Circuit assembly selecting and matching method and system and medium

Abstract

The invention relates to the technical field of circuit component selection and matching, in particular to a circuit component selection and matching method and system and a medium. The method comprises the following steps: acquiring rated voltages and rated currents of different elements through circuit assembly information; establishing a blank circuit simulation space by adopting the blank circuit design information; performing overload thermal density quantification on the rated voltage and the rated current of the different elements according to the blank circuit simulation space, and performing element performance degradation comparison among the different elements to obtain element performance degradation ratios among the different elements; performing periodic performance degradation prediction based on the element performance degradation ratio to obtain a periodic performance degradation prediction report among different elements; and carrying out stability test on the periodic performance decline prediction report, and carrying out circuit element selection and matching based on the stability test. According to the invention, the circuit component selection and matching technology is optimized, so that the circuit component selection and matching technology is more perfect.

Description

Technical Field

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

[0002] The functional requirements of electronic circuits have become more diverse and complex. This trend makes it difficult for traditional selection methods to meet the needs of efficiency, precision and customization. For example, different application scenarios have very different requirements for the performance, volume, cost, power consumption and other requirements of circuit components. How to optimize the overall performance while meeting specific requirements has become a major challenge. At the same time, there are many types of electronic components and complex parameters, which requires the most suitable combination to be quickly found among a large number of optional components during the selection process. In addition, the rapid update and iteration of components in the market and the increasing prominence of supply chain issues have also increased the difficulty of selecting circuit components. Design engineers must consider the compatibility, availability and future scalability of components. For example, some high-performance components cannot be purchased in a short period of time, or the design progress is affected by supply chain disruptions. Furthermore, different component manufacturers will provide similar products with slightly different parameters, which further increases the complexity of selection. 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. These multi-dimensional requirements require the selection method to comprehensively consider multiple factors, rather than focusing on a single performance indicator. At the same time, modern electronic products pursue higher integration and smaller size, which means that when selecting circuits, it is necessary to pay strict attention to the rationality of component layout and the feasibility of the overall design. However, a traditional method of selecting circuit components has the problems of poor recognition of circuit component component performance degradation and large errors in circuit component component selection. Summary of the invention

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

[0004] To achieve the above object, a circuit component selection method is provided, the method comprising the following steps: Step S1: obtaining blank circuit design information and circuit component information to be tested; obtaining rated voltages and rated currents of different components through the circuit component information; and establishing a blank circuit simulation space using the blank circuit design information; 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 S3: 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 test on the periodic performance degradation prediction report and use it to select circuit components.

[0005] The present invention obtains the design information of the blank circuit, and combines the specific parameter information of the circuit component to be tested, such as the rated voltage, the rated current, etc. Through the in-depth analysis of these parameters, the performance requirements and load capacity of each circuit component in the working process can be clarified. In addition, a simulation space is established using the design information of the blank circuit to lay the foundation for subsequent circuit simulation and analysis. Through the establishment of 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 subsequent steps. By analyzing the circuit simulation space, the rated voltage and rated current of each component are quantitatively calculated for the overload heat density. The purpose of this process is to evaluate the heat generated by the component due to overload under different working conditions and its impact on the life of the component. Through the quantitative overload heat density value obtained, the performance degradation comparison between different components is further performed to analyze the trend of 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, periodic performance degradation prediction is performed. By predicting the decay trend, it is possible to foresee the performance changes of each component during long-term use and evaluate its impact on the stability of the entire circuit system. On this basis, a stability test is performed to ensure the stability and reliability of the circuit as a whole under different working environments. Through this process, a scientific basis can be provided for the selection of circuit components, and components with high stability in long-term use can be selected to avoid circuit system failure or efficiency reduction due to performance decay, thereby improving the long-term reliability of the circuit design. Therefore, the present invention is an optimization of a traditional method for selecting circuit components, which solves the problems of a traditional method for selecting circuit components, such as poor ability to identify performance decay of circuit component components and large errors in the selection of circuit component components, improves the ability to identify performance decay of circuit component components, and reduces the errors in the selection of circuit component components.

[0006] Preferably, 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.

[0007] Preferably, step S2 comprises the following steps: 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: quantifying the overload heat density according to the average time-series fluctuation voltage and the average time-series fluctuation current to obtain a quantified value of the component overload heat density; Step S24: performing a comparison of component performance degradation between different components based on the component overload heat density quantification value to obtain a performance degradation ratio between different components.

[0008] 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.

[0009] Preferably, step S23 includes the following steps: 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 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; Step S223: identifying the temperature rise diffusion range based on the component temperature rise rate to obtain the temperature rise diffusion range; Step S224: 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 S225: 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 S226: quantify the overload heat density according to the component heating rate, the temperature rising range overlap data and the heat diffusion equidistant range quantity to obtain the component overload heat density quantization value.

[0010] The present invention extracts the extreme value intervals of the fluctuating voltage and the fluctuating current by analyzing the average time-series fluctuating voltage and current fluctuation data obtained in step S22. The fluctuation extreme value interval refers to the interval between the maximum and minimum values ​​reached by the voltage and current under a specific time sequence. This analysis helps to identify the extreme fluctuation range of the electrical performance of the component 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 the component. Based on the fluctuating voltage extreme value interval and the fluctuating current extreme value interval extracted in step S231, the component heating rate analysis is performed. This analysis calculates the heating rate of the component under different fluctuation conditions by evaluating the thermal effect of current and voltage fluctuations on the component. The heating rate reflects the speed at which the temperature of the component rises under a specific working environment, which 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, thereby affecting their performance degradation. According to the component heating rate, the temperature rise diffusion range is identified. The temperature rise diffusion range refers to the temperature distribution range of the surrounding area affected by the temperature rise of a component within a certain period of time. By calculating and identifying this range, the potential impact of 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 temperature rise diffusion range, data support can be provided for thermal management and temperature interference evaluation between components, helping to optimize circuit layout and reduce the negative effects of temperature interaction. By analyzing the temperature rise diffusion ranges of different components, the temperature rise range overlap data between them can be identified. This process compares the temperature rise diffusion ranges of each component to evaluate which components have overlapping heat-affected zones, thereby revealing the potential risk of thermal interference. Overlapping temperature ranges means that multiple components will work in the same heat-affected zone, increasing the risk of local overheating and performance degradation. Identifying these overlapping data helps optimize the layout and heat dissipation design of components and ensure the effectiveness of thermal management measures. Based on the component heating rate, temperature rise range overlap data and heat diffusion isodistance range quantity, a comprehensive analysis is performed to obtain the heat diffusion isodistance range quantity. The heat diffusion isodistance range quantity refers to the isodistance range of temperature rise at different distances around a component within a certain period of time. Through this analysis, we can further understand the speed and range of heat diffusion of components under overload conditions, and help identify which components have a greater thermal impact on the surrounding environment. This data is essential for optimizing thermal design, selecting appropriate heat dissipation solutions, and ensuring circuit stability. Finally, based on the heating rate, heating range overlap data, and heat diffusion equidistant range in the previous steps, the overload heat density is quantitatively calculated. This process combines the heat diffusion range, heating rate, and physical properties of the component to derive the heat density value of the component under overload conditions. The quantitative value of overload heat density reflects the degree of heat accumulation generated by the component under high load conditions and is a key indicator for evaluating component performance degradation.Accurate quantification of thermal density 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.

[0011] Preferably, step S225 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.

[0012] The present invention performs gradient partitioning of the thermal field according to the determined component heating rate so as to more accurately analyze the thermal diffusion. The thermal field partitioning is divided into multiple intervals according to different temperature ranges, and a fixed temperature gradient is set between each interval, for example, 20℃-30℃ is set as the first gradient zone, 30℃-40℃ is set as the second gradient zone, etc. This partitioning process helps to refine the analysis of temperature changes and facilitates the subsequent independent evaluation of the temperature change characteristics in each temperature gradient zone. Through this partitioning, the different temperature ranges of the thermal field and the laws of thermal diffusion can be accurately identified, providing data support for thermal analysis and optimization. According to the aforementioned thermal field gradient partitioning and the determined component heating rate, the temperature diffusion amount of each temperature gradient zone is identified. If the heating rate of the component is 5℃ per minute, there will be differences in the temperature rise rate in different gradient zones. Taking 20℃-30℃ (first gradient zone) as an example, if the temperature rises by 10℃ in 2 minutes, it will take 3 minutes for 30℃-40℃ (second gradient zone) to rise by 10℃. This analysis helps to reveal the differences in heat diffusion in different temperature gradient zones and further understand the characteristics of heat diffusion of components in different temperature ranges. This is the basis for analyzing the thermal behavior of components and can guide the optimization of subsequent thermal management strategies. By comparing the temperature rise rates in different temperature gradient zones, the temperature distribution diffusion difference is obtained. Although the temperature diffusion amount in each gradient zone is the same (such as 10°C), the temperature distribution diffusion characteristics are also different due to the different heating times in different gradient zones. 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 understand the heat propagation characteristics of components in the thermal field, and provides a basis for accurately predicting the thermal behavior of components and their impact on the surrounding environment. Based on the aforementioned temperature distribution diffusion difference, the heat diffusion isotropic range quantity analysis is performed. The isotropic range is set to 5°C, and the uniformity and directionality of temperature diffusion in each temperature range are evaluated. During the analysis, it is necessary to identify whether the temperature diffusion in different temperature zones is uniform and whether there is asymmetry in the diffusion direction. For example, the heat diffusion in one direction is faster than in other directions, which helps to determine whether the heat is evenly distributed around the component. This analysis result is of great significance for optimizing thermal management, improving heat dissipation performance, and improving the long-term stability of components. By quantifying the isometric range of heat diffusion, designers can develop more effective heat dissipation strategies to prevent performance degradation caused by overheating. Through the aforementioned temperature diffusion analysis, the heat diffusion isometric range quantity is finally obtained. This quantity measures the thermal diffusion characteristics of the component under different temperature gradients, including the temperature diffusion range and uniformity in each temperature gradient zone. The analysis results of the heat diffusion isometric range quantity can reveal the temperature propagation mode in the thermal field and guide component layout, heat dissipation design and thermal management optimization. This data can also be used to predict the temperature change trend of components in long-term operation, avoid failures caused by local overheating, and thus improve the overall reliability of the system.

[0013] Preferably, step S24 comprises 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.

[0014] The present invention first performs point-by-point differential calculation based on the quantized value of the overload heat density of the component to calculate the rate of change between two adjacent points. By using the differential formula, the thermal load change 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 to obtain a difference sequence. This difference sequence helps to reveal the similarities and differences in thermal load changes of different components, and provides data support for subsequent power decay assessments. Through this point-by-point differential analysis, the thermal stress and load changes of components in long-term use can be revealed, providing an accurate basis for thermal performance analysis. By calculating the pairwise difference sequences of all components, the average values ​​of these difference sequences are obtained, and then the point-by-point thermal load change mean difference D is obtained. The point-by-point thermal load change mean difference D reflects the degree of difference in thermal load changes between different components. The larger the value, the more obvious the difference in thermal load changes between components. The power degradation level is divided according to the D value: when D<0.1, the power degradation is slight and is divided into level 1; when 0.1<=D<0.3, the power degradation is moderate and is divided into level 2; when D>=0.3, the power degradation is obvious and is divided into level 3. Through this process, the degree of power degradation of each component can be accurately evaluated, providing clear guidance for subsequent thermal management and performance optimization. Using the data of the point-by-point thermal load change mean difference D and the power degradation level of the component, the maximum entropy value H of power degradation is estimated by the maximum entropy principle. The maximum entropy principle is a statistical method that infers the most state by maximizing uncertainty. Here, the maximum entropy principle is used to establish a probabilistic model, using the known point-by-point thermal load change mean difference and power degradation level data as constraints, and optimizing to calculate the maximum entropy value H of power degradation. The maximum entropy value H provides a comprehensive measure that reflects the uncertainty and potential risks in the power degradation process of the component, providing an important basis for the long-term reliability evaluation of the component. Based on the previously calculated maximum entropy value H of power degradation, the performance degradation between different components is compared. By comparing the maximum entropy values ​​H of different components, the decay differences of each component during the change of thermal load can be revealed. A higher H value means that the decay process of the component is more uncertain and complex, and there is a higher risk of overload or performance degradation. A lower H value indicates that the decay process of the component is relatively stable. This comparative analysis can help designers identify which components are more prone to overheating or performance degradation in long-term use, thereby optimizing component selection and thermal management strategies to ensure the overall stability and reliability of the system. By performing a comparative analysis of the performance degradation of multiple components, the performance degradation ratio between different components is obtained. This ratio reflects the relative performance differences of different components in terms of thermal load, power degradation, etc., and can reveal which components have performance degradation due to heat accumulation, overload or design defects.The degradation ratio obtained 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 efficiency and reliability of the system in long-term operation. Through this analysis, more scientific decisions can be made in component selection and thermal management strategies.

[0015] Preferably, performing 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.

[0016] The present invention collects the performance parameters of components under different working cycles, and records them point by point with the comparison value. 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 with significant correlation with the decay is screened out to form the time series correlation data of performance decay. This process helps to identify the changing law of component performance and provides a basis for subsequent decay prediction. In the weighted processing of time series data, the data of each time point is weighted according to its importance in the time distribution through the time weight calculation method. 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, thereby enhancing the model's prediction accuracy for the periodic decay trend. The weighted results are finally merged to form the time series point weighted data, providing high-quality data support for further analysis. When predicting periodic performance degradation, firstly, based on the weighted results 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 changes of different components. Through the prediction and analysis of different time periods, the degradation trend and degradation rate of each component can be revealed. In addition, by dividing and predicting the periodicity according to different time periods, the degradation mode of the components in different working cycles can be understood in detail. Finally, a report containing the performance degradation cycle, degradation rate and trend line of each component is formed to provide a scientific basis for maintenance and replacement strategies. Based on the data analysis results obtained in the above steps, a periodic performance degradation prediction report containing each component is generated. The report content should include detailed information such as the degradation cycle, degradation rate, trend line, etc. of each component, and clearly show the regularity of component performance degradation and its future change trend. This report can provide decision support for equipment maintenance management, preventive maintenance and optimization of the use cycle, help extend the service life of components, reduce equipment failure rate, and improve the overall reliability of the system.

[0017] Preferably, performing stability testing on the periodic performance degradation prediction report comprises 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.

[0018] The present invention can quantify the degree of fluctuation of the decay process by extracting the decay data of current and voltage from the report and calculating the variance. The larger the variance, the greater the decay fluctuation of the component under different load conditions, and there is a risk of instability; conversely, the smaller the variance, the more stable the decay of the component. Therefore, the calculation of the variance provides quantitative basic data for subsequent stability testing. Load characteristic analysis 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 then provides a basis for the calculation of current-voltage decay load characteristic data. Through this process, the decay rate of current and voltage during load changes can be obtained, thereby inferring whether the decay process of the component is stable. By analyzing the current-voltage decay load characteristic data, calculating the rate of change of current and voltage, drawing a trend curve, and evaluating its slope and rate of change, it can be judged whether the decay process tends to be stable. If the trend curve is linear and gentle, it means that the decay process tends to be stable, and the load fluctuation in the system has little effect on the performance of the component. This evaluation provides an intuitive basis for judging the stability of the component. By performing piecewise linear fitting on the trend curve and extracting the slope value and correlation coefficient of each segment, it is possible to verify whether the decay process is close to 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 of the entire curve and its standard deviation will help further confirm the linear and gentle characteristics of the curve. This analysis can accurately determine the stability of the decay process and ensure the long-term reliability of the component. Performing fluctuation range analysis on the fitted curve and using the difference method to calculate the incremental changes between the points of the curve can help evaluate whether there are large fluctuations in the decay process. If the incremental changes are concentrated and the range is small, it means that the decay process is relatively stable and is not prone to large fluctuations. By drawing a fluctuation histogram for evaluation, the stability of the component decay can be further confirmed, reducing the risk of failure in actual 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 by the change trend of the cumulative deviation curve. If the cumulative deviation is close to zero and there is no obvious fluctuation, it means that the decay process is stable, the trend is close to linear and there is no large fluctuation. This process provides strong support for the final stability judgment of the component and ensures that the component maintains good performance in actual applications.

[0019] Preferably, the present invention further provides a circuit component selection system for executing the circuit component selection method as described above, the circuit component selection system comprising: 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.

[0020] Preferably, the present invention further provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed, implements the circuit component selection method as described in any one of the above.

[0021] The beneficial effects of the present invention are to obtain the design information of the blank circuit, and combine it with the specific parameter information of the circuit component 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, a simulation space is established using the design information of the blank circuit to lay the foundation for subsequent circuit simulation and analysis. Through the establishment of 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 subsequent steps. By analyzing the circuit simulation space, the overload heat density is quantitatively calculated for the rated voltage and rated current of each component. The purpose of this process is to evaluate the heat generated by the component due to overload under different working conditions and its impact on the life of the component. Through the quantitative overload heat density value obtained, the performance degradation comparison between different components is further performed to analyze the trend of 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, periodic performance degradation prediction is performed. By predicting the decay trend, it is possible to foresee the performance changes of each component during long-term use and evaluate its impact on the stability of the entire circuit system. On this basis, a stability test is performed to ensure the stability and reliability of the circuit as a whole under different working environments. Through this process, a scientific basis can be provided for the selection of circuit components, and components with high stability in long-term use can be selected to avoid circuit system failure or efficiency reduction due to performance decay, thereby improving the long-term reliability of the circuit design. Therefore, the present invention is an optimization of a traditional method for selecting circuit components, which solves the problems of a traditional method for selecting circuit components, such as poor ability to identify performance decay of circuit component components and large errors in the selection of circuit component components, improves the ability to identify performance decay of circuit component components, and reduces the errors in the selection of circuit component components. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic flow chart of the steps of a method for selecting circuit components; Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0023] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0024] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0025] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0026] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0027] To achieve this, please refer to Figure 1 to Figure 2 , a circuit component selection method, the method comprising the following steps: Step S1: obtaining blank circuit design information and circuit component information to be tested; obtaining rated voltages and rated currents of different components through the circuit component information; and establishing a blank circuit simulation space using the blank circuit design information; 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 S3: 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 test on the periodic performance degradation prediction report and use it to select circuit components.

[0028] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a flow chart of a method for selecting a circuit component according to the present invention. In this example, the method for selecting a circuit component includes the following steps: Step S1: obtaining blank circuit design information and circuit component information to be tested; obtaining rated voltages and rated currents of different components through the circuit component information; and establishing a blank circuit simulation space using the blank circuit design information; In an embodiment of the present invention, when obtaining blank circuit design information and circuit component information to be tested, the specific sources and processing processes of the two types of information should be defined in detail. For blank circuit design information, it can be obtained through physical mapping or a known circuit design parameter manual to ensure that it contains a complete circuit topology, connection method and design expected load power. For the circuit component information to be tested, it is necessary to measure the rated voltage and rated current of different components one by one through professional electrical testing equipment, such as using a DC current source and an AC signal generator to gradually increase the voltage and current and record the working upper limit parameters of the components. To establish a blank circuit simulation space, it is necessary to adopt a basic circuit drawing physical translation method, and use mathematical matrix theory to convert the connection relationship of components in the circuit into a node admittance matrix. Then, the voltage distribution of each node is calculated according to the circuit topology rules to ensure that the simulation space can truly simulate the operating state of the blank circuit. The electrical parameters of each component are logically classified to form an initial input data set, which includes data such as component type, voltage distribution, current path, etc., to provide basic support for subsequent steps.

[0029] 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; In an embodiment of the present invention, when the rated voltage and rated current of different components are quantified for overload heat density based on the blank circuit simulation space, the physical relationship between thermal power and current and voltage can be mathematically described by the thermal-electric coupling analysis method. Specifically, according to the Joule heat formula, the square value of the current under the working state of each component is multiplied by the component resistance value to obtain the corresponding thermal power output, and the thermal density distribution is calculated in combination with the surface area of ​​the component. At the same time, the voltage and current are gradually increased to the overload state under the rated parameter conditions, and the heat density change curve caused by the overload is recorded to form a data set of the overload heat density quantification value of the component. Subsequently, according to the overload heat density quantification value 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 rate of change of heat density, the performance degradation ratio of the component caused by the overload state is quantified, and the performance degradation ratio of the component between different components is obtained to form a complete performance degradation comparison matrix.

[0030] Step S3: 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 test on the periodic performance degradation prediction report and use it to select circuit components.

[0031] In an embodiment of the present invention, when predicting periodic performance degradation according to the performance degradation ratio generated in step S2, the Fourier decomposition analysis method can be used to decompose the heat density quantification data into a time series pattern of periodic changes. The degradation trend of each component in different time periods is statistically modeled, and the life degradation cycle of the component is predicted by calculating the acceleration and periodic fluctuation amplitude of the degradation trend. The periodic performance degradation prediction report includes the time node of component failure and its degradation rate. When the prediction report is tested for stability, the reliability of the prediction model will be determined by comparing the consistency of the actual operating data with the prediction results. The regression analysis method is used to calculate the residual variance, and the error range of the prediction report is judged based on the confidence interval. After the stability test results meet the accuracy requirements, the optimal configuration combination of circuit components is selected according to the degradation cycle of different components to ensure the long-term stable operation of the overall circuit under the target design parameters.

[0032] Preferably, 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.

[0033] Preferably, step S2 comprises the following steps: 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: quantifying the overload heat density according to the average time-series fluctuation voltage and the average time-series fluctuation current to obtain a quantified value of the component overload heat density; Step S24: performing a comparison of component performance degradation between different components based on the component overload heat density quantification value to obtain a performance degradation ratio between different components.

[0034] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: 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; In an 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 component, the dynamic response process of different components under the extreme load state is simulated by establishing a time-dependent charge flow model of each component. Specifically, a differential equation is used to describe the instantaneous change relationship between the current and voltage in the component, and combined with the timing control signal of the dynamic load, a gradually increasing input current and voltage are applied in the simulation environment. For each component, its instantaneous current and voltage values ​​at different time points are recorded, and the fluctuation curve of the load current and voltage over time is drawn. During the simulation process, the temperature rise, power dissipation and current overload degree of the component are monitored in real time to ensure that the acquired data is complete and the performance boundary of the component under the extreme load condition can be quantified. The output load current-voltage fluctuation curve fully reflects the dynamic response characteristics of the component, providing basic data support for subsequent analysis.

[0035] 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; In an embodiment of the present invention, when analyzing the load current-voltage fluctuation curve, the fluctuation curve is interpreted using a time series data processing method. First, the fluctuation curve is segmented according to the time series, and the current and voltage peaks and valleys in each time period are used as feature points to calculate the voltage and current amplitude changes in the segment. Then, the data of all time periods are weighted averaged to obtain the average time-series fluctuation voltage and average time-series fluctuation current under the extreme load state. During the processing, the Fourier transform method is introduced to perform frequency domain analysis on the fluctuation curve, thereby eliminating the instantaneous fluctuation interference caused by high-frequency noise and ensuring the stability and accuracy of the average fluctuation amplitude. Through this process, the average time-series fluctuation parameters under the extreme load state are extracted as key quantitative indicators, which are further used for heat and performance analysis.

[0036] Step S23: quantifying the overload heat density according to the average time-series fluctuation voltage and the average time-series fluctuation current to obtain a quantified value of the component overload heat density; In an embodiment of the present invention, the heat distribution of the component under the extreme load state is calculated according to the average time-series fluctuation voltage and the average time-series fluctuation current obtained in step S22. First, using the Joule heat formula, the square value of the current is multiplied by the equivalent resistance of the component to obtain the thermal power distribution data. Secondly, according to the surface area and heat conduction path of the component, the thermal power distribution is converted into an overload thermal density value. In the specific operation, the finite element analysis method is used to numerically simulate the heat conduction process, and the thermal field distribution of the component is accurately decomposed into the thermal density values ​​of each spatial unit. Through step-by-step iterative simulation, the overload thermal density distribution curve of the component under different load conditions is calculated, and it is quantified into the overload thermal density value as the final output data, providing a quantitative indicator for the performance comparison of the component.

[0037] Step S24: performing a comparison of component performance degradation between different components based on the component overload heat density quantification value to obtain a performance degradation ratio between different components.

[0038] In an embodiment of the present invention, based on the quantitative value of overload heat density obtained in step S23, a horizontal comparison is made on the performance degradation of different components. 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 extreme load conditions. Specifically, the fatigue crack growth rate of the component is derived using the cumulative thermal power theory, and the performance degradation ratio of the component is calculated in combination with the peak and mean changes in heat density. The comparative analysis is carried out using a matrix induction method to construct a matrix data table containing the performance degradation ratios of all components, which are classified and summarized according to component categories and load conditions. The final result is based on the differences in the degradation rates between the components, providing a reliable basis for performance evaluation and providing data support for the selection of circuit components.

[0039] Preferably, step S23 includes the following steps: 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 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; Step S223: identifying the temperature rise diffusion range based on the component temperature rise rate to obtain the temperature rise diffusion range; Step S224: 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 S225: 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 S226: quantify the overload heat density according to the component heating rate, the temperature rising range overlap data and the heat diffusion equidistant range quantity to obtain the component overload heat density quantization value.

[0040] In an embodiment of the present invention, when extracting the fluctuation extreme value interval of the average time series fluctuation voltage and the average time series fluctuation current, the original data is first decomposed by a segmented statistical method and divided into continuous and uniform small time periods. For each time period, the extreme value interval of the time period is determined by obtaining the local extreme value (including the maximum value and the minimum value) of the data and combining the fluctuation frequency in the interval. In the specific operation, a polynomial fitting algorithm is introduced to perform trend decomposition on the fluctuation curve, remove background noise and non-significant fluctuations, and use the remaining curve features for the extraction of extreme value points. Using these local extreme value points, by constructing interval boundary conditions, the fluctuation voltage extreme value interval and the fluctuation current extreme value interval are output for subsequent analysis. According to the fluctuation voltage extreme value interval and the fluctuation current extreme value interval obtained in step S231, the temperature rise rate of the component under the limit state is calculated. First, the product of the fluctuation voltage and current interval is used as the power density input, and the accumulation rate of heat energy per unit time is calculated based on the heat capacity characteristics and heat dissipation conditions of the component. Next, by establishing a heat conduction equation, simulating the temperature gradient change process inside the component, combining the time change and spatial distribution in a unified thermal model, and extracting the heating rate curve of the component. Finally, the simulation data is matched and corrected with the temperature rise data of the actual test 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 heat diffusion path is numerically simulated using the finite difference method to derive the spatial distribution of temperature in the internal and external areas of the component. By defining the temperature contour area, the boundary range of the heating diffusion is determined, and the influence of the thermal conductivity, geometry and boundary heat dissipation conditions of the material is considered at the same time, and the spatial resolution of the diffusion range is refined. Finally, the output data is used as a quantitative indicator of the thermal effect range of each component to analyze the thermal effect interaction between different components. According to the heating diffusion range of step S223, the thermal effect areas of different components are spatially overlapped. By establishing a thermal effect superposition model between components, the diffusion ranges of different components are spatially projected and the intersection area is obtained. During the operation, Boolean operators are introduced to perform logical operations on the diffusion range, and the shape, area and distribution characteristics of the overlapping regions are gradually identified. The data of all overlapping regions are integrated into the temperature rise range overlapping data, and used as the input for the subsequent thermal diffusion quantitative analysis. According to the temperature rise range overlapping data in step S224, the thermal diffusion equidistant range quantity is calculated in combination with the heating rate of the component. By integrating the heat flux density distribution in the overlapping area, the equidistant change amount of heat diffusion from the high temperature area to the low temperature area is derived. The heat flow field equation and gradient operator are used for high-precision simulation, and the complex temperature change process is decomposed into the linear superposition of several sub-regions, and the equidistant range quantity data is output. This data comprehensively describes the uniformity and distribution characteristics of the thermal effect interaction between different components, providing a basis for the final thermal density quantification.According to the data of steps S222, S224 and S225, the quantitative analysis of overload heat density is completed. First, the component heating rate, the overlapping data of the heating range and the heat diffusion equidistant range are matrixed to establish a multivariable input model. The heat conduction integral method is used to calculate the change in unit area heat density of the component under overload conditions. Subsequently, the quantitative results are corrected by the principle of conservation of thermal energy to eliminate the deviation introduced by the calculation error, and the final quantitative value of the component overload heat density is obtained. The quantitative value is output in a tabular form to provide direct data support for subsequent component performance evaluation and selection.

[0041] Preferably, step S225 includes the following steps: Perform thermal field gradient partitioning on the overlapping data of the temperature rise range to obtain thermal field range gradient data; According to the heating rate of the component, the temperature diffusion amount of the thermal field range gradient data is identified to obtain the temperature distribution diffusion difference amount; Based on the temperature distribution diffusion difference, the heat diffusion isometric range quantity is analyzed to obtain the heat diffusion isometric range quantity; 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.

[0042] In an embodiment of the present invention, according to the determined heating rate of the component, a fixed temperature interval is set, and each 10°C is a gradient interval to perform gradient partitioning on the thermal field area. Taking the heating rate of 5°C per minute as an example, the initial temperature point (such as 20°C) of the test area is first selected in the space, and the spatial gradient of different temperature ranges is divided by 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 zone, and 30°C-40°C is the second gradient zone. In the specific operation process, the temperature field is segmented by the spatial discretization method, and the data characterization of the gradient partition is completed by the average heat flux density and the thermal field distribution function in each interval, and the thermal field range gradient data is obtained. The data describes the temperature range and spatial distribution characteristics in each gradient zone, and provides a basis for subsequent diffusion analysis. On the basis of the determined thermal field range gradient data, the temperature diffusion amount of each gradient zone is identified according to the heating rate of the component. If the heating rate of a component is 5°C per minute, it takes 2 minutes for the temperature to rise from 20°C to 30°C in the first gradient zone, and the diffusion amount is 10°C; due to the change of thermal field characteristics, it takes 3 minutes for the temperature to rise from 30°C to 40°C in the second gradient zone, and the diffusion amount is also 10°C. In this process, the diffusion characteristics of different regions are extracted by combining the time factor and the spatial gradient change through the multi-region thermal flow coupling analysis method. The temperature diffusion path is refined and calculated by the difference method to obtain the diffusion amount of each gradient zone, which is stored in a table form, laying the foundation for the subsequent difference analysis. Based on the difference in diffusion amount and time in different gradient zones, the diffusion difference of temperature distribution is calculated. The diffusion amount of the first gradient zone and the second gradient zone is compared in time, and it is found that although the diffusion amount is 10°C, the diffusion time of the first gradient zone is 2 minutes, and the diffusion time of the second gradient zone is 3 minutes, which produces a diffusion difference in the time dimension. In the specific analysis, by constructing the temperature distribution difference function, combined with the thermal field gradient and time characteristics in the region, the inhomogeneity in the diffusion process is quantified. The weighted average method is used to calculate the overall temperature difference, which is digitized into distribution characteristic data for the next step of equidistant range quantitative analysis. Based on the temperature distribution diffusion difference, the equidistant range of heat diffusion is analyzed. The equidistant range is set to 5°C, and the uniformity and directionality of temperature diffusion in each equidistant range are studied. 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 changes and diffusion paths in each range are analyzed one by one. During the operation, the numerical integration method is used to calculate the average heat flow rate of each equidistant range, and the directionality and uniformity of temperature diffusion in each range are evaluated through vector field analysis of the heat flow direction. Finally, combined with the heat diffusion gradient and difference distribution in the equidistant range, the heat diffusion equidistant range quantity is output to provide data support for the thermal characteristic matching of subsequent circuit components.

[0043] Preferably, step S24 comprises the following steps: The point-by-point differential calculation of the component overload heat density quantization value is performed to obtain the point-by-point heat load change rate of the component; the point-by-point heat load change mean difference is obtained by comparing the point-by-point heat load change rates of different components; The component power degradation level is divided according to the point-by-point heat load change average difference to obtain the component power degradation level; Based on the point-by-point mean difference of heat load changes and the component power decay level, the maximum entropy estimation is performed to obtain the maximum entropy value of power decay; Compare the performance degradation of different components based on the maximum entropy value of power degradation to obtain the performance degradation ratio of different components; 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.

[0044] In an embodiment of the present invention, based on the quantized value of the overload heat density of the component, the heat load change rate of each component is calculated by a point-by-point differential method. Assuming that the overload heat 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}, the formula Δρ / Δt=(ρi+1-ρi) / (ti+1-ti) is used to calculate the change rate Δρi of each two adjacent points point by point. Component A is compared point by point with component B, and the difference in the change rate of the corresponding points is calculated to form a difference sequence. Similarly, all components are subjected to pairwise difference calculations, and the average value of the data set of the difference sequence is used to obtain the point-by-point heat load change mean difference D. This process systematically implements data processing and mean difference calculation through matrix operations and numerical integration methods. According to the point-by-point heat load change mean difference D, a division threshold is set to divide the power decay level of the component. The specific threshold conditions are: when D<0.1, it is defined as level 1, indicating that the power decay is slight; 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, the D value of each component is partitioned by the screening function, and the partition result is stored as a level identifier to establish a component level table. The table can be used to quickly query the power status of each component and provide clear input data for subsequent analysis. Based on the point-by-point heat load change mean difference D and the divided power decay level data, the maximum entropy estimation is performed. The probability model is established by the maximum entropy principle. Assuming that the probability distribution of the power decay process satisfies the constraint condition ΣPi×lg(Pi)=H, the entropy value H is maximized to obtain the optimal solution of the power decay distribution. In the actual implementation, the frequency distribution of all D values ​​and their corresponding levels is first collected, and it is used as a known constraint condition to solve the maximum entropy function by the Lagrange multiplier method. In this way, the maximum entropy value H of power decay is calculated, and the power entropy distribution table of the component is generated to represent the probability characteristics of different levels of power status. Based on the maximum entropy value H of power decay, the performance decay of different components is compared. The comparison method is: based on the maximum entropy value of power decay, the entropy deviation of each component is calculated, all entropy deviation data are constructed as a comparison matrix, and the performance decay ratio between components is analyzed. During the operation, the performance differences between components are further explored through the thermodynamic isometric analysis method, and the comprehensive data of the point-by-point heat load change mean difference and entropy value are combined to form a comparison chart. Finally, a component performance decay ratio table is generated to provide a scientific basis for the selection of circuit components.

[0045] Preferably, performing periodic performance degradation prediction based on the component performance degradation ratio includes the following steps: Conduct timing correlation analysis on component performance degradation ratios and performance degradation timing correlation data; Perform time series point weighting processing on the decay time series correlation data to obtain time series point weighted data; According to the time-series point weighted data, the component performance degradation ratio is used to predict the periodic performance degradation, and a periodic performance degradation prediction report between different components is obtained; 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.

[0046] In an embodiment of the present invention, in the prediction of periodic performance degradation, the performance parameters of the components in different working cycles are first systematically collected. 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 components in each cycle, the ratio change data is recorded point by point, and a set of time series data sets with time dependence are generated in chronological order. Based on the time series data set, the Pearson correlation coefficient formula is used to calculate the correlation coefficient between each time point. The data points with significant correlation coefficients (such as |r|>0.7) are screened out, and the abnormal points that do not conform to the overall trend are eliminated, and they are integrated to form performance degradation time series correlation data, providing effective input data for subsequent weighted processing. The performance degradation time series correlation data is weighted by time series point position, and the importance of the data is weighted according to the time distribution by using the time weight calculation method, and the weight distribution of each time point is fitted by the discrete point fitting method. The specific operation includes: grouping the data points in chronological order, and fitting the weight value distribution curve of the discrete points by the polynomial fitting method 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 and merged with their corresponding performance parameters to form the time-series point weighted data. The weighted data directly reflects the degree of influence 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 operation 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 period is first divided according to the time point sequence in the weighted data, and each section 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 perform trend analysis on each section of data and estimate the performance change rate of the component in different time periods. After merging the interpolation functions of all time periods, a complete periodic performance change trend curve is generated, which includes detailed information such as performance decay cycle, decay rate, and trend line. The predicted periodic performance change trend data is organized into a visual detailed report. The report content includes the component performance decay cycle, the decay rate statistics table of each time period, the trend line chart, and the performance decay comparison chart between different components. The report can clearly compare the performance degradation status of different components in periodic operation, providing a comprehensive and accurate reference for the optimization and selection of circuit components. The report generation uses data visualization technology to present trend curves and degradation cycles in the form of tables and charts, and ensures that the output content is intuitive and detailed by marking key points such as inflection points, fluctuation amplitude and other characteristic information. The final output of the periodic performance degradation prediction report can be directly used for circuit component selection analysis and improvement plan design.

[0047] Preferably, performing stability testing on the periodic performance degradation prediction report comprises 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.

[0048] In the embodiment of this method, the current decay value and voltage decay value of each component are extracted item by item from the periodic performance decay prediction report, and they are arranged in chronological order to form time series data of current and voltage decay values. The current decay value and voltage decay value are calculated respectively by the variance formula, and the calculation results are the current decay variance and voltage decay variance. The value of the variance is used to measure the volatility of the current and voltage decay data, and provide basic data for the subsequent analysis of load characteristics. The current decay variance and the voltage decay variance are jointly analyzed to establish the current-voltage decay load characteristic data. The variance value is processed by the normalization method to eliminate the influence of the dimension on the analysis results. For the normalized data, the load characteristic relationship between current and voltage is fitted using a two-dimensional scatter distribution diagram, and a regression model is constructed using the least squares method to obtain the load characteristic function. The slope and intercept of the function reflect the correlation and change trend between the current and voltage decay characteristics, laying the foundation for the subsequent trend curve calculation. According to the current-voltage decay load characteristic data, the trend curve is calculated by the piecewise linear fitting method. In piecewise linear fitting, firstly, the time series data is divided into several equal-length intervals, and the length of the interval is determined according to the total number of time points and the characteristic fluctuation frequency. For the data in each interval, the slope value and correlation coefficient of each segment are recorded using the fitting equation to generate the 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 flatness. If the average slope is close to zero and the standard deviation tends to zero, it indicates that the curve has linear flatness characteristics. The fluctuation range of the fitted trend curve is analyzed, and the incremental changes between the points of the curve are calculated by the difference method. The maximum, minimum and range of the incremental changes are statistically calculated, and the fluctuation histogram is drawn 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 point and the fitting line is accumulated and summed to generate a cumulative deviation curve, where the preset threshold is the median of all current data. The comprehensive trend curve evaluation results and fluctuation range analysis are used to determine whether the decay 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 changes are concentrated, and the cumulative deviation tends to zero, then the decay process of the component is determined to be stable. Unstable components are marked as abnormal, a stability test report is generated, and the circuit components are optimized and selected according to the report to ensure the stability and reliability of the overall circuit performance.

[0049] Preferably, the present invention further provides a circuit component selection system for executing the circuit component selection method as described above, the circuit component selection system comprising: 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.

[0050] Preferably, a computer-readable storage medium stores a computer program, and when the computer program is executed, the circuit component selection method as described in any one of the above is implemented.

[0051] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0052] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented 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 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 S2 includes the following steps: 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: quantifying the overload heat density according to the average time-series fluctuation voltage and the average time-series fluctuation current to obtain a quantified value of the component overload heat density; Step S24: performing a comparison of component performance degradation between different components based on the component overload heat density quantification value to obtain a performance degradation ratio between different components.

4. The circuit component selection method according to claim 2, characterized in that: Step S23 includes the following steps: 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 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; Step S223: identifying the temperature rise diffusion range based on the component temperature rise rate to obtain the temperature rise diffusion range; Step S224: 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 S225: 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 S226: quantify the overload heat density according to the component heating rate, the temperature rising range overlap data and the heat diffusion equidistant range quantity to obtain the component overload heat density quantization value.

5. The circuit component selection method according to claim 3, characterized in that: Step S225 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.

6. The circuit component selection method according to claim 2, 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.

7. 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.

8. 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.

9. 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.

10. 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 1-8 is implemented.

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