A waveguide bandpass filter and its optimized control system

Abstract

The present invention relates to the field of filter data technology. The present invention provides a waveguide bandpass filter and its optimized control system, which aims to address the problem of high-precision data processing increasing the complexity of the filter control system, resulting in prolonged response time. The present invention utilizes a data processing module to receive communication signals and basic data, a data screening module to filter and convert signals, a parameter adjustment module to process signal data using a prediction model and optimization algorithm, and to monitor the filter's operating status. The data monitoring module compares the processing volume in real time, and a control parameter optimization module to continuously optimize the prediction model based on the data. The present invention improves the filter's real-time data processing and adaptability, shortens response time, and enhances system stability.

Description

Technical Field

[0001] The present invention relates to the field of filter data technology, in particular to a waveguide bandpass filter and an optimization control system thereof. Background Art

[0002] Waveguide bandpass filters are frequency-selective circuits widely used in microwave equipment such as communications, electronic warfare, radar, and automatic measurement equipment. They typically consist of multiple unit modules, such as input / output coupling structures, waveguide resonant cavities, tuning mechanisms (such as adjustable screws or varactor diodes), and matching networks. These modules work together to efficiently transmit signals within a specific frequency range while suppressing unwanted out-of-band signals, thereby ensuring the performance of the communication system. The waveguide resonant cavity is the fundamental structural unit of a resonant filter, determining key filter characteristics such as the filter's center frequency and bandwidth.

[0003] The steps for using the control system within a waveguide bandpass filter generally include the following: first, system initialization: power on and perform necessary system self-tests; second, parameter setting: according to application requirements, key parameters such as the filter's center frequency, bandwidth, and attenuation characteristics are input through the control panel or software interface; third, real-time monitoring: after starting the filter, the built-in sensors and monitoring modules are used to detect the filter's operating status in real time, including indicators such as frequency response and power loss; fourth, adjustment and optimization: based on the monitoring data, the filter is fine-tuned through the control system's tuning mechanism to achieve optimal performance; and finally, stable operation and maintenance: while improving the filter's stable operation, regular system inspections and maintenance are performed to promptly identify and address potential problems to extend the equipment's service life.

[0004] During actual use, the waveguide bandpass filter control system has the following technical pain points in data processing. Since waveguide bandpass filters are usually used in high-frequency bands, such as millimeter wave and terahertz bands, the signal changes rapidly, requiring the filter control system to have extremely high real-time data processing capabilities. However, high-precision data processing increases the data processing complexity of the filter control system, and the filter cannot adaptively adjust to the optimal filter control parameters, resulting in a prolonged response time of the filter control system, making it difficult to meet real-time requirements. To solve this technical problem, the present invention provides a waveguide bandpass filter and its optimized control system. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a waveguide bandpass filter and its optimized control system, which solves the problem that high-precision data processing increases the data processing complexity of the filter control system, the filter cannot be adaptively adjusted to the optimal filter control parameters, resulting in prolonged response time of the filter control system and difficulty in meeting real-time requirements.

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

[0007] In a first aspect, the present invention provides a waveguide bandpass filter, comprising:

[0008] A data processing module receives a communication signal and basic filter data, wherein the basic filter data includes operating status data of the filter, physical parameter data of the filter, status data of the tuning mechanism, filter data processing standard values, and environmental data;

[0009] A data screening module receives a filtering frequency range, filters the communication signal according to the filtering frequency range, obtains a signal within the filtering frequency range, converts the signal within the filtering frequency range into a digital signal through an analog-to-digital converter, and obtains filtered signal data to be processed;

[0010] The parameter adjustment module substitutes the filter signal data to be processed into the preset filter signal data prediction model to obtain the filter signal data prediction result, compares the filter data processing standard value with the filter signal data prediction result to obtain the filter data prediction error, obtains the historical filter data, uses the historical filter data and the filter data prediction error to optimize the filter signal data prediction model to obtain the optimized filter signal data prediction model, receives the real-time filter signal data to be processed, substitutes the real-time filter signal data to be processed into the optimized filter signal data prediction model, outputs the real-time predicted filter signal data, collects the filter operation status information during the data processing process of the optimized filter signal data prediction model, uses the preset filter operation status detection model to detect the real-time filter operation status during the data processing process of the optimized filter signal data prediction model, obtains the filter operation status data set, and The data in the row status data set are grouped by time periods to obtain time period grouped data, the time period grouped data are sequentially substituted into the filter operation state detection model to obtain the filter operation state detection results corresponding to the data in each time period group, the filter operation state detection results including normal filter operation and abnormal filter operation, the data information of normal filter operation is screened out from the time period grouped data to obtain a normal filter operation time period data group, the data processing volume corresponding to each time period information in the normal filter operation time period data group is retrieved to obtain the data processing volume corresponding to each time period of normal filter operation, the data processing volume corresponding to each time period of normal filter operation is sorted based on the number of data processing volumes, the normal filter operation time period with the highest data processing volume sorted is used as the optimal filter operation state data processing volume, and a correspondence is established between the real-time predicted filter signal data and the optimal filter operation state data processing volume based on the time period;

[0011] The data monitoring module collects the real-time processing volume during the operation of the filter, retrieves the optimal filter operation state data processing volume corresponding to the real-time predicted filter signal data, obtains the real-time data processing volume to be compared, compares the real-time processing volume with the real-time data processing volume to be compared, and if the real-time processing volume is greater than the real-time data processing volume to be compared, retrieves the filter operation state information corresponding to the real-time processing volume, and detects the filter operation state information corresponding to the real-time processing volume through the filter operation state detection model; if the filter operation state is normal, marks the optimized filter signal data prediction model as the filter signal data prediction model to be updated; if the filter operation state is abnormal, the optimized filter signal data prediction model is operating normally;

[0012] The control parameter optimization module uses the data generated during the operation of the data processing module, the data screening module, the parameter adjustment module and the data monitoring module to optimize the prediction model of the filter signal data to be updated, and obtains the optimized prediction model of the filter signal data to be updated, and applies the optimized prediction model of the filter signal data to be updated to the parameter adjustment module to perform predictive analysis on the real-time filter signal data to be processed, receives the predicted filter signal data fed back by the parameter adjustment module, and sends the predicted filter signal data fed back by the parameter adjustment module to the data monitoring module for data processing to obtain data monitoring results. The data monitoring results include normal filter operation status and abnormal filter operation status, and the operation control parameters under the normal filter operation status are used as the optimized filter operation control parameters.

[0013] Furthermore, the waveguide bandpass filter of the present invention, the parameter adjustment module includes:

[0014] The filter signal data to be processed is substituted into a preset filter signal data prediction model, and the prediction result of the filter signal data is obtained by calculation. The prediction result of the filter signal data is compared with the standard value of the filter data processing to obtain the prediction error of the filter data. The filter signal data prediction model is optimized by a genetic algorithm. The genetic algorithm iteratively adjusts the parameters and structure of the model by simulating the genetic mechanism in the biological evolution process to minimize the prediction error, and obtains the optimized filter signal data prediction model. The optimized filter signal data prediction model is used to process the real-time filter signal data to be processed and output the real-time predicted filter signal data. The filter operation status information of the optimized model during the data processing process is also collected, and the preset filter operation status detection model is used to detect the real-time predicted filter signal data to generate a data set of the filter operation status.

[0015] Furthermore, the waveguide bandpass filter of the present invention, the data monitoring module includes:

[0016] The real-time processing volume during the operation of the filter is collected. The real-time processing volume during the operation of the filter is used to reflect the current actual workload of the filter. The optimal filter operation status data processing volume corresponding to the real-time predicted filter signal data is retrieved as a comparison benchmark to obtain the real-time data processing volume to be compared. The real-time processing volume is carefully compared with the real-time data processing volume to be compared to evaluate whether the current working state of the filter exceeds the expected optimal processing range. If the real-time processing volume is greater than the real-time data processing volume to be compared, the current workload of the filter exceeds the optimal state predicted by the model. When the current workload of the filter exceeds the optimal state predicted by the model, the filter operation status information corresponding to the real-time processing volume is retrieved to obtain detailed operation data of the filter under high load or abnormal conditions.

[0017] Furthermore, the waveguide bandpass filter of the present invention, the control parameter optimization module includes:

[0018] In the control parameter optimization module, the data generated by the data processing module, the data screening module, the parameter adjustment module and the data monitoring module during operation are collected, and the genetic algorithm is used to simulate the natural selection and genetic mechanism in the biological evolution process to intelligently adjust the parameters and structure of the model. During the optimization process, the genetic algorithm is iteratively calculated until the optimal model parameter configuration is obtained. The optimal model parameter configuration is configured in the filter signal data prediction model to be updated to obtain the optimized filter signal data prediction model to be updated. The optimized filter signal data prediction model to be updated is used to reflect the actual working state of the filter.

[0019] In a second aspect, the present invention provides a waveguide bandpass filter and an optimization control system thereof, which are applied to the waveguide bandpass filter, comprising:

[0020] A data processing module is used to receive communication signals and basic filter data, where the basic filter data includes operating status data of the filter, physical parameter data of the filter, status data of the tuning mechanism, filter data processing standard values, and environmental data;

[0021] A data screening module is used to receive a filtering frequency range, screen the communication signal according to the filtering frequency range, obtain a signal within the filtering frequency range, convert the signal within the filtering frequency range into a digital signal through an analog-to-digital converter, and obtain filtered signal data to be processed;

[0022] The parameter adjustment module is used to substitute the to-be-processed filtered signal data into the preset filtered signal data prediction model to obtain the filtered signal data prediction result, compare the filter data processing standard value with the filtered signal data prediction result to obtain the filter data prediction error, obtain historical filter data, use the historical filter data and the filter data prediction error to optimize the filtered signal data prediction model to obtain the optimized filtered signal data prediction model, receive the real-time to-be-processed filtered signal data, substitute the real-time to-be-processed filtered signal data into the optimized filtered signal data prediction model, output the real-time predicted filtered signal data, collect the filter operation status information of the optimized filtered signal data prediction model during the data processing process, use the preset filter operation status detection model to detect the real-time filter operation status of the optimized filtered signal data prediction model during the data processing process, obtain the filter operation status data set, and filter The data in the operation status data set are grouped by time periods to obtain time period grouped data, the time period grouped data are sequentially substituted into the filter operation status detection model to obtain the filter operation status detection results corresponding to the data in each time period group, the filter operation status detection results including normal filter operation and abnormal filter operation, the data information of normal filter operation is screened out from the time period grouped data to obtain a normal filter operation time period data group, the data processing volume corresponding to each time period information in the normal filter operation time period data group is retrieved to obtain the data processing volume corresponding to each time period when the filter is operating normally, the data processing volume corresponding to each time period when the filter is operating normally is sorted based on the number of data processing volumes, the normal filter operation time period with the highest data processing volume sorted is used as the optimal filter operation status data processing volume, and a correspondence is established between the real-time predicted filtering signal data and the optimal filter operation status data processing volume based on the time period;

[0023] A data monitoring module is used to collect the real-time processing volume during the operation of the filter, retrieve the optimal filter operation state data processing volume corresponding to the real-time predicted filter signal data, obtain the real-time data processing volume to be compared, compare the real-time processing volume with the real-time data processing volume to be compared, and if the real-time processing volume is greater than the real-time data processing volume to be compared, retrieve the filter operation state information corresponding to the real-time processing volume, and detect the filter operation state information corresponding to the real-time processing volume through a filter operation state detection model; if the filter operation state is normal, mark the optimized filter signal data prediction model as the filter signal data prediction model to be updated; if the filter operation state is abnormal, the optimized filter signal data prediction model is operating normally;

[0024] The control parameter optimization module is used to optimize the filter signal data prediction model to be updated using the data generated during the operation of the data processing module, the data screening module, the parameter adjustment module and the data monitoring module, to obtain the optimized filter signal data prediction model to be updated, and apply the optimized filter signal data prediction model to be updated to the parameter adjustment module to perform predictive analysis on the real-time filter signal data to be processed, receive the predicted filter signal data fed back by the parameter adjustment module, and send the predicted filter signal data fed back by the parameter adjustment module to the data monitoring module for data processing to obtain data monitoring results. The data monitoring results include normal filter operation status and abnormal filter operation status, and the operation control parameters under the normal filter operation status are used as the optimized filter operation control parameters.

[0025] Furthermore, in the waveguide bandpass filter of the present invention, the parameter adjustment module is further used to:

[0026] The filter signal data to be processed is substituted into a preset filter signal data prediction model, and the prediction result of the filter signal data is obtained by calculation. The prediction result of the filter signal data is compared with the standard value of the filter data processing to obtain the prediction error of the filter data. The filter signal data prediction model is optimized by a genetic algorithm. The genetic algorithm iteratively adjusts the parameters and structure of the model by simulating the genetic mechanism in the biological evolution process to minimize the prediction error, and obtains the optimized filter signal data prediction model. The optimized filter signal data prediction model is used to process the real-time filter signal data to be processed and output the real-time predicted filter signal data. The filter operation status information of the optimized model during the data processing process is also collected, and the preset filter operation status detection model is used to detect the real-time predicted filter signal data to generate a data set of the filter operation status.

[0027] Furthermore, the waveguide bandpass filter and the data monitoring module of the present invention are further used for:

[0028] The real-time processing volume during the operation of the filter is collected. The real-time processing volume during the operation of the filter is used to reflect the current actual workload of the filter. The optimal filter operation status data processing volume corresponding to the real-time predicted filter signal data is retrieved as a comparison benchmark to obtain the real-time data processing volume to be compared. The real-time processing volume is carefully compared with the real-time data processing volume to be compared to evaluate whether the current working state of the filter exceeds the expected optimal processing range. If the real-time processing volume is greater than the real-time data processing volume to be compared, the current workload of the filter exceeds the optimal state predicted by the model. When the current workload of the filter exceeds the optimal state predicted by the model, the filter operation status information corresponding to the real-time processing volume is retrieved to obtain detailed operation data of the filter under high load or abnormal conditions.

[0029] Furthermore, in the waveguide bandpass filter of the present invention, the control parameter optimization module is further configured to:

[0030] The data generated by the data processing module, data screening module, parameter adjustment module and data monitoring module during operation are collected, and the genetic algorithm is used to simulate the natural selection and genetic mechanism in the biological evolution process to intelligently adjust the parameters and structure of the model. During the optimization process, the genetic algorithm is iteratively calculated until the optimal model parameter configuration is obtained. The optimal model parameters are configured in the filter signal data prediction model to be updated to obtain the optimized filter signal data prediction model to be updated. The optimized filter signal data prediction model to be updated is used to reflect the actual working status of the filter.

[0031] Beneficial effects of the present invention:

[0032] The parameter adjustment module of the present invention utilizes an optimized filter signal data prediction model to predict filter signal data in real time. By continuously monitoring the filter's operating status and comparing the real-time processing capacity with the optimal processing capacity, it dynamically adjusts the filter control parameters. The control parameter optimization module uses a genetic algorithm to iteratively optimize the filter signal data prediction model, enabling the filter to adaptively adjust to its optimal operating state, thereby improving the system's adaptive capabilities.

[0033] The data monitoring module monitors the filter's real-time processing capacity in real time and triggers an alarm mechanism when necessary. By optimizing the filter's control parameters, the present invention significantly shortens the filter control system's response time and improves the system's overall stability.

[0034] The optimized control system provided by this invention can promptly detect and address potential problems during filter operation, preventing equipment damage caused by overload or abnormal operation. Through regular system inspection and maintenance, as well as optimized adjustment of filter control parameters, this invention helps extend the service life of equipment and reduce maintenance costs.

[0035] The performance of a waveguide bandpass filter directly affects the quality of the entire communication system. The present invention improves the data processing capability and adaptability of the filter, effectively improves the filtering effect of the filter, reduces the interference of out-of-band signals, and thus improves the overall performance of the communication system.

[0036] In summary, the present invention significantly improves the performance of the waveguide bandpass filter by introducing data screening, prediction model optimization, real-time monitoring and adaptive adjustment technologies, providing a strong guarantee for the reliable operation of the communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] 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.

[0038] Figure 1 This is a schematic diagram of the functional modules in the waveguide bandpass filter and its optimized control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] 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.

[0040] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0041] First, see Figure 1 The present invention provides a waveguide bandpass filter, comprising:

[0042] A data processing module receives a communication signal and basic filter data, wherein the basic filter data includes operating status data of the filter, physical parameter data of the filter, status data of the tuning mechanism, filter data processing standard values, and environmental data;

[0043] When solving the problem that high-precision data processing increases the complexity of the filter control system, resulting in the filter being unable to adaptively adjust to the optimal control parameters, thereby prolonging the response time, the data processing module is responsible for receiving communication signals and basic filter data. The basic filter data covers many key aspects of the filter, including the filter's operating status data (such as current operating mode, power consumption, etc.), physical parameter data (such as size, material properties, etc.), tuning mechanism status data (such as adjustable screw position, varactor diode voltage, etc.), filter data processing standard values ​​(such as expected filtering characteristics, attenuation indicators, etc.) and environmental data (such as temperature, humidity, etc.).

[0044] By comprehensively processing this data, the data processing module can provide a solid foundation for subsequent steps such as data screening, model prediction, and state detection. This enables the filter control system to fully understand the current state of the filter and environmental conditions, thereby more accurately predicting the filter's behavior and adaptively adjusting control parameters based on real-time conditions.

[0045] A data screening module receives a filtering frequency range, filters the communication signal according to the filtering frequency range, obtains a signal within the filtering frequency range, converts the signal within the filtering frequency range into a digital signal through an analog-to-digital converter, and obtains filtered signal data to be processed;

[0046] Data filtering receives a specified filter frequency range as input, and then filters the received communication signals based on this range, retaining only signals within the specified frequency range. This step effectively reduces the amount of data that needs to be processed later and reduces the complexity of data processing.

[0047] The signal within the filtered frequency range is fed into an analog-to-digital converter (ADC) for conversion, transforming it from analog to digital. This conversion is a critical step in the data processing process, as it enables efficient digital processing and analysis of the signal. The resulting digital signal, known as the filtered signal data, is then fed into the parameter adjustment module for further processing.

[0048] Through the efficient operation of the data screening module, the system can focus on processing signals related to the current task, avoiding unnecessary data processing overhead, thereby speeding up the processing speed and shortening the response time of the filter control system, enabling the filter to quickly and adaptively adjust to the optimal control parameters, which is crucial to meeting real-time requirements.

[0049] The parameter adjustment module substitutes the filter signal data to be processed into the preset filter signal data prediction model to obtain the filter signal data prediction result, compares the filter data processing standard value with the filter signal data prediction result to obtain the filter data prediction error, obtains the historical filter data, uses the historical filter data and the filter data prediction error to optimize the filter signal data prediction model to obtain the optimized filter signal data prediction model, receives the real-time filter signal data to be processed, substitutes the real-time filter signal data to be processed into the optimized filter signal data prediction model, outputs the real-time predicted filter signal data, collects the filter operation status information of the optimized filter signal data prediction model during the data processing process, uses the preset filter operation status detection model to detect the real-time filter operation status of the optimized filter signal data prediction model during the data processing process, obtains the filter operation status data set, and filters The data in the operation status data set are grouped by time periods to obtain time period grouped data, and the time period grouped data are sequentially substituted into the filter operation status detection model to obtain the filter operation status detection results corresponding to the data in each time period group, the filter operation status detection results including normal filter operation and abnormal filter operation, and the data information of normal filter operation is screened out from the time period grouped data to obtain a normal filter operation time period data group, and the data processing volume corresponding to the information of each time period in the normal filter operation time period data group is retrieved to obtain the data processing volume corresponding to each time period when the filter is operating normally, and the data processing volume corresponding to each time period when the filter is operating normally is sorted based on the amount of data processing volume, and the normal filter operation time period with the highest data processing volume is used as the optimal filter operation status data processing volume, and a correspondence is established between the real-time predicted filtering signal data and the optimal filter operation status data processing volume based on the time period;

[0050] The parameter adjustment module is responsible for the prediction of filter signal data, model optimization, and real-time monitoring and analysis of filter operating status. The following is a detailed description of the module's workflow:

[0051] The parameter adjustment module first receives the filter signal data to be processed, substitutes the data into a preset filter signal data prediction model, and obtains the prediction result of the filter signal data through calculation.

[0052] The prediction results are compared with the standard values ​​of the filter data processing to obtain the prediction error of the filter data.

[0053] Obtain historical filter data, which includes past filter operating status, processed data volume, operating parameters, etc. Use the historical filter data and the previously calculated filter data prediction error to optimize the filter signal data prediction model.

[0054] The optimization process aims to reduce the prediction error and improve the accuracy of the model. In the present invention, a genetic algorithm is used to iteratively adjust the parameters and structure of the model to simulate the genetic mechanism in the biological evolution process, thereby obtaining an optimized filter signal data prediction model.

[0055] Receive real-time filtered signal data to be processed, substitute this data into the optimized filtered signal data prediction model, and output real-time predicted filtered signal data.

[0056] The filter operation status information of the optimized filter signal data prediction model during the data processing process is collected. The real-time filter operation status is detected using a preset filter operation status detection model to obtain a filter operation status data set.

[0057] The data in the filter operation state data set are grouped by time period to obtain time period grouped data, and the time period grouped data are sequentially substituted into the filter operation state detection model to obtain the filter operation state detection results corresponding to the time period grouped data.

[0058] The filter operation status detection results include two situations: filter operation is normal and filter operation is abnormal.

[0059] The optimal filter operating status data processing capacity is determined by filtering out data information showing normal filter operation from the time period grouped data to obtain a data group showing normal filter operation periods. The data processing capacity corresponding to each time period in the data group showing normal filter operation periods is retrieved to obtain the data processing capacity corresponding to each time period showing normal filter operation. These data processing capacities are sorted by quantity, and the time period with the highest ranked normal filter operation capacity is determined as the optimal filter operating status data processing capacity.

[0060] A corresponding relationship is established between the real-time predicted filter signal data and the optimal filter operating state data processing capacity based on time periods. This relationship helps to quickly determine the optimal data processing capacity based on the real-time predicted filter signal data during subsequent data processing, thereby improving the filter's operating efficiency and stability.

[0061] Through the above process, the parameter adjustment module can achieve accurate prediction of the filter signal data, continuous optimization of the model, and real-time monitoring and analysis of the filter operating status, providing strong support for the optimized control of the waveguide bandpass filter.

[0062] The data monitoring module collects the real-time processing volume during the operation of the filter, retrieves the optimal filter operation state data processing volume corresponding to the real-time predicted filter signal data, obtains the real-time data processing volume to be compared, compares the real-time processing volume with the real-time data processing volume to be compared, and if the real-time processing volume is greater than the real-time data processing volume to be compared, retrieves the filter operation state information corresponding to the real-time processing volume, and detects the filter operation state information corresponding to the real-time processing volume through the filter operation state detection model; if the filter operation state is normal, marks the optimized filter signal data prediction model as the filter signal data prediction model to be updated; if the filter operation state is abnormal, the optimized filter signal data prediction model is operating normally;

[0063] The data monitoring module is responsible for real-time monitoring and evaluation of the operating status of the waveguide bandpass filter and its optimization control system. It updates the model or maintains the existing status based on the monitoring results. The following is a detailed description of the module's workflow:

[0064] Collecting real-time processing volume, the data monitoring module first collects the real-time processing volume during the operation of the filter. The real-time processing volume reflects the current actual workload of the filter and is an important basis for evaluating the operating status of the filter.

[0065] The optimal filter operating status data processing volume is retrieved. Based on the real-time predicted filter signal data, the data monitoring module retrieves the corresponding optimal filter operating status data processing volume. The optimal data processing volume is measured during the period when the filter operates normally and is most efficient. Therefore, it can be used as a benchmark for evaluating whether the current filter operating status is normal.

[0066] Compare the real-time processing capacity with the optimal processing capacity, and compare the real-time processing capacity with the real-time data processing capacity to be compared (i.e., the data processing capacity of the optimal filter operating state). This step aims to evaluate whether the current working state of the filter exceeds the expected optimal processing range.

[0067] Filter operation status detection: If the real-time processing volume is greater than the real-time data processing volume to be compared, it means that the current workload of the filter exceeds the optimal state predicted by the model. At this time, the data monitoring module will call the filter operation status information corresponding to the real-time processing volume and conduct in-depth analysis of this information through the filter operation status detection model.

[0068] Model update decision, based on the output results of the filter operation status detection model, the data monitoring module will make a decision on whether to update the filter signal data prediction model.

[0069] If the filter operation status detection result shows that the filter is operating normally and the real-time processing volume exceeds the optimal range, the data monitoring module will mark the optimized filter signal data prediction model as a model to be updated for subsequent optimization at a more appropriate time.

[0070] Through the above process, the data monitoring module can realize real-time monitoring and accurate evaluation of the filter's operating status, providing strong support for model updating and maintenance, helping to keep the waveguide bandpass filter in optimal working condition at all times and improving the performance and reliability of the communication system.

[0071] The control parameter optimization module uses the data generated during the operation of the data processing module, the data screening module, the parameter adjustment module and the data monitoring module to optimize the prediction model of the filter signal data to be updated, and obtains the optimized prediction model of the filter signal data to be updated, and applies the optimized prediction model of the filter signal data to be updated to the parameter adjustment module to perform predictive analysis on the real-time filter signal data to be processed, receives the predicted filter signal data fed back by the parameter adjustment module, and sends the predicted filter signal data fed back by the parameter adjustment module to the data monitoring module for data processing to obtain data monitoring results. The data monitoring results include normal filter operation status and abnormal filter operation status, and the operation control parameters under the normal filter operation status are used as the optimized filter operation control parameters.

[0072] The control parameter optimization module is responsible for integrating data from various modules, continuously optimizing the filter signal data prediction model, and determining the optimal filter operation control parameters. The following is a detailed description of the module's workflow:

[0073] The control parameter optimization module first collects all data generated during operation from the data processing module, data screening module, parameter adjustment module, and data monitoring module. This data covers communication signals, basic filter data, signals within the filter frequency range, pending filter signal data, filter signal data prediction results, and filter operating status information.

[0074] The control parameter optimization module uses collected data, particularly historical filter data and filter data prediction errors, to optimize the prediction model for the updated filtered signal data. This optimization process typically employs advanced algorithms, such as genetic algorithms, which simulate natural selection and genetic mechanisms in biological evolution to intelligently adjust the model's parameters and structure to minimize prediction errors and improve model accuracy and robustness.

[0075] The optimized prediction model for the updated filtered signal data is reapplied to the parameter adjustment module, which uses the model to perform prediction analysis on the real-time filtered signal data to be processed and output more accurate predicted filtered signal data.

[0076] The parameter adjustment module feeds the predicted filter signal data back to the control parameter optimization module, which then sends this data to the data monitoring module for further data processing. The data monitoring module evaluates the filter's operating status based on the predicted results and real-time processing volume and generates data monitoring results.

[0077] If the filter's operating status is determined to be normal in the data monitoring results, the control parameter optimization module will treat the current operating control parameters as the optimized filter operating control parameters. These parameters represent the configuration that can maintain the best performance and stability of the filter under the current operating conditions.

[0078] The control parameter optimization module operates in a continuous iterative process. As the filter's operating environment changes and new data is collected, the module continuously optimizes the filter signal data prediction model and adjusts the operating control parameters to ensure that the filter can always adapt to various complex operating conditions and maintain optimal working conditions.

[0079] Through the above process, the control parameter optimization module can effectively improve the performance and stability of the waveguide bandpass filter, providing a strong guarantee for the reliable operation of the communication system.

[0080] Specifically, the waveguide bandpass filter of the present invention, the parameter adjustment module includes:

[0081] The filter signal data to be processed is substituted into a preset filter signal data prediction model, and the prediction result of the filter signal data is obtained by calculation. The prediction result of the filter signal data is compared with the standard value of the filter data processing to obtain the prediction error of the filter data. The filter signal data prediction model is optimized by a genetic algorithm. The genetic algorithm iteratively adjusts the parameters and structure of the model by simulating the genetic mechanism in the biological evolution process to minimize the prediction error, and obtains the optimized filter signal data prediction model. The optimized filter signal data prediction model is used to process the real-time filter signal data to be processed and output the real-time predicted filter signal data. The filter operation status information of the optimized model during the data processing process is also collected, and the preset filter operation status detection model is used to detect the real-time predicted filter signal data to generate a data set of the filter operation status.

[0082] The parameter adjustment module in the waveguide bandpass filter of the present invention has the following detailed working process:

[0083] The parameter adjustment module first inserts the filter signal data to be processed into a preset filter signal data prediction model. This prediction model, built based on previous data analysis and filter operating principles, provides a preliminary prediction of the filter signal data. After calculation, the model outputs predictions for the filter signal data. These predictions represent the expected output of the filter given the input signal.

[0084] To calculate the prediction error, the parameter adjustment module compares the predicted results of the filtered signal data with the standard values ​​used for filter data processing. The standard values ​​are determined based on the filter's design requirements and performance indicators, reflecting the filter's output under ideal conditions. Through this comparison, the module calculates the prediction error of the filter data—the difference between the predicted results and the standard values.

[0085] To improve prediction accuracy, the parameter adjustment module uses a genetic algorithm to optimize the filtered signal data prediction model. This optimization algorithm, which mimics biological evolution, minimizes prediction error by iteratively adjusting the model's parameters and structure. During the optimization process, the genetic algorithm generates multiple candidate models and, through selection, crossover, and mutation, gradually eliminates underperforming models while retaining and improving the best ones. After multiple iterations, the genetic algorithm produces an optimized filtered signal data prediction model with reduced prediction error and higher accuracy.

[0086] The optimized filter signal data prediction model is used to process real-time, pending filter signal data. When new filter signal data is input, the model quickly outputs real-time predicted filter signal data. While processing real-time data, the parameter adjustment module also collects filter operating status information. This information reflects the filter's actual operating conditions during real-time data processing. To ensure proper filter operation, the parameter adjustment module uses a preset filter operating status detection model to test the real-time predicted filter signal data. This detection model can identify anomalies in filter operation and generate a data set on the filter's operating status.

[0087] Through the above steps, the parameter adjustment module can effectively improve the prediction accuracy of the filtered signal data and monitor the operating status of the filter in real time to ensure the normal operation of the filter. This optimized filtered signal data prediction model not only improves the performance of the filter, but also provides strong support for subsequent data monitoring and control parameter optimization.

[0088] Specifically, the waveguide bandpass filter and the data monitoring module of the present invention include:

[0089] The real-time processing volume during the operation of the filter is collected. The real-time processing volume during the operation of the filter is used to reflect the current actual workload of the filter. The optimal filter operation status data processing volume corresponding to the real-time predicted filter signal data is retrieved as a comparison benchmark to obtain the real-time data processing volume to be compared. The real-time processing volume is carefully compared with the real-time data processing volume to be compared to evaluate whether the current working state of the filter exceeds the expected optimal processing range. If the real-time processing volume is greater than the real-time data processing volume to be compared, the current workload of the filter exceeds the optimal state predicted by the model. When the current workload of the filter exceeds the optimal state predicted by the model, the filter operation status information corresponding to the real-time processing volume is retrieved to obtain detailed operation data of the filter under high load or abnormal conditions.

[0090] Specifically, the data monitoring module in the waveguide bandpass filter of the present invention has the following detailed working process:

[0091] The data monitoring module first collects the real-time processing volume during the operation of the filter. The real-time processing volume reflects the actual workload of the filter at the current moment and can understand the operating status and processing capacity of the filter in real time.

[0092] The data monitoring module retrieves the optimal filter operating state data throughput corresponding to the real-time predicted filter signal data. This optimal throughput is measured during the period when the filter is operating normally and most efficiently, and represents the filter's ideal processing capacity.

[0093] Using this optimal processing volume as a comparison benchmark, it is possible to evaluate whether the current working state of the filter is within the optimal range.

[0094] Comparing real-time throughput with optimal throughput: The data monitoring module carefully compares the real-time throughput with the real-time data throughput to be compared (i.e., the optimal filter operating state data throughput). By comparing these two indicators, it is possible to assess whether the filter's current operating state exceeds the expected optimal processing range.

[0095] If the real-time processing volume is less than or equal to the real-time processing volume of the data to be compared, it means that the current workload of the filter is within the optimal state range predicted by the model and the filter operates normally.

[0096] If the real-time processing volume is greater than the real-time data processing volume to be compared, it indicates that the current workload of the filter exceeds the optimal state predicted by the model, and there is an overload or abnormal operation.

[0097] Retrieving filter operating status information: When the real-time processing capacity exceeds the optimal capacity, the data monitoring module further retrieves the filter operating status information corresponding to the real-time processing capacity. This information includes key parameters such as filter temperature, voltage, and current, as well as any abnormal alarms or error codes. By analyzing this detailed operating data, the specific performance of the filter under high load or abnormal conditions can be more accurately determined, providing a basis for subsequent optimization and adjustment.

[0098] If the data monitoring module finds that the filter is operating under high load or abnormal conditions, it will immediately trigger the alarm mechanism to notify the system administrator or relevant maintenance personnel.

[0099] At the same time, the module will also record these abnormal events for subsequent troubleshooting and analysis.

[0100] Through the above steps, the data monitoring module can monitor the operating status of the filter in real time, promptly detect and handle potential overload or abnormal operation problems, and improve the stable and reliable operation of the filter.

[0101] Specifically, the waveguide bandpass filter of the present invention, the control parameter optimization module includes:

[0102] In the control parameter optimization module, the data generated by the data processing module, the data screening module, the parameter adjustment module and the data monitoring module during operation are collected, and the genetic algorithm is used to simulate the natural selection and genetic mechanism in the biological evolution process to intelligently adjust the parameters and structure of the model. During the optimization process, the genetic algorithm is iteratively calculated until the optimal model parameter configuration is obtained. The optimal model parameter configuration is configured in the filter signal data prediction model to be updated to obtain the optimized filter signal data prediction model to be updated. The optimized filter signal data prediction model to be updated is used to reflect the actual working state of the filter.

[0103] Specifically, the control parameter optimization module in the waveguide bandpass filter of the present invention has the following detailed working process:

[0104] The control parameter optimization module first collects data generated by the data processing module, data screening module, parameter adjustment module, and data monitoring module during operation. This data covers multiple aspects of the filter, including communication signals, filter operating status, physical parameters, tuning mechanism status, environmental data, filter frequency range, pending filter signal data, filter signal data prediction results, and filter operating status information.

[0105] The collected data is preprocessed, including data cleaning, format conversion, and outlier processing, to improve its accuracy and consistency. Based on this preprocessed data, the control parameter optimization module prepares to optimize the prediction model for the updated filtered signal data. This involves determining the optimization objective function (e.g., minimizing prediction error), constraints (e.g., physical limitations of the filter, performance requirements), and an optimization algorithm (in this case, a genetic algorithm).

[0106] Genetic Algorithm Optimization: This algorithm uses genetic algorithms to intelligently adjust model parameters and structure. Genetic algorithms mimic the natural selection and inheritance mechanisms of biological evolution, gradually approaching the optimal solution through iterative calculations. In each generation, the algorithm selects a set of candidate model parameters (called "individuals"), evaluates their performance against the objective function, and generates the next generation of candidate model parameters through operations such as crossover and mutation. This process continues until a stopping condition is met (such as reaching a preset number of iterations or convergence of the objective function value).

[0107] Model parameter configuration: When the genetic algorithm converges to the optimal solution, the control parameter optimization module configures the optimal model parameters in the filter signal data prediction model to be updated, and obtains the optimized filter signal data prediction model to be updated.

[0108] Use new data to verify the optimized model and improve its performance and stability in practical applications.

[0109] The optimized prediction model of the filter signal data to be updated is applied to the parameter adjustment module to perform prediction analysis on the real-time filter signal data to be processed.

[0110] The parameter adjustment module feeds back the predicted filtering signal data to the control parameter optimization module, and the data monitoring module also provides real-time operating status information of the filter.

[0111] Based on this feedback, the control parameter optimization module can further adjust and optimize the model parameters, forming a continuous iterative optimization process. Through these steps, the control parameter optimization module can continuously improve the accuracy and robustness of the filter signal data prediction model, making it better reflect the actual operating state of the filter, thereby improving the performance and stability of the entire waveguide bandpass filter.

[0112] In a second aspect, the present invention provides a waveguide bandpass filter and an optimization control system thereof, which are applied to the waveguide bandpass filter, comprising:

[0113] A data processing module is used to receive communication signals and basic filter data, where the basic filter data includes operating status data of the filter, physical parameter data of the filter, status data of the tuning mechanism, filter data processing standard values, and environmental data;

[0114] A data screening module is used to receive a filtering frequency range, screen the communication signal according to the filtering frequency range, obtain a signal within the filtering frequency range, convert the signal within the filtering frequency range into a digital signal through an analog-to-digital converter, and obtain filtered signal data to be processed;

[0115] The parameter adjustment module is used to substitute the to-be-processed filtered signal data into the preset filtered signal data prediction model to obtain the filtered signal data prediction result, compare the filter data processing standard value with the filtered signal data prediction result to obtain the filter data prediction error, obtain historical filter data, use the historical filter data and the filter data prediction error to optimize the filtered signal data prediction model to obtain the optimized filtered signal data prediction model, receive the real-time to-be-processed filtered signal data, substitute the real-time to-be-processed filtered signal data into the optimized filtered signal data prediction model, output the real-time predicted filtered signal data, collect the filter operation status information of the optimized filtered signal data prediction model during the data processing process, use the preset filter operation status detection model to detect the real-time filter operation status of the optimized filtered signal data prediction model during the data processing process, obtain the filter operation status data set, and filter The data in the operation status data set are grouped by time periods to obtain time period grouped data, the time period grouped data are sequentially substituted into the filter operation status detection model to obtain the filter operation status detection results corresponding to the data in each time period group, the filter operation status detection results including normal filter operation and abnormal filter operation, the data information of normal filter operation is screened out from the time period grouped data to obtain a normal filter operation time period data group, the data processing volume corresponding to each time period information in the normal filter operation time period data group is retrieved to obtain the data processing volume corresponding to each time period when the filter is operating normally, the data processing volume corresponding to each time period when the filter is operating normally is sorted based on the number of data processing volumes, the normal filter operation time period with the highest data processing volume sorted is used as the optimal filter operation status data processing volume, and a correspondence is established between the real-time predicted filtering signal data and the optimal filter operation status data processing volume based on the time period;

[0116] A data monitoring module is used to collect the real-time processing volume during the operation of the filter, retrieve the optimal filter operation state data processing volume corresponding to the real-time predicted filter signal data, obtain the real-time data processing volume to be compared, compare the real-time processing volume with the real-time data processing volume to be compared, and if the real-time processing volume is greater than the real-time data processing volume to be compared, retrieve the filter operation state information corresponding to the real-time processing volume, and detect the filter operation state information corresponding to the real-time processing volume through a filter operation state detection model; if the filter operation state is normal, mark the optimized filter signal data prediction model as the filter signal data prediction model to be updated; if the filter operation state is abnormal, the optimized filter signal data prediction model is operating normally;

[0117] The control parameter optimization module is used to optimize the filter signal data prediction model to be updated using the data generated during the operation of the data processing module, the data screening module, the parameter adjustment module and the data monitoring module, to obtain the optimized filter signal data prediction model to be updated, and apply the optimized filter signal data prediction model to be updated to the parameter adjustment module to perform predictive analysis on the real-time filter signal data to be processed, receive the predicted filter signal data fed back by the parameter adjustment module, and send the predicted filter signal data fed back by the parameter adjustment module to the data monitoring module for data processing to obtain data monitoring results. The data monitoring results include normal filter operation status and abnormal filter operation status, and the operation control parameters under the normal filter operation status are used as the optimized filter operation control parameters.

[0118] Specifically, the waveguide bandpass filter of the present invention, the parameter adjustment module is further used to:

[0119] The filter signal data to be processed is substituted into a preset filter signal data prediction model, and the prediction result of the filter signal data is obtained by calculation. The prediction result of the filter signal data is compared with the standard value of the filter data processing to obtain the prediction error of the filter data. The filter signal data prediction model is optimized by a genetic algorithm. The genetic algorithm iteratively adjusts the parameters and structure of the model by simulating the genetic mechanism in the biological evolution process to minimize the prediction error, and obtains the optimized filter signal data prediction model. The optimized filter signal data prediction model is used to process the real-time filter signal data to be processed and output the real-time predicted filter signal data. The filter operation status information of the optimized model during the data processing process is also collected, and the preset filter operation status detection model is used to detect the real-time predicted filter signal data to generate a data set of the filter operation status.

[0120] Specifically, the waveguide bandpass filter and the data monitoring module of the present invention are further used to:

[0121] The real-time processing volume during the operation of the filter is collected. The real-time processing volume during the operation of the filter is used to reflect the current actual workload of the filter. The optimal filter operation status data processing volume corresponding to the real-time predicted filter signal data is retrieved as a comparison benchmark to obtain the real-time data processing volume to be compared. The real-time processing volume is carefully compared with the real-time data processing volume to be compared to evaluate whether the current working state of the filter exceeds the expected optimal processing range. If the real-time processing volume is greater than the real-time data processing volume to be compared, the current workload of the filter exceeds the optimal state predicted by the model. When the current workload of the filter exceeds the optimal state predicted by the model, the filter operation status information corresponding to the real-time processing volume is retrieved to obtain detailed operation data of the filter under high load or abnormal conditions.

[0122] Specifically, the waveguide bandpass filter of the present invention, the control parameter optimization module is further used to:

[0123] The data generated by the data processing module, data screening module, parameter adjustment module and data monitoring module during operation are collected, and the genetic algorithm is used to simulate the natural selection and genetic mechanism in the biological evolution process to intelligently adjust the parameters and structure of the model. During the optimization process, the genetic algorithm is iteratively calculated until the optimal model parameter configuration is obtained. The optimal model parameters are configured in the filter signal data prediction model to be updated to obtain the optimized filter signal data prediction model to be updated. The optimized filter signal data prediction model to be updated is used to reflect the actual working status of the filter.

[0124] The technical solution of the present invention solves the problem that high-precision data processing increases the data processing complexity of the filter control system through the following steps, and the filter cannot be adaptively adjusted to the optimal filter control parameters, resulting in a prolonged response time of the filter control system and difficulty in meeting real-time requirements:

[0125] The present invention uses a data screening module to systemically receive a filtering frequency range, screen the communication signal, and convert the screened signal into a digital signal, effectively reducing the amount of data to be subsequently processed and improving processing efficiency.

[0126] The filter signal data prediction model is optimized. The parameter adjustment module uses the preset filter signal data prediction model to predict the pending filter signal data and calculates the prediction error by comparing it with the filter data processing standard value. The system then optimizes the model using historical filter data and the prediction error to obtain the optimized filter signal data prediction model. This step significantly improves the model's prediction accuracy and adaptability while reducing unnecessary computational overhead.

[0127] The parameter adjustment module also collects and detects filter operating status information during model data processing, filters out data from periods of normal filter operation, and determines the optimal filter operating status data processing volume. This process ensures the filter operates at its optimal state, improving system response speed and stability.

[0128] The data monitoring module collects the real-time processing volume of the filter during operation and compares it with the optimal filter operating state data processing volume corresponding to the real-time predicted filter signal data. If the real-time processing volume exceeds the expected value, the system further checks the filter operating status. If the status is normal, the current model is marked as a candidate for update and further optimization.

[0129] The control parameter optimization module uses the data generated during the operation of each module to further optimize the prediction model of the updated filter signal data through a genetic algorithm. The optimized model is then applied to the real-time prediction analysis of the processed filter signal data. The system dynamically adjusts the filter control parameters based on the prediction results, ensuring that the filter always operates in the optimal state.

[0130] Through the above steps, the present invention effectively solves the complexity and real-time challenges brought about by high-precision data processing, realizes adaptive adjustment and rapid response of the filter control system, and improves the overall performance and stability of the filter.

Claims

1. A waveguide bandpass filter, characterized in that: include: A data processing module receives a communication signal and basic filter data, wherein the basic filter data includes operating status data of the filter, physical parameter data of the filter, status data of the tuning mechanism, filter data processing standard values, and environmental data; A data screening module receives a filtering frequency range, filters the communication signal according to the filtering frequency range, obtains a signal within the filtering frequency range, converts the signal within the filtering frequency range into a digital signal through an analog-to-digital converter, and obtains filtered signal data to be processed; The parameter adjustment module substitutes the filter signal data to be processed into the preset filter signal data prediction model to obtain the filter signal data prediction result, compares the filter data processing standard value with the filter signal data prediction result to obtain the filter data prediction error, obtains the historical filter data, uses the historical filter data and the filter data prediction error to optimize the filter signal data prediction model to obtain the optimized filter signal data prediction model, receives the real-time filter signal data to be processed, substitutes the real-time filter signal data to be processed into the optimized filter signal data prediction model, outputs the real-time predicted filter signal data, collects the filter operation status information during the data processing process of the optimized filter signal data prediction model, uses the preset filter operation status detection model to detect the real-time filter operation status during the data processing process of the optimized filter signal data prediction model, obtains the filter operation status data set, and The data in the row status data set are grouped by time periods to obtain time period grouped data, the time period grouped data are sequentially substituted into the filter operation state detection model to obtain the filter operation state detection results corresponding to the data in each time period group, the filter operation state detection results including normal filter operation and abnormal filter operation, the data information of normal filter operation is screened out from the time period grouped data to obtain a normal filter operation time period data group, the data processing volume corresponding to each time period information in the normal filter operation time period data group is retrieved to obtain the data processing volume corresponding to each time period of normal filter operation, the data processing volume corresponding to each time period of normal filter operation is sorted based on the number of data processing volumes, the normal filter operation time period with the highest data processing volume sorted is used as the optimal filter operation state data processing volume, and a correspondence is established between the real-time predicted filter signal data and the optimal filter operation state data processing volume based on the time period; A data monitoring module collects the real-time processing volume during the operation of the filter, retrieves the optimal filter operation state data processing volume corresponding to the real-time predicted filter signal data, obtains the real-time data processing volume to be compared, compares the real-time processing volume with the real-time data processing volume to be compared, and if the real-time processing volume is greater than the real-time data processing volume to be compared, retrieves the filter operation state information corresponding to the real-time processing volume, and detects the filter operation state information corresponding to the real-time processing volume through a filter operation state detection model; if the filter operation state is normal, marks the optimized filter signal data prediction model as the filter signal data prediction model to be updated; if the filter operation state is abnormal, the optimized filter signal data prediction model is not marked as to be updated; The control parameter optimization module uses the data generated during the operation of the data processing module, the data screening module, the parameter adjustment module and the data monitoring module to optimize the prediction model of the filter signal data to be updated, and obtains the optimized prediction model of the filter signal data to be updated, and applies the optimized prediction model of the filter signal data to be updated to the parameter adjustment module to perform predictive analysis on the real-time filter signal data to be processed, receives the predicted filter signal data fed back by the parameter adjustment module, and sends the predicted filter signal data fed back by the parameter adjustment module to the data monitoring module for data processing to obtain data monitoring results. The data monitoring results include normal filter operation status and abnormal filter operation status, and the operation control parameters under the normal filter operation status are used as the optimized filter operation control parameters.

2. The waveguide bandpass filter according to claim 1, wherein The parameter adjustment module includes: The filter signal data to be processed is substituted into a preset filter signal data prediction model, and the prediction result of the filter signal data is obtained by calculation. The prediction result of the filter signal data is compared with the standard value of the filter data processing to obtain the prediction error of the filter data. The filter signal data prediction model is optimized by a genetic algorithm. The genetic algorithm iteratively adjusts the parameters and structure of the model by simulating the genetic mechanism in the biological evolution process to minimize the prediction error, and obtains the optimized filter signal data prediction model. The optimized filter signal data prediction model is used to process the real-time filter signal data to be processed and output the real-time predicted filter signal data. The filter operation status information of the optimized model during the data processing process is also collected, and the preset filter operation status detection model is used to detect the real-time predicted filter signal data to generate a data set of the filter operation status.

3. The waveguide bandpass filter according to claim 1, wherein The data monitoring module includes: The real-time processing volume during the operation of the filter is collected. The real-time processing volume during the operation of the filter is used to reflect the current actual workload of the filter. The optimal filter operation status data processing volume corresponding to the real-time predicted filter signal data is retrieved as a comparison benchmark to obtain the real-time data processing volume to be compared. The real-time processing volume is carefully compared with the real-time data processing volume to be compared to evaluate whether the current working state of the filter exceeds the expected optimal processing range. If the real-time processing volume is greater than the real-time data processing volume to be compared, the current workload of the filter exceeds the optimal state predicted by the model. When the current workload of the filter exceeds the optimal state predicted by the model, the filter operation status information corresponding to the real-time processing volume is retrieved to obtain detailed operation data of the filter under high load or abnormal conditions.

4. The waveguide bandpass filter according to claim 1, wherein The control parameter optimization module includes: In the control parameter optimization module, the data generated by the data processing module, the data screening module, the parameter adjustment module and the data monitoring module during operation are collected, and the genetic algorithm is used to simulate the natural selection and genetic mechanism in the biological evolution process to intelligently adjust the parameters and structure of the model. During the optimization process, the genetic algorithm is iteratively calculated until the optimal model parameter configuration is obtained. The optimal model parameter configuration is configured in the filter signal data prediction model to be updated to obtain the optimized filter signal data prediction model to be updated. The optimized filter signal data prediction model to be updated is used to reflect the actual working state of the filter.

5. A waveguide bandpass filter optimization control system, applied to the waveguide bandpass filter according to any one of claims 1 to 4, characterized in that: include: A data processing module is used to receive communication signals and basic filter data, where the basic filter data includes operating status data of the filter, physical parameter data of the filter, status data of the tuning mechanism, filter data processing standard values, and environmental data; A data screening module is used to receive a filtering frequency range, screen the communication signal according to the filtering frequency range, obtain a signal within the filtering frequency range, convert the signal within the filtering frequency range into a digital signal through an analog-to-digital converter, and obtain filtered signal data to be processed; The parameter adjustment module is used to substitute the to-be-processed filtered signal data into the preset filtered signal data prediction model to obtain the filtered signal data prediction result, compare the filter data processing standard value with the filtered signal data prediction result to obtain the filter data prediction error, obtain historical filter data, use the historical filter data and the filter data prediction error to optimize the filtered signal data prediction model to obtain the optimized filtered signal data prediction model, receive the real-time to-be-processed filtered signal data, substitute the real-time to-be-processed filtered signal data into the optimized filtered signal data prediction model, output the real-time predicted filtered signal data, collect the filter operation status information of the optimized filtered signal data prediction model during the data processing process, use the preset filter operation status detection model to detect the real-time filter operation status of the optimized filtered signal data prediction model during the data processing process, obtain the filter operation status data set, and filter The data in the operation status data set are grouped by time periods to obtain time period grouped data, the time period grouped data are sequentially substituted into the filter operation status detection model to obtain the filter operation status detection results corresponding to the data in each time period group, the filter operation status detection results including normal filter operation and abnormal filter operation, the data information of normal filter operation is screened out from the time period grouped data to obtain a normal filter operation time period data group, the data processing volume corresponding to each time period information in the normal filter operation time period data group is retrieved to obtain the data processing volume corresponding to each time period when the filter is operating normally, the data processing volume corresponding to each time period when the filter is operating normally is sorted based on the number of data processing volumes, the normal filter operation time period with the highest data processing volume sorted is used as the optimal filter operation status data processing volume, and a correspondence is established between the real-time predicted filtering signal data and the optimal filter operation status data processing volume based on the time period; A data monitoring module is used to collect the real-time processing volume during the operation of the filter, retrieve the optimal filter operation state data processing volume corresponding to the real-time predicted filter signal data, obtain the real-time data processing volume to be compared, compare the real-time processing volume with the real-time data processing volume to be compared, and if the real-time processing volume is greater than the real-time data processing volume to be compared, retrieve the filter operation state information corresponding to the real-time processing volume, and detect the filter operation state information corresponding to the real-time processing volume through a filter operation state detection model; if the filter operation state is normal, mark the optimized filter signal data prediction model as the filter signal data prediction model to be updated; if the filter operation state is abnormal, the optimized filter signal data prediction model is not marked as to be updated; The control parameter optimization module is used to optimize the filter signal data prediction model to be updated using the data generated during the operation of the data processing module, the data screening module, the parameter adjustment module and the data monitoring module, to obtain the optimized filter signal data prediction model to be updated, and apply the optimized filter signal data prediction model to be updated to the parameter adjustment module to perform predictive analysis on the real-time filter signal data to be processed, receive the predicted filter signal data fed back by the parameter adjustment module, and send the predicted filter signal data fed back by the parameter adjustment module to the data monitoring module for data processing to obtain data monitoring results. The data monitoring results include normal filter operation status and abnormal filter operation status, and the operation control parameters under the normal filter operation status are used as the optimized filter operation control parameters.

6. The waveguide bandpass filter optimization control system according to claim 5, characterized in that: The parameter adjustment module is further used to: The filter signal data to be processed is substituted into a preset filter signal data prediction model, and the prediction result of the filter signal data is obtained by calculation. The prediction result of the filter signal data is compared with the standard value of the filter data processing to obtain the prediction error of the filter data. The filter signal data prediction model is optimized by a genetic algorithm. The genetic algorithm iteratively adjusts the parameters and structure of the model by simulating the genetic mechanism in the biological evolution process to minimize the prediction error, and obtains the optimized filter signal data prediction model. The optimized filter signal data prediction model is used to process the real-time filter signal data to be processed and output the real-time predicted filter signal data. The filter operation status information of the optimized model during the data processing process is also collected, and the preset filter operation status detection model is used to detect the real-time predicted filter signal data to generate a data set of the filter operation status.

7. The waveguide bandpass filter optimization control system according to claim 5, characterized in that: The data monitoring module is further used to: The real-time processing volume during the operation of the filter is collected. The real-time processing volume during the operation of the filter is used to reflect the current actual workload of the filter. The optimal filter operation status data processing volume corresponding to the real-time predicted filter signal data is retrieved as a comparison benchmark to obtain the real-time data processing volume to be compared. The real-time processing volume is carefully compared with the real-time data processing volume to be compared to evaluate whether the current working state of the filter exceeds the expected optimal processing range. If the real-time processing volume is greater than the real-time data processing volume to be compared, the current workload of the filter exceeds the optimal state predicted by the model. When the current workload of the filter exceeds the optimal state predicted by the model, the filter operation status information corresponding to the real-time processing volume is retrieved to obtain detailed operation data of the filter under high load or abnormal conditions.

8. The waveguide bandpass filter optimization control system according to claim 5 is characterized in that: The control parameter optimization module is further used to: The data generated by the data processing module, data screening module, parameter adjustment module and data monitoring module during operation are collected, and the genetic algorithm is used to simulate the natural selection and genetic mechanism in the biological evolution process to intelligently adjust the parameters and structure of the model. During the optimization process, the genetic algorithm is iteratively calculated until the optimal model parameter configuration is obtained. The optimal model parameters are configured in the filter signal data prediction model to be updated to obtain the optimized filter signal data prediction model to be updated. The optimized filter signal data prediction model to be updated is used to reflect the actual working status of the filter.

Images