A method and system for calculating power consumption frequency based on temperature rise of ceramic capacitors

Abstract

The embodiment of the present application provides a method and system for calculating the power consumption frequency based on the temperature rise of a ceramic capacitor, which is applied to the field of electrical data processing technology of ceramic capacitors. The power consumption frequency calculation method: obtains the detected temperature rise value corresponding to the ceramic capacitor in the target time period, and the detected temperature rise value is the surface temperature rise value of the ceramic capacitor in the target time period. Obtain the output signal delay value and the output signal phase shift value of the ceramic capacitor in the target time period. Perform binary feature encoding on the detected temperature rise value, the output signal delay value and the output signal phase shift value respectively to obtain the corresponding temperature rise characteristic value, delay characteristic value and phase shift characteristic value. Input the temperature rise characteristic value, the delay characteristic value and the phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor, and the power consumption frequency includes at least one of the following frequency information: the current operating frequency of the ceramic capacitor and the current noise frequency of the ceramic capacitor. This embodiment realizes the accurate calculation of the power consumption frequency.

Description

Technical Field

[0001] The present application relates to the technical field of electronic device electrical data processing, and in particular to a method and system for calculating electrical frequency based on the temperature rise of ceramic capacitors. Background Art

[0002] Ceramic capacitors are a common electronic component that uses ceramic materials as dielectrics to store and release charge. This type of ceramic capacitor is characterized by high dielectric constant, good stability, excellent insulation performance, small size, and high temperature resistance. They do not require a positive or negative pole to work, and are suitable for filtering and coupling high-frequency signals, as well as as a voltage reference. Due to its simple manufacturing process and relatively low cost, it is widely used in many circuit designs.

[0003] Generally, the surface temperature value of a ceramic capacitor is related to its capacitance and operating frequency. Therefore, ideally, the power consumption frequency of a ceramic capacitor can be detected by the change in its surface temperature. However, in practice, the impedance characteristic changes of a ceramic capacitor are very complex, and it not only exhibits capacitance characteristics, but also inductance characteristics and resistance characteristics. Moreover, the changes in these characteristics are determined by a variety of factors such as the temperature and operating frequency of the ceramic capacitor in a complex situation. In addition, ceramic capacitors are of many types and have a wide range of application scenarios, and have different operating frequency ranges in different design scenarios. Therefore, it is difficult to obtain the accurate power consumption frequency through a simple temperature-frequency mapping relationship, and it is difficult to accurately and quickly calculate and detect ceramic capacitors in all application scenarios. Summary of the invention

[0004] The embodiments of the present application provide a method and system for calculating the power consumption frequency based on the temperature rise of ceramic capacitors, which realize accurate and rapid calculation and detection of ceramic capacitors in all application scenarios.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0006] In the first aspect, a method for calculating the power consumption frequency based on the temperature rise of a ceramic capacitor is provided, the method comprising: obtaining a detected temperature rise value corresponding to the ceramic capacitor in a target time period, the detected temperature rise value being the surface temperature rise value of the ceramic capacitor in the target time period. Obtaining the output signal delay value and the output signal phase shift value of the ceramic capacitor in the target time period. Performing binary feature encoding on the detected temperature rise value, the output signal delay value, and the output signal phase shift value, respectively, to obtain the corresponding temperature rise characteristic value, delay characteristic value, and phase shift characteristic value. Inputting the temperature rise characteristic value, the delay characteristic value, and the phase shift characteristic value into a neural network model, the power consumption frequency of the ceramic capacitor is obtained, and the power consumption frequency includes at least one of the following frequency information: the current operating frequency of the ceramic capacitor and the current noise frequency of the ceramic capacitor.

[0007] The above embodiment has the following advantages: the traditional method uses the mapping relationship of temperature rise-frequency to directly calculate the frequency. However, the relationship between temperature rise and frequency is determined by many complex factors. Although the power consumption frequency can be obtained quickly by this method, its calculation accuracy is low. At the same time, it is also difficult to directly predict the working frequency based on the temperature rise information using the neural network model. To this end, the embodiment of the present application analyzes the characteristics of the ceramic capacitor, combines the interaction relationship between temperature, capacitance change, inductance change and frequency, and collects the multi-dimensional features of the ceramic capacitor. In addition, because the impedance characteristics such as inductance and capacitance of the working ceramic capacitor are difficult to obtain, the embodiment of the present application characterizes the changes in capacitance characteristics and inductance characteristics by the delay characteristics and phase shift characteristics between the capacitance characteristics and inductance characteristics and the output signal of the ceramic capacitor. Based on the above multi-dimensional features, the prediction calculation of the neural network model can improve the calculation accuracy of the power consumption frequency. At the same time, the unified binary encoding of the multi-dimensional features can quantize the weights between different features, and can make the model input more unified and simple, and simplify the complexity of model training. Based on the above method, the power consumption frequency of the ceramic capacitor can be quickly, lightly and accurately calculated.

[0008] In a possible implementation, before inputting the temperature rise characteristic value, the time delay characteristic value, and the phase shift characteristic value into the neural network model, the method further includes: sending a time domain detection signal to the output end of the ceramic capacitor within the target time period, obtaining an impedance reflection signal corresponding to the time domain detection signal, and obtaining an impedance real part characteristic value and an impedance imaginary part characteristic value according to the impedance reflection signal.

[0009] Exemplarily, the above-mentioned inputting of temperature rise characteristic value, time delay characteristic value and phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor includes: inputting the temperature rise characteristic value, time delay characteristic value, phase shift characteristic value, impedance real part characteristic value and impedance imaginary part eigenvalue into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

[0010] The above embodiment has the following advantages: the power frequency is related to the impedance characteristics (including inductance characteristics and capacitance characteristics) of the ceramic capacitor. In some scenarios where the impedance characteristics can be detected in the working state of the ceramic capacitor, its impedance characteristics can also be detected based on the time domain reflection detection technology. The real impedance eigenvalue and the imaginary impedance eigenvalue corresponding to the impedance characteristics are combined as the input features of the neural network model to improve the detection accuracy.

[0011] In a possible implementation, before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model, the method further includes: obtaining the ambient temperature value of the ceramic capacitor within the target time period, performing binary characteristic encoding on the ambient temperature value, and obtaining the ambient temperature characteristic value.

[0012] Exemplarily, the above-mentioned inputting temperature rise characteristic value, time delay characteristic value and phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor includes: inputting temperature rise characteristic value, time delay characteristic value, phase shift characteristic value and ambient temperature characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

[0013] The above embodiment has the following advantages: the value of temperature rise also interacts with the ambient temperature. Therefore, in order to eliminate environmental errors and pay attention to the characteristic correlation between surface temperature rise and ambient temperature, characteristic values ​​related to ambient temperature can also be input into the neural network model to improve technical accuracy.

[0014] In the second aspect, the embodiment of the present application also provides a power frequency calculation system based on the temperature rise of ceramic capacitors, the power frequency calculation system includes a data processor and a sampling circuit, the sampling circuit is used to be electrically connected to the ceramic capacitor, and a neural network model is written in the data processor. The data processor is used to: obtain the detected temperature rise value corresponding to the ceramic capacitor in the target time period through the sampling circuit, and the detected temperature rise value is the surface temperature rise value of the ceramic capacitor in the target time period. Obtain the output signal delay value and the output signal phase shift value of the ceramic capacitor in the target time period. Binary feature encoding is performed on the detected temperature rise value, the output signal delay value and the output signal phase shift value respectively to obtain the corresponding temperature rise feature value, delay feature value and phase shift feature value. The temperature rise feature value, the delay feature value and the phase shift feature value are input into the neural network model to obtain the power frequency of the ceramic capacitor, and the power frequency includes at least one of the following frequency information: the current operating frequency of the ceramic capacitor and the current clutter frequency of the ceramic capacitor.

[0015] In a possible implementation, the power frequency calculation system further includes a time domain detector, which is electrically connected to the data processor and the ceramic capacitor, respectively. Before the temperature rise characteristic value, the time delay characteristic value, and the phase shift characteristic value are input into the neural network model, the data processor is also used to: control the time domain detector to send a time domain detection signal to the output end of the ceramic capacitor within the target time period, and obtain an impedance reflection signal corresponding to the time domain detection signal through the time domain detector. Obtain the impedance real part characteristic value and the impedance imaginary part characteristic value according to the impedance reflection signal.

[0016] Exemplarily, the above-mentioned inputting of temperature rise characteristic value, time delay characteristic value and phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor includes: inputting the temperature rise characteristic value, time delay characteristic value, phase shift characteristic value, impedance real part characteristic value and impedance imaginary part eigenvalue into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

[0017] In a possible implementation, before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model, the data processor is also used to: obtain the ambient temperature value of the ceramic capacitor within the target time period through a sampling circuit, perform binary characteristic encoding on the ambient temperature value, and obtain the ambient temperature characteristic value.

[0018] Exemplarily, the above-mentioned inputting temperature rise characteristic value, time delay characteristic value and phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor includes: inputting temperature rise characteristic value, time delay characteristic value, phase shift characteristic value and ambient temperature characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

[0019] Regarding the technical principles and beneficial effects of the above-mentioned second aspect, reference may be made to the relevant description of the above-mentioned first aspect, so they will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the impedance characteristic-frequency response relationship of a ceramic capacitor provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of a system for calculating power consumption frequency based on temperature rise of ceramic capacitors provided in an embodiment of the present application Figure 1 ;

[0022] Figure 3 A schematic diagram of a system for calculating power consumption frequency based on temperature rise of ceramic capacitors provided in an embodiment of the present application Figure 2 ;

[0023] Figure 4 A flow chart of a method for calculating power consumption frequency based on temperature rise of ceramic capacitors provided in an embodiment of the present application;

[0024] Figure 5 A neural network model of a method for calculating the power frequency based on the temperature rise of a ceramic capacitor provided in an embodiment of the present application is a schematic diagram of a frequency calculation structure obtained based on input eigenvalue calculation. DETAILED DESCRIPTION

[0025] It should be noted that the terms "first", "second", etc. involved in the embodiments of the present application are only used to distinguish features of the same type and cannot be understood as indicating relative importance, quantity, order, etc.

[0026] The terms "exemplary" or "for example" and the like in the embodiments of the present application are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the related concepts in a specific way.

[0027] The terms "coupling" and "connection" involved in the embodiments of the present application should be understood in a broad sense. For example, they may refer to a direct physical connection, or an indirect connection achieved through electronic devices, such as a connection achieved through resistors, inductors, capacitors or other electronic devices.

[0028] First, some basic concepts involved in the embodiments of the present application are explained:

[0029] Ceramic capacitors are generally classified based on their temperature coefficient. Among them, the operating temperature range of Class I ceramics (super stable type) is -55℃~+125℃, and its capacity change does not exceed ±30ppm / ℃. Class II / Class III ceramics (stable type). Among them, the X7R type has a lower temperature limit of -55℃ and an upper temperature limit of +125℃, and the maximum capacity change is ±15%. Among them, the Z5U type and Y5V type represent operating temperatures of 10℃~+85℃ and -30℃~+85℃ respectively, and the corresponding maximum capacity changes are +22℃~-56% and 30℃~82%, both of which belong to Class II ceramics. The advantages of Class II and Class III are high volume utilization, that is, they can provide higher capacitance values ​​when the external dimensions are the same, and are suitable for lines with high capacitance values ​​and not too high requirements for stable performance.

[0030] Ceramic capacitors have some typical electrical properties, including dielectric constant, insulation resistance, loss and quality factor and withstand voltage.

[0031] Ceramic capacitors have different performances depending on the type of ceramic dielectric. In addition, environmental factors, including temperature, voltage, frequency and time (aging), have different effects on the capacitance of different dielectrics. The higher the dielectric constant, the worse the stability, reliability and durability.

[0032] The energy consumed by heat per unit time under the action of an external voltage is called the loss of a ceramic capacitor. An ideal ceramic capacitor stores all the energy obtained from the power supply in the dielectric of the ceramic capacitor without any form of energy consumption. But in fact, ceramic capacitors consume energy under the action of an external voltage. The leakage current of the ceramic dielectric, slow polarization (the electric dipole moment deflects under the action of the electric field), and the equivalent resistance of the metal parts of the inner and outer electrodes will consume a part of the energy, forming the loss of the ceramic capacitor. Excessive ceramic capacitor loss will generate heat and increase the temperature of the ceramic capacitor, causing the circuit working state to be unstable and accelerating the aging of the ceramic capacitor.

[0033] The frequency characteristics of ceramic capacitors refer to the relationship between the capacitance and other parameters of ceramic capacitors and the frequency. Generally speaking, when ceramic capacitors work at high frequencies, as the operating frequency increases, the capacitance will decrease due to the decrease in the dielectric constant of the insulating medium, while the loss will increase, and it will affect the distributed parameters of the ceramic capacitor. When working at high frequencies, factors such as the pole piece resistance, the resistance between the lead and the pole piece, the pole piece's own inductance, and the lead inductance will affect the performance of the ceramic capacitor. All of these limit the frequency of use of ceramic capacitors. In order to ensure the stability of ceramic capacitors, the limit operating frequency of ceramic capacitors should generally be selected at 1 / 3 - 1 / 2 of the inherent resonant frequency of ceramic capacitors. The capacitance of ceramic capacitors is strongly related to frequency. High-voltage ceramic capacitors with large specifications and capacitance have a poor response to high frequencies but a very good response to low frequencies, while ceramic capacitors with small specifications and capacitance have a poor response to low frequencies but a very good response to high frequencies.

[0034] The capacitance characteristics and frequency response characteristics of ceramic capacitors are designed according to the actual application scenarios. For example, when ceramic capacitors are used to prepare impedance characteristic circuits, filters, coupling circuits or other electronic circuits in wireless communication scenarios, ceramic capacitors are usually required to operate at higher operating frequencies, such as operating in the range of several kHz to hundreds of kHz or even higher. If they are used in energy supply devices or other electronic devices, ceramic capacitors may operate in the range of tens of Hz to thousands of Hz. In circuits where ceramic capacitors are subjected to alternating current (such as in filtering or coupling applications), some self-heating may be generated due to the effective series resistance (ESR) of the capacitor. ESR causes resistance loss, thereby converting part of the electrical energy in the capacitor into heat. In some cases, the operating frequency of ceramic capacitors is relatively stable and their capacitance is also regionally stable. However, some noise generated during operation may cause dielectric loss and cause some heating, causing the ceramic capacitor to heat up. Or, in other cases, the operating frequency of the ceramic capacitor itself is relatively high, and under continuous high operating frequency, the dielectric loss of the ceramic capacitor increases and heats up.

[0035] In many application scenarios, there is a need to detect the operating frequency or spurious frequency of some ceramic capacitors. For example, in the product research and development stage, production quality inspection stage, product working stage and factory maintenance stage. In some cases, it may be important to monitor the operating frequency or spurious frequency of ceramic capacitors when they are working. The following are some possible reasons for the detection needs:

[0036] Demand 1. For performance and stability: Capacitors (especially when used in combination with inductors) can form resonant circuits. Operating at or near the resonant frequency can result in large current oscillations and potential circuit instability. Capacitor characteristics: The impedance of a capacitor is frequency dependent. At higher frequencies, the equivalent series resistance and equivalent series inductance become significant, affecting performance.

[0037] Demand 2: For circuit design and analysis: In applications such as filtering, the performance of capacitors is directly related to the operating frequency. Testing can ensure that the capacitors can effectively filter the required frequency.

[0038] Requirement 3: Signal integrity: In high-speed digital or RF circuits, stray frequencies may cause signal integrity issues. Detection helps ensure the integrity of signal transmission.

[0039] Demand 4: For safety and reliability: At certain frequencies, especially in high-frequency applications, capacitors may be subjected to higher voltage stress, which may cause failure. Monitoring helps prevent capacitors from being overstressed. High-frequency operation causes heating due to dielectric loss. Continuous monitoring helps detect overheating and prevent damage.

[0040] Requirement 5: For compliance and testing needs: Some applications need to comply with specific electromagnetic interference (EMI) standards. Detecting frequency characteristics helps ensure compliance. At the same time, in diagnostic and troubleshooting scenarios, monitoring operating frequency and spurious frequencies helps diagnose problems.

[0041] How to achieve frequency detection of ceramic capacitors is a big problem. The loss of ceramic capacitors is related to the amount of heat dissipation. And the loss of ceramic capacitors is related to the change of frequency. In theory, this correlation should be presented as a linear change. But in reality, ceramic capacitors do not only show capacitance characteristics. They also have inductance characteristics and resistance characteristics. Moreover, as the frequency changes, the inductance characteristics and capacitance characteristics will also show dynamic changes on the ceramic capacitor. This makes it difficult to fully calculate the precise correspondence between the temperature and frequency changes of ceramic capacitors. Therefore, it is difficult to calculate the accurate frequency information of ceramic capacitors simply based on the temperature change relationship under different frequency responses. The specific reasons are as follows:

[0042] As frequency increases, dielectric losses also increase, causing more power to be dissipated as heat, which causes the temperature of the capacitor to rise. ESR also increases power dissipation. At higher frequencies, ESR can increase due to skin effect and other parasitic effects, causing additional heating. Ceramic capacitors have a temperature coefficient that defines how their capacitance changes with temperature. For example, Class I ceramics (C0G / NP0) have very stable capacitance over a wide temperature range, while Class II (X7R) and Class III (Y5V) ceramics can change capacitance significantly with temperature. Temperature rise is a function of power dissipation and depends on frequency, voltage, capacitance, and dissipation factor. The higher the frequency, the greater the dielectric losses and ESR, resulting in higher power dissipation and temperature rise. As the temperature of the capacitor increases due to the increased power dissipation at higher frequencies, the capacitance value changes according to the temperature coefficient of the dielectric material. Since the effective capacitance decreases at higher frequencies. This is more pronounced near and above the self-resonant frequency. The dielectric constant of the ceramic dielectric material also changes with frequency, affecting the capacitance value.

[0043] Ceramic capacitors have inductive properties, and ESL is the inherent inductance in the capacitor structure (including leads and internal connections). As the frequency increases, the effect of ESL becomes more significant. The change in the impedance characteristics of a ceramic capacitor is determined by its self-resonant frequency. Figure 1 As shown, this is a schematic diagram of how the electrical characteristics of ceramic capacitors respond to frequency. Figure 1 In the figure, the solid line is a schematic diagram of the impedance characteristics of ceramic capacitors changing with frequency; the dotted line is a schematic diagram of the loss of ceramic capacitors changing with frequency. Among them, point A is the self-resonant frequency point. In the frequency range before the self-resonant frequency, the change of the solid line represents the change of the capacitance characteristics of the ceramic capacitor. At this time, the ceramic capacitor is equivalent to a capacitor. In the frequency range after the self-resonant frequency, the change of the solid line represents the change of the capacitance characteristics of the ceramic capacitor. At this time, the ceramic capacitor is mainly equivalent to an inductor device rather than a capacitor device. In addition, the part of the dotted line that shows a downward trend with the increase of frequency represents the value of the dielectric loss. The part of the dotted line that shows an upward trend with the increase of frequency represents the electrode loss of the ceramic capacitor. In the low frequency range, the actual capacitance of the ceramic capacitor is close to that of the ideal capacitor, which is capacitive, and the complex impedance is inversely proportional to the frequency. At point A, it is its resonance point, at which the dotted lines and realize intersection, the impedance value at this time is the smallest, and the capacitor filtering effect is the best. In the high frequency range after the resonance point, the ceramic capacitor is inductive, and the complex impedance is proportional to the frequency. Therefore, the temperature rise-frequency relationship is complex.

[0044] In order to accurately calculate the power consumption frequency of ceramic capacitors based on their temperature rise, such as Figure 2 As shown, the embodiment of the present application provides a power frequency calculation system 1000 based on the temperature rise of a ceramic capacitor C. The power frequency calculation system 1000 includes a data processor 100 and a sampling circuit 200. The sampling circuit 200 is used to be electrically connected to the ceramic capacitor C. A neural network model is written in the data processor 100. The data processor 100 is used to:

[0045] The detected temperature rise value corresponding to the ceramic capacitor C in the target time period is obtained by the sampling circuit 200, and the detected temperature rise value is the surface temperature rise value of the ceramic capacitor C in the target time period. The output signal delay value and the output signal phase shift value of the ceramic capacitor C in the target time period are obtained. The detected temperature rise value, the output signal delay value and the output signal phase shift value are respectively binary-characterized to obtain the corresponding temperature rise characteristic value, delay characteristic value and phase shift characteristic value. The temperature rise characteristic value, the delay characteristic value and the phase shift characteristic value are input into the neural network model to obtain the power consumption frequency of the ceramic capacitor C, and the power consumption frequency includes at least one of the following frequency information: the current operating frequency of the ceramic capacitor C and the current noise frequency of the ceramic capacitor C.

[0046] For the testing of the operating frequency and capacitance of the ceramic capacitor C, the traditional method basically requires complex testing and calculation based on an oscilloscope or the like. Moreover, this method is difficult to perform when the ceramic capacitor C is in normal working condition. When the ceramic capacitor C is in working condition, the current prior art only proposes a calculation test method based on the mapping relationship between the surface temperature rise and frequency of the ceramic capacitor C for direct conversion. However, it can be seen from the above analysis that the calculation accuracy of this method is poor. When the traditional neural network model is used to realize the prediction of frequency, if only the temperature rise is used to calculate the frequency, the input feature dimension is low, and a very large sample size and algorithm accuracy are required to achieve the calculation accuracy of the above-mentioned quick calculation method of temperature rise and frequency mapping. Therefore, the neural network model cannot be directly applied to this scenario.

[0047] Based on this, in this application Figure 2 In the illustrated embodiment, by analyzing the characteristics of the ceramic capacitor C, the correlation characteristics of temperature rise-frequency are excavated, and the relationship between temperature rise and frequency is also interrelated with the changes in the inductance and capacitance of the ceramic capacitor C. In particular, as the frequency increases, the characteristics of the ceramic capacitor C will be more inclined to inductive, especially in high-frequency application scenarios and scenarios where clutter signals are generated, the heat will also increase. However, when the ceramic capacitor C is in a working state, in some application scenarios, the various electrical parameters of the ceramic capacitor C can be calculated based on open-loop control and / or closed-loop control to obtain the corresponding inductance and capacitance, but this method is complex to calculate and will easily interfere with the normal operation of the ceramic capacitor C or increase the control cost of the device operation. In other application scenarios, it is even impossible to apply some detection signals to the ceramic capacitor C, which will interfere with the normal operation of the device. Therefore, how to collect features other than temperature is a major problem.

[0048] Based on the analysis, the applicant found that the capacitance and inductance of the ceramic capacitor C are dynamically changing. In this case, the capacitance and inductance of the ceramic capacitor C will produce different changes in the electrical signal it outputs. In theory, both inductance and capacitance will have a time delay effect on the signal. However, the equivalent capacitance of the ceramic capacitor C is mainly based on the length change of the effective level caused by the charging and discharging of the capacitor. And its equivalent inductance is manifested as the delay caused by the change of phase. Therefore, the delay value of the output signal of the ceramic capacitor C is related to the characteristics of capacitance and inductance. Based on the collection of temperature rise value, delay value and phase shift value, the corresponding encoded feature value is obtained based on binary coding. Based on the unified feature form after encoding, it is input into the neural network model algorithm for model training. Based on these multi-dimensional feature information, the calculation and prediction of power consumption frequency can be more accurately realized.

[0049] In some possible implementations, in some application scenarios of ceramic capacitors C, time domain reflectometry (TDR) detection can also be performed in the working state of ceramic capacitor C. A time domain detection signal is sent to the output end of ceramic capacitor C through time domain reflection technology. The time domain detection signal is transmitted back from the output end of ceramic capacitor C to ceramic capacitor C, and a reflection signal is generated during the transmission process. The reflection signal is transmitted from the output end of ceramic capacitor C to the time domain reflection detection device. The impedance information of ceramic capacitor C (i.e., the change information of capacitance and inductance) can be detected based on the reflection signal. At this time, if Figure 3 As shown, the power frequency calculation system 1000 also includes a time domain detector 300, which is electrically connected to the data processor 100 and the ceramic capacitor C, respectively. Before the temperature rise characteristic value, the time delay characteristic value, and the phase shift characteristic value are input into the neural network model, the data processor 100 is also used to: control the time domain detector 300 to send a time domain detection signal to the output end of the ceramic capacitor C within the target time period, and obtain an impedance reflection signal corresponding to the time domain detection signal through the time domain detector 300. The impedance real part characteristic value and the impedance imaginary part characteristic value are obtained according to the impedance reflection signal.

[0050] Exemplarily, the above-mentioned inputting of the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor C includes: inputting the temperature rise characteristic value, the time delay characteristic value, the phase shift characteristic value, the real part characteristic value of impedance and the imaginary part characteristic value of impedance into the neural network model to obtain the power consumption frequency of the ceramic capacitor C.

[0051] In the above Figure 3 In the embodiment of the present invention, the impedance characteristic can also be detected based on the time domain detector 300. The data processor obtains the impedance real part characteristic value and the impedance imaginary part characteristic value based on the impedance characteristic detection, and Figure 2 The characteristic values ​​obtained by the embodiments can be combined to improve the calculation accuracy of the neural network algorithm model.

[0052] Exemplarily, when performing impedance detection, a filter and an attenuator may be provided between the time domain detector 300 and the output end of the ceramic capacitor C. The signal output by the time domain detector 300 is attenuated and reduced by the attenuator so as to minimize interference with the normal operation of the ceramic capacitor C while satisfying the time domain detection. At the same time, the filter can also improve the detection accuracy and reduce interference. In addition, for some scenarios, the ceramic capacitor C operates based on a certain working time slot, and can also perform time domain reflection detection during its non-working time slot. The time domain detector 300 usually uses pulses of several hundred millivolts based on a very short duration (nanosecond range) to achieve impedance detection.

[0053] In some possible implementations, before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model, the data processor 100 is further used to: obtain the ambient temperature value of the ceramic capacitor C in the target time period through the sampling circuit 200, perform binary feature encoding on the ambient temperature value, and obtain the ambient temperature characteristic value. Exemplarily, the above-mentioned inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor C includes: inputting the temperature rise characteristic value, the time delay characteristic value, the phase shift characteristic value and the ambient temperature characteristic value into the neural network model to obtain the power consumption frequency of the ceramic capacitor C. In the embodiment of the present application, the value of the temperature rise also interacts with the ambient temperature. Therefore, in order to eliminate environmental errors and pay attention to the characteristic correlation between the surface temperature rise and the ambient temperature, characteristic values ​​related to the ambient temperature can also be input into the neural network model to improve the technical accuracy.

[0054] Based on the above Figure 2 and Figure 3 The power consumption frequency calculation system 1000 of the structure shown can perform the following Figure 4 The power frequency calculation method based on the temperature rise of the ceramic capacitor C includes the operations of step S100 and step S300:

[0055] S100. Train a neural network model.

[0056] Before calculating the power consumption frequency, the neural network model needs to be trained. Usually, according to the actual detection scenario required, multiple ceramic capacitors C in the application scenario can be used for data collection. The collected data includes parameters such as frequency and temperature rise, ambient temperature, capacitance value, inductance value, delay value and phase shift value to generate relevant sample sets. The neural network model is trained based on the sample set. This application does not limit the neural network model on which it is based.

[0057] Exemplarily, the training may be implemented by using a BP neural network model, a RBF neural network model, or the like.

[0058] Exemplarily, a dual neural network model can be used to implement training, wherein the dual neural network model includes a weight calculation model and a parameter operation model. The weight of each eigenvalue is calculated based on the weight calculation model. All eigenvalues ​​are input into the parameter operation model, and frequency calculation is performed based on the determined weight.

[0059] S200: Obtain characteristic values.

[0060] Exemplarily, a detected temperature rise value corresponding to the ceramic capacitor C in the target time period may be obtained, where the detected temperature rise value is a surface temperature rise value of the ceramic capacitor C in the target time period.

[0061] Exemplarily, the output signal delay value and the output signal phase shift value of the ceramic capacitor C within the target time period may be acquired.

[0062] Exemplarily, within the target time period, a time domain detection signal is sent to the output end of the ceramic capacitor C to obtain an impedance reflection signal corresponding to the time domain detection signal; and the impedance real part eigenvalue and the impedance imaginary part eigenvalue are obtained according to the impedance reflection signal.

[0063] Exemplarily, the ambient temperature value of the ceramic capacitor C within the target time period may be acquired, and the ambient temperature value may be binary-characterized to obtain an ambient temperature characteristic value.

[0064] S300, inputting the characteristic value into the neural network operation model to obtain the power consumption frequency.

[0065] In some possible implementations, such as Figure 5 As shown in FIG. 1 , taking the BP neural network model as an example, it includes a cascaded input layer, a hidden layer and an output layer. The feature value is input into the input layer, and the output of the prediction result is realized through the output layer.

[0066] Exemplarily, the weight values ​​of the parameter operation model in the operation of the input layer, the hidden layer and the output layer can be calculated and determined based on the weight calculation model.

[0067] The data processor involved in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a programmable logic device (PLD), or other integrated chips.

[0068] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0069] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0071] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0072] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one device or distributed on multiple devices. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0073] In addition, each functional module in each embodiment of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device.

[0074] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, 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 process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or may include one or more servers, data centers and other data storage devices that can be integrated with the medium. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0075] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for calculating the power consumption frequency based on the temperature rise of ceramic capacitors, characterized in that: The method comprises: Obtain a detected temperature rise value corresponding to the ceramic capacitor in the target time period, wherein the detected temperature rise value is a surface temperature rise value of the ceramic capacitor in the target time period; obtain an output signal delay value and an output signal phase shift value of the ceramic capacitor in the target time period; The detected temperature rise value, the output signal delay value and the output signal phase shift value are respectively binary-characterized to obtain corresponding temperature rise characteristic values, delay characteristic values ​​and phase shift characteristic values; The temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value are input into a neural network model to obtain the power consumption frequency of the ceramic capacitor, and the power consumption frequency includes at least one of the following frequency information: the current operating frequency of the ceramic capacitor and the current noise frequency of the ceramic capacitor.

2. The method for calculating the power consumption frequency based on the temperature rise of ceramic capacitors according to claim 1, characterized in that: Before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into a neural network model, the method further includes: In the target time period, sending a time domain detection signal to the output end of the ceramic capacitor to obtain an impedance reflection signal corresponding to the time domain detection signal; The real impedance part characteristic value and the imaginary impedance part characteristic value are obtained according to the impedance reflection signal.

3. A method for calculating power consumption frequency based on temperature rise of ceramic capacitors according to claim 2, characterized in that: The step of inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into a neural network model to obtain the power consumption frequency of the ceramic capacitor comprises: The temperature rise characteristic value, the time delay characteristic value, the phase shift characteristic value, the impedance real part characteristic value and the impedance imaginary part characteristic value are input into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

4. A method for calculating the power consumption frequency based on the temperature rise of a ceramic capacitor according to any one of claims 1 to 3, characterized in that: Before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into a neural network model, the method further includes: The ambient temperature value of the ceramic capacitor within the target time period is obtained, and the ambient temperature value is binary-characterized to obtain an ambient temperature characteristic value.

5. A method for calculating the power consumption frequency based on the temperature rise of ceramic capacitors according to claim 4, characterized in that: The step of inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into a neural network model to obtain the power consumption frequency of the ceramic capacitor comprises: The temperature rise characteristic value, the time delay characteristic value, the phase shift characteristic value and the ambient temperature characteristic value are input into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

6. A system for calculating power consumption frequency based on temperature rise of ceramic capacitors, characterized in that: The invention comprises a data processor and a sampling circuit, wherein the sampling circuit is used to be electrically connected to the ceramic capacitor, and a neural network model is written into the data processor; the data processor is used to: Acquiring, by the sampling circuit, a detected temperature rise value corresponding to the ceramic capacitor in the target time period, the detected temperature rise value being a surface temperature rise value of the ceramic capacitor in the target time period; Obtaining an output signal delay value and an output signal phase shift value of the ceramic capacitor within the target time period; The detected temperature rise value, the output signal delay value and the output signal phase shift value are respectively binary-characterized to obtain corresponding temperature rise characteristic values, delay characteristic values ​​and phase shift characteristic values; The temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value are input into the neural network model to obtain the power consumption frequency of the ceramic capacitor, and the power consumption frequency includes at least one of the following frequency information: the current operating frequency of the ceramic capacitor and the current noise frequency of the ceramic capacitor.

7. The power frequency calculation system based on the temperature rise of ceramic capacitors according to claim 6, characterized in that: The power frequency calculation system further includes a time domain detector, which is electrically connected to the data processor and the ceramic capacitor respectively; before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model, the data processor is further used to: In the target time period, controlling the time domain detector to send a time domain detection signal to the output end of the ceramic capacitor, and obtaining an impedance reflection signal corresponding to the time domain detection signal through the time domain detector; The real impedance part characteristic value and the imaginary impedance part characteristic value are obtained according to the impedance reflection signal.

8. The power consumption frequency calculation system based on the temperature rise of ceramic capacitors according to claim 7, characterized in that: The step of inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into a neural network model to obtain the power consumption frequency of the ceramic capacitor comprises: The temperature rise characteristic value, the time delay characteristic value, the phase shift characteristic value, the impedance real part characteristic value and the impedance imaginary part characteristic value are input into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

9. A system for calculating power consumption frequency based on temperature rise of ceramic capacitors according to any one of claims 6 to 8, characterized in that: Before inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into the neural network model, the data processor is further used for: The ambient temperature value of the ceramic capacitor within the target time period is acquired through the sampling circuit, and the ambient temperature value is binary-characterized to obtain an ambient temperature characteristic value.

10. The power consumption frequency calculation system based on the temperature rise of ceramic capacitors according to claim 9, characterized in that: The step of inputting the temperature rise characteristic value, the time delay characteristic value and the phase shift characteristic value into a neural network model to obtain the power consumption frequency of the ceramic capacitor comprises: The temperature rise characteristic value, the time delay characteristic value, the phase shift characteristic value and the ambient temperature characteristic value are input into the neural network model to obtain the power consumption frequency of the ceramic capacitor.

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