An impedance detection optimization method and system based on micro equivalent inductance

Abstract

The present invention discloses an impedance detection optimization method and system based on a micro equivalent inductor, including: constructing a micro equivalent inductor model, setting a test scheme for a device under test, testing a target device under test, analyzing the deviation between the actual impedance and the predicted impedance, and determining whether parameter optimization is required; using a particle swarm optimization algorithm to optimize the current micro equivalent inductor model, and using the optimized micro inductor equivalent model to perform impedance detection to obtain impedance detection information; obtaining the impedance spectrum characteristics of the target device under test based on the impedance detection information, using a sliding window analysis method to identify abnormal fluctuations, and determining whether maintenance is required and issuing a maintenance warning; constructing a model optimization scheme database, formulating a model optimization strategy using real-time detection environment characteristics and real-time detection device characteristics, and setting an optimization parameter interval and an optimization parameter category. It adapts to different detection environments and device conditions to improve the accuracy and comprehensiveness of impedance detection.

Description

Technical Field

[0001] The present invention relates to the technical field of equivalent impedance detection, and in particular to an impedance detection optimization method and system based on a micro equivalent inductor. Background Art

[0002] With the increase in the complexity of electronic devices, especially in the fields of high-speed signal transmission, radio frequency communication, etc., the parasitic effects in the circuit and the existence of non-linear elements will cause impedance changes, resulting in signal distortion or reduced power transmission efficiency. Traditional impedance detection methods often have problems such as large measurement errors and slow response speeds when facing high-frequency and high-precision requirements. At the same time, due to the complexity of the circuit and the changes in its internal structure and environment, traditional impedance detection methods often cannot achieve accurate and comprehensive detection when facing complex circuits. Therefore, how to achieve accurate and comprehensive impedance detection has become an urgent problem to be solved. Summary of the Invention

[0003] The present invention overcomes the defects of the prior art and provides an impedance detection optimization method and system based on a micro equivalent inductor, and an important purpose thereof is to improve the accuracy and comprehensiveness of impedance detection.

[0004] To achieve the above object, a first aspect of the present invention provides an impedance detection optimization method based on a micro equivalent inductor, including:

[0005] Obtain the structure information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and construct a micro equivalent inductor model;

[0006] Set the test scheme of the device under test, perform impedance detection tests on the target device under test, analyze the deviation between the actual impedance and the predicted impedance, and determine whether it is necessary to optimize the parameters of the current micro equivalent inductor model to obtain the first judgment result information;

[0007] If the first judgment result information is that optimization is required, then optimize the current micro equivalent inductor model based on the particle swarm optimization algorithm, and perform impedance detection using the optimized micro inductor equivalent model to obtain impedance detection information;

[0008] Obtain the impedance spectrum characteristics of the target device under test based on the impedance detection information, use the sliding window analysis method to identify abnormal fluctuations, and determine whether maintenance is required and issue a maintenance warning;

[0009] Construct a model optimization scheme database, formulate a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter interval and optimization parameter category.

[0010] In this solution, obtaining the structure information of the device under test, extracting the internal circuit characteristics and internal component characteristics of the target device under test, and constructing a micro equivalent inductance model specifically include:

[0011] Obtain the structure information of the device under test, perform feature extraction on the structure information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and obtain the device under test feature information;

[0012] Draw the internal circuit diagram of the target device under test according to the device under test feature information, perform inductance characteristic analysis based on the drawn internal circuit diagram of the device under test, and select the corresponding type of inductance model according to the inductance characteristic analysis result;

[0013] Use the device under test feature information to extract the internal component specification characteristics of the device under test, combine the selected inductance model type to construct an initial micro equivalent inductance model, and import it into the simulation software for testing;

[0014] Obtain the product specification of the device under test, set the test reference parameters according to the product specification and calculate the expected response value, perform simulation testing according to the test reference parameters to obtain the test result and calculate the deviation from the expected response value, and perform equivalent component parameter adjustment to obtain a micro equivalent inductance model that meets the expectations.

[0015] In this solution, setting the test scheme for the device under test, performing impedance detection testing on the target device under test, analyzing the deviation between the actual impedance and the predicted impedance, and determining whether parameter optimization of the current micro equivalent inductance model is required to obtain the first judgment result information specifically include:

[0016] Obtain the product specification of the device under test, select the test signals supported by the target device under test according to the product specification of the device under test and set the corresponding test parameters to form a test scheme for the device under test;

[0017] Test the target device under test according to the device under test test scheme, use a signal generator to send the set test signal to the target device under test, and use a monitoring device to obtain the actual test response of the target device under test to obtain the actual test response information;

[0018] Perform simulation testing using the constructed micro equivalent inductance model according to the device under test test scheme to obtain simulation test response information, and calculate the impedance prediction value of the target device under test for equivalent simulation according to the simulation test response information to obtain prediction impedance information;

[0019] Calculate the actual impedance of the device under test based on the actual test response information to obtain the actual impedance information, perform an operation on the actual impedance information and the prediction impedance information, analyze the deviation between the actual impedance and the prediction impedance, and obtain the deviation value information;

[0020] Judge the deviation value information against a preset threshold to determine whether the current micro-equivalent inductance model needs to optimize its model parameters, and obtain the first judgment result information.

[0021] In this solution, if the first judgment result information indicates a need for optimization, then optimize the current micro-equivalent inductance model based on the particle swarm optimization algorithm, and use the optimized micro-inductance equivalent model to perform impedance detection, which specifically includes:

[0022] Introduce the particle swarm optimization algorithm to optimize the current micro-equivalent inductance model, preset several optimization parameter targets, construct an objective function based on the error between the actual impedance and the predicted impedance, and define the number of particles;

[0023] Randomly generate initial particles in the parameter space based on the preset optimization parameter targets and set the initial particle velocities, where each particle represents a single potential parameter or a combination of potential parameters for optimizing the current micro-equivalent inductance model, to obtain the initial particle swarm;

[0024] Calculate the objective function values corresponding to each particle in the initial particle swarm according to the constructed objective function, analyze the individual optimal position and the global optimal position within the current particle swarm based on the calculated objective function values, and perform particle swarm update;

[0025] Obtain the deviation value information, set an error tolerance interval and an error penalty interval according to the deviation value information, construct constraint conditions and a penalty function, and judge whether the updated particle swarm meets the constraint conditions. If it does not meet the constraint conditions, then penalize the target particle swarm according to the constructed penalty function;

[0026] Perform iterative update until the maximum update number is reached or the stop criterion is met, output the final optimized particle swarm, extract the objective function values of each particle in the final optimized particle swarm, sort them, and select the optimal particle according to the sorting result to generate an optimization plan to optimize the target micro-equivalent inductance model;

[0027] Perform impedance detection on the target device to be measured based on the optimized micro-equivalent inductance model, and obtain impedance detection information.

[0028] In this solution, obtain the impedance spectrum characteristics of the target device to be measured based on the impedance detection information, use the sliding window analysis method to identify abnormal fluctuations, judge whether maintenance is required, and perform maintenance warning, which specifically includes:

[0029] Obtain the impedance detection information, generate an impedance characteristic curve according to the impedance detection information, perform feature extraction on the generated impedance characteristic curve, and obtain the impedance spectrum characteristics of the target device to be measured;

[0030] Obtain the impedance amplitude spectrum and phase spectrum of the target device under test according to the impedance spectrum characteristics of the target device under test, and perform anomaly detection based on the sliding window analysis method, presetting the window size and time step;

[0031] Define the starting position of the window, and respectively detect the impedance amplitude spectrum and phase spectrum of the target device under test according to the preset time step, analyze the differences in the characteristics within each window and identify abnormal fluctuations to obtain impedance anomaly detection information;

[0032] Construct a performance evaluation rule, use the impedance anomaly detection information to perform performance evaluation on the target device under test, obtain performance evaluation information, and judge whether the target device under test needs to be repaired and give a warning prompt according to the performance evaluation information.

[0033] In this solution, the model optimization scheme database is constructed, and a model optimization strategy is formulated by using the real-time detection environment characteristics and real-time detection device characteristics, and the optimization parameter interval and optimization parameter category are set, specifically including:

[0034] Obtain the historical optimization parameters of the micro equivalent inductance model and the impedance detection status information, where the impedance detection status information includes the impedance detection environment status information and the impedance detection device status information;

[0035] Extract features from the impedance detection status information, extract the impedance detection environment characteristics and impedance detection device characteristics corresponding to each historical optimization parameter to obtain the first feature information;

[0036] Associate each historical optimization parameter with the first feature information, and construct an optimization parameter feature portrait based on the historical optimization parameters, detection environment characteristics and impedance detection device characteristics;

[0037] Calculate the Euclidean distance between each optimization parameter feature portrait, perform clustering analysis using the K-means algorithm, obtain several optimization parameter feature portrait sets, extract the optimization parameter features corresponding to each optimization parameter feature portrait set, and generate an optimization parameter interval;

[0038] Based on the optimization parameter interval, construct a model optimization strategy corresponding to the optimization parameter feature portrait set to form a model optimization scheme database. When performing the next optimization of the micro equivalent inductance model, obtain the real-time detection environment characteristics and real-time detection device characteristics;

[0039] Calculate the cosine similarity between each optimization parameter feature portrait in the model optimization scheme database, and select the corresponding model optimization strategy according to the calculated cosine similarity to set the optimization parameter interval and optimization parameter category.

[0040] In a second aspect of the present invention, an impedance detection optimization system based on a micro equivalent inductance is provided. The system includes: a memory and a processor. The memory contains an impedance detection optimization method program based on a micro equivalent inductance. When the impedance detection optimization method program based on a micro equivalent inductance is executed by the processor, the following steps are implemented:

[0041] Obtain the structure information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and construct a micro equivalent inductance model;

[0042] Set the test scheme for the device under test, perform impedance detection tests on the target device under test, analyze the deviation between the actual impedance and the predicted impedance, and determine whether it is necessary to optimize the parameters of the current micro equivalent inductance model to obtain the first judgment result information;

[0043] If the first judgment result information indicates that optimization is required, optimize the current micro equivalent inductance model based on the particle swarm optimization algorithm, and perform impedance detection using the optimized micro inductance equivalent model to obtain impedance detection information;

[0044] Obtain the impedance spectrum characteristics of the target device under test based on the impedance detection information, use the sliding window analysis method to identify abnormal fluctuations, and determine whether maintenance is required and issue a maintenance warning;

[0045] Construct a model optimization scheme database, formulate a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter range and optimization parameter category.

[0046] The present invention discloses an impedance detection optimization method and system based on a micro equivalent inductance, including: constructing a micro equivalent inductance model, setting a test scheme for the device under test, testing the target device under test, analyzing the deviation between the actual impedance and the predicted impedance, and determining whether parameter optimization is required; optimizing the current micro equivalent inductance model based on the particle swarm optimization algorithm, and performing impedance detection using the optimized micro inductance equivalent model to obtain impedance detection information; obtaining the impedance spectrum characteristics of the target device under test based on the impedance detection information, using the sliding window analysis method to identify abnormal fluctuations, and determining whether maintenance is required and issuing a maintenance warning; constructing a model optimization scheme database, formulating a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and setting the optimization parameter range and optimization parameter category. Adapt to different detection environments and device conditions to improve the accuracy and comprehensiveness of impedance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] To more clearly illustrate the technical solutions in the embodiments or exemplifications of the present invention, the following will briefly introduce the drawings required for use in the description of the embodiments or exemplifications. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings shown.

[0048] Figure 1 Flowchart of an impedance detection optimization method based on micro equivalent inductance provided by an embodiment of the present invention;

[0049] Figure 2 Flowchart of impedance detection for a device to be detected provided by an embodiment of the present invention;

[0050] Figure 3 Block diagram of an impedance detection optimization system based on micro equivalent inductance provided by an embodiment of the present invention;

[0051] The realization, functional characteristics and advantages of the object of the present invention will be further described in combination with the embodiments with reference to the drawings. Detailed implementation manners

[0052] In order to be able to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below in combination with the drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0053] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0054] Figure 1 Flowchart of an impedance detection optimization method based on micro equivalent inductance provided by an embodiment of the present invention;

[0055] As Figure 1 shown, the present invention provides a flowchart of an impedance detection optimization method based on micro equivalent inductance, including:

[0056] S102, obtaining the structure information of the device to be detected, extracting the internal circuit characteristics and internal component characteristics of the target device to be detected, and constructing a micro equivalent inductance model;

[0057] S104, setting a test scheme for the device to be detected, performing an impedance detection test on the target device to be detected, analyzing the deviation between the actual impedance and the predicted impedance, and judging whether it is necessary to optimize the parameters of the current micro equivalent inductance model to obtain the first judgment result information;

[0058] S106. If the first judgment result information indicates that optimization is required, optimize the current micro equivalent inductance model based on the particle swarm optimization algorithm, and use the optimized micro inductance equivalent model to perform impedance detection to obtain impedance detection information.

[0059] S108. Obtain the impedance spectrum characteristics of the target device under test based on the impedance detection information, use the sliding window analysis method to identify abnormal fluctuations, and determine whether maintenance is required and issue a maintenance warning.

[0060] S110. Construct a model optimization scheme database, formulate a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter range and optimization parameter category.

[0061] It should be noted that the present invention provides an impedance detection optimization method and system based on micro equivalent inductance. By constructing a micro equivalent inductance model of the device under test, when performing impedance detection, first set the test scheme of the device under test, perform impedance detection tests on the target device under test, calculate the actual impedance through test feedback, and use the micro equivalent inductance model to obtain the predicted impedance. Since the micro equivalent inductance model is constructed based on the intact state of the device under test, but in the actual impedance detection process, factors such as use loss and environmental impact exist, resulting in a difference between the actual value and the predicted value. Therefore, analyze the deviation between the actual impedance and the predicted impedance, and determine whether model parameter optimization is required according to the deviation value, so as to improve the adaptation degree of the model and the actual situation. Optimize the current micro equivalent inductance model based on the particle swarm optimization algorithm, and use the optimized micro inductance equivalent model to perform impedance detection to obtain impedance detection information. Then, obtain the impedance spectrum characteristics of the target device under test based on the impedance detection information, use the sliding window analysis method to identify abnormal fluctuations, and determine whether maintenance is required and issue a maintenance warning. Finally, since there is one or more combinations of optimization parameters during model optimization, it may lead to a long optimization adaptation time. Therefore, construct a model optimization scheme database using historical optimization parameters, formulate a model optimization strategy through real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter range and optimization parameter category. Adapt to different detection environments and device conditions to improve the accuracy and comprehensiveness of impedance detection.

[0062] Further, in a preferred embodiment of the present invention, the obtaining the structure information of the device under test, extracting the internal circuit characteristics and internal component characteristics of the target device under test, and constructing a micro equivalent inductance model specifically includes:

[0063] Obtain the structure information of the device under test, perform feature extraction on the structure information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and obtain the device under test feature information.

[0064] Draw the internal circuit diagram of the target device under test according to the characteristic information of the device under test, perform inductance characteristic analysis based on the drawn internal circuit diagram of the device under test, and select the corresponding type of inductance model according to the inductance characteristic analysis result;

[0065] Extract the internal component specification characteristics of the device under test using the characteristic information of the device under test, construct an initial micro-equivalent inductance model in combination with the selected inductance model type, and import it into the simulation software for testing;

[0066] Obtain the product specification of the device under test, set the test reference parameters according to the product specification and calculate the expected response value, perform simulation testing according to the test reference parameters to obtain the test result and calculate the deviation from the expected response value, and adjust the equivalent component parameters to obtain a micro-equivalent inductance model that meets the expectations.

[0067] It should be noted that, first, obtain the structural information of the device under test, extract the main characteristics of the internal circuit of the device under test, including the internal circuit connection relationship and structure, as well as the distribution and type of components, etc. Next, draw the internal circuit diagram of the target device under test according to the extracted characteristics of the device under test. Analyze the inductance characteristics of the device under test and select an inductance model type that matches the device under test, such as an ideal inductance model and an RLC model, etc., to construct an initial micro-equivalent inductance model. At the same time, obtain the product specification of the device under test, set the corresponding test reference parameters according to the technical requirements and standard parameters in the specification, and calculate the expected response value. Then, use the reference parameters to perform simulation testing to obtain the preliminary test result. By comparing the deviation between the actual result of the simulation testing and the expected response value, judge the accuracy of the model, and further adjust the equivalent component parameters in the model. After multiple iterative adjustments, obtain a micro-equivalent inductance model that meets the expected response value to ensure that it can accurately reflect the true inductance characteristics of the device under test.

[0068] Furthermore, in a preferred embodiment of the present invention, the test scheme of the device under test is set, the impedance detection test is performed on the target device under test, the deviation between the actual impedance and the predicted impedance is analyzed, and it is judged whether it is necessary to optimize the parameters of the current micro-equivalent inductance model to obtain the first judgment result information, specifically including:

[0069] Obtain the product specification of the device under test, select the test signals supported by the target device under test according to the product specification of the device under test and set the corresponding test parameters to form the test scheme of the device under test;

[0070] Test the target device under test according to the test scheme of the device under test, use a signal generator to send the set test signal to the target device under test, and use a monitoring device to obtain the actual test response of the target device under test to obtain the actual test response information;

[0071] According to the test scheme of the device under test, use the constructed micro equivalent inductance model to conduct simulation tests to obtain simulation test response information, calculate the impedance prediction value of the target device under test for equivalent simulation based on the simulation test response information, and obtain prediction impedance information;

[0072] Calculate the actual impedance of the device under test based on the actual test response information to obtain actual impedance information, perform an operation on the actual impedance information and the prediction impedance information, analyze the deviation between the actual impedance and the prediction impedance, and obtain deviation value information;

[0073] Judge the deviation value information against a preset threshold to determine whether the current micro equivalent inductance model needs to be optimized in terms of model parameters, and obtain the first judgment result information.

[0074] It should be noted that by using the product specification of the device under test, understand the types of test signals it supports, and based on this, select suitable test signals for the target device under test and set corresponding test parameters to form a complete test scheme for the device under test. Use a signal generator to send the set test signal to the device under test, and collect the actual test response of the target device under test through a monitoring device, including information such as current and voltage of the device under test under the action of the test signal. Next, use the previously constructed micro equivalent inductance model to conduct simulation tests according to the set test scheme of the device under test to obtain the simulation test response information of the target device under test. Calculate the impedance prediction value of the target device under test based on the simulation test response information, thereby forming prediction impedance information. Subsequently, calculate the actual impedance of the device under test based on the actual test response information to form actual impedance information, compare and analyze the actual impedance information with the impedance information obtained by simulation prediction, and calculate the difference between the two. The magnitude of the deviation reflects the degree of conformity between the model prediction and the actual test results. Finally, determine whether the current micro equivalent inductance model needs to be optimized in terms of parameters. If the deviation exceeds the threshold, it indicates that there is a large mismatch between the model and the actual situation, and the parameters of the model need to be adjusted and optimized according to the deviation to obtain more accurate prediction results and ensure the accuracy and practicality of the model.

[0075] Further, in a preferred embodiment of the present invention, if the first judgment result information indicates a need for optimization, then optimize the current micro equivalent inductance model based on the particle swarm optimization algorithm, and use the optimized micro inductance equivalent model for impedance detection, specifically including:

[0076] Introduce the particle swarm optimization algorithm to optimize the current micro equivalent inductance model, preset several optimization parameter targets, construct an objective function based on the error between the actual impedance and the prediction impedance, and define the number of particles;

[0077] Randomly generate initial particles in the parameter space based on the preset optimization parameter objectives and set the initial particle velocities, where each particle represents a single potential parameter or a combination of potential parameters for optimizing the current micro equivalent inductance model, to obtain an initial particle swarm;

[0078] Calculate the objective function values corresponding to each particle in the initial particle swarm according to the constructed objective function, analyze the individual optimal position and the global optimal position within the current particle swarm based on the calculated objective function values, and perform particle swarm update;

[0079] Obtain the deviation value information, set the error tolerance interval and the error penalty interval according to the deviation value information, construct the constraint conditions and the penalty function, and determine whether the updated particle swarm meets the constraint conditions. If it does not meet the constraint conditions, then penalize the target particle swarm according to the constructed penalty function;

[0080] Perform iterative update until the maximum number of updates is reached or the stop criterion is met, output the final optimized particle swarm, extract the objective function values of each particle in the final optimized particle swarm, sort them, and select the optimal particle according to the sorting result to generate an optimization scheme to optimize the target micro equivalent inductance model;

[0081] Perform impedance detection on the target device under test based on the optimized micro equivalent inductance model to obtain impedance detection information.

[0082] It should be noted that during the model optimization process, the particle swarm optimization algorithm is used for model optimization. Several optimization parameter objectives are preset, such as inductance value, resistance, and capacitance, etc. During optimization, it can be a single optimization parameter or a combination of multiple optimization parameters to generate an initial particle swarm. After generating the initial particle swarm, calculate the objective function value corresponding to each particle. The level of the objective function value reflects the quality of the parameter combination represented by the particle. Based on the objective function value, determine the individual optimal position and the global optimal position in the current particle swarm. At the same time, use the deviation value information to set the error tolerance interval and the penalty interval, construct the constraint conditions and the penalty function. The error tolerance interval represents the degree of neglect of the error between the predicted impedance and the actual impedance, and the penalty interval represents the degree to which the error between the predicted impedance and the actual impedance exceeds the error before optimization, so as to accelerate the convergence speed of optimization and ensure the accuracy of the final result. Through continuous iterative update, until the set maximum number of updates is reached or the stop criterion is met. Gradually converge to an optimal particle swarm, sort each particle according to the objective function values of each particle in the optimized particle swarm, and select the optimal particle according to the sorting result. Generate an optimization scheme through the parameter combination represented by this optimal particle to optimize and adjust the current micro equivalent inductance model. Finally, perform impedance detection on the target device under test based on the optimized micro equivalent inductance model, so as to perform impedance detection more accurately.

[0083] Further, in a preferred embodiment of the present invention, the impedance spectrum characteristics of the target device to be measured are obtained based on the impedance detection information, and the sliding window analysis method is used to identify abnormal fluctuations, determine whether maintenance is required, and issue a maintenance warning, which specifically includes:

[0084] Obtain impedance detection information, generate an impedance characteristic curve according to the impedance detection information, extract features from the generated impedance characteristic curve, and obtain the impedance spectrum characteristics of the target device to be measured;

[0085] Obtain the impedance amplitude spectrum and phase spectrum of the target device to be measured according to the impedance spectrum characteristics of the target device to be measured, and perform anomaly detection based on the sliding window analysis method, and preset the window size and time step;

[0086] Define the starting position of the window, and detect the impedance amplitude spectrum and phase spectrum of the target device to be measured respectively according to the preset time step, analyze the differences in the characteristics within each window, and identify abnormal fluctuations to obtain impedance anomaly detection information;

[0087] Construct a performance evaluation rule, use the impedance anomaly detection information to evaluate the performance of the target device to be measured, obtain performance evaluation information, and judge whether the target device to be measured needs to be repaired and give a warning prompt according to the performance evaluation information.

[0088] It should be noted that, first, obtain impedance detection information to generate an impedance characteristic curve reflecting the characteristics of the target device to be measured. Then, extract features from the generated impedance characteristic curve to obtain the impedance spectrum characteristics of the target device to be measured. Based on the extracted impedance spectrum characteristics, further obtain the impedance amplitude spectrum and phase spectrum of the target device to be measured. These two spectral diagrams respectively reflect the impedance amplitude and phase changes of the device to be measured at different frequencies, and the sliding window analysis method is used for abnormal fluctuation detection and identification. Subsequently, construct a performance evaluation rule, use the obtained impedance anomaly detection information to evaluate the overall performance of the target device to be measured, and judge the operating status of the device to be measured. Based on the performance evaluation results, judge whether the target device to be measured needs to be repaired. If potential risks or anomalies are detected in the device, a warning prompt is issued to take maintenance or replacement measures to ensure the normal operation of the equipment and avoid further fault expansion.

[0089] Further, in a preferred embodiment of the present invention, the model optimization scheme database is constructed, and the model optimization strategy is formulated by using the real-time detection environment characteristics and real-time detection device characteristics, and the optimization parameter interval and optimization parameter category are set, which specifically includes:

[0090] Obtain the historical optimization parameters of the micro-equivalent inductance model and the impedance detection status information, where the impedance detection status information includes impedance detection environment status information and impedance detection device status information;

[0091] Extract features from the impedance detection status information, extract the impedance detection environment features and impedance detection device features corresponding to each historical optimization parameter, and obtain the first feature information;

[0092] Associate each historical optimization parameter with the first feature information, and construct an optimization parameter feature portrait based on the historical optimization parameter, detection environment feature, and impedance detection device feature;

[0093] Calculate the Euclidean distance between each optimization parameter feature portrait, perform clustering analysis using the K-means algorithm, obtain several optimization parameter feature portrait sets, extract the optimization parameter features corresponding to each optimization parameter feature portrait set, and generate an optimization parameter interval;

[0094] Based on the optimization parameter interval, construct a model optimization strategy corresponding to the optimization parameter feature portrait set, form a model optimization scheme database, and when performing the next micro equivalent inductance model optimization, obtain the real-time detection environment feature and real-time detection device feature;

[0095] Calculate the cosine similarity between each optimization parameter feature portrait in the model optimization scheme database, and select the corresponding model optimization strategy according to the calculated cosine similarity to set the optimization parameter interval and optimization parameter category.

[0096] It should be noted that a model optimization scheme database is constructed using historical optimization parameters, and a model optimization strategy is formulated through real-time detection environment features and real-time detection device features to set the optimization parameter interval and optimization parameter category. It adapts to different detection environments and device conditions to improve the accuracy and comprehensiveness of impedance detection, and further speeds up the efficiency of impedance detection optimization. Each historical optimization parameter is associated with the corresponding detection environment feature and impedance detection device feature to generate a feature portrait of each historical optimization parameter, which is used to represent the optimization scheme adopted by the optimization parameter under different environmental and device conditions, and helps with pattern recognition and the formulation of optimization strategies. By calculating the Euclidean distance between each optimization parameter feature portrait and using the K-means clustering algorithm for analysis, several sets of optimization parameter feature portraits are formed. Then, the optimization parameter features corresponding to each optimization parameter feature portrait set are extracted, and the corresponding optimization parameter interval is generated. Thereby, the initial value of the optimization parameter during model optimization is further limited, unnecessary iteration processes are reduced, and the efficiency and accuracy of the optimization process are improved.

[0097] Figure 2 This is the flowchart for impedance detection of the device to be detected provided by an embodiment of the present invention;

[0098] As Figure 2 shown, the present invention provides a flowchart for impedance detection of the device to be detected, including:

[0099] S202. Set the test plan for the device under test, perform impedance detection on the target device under test to obtain the actual impedance information, and use the micro equivalent inductance model to perform simulation detection to obtain the predicted impedance information;

[0100] S204. Based on the deviation between the actual impedance and the predicted impedance, determine whether model optimization is required. If model optimization is required, obtain the real-time impedance detection status information;

[0101] S206. Calculate the similarity between the real-time impedance detection status information and the model optimization scheme database, and determine whether there is a suitable model optimization scheme according to the calculation result;

[0102] S208. If there is a suitable model optimization scheme, select the corresponding model optimization scheme, set the initial optimization parameter range and optimization parameter category, and perform model optimization;

[0103] S210. If there is no suitable model optimization scheme, set the constraint conditions based on the deviation between the actual impedance and the predicted impedance, and perform model optimization;

[0104] S212. Perform impedance detection based on the optimized micro-inductance equivalent model, perform performance evaluation to determine whether maintenance is required, and issue a maintenance warning.

[0105] It should be noted that, first, the test scheme of the device under test is set, and the impedance detection test is performed on the target device under test to obtain the actual impedance information. At the same time, the established micro equivalent inductor model is used for simulation detection to obtain the predicted impedance information. By comparing the actual impedance with the predicted impedance, the accuracy of the current detection result is judged. Next, based on the deviation between the actual impedance and the predicted impedance, it is judged whether the model needs to be optimized. If it is determined that model optimization is required, real-time impedance detection status information is further obtained, and the real-time impedance detection status information includes real-time impedance detection environment status information and real-time impedance detection device status information. The obtained real-time impedance detection status information is calculated for similarity with the information stored in the model optimization scheme database to determine whether there is an adapted model optimization scheme, so as to find suitable optimization parameters more quickly. If it is determined that there is an adapted model optimization scheme, the corresponding scheme is selected, and the initial optimization parameter interval and optimization parameter category are set according to the scheme. The model optimization scheme includes the corresponding optimization parameter interval and category, so as to reduce the workload when performing model optimization. However, if it is determined that there is no suitable model optimization solution, the corresponding constraints are set according to the deviation between the actual impedance and the predicted impedance, and then the model is optimized, that is, the iterative optimization of parameter combinations and parameter changes is performed normally until the optimization result is obtained. Finally, the impedance detection is re-performed based on the optimized micro-equivalent inductor model. The accuracy of the detection is improved through the optimized model, and the performance of the detection results is evaluated. If the evaluation results show that there is a need for maintenance, the system will issue a maintenance warning to ensure the normal operation of the device under test.

[0106] Figure 3 An impedance detection optimization system 3 based on micro equivalent inductance is provided in one embodiment of the present invention. The system includes: a memory 31 and a processor 32. The memory 31 contains an impedance detection optimization method program based on micro equivalent inductance. When the impedance detection optimization method program based on micro equivalent inductance is executed by the processor 32, the following steps are implemented:

[0107] Obtain the structural information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and build a micro equivalent inductor model;

[0108] Setting a test plan for the device under test, performing an impedance detection test on the target device under test, analyzing the deviation between the actual impedance and the predicted impedance, determining whether it is necessary to perform parameter optimization on the current micro equivalent inductor model, and obtaining first determination result information;

[0109] If the first judgment result information is that optimization is required, the current micro equivalent inductor model is optimized based on a particle swarm optimization algorithm, and impedance detection is performed using the optimized micro equivalent inductor model to obtain impedance detection information;

[0110] Obtain the impedance spectrum characteristics of the target device to be measured based on the impedance detection information, use the sliding window analysis method to identify abnormal fluctuations, and determine whether maintenance is required and issue a maintenance warning;

[0111] Construct a model optimization scheme database, formulate a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter interval and optimization parameter category.

[0112] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the couplings, direct couplings, or communication connections between the various components shown or discussed can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0113] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, in each embodiment of the present invention, the various functional units can all be integrated in one processing unit, or each unit can be separately used as one unit, or two or more units can be integrated in one unit; the above-mentioned integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0115] Those of ordinary skill in the art can understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0116] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0117] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An impedance detection optimization method based on micro equivalent inductance, characterized in that Including: Obtain the structure information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and construct a micro-equivalent inductance model; Set the test scheme for the device under test, conduct impedance detection tests on the target device under test, analyze the deviation between the actual impedance and the predicted impedance, and determine whether it is necessary to optimize the parameters of the current micro-equivalent inductance model to obtain the first judgment result information; If the first judgment result information indicates the need for optimization, optimize the current micro-equivalent inductance model based on the particle swarm optimization algorithm, and use the optimized micro-inductance equivalent model for impedance detection to obtain impedance detection information; Obtain the impedance spectrum characteristics of the target device under test based on the impedance detection information, use the sliding window analysis method for anomaly fluctuation identification, and determine whether maintenance is required and issue a maintenance warning; Construct a model optimization scheme database, formulate a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter range and optimization parameter category; Among them, the construction of the model optimization scheme database, formulating a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and setting the optimization parameter range and optimization parameter category specifically include: Obtain the historical optimization parameters of the micro-equivalent inductance model and the impedance detection status information, where the impedance detection status information includes the impedance detection environment status information and the impedance detection device status information; Extract the characteristics of the impedance detection status information, extract the impedance detection environment characteristics and impedance detection device characteristics corresponding to each historical optimization parameter to obtain the first characteristic information; Associate each historical optimization parameter with the first characteristic information, and form an optimization parameter characteristic portrait based on the historical optimization parameters, detection environment characteristics, and impedance detection device characteristics; Calculate the Euclidean distance between each optimization parameter characteristic portrait, use the K-means algorithm for clustering analysis, obtain several optimization parameter characteristic portrait sets, extract the optimization parameter characteristics corresponding to each optimization parameter characteristic portrait set, and generate an optimization parameter range; Based on the optimization parameter range, form a model optimization strategy corresponding to the optimization parameter characteristic portrait set to construct a model optimization scheme database. When performing the next optimization of the micro-equivalent inductance model, obtain the real-time detection environment characteristics and real-time detection device characteristics; Calculate the cosine similarity between each optimization parameter characteristic portrait in the model optimization scheme database, and select the corresponding model optimization strategy according to the calculated cosine similarity to set the optimization parameter range and optimization parameter category.

2. The impedance detection optimization method based on micro equivalent inductance according to claim 1, characterized in that, The obtaining of the structure information of the device under test, extracting the internal circuit characteristics and internal component characteristics of the target device under test, and constructing a micro-equivalent inductance model specifically include: Obtain the structure information of the device under test, extract the characteristics of the device under test structure information, extract the internal circuit characteristics and internal component characteristics of the target device under test to obtain the device under test characteristic information; Draw the internal circuit diagram of the target device under test according to the device under test characteristic information, conduct inductance characteristic analysis based on the drawn internal circuit diagram of the device under test, and select the corresponding type of inductance model according to the inductance characteristic analysis result; Extract the internal component specifications of the DUT using the DUT characteristic information, construct an initial micro-equivalent inductance model in combination with the selected inductance model type, and import it into the simulation software for testing; Obtain the product specification of the DUT, set the test reference parameters according to the product specification and calculate the expected response value, perform a simulation test according to the test reference parameters to obtain the test result and calculate the deviation from the expected response value, and adjust the equivalent component parameters to obtain a micro-equivalent inductance model that meets the expectations.

3. An impedance detection optimization method based on micro equivalent inductance according to claim 1, characterized in that Set the test scheme for the DUT, perform an impedance detection test on the target DUT, analyze the deviation between the actual impedance and the predicted impedance, and determine whether parameter optimization of the current micro-equivalent inductance model is required to obtain the first judgment result information, specifically including: Obtain the product specification of the DUT, select the test signals supported by the target DUT according to the product specification of the DUT and set the corresponding test parameters to form the test scheme for the DUT; Test the target DUT according to the test scheme for the DUT, use a signal generator to send the set test signal to the target DUT, and use a monitoring device to obtain the actual test response of the target DUT to obtain the actual test response information; Perform a simulation test using the constructed micro-equivalent inductance model according to the test scheme for the DUT to obtain the simulation test response information, and calculate the impedance prediction value of the target DUT for equivalent simulation according to the simulation test response information to obtain the predicted impedance information; Calculate the actual impedance of the DUT based on the actual test response information to obtain the actual impedance information, perform an operation on the actual impedance information and the predicted impedance information, and analyze the deviation between the actual impedance and the predicted impedance to obtain the deviation value information; Compare the deviation value information with a preset threshold to determine whether the current micro-equivalent inductance model needs to be optimized in terms of model parameters to obtain the first judgment result information.

4. An impedance detection optimization method based on micro equivalent inductance according to claim 1, characterized in that If the first judgment result information indicates that optimization is required, optimize the current micro-equivalent inductance model based on the particle swarm optimization algorithm, and perform impedance detection using the optimized micro-inductance equivalent model, specifically including: Introduce the particle swarm optimization algorithm to optimize the current micro-equivalent inductance model, preset several optimization parameter targets, construct an objective function based on the error between the actual impedance and the predicted impedance, and define the number of particles; Randomly generate initial particles in the parameter space based on the preset optimization parameter targets and set the initial particle velocities, where each particle represents a single potential parameter or a combination of potential parameters for optimizing the current micro-equivalent inductance model to obtain the initial particle swarm; Calculate the objective function values corresponding to each particle in the initial particle swarm according to the constructed objective function, analyze the individual optimal position and the global optimal position within the current particle swarm based on the calculated objective function values, and update the particle swarm; Obtain the deviation value information, set the error tolerance interval and the error penalty interval according to the deviation value information and construct the constraint conditions and the penalty function, determine whether the updated particle swarm meets the constraint conditions, and if not, punish the target particle swarm according to the constructed penalty function; Iteratively update until the maximum number of updates is reached or the stopping criterion is met, output the final optimized particle swarm, extract the objective function values of each particle in the final optimized particle swarm, sort them, and select the optimal particle according to the sorting result to generate an optimization plan to optimize the target micro equivalent inductance model; Based on the optimized micro equivalent inductance model, impedance detection is performed on the target device under test to obtain impedance detection information.

5. The impedance detection optimization method based on micro equivalent inductance according to claim 1, wherein Based on the impedance detection information, obtain the impedance spectrum characteristics of the target device under test, use the sliding window analysis method to identify abnormal fluctuations, and judge whether maintenance is required and give a maintenance warning. Specifically, it includes: Obtain impedance detection information, generate an impedance characteristic curve according to the impedance detection information, extract features from the generated impedance characteristic curve, and obtain the impedance spectrum characteristics of the target device under test; Obtain the impedance amplitude spectrum and phase spectrum of the target device under test according to the impedance spectrum characteristics of the target device under test, and perform anomaly detection based on the sliding window analysis method, presetting the window size and time step; Define the starting position of the window and detect the impedance amplitude spectrum and phase spectrum of the target device under test respectively according to the preset time step, analyze the differences in features within each window and identify abnormal fluctuations to obtain impedance anomaly detection information; Construct a performance evaluation rule, use the impedance anomaly detection information to evaluate the performance of the target device under test to obtain performance evaluation information, and judge whether the target device under test needs to be repaired and give a warning prompt according to the performance evaluation information.

6. An impedance detection optimization system based on micro equivalent inductance, characterized in that, The system includes: a memory and a processor. The memory contains a program for the impedance detection optimization method based on the micro equivalent inductance. When the program for the impedance detection optimization method based on the micro equivalent inductance is executed by the processor, the following steps are implemented: Obtain the structural information of the device under test, extract the internal circuit characteristics and internal component characteristics of the target device under test, and construct a micro equivalent inductance model; Set the test plan for the device under test, perform impedance detection tests on the target device under test, analyze the deviation between the actual impedance and the predicted impedance, and judge whether the parameters of the current micro equivalent inductance model need to be optimized to obtain the first judgment result information; If the first judgment result information indicates that optimization is required, optimize the current micro equivalent inductance model based on the particle swarm optimization algorithm, and perform impedance detection using the optimized micro inductance equivalent model to obtain impedance detection information; Based on the impedance detection information, obtain the impedance spectrum characteristics of the target device under test, use the sliding window analysis method to identify abnormal fluctuations, and judge whether maintenance is required and give a maintenance warning; Construct a model optimization plan database, formulate a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and set the optimization parameter range and optimization parameter category; Among them, the construction of the model optimization plan database, formulating a model optimization strategy using the real-time detection environment characteristics and real-time detection device characteristics, and setting the optimization parameter range and optimization parameter category specifically include: Obtain the historical optimization parameters of the micro equivalent inductance model and the impedance detection status information, where the impedance detection status information includes the impedance detection environment status information and the impedance detection device status information; Extract features from the impedance detection status information, extract the impedance detection environment features and impedance detection device features corresponding to each historical optimization parameter, and obtain the first feature information; Associate each historical optimization parameter with the first feature information, and construct an optimization parameter feature portrait based on the historical optimization parameter, detection environment feature and impedance detection device feature; Calculate the Euclidean distance between each optimization parameter feature portrait, perform clustering analysis using the K-means algorithm, obtain several optimization parameter feature portrait sets, extract the optimization parameter features corresponding to each optimization parameter feature portrait set, and generate an optimization parameter interval; Based on the optimization parameter interval, construct a model optimization strategy corresponding to the optimization parameter feature portrait set, form a model optimization scheme database, and when performing the next micro equivalent inductance model optimization, obtain the real-time detection environment feature and real-time detection device feature; Calculate the cosine similarity between each optimization parameter feature portrait in the model optimization scheme database, and select the corresponding model optimization strategy according to the calculated cosine similarity to set the optimization parameter interval and optimization parameter category.

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