Automatic matching method of high-frequency and high-precision filter

Abstract

The invention relates to the technical field of filter data processing, and provides an automatic matching method for a high-frequency and high-precision filter, which comprises the following steps of: acquiring operation data and signals of the filter, converting the operation data and signals into digital signals, and constructing an automatic matching simulation model based on historical data. And receiving a matching task demand, performing simulation operation to obtain a matching result, and comparing the matching result with an actual matching result. And if the difference exceeds a preset range, detecting and optimizing a matching rule. And meanwhile, constructing an operation data simulation model by utilizing task demand historical data and operation historical data, generating simulation data for matching, extracting data features for unsuccessful matching conditions, and optimizing a matching rule by adopting a genetic algorithm. Automatic matching of the high-frequency and high-precision filter is achieved, matching efficiency and accuracy are improved, the method is suitable for the fields of communication, radar, sonar and the like, and the problem that an automatic filter matching system in the prior art cannot effectively deal with complexity and variability of high-frequency signals is solved.

Description

Technical Field

[0001] The 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 automatic matching system of high-frequency and high-precision filters is a signal data processing tool. The automatic matching system of high-frequency and high-precision filters uses automation technology and high-precision filter design principles to achieve accurate filtering and matching of high-frequency signals. The automatic matching system of high-frequency and high-precision filters automatically adjusts the parameters and structure of the filter to maximize the ratio of the instantaneous power of the signal to the average power of the noise, that is, the signal-to-noise ratio (SNR), thereby extracting useful signal components and suppressing noise interference. The automatic matching process of high-frequency and high-precision filters not only improves the filtering efficiency, but also ensures the accuracy of the filtering results. It is widely used in communications, radar, sonar and other fields.

[0003] When using an automated matching system for high-frequency and high-precision filters, you first need to clarify the characteristics of the signal to be processed and the filtering target. Input the signal to be processed into the system and set the corresponding filtering parameters, such as center frequency, bandwidth, etc. The system will automatically adjust the filter transfer function based on these parameters and the preset algorithm to achieve the best filtering effect. During the filtering process, the system will also monitor the filtering effect in real time and make 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, it is necessary 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. Therefore, 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 prior art, the present invention provides an automatic matching method for a high-frequency and high-precision filter, which solves the problem that in order to achieve accurate filtering of high-frequency signals, the high-frequency and high-precision filters need to continuously optimize the parameters of the automatic matching of the filters, and the complexity and variability of high-frequency signals make the filters, resulting in the problem that the automatic matching system of the optimized 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: The present invention provides an automatic matching method for a high-frequency and high-precision filter, comprising: Step S101, obtaining filter operation data and filter signals, converting the filter signals into digital signals through an analog-to-digital converter to obtain filter digital signals, wherein the filter operation data includes filter parameter data, environmental noise data, and filter effect evaluation data; Step S102, obtaining filter operation history data, using the filter operation history data to construct an automatic matching simulation model of the filter, obtaining the automatic matching simulation model of the filter, receiving the automatic matching task requirements of the filter, substituting the automatic matching task requirements of the filter into the automatic matching simulation model of the filter, and obtaining the automatic matching simulation result of the filter; Step S103, matching the filter digital signal through a preset automatic matching rule of the filter to obtain an automatic matching result of the filter, comparing the automatic matching simulation result of the filter with the automatic matching simulation result of the filter, and if the comparison result between the automatic matching simulation result of the filter and the automatic matching simulation result of the filter is outside a preset error range, testing 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 using the filter operation data simulation model to generate filter operation simulation data; 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, the simulation data matching result 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, 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, the preset automatic matching rules of the filter are optimized based on the matching failure data feature and the automatic matching task requirements of the filter, and the filter is automatically matched using the optimized automatic matching rules of the filter.

[0007] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S102, comprises: The step of using the filter operation history data to construct an automatic matching simulation model of the filter includes: obtaining the historical operation data of the filter, the historical operation data of the filter including the parameter data of the filter, the environmental noise data and the filtering effect evaluation data, using the historical operation data of the filter to train the deep learning model to obtain the automatic matching simulation model of the filter, the automatic matching simulation model of the filter is used to simulate the working state and filtering effect of the filter under different conditions; The automatic matching task requirements of the filter are received, and the automatic matching task requirements of the filter are substituted 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 for subsequent matching rule optimization and automatic matching.

[0008] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S102, comprises: 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 includes 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 and outputs the filter automatic matching simulation results based on the input filter automatic matching task requirements and the historical operation data of the filter. The filter automatic matching simulation results include the filtering effect prediction and optimal parameter configuration of the filter under different parameter configurations.

[0009] Furthermore, the automatic matching method of the high-frequency and high-precision filter of the present invention, step S103, comprises: The filter digital signal is matched by a preset filter automatic matching rule to obtain the filter automatic matching result, and the steps include: 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 includes comparative matching of filtering of the filter digital signal and feature extraction and comparative matching of the filter digital signal, and 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.

[0010] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S104, comprises: 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 includes 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 includes the filter's 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 operating 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 operating history data to obtain a filter operating data simulation model, which is used to generate filter operating simulation data based on the filter's historical performance and task requirements.

[0011] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S105, comprises: The step of matching the filter operation simulation data generated by the filter operation data simulation model through the preset filter automatic matching rule to obtain the simulation data matching result includes: using the constructed filter operation data simulation model to generate the filter operation simulation data, the filter operation simulation data simulates 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 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 matching success information and simulation data matching failure information.

[0012] Furthermore, the automatic matching method for high-frequency and high-precision filters of the present invention, step S105, comprises: The step of using a genetic algorithm to optimize the preset automatic matching rules of the filter based on the characteristics of the unsuccessful matching data and the automatic matching task requirements of the filter includes: extracting data features of the filter operation data corresponding to the unsuccessful matching information of the simulation data to reflect the reasons why the filter fails to match successfully under specific conditions; According to the data characteristics of the filter operation data corresponding to the unsuccessful information of simulation data matching and the requirements of the automatic matching task of the filter, 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 automatic matching rules of the filter, and 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 stop condition, which includes the number of iterations and the fitness threshold, the optimized filter automatic matching rule is output.

[0013] Beneficial effects of the present invention: 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 cumbersome process of manual intervention and repeated adjustments required in traditional methods, and reduces the probability of human error.

[0014] The present invention can continuously optimize the automatic matching rules and improve the accuracy and stability of the filtering effect. In particular, for the complexity and variability of high-frequency and high-precision filters, the present invention simulates and optimizes the filtering effect of the filter under different conditions to achieve the expected goal.

[0015] 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 automatic matching rules to adapt to changing filtering requirements and signal environments.

[0016] 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 and improves the efficiency and accuracy of parameter optimization by constructing an automated matching simulation model, which can simulate the working state and filtering effect of the filter under different conditions. High-frequency signals are complex and variable, and traditional automated matching systems for filters 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 a genetic algorithm for rule optimization.

[0017] The method of the present invention not only improves the filtering efficiency, but also ensures the accuracy of the filtering results, and is widely used in the fields of communication, radar, sonar, etc. This overcomes the problem that the automatic matching system of the filter in the prior art cannot be applied to high-frequency and high-precision filters, and enhances the practicality and market competitiveness of the system. Through automated and intelligent matching and optimization, the present invention reduces the operation and maintenance costs of the filter. The operation and maintenance personnel do not need to frequently adjust the filter parameters or deal with matching failures, and can focus more on other important tasks.

[0018] In summary, the present invention realizes 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

[0019] 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.

[0020] Figure 1 A schematic flow chart of an intelligent radio frequency switch control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0022] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0023] The present invention provides an automatic matching method for a high-frequency and high-precision filter, comprising: Step S101, obtaining filter operation data and filter signals, converting the filter signals into digital signals through an analog-to-digital converter to obtain filter digital signals, wherein the filter operation data includes filter parameter data, environmental noise data, and filter effect evaluation data; In step S101, the filter operation data and filter signal need to be obtained first. The filter operation data includes the filter parameter data, environmental noise data and filter effect evaluation data. The filter signal is converted into a digital signal through an analog-to-digital converter (ADC) to obtain a filter digital signal. The continuously changing analog signal is converted into a discrete digital signal to facilitate subsequent digital signal processing and analysis. Through this process, an accurate filter digital signal can be obtained, which provides a basis for subsequent automated matching and filter effect evaluation.

[0024] Step S102, obtaining filter operation history data, using the filter operation history data to construct an automatic matching simulation model of the filter, obtaining the automatic matching simulation model of the filter, receiving the automatic matching task requirements of the filter, substituting the automatic matching task requirements of the filter into the automatic matching simulation model of the filter, and obtaining the automatic matching simulation result of the filter; In step S102, the operation history data of the filter including the parameter setting of the filter, the environmental noise record, the evaluation of the filtering effect, etc. is obtained, which is the basis for building the automatic matching simulation model. Then, using these historical data and combining with advanced modeling technology, the automatic matching simulation model of the filter is built. This model can simulate the performance of the filter under different working environments and task requirements, and provide accurate simulation results for subsequent automatic matching.

[0025] After the model is built, the automatic matching task requirements of the receiving filter are described, which describe the performance indicators and working conditions that the filter needs to achieve. Then, the requirements are substituted into the constructed automatic matching simulation model for simulation operation. Through simulation operation, the model can output the automatic matching simulation results of the filter, including the filtering effect of the filter under different conditions, parameter configuration suggestions, etc., which provides an important reference for the subsequent optimization of automatic matching rules and practical applications.

[0026] Step S103, matching the filter digital signal through a preset automatic matching rule of the filter to obtain an automatic matching result of the filter, comparing the automatic matching simulation result of the filter with the automatic matching simulation result of the filter, and if the comparison result between the automatic matching simulation result of the filter and the automatic matching simulation result of the filter is outside a preset error range, testing the automatic matching rule of the filter; In step S103, the filter digital signal obtained from the analog-to-digital converter is matched using the preset filter automatic matching rules. The purpose of this step is to automatically adjust the filter parameters according to the current signal characteristics to achieve the best filtering effect and output the filter automatic matching results.

[0027] The automated matching results are compared with the simulation results previously obtained 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.

[0028] If the comparison results show that the difference between the actual results of the filter's automated matching and the simulated results exceeds the preset error range, this indicates that the current automated matching rules have problems or are not accurate enough. In this case, the system needs to trigger a detection process for the filter's automated matching rules, which includes re-evaluating the rules, adjusting parameters, or adopting new matching algorithms to improve the automated matching system's ability to accurately adapt to the actual operating characteristics of the filter and the complexity and variability of high-frequency signals.

[0029] Through such steps, the automatic matching rules of the filter can be continuously optimized to improve the accuracy and stability of matching high-frequency and high-precision filters.

[0030] 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 using the filter operation data simulation model to generate filter operation simulation data; In step S104, the historical data of the automated matching task requirements of the filter are obtained, which covers the task requirements of the filter in various application scenarios in the past, including filtering targets, working environments, performance requirements, etc. These data are important inputs for building a filter operation data simulation model.

[0031] Next, the historical data of the filter's automated matching task requirements and the filter's operating history data are combined to jointly construct a filter operating data simulation model, which aims to simulate the actual performance of the filter under different task requirements and operating conditions, including its filtering effect, stability, energy consumption and other indicators. By building such a model, the performance and matching strategy of the filter can be deeply studied and optimized without relying on the actual filter hardware.

[0032] Finally, the filter operation data simulation model is used to generate filter operation simulation data. The data simulates the various operating states of the filter, providing valuable reference for subsequent automatic matching rule optimization and filter performance evaluation. By comparing the simulation data with the actual data, the behavior characteristics of the filter can be understood more accurately, and then its automatic matching strategy can be optimized to improve the filtering effect and overall performance.

[0033] 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, the simulation data matching result 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, 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, the preset automatic matching rules of the filter are optimized based on the matching failure data feature and the automatic matching task requirements of the filter, and the filter is automatically matched using the optimized automatic matching rules of the filter.

[0034] In step S105, the filter operation simulation data generated by the filter operation data simulation model is matched by the preset filter automatic matching rule. The purpose of this step is to verify the validity of the automatic matching rule in the virtual environment and obtain the simulation data matching result.

[0035] The simulation data matching result contains two parts of information: simulation data matching success information and simulation data matching failure information. For the matching success information, it can be confirmed that the current automatic matching rules are valid under certain conditions. For the matching failure information, further analysis is required.

[0036] 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.

[0037] By extracting features from these data, we can obtain the data features of unsuccessful matching, which reveal the performance bottleneck of the filter under certain specific conditions, improper parameter settings or limitations of automated matching rules.

[0038] Based on the data features of these unsuccessful matches and the requirements of the filter's automated matching task, the preset filter's 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.

[0039] Finally, the optimized filter automatic matching rules are used to perform actual automatic matching of the filter. Through this step, it is expected that the effectiveness of the optimized rules can be verified in actual applications, and the filtering effect and performance of the filter can be further improved. Specifically, the automatic matching method of the high-frequency and high-precision filter of the present invention, step S102, comprises: The step of using the filter operation history data to construct an automatic matching simulation model of the filter includes: obtaining the historical operation data of the filter, the historical operation data of the filter including the parameter data of the filter, the environmental noise data and the filtering effect evaluation data, using the historical operation data of the filter to train the deep learning model to obtain the automatic matching simulation model of the filter, the automatic matching simulation model of the filter is used to simulate the working state and filtering effect of the filter under different conditions; The automatic matching task requirements of the filter are received, and the automatic matching task requirements of the filter are substituted 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 for subsequent matching rule optimization and automatic matching.

[0040] Collect comprehensive operating data from the filter's history, including various filter parameter settings (such as frequency response, bandwidth, blocking attenuation, etc.), environmental noise characteristics (such as background noise level, noise type, etc.), and filtering effect evaluation data (such as signal quality after filtering, error rate, etc.).

[0041] Choose a suitable deep learning architecture, such as convolutional neural network (CNN), recurrent neural network (RNN), or long short-term memory network (LSTM), which can handle sequential data and complex patterns.

[0042] 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.

[0043] 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.

[0044] According to the actual application scenario, determine the automated matching task requirements of the filter, which include specific filtering objectives, performance indicators, working environment, etc.

[0045] The filter's automated matching task requirements are used as input and substituted into the constructed filter's automated matching simulation model.

[0046] 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.

[0047] After the simulation operation is completed, the automatic matching simulation results of the filter are output from the model. These results provide the expected performance of the filter under different conditions and provide a reference for subsequent matching rule optimization and automatic matching.

[0048] Through this step, an automated matching simulation model that can accurately simulate the working state of the filter was constructed, and the simulation results of the filter under different conditions were obtained using this model. These results not only help to understand the performance characteristics of the filter, but also provide data support for the subsequent optimization of automated matching rules.

[0049] Specifically, the automatic matching method of the high-frequency and high-precision filter of the present invention, step S102, comprises: 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 includes 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 and outputs the filter automatic matching simulation results based on the input filter automatic matching task requirements and the historical operation data of the filter. The filter automatic matching simulation results include the filtering effect prediction and optimal parameter configuration of the filter under different parameter configurations.

[0050] Receive the task requirements for automatic filter matching from the user or system. These requirements describe in detail the target filtering effect of the filter, such as the desired frequency response, blocking attenuation, passband fluctuation, etc.; at the same time, they also include the specific environment in which the filter will work, such as environmental noise characteristics, temperature range, etc.; as well as any restrictions, such as power consumption limit, physical size limit or cost budget, etc.

[0051] The received filter automation matching task requirements are used as input and substituted into the previously constructed filter automation matching simulation model. The filter automation 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.

[0052] The filter automatic matching simulation model performs simulation operations based on the input task requirements and the historical operation data of the filter. During the operation, the filter automatic matching simulation model will consider various parameter configurations of the filter and simulate the filtering effects under these configurations.

[0053] After the operation is completed, the filter automatic matching simulation model outputs the filter automatic matching simulation results. The filter automatic matching simulation results usually include the prediction of the filtering effect of the filter under different parameter configurations, such as the signal quality, error rate or signal-to-noise ratio after filtering; at the same time, it will also give the predicted optimal parameter configuration under the current task requirements and constraints. These configurations can enable the filter to achieve the best filtering effect.

[0054] Through this step, we can quickly obtain the prediction of the filtering effect of the filter under different parameter configurations according to the specific task requirements and constraints, and find the optimal parameter configuration scheme. This provides strong support for the subsequent automatic matching of filters, which can significantly improve the matching efficiency and filtering effect.

[0055] Specifically, the automatic matching method of the high-frequency and high-precision filter of the present invention, step S103, comprises: The filter digital signal is matched by a preset filter automatic matching rule to obtain the filter automatic matching result, and the steps include: 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 includes comparative matching of filtering of the filter digital signal and feature extraction and comparative matching of the filter digital signal, and 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.

[0056] 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.

[0057] Next, the obtained filter digital signal is processed according to the preset filter automatic matching rules. These rules are formulated based on factors such as the filter's historical operating data, target filtering effect, and working environment.

[0058] Filter automatic matching rules usually include two matching methods: filtering contrast matching and feature extraction contrast matching.

[0059] 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.

[0060] 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.

[0061] According to the matching rules, the data obtained from the comparison and matching are processed, including calculating errors, evaluating the matching degree, determining the optimal parameter configuration, etc.

[0062] Output the automatic matching results of the filter. Finally, output the automatic matching results of the filter. These results usually include the matching degree (such as similarity, error rate, etc.) and parameter configuration (such as the optimal value of the filter parameter, adjustment suggestions, etc.).

[0063] Through this step, the filter digital signal can be matched automatically and the parameter configuration closest to the target filtering effect can be quickly found. This not only improves the matching efficiency, but also improves the performance of the filter to meet the needs of practical applications. At the same time, the output matching results and parameter configuration provide strong support for subsequent optimization and adjustment.

[0064] Specifically, the automatic matching method for high-frequency and high-precision filters of the present invention, step S104, comprises: 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 includes 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 includes the filter's 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 operating 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 operating history data to obtain a filter operating data simulation model, which is used to generate filter operating simulation data based on the filter's historical performance and task requirements.

[0065] The historical data of the automated matching task requirements for the filter are obtained, including past matching task descriptions, target filtering effects (such as expected frequency response, blocking attenuation, etc.), and working environment information (such as temperature, humidity, electromagnetic interference, etc.).

[0066] Obtain the filter's operating history data, which includes the filter's parameter settings (such as filter type, order, cutoff frequency, etc.), actual filtering effects (such as signal quality after filtering, error rate, etc.), and environmental noise records (such as background noise level, noise type, etc.).

[0067] Data feature extraction is performed on the acquired filter automatic matching task requirement historical data and filter operation historical data. This step aims to extract key information useful for model training from the original data, such as the common characteristics of task requirements and the changing trend of filter performance.

[0068] The extracted data features to be matched are matched in the preset algorithm model library. The algorithm model library contains various types of models, such as neural networks, support vector machines, decision trees, etc. These models are suitable for different data features and prediction requirements. According to the matching results, the most suitable model is selected as the model to be trained. This step improves the selected model's ability to best fit the filter's historical performance and task requirements.

[0069] The model to be trained is trained using the historical data of the filter's automated matching task requirements and the historical data of the filter's operation. During the training process, the model learns the relationship between the filter parameter settings, the actual filtering effect, and the environmental noise. Through iterative training, the model gradually optimizes its parameters to improve the simulation accuracy of the filter's operation data.

[0070] After the training is completed, the filter operation data simulation model is obtained. This model can generate filter operation simulation data based on the filter's historical performance and task requirements. These data can be used for subsequent automatic matching rule optimization, filter performance evaluation, etc.

[0071] Through this step, a filter operation data simulation model that can accurately simulate the filter operation status is constructed. This model not only takes into account the historical performance and environmental factors of the filter, 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.

[0072] Specifically, the automatic matching method for high-frequency and high-precision filters of the present invention, step S105, comprises: The step of matching the filter operation simulation data generated by the filter operation data simulation model through the preset filter automatic matching rule to obtain the simulation data matching result includes: using the constructed filter operation data simulation model to generate the filter operation simulation data, the filter operation simulation data simulates 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 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 matching success information and simulation data matching failure information.

[0073] Generate filter operation simulation data, use the previously constructed filter operation data simulation model to generate filter operation simulation data under different working environments and task requirements. These data should fully reflect the performance of the filter under various conditions, including filtering effect, parameter changes, environmental adaptability, etc.

[0074] The generated filter operation simulation data is input into the preset filter automation matching rules. These rules are formulated based on the filter's historical operation data, target filtering effect, working environment and other factors to evaluate whether the simulation data meets expectations.

[0075] During the matching process, the filter operation simulation data is processed, analyzed and compared according to the preset filter automatic matching rules. This includes filter effect evaluation, parameter consistency check, environmental adaptability analysis, etc.

[0076] By comparing the simulation data with the expected filtering effect and task requirements, it is determined whether the filter operation simulation data meets expectations. This step is to verify the accuracy and reliability of the simulation data, as well as the performance of the filter under different conditions.

[0077] According to the result of the matching operation, the simulation data matching result is output, which generally includes the simulation data matching success information and the simulation data matching failure information.

[0078] Simulation data matching success information: If the simulation data meets expectations, the matching success information is output, which includes the matching filter parameter configuration, expected filtering effect, etc.

[0079] Information on unsuccessful simulation data matching: If the simulation data does not meet expectations, information on unsuccessful matching will be output, including reasons for the mismatch, recommended adjustment plans, etc.

[0080] Through this step, the filter's automatic matching rules can be verified and optimized using the 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. At the same time, the output simulation data matching results provide valuable information about the filter's performance, which helps to better understand the filter's behavioral characteristics.

[0081] Specifically, the automatic matching method for high-frequency and high-precision filters of the present invention, step S105, comprises: The step of using a genetic algorithm to optimize the preset automatic matching rules of the filter based on the characteristics of the unsuccessful matching data and the automatic matching task requirements of the filter includes: extracting data features of the filter operation data corresponding to the unsuccessful matching information of the simulation data to reflect the reasons why the filter fails to match successfully under specific conditions; According to the data characteristics of the filter operation data corresponding to the unsuccessful information of simulation data matching and the requirements of the automatic matching task of the filter, 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 automatic matching rules of the filter, and 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 stop condition, which includes the number of iterations and the fitness threshold, the optimized filter automatic matching rule is output.

[0082] Data feature extraction is performed on the filter operation data corresponding to the unsuccessful matching information of the simulation data. The purpose of this step is to identify the key factors or features that caused the filter to fail to match successfully under specific conditions. These features include specific frequency response deviations, environmental noise effects, improper parameter settings, etc.

[0083] According to the data characteristics of the filter operation data corresponding to the unsuccessful information of the simulation data matching and the requirements of the filter's automated matching task, the optimization objectives and constraints of the genetic algorithm are clarified. The optimization goal is to minimize the error between the filtering effect and the actual demand, or to maximize the probability of successful matching. The constraints include the physical limitations of the filter, power consumption requirements, response time, etc.

[0084] Initialize a population containing multiple potential solutions, that is, different configurations of the filter automation matching rules. These configurations can be combinations of filter parameters, variants of matching rules, etc. Each individual in the population represents a solution.

[0085] The selection, crossover and mutation mechanisms in the genetic algorithm are used to iteratively search for the optimal solution.

[0086] Selection: According to the optimization objectives and constraints, the fitness of each solution in the population is evaluated, and individuals with higher fitness are selected as parents.

[0087] Crossover: Perform a crossover operation on the selected parent individuals to generate new solutions (offspring). The crossover operation can be achieved by exchanging some genes or parameters of the parent individuals.

[0088] Mutation: Perform mutation operations on offspring individuals to introduce new genes or parameter mutations to increase the diversity of the population.

[0089] 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.

[0090] When the genetic algorithm reaches the preset stopping condition, the iteration stops. The stopping conditions include reaching the maximum number of iterations, the fitness reaching the preset threshold, and the population diversity falling below a certain level.

[0091] 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, thereby improving the success rate and accuracy of matching.

[0092] Through this step, the powerful search capability of the genetic algorithm can be used to optimize the preset filter automatic matching rules to solve the problem of unsuccessful matching of simulated data. The optimized rules can more accurately reflect the actual performance of the filter and improve the efficiency and accuracy of automatic matching.

[0093] The present invention provides an automated matching method for a high-frequency and high-precision filter, which aims to solve the problem that the high-frequency and high-precision filter needs to continuously optimize automated matching parameters in the process of realizing accurate filtering of high-frequency signals, and the optimized automated matching system of the filter cannot be applied to the high-frequency and high-precision filter due to the complexity and variability of the high-frequency signal.

[0094] The filter operation data and the filter signal are obtained, wherein the filter operation data includes the parameter data of the filter, the environmental noise data and the filter effect evaluation data. The filter signal is converted into a digital signal through an analog-to-digital converter to obtain a filter digital signal.

[0095] Use the filter operation history data to train the deep learning model and obtain the filter automatic matching simulation model. Receive the filter automatic matching task requirements, substitute these requirements into the constructed simulation model, perform simulation operations, and obtain the filter automatic matching simulation results.

[0096] The filter digital signal is matched according to a preset automatic matching rule to obtain an automatic matching result of the filter.

[0097] 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.

[0098] Obtain the historical data of the filter's automated matching task requirements and the filter's historical operation data, and use these data to build a filter operation data simulation model to generate filter operation simulation data.

[0099] The filter operation simulation data is matched through the preset automatic matching rules to obtain the simulation data matching results. The information of unsuccessful simulation data matching is analyzed to extract the features of unsuccessful matching data. Based on these features and the filter automatic matching task requirements, the preset automatic matching rules are optimized using genetic algorithms.

[0100] The present invention realizes the automatic matching and intelligent optimization of the filter by constructing an automatic matching simulation model and an operation data simulation model, thereby improving the matching efficiency and accuracy. By continuously optimizing the automatic matching rules, the present invention can be applied to the complexity and variability of high-frequency and high-precision filters, and improve the accuracy and stability of the filtering effect. 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 the automatic matching rules.

[0101] The present invention can simulate the working state and filtering effect of the filter under different conditions by constructing an automated matching simulation model, thereby avoiding the problem of the need to continuously optimize automated matching parameters in traditional methods. 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 the fields of communications, radar, and sonar, and overcomes the problem that the automated matching system of the filter in the prior art cannot be applied to high-frequency and high-precision filters.

[0102] 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 the prior art, 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 filter signals, converting the filter signals into digital signals through an analog-to-digital converter to obtain filter digital signals, wherein the filter operation data includes filter parameter data, environmental noise data, and filter effect evaluation data; Step S102, obtaining filter operation history data, using the filter operation history data to construct an automatic matching simulation model of the filter, obtaining the automatic matching simulation model of the filter, receiving the automatic matching task requirements of the filter, substituting the automatic matching task requirements of the filter into the automatic matching simulation model of the filter, and obtaining the automatic matching simulation result of the filter; Step S103, matching the filter digital signal through a preset automatic matching rule of the filter to obtain an automatic matching result of the filter, comparing the automatic matching simulation result of the filter with the automatic matching simulation result of the filter, and if the comparison result between the automatic matching simulation result of the filter and the automatic matching simulation result of the filter is outside a preset error range, testing 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 using the filter operation data simulation model to generate filter operation simulation data; 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, the simulation data matching result 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, 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, the preset automatic matching rules of the filter are optimized based on the matching failure data feature and the automatic matching task requirements of the filter, 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, characterized in that: The step S102 includes: The step of using the filter operation history data to construct an automatic matching simulation model of the filter includes: obtaining the historical operation data of the filter, the historical operation data of the filter including the parameter data of the filter, the environmental noise data and the filtering effect evaluation data, using the historical operation data of the filter to train the deep learning model to obtain the automatic matching simulation model of the filter, the automatic matching simulation model of the filter is used to simulate the working state and filtering effect of the filter under different conditions; The automatic matching task requirements of the filter are received, and the automatic matching task requirements of the filter are substituted 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 for subsequent matching rule optimization and automatic matching.

3. The automatic matching method for high-frequency and high-precision filters according to claim 2, characterized in that: 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 includes 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 and outputs the filter automatic matching simulation results based on the input filter automatic matching task requirements and the historical operation data of the filter. 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, characterized in that: The step S103 includes: The filter digital signal is matched by a preset filter automatic matching rule to obtain the filter automatic matching result, and the steps include: 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 includes comparative matching of filtering of the filter digital signal and feature extraction and comparative matching of the filter digital signal, and 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, characterized in that: 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 includes 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 includes the filter's 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 operating 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 operating history data to obtain a filter operating data simulation model, which is used to generate filter operating 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, characterized in that: The step S105 includes: The step of matching the filter operation simulation data generated by the filter operation data simulation model through the preset filter automatic matching rule to obtain the simulation data matching result includes: using the constructed filter operation data simulation model to generate the filter operation simulation data, the filter operation simulation data simulates 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 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 matching success information and simulation data matching failure information.

7. The automatic matching method for high-frequency and high-precision filters according to claim 6, characterized in that: The step S105 includes: The step of using a genetic algorithm to optimize the preset automatic matching rules of the filter based on the characteristics of the unsuccessful matching data and the automatic matching task requirements of the filter includes: extracting data features of the filter operation data corresponding to the unsuccessful matching information of the simulation data to reflect the reasons why the filter fails to match successfully under specific conditions; According to the data characteristics of the filter operation data corresponding to the unsuccessful information of simulation data matching and the requirements of the automatic matching task of the filter, 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 automatic matching rules of the filter, and 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 stop condition, which includes the number of iterations and the fitness threshold, the optimized filter automatic matching rule is output.

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