A method, system and medium for selecting and matching circuit components

Abstract

The present invention relates to the technical field of circuit component selection and matching, and particularly relates to a method, a system and a medium for circuit component selection and matching. The method includes the following steps: obtaining the rated voltage and rated current of different components through circuit component information; establishing a blank circuit simulation space by using blank circuit design information; quantifying the overload thermal density of the rated voltage and rated current of different components according to the blank circuit simulation space, and comparing the component performance degradation between different components to obtain the component performance degradation ratio between different components; performing periodic performance degradation prediction based on the component performance degradation ratio to obtain a periodic performance degradation prediction report between different components; performing a stability test on the periodic performance degradation prediction report, and selecting and matching circuit components based on this. The present invention makes the circuit component selection and matching technology more perfect through the optimization of the circuit component selection and matching technology.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit component selection and matching, and particularly to a method, system and medium for circuit component selection and matching. Background Art

[0002] The functional requirements of electronic circuits have become more diverse and complex. This trend makes it difficult for traditional selection and matching methods to meet the requirements of high efficiency, accuracy and customization. For example, different application scenarios have widely different requirements for the performance, volume, cost, power consumption, etc. of circuit components. How to optimize the overall performance while meeting specific requirements has become a major challenge. At the same time, there is a wide variety of electronic components with complex parameters, making it necessary to quickly find the most suitable combination among a large number of optional components during the selection and matching process. In addition, the update and iteration speed of components in the market is fast, and supply chain problems are becoming increasingly prominent, which also increases the difficulty of circuit component selection and matching. Design engineers must consider component compatibility, availability and future scalability. For example, some high-performance components cannot be purchased in a short time, or the design progress is affected due to supply chain disruptions. Moreover, different component manufacturers will provide similar products with slightly different parameters, which further increases the complexity of the selection and matching work. At the technical level, modern circuit design often involves the cross-application of multiple disciplines, including analog and digital circuits, electromagnetic compatibility design, thermal management, and mechanical structure design, etc. These multi-dimensional requirements prompt the selection and matching method to comprehensively consider various factors rather than simply focusing on a single performance index. At the same time, modern electronic products pursue higher integration and smaller volume, which means that the layout rationality of components and the feasibility of the overall design need to be strictly considered during circuit selection and matching. However, there are problems with traditional circuit component selection and matching methods, such as poor recognition ability of the performance decline of circuit component elements and large errors in the selection of circuit component elements. Summary of the Invention

[0003] Based on this, it is necessary to provide a method, system and medium for circuit component selection and matching to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for circuit component selection and matching, the method includes the following steps:

[0005] Step S1: Obtain blank circuit design information and circuit component information to be tested; obtain the rated voltage and rated current of different components through the circuit component information; establish a blank circuit simulation space using the blank circuit design information;

[0006] Step S2: Quantify the overload thermal density of the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload thermal density quantization value of the components; perform component performance decline comparison between different components based on the component overload thermal density quantization value to obtain the component performance decline ratio between different components;

[0007] Step S3: Based on the component performance degradation ratio, perform periodic performance degradation prediction to obtain a periodic performance degradation prediction report for different components; conduct a stability test on the periodic performance degradation prediction report and select circuit components accordingly.

[0008] The present invention obtains the design information of a blank circuit and combines it with the specific parameter information of the circuit components to be tested, such as rated voltage, rated current, etc. Through in-depth analysis of these parameters, the performance requirements and load capacity of each circuit component during operation can be clarified. In addition, using the design information of the blank circuit, a simulation space is established to lay a foundation for subsequent circuit simulation and analysis. By establishing the simulation space, the working state of each component in the circuit can be evaluated more intuitively, and accurate input data can be provided for the subsequent steps. By analyzing the circuit simulation space, for the rated voltage and rated current of each component, the quantification calculation of the overload heat density is carried out. The purpose of this process is to evaluate the heat generated by the component due to overload and its impact on the component life under different working conditions. Through the quantified overload heat density value, further compare the performance degradation between different components and analyze the trend of their relative performance degradation. This comparison can help clarify which components are prone to performance degradation due to overload and provide data support for circuit design optimization and component selection. According to the performance degradation ratio of different components obtained in Step S2, perform periodic performance degradation prediction. By predicting the degradation trend, the performance changes of each component during long-term use can be foreseen, and its impact on the stability of the entire circuit system can be evaluated. On this basis, conduct a stability test to ensure the overall stability and reliability of the circuit under different working environments. Through this process, a scientific basis can be provided for the selection of circuit components, and components with high stability during long-term use can be selected to avoid circuit system failure or efficiency decline caused by performance degradation, thereby improving the long-term reliability of circuit design. Therefore, the present invention makes an optimization treatment for a traditional method of selecting circuit components, solves the problems of poor recognition ability of component performance degradation and large error in selecting circuit component elements in the traditional method of selecting circuit components, improves the ability to recognize component performance degradation of circuit components, and reduces the error in selecting circuit component elements.

[0009] Preferably, the blank circuit design information includes circuit topology structure, circuit line routing, circuit board layer materials, and the number of layers; the circuit component information includes resistor, capacitor, inductor, semiconductor device component categories, component rated parameters, and thermal performance parameters.

[0010] Preferably, Step S2 includes the following steps:

[0011] Step S21: According to the blank circuit simulation space, perform element limit bearing time series simulation on the rated voltage and rated current of different elements to obtain the load current-voltage fluctuation curve under the element time series limit bearing state;

[0012] Step S22: Identify the average time series voltage and current fluctuation amplitude of the load current-voltage fluctuation curve to obtain the average time series fluctuation voltage and average time series fluctuation current under the limit bearing state;

[0013] Step S23: Quantify the overload heat density according to the average time series fluctuation voltage and average time series fluctuation current to obtain the overload heat density quantization value of the element;

[0014] Step S24: Based on the overload heat density quantization value of the element, conduct a comparison of the element performance degradation between different elements to obtain the performance degradation ratio between different elements.

[0015] The present invention first simulates the extreme load state of different components under rated voltage and rated current according to the blank circuit simulation space. This simulation aims to explore the working performance of each component in the circuit under high load, especially in the situation of overload and load fluctuation. Through simulation, the load current and voltage fluctuation curves of each component are obtained, so as to accurately describe the dynamic response and working performance of the component under extreme load conditions. This process helps to determine the extreme load capacity of the component under different working conditions and provides basic data for subsequent analysis. Based on the load current-voltage fluctuation curve generated in step S21, the average timing voltage and current fluctuation amplitude are identified. By analyzing the fluctuation curve, the average voltage fluctuation and current fluctuation amplitude under the extreme load state are extracted. These fluctuation amplitudes reflect the working stability of the component under high load and the fluctuation range of its electrical performance, and provide quantitative fluctuation indicators for subsequent overload heat density calculation and performance evaluation. This step can help identify which components show large fluctuations under high load, thereby affecting the overall reliability of the circuit. According to the average timing fluctuation voltage and current data obtained in step S22, the overload heat density of the component is quantified. The calculation of overload heat density evaluates the thermal load of the component by considering the heat accumulation generated by current and voltage fluctuations. Overload heat density is an important indicator for judging whether a component is prone to performance degradation due to overheating. By performing thermal analysis on these fluctuation amplitudes, it is possible to determine which components will encounter overheating problems when subjected to high loads, thereby providing early warning of the risk of failure in the circuit. Based on the quantified value of overload heat density obtained in step S23, a performance degradation comparison between different components is performed. Based on the impact of overload heat density on component performance, the performance degradation ratio between each component is calculated. This ratio represents the degradation trend of different components in long-term use, which can help designers evaluate and compare the reliability and durability of different components. Through in-depth analysis of the component degradation ratio, it is possible to find out which components are prone to large performance degradation, thereby providing a basis for the selection and optimization of components in circuit design, ensuring the long-term stability and efficient operation of the circuit.

[0016] Preferably, step S23 includes the following steps:

[0017] Step S231: extracting the fluctuation extreme value interval of the average time-series fluctuation voltage and the average time-series fluctuation current to obtain the fluctuation voltage extreme value interval and the fluctuation current extreme value interval;

[0018] Step S222: analyzing the component heating rate according to the fluctuation voltage extreme value interval and the fluctuation current extreme value interval to obtain the component heating rate;

[0019] Step S223: identifying the temperature rise diffusion range based on the component temperature rise rate to obtain the temperature rise diffusion range;

[0020] Step S224: Based on the temperature rise diffusion range, identify the overlap of the temperature rise ranges between different components to obtain the temperature rise range overlap data;

[0021] Step S225: According to the component temperature rise rate, perform an analysis of the isothermal diffusion range quantity on the temperature rise range overlap data to obtain the isothermal diffusion range quantity;

[0022] Step S226: Quantify the overload heat density based on the component temperature rise rate, the temperature rise range overlap data, and the isothermal diffusion range quantity to obtain the component overload heat density quantization value.

[0023] The present invention analyzes the average timing fluctuation voltage and current fluctuation data obtained in step S22 to extract the extreme value intervals of the fluctuation voltage and fluctuation current. The extreme value interval of fluctuations refers to the interval of the maximum and minimum values reached by the voltage and current at a specific timing. This analysis helps to identify the extreme fluctuation range of the electrical performance of components under different load conditions. By accurately extracting these extreme value intervals, key parameters can be provided for subsequent thermal analysis, helping to determine the time period and area where overload heat is generated in the circuit, and providing a theoretical basis for the thermal management of components. Based on the extreme value interval of the fluctuation voltage and the extreme value interval of the fluctuation current extracted in step S231, the component heating rate analysis is carried out. This analysis calculates the heating rate of the component under different fluctuation conditions by evaluating the thermal effect generated by the current and voltage fluctuations on the component. The heating rate reflects how fast the temperature of the component rises under a specific working environment, and directly affects the life and stability of the component. By accurately calculating the heating rate, it is possible to identify which components are prone to overheating under high load and fluctuation conditions, thus affecting their performance degradation. According to the component heating rate, the identification of the heating diffusion range is carried out. The heating diffusion range refers to the range of the temperature distribution of the surrounding area affected by the temperature rise of the component within a certain time. By calculating and identifying this range, the potential impact of the temperature rise on other components around the component can be revealed, especially for those components with large temperature changes under high load conditions. By identifying the heating diffusion range, data support can be provided for the thermal management and temperature interference evaluation between components, helping to optimize the circuit layout and reduce the negative effects brought by temperature interaction. By analyzing the heating diffusion ranges of different components, the overlapping data of their heating ranges are identified. This process evaluates which components have overlapping thermal influence areas by comparing the heating diffusion ranges of each component, thus revealing the potential risk of thermal interference. The overlapping of the heating ranges means that multiple components will work in the same thermal influence area, thus increasing the risk of local overheating and performance degradation. Identifying this overlapping data helps to optimize the layout and heat dissipation design of the components and ensure the effectiveness of thermal management measures. Based on the comprehensive analysis of the component heating rate, the overlapping data of the heating ranges, and the equidistant range quantity of heat diffusion, the equidistant range quantity of heat diffusion is obtained. The equidistant range quantity of heat diffusion refers to the equidistant range of the temperature rise at different distances around the component within a certain time. Through this analysis, the speed and range of heat diffusion of the component under overload conditions can be further understood, helping to identify which components have a greater thermal impact on the surrounding environment. This data is crucial for optimizing the thermal design, selecting a suitable heat dissipation solution, and ensuring the stability of the circuit. Finally, based on the heating rate, the overlapping data of the heating ranges, and the equidistant range quantity of heat diffusion in the previous steps, the quantification calculation of the overload heat density is carried out. This process combines the heat diffusion range, the heating rate with the physical characteristics of the component to obtain the heat density value of the component under overload conditions. The quantified value of the overload heat density reflects the degree of heat accumulation generated by the component under high load conditions and is a key indicator for evaluating the performance degradation of the component.Precise thermal density quantification can help designers identify which components are prone to performance degradation due to overheating, providing a scientific basis for the long-term stability and reliability of the circuit.

[0024] Preferably, step S225 includes the following steps:

[0025] According to the determined component heating rate, perform thermal field gradient partitioning. Taking 10°C as a gradient interval, divide 20°C - 30°C into the first gradient zone and 30°C to 40°C into the second gradient zone, thereby obtaining thermal field range gradient data;

[0026] If the heating rate of a certain component is 5°C per minute, the temperature rise speed is different in different gradient zones. Based on this heating rate, identify the temperature diffusion amount for the thermal field range gradient data;

[0027] In the first gradient zone, the temperature rises from 20°C to 30°C in 2 minutes, and the diffusion amount is 10°C. In the second gradient zone, due to the change in thermal field characteristics, it takes 3 minutes to rise from 30°C to 40°C, and the diffusion amount is also 10°C. Due to the time difference, the temperature distribution diffusion difference amount is obtained;

[0028] Based on the temperature distribution diffusion difference amount, perform thermal diffusion equidistant range amount analysis. Set the equidistant range to 5°C, analyze the uniformity and directionality of temperature diffusion within each equidistant range, and then obtain the thermal diffusion equidistant range amount.

[0029] According to the determined heating rate of the component, the thermal field is divided into gradient zones to more accurately analyze the heat diffusion situation. The thermal field zoning is divided into multiple intervals based on different temperature ranges, and a fixed temperature gradient is set between each interval. For example, 20°C - 30°C is set as the first gradient zone, 30°C - 40°C is set as the second gradient zone, etc. This zoning process helps to refine the analysis of temperature changes and facilitates the subsequent independent evaluation of the temperature change characteristics within each temperature gradient zone. Through this zoning, different temperature ranges in the thermal field and the laws of their heat diffusion can be accurately identified, providing data support for thermal analysis and optimization. Based on the aforementioned thermal field gradient zoning and the determined heating rate of the component, the temperature diffusion amount in each temperature gradient zone is identified. If the heating rate of the component is 5°C per minute, then the temperature rise rate will vary in different gradient zones. Taking 20°C - 30°C (the first gradient zone) as an example, if the temperature rises by 10°C in 2 minutes, while for 30°C - 40°C (the second gradient zone), it takes 3 minutes to rise by 10°C. This analysis helps to reveal the heat diffusion differences in different temperature gradient zones and further understand the heat diffusion characteristics of the component within different temperature ranges. This is the basis for analyzing the thermal behavior of the component and can guide the optimization of subsequent thermal management strategies. By comparing the temperature rise rates in different temperature gradient zones, the difference amount of temperature distribution diffusion is obtained. Although the temperature diffusion amount in each gradient zone is the same (such as 10°C), due to the different heating times in different gradient zones, the diffusion characteristics of the temperature distribution also vary. By comparing these differences, the heat diffusion rate and change law in different temperature intervals can be quantified. This analysis reveals the temperature diffusion differences between different gradient zones, helps to understand the heat propagation characteristics of the component in the thermal field, and provides a basis for accurately predicting the thermal behavior of the component and its impact on the surrounding environment. Based on the aforementioned difference amount of temperature distribution diffusion, an analysis of the equidistant range of heat diffusion is carried out. The equidistant range is set to 5°C, and the temperature diffusion uniformity and directionality within each temperature range are evaluated. During the analysis process, it is necessary to identify whether the temperature diffusion is uniform within different temperature zones and whether there is an asymmetry in the diffusion direction. For example, the heat diffusion in a certain direction is faster than in other directions, which helps to determine whether the heat is evenly distributed around the component. The results of this analysis are of great significance for optimizing thermal management, improving heat dissipation performance, and enhancing the long-term stability of the component. By quantifying the equidistant range of heat diffusion, it can help designers formulate more effective heat dissipation strategies and prevent performance degradation caused by overheating. Through the aforementioned temperature diffusion analysis, the equidistant range amount of heat diffusion is finally obtained. This quantity measures the heat diffusion characteristics of the component under different temperature gradients, specifically including the temperature diffusion range and uniformity within each temperature gradient zone. The analysis results of the equidistant range amount of heat diffusion can reveal the propagation mode of temperature in the thermal field, guide component layout, heat dissipation design, and thermal management optimization. This data can also be used to predict the temperature change trend of the component during long-term operation, avoid failures caused by local overheating, and thus improve the overall reliability of the system.

[0030] Preferably, step S24 includes the following steps:

[0031] Based on the component overload thermal density quantization value, perform point-by-point differential calculation, use the differential formula to calculate the change rate between adjacent two points, then compare the component point-by-point heat load change rates between different components to obtain the average difference of the point-by-point heat load change. Subtract the point-by-point heat load change rate of component A from the corresponding point change rate of component B to obtain a difference sequence;

[0032] After calculating the difference sequences for all components in pairs, find the average value of these difference sequences to obtain the average difference D of the point-by-point heat load change. Divide the component power decay levels based on the average difference D of the point-by-point heat load change;

[0033] Set a division threshold. When D < 0.1, the power decay level is level 1, indicating slight power decay; when 0.1 <= D < 0.3, it is level 2; when D >= 0.3, it is level 3, thereby determining the power decay level of each component;

[0034] Based on the average difference D of the point-by-point heat load change and the component power decay level, perform maximum entropy estimation. Establish a probability model through the maximum entropy principle, and use the known average difference D of the point-by-point heat load change and power decay level data as constraint conditions to estimate the maximum entropy value H of power decay;

[0035] Based on the maximum entropy value H of power decay, compare the component performance decay between different components, thereby obtaining the performance decay ratio between different components.

[0036] The present invention first performs point-by-point differential calculation based on the quantified overload thermal density value of the component to calculate the change rate between adjacent two points. By using the differential formula, the change of the thermal load of the component at different time points or under different working conditions can be accurately determined. Then, by comparing the point-by-point thermal load change rates of different components, the difference between each pair of components is calculated, thereby obtaining a difference sequence. This difference sequence helps to reveal the similarities and differences in the thermal load changes of different components, providing data support for subsequent power decay assessment. Through this point-by-point differential analysis, the thermal stress and load changes of the component during long-term use can be revealed, providing an accurate basis for thermal performance analysis. By calculating the pairwise difference sequences of all components and obtaining the average value of these difference sequences, the average difference D of the point-by-point thermal load change is obtained. The average difference D of the point-by-point thermal load change reflects the degree of difference in the thermal load changes of different components. The larger the value, the more obvious the difference in the thermal load changes between the components. The power decay level is divided according to the D value: when D < 0.1, it indicates that the power decay is slight and is classified as level 1; when 0.1 <= D < 0.3, the power decay is medium and is classified as level 2; when D >= 0.3, the power decay is obvious and is classified as level 3. Through this process, the power decay degree of each component can be accurately evaluated, providing clear guidance for subsequent thermal management and performance optimization. Using the data of the average difference D of the point-by-point thermal load change and the component power decay level, the maximum entropy value H of the power decay is estimated by the maximum entropy principle. The maximum entropy principle is a statistical method for inferring the most likely state by maximizing uncertainty. Here, the maximum entropy principle is used to establish a probability model, with the known average difference of the point-by-point thermal load change and the power decay level data as constraints, and the maximum entropy value H of the power decay is deduced through optimization. The maximum entropy value H provides a comprehensive measure, reflecting the uncertainty and potential risks during the power decay process of the component, providing an important basis for the long-term reliability assessment of the component. Based on the previously calculated maximum entropy value H of the power decay, the performance decay comparison between different components is carried out. By comparing the maximum entropy values H of different components, the decay differences of each component during the thermal load change process can be revealed. A higher H value means that the decay process of the component is more uncertain and complex, with a higher overload risk or performance degradation. While a lower H value indicates that the decay process of the component is relatively stable. Through this comparative analysis, it can help designers identify which components are more likely to overheat or degrade in performance during long-term use, thereby optimizing the component selection and thermal management strategy to ensure the overall stability and reliability of the system. Through the comparative analysis of the performance decay of multiple components, the performance decay ratio between different components is obtained. This ratio reflects the relative performance differences of different components in terms of thermal load, power decay, etc., and can reveal which components have performance degradation due to heat accumulation, overload or design defects.The obtained decline ratio can be used as a reference for performance evaluation and component optimization, helping to formulate effective maintenance strategies, optimize component layout, or adjust circuit design to ensure the high efficiency and reliability of the system during long-term operation. Through this analysis, more scientific decisions can be made in component selection and thermal management strategies.

[0037] Preferably, the periodic performance decline prediction based on the component performance decline ratio includes the following steps:

[0038] First, collect the performance parameters of the component in different operating cycles, record the ratio point by point, establish a time series data set in chronological order, calculate the correlation coefficient between each time point, screen out the time series data with significant correlation, and form the time series correlation data of performance decline.

[0039] When performing weighted processing on the time series points of the decline time series correlation data, use the time weight calculation method to weight the data of each time point according to the importance of its time distribution, use the discrete point fitting method to calculate the weight distribution of the data of each time point, and merge the weighted results to generate the weighted data of time series points.

[0040] When performing periodic performance decline prediction, according to the weighted results of each point in the weighted data of time series points, combined with the actual operating cycle of the component, use the multi-point fitting interpolation method to analyze the periodic performance change trends of different components, conduct periodic prediction according to the time period division method, and generate a detailed report including the performance decline cycle, decline rate, and trend line of each component, and finally form a periodic performance decline prediction report between different components.

[0041] The present invention collects the performance parameters of components under different duty cycles and records the ratio point by point. Each data point should include the performance data of the component at a specific time point, and a time series data set is constructed through these data. Then, the data is organized according to the time sequence to ensure the timeliness and coherence of the data. By calculating the correlation coefficient between adjacent time points, the time series data significantly correlated with the decline is selected to form the time series correlation data of performance decline. This process helps to identify the change law of component performance and provides a basis for subsequent decline prediction. In the weighted processing of time series data, through the time weight calculation method, the data at each time point is weighted according to its importance in the time distribution. According to the actual situation of the component performance change, the discrete point fitting method is used to calculate the weight distribution of each time point. This weighted processing process can highlight the influence of key time points, ignore or reduce the time points with less influence on the performance change, so as to enhance the prediction accuracy of the model for the periodic decline trend. The weighted results are finally merged to form the weighted data of time series points, providing high-quality data support for further analysis. When predicting the periodic performance decline, first, based on the weighted results in the weighted data of time series points and combined with the actual operating cycle of the component, the multi-point fitting interpolation method is used to analyze the periodic performance changes of different components. Through the prediction analysis of different time periods, the decline trend and decline rate of each component can be revealed. In addition, by dividing and predicting periodically according to different time periods, the decline mode of the component in different operating cycles can be understood in detail. Finally, a report including the performance decline cycle, decline rate and trend line of each component is formed, providing a scientific basis for the maintenance and replacement strategy. Based on the analysis results of the data obtained from the foregoing steps, a periodic performance decline prediction report for each component is generated. The report content should include detailed information such as the decline cycle, decline rate, and trend line of each component, clearly showing the regularity of component performance decline and its future change trend. This report can provide decision-making support for the maintenance management, preventive maintenance and optimization of the equipment usage cycle of the equipment, help extend the service life of the component, reduce the equipment failure rate, and improve the overall reliability of the system.

[0042] Preferably, the stability test of the periodic performance decline prediction report includes the following steps:

[0043] Extract the current and voltage decline values of different components from the periodic performance decline prediction report and calculate the variance to obtain the current decline variance and the voltage decline variance;

[0044] Conduct a load characteristic analysis on the current decline variance and the voltage decline variance to obtain the current-voltage decline load characteristic data;

[0045] Calculate the current and voltage decay based on the current-voltage decay load characteristic data, and obtain the rate of change during the load increase and decrease process to get a trend curve. By evaluating the slope and rate of change of the trend curve, determine whether the decay state tends to be stable;

[0046] If the trend of the trend curve is like a linear and gentle shape, it indicates that the decay process of the component in actual application tends to be stable. Finally, based on the evaluation results, perform a stability test to identify unstable components, and then reasonably select and match circuit components; specifically, the curve trend being like a linear and gentle shape means: perform piecewise linear fitting on the trend curve, extract the slope values and correlation coefficients of each segment to verify whether each segment of the curve is close to a linear distribution; calculate the average slope and its standard deviation of the entire curve. If the average slope is close to zero and the standard deviation is less than 1 and infinitely approaches zero but is not equal to zero, then preliminarily judge that the trend curve tends to be gentle; perform a fluctuation range analysis on the fitted curve, use the difference method to calculate the incremental changes between points on the curve, count the maximum value, minimum value and range of the increments, and draw a fluctuation histogram for evaluation; if the increment distribution is concentrated and the range is less than the preset value, sum up the deviations between the actual data points and the fitting line, and record the change trend of the cumulative deviation curve. If the cumulative deviation curve is close to zero and has no fluctuations, it can be determined that the curve exhibits linear and gentle characteristics.

[0047] By extracting the decay data of current and voltage from the report and calculating the variance, the present invention can quantify the fluctuation degree of the decay process. The larger the variance, the greater the decay fluctuation of the component under different load conditions, and there is a risk of instability; on the contrary, a smaller variance indicates that the component decay is relatively stable. Therefore, the calculation of variance provides the quantified basic data for the subsequent stability test. Analyzing the load characteristics of the current decay variance and the voltage decay variance can reveal the decay law of the component under different loads. This analysis helps to understand whether the decay process is affected by load fluctuations, and thus provides a basis for the calculation of the current-voltage decay load characteristic data. Through this process, the decay rates of current and voltage during the load change process can be obtained, so as to infer whether the component decay process is stable. By analyzing the current-voltage decay load characteristic data, calculating the change rates of current and voltage, plotting the trend curve, and evaluating its slope and change rate, it is possible to judge whether the decay process tends to be stable. If the trend curve shows a linear and gentle shape, it indicates that the decay process tends to be stable, and the load fluctuations in the system have little impact on the component performance. This evaluation provides an intuitive basis for the component stability judgment. By performing piecewise linear fitting on the trend curve and extracting the slope values and correlation coefficients of each segment, it can be verified whether the decay process is close to a linear distribution. If the slope of each segment is small and the correlation coefficient is high, it indicates that the decay process tends to be stable. Calculating the average slope and its standard deviation of the entire curve helps to further confirm the linear and gentle characteristics of the curve. This analysis can accurately judge the stability of the decay process and ensure the long-term reliability of the component. Analyzing the fluctuation range of the fitted curve and using the difference method to calculate the incremental changes between points on the curve can help to evaluate whether there are large fluctuations during the decay process. If the incremental changes are concentrated and the range is small, it indicates that the decay process is relatively stable and not prone to large fluctuations. By evaluating through plotting the fluctuation histogram, the stability of the component decay can be further confirmed, reducing the failure risk in practical applications. If the incremental distribution is concentrated and the range is less than the preset value, the stability of the decay curve can be further confirmed through the change trend of the cumulative deviation curve. If the cumulative deviation is close to zero and there is no obvious fluctuation, it indicates that the decay process is stable, the trend is close to linear and there are no large fluctuations. This process provides strong support for the final stability judgment of the component and ensures that the component maintains good performance in practical applications.

[0048] Preferably, the present invention further provides a circuit component selection and matching system for performing the circuit component selection and matching method as described above. The circuit component selection and matching system includes:

[0049] A simulation space construction module, configured to obtain blank circuit design information and circuit component information to be tested; obtain the rated voltage and rated current of different components through the circuit component information; establish a blank circuit simulation space using the blank circuit design information;

[0050] A performance degradation comparison module, which is used to quantify the overload thermal density of the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload thermal density quantization value of the components; and perform component performance degradation comparison between different components based on the overload thermal density quantization value of the components to obtain the component performance degradation ratio between different components.

[0051] A stability inspection module, which is used to perform periodic performance degradation prediction based on the component performance degradation ratio to obtain a periodic performance degradation prediction report between different components; perform stability inspection on the periodic performance degradation prediction report, and perform circuit component selection based on this.

[0052] Preferably, the present invention also provides a computer-readable medium, on which a computer program is stored, and when the computer program is executed, the circuit component selection method described in any one of the above is implemented.

[0053] The beneficial effects of the present invention are as follows: obtain the design information of the blank circuit and combine it with the specific parameter information of the circuit components to be tested, such as rated voltage, rated current, etc. Through in-depth analysis of these parameters, the performance requirements and load capacity of each circuit component during operation can be clarified. In addition, using the design information of the blank circuit, a simulation space is established, laying a foundation for subsequent circuit simulation and analysis. By establishing the simulation space, the working state of each component in the circuit can be more intuitively evaluated, and accurate input data can be provided for the subsequent steps. By analyzing the circuit simulation space, for the rated voltage and rated current of each component, a quantitative calculation of the overload heat density is carried out. The purpose of this process is to evaluate the heat generated by the component due to overload and its impact on the component life under different working conditions. Through the quantified overload heat density value, a further comparison of the performance degradation between different components is carried out to analyze the trend of their relative performance degradation. This comparison can help clarify which components are prone to performance degradation due to overload, providing data support for circuit design optimization and component selection. According to the performance degradation ratio of different components obtained in step S2, a periodic performance degradation prediction is carried out. By predicting the degradation trend, the performance changes of each component during long-term use can be foreseen, and its impact on the stability of the entire circuit system can be evaluated. On this basis, a stability test is carried out to ensure the overall stability and reliability of the circuit under different working environments. Through this process, a scientific basis can be provided for the selection of circuit components, choosing those components with high stability during long-term use, avoiding circuit system failure or efficiency decline caused by performance degradation, and thus improving the long-term reliability of circuit design. Therefore, the present invention is an optimization of a traditional method for selecting circuit components, solving the problems of poor recognition ability of the performance degradation of circuit component elements and large errors in the selection of circuit component elements in the traditional method, improving the ability to recognize the performance degradation of circuit component elements, and reducing the error in the selection of circuit component elements. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic flow chart of the steps of a method for selecting circuit components;

[0055] Figure 2 For Figure 1 It is a schematic detailed implementation step flow chart of step S2 in

[0056] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0058] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0059] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0060] To achieve the above object, please refer to Figures 1 to 2 , a method for selecting and matching circuit components, the method comprising the following steps:

[0061] Step S1: Obtain blank circuit design information and circuit component information to be tested; obtain the rated voltage and rated current of different components through the circuit component information; establish a blank circuit simulation space using the blank circuit design information;

[0062] Step S2: Quantify the overload thermal density of the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload thermal density quantization value of the components; compare the component performance degradation between different components based on the component overload thermal density quantization value to obtain the component performance degradation ratio between different components;

[0063] Step S3: Predict the periodic performance degradation based on the component performance degradation ratio to obtain a periodic performance degradation prediction report for different components; perform a stability test on the periodic performance degradation prediction report and select and match circuit components based on this.

[0064] In the embodiments of the present invention, refer to Figure 1As shown in the figure, it is a schematic flowchart of the steps of a method for selecting and matching circuit components according to the present invention. In this embodiment, the method for selecting and matching circuit components includes the following steps:

[0065] Step S1: Obtain blank circuit design information and circuit component information to be tested; obtain the rated voltage and rated current of different components through the circuit component information; establish a blank circuit simulation space using the blank circuit design information;

[0066] In the embodiment of the present invention, when obtaining the blank circuit design information and the circuit component information to be tested, the specific sources and processing processes of the two types of information should be defined in detail. For the blank circuit design information, it can be obtained through physical mapping or a known circuit design parameter manual to ensure that it includes a complete circuit topology, connection method, and the expected load power of the design. For the circuit component information to be tested, the rated voltage and rated current of different components need to be measured one by one through professional electrical testing equipment. For example, a DC current source and an AC signal generator are used to gradually increase the voltage and current and record the upper limit parameters of the component operation. Establishing a blank circuit simulation space requires using a basic method of physically translating circuit drawings, and converting the connection relationship of components in the circuit into a nodal admittance matrix using mathematical matrix theory. Then, according to the circuit topology rules, calculate the voltage distribution of each node to ensure that the simulation space can truly simulate the operation state of the blank circuit. The electrical parameters of each component are logically classified to form an initial input data set, including data such as component type, voltage distribution, and current path, providing a basic support for the subsequent steps.

[0067] Step S2: Quantify the overload thermal density of the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload thermal density quantification value of the components; perform a comparison of component performance degradation between different components based on the overload thermal density quantification value of the components to obtain the component performance degradation ratio between different components;

[0068] In the embodiment of the present invention, when quantifying the overload thermal density of the rated voltage and rated current of different components based on the blank circuit simulation space, a thermo - electrical coupling analysis method can be used to mathematically describe the physical relationship between thermal power and current voltage. Specifically, according to the Joule heat formula, multiply the square value of the current in the working state of each component by the resistance value of the component to obtain the corresponding thermal power output, and calculate the thermal density distribution in combination with the surface area of the component. At the same time, gradually increase the voltage and current to the overload state under the rated parameter conditions, record the thermal density change curve generated due to overload, and form an overload thermal density quantification value data set of the components. Subsequently, based on the overload thermal density quantification values of different components, each component is compared one by one to calculate its performance degradation trend. Through the derivation of the thermal fatigue theory formula, combined with the change rate of thermal density, quantify the proportion of performance degradation generated by the component under the overload state, obtain the component performance degradation ratio between different components, and form a complete performance degradation comparison matrix.

[0069] Step S3: Based on the component performance degradation ratio, perform periodic performance degradation prediction to obtain a periodic performance degradation prediction report among different components; perform a stability test on the periodic performance degradation prediction report and select circuit components accordingly.

[0070] In the embodiment of the present invention, when performing periodic performance degradation prediction according to the performance degradation ratio generated in step S2, the Fourier decomposition analysis method can be used to decompose the thermal density quantization data into a time series pattern with periodic changes. Statistically model the degradation trends of each component at different time periods, and predict the life degradation cycle of the component by calculating the acceleration of the degradation trend and the amplitude of periodic fluctuations. The periodic performance degradation prediction report includes the time node of component failure and its degradation rate. When performing a stability test on the prediction report, determine the reliability of the prediction model by comparing the consistency between the actual operation data and the prediction results. Calculate the residual variance using the regression analysis method and judge the error range of the prediction report based on the confidence interval. After the stability test results meet the accuracy requirements, select the optimal configuration combination of circuit components according to the degradation cycles of different components to ensure the long-term stable operation of the overall circuit under the target design parameters.

[0071] Preferably, the blank circuit design information includes the circuit topology structure, the circuit line routing, the circuit board layer material, and the number of layers; the circuit component information includes the resistor, capacitor, inductor, semiconductor device component type, component rated parameters, and thermal performance parameters.

[0072] Preferably, step S2 includes the following steps:

[0073] Step S21: According to the blank circuit simulation space, perform component limit bearing time series simulation on the rated voltage and rated current of different components to obtain the load current-voltage fluctuation curve under the component time series limit bearing state;

[0074] Step S22: Identify the average time series voltage and current fluctuation amplitudes of the load current-voltage fluctuation curve to obtain the average time series fluctuation voltage and average time series fluctuation current under the limit bearing state;

[0075] Step S23: Perform overload thermal density quantization according to the average time series fluctuation voltage and average time series fluctuation current to obtain the component overload thermal density quantization value;

[0076] Step S24: Based on the component overload thermal density quantization value, perform component performance degradation comparison among different components to obtain the performance degradation ratio among different components.

[0077] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0078] Step S21: According to the blank circuit simulation space, perform element limit bearing time series simulation on the rated voltage and rated current of different elements to obtain the load current-voltage fluctuation curve under the element time series limit bearing state;

[0079] In the embodiment of the present invention, according to the circuit topology relationship of the blank circuit simulation space and the rated voltage and rated current parameters of each element, by establishing a time-dependent charge flow model for each element, the dynamic response process of different elements under the limit bearing state is simulated. Specifically, a differential equation is used to describe the instantaneous change relationship between current and voltage in the element, and combined with the time series control signal of the dynamic load, a gradually increasing input current and voltage are applied in the simulation environment. For each element, record its instantaneous current and voltage values at different time points, and plot the fluctuation curve of the load current and voltage over time. During the simulation process, by monitoring the temperature rise, power dissipation, and current overload degree of the element in real time, ensure that the obtained data is complete and can quantify the element performance boundary under the limit bearing conditions. The output load current-voltage fluctuation curve fully reflects the dynamic response characteristics of the element, providing basic data support for subsequent analysis.

[0080] Step S22: Identify the average time series voltage and current fluctuation amplitude of the load current-voltage fluctuation curve to obtain the average time series fluctuation voltage and average time series fluctuation current under the limit bearing state;

[0081] In the embodiment of the present invention, when analyzing the load current-voltage fluctuation curve, a time series data processing method is used to interpret the fluctuation curve. First, the fluctuation curve is segmented according to the time series, and the peak and valley values of the current and voltage in each time period are used as characteristic points to calculate the voltage and current amplitude change amounts in this period. Then, the data of all time periods are weighted and averaged to obtain the average time series fluctuation voltage and average time series fluctuation current under the limit bearing state. During the processing, the Fourier transform method is introduced to perform frequency domain analysis on the fluctuation curve, so as to eliminate the instantaneous fluctuation interference caused by high-frequency noise and ensure the stability and accuracy of the average fluctuation amplitude. Through this process, the average time series fluctuation parameters under the limit bearing state are extracted as key quantitative indicators for further heat and performance analysis.

[0082] Step S23: Quantify the overload heat density according to the average time series fluctuation voltage and average time series fluctuation current to obtain the overload heat density quantification value of the element;

[0083] In the embodiments of the present invention, according to the average timing fluctuation voltage and the average timing fluctuation current obtained in step S22, the heat distribution of the component under the ultimate load-bearing state is calculated. First, using the Joule heat formula, the square value of the current is multiplied by the equivalent resistance of the component to obtain the heat power distribution data. Secondly, according to the surface area and the heat conduction path of the component, the heat power distribution is converted into an overload heat density value. In specific operations, a finite element analysis method is used to numerically simulate the heat conduction process, and the heat field distribution of the component is accurately decomposed into the heat density values of each spatial unit. Through step-by-step iterative simulation, the overload heat density distribution curve of the component under different load-bearing conditions is calculated, and it is quantified as an overload heat density value as the final output data, providing a quantitative index for component performance comparison.

[0084] Step S24: Based on the quantified value of the overload heat density of the component, compare the performance degradation between different components to obtain the performance degradation ratio between different components.

[0085] In the embodiments of the present invention, based on the quantified value of the overload heat density obtained in step S23, a horizontal comparison of the performance degradation between different components is carried out. The overload heat density value of each component is fitted and analyzed with its corresponding thermal fatigue life model to determine the performance degradation rate of each component under the ultimate load-bearing condition. Specifically, the cumulative heat power theory is used to deduce the fatigue crack growth rate of the component, and combined with the changes in the peak and mean values of the heat density, the performance degradation ratio of the component is calculated. The comparative analysis is carried out by means of matrix induction, constructing a matrix data table containing the performance degradation ratios of all components, and classifying and summarizing according to the component category and the load-bearing condition. The final result is based on the difference in the degradation rates between components, providing a reliable basis for performance evaluation and data support for the selection and matching of circuit components.

[0086] Preferably, step S23 includes the following steps:

[0087] Step S231: Extract the extreme value intervals of the fluctuations of the average timing fluctuation voltage and the average timing fluctuation current to obtain the extreme value interval of the fluctuation voltage and the extreme value interval of the fluctuation current;

[0088] Step S222: Analyze the component heating rate according to the extreme value interval of the fluctuation voltage and the extreme value interval of the fluctuation current to obtain the component heating rate;

[0089] Step S223: Identify the heating diffusion range based on the component heating rate to obtain the heating diffusion range;

[0090] Step S224: Identify the overlap of the heating ranges between different components based on the heating diffusion range to obtain the heating range overlap data;

[0091] Step S225: Analyze the isometric range of heat diffusion for the heating range overlap data according to the component heating rate to obtain the isometric range of heat diffusion;

[0092] Step S226: Quantify the overload thermal density based on the component heating rate, the overlapping data of the heating ranges, and the equidistant range of heat diffusion to obtain the quantified value of the component overload thermal density.

[0093] In the embodiments of the present invention, when extracting the extreme value intervals of the average timing fluctuation voltage and the average timing fluctuation current, the original data is first decomposed by the segmented statistical method into continuous and uniform small time periods. For each time period, by obtaining the local extreme values (including the maximum and minimum values) of the data and combining with the fluctuation frequency within the interval, the extreme value interval of this time period is determined. In the specific operation, a polynomial fitting algorithm is introduced to decompose the trend of the fluctuation curve, eliminate background noise and non-significant fluctuations, and use the remaining curve features for the extraction of extreme points. Using these local extreme points, by constructing interval boundary conditions, the extreme value interval of the fluctuation voltage and the extreme value interval of the fluctuation current are output for subsequent analysis. According to the extreme value intervals of the fluctuation voltage and the extreme value interval of the fluctuation current obtained in step S231, the heating rate of the component in the extreme state is calculated. First, the product of the fluctuation voltage and current intervals is used as the power density input, and based on the heat capacity characteristics and heat dissipation conditions of the component, the cumulative rate of heat energy per unit time is calculated. Then, by establishing a heat conduction equation, the temperature gradient change process inside the component is simulated, combining the time change and spatial distribution in a unified thermal model, and the heating rate curve of the component is extracted. Finally, the simulated data is matched and corrected with the actual measured temperature rise data to ensure the accuracy of the heating rate calculation. The output data is used as the basic parameter for the subsequent heat diffusion range. Based on the heating rate obtained in step S222, the heating diffusion range of the component is analyzed. The heating rate is decomposed into point heat source conditions, and the finite difference method is used to numerically simulate the heat diffusion path, and the spatial distribution of temperature in the internal and external regions of the component is deduced. By defining the temperature contour region, the boundary range of the heating diffusion is determined, and at the same time, considering the influence of the thermal conductivity, geometric shape of the material and the boundary heat dissipation conditions, the spatial resolution of the diffusion range is refined. Finally, the output data is used as a quantitative index for the heat effect range of each component to analyze the heat effect interaction between different components. According to the heating diffusion range in step S223, a spatial overlap analysis is performed on the heat effect regions of different components. By establishing a heat effect superposition model between components, the diffusion ranges of different components are spatially projected and the intersection region is obtained. During the operation process, Boolean operators are introduced to perform logical operations on the diffusion range, and the shape, area and distribution characteristics of the overlap degree of the overlapping regions are gradually identified. The data of all overlapping regions are integrated into the heating range overlap data and used as the input for the subsequent quantitative analysis of heat diffusion. According to the heating range overlap data in step S224, combined with the heating rate of the component, the isometric range quantity of heat diffusion is calculated. By integrating the heat flux density distribution in the overlapping region, the isometric change quantity of the heat diffusion from the high temperature region to the low temperature region is deduced. High-precision simulation is carried out using the heat flow field equation and the gradient operator, and the complex temperature change process is decomposed into the linear superposition of several sub-regions, and the isometric range quantity data is output. This data comprehensively describes the uniformity and distribution characteristics of the heat effect interaction between different components and provides a basis for the final heat density quantification.Complete the quantitative analysis of the overload heat density according to the data in steps S222, S224, and S225. First, matrix organize the component heating rate, heating range overlap data, and thermal diffusion equidistant range quantity to establish a multivariable input model. Use the heat conduction integral method to calculate the change in heat density per unit area of the component under overload conditions. Subsequently, correct the quantization result through the principle of conservation of thermal energy to eliminate the deviation introduced by calculation errors, and obtain the final quantization value of the component overload heat density. The quantization value is output in tabular form, providing direct data support for subsequent component performance evaluation and selection.

[0094] Preferably, step S225 includes the following steps:

[0095] Perform thermal field gradient partitioning on the heating range overlap data to obtain thermal field range gradient data;

[0096] Identify the temperature diffusion quantity of the thermal field range gradient data according to the component heating rate to obtain the temperature distribution diffusion difference quantity;

[0097] Perform thermal diffusion equidistant range quantity analysis based on the temperature distribution diffusion difference quantity to obtain the thermal diffusion equidistant range quantity;

[0098] According to the determined component heating rate, perform thermal field gradient partitioning. Taking 10°C as a gradient interval, divide 20°C - 30°C into the first gradient zone and 30°C to 40°C into the second gradient zone, thereby obtaining the thermal field range gradient data;

[0099] If the heating rate of a certain component is 5°C per minute, the temperature rise speed is different in different gradient zones. Identify the temperature diffusion quantity of the thermal field range gradient data according to this heating rate;

[0100] In the first gradient zone, the temperature rises from 20°C to 30°C in 2 minutes, and the diffusion quantity is 10°C. In the second gradient zone, due to the change in thermal field characteristics, it takes 3 minutes to rise from 30°C to 40°C, and the diffusion quantity is also 10°C. Due to the difference in time, the temperature distribution diffusion difference quantity is obtained;

[0101] Perform thermal diffusion equidistant range quantity analysis based on the temperature distribution diffusion difference quantity. Set the equidistant range to 5°C, and analyze the uniformity and directionality of temperature diffusion in each equidistant range, thereby obtaining the thermal diffusion equidistant range quantity.

[0102] In the embodiment of the present invention, according to the determined heating rate of the component, by setting a fixed temperature range, with every 10°C as a gradient interval, the thermal field area is divided into gradients. Taking the heating rate of 5°C per minute as an example, first select the initial temperature point (such as 20°C) of the test area in space, and divide the spatial gradient of different temperature ranges through the change of heat flux density. According to the temperature range from 20°C to 40°C, it is divided into two gradient intervals: 20°C - 30°C is the first gradient area, and 30°C - 40°C is the second gradient area. In the specific operation process, the space discretization method is used to segment the temperature field, and through the average heat flux density and the thermal field distribution function in each interval, the data characterization of the gradient partition is completed, and the gradient data of the thermal field range is obtained. This data describes the temperature range and spatial distribution characteristics in each gradient area, providing a basis for subsequent diffusion analysis. On the basis of the determined gradient data of the thermal field range, the temperature diffusion amount of each gradient area is identified according to the heating rate of the component. If the heating rate of a certain component is 5°C per minute, then in the first gradient area, it takes 2 minutes for the temperature to rise from 20°C to 30°C, and the diffusion amount is 10°C; in the second gradient area, due to the change of the thermal field characteristics, it takes 3 minutes for the temperature to rise from 30°C to 40°C, and the diffusion amount is also 10°C. In this process, through the multi-region heat flow coupling analysis method, combined with the time factor and the spatial gradient change, the diffusion characteristics of different regions are extracted. The differential method is used to refine the calculation of the temperature diffusion path, and the diffusion amount of each gradient area is obtained and stored in tabular form, laying a foundation for subsequent differential amount analysis. Based on the diffusion amount and time difference of different gradient areas, the diffusion difference amount of the temperature distribution is calculated. Comparing the diffusion time of the first gradient area and the second gradient area, it is found that although the diffusion amount is 10°C for both, the diffusion time of the first gradient area is 2 minutes, and the diffusion time of the second gradient area is 3 minutes, resulting in a diffusion difference in the time dimension. In the specific analysis, by constructing a temperature distribution difference function, combined with the thermal field gradient and time characteristics in the region, the non-uniformity in the diffusion process is quantified. The weighted average method is used to calculate the overall temperature difference amount, and its value is converted into distribution characteristic data for the next equidistant range quantification analysis. Based on the temperature distribution diffusion difference amount, the equidistant range amount of heat diffusion is analyzed. Set the equidistant range to 5°C, and study the temperature diffusion uniformity and directionality within each equidistant range. The thermal field area is divided into multiple equidistant range intervals (such as 20°C - 25°C, 25°C - 30°C, etc.), and the temperature change and diffusion path within each range are analyzed one by one. In the operation process, the numerical integration method is used to calculate the average heat flow rate of each equidistant range, and through the vector field analysis of the heat flow direction, the directionality and uniformity of temperature diffusion within each range are evaluated. Finally, combined with the heat diffusion gradient and difference distribution within the equidistant range, the equidistant range amount of heat diffusion is output, providing data support for the thermal characteristic matching of subsequent circuit components.

[0103] Preferably, step S24 includes the following steps:

[0104] Perform point-by-point differential calculation on the quantified value of the component overload thermal density to obtain the point-by-point thermal load change rate of the component; obtain the average difference in point-by-point thermal load change by comparing the point-by-point thermal load change rates between different components.

[0105] Divide the component power degradation levels according to the average difference in point-by-point thermal load change to obtain the component power degradation levels.

[0106] Perform maximum entropy estimation based on the average difference in point-by-point thermal load change and the component power degradation levels to obtain the maximum entropy value of power degradation.

[0107] Perform comparison of component performance degradation between different components based on the maximum entropy value of power degradation to obtain the performance degradation ratio between different components.

[0108] Perform point-by-point differential calculation based on the quantified value of the component overload thermal density, calculate the change rate between two adjacent points using the differential formula, then compare the point-by-point thermal load change rates between different components to obtain the average difference in point-by-point thermal load change. Subtract the point-by-point thermal load change rate of component A from the corresponding point change rate of component B to obtain a difference sequence.

[0109] After calculating the difference sequences for all pairs of components, find the average value of these difference sequences to obtain the average difference D in point-by-point thermal load change. Divide the component power degradation levels according to the average difference D in point-by-point thermal load change.

[0110] Set a division threshold. When D < 0.1, the power degradation level is level 1, indicating slight power degradation; when 0.1 <= D < 0.3, it is level 2; when D >= 0.3, it is level 3, thereby determining the power degradation level of each component.

[0111] Perform maximum entropy estimation based on the average difference D in point-by-point thermal load change and the component power degradation levels. Establish a probability model through the maximum entropy principle, and use the known average difference D in point-by-point thermal load change and power degradation level data as constraint conditions to estimate the maximum entropy value H of power degradation.

[0112] Perform comparison of component performance degradation between different components based on the maximum entropy value H of power degradation, thereby obtaining the performance degradation ratio between different components.

[0113] In the embodiments of the present invention, based on the quantified value of the overload thermal density of components, the change rate of the thermal load of each component is calculated by the point-by-point differentiation method. Assume that the overload thermal density data of component A is a set of discrete data points {ρ1, ρ2,..., ρn}, and the data of component B is {σ1, σ2,..., σn}. Using the formula Δρ / Δt = (ρi+1 - ρi) / (ti+1 - ti), the change rate Δρi of every two adjacent points is calculated point by point. Component A and component B are compared point by point, and the difference in the change rate of the corresponding points is calculated to form a difference sequence. By analogy, pairwise difference calculations are performed on all components, and the average value is obtained by using the data set of the difference sequence to obtain the average difference D of the point-by-point thermal load change. This process systematically realizes data processing and average difference calculation through matrix operations and numerical integration methods. According to the average difference D of the point-by-point thermal load change, a division threshold is set to divide the power decay level of the components. The specific threshold conditions are as follows: when D < 0.1, it is defined as level 1, indicating slight power decay; when 0.1 ≤ D < 0.3, it is defined as level 2; when D ≥ 0.3, it is defined as level 3, indicating severe power decay. During the operation process, the D values of each component are processed in zones through a screening function, and the division results are stored as level identifiers to establish a component level table. Using this table, the power status of each component can be quickly queried, providing clear input data for subsequent analysis. Based on the average difference D of the point-by-point thermal load change and the already divided power decay level data, maximum entropy estimation is performed. A probability model is established through the maximum entropy principle. Assume that the probability distribution of the power decay process satisfies the constraint condition ΣPi × lg(Pi) = H, and the entropy value H is maximized to obtain the optimal solution of the power decay distribution. In actual implementation, first, the frequency distribution of all D values and their corresponding levels is collected, which is used as the known constraint condition, and the maximum entropy function is solved by the Lagrange multiplier method. The maximum entropy value H of the power decay is calculated accordingly, and a power entropy distribution table of the components is generated to represent the probability characteristics of different level power states. Based on the maximum entropy value H of the power decay, the performance decay comparison between different components is carried out. The comparison method is as follows: taking the maximum entropy value of the power decay as the benchmark, the entropy value deviation of each component is calculated, and all the entropy value deviation data are constructed into a comparison matrix to analyze the performance decay ratio between the components. During the operation process, the performance differences between the components are further explored through thermodynamic isometric analysis methods, and combined with the comprehensive data of the average difference of the point-by-point thermal load change and the entropy value, a comparison chart is formed. Finally, a component performance decay ratio table is generated, providing a scientific basis for the selection and matching of circuit components.

[0114] Preferably, the periodic performance decay prediction based on the component performance decay ratio includes the following steps:

[0115] Perform a time series correlation analysis on the component performance decay ratio to obtain the performance decay time series correlation data;

[0116] Perform a weighted processing on the time series points of the decay time series correlation data to obtain the weighted time series point data;

[0117] Perform periodic performance degradation prediction on the component performance degradation ratio based on the time-series point-weighted data to obtain a periodic performance degradation prediction report for different components;

[0118] First, collect the performance parameters of the components in different operating cycles, record the comparison values point by point, establish a time-series data set in chronological order, calculate the correlation coefficients between each time point, screen out the time-series data with significant correlations, and form the time-series correlation data of performance degradation;

[0119] When performing time-series point-weighted processing on the time-series correlation data of degradation, use the time-weight calculation method to weight the data of each time point according to the importance of its time distribution, use the discrete point fitting method to calculate the weight distribution of the data of each time point, and merge the weighted results to generate the time-series point-weighted data;

[0120] When performing periodic performance degradation prediction, based on the weighted results of each point in the time-series point-weighted data, combined with the actual operating cycle of the component, use the multi-point fitting interpolation method to analyze the periodic performance change trends of different components, perform periodic prediction according to the time period division method, and generate a detailed report including the performance degradation cycle, degradation rate, and trend line of each component, and finally form a periodic performance degradation prediction report for different components.

[0121] In the embodiments of the present invention, in the prediction of periodic performance degradation, first, the performance parameters of components are systematically collected in different operating cycles. The performance parameters include characteristic data such as voltage, temperature, power load value, and related heat density distribution. By comparing the performance parameters of the same component in each cycle, the ratio change data is recorded point by point, and a set of time series data sets with time-dependent relationships is generated in chronological order. Based on this time series data set, the Pearson correlation coefficient formula is used to calculate the correlation coefficients between each time point. The data points with significant correlation coefficients (such as |r|>0.7) are selected, and the abnormal points that do not conform to the overall trend are removed, and they are integrated to form the time series correlation data of performance degradation, providing effective input data for subsequent weighting processing. The time series point weighting processing is performed on the time series correlation data of performance degradation. The time weight calculation method is used to weight the importance of the data according to the time distribution, and the weight distribution of each time point is fitted by the discrete point fitting method. The specific operations include: grouping the data points in chronological order, and using the polynomial fitting method to fit the weight value distribution curve of the discrete points to ensure that the fitting accuracy is within 0.01. After the weight fitting is completed, the weight values of all time points are multiplied by their corresponding performance parameters and combined to form the time series point weighted data. This weighted data directly reflects the influence degree of the performance change at each time point on the overall trend. Based on the time series point weighted data, by analyzing the weighted results and combining the actual operating cycle of the component, the multi-point fitting interpolation method is used to analyze the periodic performance change trend of the component. In the specific implementation, the time periods are first divided according to the time point order in the weighted data, and each period contains a certain number of time series points to improve the local accuracy of the prediction. The Lagrange interpolation method or the piecewise spline interpolation method is used to analyze the trend of each period of data and estimate the performance change rate of the component in different time periods. After combining the interpolation functions of all periods, a complete periodic performance change trend curve is generated, and the curve includes detailed information such as the performance degradation cycle, degradation rate, and trend line. The predicted periodic performance change trend data is sorted into a visual detailed report. The report content includes the component performance degradation cycle, the degradation rate statistical table of each time period, the trend line chart, and the performance degradation comparison chart between different components. Through this report, the performance degradation states of different components in periodic operation can be clearly compared, providing a comprehensive and accurate reference basis for the optimization and selection of circuit components. The report is generated using data visualization technology, presenting the trend curve and degradation cycle in the form of tables and charts, and ensuring that the output content is intuitive and detailed by annotating key feature information such as inflection points and fluctuation amplitudes. The finally output periodic performance degradation prediction report can be directly used for the selection analysis and improvement plan design of circuit components.

[0122] Preferably, the stability test of the periodic performance degradation prediction report includes the following steps:

[0123] Extract the current and voltage decay values of different components from the periodic performance degradation prediction report, and perform variance calculation to obtain the current decay variance and voltage decay variance;

[0124] Conduct load characteristic analysis on the current decay variance and voltage decay variance to obtain current-voltage decay load characteristic data;

[0125] Calculate the rates of change of current and voltage decay during the process of load increase and decrease based on the current-voltage decay load characteristic data to obtain a trend curve. By evaluating the slope and rate of change of the trend curve, determine whether the degradation state tends to be stable;

[0126] If the trend of the trend curve is like a linear and gentle shape, it indicates that the degradation process of the component in actual application tends to be stable. Finally, based on the evaluation results, conduct a stability test to identify unstable components, and then reasonably select circuit components; specifically, the trend of the curve being like a linear and gentle shape is as follows: perform piecewise linear fitting on the trend curve, extract the slope values and correlation coefficients of each segment to verify whether each segment of the curve is close to a linear distribution; calculate the average slope and its standard deviation of the entire curve. If the average slope is close to zero and the standard deviation is less than 1 and infinitely approaches zero but is not equal to zero, then initially judge that the trend curve tends to be gentle; conduct a fluctuation range analysis on the fitted curve, use the difference method to calculate the incremental changes between points on the curve, statistically calculate the maximum value, minimum value and range of the increments, and draw a fluctuation histogram for evaluation; if the increment distribution is concentrated and the range is less than the preset value, sum up the deviations between the actual data points and the fitted line, and record the change trend of the cumulative deviation curve. If the cumulative deviation curve is close to zero and has no fluctuations, it can be determined that the curve exhibits linear and gentle characteristics.

[0127] In the embodiments of this law, the current decay value and voltage decay value of each component are extracted item by item from the periodic performance degradation prediction report, arranged in chronological order to form time series data of current and voltage decay values. The current decay variance and voltage decay variance are calculated respectively through the variance formula. The value of variance is used to measure the volatility of current and voltage decay data, providing basic data for subsequent analysis of load characteristics. The current decay variance and voltage decay variance are jointly analyzed to establish current-voltage decay load characteristic data. The normalization method is used to process the variance value to eliminate the influence of dimension on the analysis result. For the normalized data, the load characteristic relationship between current and voltage is fitted using a two-dimensional scatter plot, and a regression model is constructed using the least squares method to obtain the load characteristic function. The slope and intercept of this function reflect the correlation and change trend between current and voltage decay characteristics, laying a foundation for subsequent trend curve calculation. According to the current-voltage decay load characteristic data, the piecewise linear fitting method is used to calculate the trend curve. In piecewise linear fitting, first, the time series data is divided into several equal-length intervals, and the interval length is determined according to the total number of time points and the characteristic fluctuation frequency. For the data within each interval, the slope value and correlation coefficient of each segment are recorded using the fitting equation to generate piecewise trend data. When evaluating whether the trend curve tends to be stable, the average slope and standard deviation of the entire trend curve are calculated to determine whether the curve is close to linear and flat. If the average slope is close to zero and the standard deviation tends to zero, it indicates that the curve has linear and flat characteristics. The fluctuation range of the fitted trend curve is analyzed. The difference method is used to calculate the incremental change between each point of the curve, and the maximum value, minimum value, and range of the incremental change are statistically analyzed. A fluctuation histogram is plotted to observe the incremental distribution characteristics. If the incremental distribution is concentrated and the range is less than the preset threshold, the deviation between the actual data points and the fitted line is accumulated and summed to generate an accumulated deviation curve, where the preset threshold is evaluated based on the median of all current data. Combining the trend curve evaluation result and the fluctuation range analysis, it is judged whether the degradation state of each component tends to be stable. If the average slope of the trend curve is close to zero, the standard deviation is small, the incremental change is concentrated, and the accumulated deviation tends to zero, it is determined that the degradation process of the component is stable. The unstable components are marked as abnormal to form a stability test report, and the circuit components are optimized and selected according to the report to ensure the stability and reliability of the overall circuit performance.

[0128] Preferably, the present invention also provides a circuit component selection and matching system for performing the circuit component selection and matching method as described above. This circuit component selection and matching system includes:

[0129] A simulation space construction module, used to obtain blank circuit design information and circuit component information to be tested; obtain the rated voltage and rated current of different components through the circuit component information; establish a blank circuit simulation space using the blank circuit design information;

[0130] A performance degradation comparison module, configured to perform overload thermal density quantization on the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload thermal density quantization values of the components; and perform component performance degradation comparison between different components based on the overload thermal density quantization values of the components to obtain the component performance degradation ratio between different components.

[0131] A stability inspection module, configured to perform periodic performance degradation prediction based on the component performance degradation ratio to obtain a periodic performance degradation prediction report between different components; perform stability inspection on the periodic performance degradation prediction report, and select and match circuit components accordingly.

[0132] Preferably, a computer-readable storage medium stores a computer program, and when the computer program is executed, it implements the circuit component selection and matching method described in any one of the above.

[0133] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be embraced by the present invention.

[0134] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A circuit component selection method, characterized in that: The following steps are involved: Step S1: Obtain blank circuit design information and circuit component information to be tested; The rated voltage and rated current of different components are obtained through circuit component information; blank circuit design information is used to establish a blank circuit simulation space; Step S2: quantifying the overload heat density of the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload heat density quantification value of the component; comparing the component performance degradation between different components based on the component overload heat density quantification value to obtain the component performance degradation ratio between different components; Step S2 is specifically: Step S21: performing component limit load timing simulation on the rated voltage and rated current of different components according to the blank circuit simulation space to obtain a load current-voltage fluctuation curve under the component timing limit load state; Step S22: performing average sequential voltage and current fluctuation amplitude identification on the load current-voltage fluctuation curve to obtain average sequential fluctuation voltage and average sequential fluctuation current under the extreme load state; Step S23: quantify the overload heat density according to the average time-series fluctuation voltage and the average time-series fluctuation current to obtain the quantized value of the component overload heat density; Step S23 is specifically as follows: Step S231: extracting the fluctuation extreme value interval of the average time-series fluctuation voltage and the average time-series fluctuation current to obtain the fluctuation voltage extreme value interval and the fluctuation current extreme value interval; Step S232: analyzing the component heating rate according to the fluctuation voltage extreme value interval and the fluctuation current extreme value interval to obtain the component heating rate; Step S233: identifying the temperature rise diffusion range based on the component temperature rise rate to obtain the temperature rise diffusion range; Step S234: identifying the overlap of temperature rise ranges between different components based on the temperature rise diffusion range, and obtaining temperature rise range overlap data; Step S235: performing heat diffusion isometric range quantity analysis on the temperature rise range overlap data according to the component temperature rise rate to obtain the heat diffusion isometric range quantity; Step S236: quantifying the overload heat density according to the component heating rate, the temperature rising range overlap data and the heat diffusion equidistant range quantity, and obtaining the component overload heat density quantization value; Step S24: comparing the performance degradation of different components based on the quantified value of the component overload heat density to obtain the performance degradation ratio of the different components; Step S3: performing periodic performance degradation prediction based on the component performance degradation ratio to obtain a periodic performance degradation prediction report between different components; Perform stability checks on periodic performance degradation prediction reports and use them to select circuit components.

2. The circuit component selection method according to claim 1, characterized in that: The blank circuit design information includes circuit topology, circuit line direction, circuit board layer material and number of layers; the circuit component information includes resistors, capacitors, inductors, semiconductor device component categories, component rated parameters and thermal performance parameters.

3. The circuit component selection method according to claim 1, characterized in that: Step S235 includes the following steps: According to the determined component heating rate, the thermal field gradient is partitioned, with 10°C as a gradient interval, 20°C-30°C as the first gradient zone, and 30°C to 40°C as the second gradient zone, so as to obtain the thermal field range gradient data; If the heating rate of a component is 5°C per minute, the temperature rise speed is different in different gradient areas. The temperature diffusion amount is identified based on the thermal field range gradient data according to this heating rate. In the first gradient zone, the temperature rises from 20°C to 30°C after 2 minutes, and the diffusion amount is 10°C. In the second gradient zone, due to the change of thermal field characteristics, it takes 3 minutes to rise from 30°C to 40°C, and the diffusion amount is also 10°C. Due to the difference in time, the temperature distribution diffusion difference is obtained; Based on the temperature distribution diffusion difference, the heat diffusion equidistant range quantity is analyzed. The equidistant range is set to 5°C. The uniformity and directionality of temperature diffusion in each equidistant range are analyzed to obtain the heat diffusion equidistant range quantity.

4. The circuit component selection method according to claim 1, characterized in that: Step S24 includes the following steps: Based on the quantitative value of the component overload heat density, point-by-point differential calculation is performed, and the rate of change between two adjacent points is calculated using the differential formula. Then, the point-by-point heat load change rate of different components is compared to obtain the point-by-point heat load change average difference. The point-by-point heat load change rate of component A is subtracted from the corresponding point change rate of component B to obtain a difference sequence. After calculating the difference sequence for all components in pairs, the average value of these difference sequences is calculated to obtain the point-by-point heat load change average difference D, and the power decay level of the components is divided according to the point-by-point heat load change average difference D; The division threshold is set. When D<0.1, the power decay level is level 1, indicating a slight power decay; when 0.1<=D <0.3, it is level 2; when D>=0.3, it is level 3, thereby determining the power decay level of each component; Based on the point-by-point heat load change mean difference D and the power decay level of the component, the maximum entropy estimation is performed, and a probability model is established through the maximum entropy principle. The known point-by-point heat load change mean difference and power decay level data are used as constraints to estimate the power decay maximum entropy value H. Based on the maximum entropy value H of power decay, the performance decay of different components is compared, so as to obtain the performance decay ratio of different components.

5. The circuit component selection method according to claim 1, characterized in that: The periodic performance degradation prediction based on the component performance degradation ratio includes the following steps: First, the performance parameters of the components in different working cycles are collected, and the comparison values ​​are recorded point by point. A time series data set is established in chronological order, and the correlation coefficients between each time point are calculated. The time series data with significant correlation are screened out to form performance degradation time series correlation data. When performing time-series point weighting processing on decay time-series correlation data, the time weight calculation method is used to weight the data at each time point according to the importance of its time distribution, the discrete point fitting method is used to calculate the weight distribution of the data at each time point, and the weighted results are combined to generate time-series point weighted data; When making a periodic performance degradation prediction, based on the weighted results of each point in the time series point weighted data and combined with the actual operation cycle of the components, the multi-point fitting interpolation method is used to analyze the periodic performance change trends of different components, and periodic predictions are made according to the time period division method. A detailed report containing the performance degradation cycle, degradation rate, and trend lines of each component is generated, and finally a periodic performance degradation prediction report between different components is formed.

6. The circuit component selection method according to claim 1, characterized in that: The stability check of the periodic performance degradation prediction report includes the following steps: Extract the current and voltage decay values ​​of different components from the periodic performance decay prediction report, and perform variance calculation to obtain the current decay variance and voltage decay variance; Perform load characteristic analysis on current decay variance and voltage decay variance to obtain current-voltage decay load characteristic data; According to the current-voltage decay load characteristic data, the current and voltage decay are calculated, and the rate of change during the load increase and decrease process is obtained to obtain the trend curve. By evaluating the slope and change rate of the trend curve, it is determined whether the decay state tends to be stable; If the trend curve is like a linear and gentle trend, it indicates that the decay process of the component in practical application tends to be stable. Finally, a stability test is performed based on the evaluation results to identify unstable components and then reasonably select the circuit components. Specifically, if the trend of the curve is like a linear and gentle trend, the trend curve is fitted piecewise linearly, and the slope value and correlation coefficient of each segment are extracted to verify whether each segment of the curve is close to a linear distribution; the average slope and its standard deviation of the entire curve are calculated. If the average slope is close to zero and the standard deviation is less than 1 and infinitely tends to zero but is not equal to zero, it is preliminarily judged that the trend curve tends to be gentle; the fluctuation range of the fitted curve is analyzed, and the incremental changes between the points of the curve are calculated by the difference method, the maximum value, minimum value and range of the incremental are counted, and a fluctuation histogram is drawn for evaluation; if the incremental distribution is concentrated and the range is less than the preset value, the deviations between the actual data points and the fitting line are accumulated and summed, and the changing trend of the cumulative deviation curve is recorded. If the cumulative deviation curve is close to zero and there is no fluctuation, it can be determined that the curve presents a linear and gentle characteristic.

7. A circuit component selection system, characterized in that: Used to execute the circuit component selection method according to claim 1, the circuit component selection system comprises: A simulation space construction module is used to obtain blank circuit design information and circuit component information to be tested; obtain rated voltages and rated currents of different components through circuit component information; and establish a blank circuit simulation space using blank circuit design information; The performance degradation comparison module is used to quantify the overload thermal density of the rated voltage and rated current of different components according to the blank circuit simulation space to obtain the overload thermal density quantification value of the component; compare the component performance degradation between different components based on the component overload thermal density quantification value to obtain the component performance degradation ratio between different components; The stability test module is used to predict periodic performance degradation based on the component performance degradation ratio, and obtain a periodic performance degradation prediction report between different components; perform stability testing on the periodic performance degradation prediction report, and use it to select circuit components.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the circuit component selection method as described in any one of claims 1-6 is implemented.

Images