Topology cooperation method and system for realizing filter magnetic coupling

Abstract

The invention relates to the technical field of high-end chips, and discloses a topology collaboration method and system for realizing filter magnetic coupling, and the method comprises the steps: carrying out the classification processing of circuit topology data, and obtaining inductor topology data and capacitor topology data; a key magnetic coupling path in the inductance topological data is marked, a to-be-tuned magnetic coupling topological variable is determined from the inductance topological data, the magnetic coupling topological variable is tuned, and tuned magnetic coupling data is obtained; setting a collaborative weight of each capacitor element in the capacitor topological data, and calculating a topological matching degree corresponding to each capacitor element in the capacitor topological data; performing network synthesis processing on each capacitor element in the capacitor topological data to obtain a capacitor topological network, and performing topological collaborative optimization on each capacitor element in the capacitor topological data to obtain an optimized topological capacitor; and generating a topology cooperation scheme corresponding to the filter in the filter magnetic coupling system. According to the invention, the accuracy of topology collaboration for realizing filter magnetic coupling can be improved.

Description

Technical Field

[0001] This invention relates to a topology coordination method and system for realizing magnetic coupling in filters, belonging to the field of high-end chip technology. Background Technology

[0002] Filter magnetic coupling systems are core components of power electronic converters, high-frequency inverter systems, and other equipment. They achieve functions such as electromagnetic interference suppression and harmonic filtering through the synergistic effect of inductors and capacitors, directly affecting the stability and electromagnetic compatibility performance of the entire system. As power electronics technology develops towards higher frequencies and greater integration, the performance requirements for filter magnetic coupling topologies are increasing. Efficient topology design has become a key aspect of improving the overall performance of equipment.

[0003] Existing filter magnetic coupling topology design methods mostly adopt a mode of optimizing inductors and capacitors separately, achieving the filtering target by empirically adjusting magnetic coupling parameters or optimizing the capacitor network structure separately. However, this method fails to fully consider the synergistic relationship between the inductor magnetic coupling path and the capacitor topology, and only adjusts the parameters of a single component, resulting in insufficient coordination between the two. At the same time, traditional designs lack quantitative analysis of the deviation after network synthesis, relying solely on simulation trial and error optimization, which not only increases design redundancy and cost, but also makes it difficult to guarantee the stability of the topology across the entire frequency band. Therefore, a design method that can achieve precise coordination between inductor magnetic coupling and capacitor topology is needed to improve the overall performance of filter magnetic coupling systems. Summary of the Invention

[0004] This invention provides a method and system for achieving topology coordination of magnetic coupling in filters, the main purpose of which is to improve the accuracy of topology coordination for achieving magnetic coupling in filters.

[0005] To achieve the above objectives, the present invention provides a topology coordination method for realizing magnetic coupling in filters, comprising: Obtain the circuit topology data of the filter in the filter magnetic coupling system, and classify the circuit topology data to obtain inductor topology data and capacitor topology data; Identify the magnetic coupling nodes in the inductor topology data to mark the key magnetic coupling paths in the inductor topology data, analyze the mutual inductance coupling characteristics corresponding to the key magnetic coupling paths, determine the magnetic coupling topology variables to be tuned from the inductor topology data, and perform tuning processing on the magnetic coupling topology variables to obtain tuned magnetic coupling data. Calculate the resonance coefficient corresponding to each capacitor element in the capacitor topology data to set the cooperative weight of each capacitor element in the capacitor topology data, and calculate the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the cooperative weight; Each capacitor element in the capacitor topology data is processed by network synthesis to obtain a capacitor topology network. The synthesis deviation between the capacitor topology network and each capacitor element in the capacitor topology data is calculated. Combining the synthesis deviation and the topology matching degree, topology co-optimization of each capacitor element in the capacitor topology data is performed to obtain an optimized topology capacitor. By combining the tuned magnetic coupling data and the optimized topology capacitance, a topology coordination scheme corresponding to the filter in the filter magnetic coupling system is generated.

[0006] Optionally, identifying magnetic coupling nodes in the inductor topology data to mark key magnetic coupling paths in the inductor topology data includes: Network topology analysis was performed on the magnetically coupled nodes to obtain the node connection relationships; Based on the node connection relationship, path tracing processing is performed on the magnetic coupling node to obtain the initial magnetic coupling path; Extract the characteristic path parameters from the initial magnetic coupling path; According to the preset path filtering rules, the core path segments in the initial magnetic coupling path are filtered out; Based on the core path segment, the key magnetic coupling paths in the inductor topology data are marked.

[0007] Optionally, analyzing the mutual inductance coupling characteristics corresponding to the key magnetic coupling path to determine the magnetic coupling topology variables to be tuned from the inductor topology data includes: Based on the critical magnetic coupling path, non-critical magnetic coupling structures in the inductor topology data are selected; Analyze the structural characteristic quantities corresponding to the non-critical magnetic coupling structure; Calculate the feature correlation degree between the structural feature quantity and the mutual inductance coupling characteristic; Based on the aforementioned feature correlation degree, the associated coupling structures in the non-critical magnetic coupling structures are determined; By combining the associated coupling structure and the key magnetic coupling path, the magnetic coupling topology variables to be tuned in the inductor topology data are generated.

[0008] Optionally, calculating the feature correlation degree between the structural feature quantity and the mutual inductance coupling characteristic includes: Calculate the feature sensitivity corresponding to the structural feature; Based on the sensitivity of the feature quantity, the structural feature quantity is filtered to obtain the target structural feature quantity; Calculate the correlation between the target structural features and the mutual inductance coupling characteristics; Based on the aforementioned characteristic correlation, the characteristic correlation degree between the structural characteristic quantity and the mutual inductance coupling characteristic is calculated.

[0009] Optionally, calculating the feature sensitivity corresponding to the structural feature includes: Statistically calculate the rate of change of each feature among the structural features within a preset range; Query the reference baseline value corresponding to each feature quantity in the structural feature quantities; Based on the reference benchmark value and the rate of change of the characteristic quantity, the benchmark mutual inductance and the change of mutual inductance corresponding to the structural characteristic quantity are calculated respectively. By combining the rate of change of the characteristic quantity, the reference base value, the change in mutual inductance, and the reference mutual inductance, the sensitivity of the characteristic quantity corresponding to the structural characteristic quantity is calculated.

[0010] Optionally, calculating the resonant coefficient corresponding to each capacitor element in the capacitor topology data includes: The parameters of each capacitor element in the capacitor topology data are standardized to obtain standard capacitor parameters; The standard capacitance parameters are subjected to feature enhancement processing to obtain enhanced capacitance parameters; Identify the electrical characteristics in the enhanced capacitor parameters and extract the feature identifiers corresponding to the electrical characteristics; Based on the feature identifier, the resonant feature among the electrical features is determined; Calculate the characteristic weight of the resonant feature in the enhanced capacitance parameters; Based on the aforementioned characteristic weights, the resonance coefficient corresponding to each capacitor element in the capacitor topology data is obtained.

[0011] Optionally, calculating the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the collaborative weight includes: Calculate the impedance fit of each capacitor element in the capacitor topology data; Combining the impedance fit and the cooperative weight, the matching tolerance corresponding to each capacitor element in the capacitor topology data is calculated; Extract the topological relationship corresponding to each capacitor element in the capacitor topology data; Based on the topological relationship, determine the matching mode corresponding to each capacitor element in the capacitor topology data; By combining the matching mode and the matching tolerance, an adaptive reconstruction process is performed on each capacitor element in the capacitor topology data to obtain reconstructed topology data. Obtain the electrical parameters corresponding to the reconstructed topology data and the original capacitor topology data respectively to obtain the reconstructed parameter values ​​and the original parameter values; By combining the reconstructed parameter values ​​and the original parameter values, the topology matching degree corresponding to each capacitor element in the capacitor topology data is calculated.

[0012] Optionally, the step of performing network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network includes: Perform node association analysis on each capacitor element in the capacitor topology data to obtain the element connection relationship; Determine the connection impedance value corresponding to each connection in the component connection relationship, and calculate the energy loss rate corresponding to the connection impedance value; By combining the connection impedance value and the energy loss rate, calculate the electrical coupling degree corresponding to each connection in the component connection relationship; Based on the electrical coupling degree, the capacitor topology data is reconstructed to obtain a capacitor topology network.

[0013] Optionally, calculating the combined deviation between the capacitor topology network and each capacitor element in the capacitor topology data includes: Extract the component-independent attributes corresponding to each capacitor element in the capacitor topology data; Obtain the topology network attributes in the capacitor topology network, and calculate the attribute difference degree between the component independent attributes and the topology network attributes; Based on the attribute difference degree, the combined deviation degree between the capacitor topology network and each capacitor element in the capacitor topology data is obtained.

[0014] To address the above problems, the present invention also provides a topology cooperative system for implementing magnetic coupling in filters, the system comprising: The topology data classification module is used to acquire circuit topology data related to the filter in the filter magnetic coupling system, and to classify the circuit topology data to obtain inductor topology data and capacitor topology data. The magnetic coupling topology tuning module is used to identify magnetic coupling nodes in the inductor topology data, mark key magnetic coupling paths in the inductor topology data, analyze the mutual inductance coupling characteristics corresponding to the key magnetic coupling paths, determine the magnetic coupling topology variables to be tuned from the inductor topology data, and perform tuning processing on the magnetic coupling topology variables to obtain tuned magnetic coupling data. The matching degree calculation module is used to calculate the resonance coefficient corresponding to each capacitor element in the capacitor topology data, so as to set the cooperative weight of each capacitor element in the capacitor topology data, and calculate the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the cooperative weight. The capacitor topology optimization module is used to perform network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network, calculate the synthesis deviation between the capacitor topology network and each capacitor element in the capacitor topology data, and combine the synthesis deviation and the topology matching degree to perform topology collaborative optimization on each capacitor element in the capacitor topology data to obtain an optimized topology capacitor. The topology scheme generation module is used to combine the tuned magnetic coupling data and the optimized topology capacitance to generate a topology coordination scheme corresponding to the filter in the filter magnetic coupling system.

[0015] Compared to the problems described in the background art, this invention classifies the circuit topology data into inductor topology data and capacitor topology data according to type, providing data support for subsequent related analysis. By identifying magnetic coupling nodes in the inductor topology data to mark key magnetic coupling paths, this invention can accurately locate the core coupling structure in the inductor topology data, thus providing a foundation for subsequent tuning processing. Furthermore, by calculating the resonance coefficient corresponding to each capacitor element in the capacitor topology data, this invention can quantify the resonance characteristics of each capacitor element in the circuit, thus providing a basis for subsequent collaborative optimization. By performing network synthesis processing on each capacitor element in the capacitor topology data, this invention can integrate scattered capacitor elements into a unified network structure, facilitating the analysis of the interaction relationships between elements and providing a foundation for subsequent topology optimization. Finally, by combining the tuned magnetic coupling data and the optimized topology capacitor, this invention generates a topology coordination scheme corresponding to the filter in the filter magnetic coupling system, thereby obtaining a method to improve the overall performance of the filter magnetic coupling system. Therefore, the topology coordination method and system for achieving magnetic coupling of filters provided in this embodiment of the invention can improve the accuracy of topology coordination for achieving magnetic coupling of filters. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a topology coordination method for achieving magnetic coupling in a filter, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of a module for implementing a topology cooperative system for achieving magnetic coupling of a filter, provided as an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a topology coordination method for implementing magnetic coupling in filters. The executing entity of this topology coordination method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the topology coordination method for implementing magnetic coupling in filters can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a topology coordination method for achieving magnetic coupling in a filter according to an embodiment of the present invention. In this embodiment, the topology coordination method for achieving magnetic coupling in a filter includes: S1. Obtain the circuit topology data of the filter in the filter magnetic coupling system, classify the circuit topology data, and obtain inductor topology data and capacitor topology data.

[0021] This invention classifies the circuit topology data into inductor topology data and capacitor topology data according to type, providing data support for subsequent related analysis. The filter magnetic coupling system is an electronic filtering device based on the principle of magnetic coupling, which connects the filtering components through electromagnetic induction effect. The inductor topology data and the capacitor topology data are the layout data of the corresponding inductor elements and capacitor elements in the circuit topology data, respectively. Furthermore, the classification of the circuit topology data can be achieved through clustering algorithms.

[0022] S2. Identify the magnetic coupling nodes in the inductor topology data to mark the key magnetic coupling paths in the inductor topology data, analyze the mutual inductance coupling characteristics corresponding to the key magnetic coupling paths, determine the magnetic coupling topology variables to be tuned from the inductor topology data, and perform tuning processing on the magnetic coupling topology variables to obtain tuned magnetic coupling data.

[0023] This invention identifies magnetic coupling nodes in the inductor topology data to mark key magnetic coupling paths, enabling precise location of the core coupling structure within the inductor topology data. This provides a foundation for subsequent tuning processing. The magnetic coupling nodes are the connection points where magnetic flux couples within the inductor element, and the key magnetic coupling paths are the main flow paths formed between the coupling nodes. Furthermore, the identification of magnetic coupling nodes in the inductor topology data can be achieved using netlist parsing techniques, such as depth-first search.

[0024] As an embodiment of the present invention, the step of identifying magnetic coupling nodes in the inductor topology data to mark key magnetic coupling paths in the inductor topology data includes: Network topology analysis was performed on the magnetically coupled nodes to obtain the node connection relationships; Based on the node connection relationship, path tracing processing is performed on the magnetic coupling node to obtain the initial magnetic coupling path; Extract the characteristic path parameters from the initial magnetic coupling path; According to the preset path filtering rules, the core path segments in the initial magnetic coupling path are filtered out; Based on the core path segment, the key magnetic coupling paths in the inductor topology data are marked.

[0025] Wherein, the node connection relationship represents the electrical connection state between each magnetic coupling node, the initial magnetic coupling path is a possible magnetic flux path formed by the connection of magnetic coupling nodes, the characteristic path parameters include path length, path impedance and path coupling coefficient, and the preset path screening rule is a path selection standard set in advance by analyzing the characteristics of typical magnetic coupling topology.

[0026] Furthermore, the network topology analysis of the magnetic coupling nodes can be implemented using graph theory algorithms, such as Prim's algorithm; the path tracing processing of the magnetic coupling nodes can be implemented using backtracking algorithms; the extraction of feature path parameters in the initial magnetic coupling path can be implemented using parameter scanning techniques, such as Monte Carlo scanning techniques; the selection of core path segments in the initial magnetic coupling path can be implemented using weighted scoring algorithms, such as the analytic hierarchy process (AHP); and the marking of key magnetic coupling paths in the inductor topology data can be implemented using highlighting tools in simulation software.

[0027] This invention analyzes the mutual inductance coupling characteristics corresponding to the key magnetic coupling path to determine the magnetic coupling topology variables to be tuned from the inductor topology data. This allows for the accurate identification of key adjustable parameters affecting system performance, thereby improving the filtering efficiency of the magnetic coupling system. The mutual inductance coupling characteristics represent the electromagnetic characteristics of mutual induction between coils in the magnetic coupling path. Furthermore, the analysis of the mutual inductance coupling characteristics corresponding to the key magnetic coupling path can be achieved through electromagnetic field simulation methods.

[0028] As an embodiment of the present invention, the step of analyzing the mutual inductance coupling characteristics corresponding to the key magnetic coupling path to determine the magnetic coupling topology variables to be tuned from the inductor topology data includes: Based on the critical magnetic coupling path, non-critical magnetic coupling structures in the inductor topology data are selected; Analyze the structural characteristic quantities corresponding to the non-critical magnetic coupling structure; Calculate the feature correlation degree between the structural feature quantity and the mutual inductance coupling characteristic; Based on the aforementioned feature correlation degree, the associated coupling structures in the non-critical magnetic coupling structures are determined; By combining the associated coupling structure and the key magnetic coupling path, the magnetic coupling topology variables to be tuned in the inductor topology data are generated.

[0029] The non-critical magnetic coupling structure is the structure in the inductor topology data that has little impact on the main magnetic flux path. The structural features include geometric dimensions, number of winding turns, and core material properties. The feature correlation degree represents the degree of influence between the structural features and the mutual inductance coupling characteristics. The associated coupling structure is the structure in the non-critical magnetic coupling structure that has an electromagnetic correlation with the critical magnetic coupling path.

[0030] Furthermore, the analysis of structural feature quantities corresponding to the non-critical magnetic coupling structure can be achieved through structured modeling methods. The feature correlation degree is compared with a preset correlation threshold. When the feature correlation degree is greater than the preset correlation threshold, the corresponding non-critical magnetic coupling structure is taken as the associated coupling structure. The preset correlation threshold is a pre-set benchmark value, which can be 0.8 or adjusted according to specific filtering performance requirements. Combining the associated coupling structure and the critical magnetic coupling path, the magnetic coupling topology variables to be tuned in the inductor topology data are generated. Specifically, by comparing the coupling mechanism and correlation strength of the two, the core variable dimensions affecting the mutual inductance coupling characteristics are comprehensively extracted, thereby generating the magnetic coupling topology variables to be tuned in the inductor topology data.

[0031] Furthermore, as an optional embodiment of the present invention, calculating the feature correlation degree between the structural feature quantity and the mutual inductance coupling characteristic includes: Calculate the feature sensitivity corresponding to the structural feature; Based on the sensitivity of the feature quantity, the structural feature quantity is filtered to obtain the target structural feature quantity; Calculate the correlation between the target structural features and the mutual inductance coupling characteristics; Based on the aforementioned characteristic correlation, the characteristic correlation degree between the structural characteristic quantity and the mutual inductance coupling characteristic is calculated.

[0032] The feature sensitivity represents the degree of influence of the structural feature on the system performance. The higher the feature sensitivity, the greater the importance of the corresponding structural feature. The characteristic correlation represents the correlation strength between the target structural feature and the mutual inductance coupling characteristic.

[0033] Furthermore, features with high sensitivity are selected from the structural features and retained to obtain target structural features; the correlation between the target structural features and the mutual inductance coupling characteristics can be achieved using the Pearson correlation coefficient algorithm; the weighted average of the correlations corresponding to each feature in the structural features is used to obtain the feature correlation degree.

[0034] Furthermore, as an optional embodiment of the present invention, calculating the feature sensitivity corresponding to the structural feature includes: Statistically calculate the rate of change of each feature among the structural features within a preset range; Query the reference baseline value corresponding to each feature quantity in the structural feature quantities; Based on the reference benchmark value and the rate of change of the characteristic quantity, the benchmark mutual inductance and the change of mutual inductance corresponding to the structural characteristic quantity are calculated respectively. By combining the rate of change of the characteristic quantity, the reference base value, the change in mutual inductance, and the reference mutual inductance, the sensitivity of the characteristic quantity corresponding to the structural characteristic quantity is calculated.

[0035] Wherein, the characteristic quantity change rate refers to the ratio of the variation amplitude of a single structural characteristic quantity within a preset fluctuation range to its initial value, used to quantify the degree of variation of the characteristic quantity itself; the reference benchmark value refers to the typical value of the structural characteristic quantity under standard operating conditions, providing a benchmark reference for mutual inductance calculation; the benchmark mutual inductance is the mutual inductance under standard conditions calculated based on the reference benchmark value, reflecting the coupling strength corresponding to the benchmark state of the characteristic quantity; the mutual inductance change refers to the corresponding change value of the mutual inductance when the characteristic quantity changes according to the change rate, reflecting the influence of the characteristic quantity change on the coupling characteristics.

[0036] Furthermore, the rate of change of the structural characteristic quantities within a preset range can be statistically analyzed using a sliding window method. This involves setting a window with fixed time or numerical intervals and calculating the ratio of the maximum variation amplitude of the characteristic quantity within the window to its initial value, thus achieving dynamic statistical analysis of the rate of change. Reference benchmark values ​​corresponding to the structural characteristic quantities can be retrieved through database searches or design specification lookups. The database stores characteristic quantity values ​​under historical typical operating conditions, while design specifications provide industry standard reference values. The reference mutual inductance and the change in mutual inductance corresponding to the structural characteristic quantities can be calculated using finite element simulation software. For example, using ANSYS Maxwell software, a three-dimensional magnetic coupling model is first established by inputting the reference benchmark values ​​of the structural characteristic quantities, and the reference mutual inductance is obtained. Then, the model parameters are adjusted according to the rate of change of the characteristic quantities, and the simulation is repeated. The difference between the new mutual inductance and the reference mutual inductance is then used to obtain the change in mutual inductance.

[0037] Furthermore, as another optional embodiment of the present invention, the characteristic sensitivity corresponding to the structural characteristic is calculated using the following formula, combining the characteristic change rate, the reference base value, the mutual inductance change, and the reference mutual inductance: ; Where S represents the feature sensitivity corresponding to the structural feature quantity, and N represents the number of structural feature quantities. This represents the rate of change of the i-th feature among the structural features. This represents the reference value of the i-th feature among the structural features. This represents the change in mutual inductance of the i-th structural feature quantity. The reference mutual inductance of the i-th feature among the structural features is represented by i, where i represents the sequence number corresponding to the structural feature.

[0038] This invention optimizes the electromagnetic performance of magnetic coupling topology variables by tuning them. The tuned magnetic coupling data, obtained after tuning, is the optimized state data of the magnetic coupling topology variables, directly reflecting the electromagnetic characteristic parameters and associated configuration information of the tuned magnetic coupling topology. Furthermore, the tuning of the magnetic coupling topology variables can be achieved through parameter optimization algorithms, such as genetic algorithms. Specifically, the magnetic coupling topology variables are encoded as gene sequences to form an initial population, such as the number of inductor turns and the core spacing. A fitness function is constructed with electromagnetic performance optimization objectives, such as maximizing coupling efficiency. Through selection, crossover, and mutation operations, iterative evolution is performed, ultimately selecting the variable combination corresponding to the gene sequence with the highest fitness, which is the optimal solution after tuning, thus obtaining the tuned magnetic coupling data.

[0039] S3. Calculate the resonance coefficient corresponding to each capacitor element in the capacitor topology data to set the cooperative weight of each capacitor element in the capacitor topology data. Based on the cooperative weight, calculate the topology matching degree corresponding to each capacitor element in the capacitor topology data.

[0040] This invention quantifies the resonant characteristics of each capacitor element in the circuit by calculating the resonance coefficient corresponding to each capacitor element in the capacitor topology data, thereby providing a basis for subsequent collaborative optimization. The resonance coefficient represents the degree to which a capacitor element tends to resonate at a specific frequency.

[0041] As an embodiment of the present invention, the calculation of the resonance coefficient corresponding to each capacitor element in the capacitor topology data includes: The parameters of each capacitor element in the capacitor topology data are standardized to obtain standard capacitor parameters; The standard capacitance parameters are subjected to feature enhancement processing to obtain enhanced capacitance parameters; Identify the electrical characteristics in the enhanced capacitor parameters and extract the feature identifiers corresponding to the electrical characteristics; Based on the feature identifier, the resonant feature among the electrical features is determined; Calculate the characteristic weight of the resonant feature in the enhanced capacitance parameters; Based on the aforementioned characteristic weights, the resonance coefficient corresponding to each capacitor element in the capacitor topology data is obtained.

[0042] Wherein, the standard capacitance parameter is the parameter after removing dimensions and measurement bias, the enhanced capacitance parameter is the saliency parameter after data augmentation processing, the electrical characteristic is the key indicator describing the working characteristics of the capacitor element, the feature identifier is the type mark corresponding to the electrical characteristic, the resonance characteristic is the feature parameter that directly affects the resonance behavior, and the feature weight represents the importance ratio of the resonance characteristic in the overall electrical characteristics.

[0043] Furthermore, the parameters of each capacitor element in the capacitor topology data are standardized using the Z-score standardization algorithm; the feature enhancement of the standard capacitor parameters is achieved using principal component analysis; the identification of electrical features in the enhanced capacitor parameters is achieved using clustering algorithms, such as the K-means algorithm; the feature identifiers corresponding to the electrical features are implemented using a feature classifier compiled from Python; the identifier content is retrieved, and based on the identifier content, it is analyzed whether the identifier content contains resonance-related parameters, such as the resonant frequency or quality factor, to determine the resonance features in the electrical features; the feature weights of the resonance features and the total feature weights of the enhanced capacitor parameters are calculated separately, and the ratio of the feature weights to the total feature weights is calculated to obtain the feature proportions; the feature proportions are weighted and accumulated to obtain the comprehensive feature proportion, which is used as the resonance coefficient corresponding to each capacitor element in the capacitor topology data.

[0044] Based on the resonance coefficient, this invention sets a collaborative weight for each capacitor element in the capacitor topology data. The collaborative weight can be used to determine the strength of each element's role in the system, thereby achieving precise topology control. The collaborative weight represents the contribution of the capacitor element to the collaborative work with other elements in the filtering system. Furthermore, the collaborative weight of each capacitor element in the capacitor topology data can be represented by the numerical form corresponding to the resonance coefficient. For example, if the resonance coefficient is 0.75, then the collaborative weight is set to 0.75.

[0045] This invention calculates the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the collaborative weight, and can evaluate the system adaptability of each capacitor element in the capacitor topology data under the collaborative weight. The topology matching degree represents the degree of fit between the capacitor element and the system topology under the current weight configuration.

[0046] As an embodiment of the present invention, the step of calculating the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the collaborative weight includes: Calculate the impedance fit of each capacitor element in the capacitor topology data; Combining the impedance fit and the cooperative weight, the matching tolerance corresponding to each capacitor element in the capacitor topology data is calculated; Extract the topological relationship corresponding to each capacitor element in the capacitor topology data; Based on the topological relationship, determine the matching mode corresponding to each capacitor element in the capacitor topology data; By combining the matching mode and the matching tolerance, an adaptive reconstruction process is performed on each capacitor element in the capacitor topology data to obtain reconstructed topology data. Obtain the electrical parameters corresponding to the reconstructed topology data and the original capacitor topology data respectively to obtain the reconstructed parameter values ​​and the original parameter values; By combining the reconstructed parameter values ​​and the original parameter values, the topology matching degree corresponding to each capacitor element in the capacitor topology data is calculated.

[0047] Wherein, the impedance fit represents the degree of matching between the impedance of the capacitor element and the characteristic impedance of the system; the matching tolerance represents the allowable parameter adjustment range; the topology relationship is the connection relationship of the capacitor element in the circuit; the matching mode is the specific strategy for parameter adjustment; the reconstructed topology data is the topology structure obtained after applying the matching mode and the matching tolerance to each capacitor element in the capacitor topology data to the new configuration scheme; and the electrical parameters are the key electrical indicators corresponding to the reconstructed topology data and the original capacitor topology data that can characterize the working state and performance stability of the capacitor topology in the circuit, such as core parameters like insulation resistance, temperature coefficient, parasitic inductance, and leakage current.

[0048] Furthermore, the calculation process for the impedance fit of each capacitor element in the capacitor topology data is as follows: Scan the impedance curve of each capacitor element in the capacitor topology data within the system's operating frequency band, extract the system's characteristic impedance in that frequency band, calculate the sum of squared errors between the capacitor element's impedance and the system's characteristic impedance, and obtain the impedance fit through normalization. The topological relationship corresponding to each capacitor element in the capacitor topology data can be implemented using a network graph theory algorithm. Determine the relationship type corresponding to the topological relationship, and based on the relationship type, determine the matching mode corresponding to each capacitor element in the capacitor topology data, such as a series matching mode or a parallel matching mode. The configuration scheme of each capacitor element in the capacitor topology data can be generated through an optimization algorithm, such as the particle swarm optimization algorithm, and the matching tolerance can be input as a constraint into the optimization algorithm. Combining the matching mode and the matching tolerance, an adaptive reconstruction processing can be performed on each capacitor element in the capacitor topology data through a constraint-based topology adjustment algorithm to obtain reconstructed topology data. The electrical parameters corresponding to the reconstructed topology data and the original capacitor topology data can be obtained through a parameter extraction tool, which is compiled from a scripting language, such as JAVA.

[0049] Optionally, as an optional embodiment of the present invention, the step of calculating the topology matching degree corresponding to each capacitor element in the capacitor topology data by combining the reconstructed parameter values ​​and the original parameter values ​​includes: The reconstructed parameter values ​​and the parameter dimensions corresponding to the original parameter values ​​are statistically analyzed. By combining the parameter dimensions, the reconstructed parameter values, and the original parameter values, the topology matching degree corresponding to each capacitor element in the capacitor topology data is calculated.

[0050] The parameter dimension refers to the key parameter categories that reflect the core electrical performance of the reconstructed parameter value and the original parameter value, such as capacitance, impedance, withstand voltage, equivalent series resistance, etc. Furthermore, the parameter dimension corresponding to the reconstructed parameter and the original parameter can be statistically analyzed by traversing the attribute labels of the dataset of the reconstructed parameter value and the original parameter value.

[0051] Furthermore, as another optional embodiment of the present invention, by combining the parameter dimension, the reconstructed parameter value, and the original parameter value, the topology matching degree corresponding to each capacitor element in the capacitor topology data can be calculated using the following formula: ; Where M represents the topology matching degree corresponding to each capacitor element in the capacitor topology data, D represents the number of parameter dimensions, and c represents the sequence number of the parameter dimensions. This represents the reconstruction parameter value corresponding to the c-th parameter dimension in the capacitor topology data. This represents the original parameter value corresponding to the c-th parameter dimension in the capacitor topology data, where c represents the sequence number of the parameter dimension.

[0052] S4. Perform network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network. Calculate the synthesis deviation between the capacitor topology network and each capacitor element in the capacitor topology data. Combining the synthesis deviation and the topology matching degree, perform topology collaborative optimization on each capacitor element in the capacitor topology data to obtain an optimized topology capacitor.

[0053] This invention integrates scattered capacitor elements into a unified network structure by performing network synthesis processing on each capacitor element in the capacitor topology data, thereby facilitating the analysis of the interaction relationships between elements and providing a foundation for subsequent topology optimization.

[0054] As an embodiment of the present invention, the step of performing network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network includes: Perform node association analysis on each capacitor element in the capacitor topology data to obtain the element connection relationship; Determine the connection impedance value corresponding to each connection in the component connection relationship, and calculate the energy loss rate corresponding to the connection impedance value; By combining the connection impedance value and the energy loss rate, calculate the electrical coupling degree corresponding to each connection in the component connection relationship; Based on the electrical coupling degree, the capacitor topology data is reconstructed to obtain a capacitor topology network.

[0055] Wherein, the component connection relationship represents the electrical connection topology between each capacitor component, the connection impedance value represents the equivalent impedance on the connection path, the energy loss rate represents the proportion of electrical energy lost on the connection path, and the electrical coupling degree represents the energy transfer efficiency between components at the connection point. Furthermore, the node correlation analysis of each capacitor component in the capacitor topology data can be achieved using Kirchhoff's laws. By analyzing the current equations and voltage equations of each node, a connection relationship matrix between components is established. Based on the connection relationship matrix, the corresponding component connection relationships are analyzed. The connection impedance value corresponding to each connection in the component connection relationship can be obtained by measuring with an impedance analyzer. The energy loss rate corresponding to the connection impedance value... The energy loss rate can be calculated using power loss analysis. Combining the connection impedance value and the energy loss rate, the electrical coupling degree corresponding to each connection in the component connection relationship can be calculated using a weighted reciprocal synthesis algorithm. First, the reciprocal of the connection impedance value and the reciprocal of the energy loss rate are taken respectively. Then, weights are assigned according to the proportion of their influence on the coupling characteristics and the results are summed to obtain the coupling degree reflecting the strength of the electrical association of the connection. Based on the electrical coupling degree, the capacitor topology data is reconstructed to obtain a capacitor topology network. For example, capacitor elements with electrical coupling degrees higher than a set threshold are preferentially constructed into parallel clusters, and elements with lower coupling degrees are connected in series according to the signal path to form a capacitor topology network with hierarchical association.

[0056] As an embodiment of the present invention, calculating the combined deviation between the capacitor topology network and each capacitor element in the capacitor topology data includes: Extract the component-independent attributes corresponding to each capacitor element in the capacitor topology data; Obtain the topology network attributes in the capacitor topology network, and calculate the attribute difference degree between the component independent attributes and the topology network attributes; Based on the attribute difference degree, the combined deviation degree between the capacitor topology network and each capacitor element in the capacitor topology data is obtained.

[0057] The component-independent attributes include inherent attributes such as capacitance, equivalent series resistance, and withstand voltage. The topology network attributes include system-level attributes such as node admittance and network transfer function. The attribute difference degree represents the degree of deviation between the individual characteristics of the component and the overall characteristics of the network.

[0058] Furthermore, the independent attributes of each capacitor element in the capacitor topology data can be realized through an attribute extraction function, which is compiled by a programming language; the topology network attributes in the capacitor topology network can be calculated through node analysis methods, such as equivalent impedance between nodes and loop coupling current; the attribute difference between the independent attributes of the elements and the topology network attributes can be realized through a weighted Euclidean distance algorithm; the average value between the attribute differences is calculated to obtain the combined deviation between the capacitor topology network and each capacitor element in the capacitor topology data.

[0059] This invention combines the synthesized deviation and the topology matching degree to perform topology co-optimization on each capacitor element in the capacitor topology data. This ensures the performance stability and system compatibility of the capacitor elements, thereby improving the efficiency of the entire filtering system. The optimized topology capacitor is a capacitor topology structure and corresponding element parameter configuration formed after co-optimization combining the synthesized deviation and the topology matching degree. Further, combining the synthesized deviation and the topology matching degree, topology co-optimization is performed on each capacitor element in the capacitor topology data to obtain the optimized topology capacitor. The specific steps are as follows: First, a minimum threshold for topology matching degree and a maximum threshold for synthesized deviation degree are set, and capacitor elements with topology matching degree higher than the threshold and synthesized deviation degree lower than the threshold are selected as the optimization basis. Second, for the selected elements whose synthesized deviation degree is close to the threshold, their connection relationship or attribute value is adjusted based on the topology matching degree weight to reduce the deviation. Subsequently, the adjusted element matching degree and deviation degree are repeatedly verified until all meet the dual threshold requirements. Finally, the topology structure of the qualified elements is integrated to form the optimized topology capacitor.

[0060] S5. Combining the tuned magnetic coupling data and the optimized topology capacitance, generate the topology coordination scheme corresponding to the filter in the filter magnetic coupling system.

[0061] This invention generates a topology coordination scheme for the filter in the filter magnetic coupling system by combining the tuned magnetic coupling data and the optimized topology capacitor, thereby obtaining a method to improve the overall performance of the filter magnetic coupling system. The topology coordination scheme is a comprehensive solution that optimizes the topology, spatial layout, and electrical connections of the magnetic coupling network and the capacitor network to achieve optimal filter performance. Furthermore, by combining the tuned magnetic coupling data and the optimized topology capacitor, a topology coordination scheme for the filter in the filter magnetic coupling system is generated. The specific generation steps are as follows: First, the inductance parameters and coupling coefficients are extracted from the tuned magnetic coupling data, and the capacitance configuration and equivalent series resistance of the optimized topology capacitor are obtained. Then, a magnetic coupling-capacitor hybrid topology is constructed based on the impedance matching principle, and the optimal connection relationship and spatial layout parameters between units are determined. Finally, the frequency response characteristics and stability of the scheme are verified through multi-condition simulation, forming a complete coordination scheme including structural drawings and performance indicators.

[0062] Compared to the problems described in the background art, this invention classifies the circuit topology data into inductor topology data and capacitor topology data according to type, providing data support for subsequent related analysis. By identifying magnetic coupling nodes in the inductor topology data to mark key magnetic coupling paths, this invention can accurately locate the core coupling structure in the inductor topology data, thus providing a foundation for subsequent tuning processing. Furthermore, by calculating the resonance coefficient corresponding to each capacitor element in the capacitor topology data, this invention can quantify the resonance characteristics of each capacitor element in the circuit, thus providing a basis for subsequent collaborative optimization. By performing network synthesis processing on each capacitor element in the capacitor topology data, this invention can integrate scattered capacitor elements into a unified network structure, facilitating the analysis of the interaction relationships between elements and providing a foundation for subsequent topology optimization. Finally, by combining the tuned magnetic coupling data and the optimized topology capacitor, this invention generates a topology coordination scheme corresponding to the filter in the filter magnetic coupling system, thereby obtaining a method to improve the overall performance of the filter magnetic coupling system. Therefore, the topology coordination method and system for achieving magnetic coupling of filters provided in this embodiment of the invention can improve the accuracy of topology coordination for achieving magnetic coupling of filters.

[0063] like Figure 2 The diagram shown is a functional block diagram of a topology cooperative system for realizing magnetic coupling of filters according to the present invention.

[0064] The topology coordination system 200 for implementing magnetic coupling of filters described in this invention can be installed in an electronic device. Depending on the functions implemented, the topology coordination system for implementing magnetic coupling of filters may include a topology data classification module 201, a magnetic coupling topology tuning module 202, a matching degree calculation module 203, a capacitor topology optimization module 204, and a topology scheme generation module 205. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0065] In this embodiment of the invention, the functions of each module / unit are as follows: The topology data classification module 201 is used to acquire circuit topology data related to the filter in the filter magnetic coupling system, and to classify the circuit topology data to obtain inductor topology data and capacitor topology data. The magnetic coupling topology tuning module 202 is used to identify magnetic coupling nodes in the inductor topology data, mark key magnetic coupling paths in the inductor topology data, analyze the mutual inductance coupling characteristics corresponding to the key magnetic coupling paths, determine the magnetic coupling topology variables to be tuned from the inductor topology data, and perform tuning processing on the magnetic coupling topology variables to obtain tuned magnetic coupling data. The matching degree calculation module 203 is used to calculate the resonance coefficient corresponding to each capacitor element in the capacitor topology data, so as to set the cooperative weight of each capacitor element in the capacitor topology data, and calculate the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the cooperative weight. The capacitor topology optimization module 204 is used to perform network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network, calculate the synthesis deviation between the capacitor topology network and each capacitor element in the capacitor topology data, and combine the synthesis deviation and the topology matching degree to perform topology collaborative optimization on each capacitor element in the capacitor topology data to obtain an optimized topology capacitor. The topology scheme generation module 205 is used to combine the tuned magnetic coupling data and the optimized topology capacitance to generate a topology coordination scheme corresponding to the filter in the filter magnetic coupling system.

[0066] In detail, the modules in the topology cooperative system 200 for implementing filter magnetic coupling described in this embodiment of the invention employ the same methods as described above. Figure 1 This method employs the same topology coordination technique as described in the paper for achieving magnetic coupling in filters, and can produce the same technical effect, so it will not be elaborated here.

[0067] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0068] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A topology coordination method for achieving magnetic coupling in filters, characterized in that, The method includes: Obtain the circuit topology data of the filter in the filter magnetic coupling system, and classify the circuit topology data to obtain inductor topology data and capacitor topology data; Identify the magnetic coupling nodes in the inductor topology data to mark the key magnetic coupling paths in the inductor topology data, analyze the mutual inductance coupling characteristics corresponding to the key magnetic coupling paths, determine the magnetic coupling topology variables to be tuned from the inductor topology data, and perform tuning processing on the magnetic coupling topology variables to obtain tuned magnetic coupling data. Calculate the resonance coefficient corresponding to each capacitor element in the capacitor topology data to set the cooperative weight of each capacitor element in the capacitor topology data, and calculate the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the cooperative weight; Each capacitor element in the capacitor topology data is processed by network synthesis to obtain a capacitor topology network. The synthesis deviation between the capacitor topology network and each capacitor element in the capacitor topology data is calculated. Combining the synthesis deviation and the topology matching degree, topology co-optimization of each capacitor element in the capacitor topology data is performed to obtain an optimized topology capacitor. By combining the tuned magnetic coupling data and the optimized topology capacitance, a topology coordination scheme corresponding to the filter in the filter magnetic coupling system is generated.

2. The topology coordination method for achieving magnetic coupling in a filter as described in claim 1, characterized in that, The step of identifying magnetic coupling nodes in the inductor topology data to mark key magnetic coupling paths in the inductor topology data includes: Network topology analysis was performed on the magnetically coupled nodes to obtain the node connection relationships; Based on the node connection relationship, path tracing processing is performed on the magnetic coupling node to obtain the initial magnetic coupling path; Extract the characteristic path parameters from the initial magnetic coupling path; According to the preset path filtering rules, the core path segments in the initial magnetic coupling path are filtered out; Based on the core path segment, the key magnetic coupling paths in the inductor topology data are marked.

3. The topology coordination method for achieving magnetic coupling in a filter as described in claim 1, characterized in that, The analysis of the mutual inductance coupling characteristics corresponding to the key magnetic coupling path, in order to determine the magnetic coupling topology variables to be tuned from the inductor topology data, includes: Based on the critical magnetic coupling path, non-critical magnetic coupling structures in the inductor topology data are selected; Analyze the structural characteristic quantities corresponding to the non-critical magnetic coupling structure; Calculate the feature correlation degree between the structural feature quantity and the mutual inductance coupling characteristic; Based on the aforementioned feature correlation degree, the associated coupling structures in the non-critical magnetic coupling structures are determined; By combining the associated coupling structure and the key magnetic coupling path, the magnetic coupling topology variables to be tuned in the inductor topology data are generated.

4. The topology coordination method for achieving magnetic coupling in a filter as described in claim 3, characterized in that, The calculation of the feature correlation degree between the structural feature quantity and the mutual inductance coupling characteristic includes: Calculate the feature sensitivity corresponding to the structural feature; Based on the sensitivity of the feature quantity, the structural feature quantity is filtered to obtain the target structural feature quantity; Calculate the correlation between the target structural features and the mutual inductance coupling characteristics; Based on the aforementioned characteristic correlation, the characteristic correlation degree between the structural characteristic quantity and the mutual inductance coupling characteristic is calculated.

5. A topology coordination method for achieving magnetic coupling in a filter as described in claim 4, characterized in that, The calculation of the feature sensitivity corresponding to the structural feature includes: Statistically calculate the rate of change of each feature among the structural features within a preset range; Query the reference baseline value corresponding to each feature quantity in the structural feature quantities; Based on the reference benchmark value and the rate of change of the characteristic quantity, the benchmark mutual inductance and the change of mutual inductance corresponding to the structural characteristic quantity are calculated respectively. By combining the rate of change of the characteristic quantity, the reference base value, the change in mutual inductance, and the reference mutual inductance, the sensitivity of the characteristic quantity corresponding to the structural characteristic quantity is calculated.

6. The topology coordination method for achieving magnetic coupling in a filter as described in claim 1, characterized in that, The calculation of the resonance coefficient corresponding to each capacitor element in the capacitor topology data includes: The parameters of each capacitor element in the capacitor topology data are standardized to obtain standard capacitor parameters; The standard capacitance parameters are subjected to feature enhancement processing to obtain enhanced capacitance parameters; Identify the electrical characteristics in the enhanced capacitor parameters and extract the feature identifiers corresponding to the electrical characteristics; Based on the feature identifier, the resonant feature among the electrical features is determined; Calculate the characteristic weight of the resonant feature in the enhanced capacitance parameters; Based on the aforementioned characteristic weights, the resonance coefficient corresponding to each capacitor element in the capacitor topology data is obtained.

7. A topology coordination method for achieving magnetic coupling in a filter as described in claim 1, characterized in that, The step of calculating the topology matching degree for each capacitor element in the capacitor topology data based on the collaborative weight includes: Calculate the impedance fit of each capacitor element in the capacitor topology data; Combining the impedance fit and the cooperative weight, the matching tolerance corresponding to each capacitor element in the capacitor topology data is calculated; Extract the topological relationship corresponding to each capacitor element in the capacitor topology data; Based on the topological relationship, determine the matching mode corresponding to each capacitor element in the capacitor topology data; By combining the matching mode and the matching tolerance, an adaptive reconstruction process is performed on each capacitor element in the capacitor topology data to obtain reconstructed topology data. Obtain the electrical parameters corresponding to the reconstructed topology data and the original capacitor topology data respectively to obtain the reconstructed parameter values ​​and the original parameter values; By combining the reconstructed parameter values ​​and the original parameter values, the topology matching degree corresponding to each capacitor element in the capacitor topology data is calculated.

8. A topology coordination method for achieving magnetic coupling in a filter as described in claim 1, characterized in that, The step of performing network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network includes: Perform node association analysis on each capacitor element in the capacitor topology data to obtain the element connection relationship; Determine the connection impedance value corresponding to each connection in the component connection relationship, and calculate the energy loss rate corresponding to the connection impedance value; By combining the connection impedance value and the energy loss rate, calculate the electrical coupling degree corresponding to each connection in the component connection relationship; Based on the electrical coupling degree, the capacitor topology data is reconstructed to obtain a capacitor topology network.

9. A topology coordination method for achieving magnetic coupling in a filter as described in claim 1, characterized in that, The calculation of the combined deviation between the capacitor topology network and each capacitor element in the capacitor topology data includes: Extract the component-independent attributes corresponding to each capacitor element in the capacitor topology data; Obtain the topology network attributes in the capacitor topology network, and calculate the attribute difference degree between the component independent attributes and the topology network attributes; Based on the attribute difference degree, the combined deviation degree between the capacitor topology network and each capacitor element in the capacitor topology data is obtained.

10. A topological cooperative system for implementing magnetic coupling in filters, characterized in that, The system includes: The topology data classification module is used to acquire circuit topology data related to the filter in the filter magnetic coupling system, and to classify the circuit topology data to obtain inductor topology data and capacitor topology data. The magnetic coupling topology tuning module is used to identify magnetic coupling nodes in the inductor topology data, mark key magnetic coupling paths in the inductor topology data, analyze the mutual inductance coupling characteristics corresponding to the key magnetic coupling paths, determine the magnetic coupling topology variables to be tuned from the inductor topology data, and perform tuning processing on the magnetic coupling topology variables to obtain tuned magnetic coupling data. The matching degree calculation module is used to calculate the resonance coefficient corresponding to each capacitor element in the capacitor topology data, so as to set the cooperative weight of each capacitor element in the capacitor topology data, and calculate the topology matching degree corresponding to each capacitor element in the capacitor topology data based on the cooperative weight. The capacitor topology optimization module is used to perform network synthesis processing on each capacitor element in the capacitor topology data to obtain a capacitor topology network, calculate the synthesis deviation between the capacitor topology network and each capacitor element in the capacitor topology data, and combine the synthesis deviation and the topology matching degree to perform topology collaborative optimization on each capacitor element in the capacitor topology data to obtain an optimized topology capacitor. The topology scheme generation module is used to combine the tuned magnetic coupling data and the optimized topology capacitance to generate a topology coordination scheme corresponding to the filter in the filter magnetic coupling system.

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