Impedance detection optimization method and system based on micro-equivalent inductance

Abstract

The invention discloses a micro-equivalent inductance-based impedance detection optimization method and system. The method comprises the steps of constructing a micro-equivalent inductance model, setting a to-be-tested device test scheme, testing a target to-be-tested device, analyzing a deviation between actual impedance and predicted impedance, and judging whether parameter optimization needs to be performed or not; optimizing the current micro-equivalent inductance model by using a particle swarm optimization algorithm, and performing impedance detection by using the optimized micro-equivalent inductance model to obtain impedance detection information; acquiring impedance spectrum characteristics of the target to-be-detected device based on the impedance detection information, performing abnormal fluctuation identification by using a sliding window analysis method, judging whether maintenance is needed or not, and performing maintenance early warning; and constructing a model optimization scheme database, making a model optimization strategy by utilizing real-time detection environment characteristics and real-time detection device characteristics, and setting an optimization parameter interval and an optimization parameter category. The method is suitable for different detection environments and device conditions so as 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 micro equivalent inductance. Background Art

[0002] As the complexity of electronic devices increases, especially in the fields of high-speed signal transmission and radio frequency communication, the presence of parasitic effects and nonlinear elements in the circuit 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 changes in its internal structure and environment, traditional impedance detection methods are often unable to achieve accurate and comprehensive detection when facing complex circuits. Therefore, how to achieve accurate and comprehensive impedance detection has become a problem that needs to be solved urgently. 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 micro equivalent inductance, the important purpose of which is to improve the accuracy and comprehensiveness of impedance detection.

[0004] To achieve the above-mentioned purpose, the first aspect of the present invention provides an impedance detection optimization method based on micro equivalent inductance, comprising:

[0005] 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 inductance model;

[0006] 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;

[0007] 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;

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

[0009] Build a model optimization solution database, use real-time detection environment characteristics and real-time detection device characteristics to formulate model optimization strategies, and set optimization parameter ranges and optimization parameter categories.

[0010] In this solution, the steps of obtaining the structural 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 inductor model specifically include:

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

[0012] Draw an 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 an inductance model of a corresponding type according to the result of the inductance characteristic analysis;

[0013] Extracting the internal component specification characteristics of the device under test by using the characteristic information of the device under test, building an initial micro equivalent inductor model in combination with the selected inductor model type, and importing it into the simulation software for testing;

[0014] Obtain the product specification of the device to be tested, set the test benchmark parameters according to the product specification and calculate the expected response value, perform simulation test according to the test benchmark parameters to obtain the test results and calculate the deviation from the expected response value, and adjust the equivalent component parameters to obtain a micro equivalent inductor model that meets the expectations.

[0015] In this solution, the test solution for the device under test is set, an 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 determined whether the current micro equivalent inductor model needs to be parameter optimized to obtain the first judgment result information, which specifically includes:

[0016] Obtain the product specification of the device under test, select the test signal 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 plan for the device under test;

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

[0018] According to the test scheme of the device under test, a simulation test is performed using the constructed micro equivalent inductor model to obtain simulation test response information, and an impedance prediction value of the target device under test for equivalent simulation is calculated according to the simulation test response information to obtain predicted impedance information;

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

[0020] The deviation value information is compared with a preset threshold value to determine whether the current micro equivalent inductor model needs to be optimized in model parameters, and first judgment result information is obtained.

[0021] In this solution, if the first judgment result information is that optimization is required, the current micro equivalent inductor model is optimized based on the particle swarm optimization algorithm, and the optimized micro equivalent inductor model is used to perform impedance detection, specifically including:

[0022] The particle swarm optimization algorithm is introduced to optimize the current micro equivalent inductance model, several optimization parameter targets are preset, the objective function is constructed according to the error between the actual impedance and the predicted impedance, and the number of particles is defined;

[0023] Based on the preset optimization parameter target, initial particles are randomly generated in the parameter space and the initial particle speed is set, wherein each particle represents a single potential parameter or a combination of potential parameters for optimizing the current micro equivalent inductor model, to obtain an initial particle group;

[0024] Calculate the objective function value 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 in the current particle swarm according to the calculated objective function value, and update the particle swarm;

[0025] Obtaining deviation value information, setting an error tolerance interval and an error penalty interval according to the deviation value information and constructing constraint conditions and penalty functions, determining whether the updated particle swarm meets the constraint conditions, and if not, punishing the target particle swarm according to the constructed penalty function;

[0026] Perform iterative updates until the maximum number of updates is reached or the stopping criteria are met, output the final optimized particle swarm, extract the objective function value 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 inductor model;

[0027] Based on the optimized micro equivalent inductance model, impedance detection is performed on the target device under test to obtain impedance detection information.

[0028] In this solution, the impedance spectrum characteristics of the target device under test are obtained based on the impedance detection information, and the abnormal fluctuation is identified by using the sliding window analysis method to determine whether maintenance is needed and to issue a maintenance warning, which specifically includes:

[0029] Acquire impedance detection information, generate an impedance characteristic curve according to the impedance detection information, perform feature extraction on the generated impedance characteristic curve, and acquire impedance spectrum characteristics of the target device under test;

[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, perform anomaly detection based on the sliding window analysis method, and preset the window size and time step;

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

[0032] Construct performance evaluation rules, use the impedance anomaly detection information to perform performance evaluation on the target device under test, obtain performance evaluation information, determine whether the target device under test needs maintenance based on the performance evaluation information, and issue an early warning prompt.

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

[0034] Acquire historical optimization parameters of the micro equivalent inductor model and impedance detection status information, wherein the impedance detection status information includes impedance detection environment status information and impedance detection device status information;

[0035] Performing feature extraction on the impedance detection status information, extracting impedance detection environment features and impedance detection device features corresponding to each historical optimization parameter, and obtaining first feature information;

[0036] Associating each historical optimization parameter with the first characteristic information, and forming an optimization parameter characteristic profile based on the historical optimization parameter, the detection environment characteristic, and the impedance detection device characteristic;

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

[0038] Based on the optimization parameter interval, a model optimization strategy is formed to form a corresponding optimization parameter feature portrait set, and a model optimization solution database is formed. When the next micro equivalent inductor model optimization is performed, the real-time detection environment characteristics and real-time detection device characteristics are obtained;

[0039] The cosine similarity between the feature portraits of each optimization parameter in the model optimization solution database is calculated, and the corresponding model optimization strategy is selected according to the calculated cosine similarity to set the optimization parameter interval and optimization parameter category.

[0040] A second aspect of the present invention provides an impedance detection optimization system based on micro equivalent inductance, the system comprising: a memory, a processor, the memory containing an impedance detection optimization method program based on micro equivalent inductance, the impedance detection optimization method program based on micro equivalent inductance implementing the following steps when executed by the processor:

[0041] 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 inductance model;

[0042] 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;

[0043] 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;

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

[0045] Build a model optimization solution database, use real-time detection environment characteristics and real-time detection device characteristics to formulate model optimization strategies, and set optimization parameter ranges and optimization parameter categories.

[0046] The present invention discloses an impedance detection optimization method and system based on micro-equivalent inductance, including: constructing a micro-equivalent inductance model, setting a test scheme for a device to be tested, testing a target device to be tested, analyzing the deviation between the actual impedance and the predicted impedance, and judging whether parameter optimization is required; optimizing the current micro-equivalent inductance model using a 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 to be tested based on the impedance detection information, identifying abnormal fluctuations using a sliding window analysis method, judging whether maintenance is required and performing 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 performing optimization parameter interval and optimization parameter category settings. Adapting to different detection environments and device conditions to improve the accuracy and comprehensiveness of impedance detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments or exemplary embodiments of the present invention, the drawings required for use in the embodiments or exemplary descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained according to the drawings without paying creative work.

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

[0049] Figure 2 A flow chart of impedance detection of a device to be detected provided in one embodiment of the present invention;

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

[0051] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

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

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

[0056] S102, 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 inductor model;

[0057] S104, 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;

[0058] S106: 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;

[0059] S108, acquiring impedance spectrum characteristics of the target device under test based on the impedance detection information, identifying abnormal fluctuations using a sliding window analysis method, determining whether maintenance is required, and issuing a maintenance warning;

[0060] S110, constructing a model optimization solution database, formulating a model optimization strategy using real-time detection environment characteristics and real-time detection device characteristics, and setting optimization parameter intervals and optimization parameter categories.

[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 a device under test, when performing impedance detection, the device under test test scheme is first set, the target device under test is subjected to impedance detection test, the actual impedance is calculated through test feedback, and the predicted impedance is obtained using the micro-equivalent inductance model. Because the micro-equivalent inductance model is constructed based on the intact state of the device under test, but in the actual impedance detection process, there are factors such as use loss and environmental impact, resulting in differences between the actual value and the predicted value, so the deviation between the actual impedance and the predicted impedance is analyzed, and the model parameter optimization is determined according to the deviation value, thereby improving the degree of adaptation between the model and the actual situation. The current micro-equivalent inductance model is optimized using a particle swarm optimization algorithm, and the optimized micro-inductance equivalent model is used to perform impedance detection to obtain impedance detection information. Then, the impedance spectrum characteristics of the target device under test 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 perform maintenance warning. Finally, when optimizing the model, there are one or more combinations of optimization parameters, which may lead to the optimization adaptation time process. Then, the historical optimization parameters are used to build a model optimization solution database, and the model optimization strategy is formulated through real-time detection of environmental characteristics and real-time detection of device characteristics, and the optimization parameter interval and optimization parameter category are set. Different detection environments and device conditions are adapted to improve the accuracy and comprehensiveness of impedance detection.

[0062] Further, in a preferred embodiment of the present invention, the step of obtaining the structural 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] Acquire the structure information of the device under test, perform feature extraction on the structure information of the device under test, extract the internal circuit features and internal component features of the target device under test, and obtain feature information of the device under test;

[0064] Draw an 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 an inductance model of a corresponding type according to the result of the inductance characteristic analysis;

[0065] Extracting the internal component specification characteristics of the device under test by using the characteristic information of the device under test, building an initial micro equivalent inductor model in combination with the selected inductor model type, and importing it into the simulation software for testing;

[0066] Obtain the product specification of the device to be tested, set the test benchmark parameters according to the product specification and calculate the expected response value, perform simulation test according to the test benchmark parameters to obtain the test results and calculate the deviation from the expected response value, and adjust the equivalent component parameters to obtain a micro equivalent inductor model that meets the expectations.

[0067] It should be noted that, first, the structural information of the device under test is obtained, and the main features of the internal circuit of the device under test are extracted, including the internal circuit connection relationship and structure, as well as the distribution and type of components. Next, the internal circuit diagram of the target device under test is drawn according to the extracted features of the device under test. The inductance characteristics of the device under test are analyzed to select the type of inductance model that matches the device under test, such as the ideal inductance model and the RLC model, to construct an initial micro equivalent inductance model. At the same time, the product specification of the device under test is obtained, and the corresponding test benchmark parameters are set according to the technical requirements and standard parameters in the specification, and the expected response value is calculated. Then, the benchmark parameters are used for simulation testing to obtain preliminary test results. By comparing the deviation between the actual results of the simulation test and the expected response value, the accuracy of the model is judged, and the equivalent component parameters in the model are further adjusted. After multiple iterative adjustments, a micro equivalent inductance model that meets the expected response value is obtained to ensure that it can accurately reflect the true inductance characteristics of the device under test.

[0068] Further, in a preferred embodiment of the present invention, the setting of the DUT test scheme, performing an impedance detection test on the target DUT, 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 the first determination result information specifically include:

[0069] Obtain the product specification of the device under test, select the test signal 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 plan for the device under test;

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

[0071] According to the test scheme of the device under test, a simulation test is performed using the constructed micro equivalent inductor model to obtain simulation test response information, and an impedance prediction value of the target device under test for equivalent simulation is calculated according to the simulation test response information to obtain predicted 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 operation on the actual impedance information and the predicted impedance information, analyze the deviation between the actual impedance and the predicted impedance, and obtain deviation value information;

[0073] The deviation value information is compared with a preset threshold value to determine whether the current micro equivalent inductor model needs to be optimized in model parameters, and first judgment result information is obtained.

[0074] It should be noted that the product specification of the device under test is used to understand the test signal types it supports, and based on this, the test signal suitable for the target device under test is selected and the corresponding test parameters are set 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 the monitoring equipment, including the current, voltage and other information of the device under test under the test signal. Next, use the previously constructed micro-equivalent inductor model to perform simulation tests according to the set device under test test scheme to obtain the simulation test response information of the target device under test. The impedance prediction value of the target device under test is calculated based on the simulation test response information to form the predicted impedance information. Subsequently, the actual impedance of the device under test is calculated based on the actual test response information to form the actual impedance information, and the actual impedance information is compared and analyzed with the impedance information obtained by the simulation prediction to calculate the difference between the two. The size of the deviation reflects the degree of conformity between the model prediction and the actual test results. Finally, it is determined whether the current micro-equivalent inductor model needs to be optimized. If the deviation exceeds the threshold, it indicates that there is a large mismatch between the model and the actual situation. The model parameters 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 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 inductor equivalent model, specifically including:

[0076] The particle swarm optimization algorithm is introduced to optimize the current micro equivalent inductance model, several optimization parameter targets are preset, the objective function is constructed according to the error between the actual impedance and the predicted impedance, and the number of particles is defined;

[0077] Based on the preset optimization parameter target, initial particles are randomly generated in the parameter space and the initial particle speed is set, wherein each particle represents a single potential parameter or a combination of potential parameters for optimizing the current micro equivalent inductor model, to obtain an initial particle group;

[0078] Calculate the objective function value 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 in the current particle swarm according to the calculated objective function value, and update the particle swarm;

[0079] Obtaining deviation value information, setting an error tolerance interval and an error penalty interval according to the deviation value information and constructing constraint conditions and penalty functions, determining whether the updated particle swarm meets the constraint conditions, and if not, punishing the target particle swarm according to the constructed penalty function;

[0080] Perform iterative updates until the maximum number of updates is reached or the stopping criteria are met, output the final optimized particle swarm, extract the objective function value 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 inductor model;

[0081] Based on the optimized micro equivalent inductance model, impedance detection is performed on the target device under test to obtain impedance detection information.

[0082] It should be noted that in the process of model optimization, the particle swarm optimization algorithm is used for model optimization, and several optimization parameter targets are preset, such as inductance, resistance and capacitance. When optimizing, an initial particle swarm can be generated for one optimization parameter or multiple optimization parameter groups. After the initial particle swarm is generated, the objective function value corresponding to each particle is calculated. The high or low objective function value reflects the quality of the parameter combination represented by the particle. Based on the objective function value, the individual optimal position and the global optimal position in the current particle swarm are determined. At the same time, the tolerance interval and penalty interval of the error are set using the deviation value information, and the constraint conditions and penalty functions are constructed. The error tolerance interval represents the degree of error neglect between the predicted impedance and the actual impedance, and the penalty interval represents the degree of error between the predicted impedance and the actual impedance exceeding the error before optimization, so as to accelerate the convergence speed of the optimization and ensure the accuracy of the final result. Through continuous iterative updates, until the set maximum number of updates is reached or the stopping criteria are met. Gradually converge to an optimal particle swarm, sort each particle according to the objective function value of each particle in the optimized particle swarm, and select the optimal particle according to the sorting result. The optimization scheme is generated through the parameter combination represented by the optimal particle, and the current micro equivalent inductor model is optimized and adjusted. Finally, the impedance detection of the target device under test is performed based on the optimized micro equivalent inductor 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 under test are obtained based on the impedance detection information, abnormal fluctuations are identified using a sliding window analysis method, and whether maintenance is required and maintenance warning is performed, specifically including:

[0084] Acquire impedance detection information, generate an impedance characteristic curve according to the impedance detection information, perform feature extraction on the generated impedance characteristic curve, and acquire impedance spectrum characteristics of the target device under test;

[0085] 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, perform anomaly detection based on the sliding window analysis method, and preset the window size and time step;

[0086] Define the window start position to detect the impedance amplitude spectrum and phase spectrum of the target device under test according to the preset time step, analyze the difference of features in each window and identify abnormal fluctuations to obtain impedance anomaly detection information;

[0087] Construct performance evaluation rules, use the impedance anomaly detection information to perform performance evaluation on the target device under test, obtain performance evaluation information, determine whether the target device under test needs maintenance based on the performance evaluation information, and issue an early warning prompt.

[0088] It should be noted that, first, the impedance detection information is obtained to generate an impedance characteristic curve reflecting the characteristics of the target device under test. Next, the generated impedance characteristic curve is feature extracted to obtain the impedance spectrum characteristics of the target device under test. Based on the extracted impedance spectrum characteristics, the impedance amplitude spectrum and phase spectrum of the target device under test are further obtained. These two spectra respectively reflect the impedance amplitude and phase changes of the device under test at different frequencies, and the sliding window analysis method is used for abnormal fluctuation detection and identification. Subsequently, a performance evaluation rule is constructed, and the overall performance of the target device under test is evaluated using the acquired impedance anomaly detection information to determine the operating status of the device under test. Based on the performance evaluation results, it is determined whether the target device under test needs to be repaired. If a potential risk or abnormality is detected in the device, an early warning prompt is issued to take maintenance or replacement measures to ensure the normal operation of the equipment and avoid further fault expansion.

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

[0090] Acquire historical optimization parameters of the micro equivalent inductor model and impedance detection status information, wherein the impedance detection status information includes impedance detection environment status information and impedance detection device status information;

[0091] Performing feature extraction on the impedance detection status information, extracting impedance detection environment features and impedance detection device features corresponding to each historical optimization parameter, and obtaining first feature information;

[0092] Associating each historical optimization parameter with the first characteristic information, and forming an optimization parameter characteristic profile based on the historical optimization parameter, the detection environment characteristic, and the impedance detection device characteristic;

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

[0094] Based on the optimization parameter interval, a model optimization strategy is formed to form a corresponding optimization parameter feature portrait set, and a model optimization solution database is formed. When the next micro equivalent inductor model optimization is performed, the real-time detection environment characteristics and real-time detection device characteristics are obtained;

[0095] The cosine similarity between the feature portraits of each optimization parameter in the model optimization solution database is calculated, and the corresponding model optimization strategy is selected according to the calculated cosine similarity to set the optimization parameter interval and optimization parameter category.

[0096] It should be noted that the historical optimization parameters are used to construct a model optimization solution database, and the model optimization strategy is formulated through real-time detection environment characteristics and real-time detection device characteristics, and the optimization parameter interval and optimization parameter category are set. Adapt to different detection environments and device conditions to improve the accuracy and comprehensiveness of impedance detection, and further accelerate the efficiency of impedance detection optimization. Associate each historical optimization parameter with the corresponding detection environment characteristics and impedance detection device characteristics to generate a feature portrait of each historical optimization parameter, which is used to represent the optimization scheme adopted by the optimization parameters under different environments and device conditions, and is helpful for pattern recognition and optimization strategy formulation. By calculating the Euclidean distance between each optimization parameter feature portrait, the K-means clustering algorithm is used for analysis to form a set of several optimization parameter feature portraits. Then, the optimization parameter features corresponding to each optimization parameter feature portrait set are extracted, and the corresponding optimization parameter interval is generated. Thereby further limiting the initial value of the optimization parameter when performing model optimization, reducing unnecessary iteration processes, and improving the efficiency and accuracy of the optimization process.

[0097] Figure 2 A flow chart of impedance detection of a device to be detected provided by an embodiment of the present invention;

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

[0099] S202, setting a test plan for the device under test, performing an impedance detection test on the target device under test to obtain actual impedance information, and performing simulation detection using a micro equivalent inductor model to obtain predicted impedance information;

[0100] S204, based on the deviation between the actual impedance and the predicted impedance, determining whether model optimization is required, and if model optimization is required, obtaining real-time impedance detection status information;

[0101] 2S06, performing similarity calculation between the real-time impedance detection status information and the model optimization solution database, and judging whether there is an adaptive model optimization solution based on the calculation result;

[0102] S208, if there is an adapted model optimization scheme, select the corresponding model optimization scheme to set the initial optimization parameter interval and optimization parameter category, and perform model optimization;

[0103] S210, if there is no suitable model optimization solution, setting constraint conditions based on the deviation between the actual impedance and the predicted impedance to perform model optimization;

[0104] S212, impedance detection is performed based on the optimized micro-inductor equivalent model, performance evaluation is performed to determine whether maintenance is required, and maintenance warning is issued.

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

[0111] Build a model optimization solution database, use real-time detection environment characteristics and real-time detection device characteristics to formulate model optimization strategies, and set optimization parameter ranges and optimization parameter categories.

[0112] In the 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 schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: 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 coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the 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, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

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

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

[0116] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for 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 each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0117] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An impedance detection optimization method based on micro equivalent inductance, characterized in that: include: 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; 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; 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; Acquire impedance spectrum characteristics of the target device under test based on the impedance detection information, identify abnormal fluctuations using a sliding window analysis method, determine whether maintenance is required, and issue a maintenance warning; Build a model optimization solution database, use real-time detection environment characteristics and real-time detection device characteristics to formulate model optimization strategies, and set optimization parameter ranges and optimization parameter categories.

2. According to claim 1, the impedance detection optimization method based on micro equivalent inductance is characterized in that: The step of 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 inductor model specifically includes: Acquire the structure information of the device under test, perform feature extraction on the structure information of the device under test, extract the internal circuit features and internal component features of the target device under test, and obtain feature information of the device under test; Draw an 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 an inductance model of a corresponding type according to the result of the inductance characteristic analysis; Extracting the internal component specification characteristics of the device under test by using the characteristic information of the device under test, building an initial micro equivalent inductor model in combination with the selected inductor model type, and importing it into the simulation software for testing; Obtain the product specification of the device to be tested, set the test benchmark parameters according to the product specification and calculate the expected response value, perform simulation test according to the test benchmark parameters to obtain the test results and calculate the deviation from the expected response value, and adjust the equivalent component parameters to obtain a micro equivalent inductor model that meets the expectations.

3. The impedance detection optimization method based on micro equivalent inductance according to claim 1 is characterized in that: The setting of the DUT test scheme, performing impedance detection test on the target DUT, analyzing the deviation between the actual impedance and the predicted impedance, judging whether it is necessary to optimize the parameters of the current micro equivalent inductor model, and obtaining the first judgment result information specifically includes: Obtain the product specification of the device under test, select the test signal 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 plan for the device under test; Testing the target device under test according to the device under test test scheme, using a signal generator to send a set test signal to the target device under test, and using a monitoring device to obtain an actual test response of the target device under test to obtain actual test response information; According to the test scheme of the device under test, a simulation test is performed using the constructed micro equivalent inductor model to obtain simulation test response information, and an impedance prediction value of the target device under test for equivalent simulation is calculated according to the simulation test response information to obtain predicted impedance information; Calculate the actual impedance of the device under test based on the actual test response information to obtain actual impedance information, perform operation on the actual impedance information and the predicted impedance information, analyze the deviation between the actual impedance and the predicted impedance, and obtain deviation value information; The deviation value information is compared with a preset threshold value to determine whether the current micro equivalent inductor model needs to be optimized in model parameters, and first judgment result information is obtained.

4. The impedance detection optimization method based on micro equivalent inductance according to claim 1 is characterized in that: If the first judgment result information indicates that optimization is required, optimizing the current micro equivalent inductor model based on a particle swarm optimization algorithm, and performing impedance detection using the optimized micro equivalent inductor model, specifically including: The particle swarm optimization algorithm is introduced to optimize the current micro equivalent inductance model, several optimization parameter targets are preset, the objective function is constructed according to the error between the actual impedance and the predicted impedance, and the number of particles is defined; Based on the preset optimization parameter target, initial particles are randomly generated in the parameter space and the initial particle speed is set, wherein each particle represents a single potential parameter or a combination of potential parameters for optimizing the current micro equivalent inductor model, to obtain an initial particle group; Calculate the objective function value 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 in the current particle swarm according to the calculated objective function value, and update the particle swarm; Obtaining deviation value information, setting an error tolerance interval and an error penalty interval according to the deviation value information and constructing constraint conditions and penalty functions, determining whether the updated particle swarm meets the constraint conditions, and if not, punishing the target particle swarm according to the constructed penalty function; Perform iterative updates until the maximum number of updates is reached or the stopping criteria are met, output the final optimized particle swarm, extract the objective function value 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 inductor 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 is characterized in that: The step of acquiring the impedance spectrum characteristics of the target device under test based on the impedance detection information, identifying abnormal fluctuations using a sliding window analysis method, determining whether maintenance is required, and performing maintenance warning specifically includes: Acquire impedance detection information, generate an impedance characteristic curve according to the impedance detection information, perform feature extraction on the generated impedance characteristic curve, and acquire 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, perform anomaly detection based on the sliding window analysis method, and preset the window size and time step; Define the window start position to detect the impedance amplitude spectrum and phase spectrum of the target device under test according to the preset time step, analyze the difference of features in each window and identify abnormal fluctuations to obtain impedance anomaly detection information; Construct performance evaluation rules, use the impedance anomaly detection information to perform performance evaluation on the target device under test, obtain performance evaluation information, determine whether the target device under test needs maintenance based on the performance evaluation information, and issue an early warning prompt.

6. The impedance detection optimization method based on micro equivalent inductance according to claim 1 is characterized in that: The model optimization solution database is constructed, and the model optimization strategy is formulated by using the real-time detection environment characteristics and the real-time detection device characteristics, and the optimization parameter interval and optimization parameter category are set, specifically including: Acquire historical optimization parameters of the micro equivalent inductor model and impedance detection status information, wherein the impedance detection status information includes impedance detection environment status information and impedance detection device status information; Performing feature extraction on the impedance detection status information, extracting impedance detection environment features and impedance detection device features corresponding to each historical optimization parameter, and obtaining first feature information; Associating each historical optimization parameter with the first characteristic information, and forming an optimization parameter characteristic profile based on the historical optimization parameter, the detection environment characteristic, and the impedance detection device characteristic; Calculate the Euclidean distance between the feature portraits of each optimization parameter, perform cluster analysis using the K-means algorithm, obtain a set of several feature portraits of optimization parameters, extract the optimization parameter features corresponding to each set of feature portraits of optimization parameters, and generate the optimization parameter interval; Based on the optimization parameter interval, a model optimization strategy is formed to form a corresponding optimization parameter feature portrait set, and a model optimization solution database is formed. When the next micro equivalent inductor model optimization is performed, the real-time detection environment characteristics and real-time detection device characteristics are obtained; The cosine similarity between the feature portraits of each optimization parameter in the model optimization solution database is calculated, and the corresponding model optimization strategy is selected according to the calculated cosine similarity to set the optimization parameter interval and optimization parameter category.

7. An impedance detection optimization system based on micro equivalent inductance, characterized in that: The system includes: a memory and a processor, wherein the memory contains an impedance detection optimization method program based on micro equivalent inductance, and when the impedance detection optimization method program based on 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 build a micro equivalent inductor model; 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; 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; Acquire impedance spectrum characteristics of the target device under test based on the impedance detection information, identify abnormal fluctuations using a sliding window analysis method, determine whether maintenance is required, and issue a maintenance warning; Build a model optimization solution database, use real-time detection environment characteristics and real-time detection device characteristics to formulate model optimization strategies, and set optimization parameter ranges and optimization parameter categories.

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