shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in a Read-Only Memory (ROM) 302 or the program loaded from a storage section 308 into a Random Access Memory (RAM) 303, such as executing the method in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0095] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a Liquid Crystal Display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as required. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as required so that a computer program read from it can be installed into the storage section 308 as required.

[0096] Specifically, according to the embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments of the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the Central Processing Unit (CPU) 301, various functions defined in the present invention are executed.

[0097] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, apparatus, or device. In the present invention, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.

[0098] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0099] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or may exist alone without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiments.

[0100] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0101] As used in the above embodiments, depending on the context, the term "when..." may be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" may be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0102] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state drive), etc.

[0103] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by relevant hardware instructed by a computer program. This program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The aforementioned storage media include: various media that can store program codes such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A film capacitor energization control method, characterized in that: include: Receiving an enabling parameter configuration instruction, wherein the enabling parameter configuration instruction includes a preset amplitude, frequency, and enabling duration of a sinusoidal voltage; Performing an enabling process for a first time period on the thin film capacitor to obtain current sampling data and voltage sampling data of the thin film capacitor within the first time period, wherein the first time period is shorter than the enabling time period; Calculating an equivalent impedance sequence within the first time period based on the current sampling data and the voltage sampling data, and performing Fourier analysis on the equivalent impedance sequence to obtain distribution characteristics of impedance components at different frequencies; Dividing the distribution characteristics into a number of frequency band intervals, and calculating the impedance change trend in each of the frequency band intervals to obtain an impedance change rate matrix; Determine the energy transmission efficiency of each frequency band interval based on the impedance change rate matrix, and determine the target frequency band interval with the maximum energy transmission efficiency; Generating a compensation coefficient according to the frequency range and impedance characteristics of the target frequency band interval, and adjusting the amplitude and frequency of the sinusoidal wave voltage using the compensation coefficient to obtain a target sinusoidal wave voltage; During the remaining time of the energizing time, the target sinusoidal wave voltage is used to energize the film capacitor, and the instantaneous power factor is calculated in real time; When the stability of the instantaneous power factor reaches a preset condition and the leakage current decreases below a preset threshold, an energization completion signal is generated.

2. The method according to claim 1, characterized in that The calculating the equivalent impedance sequence within the first time period based on the current sampling data and the voltage sampling data specifically includes: Calculating an instantaneous impedance value according to the current sampling data and the voltage sampling data to obtain an instantaneous impedance sequence; Performing sliding average processing on the instantaneous impedance sequence to obtain a smoothed impedance sequence; An impedance phase angle is calculated based on the smoothed impedance sequence, and an equivalent impedance sequence is obtained according to the impedance phase angle and the impedance amplitude.

3. The method according to claim 1, characterized in that The dividing the distribution characteristics into a plurality of frequency band intervals specifically includes: Calculating the power spectrum density of the distribution feature to obtain a power distribution curve; Identifying peak points and valley points on the power distribution curve, and dividing an area between adjacent peak points and valley points into a frequency band interval; The power proportion in each of the frequency band intervals is calculated, and the frequency band intervals are merged according to the power proportions to obtain a target frequency band interval set.

4. The method according to claim 1, characterized in that: After generating an enabling completion signal when the stability of the instantaneous power factor reaches a preset condition and the leakage current decreases below a preset threshold, the method further includes: Calculating the instantaneous impedance change trajectory of the film capacitor during the energization process; Performing wavelet decomposition on the instantaneous impedance change trajectory to obtain multi-scale characteristic coefficients; Constructing a stability evaluation index based on the multi-scale characteristic coefficient, and determining a minimum observation period according to the stability evaluation index; Collecting a capacitance sampling sequence and a temperature sampling sequence within the minimum observation period; Performing cross-correlation analysis on the capacitance sampling sequence and the temperature sampling sequence to obtain a temperature influence coefficient; Decoupling and compensating the capacitance sampling sequence according to the temperature influence coefficient to obtain a capacitance change curve under isothermal conditions; Calculating the fluctuation envelope of the capacitance change curve and extracting characteristic parameters of the envelope; Whether a secondary energization process is required is determined based on the characteristic parameters.

5. The method according to claim 4, characterized in that The determining whether a secondary empowerment process is required based on the characteristic parameters specifically includes: Calculating the variance and mean of the fluctuation characteristic parameters; generating a stability evaluation value according to the variance and the mean; When the stability evaluation value exceeds a preset range, it is determined that a secondary energy empowerment process is required; When the stability evaluation value is within the preset range, it is determined that no secondary energizing process is required.

6. The method according to claim 4, characterized in that Before determining whether a secondary energy empowerment process is required based on the characteristic parameters, the method further includes: Performing spectrum analysis on the capacitance variation curve to obtain spectrum distribution characteristics; Calculating the dispersion and concentration of the spectrum distribution characteristics; generating a frequency domain stability index according to the dispersion and the concentration; The frequency domain stability index and the characteristic parameter are combined and calculated to obtain a comprehensive evaluation value.

7. The method according to claim 6, characterized in that The frequency domain stability index and the characteristic parameter are combined and calculated to obtain a comprehensive evaluation value, specifically including: Weighting the frequency domain stability index and the characteristic parameter according to a preset weight coefficient; Calculating a weighted linear combination of the frequency domain stability index and the characteristic parameter; The linear combination is normalized by normalization processing to obtain a comprehensive evaluation value.

8. A film capacitor energizing control system, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a system, the system is caused to execute the method according to any one of claims 1 to 7.

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