An automated matching method for high-frequency and high-precision filters

Abstract

The present invention relates to the field of filter data processing technology. The present invention provides an automatic matching method for high-frequency and high-precision filters, which obtains filter operation data and signals and converts them into digital signals, and constructs an automatic matching simulation model based on historical data. The matching task requirements are received, and the matching results are obtained by simulation operation, and compared with the actual matching results. If the difference exceeds the preset range, the matching rules are detected and optimized. At the same time, the task requirement historical data and operation history data are used to construct an operation data simulation model, and simulation data is generated for matching. Data features are extracted for unsuccessful matching situations, and genetic algorithms are used to optimize matching rules. The present invention realizes the automatic matching of high-frequency and high-precision filters, improves matching efficiency and accuracy, is applicable to fields such as communications, radar and sonar, and overcomes the problem that the automatic filter matching system in the prior art cannot effectively cope with the complexity and variability of high-frequency signals.

Description

Technical Field

[0001] The present invention relates to the technical field of filter data processing, and in particular to an automatic matching method for high-frequency and high-precision filters. Background Art

[0002] The automated matching system for high-frequency, high-precision filters is a signal data processing tool. It leverages automation technology and high-precision filter design principles to achieve precise filtering and matching of high-frequency signals. By automatically adjusting the filter's parameters and structure to maximize the ratio of the signal's instantaneous power to the average noise power, known as the signal-to-noise ratio (SNR), the system extracts useful signal components and suppresses noise interference. This automated matching process not only improves filtering efficiency but also ensures the accuracy of the filtering results. It is widely used in fields such as communications, radar, and sonar.

[0003] When using an automated matching system for high-frequency, high-precision filters, the characteristics of the signal to be processed and the filtering objectives must be clearly defined. The signal to be processed is input into the system, and the corresponding filtering parameters, such as center frequency and bandwidth, are set. Based on these parameters and a pre-set algorithm, the system automatically adjusts the filter transfer function to achieve optimal filtering results. During the filtering process, the system also monitors the filtering effect in real time and makes fine adjustments as needed. Users can view the filtering results through the system interface and perform further processing or analysis as needed.

[0004] The automatic matching system of high-frequency and high-precision filters still has the following technical pain points in actual application. In order to achieve accurate filtering of high-frequency signals, high-frequency and high-precision filters need to continuously optimize the parameters of the automatic matching of the filters. Due to the complexity and variability of high-frequency signals, the filters, resulting in the optimized automatic matching system of the filters, cannot be applied to high-frequency and high-precision filters. For this reason, the present invention provides an automatic matching method for high-frequency and high-precision filters. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an automatic matching method for high-frequency and high-precision filters, which solves the problem that in order to achieve accurate filtering of high-frequency signals, high-frequency and high-precision filters need to continuously optimize the parameters of the automatic matching of the filters. Due to the complexity and variability of high-frequency signals, the filters, resulting in the problem that the optimized automatic matching system of the filters cannot be applied to high-frequency and high-precision filters.

[0006] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0007] The present invention provides an automatic matching method for high-frequency and high-precision filters, comprising:

[0008] Step S101, obtaining filter operation data and a filter signal, converting the filter signal into a digital signal through an analog-to-digital converter to obtain a filter digital signal, wherein the filter operation data includes filter parameter data, environmental noise data, and filtering effect evaluation data;

[0009] Step S102: Acquire filter operation history data, construct an automatic matching simulation model of the filter using the filter operation history data, obtain the automatic matching simulation model of the filter, receive the automatic matching task requirements of the filter, substitute the automatic matching task requirements of the filter into the automatic matching simulation model of the filter, and obtain the automatic matching simulation result of the filter;

[0010] Step S103: Match the filter digital signal using a preset automatic matching rule of the filter to obtain an automatic matching result of the filter, compare the automatic matching simulation result of the filter with the automatic matching simulation result of the filter, and if the comparison result of the automatic matching simulation result of the filter with the automatic matching simulation result of the filter is outside a preset error range, then test the automatic matching rule of the filter;

[0011] Step S104, obtaining the historical data of the automated matching task requirements of the filter, constructing a filter operation data simulation model with the historical data of the automated matching task requirements of the filter and the historical data of the filter operation, and generating filter operation simulation data using the filter operation data simulation model;

[0012] Step S105: The filter operation simulation data generated by the filter operation data simulation model is matched through the preset automatic matching rules of the filter to obtain the simulation data matching result, which includes the simulation data matching success information and the simulation data matching failure information. The filter operation data corresponding to the simulation data matching failure information is retrieved, and the filter operation data corresponding to the simulation data matching failure information is subjected to data feature analysis to obtain the matching failure data feature. Based on the matching failure data feature and the automatic matching task requirements of the filter, the preset automatic matching rules of the filter are optimized, and the filter is automatically matched using the optimized automatic matching rules of the filter.

[0013] Furthermore, the automated matching method for high-frequency and high-precision filters of the present invention, step S102, includes:

[0014] The step of constructing an automatic matching simulation model of the filter using the historical operation data of the filter includes: obtaining the historical operation data of the filter, the historical operation data of the filter including parameter data of the filter, environmental noise data, and filtering effect evaluation data; training a deep learning model using the historical operation data of the filter to obtain an automatic matching simulation model of the filter, the automatic matching simulation model of the filter being used to simulate the working state and filtering effect of the filter under different conditions;

[0015] Receive the automatic matching task requirements of the filter, and substitute the automatic matching task requirements of the filter into the constructed automatic matching simulation model of the filter. The automatic matching simulation model of the filter performs simulation operations to obtain the automatic matching simulation results of the filter. The automatic matching simulation results of the filter are used to provide a reference basis for subsequent matching rule optimization and automatic matching.

[0016] Furthermore, the automated matching method for high-frequency and high-precision filters of the present invention, step S102, includes:

[0017] The steps of receiving the automatic matching task requirement of the filter and substituting it into the automatic matching simulation model of the filter to obtain the automatic matching simulation result of the filter include: receiving the automatic matching task requirement of the filter, the automatic matching task requirement of the filter including the target filtering effect, working environment and restriction conditions of the filter;

[0018] The filter automatic matching task requirements are substituted into the filter automatic matching simulation model. The filter automatic matching simulation model performs simulation operations based on the input filter automatic matching task requirements and the historical operation data of the filter, and outputs the filter automatic matching simulation results. The filter automatic matching simulation results include the filtering effect prediction and optimal parameter configuration of the filter under different parameter configurations.

[0019] Furthermore, the automated matching method for high-frequency and high-precision filters of the present invention, step S103, includes:

[0020] The filter digital signal is matched by a preset filter automatic matching rule to obtain the filter automatic matching result, which includes: obtaining the filter digital signal after analog-to-digital conversion, matching the filter digital signal according to the preset filter automatic matching rule, the filter automatic matching rule including comparative matching of filtering of the filter digital signal and feature extraction and comparative matching of the filter digital signal, processing the comparative matching data according to the matching rule, and outputting the filter automatic matching result, which includes the matching degree and parameter configuration.

[0021] Furthermore, the automated matching method for high-frequency and high-precision filters of the present invention, step S104, includes:

[0022] The step of constructing a filter operation data simulation model using the filter's automated matching task demand history data and the filter's operation history data includes obtaining the filter's automated matching task demand history data, the filter's automated matching task demand history data including past matching tasks, target filtering effects, and working environment information, and also obtaining the filter's operation history data, the filter's operation history data including filter parameter settings, actual filtering effects, and environmental noise records;

[0023] Data features are extracted from the historical data of the filter's automated matching task requirements and the filter's operation history data to obtain features of the data to be matched, and the features of the data to be matched are matched in a preset algorithm model library to obtain a model to be trained. The model to be trained is trained using the historical data of the filter's automated matching task requirements and the filter's operation history data to obtain a filter operation data simulation model. The filter operation data simulation model is used to generate filter operation simulation data based on the filter's historical performance and task requirements.

[0024] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S105, includes:

[0025] Matching filter operation simulation data generated by the filter operation data simulation model through a preset filter automatic matching rule to obtain a simulation data matching result includes: using the constructed filter operation data simulation model to generate filter operation simulation data, the filter operation simulation data simulating the performance of the filter under different working environments and task requirements, and inputting the filter operation simulation data into the preset filter automatic matching rule to perform a matching operation;

[0026] During the matching process, the filter operation simulation data is processed, analyzed and compared according to the preset filter automatic matching rules to determine whether the filter operation simulation data meets the expected filtering effect and task requirements, and the simulation data matching results are output. The simulation data matching results include simulation data success information and simulation data failure information.

[0027] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S105, includes:

[0028] The steps of optimizing the preset automatic matching rules of the filter using a genetic algorithm based on the characteristics of the unsuccessful matching data and the automatic matching task requirements of the filter include: extracting data features of the filter operation data corresponding to the unsuccessful matching information of the simulated data, so as to reflect the reasons why the filter failed to match successfully under specific conditions;

[0029] Based on the data characteristics of the filter operating data corresponding to the unsuccessful simulation data matching information and the requirements of the filter automatic matching task, the optimization objectives and constraints of the genetic algorithm are defined. A population containing multiple potential solutions is initialized, that is, different configurations of the filter automatic matching rules. The selection, crossover and mutation mechanisms in the genetic algorithm are used to iteratively search for the optimal solution.

[0030] In each iteration, the fitness of each solution in the population is evaluated according to the optimization objectives and constraints, and individuals with higher fitness are selected for crossover and mutation operations to generate new solutions;

[0031] When the genetic algorithm reaches the preset stopping conditions, which include the number of iterations and the fitness threshold, the optimized filter automatic matching rules are output.

[0032] Beneficial effects of the present invention:

[0033] By constructing an automated matching simulation model and an operating data simulation model, the present invention realizes automated matching and intelligent optimization of filters, significantly improves matching efficiency and accuracy, avoids the tedious process of manual intervention and repeated adjustments required in traditional methods, and reduces the risk of human error.

[0034] The present invention can continuously optimize automated matching rules, improving the accuracy and stability of filtering effects. In particular, the present invention simulates and optimizes the complexity and variability of high-frequency, high-precision filters, ensuring that the filtering effects under different conditions meet the desired goals.

[0035] The method of the present invention can be flexibly applied to different types of filters and filtering tasks, and supports continuous optimization and expansion of automated matching rules to adapt to ever-changing filtering requirements and signal environments.

[0036] In the prior art, the automated matching parameters of high-frequency and high-precision filters need to be continuously optimized and are difficult to apply to all conditions. The present invention avoids this problem by constructing an automated matching simulation model, which can simulate the working state and filtering effect of the filter under different conditions, thereby improving the efficiency and accuracy of parameter optimization. High-frequency signals are complex and variable, and traditional filter automated matching systems are difficult to cope with. The present invention can effectively cope with these characteristics of high-frequency signals and improve the filtering effect of the filter under different conditions by constructing a filter operation data simulation model and using genetic algorithms for rule optimization.

[0037] The method of the present invention not only improves filtering efficiency but also ensures the accuracy of filtering results, and is widely used in fields such as communications, radar, and sonar. This overcomes the problem of existing automated filter matching systems being unsuitable for high-frequency, high-precision filters, enhancing the system's practicality and market competitiveness. Through automated and intelligent matching and optimization, the present invention reduces the filter's operation and maintenance costs. Operations and maintenance personnel no longer need to frequently adjust filter parameters or deal with matching failures, allowing them to focus on other important tasks.

[0038] In summary, the present invention achieves accurate filtering and matching of high-frequency signals by constructing a simulation model and optimizing automatic matching rules, and has significant technical advantages and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0040] Figure 1 This is a flow chart of the intelligent RF switch control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings.

[0042] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0043] The present invention provides an automatic matching method for high-frequency and high-precision filters, comprising:

[0044] Step S101, obtaining filter operation data and a filter signal, converting the filter signal into a digital signal through an analog-to-digital converter to obtain a filter digital signal, wherein the filter operation data includes filter parameter data, environmental noise data, and filtering effect evaluation data;

[0045] In step S101, the filter operation data and filter signal are first acquired. The filter operation data includes filter parameter data, ambient noise data, and filter effect evaluation data. The filter signal is converted into a digital signal using an analog-to-digital converter (ADC) to obtain a filter digital signal. This converts the continuously changing analog signal into a discrete digital signal to facilitate subsequent digital signal processing and analysis. This process can obtain an accurate filter digital signal, providing a foundation for subsequent automated matching and filter effect evaluation.

[0046] Step S102: Acquire filter operation history data, construct an automatic matching simulation model of the filter using the filter operation history data, obtain the automatic matching simulation model of the filter, receive the automatic matching task requirements of the filter, substitute the automatic matching task requirements of the filter into the automatic matching simulation model of the filter, and obtain the automatic matching simulation result of the filter;

[0047] In step S102, historical filter operation data, including filter parameter settings, ambient noise recording, and filter performance evaluation, is acquired. This data serves as the foundation for constructing an automated matching simulation model. Next, this historical data, combined with advanced modeling techniques, is used to construct an automated matching simulation model for the filter. This model simulates the filter's performance under various operating environments and task requirements, providing accurate simulation results for subsequent automated matching.

[0048] After the model is built, it receives the automated filter matching task requirements, which describe the required filter performance indicators and operating conditions. These requirements are then substituted into the constructed automated matching simulation model for simulation. Through these simulations, the model outputs automated matching simulation results, including the filter's filtering performance under different conditions and parameter configuration recommendations. This provides an important reference for subsequent automated matching rule optimization and practical application.

[0049] Step S103: Match the filter digital signal using a preset automatic matching rule of the filter to obtain an automatic matching result of the filter, compare the automatic matching simulation result of the filter with the automatic matching simulation result of the filter, and if the comparison result of the automatic matching simulation result of the filter with the automatic matching simulation result of the filter is outside a preset error range, then test the automatic matching rule of the filter;

[0050] In step S103, the filtered digital signal obtained from the analog-to-digital converter is matched using the preset filter automatic matching rules. This step aims to automatically adjust the filter parameters based on the current signal characteristics to achieve the best filtering effect and output the filter automatic matching results.

[0051] The automated matching results are compared with the simulation results obtained previously through the filter automated matching simulation model. This comparison process is to verify the accuracy and reliability of the automated matching rules in practical applications.

[0052] If the comparison results show that the difference between the actual filter matching results and the simulated results exceeds the preset error range, this indicates that the current automated matching rules are problematic or inaccurate. In this case, the system needs to trigger a test process for the filter matching rules. This test includes re-evaluating the rules, adjusting parameters, or adopting a new matching algorithm to improve the automated matching system's ability to accurately adapt to the actual operating characteristics of the filter and the complex and variable nature of high-frequency signals.

[0053] Through such steps, the automatic matching rules of the filter can be continuously optimized to improve the accuracy and stability of its matching for high-frequency and high-precision filters.

[0054] Step S104, obtaining the historical data of the automated matching task requirements of the filter, constructing a filter operation data simulation model with the historical data of the automated matching task requirements of the filter and the historical data of the filter operation, and generating filter operation simulation data using the filter operation data simulation model;

[0055] In step S104, historical data on the filter's automated matching task requirements is obtained. This data covers past filter task requirements in various application scenarios, including filtering objectives, operating environments, and performance requirements. This data is an important input for building a filter operation data simulation model.

[0056] Next, the team combined historical data on the filter's automated matching requirements with historical filter operation data to construct a filter operation data simulation model. This model simulates the filter's actual performance under different task requirements and operating conditions, including its filtering effectiveness, stability, energy consumption, and other indicators. This model enables in-depth research and optimization of filter performance and matching strategies without relying on actual filter hardware.

[0057] Finally, the constructed filter operation data simulation model is used to generate filter operation simulation data. This data simulates the various operating states of the filter, providing valuable reference for subsequent automated matching rule optimization and filter performance evaluation. By comparing the simulated data with the actual data, we can more accurately understand the filter's behavioral characteristics, thereby optimizing its automated matching strategy and improving filtering effectiveness and overall performance.

[0058] Step S105: The filter operation simulation data generated by the filter operation data simulation model is matched through the preset automatic matching rules of the filter to obtain the simulation data matching result, which includes the simulation data matching success information and the simulation data matching failure information. The filter operation data corresponding to the simulation data matching failure information is retrieved, and the filter operation data corresponding to the simulation data matching failure information is subjected to data feature analysis to obtain the matching failure data feature. Based on the matching failure data feature and the automatic matching task requirements of the filter, the preset automatic matching rules of the filter are optimized, and the filter is automatically matched using the optimized automatic matching rules of the filter.

[0059] In step S105, the filter operation simulation data generated by the filter operation data simulation model is matched using the preset filter automatic matching rules. The purpose of this step is to verify the validity of the automatic matching rules in a virtual environment and obtain the simulation data matching results.

[0060] Simulated data matching results contain two types of information: successful matches and unsuccessful matches. Successful matches confirm that the current automated matching rules are effective under certain conditions. Unsuccessful matches require further analysis.

[0061] Next, the filter operation data corresponding to the unsuccessful simulation data matching information is retrieved, which contains detailed status information of the filter during the simulation operation process and is the key to analyzing the reasons for the unsuccessful matching.

[0062] By extracting features from these data, we can obtain the data features of unsuccessful matching. These features reveal the performance bottlenecks of the filter under certain specific conditions, improper parameter settings or limitations of automated matching rules.

[0063] Based on the data features of these unsuccessful matches and the requirements of the filter automated matching task, the preset filter automated matching rules are optimized. The optimization process includes adjusting the parameters in the rules, improving the matching algorithm, or introducing new decision logic to improve the accuracy and robustness of automated matching.

[0064] Finally, the optimized filter automatic matching rules are used to perform actual automatic matching of the filter. Through this step, we hope to verify the effectiveness of the optimized rules in actual applications and further improve the filtering effect and performance of the filter.

[0065] Specifically, the automated matching method for high-frequency and high-precision filters of the present invention, step S102, includes:

[0066] The step of constructing an automatic matching simulation model of the filter using the historical operation data of the filter includes: obtaining the historical operation data of the filter, the historical operation data of the filter including parameter data of the filter, environmental noise data, and filtering effect evaluation data; training a deep learning model using the historical operation data of the filter to obtain an automatic matching simulation model of the filter, the automatic matching simulation model of the filter being used to simulate the working state and filtering effect of the filter under different conditions;

[0067] Receive the automatic matching task requirements of the filter, and substitute the automatic matching task requirements of the filter into the constructed automatic matching simulation model of the filter. The automatic matching simulation model of the filter performs simulation operations to obtain the automatic matching simulation results of the filter. The automatic matching simulation results of the filter are used to provide a reference basis for subsequent matching rule optimization and automatic matching.

[0068] Comprehensive operational data is collected from the filter's history, covering various filter parameter settings (such as frequency response, bandwidth, and blocking attenuation), environmental noise characteristics (such as background noise level and noise type), and filtering effect evaluation data (such as filtered signal quality and error rate).

[0069] Choose appropriate deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or long short-term memory networks (LSTMs), which are capable of processing sequential data and complex patterns.

[0070] The collected historical filter operation data is used as a training set and input into the selected deep learning model. Through the iterative training process, the model learns the complex relationship between filter parameters, environmental noise, and filtering effect.

[0071] After the training is completed, an automatic matching simulation model of the filter is obtained, which can simulate the working state and filtering effect of the filter under different conditions.

[0072] According to the actual application scenario, the requirements for the automated matching task of the filter are determined. These requirements include specific filtering objectives, performance indicators, working environment, etc.

[0073] The filter's automatic matching task requirements are used as input and substituted into the constructed filter's automatic matching simulation model.

[0074] The model performs simulation operations based on the input task requirements and historically learned knowledge to predict the working state and filtering effect of the filter under given conditions.

[0075] After the simulation is complete, the model outputs the filter’s automated matching simulation results. These results provide the expected performance of the filter under different conditions, providing a reference for subsequent matching rule optimization and automated matching.

[0076] This step built an automated matching simulation model that accurately simulates the filter's operating conditions. This model was then used to obtain simulation results for the filter under various conditions. These results not only helped us understand the filter's performance characteristics but also provided data support for subsequent optimization of automated matching rules.

[0077] Specifically, the automated matching method for high-frequency and high-precision filters of the present invention, step S102, includes:

[0078] The steps of receiving the automatic matching task requirement of the filter and substituting it into the automatic matching simulation model of the filter to obtain the automatic matching simulation result of the filter include: receiving the automatic matching task requirement of the filter, the automatic matching task requirement of the filter including the target filtering effect, working environment and restriction conditions of the filter;

[0079] The filter automatic matching task requirements are substituted into the filter automatic matching simulation model. The filter automatic matching simulation model performs simulation operations based on the input filter automatic matching task requirements and the historical operation data of the filter, and outputs the filter automatic matching simulation results. The filter automatic matching simulation results include the filtering effect prediction and optimal parameter configuration of the filter under different parameter configurations.

[0080] Receive automated filter matching requirements from the user or system. These requirements describe the filter's target filtering performance, such as the desired frequency response, block attenuation, and passband ripple. They also include the specific operating environment of the filter, such as ambient noise characteristics and temperature range, as well as any constraints, such as power consumption, physical size, or cost budget.

[0081] The received filter automatic matching task requirements are used as input and substituted into the previously constructed filter automatic matching simulation model. The filter automatic matching simulation model is based on the historical operation data of the filter and is trained through deep learning or other machine learning technologies. It can simulate the working behavior and filtering effect of the filter under different conditions.

[0082] The filter automatic matching simulation model performs simulation operations based on the input task requirements and the filter's historical operating data. During the operation, the filter automatic matching simulation model considers various filter parameter configurations and simulates the filtering effects under these configurations.

[0083] After the calculation is complete, the filter automatic matching simulation model outputs the filter automatic matching simulation results. These results typically include predictions of the filter's filtering effect under different parameter configurations, such as the signal quality, error rate, or signal-to-noise ratio after filtering. They also provide the predicted optimal parameter configurations that achieve the best filtering effect under the current task requirements and constraints.

[0084] This step allows for rapid prediction of filter effects under different parameter configurations, based on specific task requirements and constraints, and helps identify the optimal parameter configuration. This provides strong support for subsequent automated filter matching, significantly improving matching efficiency and filtering effectiveness.

[0085] Specifically, the automated matching method for high-frequency and high-precision filters of the present invention, step S103, includes:

[0086] The filter digital signal is matched by a preset filter automatic matching rule to obtain the filter automatic matching result, which includes: obtaining the filter digital signal after analog-to-digital conversion, matching the filter digital signal according to the preset filter automatic matching rule, the filter automatic matching rule including comparative matching of filtering of the filter digital signal and feature extraction and comparative matching of the filter digital signal, processing the comparative matching data according to the matching rule, and outputting the filter automatic matching result, which includes the matching degree and parameter configuration.

[0087] The converted filter digital signals are obtained from the analog-to-digital converter (ADC). These signals represent the output of the filter in the actual working environment, including noise, interference and other non-ideal factors.

[0088] Next, the acquired filter digital signal is processed according to preset filter automation matching rules. These rules are based on factors such as the filter's historical operating data, the target filtering effect, and the operating environment.

[0089] Automatic filter matching rules usually include two matching methods: filter comparison matching and feature extraction comparison matching.

[0090] Filter comparison and matching: filter the digital signal through different parameter configurations and compare it with the target filtering effect to find the parameter configuration closest to the target effect.

[0091] Feature extraction and comparison matching extracts key features (such as frequency components, amplitude, phase, etc.) from the filter digital signal and compares them with the expected features to evaluate the degree of matching.

[0092] According to the matching rules, the data obtained from the comparison and matching is processed, including calculating the error, evaluating the matching degree, and determining the optimal parameter configuration.

[0093] Finally, the automatic filter matching results are output. These results usually include the matching degree (such as similarity, error rate, etc.) and parameter configuration (such as the optimal value of the filter parameters, adjustment suggestions, etc.).

[0094] This step automatically matches the filter's digital signal, quickly finding the parameter configuration that best matches the target filtering effect. This not only improves matching efficiency but also enhances filter performance to meet practical application requirements. The output matching results and parameter configurations provide strong support for subsequent optimization and adjustment.

[0095] Specifically, the automated matching method for high-frequency and high-precision filters of the present invention, step S104, includes:

[0096] The step of constructing a filter operation data simulation model using the filter's automated matching task demand history data and the filter's operation history data includes obtaining the filter's automated matching task demand history data, the filter's automated matching task demand history data including past matching tasks, target filtering effects, and working environment information, and also obtaining the filter's operation history data, the filter's operation history data including filter parameter settings, actual filtering effects, and environmental noise records;

[0097] Data features are extracted from the historical data of the filter's automated matching task requirements and the filter's operation history data to obtain features of the data to be matched, and the features of the data to be matched are matched in a preset algorithm model library to obtain a model to be trained. The model to be trained is trained using the historical data of the filter's automated matching task requirements and the filter's operation history data to obtain a filter operation data simulation model. The filter operation data simulation model is used to generate filter operation simulation data based on the filter's historical performance and task requirements.

[0098] The historical data of the filter's automated matching task requirements is obtained, including past matching task descriptions, target filtering effects (such as desired frequency response, blocking attenuation, etc.), and working environment information (such as temperature, humidity, electromagnetic interference, etc.).

[0099] Obtain the filter's historical operating data, which includes the filter parameter settings (such as filter type, order, cutoff frequency, etc.), actual filtering effects (such as signal quality and error rate after filtering), and environmental noise records (such as background noise level and noise type, etc.).

[0100] Data feature extraction is performed on the acquired filter automated matching task requirement history data and filter operation history data. This step aims to extract key information useful for model training from the raw data, such as common characteristics of task requirements and changing trends in filter performance.

[0101] The extracted data features to be matched are then matched against a pre-set library of algorithm models. This library includes various models, such as neural networks, support vector machines, and decision trees, adapted to different data characteristics and prediction requirements. Based on the matching results, the most appropriate model is selected as the model to be trained. This step ensures that the selected model best fits the filter's historical performance and task requirements.

[0102] The training model is trained using historical data from the filter's automated matching task requirements and historical filter operation data. During training, the model learns the relationship between filter parameter settings, actual filtering performance, and ambient noise. Through iterative training, the model gradually optimizes its parameters to improve the accuracy of its simulation of filter operation data.

[0103] After training is complete, a filter operation data simulation model is generated. This model can generate filter operation simulation data based on the filter's historical performance and task requirements. This data can be used for subsequent automated matching rule optimization and filter performance evaluation.

[0104] This step built a filter operation data simulation model capable of accurately simulating the filter's operating state. This model not only takes into account the filter's historical performance and environmental factors but also improves the accuracy and reliability of the simulation data through data feature extraction and model training. This provides strong support for subsequent automated matching and filter performance optimization.

[0105] Specifically, the automated matching method for high-frequency and high-precision filters of the present invention, step S105, includes:

[0106] Matching filter operation simulation data generated by the filter operation data simulation model through a preset filter automatic matching rule to obtain a simulation data matching result includes: using the constructed filter operation data simulation model to generate filter operation simulation data, the filter operation simulation data simulating the performance of the filter under different working environments and task requirements, and inputting the filter operation simulation data into the preset filter automatic matching rule to perform a matching operation;

[0107] During the matching process, the filter operation simulation data is processed, analyzed and compared according to the preset filter automatic matching rules to determine whether the filter operation simulation data meets the expected filtering effect and task requirements, and the simulation data matching results are output. The simulation data matching results include simulation data success information and simulation data failure information.

[0108] Generate filter operation simulation data. Utilize the previously constructed filter operation data simulation model to generate filter operation simulation data under different working environments and task requirements. This data should fully reflect the filter's performance under various conditions, including filtering effect, parameter changes, and environmental adaptability.

[0109] The generated filter operation simulation data is input into the preset filter automation matching rules. These rules are formulated based on factors such as the filter's historical operation data, the target filtering effect, and the operating environment to evaluate whether the simulation data meets expectations.

[0110] During the matching process, the filter operation simulation data is processed, analyzed, and compared according to the preset filter automatic matching rules. This includes filtering effect evaluation, parameter consistency check, environmental adaptability analysis, etc.

[0111] By comparing the simulated data with the expected filtering effect and task requirements, we can determine whether the filter operation simulation data meets expectations. This step is to verify the accuracy and reliability of the simulation data and the performance of the filter under different conditions.

[0112] According to the result of the matching operation, the simulation data matching results are output, which generally include simulation data matching success information and simulation data matching failure information.

[0113] Simulated data matching success information: If the simulated data meets expectations, the matching success information is output, which includes the matching filter parameter configuration, expected filtering effect, etc.

[0114] Information on unsuccessful simulation data matching: If the simulation data does not meet expectations, information on unsuccessful matching will be output, including the reasons for the mismatch, recommended adjustment plans, etc.

[0115] This step enables the automated filter matching rules to be verified and optimized using simulation data generated by the filter operation data simulation model. This not only improves the accuracy and reliability of the matching rules but also provides strong support for subsequent filter design and optimization. Furthermore, the output simulation data matching results provide valuable information about filter performance, helping to better understand filter behavior.

[0116] Specifically, the automated matching method for high-frequency and high-precision filters of the present invention, step S105, includes:

[0117] The steps of optimizing the preset automatic matching rules of the filter using a genetic algorithm based on the characteristics of the unsuccessful matching data and the automatic matching task requirements of the filter include: extracting data features of the filter operation data corresponding to the unsuccessful matching information of the simulated data, so as to reflect the reasons why the filter failed to match successfully under specific conditions;

[0118] Based on the data characteristics of the filter operating data corresponding to the unsuccessful simulation data matching information and the requirements of the filter automatic matching task, the optimization objectives and constraints of the genetic algorithm are defined. A population containing multiple potential solutions is initialized, that is, different configurations of the filter automatic matching rules. The selection, crossover and mutation mechanisms in the genetic algorithm are used to iteratively search for the optimal solution.

[0119] In each iteration, the fitness of each solution in the population is evaluated according to the optimization objectives and constraints, and individuals with higher fitness are selected for crossover and mutation operations to generate new solutions;

[0120] When the genetic algorithm reaches the preset stopping conditions, which include the number of iterations and the fitness threshold, the optimized filter automatic matching rules are output.

[0121] Data feature extraction is performed on the filter operating data corresponding to the unsuccessful simulation data matching information. The purpose of this step is to identify the key factors or characteristics that caused the filter to fail to match successfully under specific conditions. These characteristics include specific frequency response deviations, environmental noise effects, improper parameter settings, etc.

[0122] Based on the characteristics of the filter's operating data corresponding to the unsuccessful simulation data matches and the requirements for the automated filter matching task, the optimization objective and constraints of the genetic algorithm are clearly defined. The optimization objective is to minimize the error between the filtering effect and the actual requirements or to maximize the probability of a successful match. Constraints include the filter's physical limitations, power consumption requirements, and response time.

[0123] Initialize a population containing multiple potential solutions, i.e., different configurations of the filter automation matching rules. These configurations can be combinations of filter parameters, variants of the matching rules, etc. Each individual in the population represents a solution.

[0124] The selection, crossover and mutation mechanisms in the genetic algorithm are used to iteratively search for the optimal solution.

[0125] Selection: Based on the optimization objectives and constraints, the fitness of each solution in the population is evaluated, and individuals with higher fitness are selected as parents.

[0126] Crossover: Perform a crossover operation on the selected parent individuals to generate a new solution (offspring). The crossover operation can be achieved by exchanging some genes or parameters of the parent individuals.

[0127] Mutation: Perform mutation operations on offspring individuals to introduce new genes or parameter variations to increase the diversity of the population.

[0128] In each iteration, the fitness of the newly generated solution is evaluated according to the optimization objective and constraints, and individuals with higher fitness are selected to update the population. This step promotes the population to gradually evolve towards the optimal solution.

[0129] When the genetic algorithm reaches the preset stopping conditions, the iteration stops. The stopping conditions include reaching the maximum number of iterations, fitness reaching a preset threshold, and population diversity falling below a certain level.

[0130] After the iteration stops, the optimized filter automatic matching rules are output. These rules are based on the search results of the genetic algorithm and can better adapt to the actual operating environment and task requirements of the filter, improving the success rate and accuracy of matching.

[0131] This step leverages the powerful search capabilities of genetic algorithms to optimize the preset automated filter matching rules to address the issue of unsuccessful simulation data matching. The optimized rules more accurately reflect the actual performance of the filter, improving the efficiency and accuracy of automated matching.

[0132] The present invention provides an automated matching method for high-frequency and high-precision filters, which aims to solve the problem that high-frequency and high-precision filters need to continuously optimize automated matching parameters in the process of accurately filtering high-frequency signals, and the problem that the optimized filter automated matching system cannot be applied to high-frequency and high-precision filters due to the complexity and variability of high-frequency signals.

[0133] Obtain filter operation data and filter signals, including filter parameter data, environmental noise data, and filter effect evaluation data. Convert the filter signals into digital signals through an analog-to-digital converter to obtain filter digital signals.

[0134] The deep learning model is trained using historical filter operation data to obtain an automated filter matching simulation model. The automated filter matching task requirements are received and substituted into the constructed simulation model to perform simulation operations and obtain the automated filter matching simulation results.

[0135] The filter digital signal is matched according to the preset automatic matching rules to obtain the automatic matching result of the filter.

[0136] The automated matching simulation results are compared with the actual automated matching results. If the comparison results are outside the preset error range, the automated matching rules are tested.

[0137] Obtain historical data on filter automated matching task requirements and filter operation history data. Use this data to build a filter operation data simulation model to generate filter operation simulation data.

[0138] The filter's simulated data is matched against pre-set automated matching rules to obtain matching results. Data analysis is performed on unsuccessful matches to extract features of the unsuccessful data. Based on these features and the filter's automated matching requirements, a genetic algorithm is used to optimize the pre-set automated matching rules.

[0139] By constructing an automated matching simulation model and an operational data simulation model, the present invention achieves automated matching and intelligent optimization of filters, improving matching efficiency and accuracy. By continuously optimizing automated matching rules, the present invention can adapt to the complexity and variability of high-frequency, high-precision filters, improving the accuracy and stability of filtering effects. The method of the present invention can be flexibly applied to different types of filters and filtering tasks, while supporting the continuous optimization and expansion of automated matching rules.

[0140] By constructing an automated matching simulation model, the present invention can simulate the working state and filtering effect of the filter under different conditions, thereby avoiding the problem of the traditional method that requires continuous optimization of automated matching parameters. By constructing a filter operation data simulation model and using a genetic algorithm for rule optimization, the present invention can cope with the complexity and variability of high-frequency signals and improve the filtering effect of the filter under different conditions. The method of the present invention not only improves the filtering efficiency, but also ensures the accuracy of the filtering results. It is widely used in fields such as communications, radar, and sonar, and overcomes the problem that the automated filter matching system in the prior art cannot be applied to high-frequency and high-precision filters.

[0141] In summary, the present invention provides an automated matching method for high-frequency and high-precision filters. By constructing a simulation model and optimizing automated matching rules, it achieves accurate filtering and matching of high-frequency signals, overcomes the limitations of existing technologies, and has significant technical advantages and application prospects.

Claims

1. An automated matching method for high-frequency and high-precision filters, characterized in that: include: Step S101, obtaining filter operation data and a filter signal, converting the filter signal into a digital signal through an analog-to-digital converter to obtain a filter digital signal, wherein the filter operation data includes filter parameter data, environmental noise data, and filtering effect evaluation data; Step S102: Acquire filter operation history data, construct an automatic matching simulation model of the filter using the filter operation history data, obtain the automatic matching simulation model of the filter, receive the automatic matching task requirements of the filter, substitute the automatic matching task requirements of the filter into the automatic matching simulation model of the filter, and obtain the automatic matching simulation result of the filter; Step S103: Match the filter digital signal using a preset automatic matching rule of the filter to obtain an automatic matching result of the filter, compare the automatic matching simulation result of the filter with the automatic matching simulation result of the filter, and if the comparison result of the automatic matching simulation result of the filter with the automatic matching simulation result of the filter is outside a preset error range, then test the automatic matching rule of the filter; Step S104, obtaining the historical data of the automated matching task requirements of the filter, constructing a filter operation data simulation model with the historical data of the automated matching task requirements of the filter and the historical data of the filter operation, and generating filter operation simulation data using the filter operation data simulation model; Step S105: The filter operation simulation data generated by the filter operation data simulation model is matched through the preset automatic matching rules of the filter to obtain the simulation data matching result, which includes the simulation data matching success information and the simulation data matching failure information. The filter operation data corresponding to the simulation data matching failure information is retrieved, and the filter operation data corresponding to the simulation data matching failure information is subjected to data feature analysis to obtain the matching failure data feature. Based on the matching failure data feature and the automatic matching task requirements of the filter, the preset automatic matching rules of the filter are optimized, and the filter is automatically matched using the optimized automatic matching rules of the filter.

2. The automatic matching method for high-frequency and high-precision filters according to claim 1, wherein: The step S102 includes: The step of constructing an automatic matching simulation model of the filter using the historical operation data of the filter includes: obtaining the historical operation data of the filter, the historical operation data of the filter including parameter data of the filter, environmental noise data, and filtering effect evaluation data; training a deep learning model using the historical operation data of the filter to obtain an automatic matching simulation model of the filter, the automatic matching simulation model of the filter being used to simulate the working state and filtering effect of the filter under different conditions; Receive the automatic matching task requirements of the filter, and substitute the automatic matching task requirements of the filter into the constructed automatic matching simulation model of the filter. The automatic matching simulation model of the filter performs simulation operations to obtain the automatic matching simulation results of the filter. The automatic matching simulation results of the filter are used to provide a reference basis for subsequent matching rule optimization and automatic matching.

3. The automatic matching method for high-frequency and high-precision filters according to claim 2, wherein: The step S102 includes: The steps of receiving the automatic matching task requirement of the filter and substituting it into the automatic matching simulation model of the filter to obtain the automatic matching simulation result of the filter include: receiving the automatic matching task requirement of the filter, the automatic matching task requirement of the filter including the target filtering effect, working environment and restriction conditions of the filter; The filter automatic matching task requirements are substituted into the filter automatic matching simulation model. The filter automatic matching simulation model performs simulation operations based on the input filter automatic matching task requirements and the historical operation data of the filter, and outputs the filter automatic matching simulation results. The filter automatic matching simulation results include the filtering effect prediction and optimal parameter configuration of the filter under different parameter configurations.

4. The automatic matching method for high-frequency and high-precision filters according to claim 1, wherein: The step S103 includes: The filter digital signal is matched by a preset filter automatic matching rule to obtain the filter automatic matching result, which includes: obtaining the filter digital signal after analog-to-digital conversion, matching the filter digital signal according to the preset filter automatic matching rule, the filter automatic matching rule including comparative matching of filtering of the filter digital signal and feature extraction and comparative matching of the filter digital signal, processing the comparative matching data according to the matching rule, and outputting the filter automatic matching result, which includes the matching degree and parameter configuration.

5. The automatic matching method for high-frequency and high-precision filters according to claim 1, wherein: The step S104 includes: The step of constructing a filter operation data simulation model using the filter's automated matching task demand history data and the filter's operation history data includes obtaining the filter's automated matching task demand history data, the filter's automated matching task demand history data including past matching tasks, target filtering effects, and working environment information, and also obtaining the filter's operation history data, the filter's operation history data including filter parameter settings, actual filtering effects, and environmental noise records; Data features are extracted from the historical data of the filter's automated matching task requirements and the filter's operation history data to obtain features of the data to be matched, and the features of the data to be matched are matched in a preset algorithm model library to obtain a model to be trained. The model to be trained is trained using the historical data of the filter's automated matching task requirements and the filter's operation history data to obtain a filter operation data simulation model. The filter operation data simulation model is used to generate filter operation simulation data based on the filter's historical performance and task requirements.

6. The automatic matching method for high-frequency and high-precision filters according to claim 1, wherein: The step S105 includes: Matching filter operation simulation data generated by the filter operation data simulation model through a preset filter automatic matching rule to obtain a simulation data matching result includes: using the constructed filter operation data simulation model to generate filter operation simulation data, the filter operation simulation data simulating the performance of the filter under different working environments and task requirements, and inputting the filter operation simulation data into the preset filter automatic matching rule to perform a matching operation; During the matching process, the filter operation simulation data is processed, analyzed and compared according to the preset filter automatic matching rules to determine whether the filter operation simulation data meets the expected filtering effect and task requirements, and the simulation data matching results are output. The simulation data matching results include simulation data success information and simulation data failure information.

7. The automatic matching method for high-frequency and high-precision filters according to claim 6, wherein: The step S105 includes: The steps of optimizing the preset automatic matching rules of the filter using a genetic algorithm based on the characteristics of the unsuccessful matching data and the automatic matching task requirements of the filter include: extracting data features of the filter operation data corresponding to the unsuccessful matching information of the simulated data, so as to reflect the reasons why the filter failed to match successfully under specific conditions; Based on the data characteristics of the filter operating data corresponding to the unsuccessful simulation data matching information and the requirements of the filter automatic matching task, the optimization objectives and constraints of the genetic algorithm are defined. A population containing multiple potential solutions is initialized, that is, different configurations of the filter automatic matching rules. The selection, crossover and mutation mechanisms in the genetic algorithm are used to iteratively search for the optimal solution. In each iteration, the fitness of each solution in the population is evaluated according to the optimization objectives and constraints, and individuals with higher fitness are selected for crossover and mutation operations to generate new solutions; When the genetic algorithm reaches the preset stopping conditions, which include the number of iterations and the fitness threshold, the optimized filter automatic matching rules are output.

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