CN117520963B - Power amplifier protection method and system based on output power real-time monitoring - Google Patents
Power amplifier protection method and system based on output power real-time monitoring Download PDFInfo
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Abstract
The invention relates to the technical field of output power monitoring, in particular to a power amplifier protection method and system based on real-time output power monitoring, comprising the following steps: based on the power amplifier output power data, a convolutional neural network is adopted to perform feature extraction, a time sequence analysis is combined to perform real-time data monitoring, a cluster analysis algorithm is adopted to perform pattern analysis, and power monitoring data are generated. In the invention, abnormal power mode identification is enhanced through a random forest algorithm, diagnosis speed and accuracy are improved, main component analysis is combined with a genetic algorithm to perform environmental factor analysis and threshold adjustment, adaptability and response capability are enhanced, autoregressive moving average model and seasonal decomposition time sequence are subjected to trend analysis, comprehensive power variation trend prediction is provided, a Kalman filter and a simulated annealing algorithm optimize DSP parameter setting, overall operation efficiency and stability are improved, and a deep belief network is combined with reverse propagation algorithm fault mode identification to improve fault diagnosis accuracy and efficiency.
Description
Technical Field
The invention relates to the technical field of output power monitoring, in particular to a power amplifier protection method and system based on output power real-time monitoring.
Background
In the field of output power monitoring technology, which focuses on real-time monitoring and adjusting the output power of a power amplifier to ensure its stable, efficient operation while preventing overload and potential damage, the key of the technology is to accurately monitor the output power of the power amplifier and to make a fast response based on real-time data to adjust or protect equipment, often involving the use of advanced sensors and control systems, in order to improve the reliability and efficiency of the power amplifier while reducing the risk due to overheating, over-currents or other electrical faults.
The power amplifier protection method based on real-time monitoring of output power is a technology for a power amplifier, and is mainly aimed at ensuring that the power amplifier operates under safe and optimal working conditions.
The anomaly detection of the traditional power amplifier protection method depends on a simple algorithm, so that the application effect of the traditional power amplifier protection method in a complex environment is limited, the traditional method is weak in environmental factor analysis and threshold adjustment, the adaptability and flexibility of the traditional power amplifier protection method in a dynamic change environment are insufficient, the traditional method lacks an effective prediction mechanism and parameter optimization strategies in the aspect of long-term and short-term power trend prediction, and the traditional method is difficult to analyze fault types and cause identification in the aspect of fault diagnosis.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a power amplifier protection method and system based on real-time monitoring of output power.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a power amplifier protection method based on output power real-time monitoring comprises the following steps:
s1: based on the power amplifier output power data, a convolutional neural network is adopted to perform feature extraction, a time sequence analysis is combined to perform real-time data monitoring, a cluster analysis algorithm is adopted to perform pattern analysis, and power monitoring data are generated;
s2: based on the power monitoring data, adopting a random forest algorithm to perform anomaly detection and anomaly mode analysis to generate an anomaly power mode;
S3: based on the abnormal power mode, performing environmental factor analysis by adopting principal component analysis, and performing threshold adjustment by adopting a genetic algorithm to generate an optimized power threshold;
s4: based on historical power data, adopting an autoregressive moving average model to perform trend analysis, and adopting seasonal decomposition time sequence and time sequence modeling to generate a power trend prediction report;
s5: based on the power trend prediction report, adopting a Kalman filter to adjust DSP parameters, and performing parameter optimization by using a simulated annealing algorithm to generate DSP parameter setting;
s6: based on the power monitoring data and the power trend prediction report, a deep belief network is adopted to identify a fault mode, and a back propagation algorithm is used for model training to generate a fault diagnosis report;
s7: based on the fault diagnosis report, adopting a cyclic neural network model to adjust operation parameters, and generating a power amplifier operation adjustment scheme;
the power monitoring data comprises power level, power fluctuation and historical comparison data, the abnormal power mode comprises power mutation, high power abnormality and low power abnormality, the optimized power threshold is specifically maximum and minimum power limitation based on environmental change, the power trend prediction report comprises short-term and long-term power change trend prediction, the DSP parameter setting is specifically adjusted compression ratio and threshold parameter, and the fault diagnosis report comprises fault type and fault reason.
As a further scheme of the invention, based on power amplifier output power data, a convolutional neural network is adopted to perform feature extraction, a time sequence analysis is combined to perform real-time data monitoring, a cluster analysis algorithm is adopted to perform pattern analysis, and the step of generating power monitoring data specifically comprises the following steps:
s101: based on the power amplifier output power data, a convolutional neural network is adopted to extract key power characteristics and generate power characteristic data;
s102: based on the power characteristic data, adopting an autoregressive moving average model to perform time sequence analysis to generate a time sequence analysis result;
s103: based on the time sequence analysis result, classifying the data by adopting a K-means clustering algorithm, and identifying a differentiated power mode to generate power monitoring data.
As a further scheme of the present invention, based on the power monitoring data, a random forest algorithm is adopted to perform anomaly detection and anomaly pattern analysis, and the step of generating an anomaly power pattern specifically includes:
s201: and based on the power monitoring data, adopting Z-score standardization to convert the data into a standardized format and generating standardized power monitoring data.
S202: based on the standardized power monitoring data, adopting a random forest algorithm to perform abnormal pattern detection analysis and generate a preliminary abnormal pattern recognition result;
S203: and based on the preliminary abnormal mode identification result, performing deviation analysis by adopting variance analysis, confirming and refining the abnormal mode characteristics, and generating an abnormal power mode.
As a further scheme of the invention, based on the abnormal power mode, the main component analysis is adopted to analyze environmental factors, and the genetic algorithm is adopted to adjust the threshold value, so that the step of generating the optimized power threshold value is specifically as follows:
s301: based on the abnormal power mode, adopting principal component analysis to analyze the relevance between the power mode and environmental factors and generate an environmental factor analysis report;
s302: based on the environmental factor analysis report, predicting the influence of the differentiated environmental factors on the power threshold by adopting a logistic regression model, and generating environmental influence prediction data;
s303: and based on the environmental impact prediction data, adopting a genetic algorithm, and carrying out dynamic threshold adjustment by combining historical power threshold data to generate an optimized power threshold.
As a further scheme of the invention, based on historical power data, an autoregressive moving average model is adopted to carry out trend analysis, seasonal decomposition time sequence and time sequence modeling are adopted, and the steps for generating a power trend prediction report are specifically as follows:
S401: based on historical power data, an autoregressive moving average model is adopted to analyze the long-term trend of the power data, identify potential period fluctuation and generate a long-term trend analysis result;
s402: based on the long-term trend analysis result, carrying out seasonal analysis on the power data by adopting a seasonal decomposition time sequence algorithm to generate seasonal analysis data;
s403: based on the seasonal analysis data, a Gaussian process regression model is adopted to conduct time sequence modeling, predict power trend and generate a power trend prediction report.
As a further scheme of the invention, based on the power trend prediction report, a Kalman filter is adopted to adjust DSP parameters, and a simulated annealing algorithm is used to optimize parameters, and the step of generating DSP parameter settings is specifically as follows:
s501: based on the power trend prediction report, adopting a Kalman filter to adjust DSP parameter setting and generating initial DSP adjustment parameters;
s502: based on the initial DSP adjustment parameters, adopting signal intensity analysis, adopting fast Fourier transform to perform signal intensity analysis, analyzing the influence of the adjustment parameters on signal processing, and generating a spectrum analysis result;
S503: based on the spectrum analysis result, adopting a simulated annealing algorithm to carry out parameter adjustment, and generating DSP parameter setting.
As a further scheme of the invention, based on the power monitoring data and the power trend prediction report, a deep belief network is adopted to identify a fault mode, and a back propagation algorithm is used for model training, so that the steps of generating the fault diagnosis report are specifically as follows:
s601: based on the power monitoring data and the power trend prediction report, adopting a convolutional neural network to perform feature extraction, adopting a deep belief network to calibrate fault types and generating a preliminary fault identification result;
s602: based on the preliminary fault recognition result, optimizing a deep belief network by using a back propagation algorithm, and generating a trained fault recognition model;
s603: and carrying out fault mode analysis and confirmation based on the trained fault recognition model to generate a fault diagnosis report.
As a further scheme of the invention, based on the fault diagnosis report, a cyclic neural network model is adopted to adjust operation parameters, and the steps for generating a successful release operation adjustment scheme are specifically as follows:
s701: based on the fault diagnosis report, a cyclic neural network model is adopted to analyze the influence of a fault mode on the performance of the power amplifier, and a preliminary operation adjustment scheme is generated;
S702: based on the preliminary operation adjustment scheme, adopting Monte Carlo simulation to evaluate the effect and feasibility of the adjustment scheme and generating a simulation test result;
s703: and based on the simulation test result, adjusting the operation parameters of the power amplifier by adopting linear programming to generate a power amplifier operation adjustment scheme.
The power amplifier protection system based on the output power real-time monitoring is used for executing the power amplifier protection method based on the output power real-time monitoring, and comprises a power characteristic extraction module, a power monitoring module, an abnormal mode identification module, an environmental factor analysis module, a trend analysis and DSP adjustment module, a fault type calibration module, a fault diagnosis module and an operation parameter adjustment module.
As a further scheme of the invention, the power characteristic extraction module adopts a convolutional neural network to extract characteristics based on power amplifier output power data, utilizes an autoregressive moving average model to perform time sequence analysis, classifies the power data through a K-means clustering algorithm, and generates power monitoring data;
the power monitoring module converts data into a standardized format by adopting a Z score standardization method based on power monitoring data, and performs abnormal pattern detection analysis by a random forest algorithm to generate preliminary abnormal pattern identification data;
The abnormal pattern recognition module performs deviation analysis by adopting an analysis of variance method based on the preliminary abnormal pattern recognition data to generate abnormal power pattern data;
the environmental factor analysis module analyzes the association between the power mode and the environmental factor through principal component analysis based on the abnormal power mode data, predicts the influence of the environmental factor on the power threshold value by utilizing a logistic regression model, and generates environmental influence prediction data;
the trend analysis and DSP adjustment module is used for carrying out trend analysis by combining historical power data, applying an autoregressive moving average model, carrying out seasonal analysis by using a seasonal decomposition time sequence algorithm, carrying out time sequence modeling by using a Gaussian process regression model, predicting power trend, and carrying out DSP parameter adjustment by using a Kalman filter to generate DSP parameter adjustment data;
the fault type calibration module is used for carrying out feature extraction by using a convolutional neural network based on the power monitoring data and the DSP parameter adjustment data and calibrating the fault type by combining a deep belief network to generate preliminary fault identification data;
the fault diagnosis module optimizes a deep belief network by using a back propagation algorithm based on the preliminary fault identification data, and performs fault mode analysis to generate a fault diagnosis report;
The operation parameter adjustment module adopts a cyclic neural network model to analyze fault influence based on fault diagnosis reports, formulates a preliminary operation adjustment scheme, adopts Monte Carlo simulation and linear programming to adjust operation parameters of the power amplifier, and generates the operation adjustment scheme of the power amplifier.
Compared with the prior art, the invention has the advantages and positive effects that:
in the invention, the abnormal detection and mode analysis are carried out through a random forest algorithm, so that the recognition capability of an abnormal power mode is enhanced, the rapidity and the accuracy of diagnosis are improved, the main component analysis is combined with a genetic algorithm to carry out environmental factor analysis and threshold adjustment, more flexible adaptability and response capability are provided, the environment change can be effectively treated, the trend analysis is carried out by combining an autoregressive moving average model and a seasonal decomposition time sequence, more comprehensive power change trend prediction can be provided, reliable basis is provided for subsequent decision, the application of a Kalman filter and a simulated annealing algorithm is optimized in terms of DSP parameter adjustment, the parameter setting is optimized, the overall operation efficiency and stability are improved, and the fault mode recognition of a deep belief network combined with a counter propagation algorithm is improved, so that the accuracy and the efficiency of fault diagnosis are improved.
Drawings
FIG. 1 is a schematic workflow diagram of the present invention;
FIG. 2 is a S1 refinement flowchart of the present invention;
FIG. 3 is a S2 refinement flowchart of the present invention;
FIG. 4 is a S3 refinement flowchart of the present invention;
FIG. 5 is a S4 refinement flowchart of the present invention;
FIG. 6 is a S5 refinement flowchart of the present invention;
FIG. 7 is a S6 refinement flowchart of the present invention;
FIG. 8 is a S7 refinement flowchart of the present invention;
fig. 9 is a system flow diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1, the present invention provides a technical solution: a power amplifier protection method based on output power real-time monitoring comprises the following steps:
s1: based on the power amplifier output power data, a convolutional neural network is adopted to perform feature extraction, a time sequence analysis is combined to perform real-time data monitoring, a cluster analysis algorithm is adopted to perform pattern analysis, and power monitoring data are generated;
s2: based on the power monitoring data, adopting a random forest algorithm to perform anomaly detection and anomaly mode analysis to generate an anomaly power mode;
s3: based on the abnormal power mode, performing environmental factor analysis by adopting principal component analysis, and performing threshold adjustment by adopting a genetic algorithm to generate an optimized power threshold;
s4: based on historical power data, adopting an autoregressive moving average model to perform trend analysis, and adopting seasonal decomposition time sequence and time sequence modeling to generate a power trend prediction report;
s5: based on the power trend prediction report, adopting a Kalman filter to adjust DSP parameters, and carrying out parameter optimization by using a simulated annealing algorithm to generate DSP parameter setting;
s6: based on the power monitoring data and the power trend prediction report, a deep belief network is adopted to identify a fault mode, and a back propagation algorithm is used for model training to generate a fault diagnosis report;
S7: based on the fault diagnosis report, adopting a cyclic neural network model to adjust operation parameters, and generating a power amplifier operation adjustment scheme;
the power monitoring data comprises power level, power fluctuation and historical comparison data, the abnormal power mode comprises power mutation, high power abnormality and low power abnormality, the optimized power threshold is specifically maximum and minimum power limit based on environmental change, the power trend prediction report comprises short-term and long-term power change trend prediction, the DSP parameter setting is specifically adjusted compression ratio and threshold parameter, and the fault diagnosis report comprises fault type and fault cause.
By adopting the convolutional neural network to perform feature extraction and time sequence analysis, a large amount of power amplifier output power data can be efficiently processed and analyzed, so that the data processing speed is improved, the understanding and recognition capability of the power data features are enhanced, the real-time data monitoring is more accurate, the pattern analysis is performed by using a cluster analysis algorithm, the recognition capability of the power data patterns is further enhanced, and the method can effectively distinguish normal power patterns from abnormal power patterns.
The application of the random forest algorithm is particularly important in anomaly detection and anomaly mode analysis, and the random forest algorithm improves the accuracy of anomaly detection by constructing a plurality of decision trees and synthesizing the prediction results of the decision trees, and can more accurately identify various anomalies such as power mutation, high-power anomalies, low-power anomalies and the like, so that corresponding protection measures are timely carried out, and the risk of potential equipment damage or performance reduction is reduced.
The application of principal component analysis in environmental factor analysis and genetic algorithm in threshold adjustment can adjust the maximum and minimum power limits according to environmental changes, so as to maintain optimal performance in different operating environments, and the dynamic adjustment mechanism is important for long-time operation of the power amplifier.
The application of the autoregressive moving average model and the seasonal decomposition time series is particularly critical in the aspect of trend analysis, can provide detailed prediction about short-term and long-term change trend of the power data, helps operation and maintenance personnel to better understand the change mode of the power data, and accordingly makes more effective maintenance and adjustment decisions, thereby not only improving the operation efficiency, but also being beneficial to preventing potential faults.
The application of the deep belief network and the back propagation algorithm in fault mode identification and the application of the cyclic neural network model in operation parameter adjustment greatly improve the accuracy and timeliness of fault diagnosis, can quickly and accurately identify the type and cause of the fault, and adjust the operation parameters accordingly, thereby minimizing downtime and maintenance cost caused by the fault.
Referring to fig. 2, based on power amplifier output power data, the steps of performing feature extraction by using a convolutional neural network, performing real-time data monitoring by combining time sequence analysis, and performing pattern analysis by using a cluster analysis algorithm are specifically as follows:
S101: based on the power amplifier output power data, a convolutional neural network is adopted to extract key power characteristics and generate power characteristic data;
s102: based on the power characteristic data, adopting an autoregressive moving average model to perform time sequence analysis to generate a time sequence analysis result;
s103: based on the time sequence analysis result, the data are classified by adopting a K-means clustering algorithm, and differentiated power modes are identified to generate power monitoring data.
In step S101, by collecting the power data output by the power amplification device, feature extraction is performed on the data by using a convolutional neural network, and the convolutional neural network can effectively identify key features in the power data, such as power fluctuation, peak value, and the like, through its hierarchical structure, and in this process, the convolutional neural network model is trained to ensure that it can accurately capture the key information, so as to generate a data set containing important power features.
In step S102, based on the power characteristic data, time series analysis is performed using an autoregressive moving average model, which is specifically designed to analyze and predict time series data, so that time correlation and trend of the data can be effectively identified, and time series analysis results are generated by analyzing changes of the power data with time, such as periodic fluctuation, rising or falling of long-term trend, and the like.
In step S103, based on the result of the time series analysis, the power data is further classified by using a K-means clustering algorithm, and this clustering analysis helps to divide the power data into different categories or groups, and classifies the power data based on the similarity of the power characteristics and the time series behavior, so that different power modes, such as a normal operation mode, an abnormal fluctuation mode, and the like, can be identified, and this step finally generates power monitoring data, which reflects the operation state and possible abnormal modes of the power amplification device, and provides key information for subsequent analysis and decision.
Referring to fig. 3, based on the power monitoring data, the steps of performing anomaly detection and anomaly pattern analysis by adopting a random forest algorithm to generate an anomaly power pattern specifically include:
s201: based on the power monitoring data, Z-score standardization is adopted, the data is converted into a standardized format, and standardized power monitoring data is generated.
S202: based on the standardized power monitoring data, adopting a random forest algorithm to perform abnormal pattern detection analysis and generate a preliminary abnormal pattern recognition result;
s203: based on the primary abnormal pattern recognition result, performing deviation analysis by adopting variance analysis, and confirming and refining the abnormal pattern characteristics to generate an abnormal power pattern.
In step S201, the output power monitoring data of the power amplifier is converted by the Z-score normalization method to obtain data in a standardized format, and the process involves converting the original data into a format with zero mean and unit variance, so that the power data from different time points and conditions can be effectively compared and analyzed, and a uniform data base is provided for subsequent anomaly detection.
In step S202, an anomaly pattern detection is performed on the normalized power monitoring data using a random forest algorithm, which improves accuracy and robustness by constructing a plurality of decision trees and synthesizing prediction results, and generates a preliminary anomaly pattern recognition result by recognizing data points or patterns significantly different from the conventional power distribution pattern, thereby providing a basis for accurately recognizing and responding to potential power anomalies.
In step S203, the variance analysis is applied to further bias analysis of the initially identified abnormal patterns, which makes it possible to quantitatively analyze the degree of difference between different power patterns and confirm the specific features of the abnormal patterns, by which the features of the abnormal power patterns are depicted more carefully and accurately, providing key information for taking corresponding protection measures.
Referring to fig. 4, based on the abnormal power mode, the steps of performing environmental factor analysis by adopting principal component analysis and performing threshold adjustment by adopting a genetic algorithm, and generating an optimized power threshold are specifically as follows:
s301: based on the abnormal power mode, adopting principal component analysis to analyze the relevance between the power mode and the environmental factors and generate an environmental factor analysis report;
s302: based on the environmental factor analysis report, predicting the influence of the differentiated environmental factors on the power threshold by adopting a logistic regression model, and generating environmental influence prediction data;
s303: based on the environmental impact prediction data, adopting a genetic algorithm, and combining historical power threshold data to perform dynamic threshold adjustment to generate an optimized power threshold.
In step S301, the abnormal power pattern is deeply analyzed by the principal component analysis method, and its relevance to various environmental factors such as temperature, humidity, etc. is explored, and this analysis helps to reveal potential links between power anomalies and specific environmental conditions, and the results are summarized into an environmental factor analysis report, which provides critical environmental background information for subsequent data processing.
In step S302, the influence of different environmental factors on the power threshold is predicted based on the environmental factor analysis report by utilizing the logistic regression model, and the influence degree of environmental variables on the power threshold can be quantified by the statistical method, so that environmental influence prediction data is generated, and a scientific basis is provided for optimizing and adjusting the power threshold.
In step S303, the power threshold is dynamically adjusted by combining the genetic algorithm and the environmental impact prediction data, and the adjustment is based on the historical power threshold data and the predicted environmental impact, and the genetic algorithm is adopted to optimally search the optimal threshold setting, so as to generate a final optimal power threshold, thereby ensuring that the power amplifier system can be flexibly adjusted according to the environmental change and improving the adaptability and efficiency of the power amplifier system.
Referring to fig. 5, based on historical power data, using an autoregressive moving average model to perform trend analysis, and using seasonal decomposition time series and time series modeling, the steps of generating a power trend prediction report are specifically as follows:
s401: based on historical power data, an autoregressive moving average model is adopted to analyze the long-term trend of the power data, identify potential period fluctuation and generate a long-term trend analysis result;
s402: based on the long-term trend analysis result, seasonal analysis is carried out on the power data by adopting a seasonal decomposition time sequence algorithm, so as to generate seasonal analysis data;
s403: based on seasonal analysis data, a Gaussian process regression model is adopted to conduct time sequence modeling, power trend is predicted, and a power trend prediction report is generated.
In step S401, historical power data is analyzed by an autoregressive moving average model, focusing on identifying long-term trends and potential periodic fluctuations of the power data, the analysis reveals long-term patterns and regularity of power variation, a basis is provided for predicting future power trends, and long-term trend analysis results are generated.
In step S402, the long-term trend analysis results are further processed using a seasonal decomposition time series algorithm, focusing on the seasonal variation of the power data, which analysis helps to understand how the power varies with the season or a specific period of time, thereby generating seasonal analysis data that provides an important perspective for understanding and predicting seasonal fluctuations of the power data.
In step S403, a Gaussian process regression model is combined for time series modeling, future power trend is predicted based on seasonal analysis data, the method utilizes the strong capability of the Gaussian process regression model to simulate and predict complex dynamic behaviors of the power data, an effective tool is provided for accurately predicting the power trend, a power trend prediction report is finally generated, and key information is provided for preventive maintenance and operation optimization of a power amplifier system.
Referring to fig. 6, based on a power trend prediction report, a kalman filter is adopted to adjust DSP parameters, and a simulated annealing algorithm is used to perform parameter optimization, so that the steps for generating DSP parameter settings are specifically as follows:
s501: based on the power trend prediction report, adopting a Kalman filter to adjust the DSP parameter setting and generating initial DSP adjustment parameters;
s502: based on the initial DSP adjustment parameters, adopting signal intensity analysis, adopting fast Fourier transform to perform signal intensity analysis, analyzing the influence of the adjustment parameters on signal processing, and generating a spectrum analysis result;
s503: based on the spectrum analysis result, adopting a simulated annealing algorithm to carry out parameter adjustment, and generating DSP parameter setting.
In step S501, the power trend prediction report is analyzed by using a kalman filter, so as to adjust DSP parameters, where the kalman filter is used as an effective estimation algorithm, and can accurately adjust parameter settings of the DSP, such as gain control and frequency adjustment, based on the prediction report, so as to generate initial DSP adjustment parameters, which is to ensure that the DSP system can adjust its processing strategy according to the predicted power trend, so as to adapt to different operating conditions.
In step S502, the initial DSP adjustment parameters are further analyzed by using signal strength analysis and fast fourier transform, where the fast fourier transform is used as a high-efficiency signal processing tool for converting signals from time domain to frequency domain, and analyzing specific effects of the adjusted parameters on signal processing, where the objective of this step is to generate a spectrum analysis result, so as to ensure that DSP parameter adjustment is not only based on theoretical prediction, but also can achieve an optimal effect in actual signal processing.
In step S503, based on the spectrum analysis result, the simulated annealing algorithm is used to further adjust DSP parameters, and the simulated annealing algorithm finds a global optimal solution through an annealing process in a simulated physical process, which is particularly effective in DSP parameter optimization.
Referring to fig. 7, based on the power monitoring data and the power trend prediction report, the fault mode is identified by adopting a deep belief network, and model training is performed by using a back propagation algorithm, so that the steps of generating the fault diagnosis report are specifically as follows:
S601: based on the power monitoring data and the power trend prediction report, adopting a convolutional neural network to perform feature extraction, adopting a deep belief network to calibrate fault types and generating a preliminary fault identification result;
s602: based on the preliminary fault recognition result, optimizing a deep belief network by using a back propagation algorithm, and generating a trained fault recognition model;
s603: and carrying out fault mode analysis and confirmation based on the trained fault recognition model to generate a fault diagnosis report.
In step S601, advanced feature extraction is performed by combining the power monitoring data and the power trend prediction report, where the convolutional neural network is mainly used to analyze and identify key patterns and abnormal features in the power data, and further analyze these features by using the deep belief network to calibrate specific fault types, where the process involves identifying potential fault signals from complex data, and finally generating a preliminary fault identification result.
In step S602, the deep belief network is trained using a back propagation algorithm to improve its accuracy and reliability in fault identification, and the training process involves adjusting weights and deviations in the network to ensure that the network can accurately identify various fault types, and by this optimization, a trained and adjusted fault identification model can be generated.
In step S603, the failure mode is carefully analyzed and confirmed by using the trained failure recognition model, which includes analyzing the failure data by using the model, identifying the specific failure mode and the cause, and based on the analysis result, generating an exhaustive failure diagnosis report, which provides important information and guidance for subsequent maintenance and adjustment.
Referring to fig. 8, based on the fault diagnosis report, the operation parameter adjustment is performed by using a cyclic neural network model, and the steps for generating the successful release operation adjustment scheme are specifically as follows:
s701: based on a fault diagnosis report, a cyclic neural network model is adopted to analyze the influence of a fault mode on the performance of the power amplifier, and a preliminary operation adjustment scheme is generated;
s702: based on the preliminary operation adjustment scheme, adopting Monte Carlo simulation to evaluate the effect and feasibility of the adjustment scheme and generating a simulation test result;
s703: based on the simulation test result, linear programming is adopted to adjust the operation parameters of the power amplifier, and a power amplifier operation adjustment scheme is generated.
In step S701, by analyzing the fault diagnosis report using the recurrent neural network model, the system can evaluate the influence of the fault mode on the performance of the power amplifier in detail, and the recurrent neural network is adapted to process the sequence data so that it can extract the fault mode and the characteristics related to the time sequence from the fault diagnosis report, thereby generating a preliminary operation adjustment scheme, which aims at guiding how to adjust the power amplifier to cope with the detected fault, and ensures that the power amplifier maintains the optimal performance.
In step S702, the effect and feasibility of the preliminary operation adjustment scheme are evaluated by using monte carlo simulation, the monte carlo simulation performs multiple simulation operations on the scheme through random sampling and statistical analysis, and the performance of the scheme under different conditions is evaluated.
In step S703, the power amplifier operation parameters are adjusted by using a linear programming method based on the simulation test result, and the linear programming is used as an optimization algorithm, so that an optimal parameter adjustment scheme can be found under a given constraint condition to achieve an expected performance target.
Referring to fig. 9, an output power real-time monitoring-based power amplifier protection system is used for executing the output power real-time monitoring-based power amplifier protection method, and the system includes a power feature extraction module, a power monitoring module, an abnormal mode identification module, an environmental factor analysis module, a trend analysis and DSP adjustment module, a fault type calibration module, a fault diagnosis module, and an operation parameter adjustment module.
The power characteristic extraction module is used for carrying out characteristic extraction by adopting a convolutional neural network based on power amplification output power data, carrying out time sequence analysis by utilizing an autoregressive moving average model, classifying the power data by a K-means clustering algorithm, and generating power monitoring data;
the power monitoring module converts the data into a standardized format by adopting a Z score standardization method based on the power monitoring data, and performs abnormal pattern detection analysis by a random forest algorithm to generate preliminary abnormal pattern identification data;
the abnormal pattern recognition module performs deviation analysis by adopting an analysis of variance method based on the preliminary abnormal pattern recognition data to generate abnormal power pattern data;
the environmental factor analysis module analyzes the association between the power mode and the environmental factor through principal component analysis based on the abnormal power mode data, predicts the influence of the environmental factor on the power threshold by utilizing a logistic regression model, and generates environmental influence prediction data;
the trend analysis and DSP adjustment module is used for carrying out trend analysis by combining historical power data and applying an autoregressive moving average model, carrying out seasonal analysis by using a seasonal decomposition time sequence algorithm, carrying out time sequence modeling by using a Gaussian process regression model, predicting power trend, and carrying out DSP parameter adjustment by using a Kalman filter to generate DSP parameter adjustment data;
The fault type calibration module is used for carrying out feature extraction by using a convolutional neural network based on the power monitoring data and the DSP parameter adjustment data, and calibrating the fault type by combining a deep belief network to generate preliminary fault identification data;
the fault diagnosis module optimizes a deep belief network by using a back propagation algorithm based on the preliminary fault identification data, and performs fault mode analysis to generate a fault diagnosis report;
the operation parameter adjustment module adopts a cyclic neural network model to analyze fault influence based on fault diagnosis reports, formulates a preliminary operation adjustment scheme, adopts Monte Carlo simulation and linear programming to adjust operation parameters of the power amplifier, and generates the operation adjustment scheme of the power amplifier.
The power characteristic extraction module combines a convolutional neural network, an autoregressive moving average model and a K-means clustering algorithm, so that key characteristics of power amplifier output power can be efficiently and accurately extracted and analyzed, data can be effectively classified, the speed and accuracy of data processing are greatly improved, and the system can rapidly respond to various state changes through accurate real-time monitoring, so that the overall operation efficiency and stability are improved.
The power monitoring module adopts a Z-score standardization method and a random forest algorithm, so that data can be standardized effectively, an abnormal mode can be identified accurately, and the efficient abnormal detection mechanism can discover potential problems in time, thereby preventing damage of power amplifier equipment, and reducing maintenance cost and downtime.
The abnormal pattern recognition module performs deep deviation analysis through an analysis of variance method, and can more accurately determine specific characteristics of the abnormal power pattern, which is helpful for the system to more accurately diagnose problems and take targeted protection measures.
The environmental factor analysis module combines principal component analysis and a logistic regression model, so that the system can dynamically adjust the power threshold according to environmental changes, and the flexibility is important to coping with changeable running environments, and is beneficial to improving the adaptability and reliability of the system.
The trend analysis and the application of the DSP adjustment module are combined with an autoregressive moving average model, a seasonal decomposition time sequence algorithm and a Gaussian process regression model, so that deep trend analysis and accurate power prediction are provided for the system. The method is not only beneficial to formulating a more effective maintenance strategy, but also can optimize the DSP parameter setting and further improve the overall performance of the system.
The integration of the fault type calibration module and the fault diagnosis module provides high-efficiency and accurate fault diagnosis capability through a deep belief network and a back propagation algorithm, and the advanced fault diagnosis mechanism can rapidly and accurately identify the type and the reason of the fault, so that the reliability and the safety of the system are greatly improved.
The operation parameter adjustment module adopts a cyclic neural network model, monte Carlo simulation and linear programming, provides a flexible operation adjustment scheme for the system, is beneficial to quickly recovering the normal operation of the system after the fault is found, and ensures the optimal performance of the power amplifier.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
Claims (7)
1. The power amplifier protection method based on output power real-time monitoring is characterized by comprising the following steps of:
Based on the power amplifier output power data, a convolutional neural network is adopted to perform feature extraction, a time sequence analysis is combined to perform real-time data monitoring, a cluster analysis algorithm is adopted to perform pattern analysis, and power monitoring data are generated;
based on the power monitoring data, adopting a random forest algorithm to perform anomaly detection and anomaly mode analysis to generate an anomaly power mode;
based on the abnormal power mode, performing environmental factor analysis by adopting principal component analysis, and performing threshold adjustment by adopting a genetic algorithm to generate an optimized power threshold;
based on historical power data, adopting an autoregressive moving average model to perform trend analysis, and adopting seasonal decomposition time sequence and time sequence modeling to generate a power trend prediction report;
based on the power trend prediction report, adopting a Kalman filter to adjust DSP parameters, and performing parameter optimization by using a simulated annealing algorithm to generate DSP parameter setting;
based on the power monitoring data and the power trend prediction report, a deep belief network is adopted to identify a fault mode, and a back propagation algorithm is used for model training to generate a fault diagnosis report;
Based on the fault diagnosis report, adopting a cyclic neural network model to adjust operation parameters, and generating a power amplifier operation adjustment scheme;
the power monitoring data comprises power level, power fluctuation and historical comparison data, the abnormal power mode comprises power mutation, high power abnormality and low power abnormality, the optimized power threshold is specifically a maximum and minimum power limit based on environmental change, the power trend prediction report comprises short-term and long-term power change trend prediction, the DSP parameter setting is specifically an adjusted compression ratio and threshold parameter, and the fault diagnosis report comprises fault type and fault cause;
based on the abnormal power mode, adopting principal component analysis to analyze environmental factors, adopting a genetic algorithm to adjust a threshold value, and generating an optimized power threshold value specifically comprises the following steps:
based on the abnormal power mode, adopting principal component analysis to analyze the relevance between the power mode and environmental factors and generate an environmental factor analysis report;
based on the environmental factor analysis report, predicting the influence of the differentiated environmental factors on the power threshold by adopting a logistic regression model, and generating environmental influence prediction data;
Based on the environmental impact prediction data, adopting a genetic algorithm, and combining historical power threshold data to perform dynamic threshold adjustment to generate an optimized power threshold;
based on historical power data, adopting an autoregressive moving average model to perform trend analysis, and adopting seasonal decomposition time sequence and time sequence modeling to generate a power trend prediction report, wherein the steps of generating the power trend prediction report specifically comprise:
based on historical power data, an autoregressive moving average model is adopted to analyze the long-term trend of the power data, identify potential period fluctuation and generate a long-term trend analysis result;
based on the long-term trend analysis result, carrying out seasonal analysis on the power data by adopting a seasonal decomposition time sequence algorithm to generate seasonal analysis data;
based on the seasonal analysis data, a Gaussian process regression model is adopted to conduct time sequence modeling, predict power trend and generate a power trend prediction report;
based on the power trend prediction report, a Kalman filter is adopted to adjust DSP parameters, and a simulated annealing algorithm is used to optimize parameters, so that the DSP parameter setting generation step specifically comprises the following steps:
based on the power trend prediction report, adopting a Kalman filter to adjust DSP parameter setting and generating initial DSP adjustment parameters;
Based on the initial DSP adjustment parameters, adopting signal intensity analysis, adopting fast Fourier transform to perform signal intensity analysis, analyzing the influence of the adjustment parameters on signal processing, and generating a spectrum analysis result;
based on the spectrum analysis result, adopting a simulated annealing algorithm to carry out parameter adjustment, and generating DSP parameter setting.
2. The power amplifier protection method based on real-time monitoring of output power according to claim 1, wherein the steps of generating power monitoring data by performing feature extraction, real-time data monitoring in combination with time sequence analysis and pattern analysis in combination with a cluster analysis algorithm based on power amplifier output power data are specifically as follows:
based on the power amplifier output power data, a convolutional neural network is adopted to extract key power characteristics and generate power characteristic data;
based on the power characteristic data, adopting an autoregressive moving average model to perform time sequence analysis to generate a time sequence analysis result;
based on the time sequence analysis result, classifying the data by adopting a K-means clustering algorithm, and identifying a differentiated power mode to generate power monitoring data.
3. The power amplifier protection method based on real-time output power monitoring according to claim 1, wherein the step of performing anomaly detection and anomaly pattern analysis by adopting a random forest algorithm based on the power monitoring data to generate an anomaly power pattern comprises the following steps:
based on the power monitoring data, adopting Z score standardization, converting the data into a standardized format, and generating standardized power monitoring data;
based on the standardized power monitoring data, adopting a random forest algorithm to perform abnormal pattern detection analysis and generate a preliminary abnormal pattern recognition result;
and based on the preliminary abnormal mode identification result, performing deviation analysis by adopting variance analysis, confirming and refining the abnormal mode characteristics, and generating an abnormal power mode.
4. The power amplifier protection method based on real-time output power monitoring according to claim 1, wherein based on the power monitoring data and the power trend prediction report, a deep belief network is adopted to perform fault mode identification, and a back propagation algorithm is used to perform model training, so that the step of generating a fault diagnosis report is specifically as follows:
based on the power monitoring data and the power trend prediction report, adopting a convolutional neural network to perform feature extraction, adopting a deep belief network to calibrate fault types and generating a preliminary fault identification result;
Based on the preliminary fault recognition result, optimizing a deep belief network by using a back propagation algorithm, and generating a trained fault recognition model;
and carrying out fault mode analysis and confirmation based on the trained fault recognition model to generate a fault diagnosis report.
5. The power amplifier protection method based on output power real-time monitoring according to claim 1, wherein based on the fault diagnosis report, a cyclic neural network model is adopted to adjust operation parameters, and the step of generating a power amplifier operation adjustment scheme is specifically as follows:
based on the fault diagnosis report, a cyclic neural network model is adopted to analyze the influence of a fault mode on the performance of the power amplifier, and a preliminary operation adjustment scheme is generated;
based on the preliminary operation adjustment scheme, adopting Monte Carlo simulation to evaluate the effect and feasibility of the adjustment scheme and generating a simulation test result;
and based on the simulation test result, adjusting the operation parameters of the power amplifier by adopting linear programming to generate a power amplifier operation adjustment scheme.
6. The power amplifier protection system based on output power real-time monitoring is characterized in that the power amplifier protection method based on output power real-time monitoring according to any one of claims 1-5 comprises a power characteristic extraction module, a power monitoring module, an abnormal mode identification module, an environmental factor analysis module, a trend analysis and DSP adjustment module, a fault type calibration module, a fault diagnosis module and an operation parameter adjustment module.
7. The power amplifier protection system based on real-time monitoring of output power according to claim 6, wherein the power feature extraction module performs feature extraction based on power amplifier output power data by using a convolutional neural network, performs time series analysis by using an autoregressive moving average model, and classifies the power data by a K-means clustering algorithm to generate power monitoring data;
the power monitoring module converts data into a standardized format by adopting a Z score standardization method based on power monitoring data, and performs abnormal pattern detection analysis by a random forest algorithm to generate preliminary abnormal pattern identification data;
the abnormal pattern recognition module performs deviation analysis by adopting an analysis of variance method based on the preliminary abnormal pattern recognition data to generate abnormal power pattern data;
the environmental factor analysis module analyzes the association between the power mode and the environmental factor through principal component analysis based on the abnormal power mode data, predicts the influence of the environmental factor on the power threshold value by utilizing a logistic regression model, and generates environmental influence prediction data;
the trend analysis and DSP adjustment module is used for carrying out trend analysis by combining historical power data, applying an autoregressive moving average model, carrying out seasonal analysis by using a seasonal decomposition time sequence algorithm, carrying out time sequence modeling by using a Gaussian process regression model, predicting power trend, and carrying out DSP parameter adjustment by using a Kalman filter to generate DSP parameter adjustment data;
The fault type calibration module is used for carrying out feature extraction by using a convolutional neural network based on the power monitoring data and the DSP parameter adjustment data and calibrating the fault type by combining a deep belief network to generate preliminary fault identification data;
the fault diagnosis module optimizes a deep belief network by using a back propagation algorithm based on the preliminary fault identification data, and performs fault mode analysis to generate a fault diagnosis report;
the operation parameter adjustment module adopts a cyclic neural network model to analyze fault influence based on fault diagnosis reports, formulates a preliminary operation adjustment scheme, adopts Monte Carlo simulation and linear programming to adjust operation parameters of the power amplifier, and generates the operation adjustment scheme of the power amplifier.
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