Switching power supply auxiliary design method and system

Abstract

The present invention provides a switching power supply auxiliary design method and system, which includes: performing simulation and physical experimental tests based on influencing parameters and experimental circuits to obtain data on each influencing parameter under each test; performing linear regression on each influencing parameter under each test to obtain a regression function of output voltage ripple and other influencing parameters; obtaining all influencing parameters with causal relationships and the causal effect value corresponding to each causal relationship based on the causal inference results; defining each influencing parameter as an entity, summarizing the entity relationships between all entities, and associating the corresponding regression coefficients or causal effect values based on the entity relationships to obtain a power supply knowledge graph; obtaining power supply design requirements, searching for parameters to be debugged from the power supply knowledge graph based on the power supply design requirements, and optimizing the power supply design solution based on the parameters to be debugged. The present invention can greatly shorten the design cycle of switching power supplies and promote the intelligentization of power supply design.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply auxiliary design, and in particular to a switching power supply auxiliary design method and system. Background Art

[0002] With the increasing demand for miniaturization and efficiency in electronic devices, low-power design and high-efficiency conversion in switching power supplies are becoming increasingly important. However, traditional design methods struggle to fully consider the complex impact of multiple factors on power supply performance, making the design process complex and time-consuming. Summary of the Invention

[0003] The purpose of the present invention is to provide a switching power supply auxiliary design method and system, aiming to solve the problem that traditional technology is difficult to fully consider the complex impact of multiple factors on power supply performance, resulting in a complex and time-consuming design process.

[0004] In a first aspect, the present invention provides a switching power supply auxiliary design method, the method comprising:

[0005] Build an experimental circuit, select multiple influencing parameters that affect power supply performance, and perform simulation and physical experimental tests based on the influencing parameters and the experimental circuit to obtain data on each influencing parameter under each test;

[0006] Performing linear regression on each influencing parameter under each test to obtain a regression function of the output voltage ripple and other influencing parameters, and determining whether the regression function meets the preset fitting conditions;

[0007] If the regression function satisfies the preset fitting conditions, the regression coefficients of the output voltage ripple and other influencing parameters are obtained according to the regression function, and causal discovery is performed on any two influencing parameters based on the PC algorithm. All influencing parameters with causal relationships are obtained according to the causal discovery results, and the causal effect values corresponding to the influencing parameters and each causal relationship are obtained through the structural equation model;

[0008] Define each influencing parameter as an entity, summarize the entity relationships between all entities, and associate the corresponding regression coefficients or causal effect values according to the entity relationships to obtain the power supply knowledge graph;

[0009] The power supply design requirements are obtained, and parameters to be debugged are searched from the power supply knowledge graph according to the power supply design requirements, and the power supply design solution is optimized according to the parameters to be debugged.

[0010] In summary, the aforementioned switching power supply assisted design method can quickly locate the root cause of a problem through knowledge graph query. This causal inference and knowledge graph-based design-assisted method can systematically reveal causal relationships and provide more accurate optimization solutions, thereby significantly improving design efficiency, accuracy, and intelligence. The advantages of the embodiments of the present invention are particularly prominent in complex power supply designs, not only improving the quality of design decisions but also significantly shortening the design cycle, thereby promoting the intelligentization of power supply design.

[0011] Furthermore, the influencing parameters include inductor core material, output capacitance, switching frequency, input voltage, output voltage, output voltage ripple, inductor current ripple, maximum current limit, and control voltage;

[0012] The physical experiment test includes a first test experiment and a second test experiment, and the steps of the first test experiment include:

[0013] Step 1.1. Select a 220μF output capacitor, a 15μH powder core inductor, set the switching frequency to 500kHz, turn on the oscilloscope, turn on the power supply, set the voltage to 12V, and set the current limit to 1.5A. Turn on the electronic load, set it to constant current mode, and set the current to 1.5A.

[0014] Step 2.1. Turn on the power supply "OUT ON" button. The output voltage Vout waveform displayed on the oscilloscope is a flat horizontal line of 3.3V.

[0015] Step 3.1. Turn on the "LOAD ON" button of the electronic load. A triangle waveform with an average value of 1.5A will be displayed on the inductor current channel of the oscilloscope, and a flat horizontal line with a level of 3.3V will be displayed on the output voltage channel Vout.

[0016] Step 4.1. Read the output voltage Vout, measure the output voltage ripple and inductor current ripple, and record these values. Then change the input voltage Vin and load current values and repeat this step.

[0017] Step 5.1. Turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, then connect the 15μH ferrite core inductor and repeat steps 2.1 to 4.1.

[0018] Step 6.1. Set the switching frequency to 500kHz and repeat steps 2.1 to 5.1.

[0019] Step 7.1. At the end of the measurement, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, and then turn off all instruments.

[0020] Furthermore, the steps of the second test experiment include:

[0021] Step 1.2: Select a 220μF output capacitor, a 15μH powder core inductor as the inductor core material X, set the switching frequency to 250kHz, turn on the oscilloscope, turn on the power supply, set the voltage to 12V, and set the current limit to 1.5A. Turn on the electronic load, set it to constant current mode, and set the current to 1A.

[0022] Step 2.2. Turn on the power supply "OUT ON" button. The output voltage waveform displayed on the oscilloscope is a flat horizontal line of 3.3V.

[0023] Step 3.2. Turn on the electronic load "LOAD ON". A triangle waveform with an average value of 1A should be displayed on the inductor current channel of the oscilloscope. A flat horizontal line with a very small ripple between 500mV and 1V should be seen on the control voltage channel. A flat horizontal line with a level of 3.3V should be seen on the output voltage channel.

[0024] Step 4.2: Slowly increase the current of the electronic load and detect the maximum current limit value when the output voltage is stabilized at 3.3V. When the output voltage is no longer stable, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the power supply, then record the maximum current limit and the corresponding control voltage value. Change the input voltage value and repeat this step;

[0025] Step 5.2: Select a 15μH ferrite core inductor or a 15μH magnetic powder core inductor, a 10μF output capacitor or a 220μF output capacitor, and a switching frequency of 250kHz or 500kHz. Repeat steps 2.2 to 4.2 for these three combinations.

[0026] Step 6.2: At the end of the measurement, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, and then turn off all instruments.

[0027] Furthermore, the steps of performing linear regression on each influencing parameter under each test to obtain a regression function of the output voltage ripple and other influencing parameters, and determining whether the regression function satisfies a preset fitting condition include:

[0028] Linear regression is performed according to the following formula:

[0029] ;

[0030] in, Indicates the inductor core material, Indicates the input voltage, Indicates the maximum limiting current, represents the control voltage, represents the switching frequency, Indicates the output voltage, represents the output voltage ripple, represents the inductor current ripple, represents the output capacitance, represents the intercept term, 、 、 、 、 、 、 、 are regression coefficients, represents the error term;

[0031] The preset fitting conditions include whether the multivariate determination coefficient after the regression function adjustment is greater than a first preset threshold, and whether the p-value of the regression function is greater than a second preset threshold;

[0032] The adjusted multivariate determination coefficient was calculated according to the following formula:

[0033] ;

[0034] in, represents the adjusted multiple determination coefficient, represents the original multivariate determination coefficient, represents the experimental data capacity, Indicates the number of independent variables;

[0035] The original multivariate determination coefficient was calculated according to the following formula:

[0036] ;

[0037] in, represents the residual sum of squares, represents the total sum of squares, represents the fitting value of the qth experimental data, represents the average value of the experimental data, represents the qth experimental data, and q represents the number of experimental data.

[0038] Furthermore, the step of performing causal discovery on any two influencing parameters based on the PC algorithm and obtaining all influencing parameters with causal relationships according to the causal discovery results includes:

[0039] Determine each pair of influencing parameters and Whether it is independent under a given set of variables Y:

[0040] ;

[0041] If independent, delete and The edges between them are constructed, and by identifying the V-shaped structure and applying the transitivity rule, the direction of the edges is determined, and a directed acyclic graph is obtained to represent the causal relationship between the influencing parameters.

[0042] Furthermore, the step of obtaining the causal effect value corresponding to the influencing parameter and each causal relationship through the structural equation model includes:

[0043] The causal effect value is calculated according to the following formula:

[0044] ;

[0045] in, 、 Represent the i-th and j-th influencing parameters respectively, represents the error term, Influencing parameters Influencing parameters The causal effect value of Influencing parameters and influencing parameters The covariance between Influencing parameters The variance of .

[0046] Furthermore, the steps of obtaining power supply design requirements, searching for parameters to be debugged from the power supply knowledge graph according to the power supply design requirements, and optimizing the power supply design solution according to the parameters to be debugged include:

[0047] Acquire at least one power supply design requirement, each of the power supply design requirements including a target impact parameter and a value corresponding to the target impact parameter;

[0048] Parameters to be debugged that are related to each target influencing parameter are searched from the power supply knowledge graph, and power supply design results are optimized according to the parameters to be debugged to obtain the optimal design solution.

[0049] In a second aspect, the present invention provides a switching power supply auxiliary design system, the system comprising:

[0050] A simulation and physical experiment platform building module is used to build an experimental circuit, select multiple influencing parameters that affect power supply performance, and perform simulation and physical experiment tests based on the influencing parameters and experimental circuit to obtain data on each influencing parameter under each test;

[0051] A linear regression module is used to perform linear regression on each influencing parameter under each test, obtain a regression function of the output voltage ripple and other influencing parameters, and determine whether the regression function meets the preset fitting conditions;

[0052] a causal inference module, configured to obtain, if the regression function satisfies a preset fitting condition, the regression coefficients of the output voltage ripple and other influencing parameters according to the regression function, perform causal discovery on any two influencing parameters based on a PC algorithm, obtain all influencing parameters with causal relationships based on the causal discovery results, and then derive the causal effect value corresponding to the influencing parameter and each causal relationship through a structural equation model;

[0053] The knowledge graph construction module is used to define each influencing parameter as an entity, summarize the entity relationships between all entities, and associate the corresponding regression coefficients or causal effect values according to the entity relationships to obtain the power knowledge graph;

[0054] The auxiliary design module is used to obtain power supply design requirements, search for parameters to be debugged from the power supply knowledge graph according to the power supply design requirements, and optimize the power supply design solution according to the parameters to be debugged.

[0055] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned switching power supply auxiliary design method.

[0056] In a fourth aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein:

[0057] The memory is used to store computer programs;

[0058] When the processor is used to execute the computer program stored in the memory, the above-mentioned switching power supply auxiliary design method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a switching power supply auxiliary design method proposed in one embodiment of the present invention;

[0060] Figure 2 A simplified schematic diagram of a buck regulator according to an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of a cause-effect relationship that has been clarified based on prior knowledge according to an embodiment of the present invention;

[0062] Figure 4 A schematic diagram of the cause-effect relationship among the five variables of the inductor core material, output capacitance, output voltage, switching frequency, and maximum current limit according to an embodiment of the present invention;

[0063] Figure 5 A general diagram of cause and effect relationships illustrating an embodiment of the present invention;

[0064] Figure 6 A cause-and-effect diagram of the inductor core material and power supply performance indicators according to an embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram of the structure of a power supply knowledge graph containing causal relationships according to an embodiment of the present invention;

[0066] Figure 8 A schematic diagram illustrating factors affecting output voltage ripple according to an embodiment of the present invention;

[0067] Figure 9 (a) is a schematic diagram showing the effect of switching frequency on other performance indicators according to an embodiment of the present invention. Figure 9 (b) is a schematic diagram showing the influence of the inductor core material on other performance indicators according to an embodiment of the present invention. Figure 9 (c) is a schematic diagram showing the influence of the control voltage on other performance indicators according to an embodiment of the present invention;

[0068] Figure 10 This is a schematic diagram of the structure of a switching power supply auxiliary design system proposed in one embodiment of the present invention.

[0069] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the invention belongs. The words "including" and similar words used in this article mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0071] like Figure 1 As shown, an embodiment of the present invention provides a switching power supply auxiliary design method, the method comprising steps S101 to S105, wherein:

[0072] Step S101: constructing an experimental circuit, selecting multiple influencing parameters that affect power supply performance, and performing simulation and physical experimental tests based on the influencing parameters and the experimental circuit to obtain data on each influencing parameter under each test;

[0073] It should be noted that the Buck circuit is one of the most common switching power supply topologies and is widely used in various electronic devices. Studying the influence and causal relationship in the Buck circuit has broad practical significance and can provide a reference for the design of other types of switching power supplies. The TPS54160 is a buck-type integrated voltage regulator chip with an internal integrated NMOS switch. It has an input voltage range of 3.5V to 60V, a maximum output current of 1.5A, and a stable output voltage of 3.3V. The switching frequency range is 100kHz to 2MHz. It is used to study the effects of different inductor core material types and operating conditions under core saturation on inductor current ripple. The simplified schematic diagram of the buck regulator is shown below. Figure 2 shown.

[0074] Furthermore, simulations were conducted using a simulation circuit diagram constructed using the Texas Instruments (TI) WEBENCH power supply online experiment platform. Ferrite and powder core, two common inductor core materials, were used. The experimental results show that, under the same nominal inductance and saturation conditions, the powder core inductor produces less current ripple than the ferrite inductor under heavy load current. As the load current increases, the relative increase in ripple amplitude for the ferrite inductor is greater than that for the powder core inductor. To validate the simulation results, physical experiments were then conducted.

[0075] Using a regular circuit board, perform the following two tests:

[0076] In order to obtain data on seven variables, namely, inductor core material, output capacitance, switching frequency, input voltage, output voltage, output voltage ripple, and inductor current ripple, the first test experiment was conducted with the following steps:

[0077] Step 1.1. Select a 220μF output capacitor, a 15μH powder core inductor, set the switching frequency to 500kHz, turn on the oscilloscope, turn on the power supply, set the voltage to 12V, and set the current limit to 1.5A. Turn on the electronic load, set it to constant current mode, and set the current to 1.5A.

[0078] Step 2.1. Turn on the power supply "OUT ON" button. The output voltage Vout waveform displayed on the oscilloscope is a flat horizontal line of 3.3V.

[0079] Step 3.1. Turn on the "LOAD ON" button of the electronic load. A triangle waveform with an average value of 1.5A will be displayed on the inductor current channel of the oscilloscope, and a flat horizontal line with a level of 3.3V will be displayed on the output voltage channel Vout.

[0080] Step 4.1. Read the output voltage Vout, measure the output voltage ripple and inductor current ripple, and record these values. Then change the input voltage Vin and load current values and repeat this step.

[0081] Step 5.1. Turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, then connect the 15μH ferrite core inductor and repeat steps 2.1 to 4.1.

[0082] Step 6.1. Set the switching frequency to 500kHz and repeat steps 2.1 to 5.1.

[0083] Step 7.1. At the end of the measurement, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, and then turn off all instruments.

[0084] In order to obtain data on the six variables of inductor core material, output capacitance, switching frequency, input voltage, maximum current limit, and control voltage, a second test experiment was conducted. The steps are as follows:

[0085] Step 1.2: Select a 220μF output capacitor, a 15μH powder core inductor as the inductor core material X, set the switching frequency to 250kHz, turn on the oscilloscope, turn on the power supply, set the voltage to 12V, and set the current limit to 1.5A. Turn on the electronic load, set it to constant current mode, and set the current to 1A.

[0086] Step 2.2. Turn on the power supply "OUT ON" button. The output voltage waveform displayed on the oscilloscope is a flat horizontal line of 3.3V.

[0087] Step 3.2. Turn on the electronic load "LOAD ON". A triangle waveform with an average value of 1A should be displayed on the inductor current channel of the oscilloscope. A flat horizontal line with a very small ripple between 500mV and 1V should be seen on the control voltage channel. A flat horizontal line with a level of 3.3V should be seen on the output voltage channel.

[0088] Step 4.2: Slowly increase the current of the electronic load and detect the maximum current limit value when the output voltage is stabilized at 3.3V. When the output voltage is no longer stable, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the power supply, then record the maximum current limit and the corresponding control voltage value. Change the input voltage value and repeat this step;

[0089] Step 5.2: Select a 15μH ferrite core inductor or a 15μH magnetic powder core inductor, a 10μF output capacitor or a 220μF output capacitor, and a switching frequency of 250kHz or 500kHz. Repeat steps 2.2 to 4.2 for these three combinations.

[0090] Step 6.2: At the end of the measurement, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, and then turn off all instruments.

[0091] Comprehensive tests 1 and 2 yield a total of nine variables: inductor core material, output capacitance, switching frequency, input voltage, maximum current limit, control voltage, output voltage, output voltage ripple, and inductor current ripple.

[0092] In the physical experiment, the experimental phenomenon is consistent with the simulation results. Ferrite inductors are more likely to saturate when the current increases, resulting in increased current ripple. Under the same conditions, magnetic powder core inductors show a lower degree of saturation, resulting in relatively small current ripple. The type of core material has a significant impact on power supply performance, especially on inductor current ripple and output voltage ripple. Therefore, the appropriate selection of inductor core material is of great significance for optimizing the design of switching power supplies and improving their performance. A total of 200 sets of experimental data were collected through comprehensive simulation and physical experiments. Some of the data are shown in Table 1:

[0093] Table 1

[0094] ;

[0095] in, Indicates the inductor core material, is a binary variable, “1” represents ferrite core material, “0” represents magnetic powder core material, Indicates the input voltage, Indicates the maximum limiting current, represents the control voltage, represents the switching frequency, Indicates the output voltage, represents the output voltage ripple, represents the inductor current ripple, Represents the output capacitance.

[0096] Step S102: performing linear regression on each influencing parameter under each test to obtain a regression function of the output voltage ripple and other influencing parameters, and determining whether the regression function meets a preset fitting condition;

[0097] It should be noted that the linear regression is performed according to the following formula:

[0098] ;

[0099] in, Indicates the inductor core material, Indicates the input voltage, Indicates the maximum limiting current, represents the control voltage, represents the switching frequency, Indicates the output voltage, represents the output voltage ripple, represents the inductor current ripple, represents the output capacitance, represents the intercept term, 、 、 、 、 、 、 、 are regression coefficients, represents the error term;

[0100] The preset fitting conditions include whether the multivariate determination coefficient after the regression function adjustment is greater than a first preset threshold, and whether the p-value of the regression function is greater than a second preset threshold;

[0101] The adjusted multivariate determination coefficient was calculated according to the following formula:

[0102] ;

[0103] in, represents the adjusted multiple determination coefficient, represents the original multivariate determination coefficient, represents the experimental data capacity, Indicates the number of independent variables;

[0104] The original multivariate determination coefficient was calculated according to the following formula:

[0105] ;

[0106] in, represents the residual sum of squares, represents the total sum of squares, represents the fitting value of the qth experimental data, represents the average value of the experimental data, represents the qth experimental data, and q represents the number of experimental data.

[0107] The adjusted coefficient of determination is an improvement on the multivariate coefficient of determination. It more accurately reflects the model's fit when introducing new independent variables, avoiding overfitting caused by adding too many meaningless independent variables. For example, as shown in Tables 2 and 3 below, the adjusted coefficient of determination, p-value, and regression coefficient of each influencing parameter are as follows:

[0108] Table 2

[0109] ;

[0110] Table 3

[0111] ;

[0112] In Table 3, the regression coefficient between inductor core material and output voltage ripple is 0.6828, indicating that the inductor core material has a significant impact on output voltage ripple. However, the regression coefficient between maximum current limit and output voltage ripple is 0.1963, indicating that the maximum current limit has a weaker impact on output voltage ripple. Next, we analyze the causal relationships between these variables.

[0113] Step S103: If the regression function satisfies the preset fitting condition, the regression coefficients of the output voltage ripple and other influencing parameters are obtained according to the regression function, and causal discovery is performed on any two influencing parameters based on the PC algorithm. All influencing parameters with causal relationships are obtained according to the causal discovery results, and the causal effect value corresponding to the influencing parameter and each causal relationship is obtained through the structural equation model;

[0114] It should be noted that if the multivariate determination coefficient after adjustment of the regression function is greater than the first preset threshold and the p-value of the regression function is greater than the second preset threshold, it means that the regression function meets the preset fitting conditions, and causal inference is performed at this time. Otherwise, it means that the preset fitting conditions are not met, that is, the obtained regression function is not good. At this time, it is necessary to reselect the fitting function until the preset fitting conditions are met.

[0115] Among the nine variables obtained in Table 1, the causal relationships that have been clearly established based on prior knowledge are as follows: Figure 3 As shown in Figure 2, the causal relationship between input voltage → inductor current ripple, switching frequency → inductor current ripple, and inductor core material → inductor current ripple can be derived from the following formula:

[0116] ;

[0117] Where D is the duty cycle.

[0118] The causal relationship between switching frequency → output voltage ripple, inductor core material → output voltage ripple, and switching frequency → output voltage can be derived from the following formula:

[0119] ;

[0120] Where ESR is the equivalent series resistance of the output capacitor.

[0121] The characteristics of the inductor core material (such as magnetic permeability, saturation flux density, loss, etc.) directly affect the maximum limiting current of the inductor. The relationship between the saturation flux density B and the current I in the core material is:

[0122] ;

[0123] Where μ is the core's permeability, N is the number of turns in the coil, I is the current, and l is the magnetic path length. This formula shows the causal relationship between inductor core material and maximum current limit.

[0124] Other causal relationships are derived:

[0125] Control voltage → output voltage ripple: The control voltage maintains output voltage stability by regulating the on / off state of switching devices. The response speed of the control voltage directly affects the magnitude of output voltage ripple. A well-designed feedback control system can effectively reduce output voltage ripple. Conversely, a slow control voltage response or an unstable feedback loop can result in larger ripple.

[0126] Control voltage → maximum current limit: In peak current control mode, the control voltage directly controls the peak current of the switching tube. The peak current determines the maximum inductor current, which is directly related to the maximum output current of the power supply.

[0127] Output Capacitor → Control Voltage: The size of the capacitor directly affects the power supply's output voltage response speed (control voltage) and its ability to withstand load changes. When the load current changes suddenly, the output capacitor helps the power supply stabilize the output voltage.

[0128] The causal relationship between the inductor core material and the output capacitance and output voltage, as well as the causal relationship between the switching frequency and the maximum current limit are still unclear. Based on the collected experimental data, the PC algorithm is used to perform causal discovery on these five variables. The PC algorithm is good at processing high-dimensional data and can accurately identify direct causal relationships between many variables. At the same time, it provides a graphical display of the relationship between variables, which is suitable for causal discovery of multiple variables in this study. The core of the PC algorithm is to use conditional independence tests to gradually construct an undirected graph G of the variable set Y. In the process of constructing the undirected graph, the PC algorithm gradually tests each pair of influencing parameters. and Whether it is independent under a given set of variables Y. If the conditions are met: , then delete and For example, if the inductor core material and input voltage are independent in the set of nine variables, the edge between them can be deleted. Based on conditional independence, the direction of the edges is further determined by identifying the V-shaped structure and applying the transitive rule, ultimately resulting in a directed acyclic graph (DAG).

[0129] Finally, the causal relationship between the five influencing parameters, namely, inductor core material, output capacitance, output voltage, switching frequency, and maximum current limit, is obtained as follows: Figure 4 As shown, the black dotted arrows represent the causal relationships discovered based on the PC algorithm, and the black solid arrows represent the causal relationships mentioned above based on prior knowledge. Figure 3 and Figure 4 The overall cause-effect relationship diagram is as follows Figure 5 shown.

[0130] Furthermore, in some embodiments, to obtain the causal effect value, the sem() function in the lavaan package is introduced to fit the model and estimate the causal effect. For example, in a specific structural equation model, maximum likelihood estimation is used to estimate the path coefficient:

[0131] ;

[0132] in, 、 Represent the i-th and j-th influencing parameters respectively, represents the error term, Influencing parameters Influencing parameters The causal effect value of Influencing parameters and influencing parameters The covariance between Influencing parameters The path coefficient can be automatically extracted after model fitting through the sem() function, and the causal effect diagram of the inductor core material and power supply performance is finally obtained. Figure 6 shown.

[0133] Step S104: define each influencing parameter as an entity, summarize the entity relationships between all entities, and associate the corresponding regression coefficients or causal effect values according to the entity relationships to obtain a power knowledge graph;

[0134] In this step, the causal graph is combined with the knowledge graph, making the knowledge graph no longer just a model of correlations but also encompassing the core causal relationships in power supply design. This ultimately results in a more comprehensive power supply knowledge graph with causal relationships. The knowledge graph modeling tool used in this study is the Neo4j graph database. Neo4j's primary function is to provide underlying storage and query capabilities. It supports the Cypher query language, which helps users easily retrieve and analyze information in the knowledge graph.

[0135] Taking the experiment on the impact of inductor core materials on power supply performance as an example, we constructed a power supply knowledge graph containing causal relationships. We first used TI's TI-PMLK experiment guide, the HowNet database, and literature as declarative knowledge sources. We extracted and integrated this knowledge, then combined it with a structural equation model to map the causal graph to a triple table. For example, the triple table for control voltage → output voltage ripple is: Entity 1: Control Voltage, Relationship: Causal, Entity 2: Output Voltage Ripple. The same applies to other causal relationships. We extracted and converted a total of 54 nodes and four types of relationships (a total of 106 relationships), forming a CSV file of "node-relationship-node" triples. We then added regression coefficients and causal effect data to the CSV file, resulting in some data, as shown in Table 4.

[0136] Table 4

[0137] ;

[0138] Finally, a power knowledge graph with causal relationships is obtained in Neo4j. The power knowledge graph with causal relationships (partial) is as follows: Figure 7 shown.

[0139] Step S105: obtaining power supply design requirements, searching for parameters to be debugged from the power supply knowledge graph according to the power supply design requirements, and optimizing the power supply design solution according to the parameters to be debugged.

[0140] In this step, it is first necessary to obtain at least one power supply design requirement, each of which includes a target influencing parameter and a numerical value corresponding to the target influencing parameter; then, the parameters to be debugged that are related to each of the target influencing parameters are searched from the power supply knowledge graph, and the power supply design results are optimized according to the parameters to be debugged to obtain the optimal design solution.

[0141] For example, using the TI power management chip TPS54160 to build a Buck circuit for design, the power supply designer completes the following design tasks. The specific design and debugging tasks (design requirements) are shown in Table 5:

[0142] Table 5

[0143] ;

[0144] Initial measurements showed that the inductor core material was ferrite, and the actual output voltage ripple was 51mV. The design requirements are shown in Table 5. Taking the requirement of "output voltage ripple ≤ 45mV" in Table 5 as an example, facing this problem, the power supply designer first used the Neo4j knowledge graph to query which factors may affect the output voltage ripple. The designer can set up a "cause and effect" relationship and obtain the factors affecting the output voltage ripple by querying the knowledge graph. Figure 8 As shown, by viewing Figure 8 It can be seen that the factors affecting the output voltage ripple include the switching frequency, the inductor core material, and the control voltage. Therefore, the first thing to consider is to adjust these three factors to reduce the output voltage ripple. Due to the complexity of the power supply, these three factors will also affect other power supply performance, causing other indicators to fail to meet the standards during adjustment. Therefore, the designer also needs to find out which other performance indicators are affected by these three factors. By querying, we can get the impact of these factors on other performance indicators. Figure 9 (a) to Figure 9 (c). Figure 9 (a) to Figure 9 (c) As can be seen, the switching frequency and inductor core material have many influencing factors. Adjusting the switching frequency or replacing the inductor core material may cause other performance indicators to fail. The control voltage only affects the maximum current limit. Therefore, while ensuring that the maximum current limit is met, the control voltage should be adjusted to reduce the output voltage ripple. Ultimately, increasing the control voltage to 0.97V reduced the output voltage ripple to 42mV, indicating that the goal was achieved. The same method was used for the other tasks. The final optimization results are shown in Table 6, indicating that the task was completed:

[0145] Table 6

[0146] ;

[0147] As shown in Table 5, design and debugging tasks involve many complex influencing factors. This example demonstrates that knowledge graph query methods can quickly locate the root cause of the problem. The design-assisted method based on causal inference and knowledge graphs can systematically reveal causal relationships and provide more accurate optimization solutions, thereby greatly improving the efficiency, accuracy, and intelligence of the design. This research method is particularly advantageous in complex power supply designs, not only improving the quality of design decisions but also significantly shortening the design cycle, promoting the intelligentization of power supply design.

[0148] In summary, by applying knowledge graphs to power supply design, the sharing and reusability of knowledge in the power supply field are improved compared to traditional power supply knowledge acquisition; and causal inference technology helps realize power supply design from a causal perspective, which is different from previous research methods. It crosses from the field of natural science to the field of data science and solves the power supply design ideas from the root; in addition, the hidden causal relationship is discovered through the causal discovery method, which enhances the scientificity and accuracy of power supply design; moreover, a method of combining causal inference with knowledge graphs is proposed, which enables the knowledge graph to be expanded from correlation analysis to causal relationship modeling, providing new ideas for complex system design.

[0149] like Figure 10 An embodiment of the present invention further provides a switching power supply auxiliary design system, the system comprising:

[0150] The simulation and physical experiment platform building module 10 is used to build an experimental circuit, select multiple influencing parameters that affect the power supply performance, and perform simulation and physical experiment tests based on the influencing parameters and the experimental circuit to obtain data on each influencing parameter under each test;

[0151] The linear regression module 20 is used to perform linear regression on each influencing parameter under each test, obtain a regression function of the output voltage ripple and other influencing parameters, and determine whether the regression function meets the preset fitting conditions;

[0152] a causal inference module 30 for obtaining, if the regression function satisfies a preset fitting condition, regression coefficients of the output voltage ripple and other influencing parameters based on the regression function, performing causal discovery on any two influencing parameters based on a PC algorithm, obtaining all influencing parameters with causal relationships based on the causal discovery results, and then deriving causal effect values corresponding to the influencing parameters and each causal relationship through a structural equation model;

[0153] The knowledge graph construction module 40 is used to define each influencing parameter as an entity, summarize the entity relationships between all entities, and associate the corresponding regression coefficients or causal effect values according to the entity relationships to obtain a power knowledge graph;

[0154] The auxiliary design module 50 is used to obtain power supply design requirements, search for parameters to be debugged from the power supply knowledge graph according to the power supply design requirements, and optimize the power supply design solution according to the parameters to be debugged.

[0155] Another aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the above-mentioned switching power supply auxiliary design method.

[0156] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0157] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0158] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0159] While the embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations of these embodiments are possible. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as set forth in the claims. Furthermore, the invention described herein is susceptible to other embodiments and may be practiced or implemented in a variety of ways.

Claims

1. A switching power supply auxiliary design method, characterized in that: The method comprises: Build an experimental circuit, select multiple influencing parameters that affect power supply performance, and perform simulation and physical experimental tests based on the influencing parameters and the experimental circuit to obtain data on each influencing parameter under each test. The influencing parameters include inductor core material, output capacitance, switching frequency, input voltage, maximum current limit, control voltage, output voltage, output voltage ripple, and inductor current ripple; Performing linear regression on each influencing parameter under each test to obtain a regression function of the output voltage ripple and other influencing parameters, and determining whether the regression function meets the preset fitting conditions; Linear regression is performed according to the following formula: ； in, Indicates the inductor core material, Indicates the input voltage, Indicates the maximum limiting current, represents the control voltage, represents the switching frequency, Indicates the output voltage, represents the output voltage ripple, represents the inductor current ripple, represents the output capacitance, represents the intercept term, 、 、 、 、 、 、 、 are regression coefficients, represents the error term; If the regression function satisfies the preset fitting conditions, the regression coefficients of the output voltage ripple and other influencing parameters are obtained according to the regression function, and causal discovery is performed on any two influencing parameters based on the PC algorithm. All influencing parameters with causal relationships are obtained according to the causal discovery results, and the causal effect values corresponding to the influencing parameters and each causal relationship are obtained through the structural equation model; The PC algorithm gradually tests each pair of influencing parameters and Is it independent under a given set of variables Y, if the conditions are met: , then delete and The edges between them are further determined based on conditional independence by identifying the V-shaped structure and applying the transitivity rule, and finally a directed acyclic graph is obtained; The expression of the structural equation model is: ； in, 、 Represent the i-th and j-th influencing parameters respectively, represents the error term, Influencing parameters Influencing parameters The causal effect value of Influencing parameters and influencing parameters The covariance between Influencing parameters variance; Define each influencing parameter as an entity, summarize the entity relationships between all entities, and associate the corresponding regression coefficients or causal effect values according to the entity relationships to obtain the power supply knowledge graph; The knowledge was extracted and integrated, and then combined with the structural equation model. The causal diagram and triple table were mapped one by one. A total of 54 nodes and four types of relationships were extracted and converted to form a CSV of "node-relationship-node" triples. The regression coefficient and causal effect data were then added to the CSV table to obtain the power supply knowledge graph. The power supply design requirements are obtained, and parameters to be debugged are searched from the power supply knowledge graph according to the power supply design requirements, and the power supply design solution is optimized according to the parameters to be debugged.

2. The switching power supply auxiliary design method according to claim 1, characterized in that: The physical experiment test includes a first test experiment and a second test experiment, and the steps of the first test experiment include: Step 1.

1. Select a 220μF output capacitor, a 15μH powder core inductor, set the switching frequency to 500kHz, turn on the oscilloscope, turn on the power supply, set the voltage to 12V, and set the current limit to 1.5A. Turn on the electronic load, set it to constant current mode, and set the current to 1.5A. Step 2.

1. Turn on the power supply "OUT ON" button. The output voltage Vout waveform displayed on the oscilloscope is a flat horizontal line of 3.3V. Step 3.

1. Turn on the "LOAD ON" button of the electronic load. A triangular waveform with an average value of 1.5A will be displayed on the inductor current channel of the oscilloscope, and a flat horizontal line of 3.3V will be displayed on the output voltage channel Vout. Step 4.

1. Read the output voltage Vout, measure the output voltage ripple and inductor current ripple, and record these values. Then change the input voltage Vin and load current values and repeat this step. Step 5.

1. Turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, then connect the 15μH ferrite core inductor and repeat steps 2.1 to 4.

1. Step 6.

1. Set the switching frequency to 500kHz and repeat steps 2.1 to 5.

1. Step 7.

1. At the end of the measurement, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, and then turn off all instruments.

3. The switching power supply auxiliary design method according to claim 2, characterized in that: The steps of the second test experiment include: Step 1.2: Select a 220μF output capacitor, a 15μH powder core inductor as the inductor core material X, set the switching frequency to 250kHz, turn on the oscilloscope, turn on the power supply, set the voltage to 12V, and set the current limit to 1.5A. Turn on the electronic load, set it to constant current mode, and set the current to 1A. Step 2.

2. Turn on the power supply "OUT ON" button. The output voltage waveform displayed on the oscilloscope is a flat horizontal line of 3.3V. Step 3.

2. Turn on the "LOAD ON" button of the electronic load. A triangular waveform with an average value of 1A should be displayed on the oscilloscope's inductor current channel. A flat horizontal line with minimal ripple between 500mV and 1V should be displayed on the control voltage channel. A flat horizontal line with a level of 3.3V should be displayed on the output voltage channel. Step 4.2: Slowly increase the current of the electronic load and detect the maximum current limit value when the output voltage is stabilized at 3.3V. When the output voltage is no longer stable, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the power supply. Then record the maximum current limit and the corresponding control voltage value. Change the input voltage value and repeat this step. Step 5.2: Select a 15μH ferrite core inductor or a 15μH magnetic powder core inductor, a 10μF output capacitor or a 220μF output capacitor, and a switching frequency of 250kHz or 500kHz. Repeat steps 2.2 to 4.2 for these three combinations. Step 6.2: At the end of the measurement, turn off the "LOAD ON" button of the electronic load and the "OUT ON" button of the DC power supply, and then turn off all instruments.

4. The switching power supply auxiliary design method according to claim 1, characterized in that: The steps of performing linear regression on each influencing parameter under each test to obtain a regression function of the output voltage ripple and other influencing parameters, and determining whether the regression function meets a preset fitting condition include: The preset fitting conditions include whether the multivariate determination coefficient after the regression function adjustment is greater than a first preset threshold, and whether the p-value of the regression function is greater than a second preset threshold; The adjusted multivariate determination coefficient was calculated according to the following formula: ； in, represents the adjusted multiple determination coefficient, represents the original multivariate determination coefficient, represents the experimental data capacity, Indicates the number of independent variables; The original multivariate determination coefficient was calculated according to the following formula: ； in, represents the residual sum of squares, represents the total sum of squares, represents the fitting value of the qth experimental data, represents the average value of the experimental data, represents the qth experimental data, and q represents the number of experimental data.

5. The switching power supply auxiliary design method according to claim 1, characterized in that: The steps of obtaining power supply design requirements, searching for parameters to be debugged from the power supply knowledge graph according to the power supply design requirements, and optimizing the power supply design solution according to the parameters to be debugged include: Acquire at least one power supply design requirement, each of the power supply design requirements including a target impact parameter and a value corresponding to the target impact parameter; Parameters to be debugged that are related to each target influencing parameter are searched from the power supply knowledge graph, and power supply design results are optimized according to the parameters to be debugged to obtain the optimal design solution.

6. A switching power supply auxiliary design system, characterized in that: The system comprises: A simulation and physical experiment platform building module is used to build an experimental circuit, select multiple influencing parameters that affect power supply performance, and perform simulation and physical experiment tests based on the influencing parameters and experimental circuit to obtain data on each influencing parameter under each test. The influencing parameters include inductor core material, output capacitance, switching frequency, input voltage, maximum current limit, control voltage, output voltage, output voltage ripple, and inductor current ripple; A linear regression module is used to perform linear regression on each influencing parameter under each test, obtain a regression function of the output voltage ripple and other influencing parameters, and determine whether the regression function meets the preset fitting conditions; Linear regression is performed according to the following formula: ； in, Indicates the inductor core material, Indicates the input voltage, Indicates the maximum limiting current, represents the control voltage, represents the switching frequency, Indicates the output voltage, represents the output voltage ripple, represents the inductor current ripple, represents the output capacitance, represents the intercept term, 、 、 、 、 、 、 、 are regression coefficients, represents the error term; a causal inference module, configured to obtain, if the regression function satisfies a preset fitting condition, the regression coefficients of the output voltage ripple and other influencing parameters according to the regression function, perform causal discovery on any two influencing parameters based on a PC algorithm, obtain all influencing parameters with causal relationships based on the causal discovery results, and then derive the causal effect value corresponding to the influencing parameter and each causal relationship through a structural equation model; The PC algorithm gradually tests each pair of influencing parameters and Is it independent under a given set of variables Y, if the conditions are met: , then delete and The edges between them are further determined based on conditional independence by identifying the V-shaped structure and applying the transitivity rule, and finally a directed acyclic graph is obtained; The expression of the structural equation model is: ； in, 、 Represent the i-th and j-th influencing parameters respectively, represents the error term, Influencing parameters Influencing parameters The causal effect value of Influencing parameters and influencing parameters The covariance between Influencing parameters variance; The knowledge graph construction module is used to define each influencing parameter as an entity, summarize the entity relationships between all entities, and associate the corresponding regression coefficients or causal effect values according to the entity relationships to obtain the power knowledge graph; The knowledge was extracted and integrated, and then combined with the structural equation model. The causal diagram and triple table were mapped one by one. A total of 54 nodes and four types of relationships were extracted and converted to form a CSV of "node-relationship-node" triples. The regression coefficient and causal effect data were then added to the CSV table to obtain the power supply knowledge graph. The auxiliary design module is used to obtain power supply design requirements, search for parameters to be debugged from the power supply knowledge graph according to the power supply design requirements, and optimize the power supply design solution according to the parameters to be debugged.

7. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by a processor, implement the switching power supply auxiliary design method according to any one of claims 1 to 5.

8. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the switching power supply auxiliary design method according to any one of claims 1 to 5.

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