Multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent regulation and control system

Abstract

The invention provides an intelligent regulation and control system for wavelength and power data of a multi-wavelength tunable ultrafast fiber laser, and relates to the technical field of data processing, and the method comprises a data collection module which is used for obtaining the wavelength data and the power data of the multi-wavelength tunable ultrafast fiber laser in real time; the data processing module is used for receiving and processing the obtained wavelength data and power data, and performing data analysis, filtering and abnormal value detection to obtain a data analysis result; the intelligent regulation and control module is used for automatically adjusting parameters of the multi-wavelength tunable ultrafast fiber laser according to the data processing and analysis result so as to regulate and control the wavelength and the power; and the data management module is used for storing the obtained wavelength data, power data and related data in the regulation and control process. According to the invention, the wavelength and power of the multi-wavelength tunable ultrafast fiber laser are efficiently, intelligently and remotely regulated and controlled, the system performance is self-optimized, and the regulation and control precision, the response speed and the system stability of the laser are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a wavelength and power data intelligent control system for a multi-wavelength tunable ultrafast fiber laser. Background Art

[0002] In traditional technologies, due to the limitations of sensor accuracy and actuator response speed, the control accuracy of wavelength and power may not reach the optimal level. This may cause certain effects on signal quality in high-speed optical communications or high-precision applications, such as wavelength drift and power fluctuation, which in turn affects the stability of the communication system and the accuracy of data transmission.

[0003] Although existing technologies have made efforts to improve response speed, the response speed of traditional systems may still not be fast enough when faced with rapidly changing communication needs or emergencies. This may lead to delays in wavelength switching or power adjustment, and the dynamic requirements of the communication system cannot be met immediately, affecting communication efficiency and user experience. The system's adaptive ability is relatively weak to changes in external factors such as ambient temperature and vibration. This may cause laser performance to drift during long-term operation or in harsh environments, requiring manual intervention for calibration and adjustment, increasing operation and maintenance costs and workload.

[0004] In the process of achieving wavelength and power regulation, traditional technologies may consume more energy and resources, such as electricity, cooling systems, etc. At the same time, due to the limitations of regulation accuracy and response speed, the energy efficiency ratio may not be ideal, that is, more resources need to be invested to achieve the desired regulation effect. Although certain intelligent elements may have been incorporated into traditional technologies, the level of intelligence is still limited in terms of data processing, decision making, and execution control. This may result in the system being unable to make the final regulation decision when faced with complex or unknown situations, limiting the potential performance and flexibility of the system. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system to achieve automatic adjustment and optimization of wavelength and power and improve the performance and stability of the laser.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows: Multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system, including: A data acquisition module, used to obtain wavelength data and power data of multi-wavelength tunable ultrafast fiber lasers in real time; A data processing module is used to receive and process the acquired wavelength data and power data, perform data analysis, filtering and outlier detection, and obtain data analysis results; An intelligent control module is used to automatically adjust the parameters of the multi-wavelength tunable ultrafast fiber laser according to the data processing and analysis results to control the wavelength and power; A data management module is used to store the acquired wavelength data, power data and related data in the control process; Intelligent interaction module, which is used to provide a multi-modal interactive interface, respond to user needs in real time through an intelligent assistant, and allow multi-user collaboration and remote access; The self-optimization module is used to monitor key indicators of data processing speed, storage capacity, and energy consumption in real time, automatically execute optimization plans and adjust related system configurations, including processor allocation, memory management, and predict system life and failures.

[0007] Further, the acquired wavelength data and power data are received and processed, and data analysis, filtering and outlier detection are performed to obtain data analysis results, including: Receiving wavelength data and power data, and sorting the data to obtain sorted data; Analyze the sorted data and extract key information, including wavelength distribution and power change trend; Filtering the extracted key information to obtain filtered data, and comparing the filtered data with a preset calibration standard value, adjusting the data value through linear transformation to obtain calibrated data; The calibrated data is analyzed and outliers are detected to identify and process abnormal points or outliers in the data and obtain data analysis results.

[0008] Furthermore, the calibrated data is analyzed and outliers are detected to identify and process the outliers or outliers in the data, and the data analysis results are obtained, including: Initialize the parameters of the artificial bee colony according to the characteristics of the calibrated data, including the colony size, number of iterations, and search space range; Define a fitness function to measure whether a data point is an outlier, and simulate hired bees to randomly select data points in the search space; Calculate the fitness value of the data point, and determine whether the corresponding data point is a potential outlier based on the size of the fitness value; The scout bees search for potential outliers based on the potential outliers provided by the employed bees. During the search, they update the fitness values ​​of the data points and randomly search new areas through the scout bees. Repeat the stages of employed bees, observer bees, and scout bees. In each iteration, the fitness value of the data point and the list of outliers are updated until the preset number of iterations is reached and the list of outliers is determined. According to the outlier list, outliers in the calibrated data are processed, including deletion, correction, or retention and annotation; Analyze the remaining data except the outliers, including calculating statistics and drawing data distribution graphs to obtain data analysis results.

[0009] Furthermore, the fitness value calculation formula of the data point is: ; in, Indicates The fitness value of each data point; , , , , represents the weight coefficient; Indicates data points; Indicates The wavelength value of each data point; Represents the mean wavelength of all data points in the data set; represents the wavelength standard deviation of all data points in the data set; Indicates The power value of each data point; Represents the power mean of all data points in the data set; represents the standard deviation of the power of all data points in the data set; Indicates The difference between the skewness of a data point and the skewness of the data set; Indicates The difference between the kurtosis of a data point and the kurtosis of the data set; It is The local density of the data points.

[0010] Furthermore, according to the data processing and analysis results, the parameters of the multi-wavelength tunable ultrafast fiber laser are automatically adjusted to control the wavelength and power, including: Receive and interpret the analysis results from the data processing module, including the current status of wavelength and power, deviations from target values, and detected anomalies; According to the analysis results, a control plan is formulated. If the wavelength deviates from the target value, the wavelength tuning mechanism of the laser is adjusted; if the power decreases, the pump power is increased or the optical path is optimized; According to the control scheme, calculate the adjusted parameter values, including current, voltage, temperature, and grating position; The parameter values ​​are sent to the laser control system, which automatically adjusts the corresponding parameters according to the instruction content to achieve wavelength and power regulation.

[0011] Furthermore, a control scheme is formulated based on the analysis results. If the wavelength deviates from the target value, the wavelength tuning mechanism of the laser is adjusted; if the power decreases, the pump power is increased or the optical path is optimized, including: Parse the analysis results of the data processing module to obtain the wavelength deviation and power deviation, and initialize the proportional coefficient, integral coefficient and differential coefficient for wavelength control and power control respectively; Calculating a proportional control signal and an integral control signal of the wavelength deviation, and calculating a rate of change of the wavelength deviation to obtain a differential control signal; Adding the proportional control signal, the integral control signal and the differential control signal to obtain a total control signal for wavelength control; Determining parameters for adjusting the wavelength tuning mechanism, including current, voltage or grating position, based on the calculated wavelength control total control signal; If the wavelength deviates from the target value, the wavelength tuning mechanism is adjusted according to the control signal, and the proportional, integral and differential control signals of the power control are calculated, and the proportional, integral and differential control signals are added to obtain the total control signal of the power control; According to the total control signal of power control, it is determined to increase the pump power or optimize the optical path parameters. If the power decreases, the pump power is increased or the optical path parameters are adjusted according to the value of the control signal.

[0012] Furthermore, the calculation formula of the total control signal of wavelength control is: ; in, Indicates time The total control signal for wavelength control when represents the time-varying proportionality coefficient; Represents the logarithm of the natural base; Indicates time; Indicates time delay; represents the integral coefficient; represents the integral variable; represents the attenuation coefficient; represents the differential coefficient; represents the coefficient of the nonlinear term; represents the system of higher-order derivative terms; Indicates wavelength deviation Small changes in Indicates time Small changes in Represents the time variable A small change in is an integral variable used to calculate the integral control signal of the wavelength deviation.

[0013] Furthermore, the calculation formula of the total control signal of power control is: ; in, Indicates time The total control signal for power control when Indicates at time Delayed The power deviation after , Represent the first integral coefficient and the second integral coefficient respectively; , Respectively represent the attenuation coefficient of the first integral term and the attenuation coefficient of the second integral term; Indicates power deviation; Indicates power deviation The differential of Indicates the influence coefficient of power deviation; represents the coefficient of the cosine term; represents the angular frequency of the cosine term.

[0014] Furthermore, real-time monitoring of key indicators of data processing speed, storage capacity, and energy consumption, automatic execution of optimization plans and adjustment of related system configurations, including processor allocation, memory management, and prediction of system life and failure points, including: Analyze the collected real-time data, calculate the average value, peak value, and volatility statistics of each key indicator, and obtain the statistical analysis results of the key indicators; Based on the statistical analysis results of key indicators, performance bottlenecks, resource waste or potential failure points are identified, and a performance evaluation report is obtained, including a list of problems and priority ranking; According to the performance evaluation report, formulate optimization plans for processor allocation and memory management system configuration to obtain the optimized system configuration status; Based on the optimized system configuration status, historical data sets, and real-time data sets, the prediction model is used to predict the system life, and potential failure points are predicted based on the statistical analysis results of key indicators and performance evaluation reports.

[0015] Furthermore, based on the optimized system configuration status, historical data sets, and real-time data sets, the prediction model is used to predict the system life, and based on the statistical analysis results of key indicators and performance evaluation reports, potential failure points are predicted, including: Integrate the optimized system configuration status, historical data set and real-time data set to obtain an integrated data set; Conduct statistical analysis of key indicators on the integrated data set and generate key indicator statistical analysis results; Based on the statistical analysis results and performance evaluation report, time series analysis is determined as the forecasting model; Using historical data sets to train the time series analysis model, and adjusting the parameters of the time series analysis model to obtain a trained time series analysis model; Input the real-time data set into the trained time series analysis model to predict the remaining life of the system or the performance trend in the future to generate a system life prediction report, including prediction results, confidence intervals and prediction trend graphs; Predict potential failure points in the system based on statistical analysis results of key indicators, performance evaluation reports, and trained time series analysis models.

[0016] The above solution of the present invention includes at least the following beneficial effects: The data acquisition module acquires accurate wavelength and power data in real time, providing a high-quality information foundation for the data processing module. Combined with advanced data processing algorithms, the system can analyze data more accurately, filter out noise, and detect outliers, thereby significantly improving the control accuracy of wavelength and power. This helps to reduce signal distortion and transmission errors, improve the stability of the communication system and the accuracy of data transmission. Based on the results of data processing and analysis, the intelligent control module can quickly and automatically adjust the parameters of the laser to respond to rapidly changing communication needs or emergencies. This real-time, automatic control mechanism shortens the response time, improves the dynamic performance of the system, and ensures the efficient operation of the communication system.

[0017] The self-optimization module can automatically execute optimization plans and adjust system configurations by real-time monitoring of key indicators such as data processing speed, storage capacity, and energy consumption. This adaptive capability enables the system to better adapt to different working environments and load conditions, reducing the need for manual intervention and reducing operation and maintenance costs. At the same time, the flexibility of the system has also been improved, and it can be more easily integrated into various complex communication networks. Through intelligent control and self-optimization, the system can more reasonably allocate resources, such as processors and memory, thereby improving resource utilization efficiency. In addition, real-time monitoring and optimization of energy consumption help reduce the overall energy consumption of the system and improve energy efficiency.

[0018] The intelligent interaction module provides a multi-mode interactive interface and intelligent assistant, allowing users to interact with the system more conveniently and respond to user needs in real time. At the same time, the multi-user collaboration and remote access functions promote collaboration and information sharing between teams, improving work efficiency. The self-optimization module also has the ability to predict system life and failures. By monitoring key indicators and performance data, it can detect potential problems in advance and take corresponding preventive measures. This helps reduce the risk of system failure, extend the service life of the system, and reduce downtime and maintenance costs caused by failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of a multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system provided by an embodiment of the present invention.

[0020] Figure 2 The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system provided by the embodiment of the present invention analyzes the calibrated data and detects outliers, identifies and processes abnormal points or outliers in the data, and obtains data analysis results. DETAILED DESCRIPTION

[0021] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0022] like Figure 1 As shown, an embodiment of the present invention proposes a multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system, including: A data acquisition module 10 is used to obtain wavelength data and power data of a multi-wavelength tunable ultrafast fiber laser in real time; The data processing module 11 is used to receive and process the acquired wavelength data and power data, perform data analysis, filtering and outlier detection, and obtain data analysis results; An intelligent control module 12 is used to automatically adjust the parameters of the multi-wavelength tunable ultrafast fiber laser according to the data processing and analysis results to control the wavelength and power; A data management module 13, used to store the acquired wavelength data, power data and related data in the control process; Intelligent interaction module 14, used to provide a multi-mode interactive interface, respond to user needs in real time through an intelligent assistant, and allow multi-user collaboration and remote access; The self-optimization module 15 is used to monitor the data processing speed, storage capacity, and key energy consumption indicators in real time, automatically execute the optimization plan and adjust the relevant system configuration, including processor allocation, memory management, and predict system life and failure.

[0023] In an embodiment of the present invention, the data acquisition module can obtain the wavelength and power data of the laser in real time to ensure the timeliness and accuracy of the information. The data processing module further analyzes, filters and detects outliers on these data to obtain more accurate data analysis results. The intelligent control module can automatically adjust the parameters of the laser according to the data processing and analysis results to achieve precise control of the wavelength and power. This intelligent control method not only improves the control efficiency, but also avoids the errors that may be caused by human operation, and further improves the stability and performance of the system.

[0024] The data management module can store all relevant acquired data, including wavelength data, power data, and data during the control process. The intelligent interaction module provides a multi-mode interactive interface that can respond to user needs in real time and supports multi-user collaboration and remote access. This not only improves the user experience, but also enhances the efficiency of teamwork, making the system more flexible and easy to use. The self-optimization module can monitor the key indicators of the system in real time, such as data processing speed, storage capacity, and energy consumption, and automatically execute optimization plans and adjust system configurations according to actual conditions. This not only ensures the efficient operation of the system, but also extends the service life of the system. At the same time, by predicting system life and failures, maintenance preparations can be made in advance, reducing unexpected downtime and improving system reliability.

[0025] In a preferred embodiment of the present invention, real-time acquisition of wavelength data and power data of a multi-wavelength tunable ultrafast fiber laser may include: Select data acquisition equipment suitable for multi-wavelength tunable ultrafast fiber lasers, such as high-speed photodetectors, spectrum analyzers, etc. These devices can capture the wavelength and power information of the laser in real time. According to the output interface of the data acquisition device (such as USB, GPIB, Ethernet, etc.), configure the interface connected to the laser control system. Ensure unimpeded communication between the data acquisition device and the control system. According to the type and interface of the data acquisition device, read and record the wavelength data and power data output by the data acquisition device in real time. Set the appropriate sampling frequency and data accuracy according to the characteristics of the laser and application requirements. High sampling frequency can capture faster changes, while high accuracy can ensure the accuracy of the data.

[0026] Set the data storage method and format in the data acquisition software. You can choose to save the data in real time to a local file, database or cloud storage. Before the laser works, start the data acquisition software and ensure that the connection between the data acquisition device and the laser is normal. The wavelength data and power data output by the data acquisition device are read in real time through the data acquisition software. These data are transmitted in the form of digital signals and converted into readable values. The acquired wavelength data and power data are displayed in real time on the interface of the data acquisition software. Charts, curves or digital displays can be used so that the operator can intuitively monitor the status of the laser. Perform necessary preprocessing on the wavelength data and power data acquired in real time, such as noise removal and smoothing, to improve the quality of the data. During the data acquisition process, monitor possible errors or abnormal conditions in real time, such as data acquisition device failure, communication interruption, etc. Once an error is found, take appropriate measures immediately, such as restarting the data acquisition device, re-establishing the communication connection, etc. Record the key events and operations in the data acquisition process in detail, including the start and end time of data acquisition, the range of data collected, errors that occurred and how to deal with them.

[0027] In a preferred embodiment of the present invention, receiving and processing the acquired wavelength data and power data, performing data analysis, filtering and outlier detection, and obtaining data analysis results may include: Receiving wavelength data and power data, and sorting the data to obtain sorted data; Analyze the sorted data and extract key information, including wavelength distribution and power change trend; Filtering the extracted key information to obtain filtered data, and comparing the filtered data with a preset calibration standard value, adjusting the data value through linear transformation to obtain calibrated data; The calibrated data is analyzed and outliers are detected to identify and process abnormal points or outliers in the data and obtain data analysis results.

[0028] In the embodiment of the present invention, the wavelength data and power data output by the multi-wavelength tunable ultrafast fiber laser are received in real time through the previously configured data interface. The received raw data is preprocessed, including removing duplicate data, filling missing values, and synchronizing timestamps. The processed data is formatted into a unified data structure to obtain a sorted data set. The statistical characteristics of the wavelength data, such as the average value, are calculated. ;in, is the wavelength value of the data point; is the number of data points; It is Wavelength value, standard deviation of each data point . To analyze the wavelength distribution, divide the wavelength data into several intervals of equal width (or "boxes"), and then count the number of data points in each interval. The height of the histogram represents the frequency or relative frequency of the data points in each interval. Through the histogram, we can get a preliminary understanding of the distribution shape of the wavelength data, such as whether it is concentrated, skewed, whether there are multiple peaks, etc. Kernel density estimation is a non-parametric statistical method used to estimate the probability density function of a random variable. Compared with the histogram, kernel density estimation can provide a smoother curve and is not affected by the way the intervals are divided. In kernel density estimation, each data point is regarded as a "kernel" (Gaussian kernel) with a specific shape and width, and then these kernel functions are superimposed to form an overall probability density estimate. Kernel density estimation is expressed as: ,in, is the wavelength The probability density function of is the number of data points; is a kernel function (such as a Gaussian kernel), is the bandwidth parameter, which controls the width of the kernel function. Once the probability density function of the wavelength distribution is obtained through kernel density estimation, , you can use a graphics drawing tool (such as MATLAB, Python's matplotlib library, etc.) to plot this function. This will be a continuous curve showing the probability density of wavelength at various values.

[0029] Similarly, statistical characteristics of power data, such as the mean ; Indicates The power value and standard deviation of each data point . Analyze the power change trend and obtain the power change model over time. Apply appropriate filters, such as low-pass filter, median filter, etc., to the wavelength data and power data to reduce noise and interference in the data. Compare the filtered data with the preset calibration standard value. Adjust the data value through linear transformation (such as scaling and offset) to align it with the standard value to obtain a calibrated data set. Apply an outlier detection algorithm to the calibrated data. Identify abnormal points or outliers in the data.

[0030] Assume that the system receives a series of wavelength data and power data. In the sorting stage, the system converts this data into a unified format and synchronizes the timestamps. Next, in the data analysis stage, the system finds that the wavelength data is mainly distributed in the range of 1530-1565nm, while the power data shows a periodic fluctuation trend. In the filtering stage, the system uses a low-pass filter to reduce the high-frequency noise in the power data. Then, in the calibration stage, the system compares the filtered power data with the preset standard power curve and adjusts the data values ​​through linear transformation to make it better fit the standard curve. Finally, in the outlier detection stage, the system uses statistical models to identify several outliers in the power data, whose values ​​are significantly higher or lower than the expected range. The system replaces these outliers with the average of their adjacent data to obtain the final data analysis results.

[0031] Through filtering and calibration steps, noise and interference in the data can be effectively removed, measurement errors can be reduced, and the accuracy and reliability of the data can be improved. Extracting key information such as wavelength distribution and power change trends can help to more intuitively understand the physical phenomena and laws behind the data and enhance the interpretability of the data. The outlier detection and processing steps can timely detect and handle anomalies in the data. Based on the data results that have been fully processed and analyzed, accurate decisions can be made more efficiently, reducing misjudgment and decision-making risks.

[0032] In another preferred embodiment of the present invention, analyzing and detecting outliers on the calibrated data, identifying and processing outliers or outliers in the data, and obtaining data analysis results may include: Initialize the parameters of the artificial bee colony according to the characteristics of the calibrated data, including the colony size, number of iterations, and search space range; Define a fitness function to measure whether a data point is an outlier, and simulate hired bees to randomly select data points in the search space; Calculate the fitness value of the data point, and determine whether the corresponding data point is a potential outlier based on the size of the fitness value; The scout bees search for potential outliers based on the potential outliers provided by the employed bees. During the search, they update the fitness values ​​of the data points and randomly search new areas through the scout bees. Repeat the stages of employed bees, observer bees, and scout bees. In each iteration, the fitness value of the data point and the list of outliers are updated until the preset number of iterations is reached and the list of outliers is determined. According to the outlier list, outliers in the calibrated data are processed, including deletion, correction, or retention and annotation; Analyze the remaining data except the outliers, including calculating statistics and drawing data distribution graphs to obtain data analysis results.

[0033] In an embodiment of the present invention, the number of employed bees, observer bees and scout bees is set. The maximum number of iterations of the algorithm is set. The search range of the data point is determined according to the characteristics of the calibrated data. A fitness function is designed to evaluate whether the data point is an outlier. For example, a distance-based method such as Mahalanobis distance, Euclidean distance, etc. can be used to calculate the sum of the distances between the data point and the remaining data points as the fitness value of the data point. The larger the distance, the more likely the point is an outlier. The employed bees randomly select data points in the search space and calculate their fitness values. According to the size of the fitness value, it is determined whether the corresponding data point is a potential outlier. For example, a threshold can be set, and when the fitness value of the data point exceeds the threshold, it is considered to be a potential outlier. The observer bee performs further search based on the potential outlier information provided by the employed bee. During the search process, the fitness value of the data point is updated. If a better potential outlier (i.e., a higher fitness value) is found, the original potential outlier is replaced. Through the scout bee mechanism, a new area is randomly searched. Repeat the stages of employed bees and observer bees, and update the fitness value of the data point and the outlier list after each iteration. After reaching the preset number of iterations, the iteration is stopped and the final list of outliers is determined.

[0034] According to the outlier list, outliers in the calibrated data are processed. The processing methods may include deletion, correction (such as using interpolation methods to replace outliers) or retention and annotation (such as marking outliers in the data set). Further analysis of the processed data, including calculation of statistics (such as mean, variance, etc.), drawing of data distribution diagrams (such as histograms, scatter plots, etc.), to obtain the final data analysis results.

[0035] Suppose there is a set of calibrated power data, and you want to detect outliers in it through the artificial bee colony algorithm. Set the colony size to 10 (5 employed bees and 5 observation bees), the number of iterations to 100, and the search space range to the maximum and minimum values ​​of the power data. Use the Euclidean distance as the fitness function to calculate the sum of the distances between each data point and other data points. Each employed bee randomly selects a data point and calculates its fitness value. If the fitness value of a data point exceeds the preset threshold (such as the mean of the fitness values ​​of all data points plus twice the standard deviation), it is considered a potential outlier. The observation bee conducts further search and update based on the potential outlier information provided by the employed bee. At the same time, the scout bee randomly searches new areas. After 100 iterations, the final outlier list is obtained. According to the outlier list, the outliers in the calibrated data are processed. For example, you can choose to delete these outliers or replace them with the average of the adjacent data. Perform statistical analysis on the processed power data, such as calculating the average power, drawing a power distribution histogram, etc., to obtain the final data analysis results.

[0036] Through outlier detection and processing, outliers in the data can be effectively identified and removed or corrected, thereby reducing data noise and interference and improving the overall quality of the data. Outliers often have an adverse effect on the results of data analysis. Accurately identifying and processing outliers through artificial bee colony algorithms can enhance the reliability of the remaining data. After removing or correcting outliers, analyzing the remaining data can more accurately reflect the true distribution and characteristics of the data and improve the accuracy and effectiveness of statistical analysis. By drawing a distribution map of the processed data, the structure and trend of the data can be more clearly displayed, helping researchers to better understand and interpret the data and discover potential data laws and patterns. Based on the data analysis results after outlier processing, decision makers can make accurate judgments more efficiently, reduce decision-making errors caused by data anomalies, and improve the scientificity and effectiveness of decision-making.

[0037] In another preferred embodiment of the present invention, the fitness value calculation formula of the data point is: ; in, Indicates The fitness value of each data point; , , , , represents the weight coefficient; Indicates data points; Indicates The wavelength value of each data point; Represents the mean wavelength of all data points in the data set; represents the wavelength standard deviation of all data points in the data set; Indicates The power value of each data point; Represents the power mean of all data points in the data set; represents the standard deviation of the power of all data points in the data set; Indicates The difference between the skewness of a data point and the skewness of the data set; Indicates The difference between the kurtosis of a data point and the kurtosis of the data set; It is The local density of the data points.

[0038] In the embodiment of the present invention, all calibrated data points are collected, each of which contains a wavelength value. and power value . Calculate the mean of the wavelength values ​​in the entire data set and standard deviation . Calculate the mean of the power values ​​in the entire data set and standard deviation . Calculate the skewness and kurtosis of the entire data set so that the subsequent calculation of the data points and . Set the weight coefficient , , , , These coefficients can be determined empirically or experimentally to reflect the importance of different indicators in fitness evaluation. For each data point in the dataset , perform the following steps: Compute the normalized squared difference of the wavelength values: .

[0039] Calculate the normalized squared difference of the power values: .

[0040] Calculate the absolute value of the difference between the skewness of a data point and the skewness of the data set (This value will be larger if the skewness of the data point is more extreme than the skewness of the data set).

[0041] Calculate the absolute value of the difference between the kurtosis of a data point and the kurtosis of the data set (Reflects the difference between the sharpness of the data point and the overall data set). Use all calculated values ​​and weight coefficients to calculate according to the given fitness function formula .

[0042] The fitness value calculation formula combines multiple features of the data point, including wavelength value, power value, local density (through This multi-dimensional evaluation method enables the algorithm to identify anomalies more comprehensively and accurately, improving the accuracy and reliability of anomaly detection. , , , , , the importance of different features in anomaly detection can be flexibly adjusted. This flexibility enables the algorithm to adapt to the data characteristics in different scenarios, enhancing the versatility and practicality of the algorithm. By standardizing the wavelength value and power value (i.e., subtracting the mean and dividing by the standard deviation), the algorithm can effectively eliminate the impact of dimension and value range differences on anomaly detection. At the same time, the difference in skewness and kurtosis is used as an evaluation indicator.

[0043] In the formula The term takes into account the local density information of the data points, allowing the algorithm to pay attention to global features while also taking into account local features. This combination of local and global methods helps to more accurately identify abnormal points that are different from surrounding data points. By calculating the fitness value of each data point, a quantitative indicator can be generated to measure the degree of abnormality of the data point. This makes the results easier to visualize and interpret, making it easier for users to understand and analyze abnormal phenomena in the data.

[0044] In a preferred embodiment of the present invention, according to the data processing and analysis results, automatically adjusting the parameters of the multi-wavelength tunable ultrafast fiber laser to control the wavelength and power may include: Receive and interpret the analysis results from the data processing module, including the current status of wavelength and power, deviations from target values, and detected anomalies; According to the analysis results, a control plan is formulated. If the wavelength deviates from the target value, the wavelength tuning mechanism of the laser is adjusted; if the power decreases, the pump power is increased or the optical path is optimized; According to the control scheme, calculate the adjusted parameter values, including current, voltage, temperature, and grating position; The parameter values ​​are sent to the laser control system, which automatically adjusts the corresponding parameters according to the instruction content to achieve wavelength and power regulation.

[0045] In an embodiment of the present invention, the data processing module monitors the output state of the multi-wavelength tunable ultrafast fiber laser in real time, including key parameters such as wavelength and power. The module compares the currently monitored wavelength and power data with the preset target value and calculates the deviation value. At the same time, the data processing module also detects whether there is an abnormality in the output, such as wavelength drift, power fluctuation, etc., through an algorithm. The data processing module packages the above analysis results and sends them to the control system through the communication interface. After receiving the analysis results, the control system first analyzes the current state of the wavelength and power, as well as the deviation from the target value. If it is found that the wavelength deviates from the target value, the control system will formulate a wavelength adjustment plan, such as adjusting the wavelength tuning mechanism of the laser (such as rotating the grating or changing the state of the intracavity filter element) to return the wavelength to the target value. If it is found that the power drops, the control system will consider increasing the pump power or optimizing the optical path to increase the output power. The specific plan includes adjusting the current or voltage of the pump source, or optimizing the optical path transmission efficiency by fine-tuning optical path components such as optical fiber connectors and reflectors. Based on the formulated control plan, the control system will further calculate the specific parameter values ​​that need to be adjusted. For wavelength adjustment, it is necessary to calculate the angle of grating rotation or the adjustment amount of the filter element in the cavity. For power adjustment, it is necessary to calculate the increase in pump source current or voltage, or the specific position and angle of fine-tuning of optical path elements. The control system sends the calculated parameter values ​​to the control system of the laser through the communication interface. After receiving the parameter values, the control system converts them into specific control instructions, such as PWM wave signals, stepper motor drive signals, etc. The control system automatically adjusts the corresponding parameters of the laser according to the content of the instructions, such as driving the wavelength tuning mechanism to operate, adjusting the current or voltage of the pump source, etc. During the adjustment process, the control system also monitors the output status of the laser in real time to ensure that the adjustment effect meets expectations. If necessary, the control system can also make iterative adjustments to further optimize the output performance.

[0046] Assume that the current output wavelength of the multi-wavelength tunable ultrafast fiber laser is 1540nm and the power is 10mW, while the target wavelength is 1550nm and the target power is 20mW. The data processing module detects that the wavelength deviates from the target value by 10nm and the power is 10mW lower than the target value, and sends this information to the control system. After receiving the data, the control system first formulates a wavelength adjustment plan: by rotating the grating, the wavelength moves 10nm in the long-wave direction. At the same time, a power increase plan is formulated: increase the current of the pump source to increase the output power. Then the control system calculates the specific angle that the grating needs to be rotated and the increase in the pump source current.

[0047] Next, the control system sends the calculated parameter values ​​to the laser control system. The control system drives the grating to rotate the corresponding angle according to the received instructions and adjusts the current of the pump source. After a period of time, the output wavelength and power of the laser gradually approach and reach the target value. At this time, the control system will monitor the output status in real time and make necessary fine-tuning to ensure stable output near the target value.

[0048] By automatically receiving and parsing the analysis results of the data processing module, the system can accurately grasp the current state of wavelength and power and its deviation from the target value. This makes the control process more precise and can quickly adjust the parameters to the target value, thereby improving the accuracy and efficiency of the control. The system can monitor the output state of the laser in real time and formulate corresponding control plans based on the analysis results. This real-time response and adaptive adjustment capability enables the laser to maintain stable output in various environments and effectively respond to external interference and internal changes. Traditional laser parameter adjustment requires manual work, which is not only inefficient, but also prone to human errors. The automatic adjustment function of this system reduces the need for manual intervention, reduces the impact of human factors, and improves the reliability and stability of the system. The system can adjust the pump power and optical path configuration according to actual needs, avoiding unnecessary energy waste.

[0049] In another preferred embodiment of the present invention, a control scheme is formulated according to the analysis results. If the wavelength deviates from the target value, the wavelength tuning mechanism of the laser is adjusted; if the power decreases, the pump power is increased or the optical path is optimized, which may include: Parse the analysis results of the data processing module to obtain the wavelength deviation and power deviation, and initialize the proportional coefficient, integral coefficient and differential coefficient for wavelength control and power control respectively; Calculating a proportional control signal and an integral control signal of the wavelength deviation, and calculating a rate of change of the wavelength deviation to obtain a differential control signal; Adding the proportional control signal, the integral control signal and the differential control signal to obtain a total control signal for wavelength control; Determining parameters for adjusting the wavelength tuning mechanism, including current, voltage or grating position, based on the calculated wavelength control total control signal; If the wavelength deviates from the target value, the wavelength tuning mechanism is adjusted according to the control signal, and the proportional, integral and differential control signals of the power control are calculated, and the proportional, integral and differential control signals are added to obtain the total control signal of the power control; According to the total control signal of power control, it is determined to increase the pump power or optimize the optical path parameters. If the power decreases, the pump power is increased or the optical path parameters are adjusted according to the value of the control signal.

[0050] In an embodiment of the present invention, the control system receives the analysis result sent by the data processing module, which includes the actual measured values ​​of the current wavelength and power and their deviations from the target values. The initial proportional coefficient, integral coefficient and differential coefficient are set for the wavelength control and the power control respectively. According to the wavelength deviation (the difference between the actual wavelength and the target wavelength), the proportional control signal is calculated. The integral of the wavelength deviation is calculated and multiplied by the integral coefficient to obtain the integral control signal. The rate of change of the wavelength deviation (i.e., the difference between the current wavelength deviation and the last wavelength deviation divided by the time interval) is calculated and multiplied by the differential coefficient to obtain the differential control signal. The proportional, integral and differential control signals are added to obtain the total control signal of the wavelength control. According to the total control signal of the wavelength control, the parameters of the wavelength tuning mechanism that need to be adjusted, such as current, voltage or grating position, are determined.

[0051] If the wavelength deviates from the target value, the wavelength tuning mechanism is adjusted through the control system according to the value of the total control signal to make the wavelength approach the target value. While adjusting the wavelength tuning mechanism, the proportional control signal of the power control is calculated according to the power deviation (the difference between the actual power and the target power). The integral of the power deviation is calculated and multiplied by the integral coefficient to obtain the integral control signal of the power control. The rate of change of the power deviation is calculated and multiplied by the differential coefficient to obtain the differential control signal of the power control. The proportional, integral and differential control signals are added to obtain the total control signal of the power control. According to the total control signal of the power control, it is determined that the pump power needs to be increased or the optical path parameters need to be optimized. If the power drops, according to the value of the total control signal, the pump power is increased or the optical path parameters are adjusted through the control system, such as optimizing the optical fiber connection, adjusting the reflector angle, etc., to increase the output power to the target value.

[0052] Assume that the target wavelength is 1550nm, the actual measured wavelength is 1548nm, and the wavelength deviation is -2nm; the target power is 20mW, the actual measured power is 18mW, and the power deviation is -2mW. After receiving these data, the control system begins to adjust. For wavelength control, the control system calculates the proportional, integral, and differential control signals based on the wavelength deviation, and adds them together to obtain the total control signal for wavelength control. Then, the control system adjusts the wavelength tuning mechanism according to this total control signal, such as by changing the grating position to move the wavelength toward the long-wave direction to approach the target wavelength. At the same time, for power control, the control system also calculates the proportional, integral, and differential control signals based on the power deviation, and adds them together to obtain the total control signal for power control. Based on this total control signal, the control system decides to increase the pump power or optimize the optical path parameters to increase the output power. For example, the control system may increase the current of the pump source to increase the pump power, or fine-tune optical path components such as optical fiber connectors and reflectors to optimize the optical path transmission efficiency.

[0053] By analyzing the analysis results of the data processing module received by the control system, the wavelength deviation and power deviation can be accurately obtained. Combined with the initialized proportional coefficient, integral coefficient and differential coefficient, an accurate control signal can be calculated to achieve high-precision control of wavelength and power. At the same time, this control method has a fast response speed and can adjust the output state of the laser in time. The proportional-integral-differential (PID) control strategy can comprehensively consider the current deviation, historical deviation and deviation change trend to formulate a more reasonable control plan. This control strategy helps to reduce the overshoot of the system, shorten the adjustment time and improve the stability of the system. According to the calculated total control signal of wavelength control and power control, the control system can automatically adjust the parameters of the wavelength tuning mechanism and the pump power or optical path parameters without manual intervention. This automated control method not only improves work efficiency, but also reduces the possibility of human operation errors. By accurately controlling the pump power and optimizing the optical path parameters, unnecessary energy consumption can be reduced while ensuring the output performance of the laser. This intelligent resource utilization and energy consumption management method helps to reduce operating costs and improve energy efficiency. By adjusting the wavelength and power in time, the laser can be kept in the best working state, thereby improving its output performance and service life. At the same time, this control scheme can also effectively deal with external interference and internal changes to ensure stable and reliable operation of the laser.

[0054] In another preferred embodiment of the present invention, the calculation formula of the total control signal of wavelength control is: ; in, Indicates time The total control signal for wavelength control when represents the time-varying proportionality coefficient; Represents the logarithm of the natural base; Indicates time; Indicates time delay; represents the integral coefficient; represents the integral variable; represents the attenuation coefficient; represents the differential coefficient; represents the coefficient of the nonlinear term; represents the system of higher-order derivative terms; Indicates wavelength deviation Small changes in Indicates time Small changes in Represents the time variable A small change in is an integral variable used to calculate the integral control signal of the wavelength deviation.

[0055] In the embodiment of the present invention, the time-varying proportionality coefficient is set , integral coefficient , differential coefficient , nonlinear term coefficient , coefficients of higher-order derivatives , and the attenuation coefficient . Determine the time delay Receive the current time from the data processing module Wavelength deviation , which is the difference between the actual wavelength and the target wavelength. Using the formula Calculate the proportional control signal. Wavelength deviation Integrate while taking into account the attenuation coefficient , using the formula To calculate the integral control signal. Calculate the wavelength deviation About Time The differential of, that is, the rate of change of wavelength deviation, is calculated using the formula To get the differential control signal. Considering the nonlinear factors, use the formula To calculate the nonlinear control signal, we use the formula To obtain the high-order derivative control signal. Add the proportional, integral, differential (and optional nonlinear and high-order derivative) control signals calculated above to obtain the time The total control signal of wavelength control is According to the total control signal , the parameters of the wavelength tuning mechanism (such as current, voltage or grating position) are adjusted through the laser control system to achieve precise control of the wavelength.

[0056] By adopting the total control signal calculation formula including proportional, integral, differential, nonlinear and high-order derivative terms, the output wavelength of the laser can be controlled more accurately. This control method comprehensively considers the current value, historical accumulation, change trend, nonlinear characteristics and rapid changes of the wavelength deviation, thereby achieving high-precision tracking and adjustment of the wavelength. The proportional term can quickly respond to the wavelength deviation and reduce the adjustment time; the integral term can eliminate the static error of the system and improve the control accuracy; the differential term can predict the change trend of the wavelength deviation, make adjustments in advance, and enhance the stability of the system. The combined effect of these terms enables the system to reach the target wavelength quickly and stably. By introducing nonlinear terms and high-order derivative terms, the control system can better adapt to the nonlinear characteristics and rapid dynamic changes of the laser under different working conditions. This highly adaptable feature enables the control system to maintain excellent control performance in various complex environments.

[0057] The attenuation coefficient can gradually reduce the impact of historical deviations during the integration process, thereby suppressing integral saturation and noise interference. This helps to improve the anti-interference ability of the control system and ensure that the laser can maintain stable output when subjected to external interference. By adjusting the proportional coefficient, integral coefficient, differential coefficient, nonlinear term coefficient, and high-order derivative term coefficient, the control strategy can be flexibly adjusted to meet the needs of different application scenarios. This flexible adjustability enables the control system to be optimized according to different situations.

[0058] In another preferred embodiment of the present invention, the calculation formula of the total control signal of power control is: ; in, Indicates time The total control signal for power control when Indicates at time Delayed The power deviation after , Represent the first integral coefficient and the second integral coefficient respectively; , Respectively represent the attenuation coefficient of the first integral term and the attenuation coefficient of the second integral term; Indicates power deviation; Indicates power deviation The differential of Indicates the influence coefficient of power deviation; represents the coefficient of the cosine term; represents the angular frequency of the cosine term.

[0059] In the embodiment of the present invention, the time-varying proportionality coefficient is set , the first and second integration coefficients , , the attenuation coefficients of the first and second integral terms and , differential coefficient , the influence coefficient of power deviation , the coefficient of the cosine term , and the angular frequency of the cosine term . Determine the time delay Receive the current time Delayed Power deviation after , which is the difference between the actual power and the target power. Using the formula Calculate the proportional control signal. Power Deviation Integrate and consider the first attenuation coefficient , using the formula To calculate the first integral control signal.

[0060] Similarly, the square of the power deviation is integrated, taking into account the second attenuation factor , using the formula Calculate the second integral control signal. Calculate the power deviation About Time The differential of, that is, the rate of change of the power deviation, is calculated using the formula To get the differential control signal. Use the formula To calculate the direct impact signal of power deviation. Considering the periodic impact, use the formula To calculate the cosine control signal. Add the above calculated proportion, integral, differential, power deviation effect and cosine control signal to get the time The total control signal of power control when According to the total control signal , adjust the power output through the power regulation system to achieve precise power control.

[0061] By comprehensively applying proportional, integral, differential and nonlinear control strategies, high-precision control of power output can be achieved. The proportional term responds quickly to power deviations, the integral term eliminates steady-state errors, and the differential term predicts the trend of deviation changes, thereby ensuring that the system can quickly and accurately reach and maintain the target power output. Two integral terms, one of which considers the square of the power deviation, enable the control system to better adapt to nonlinear characteristics. This design enhances the stability and control accuracy of the system under different operating conditions, especially when the power deviation is large or complex. The attenuation coefficient included in the integral term enables the system to gradually reduce its dependence on historical power deviations, thereby avoiding integral saturation and over-regulation. This attenuation memory capability improves the robustness of the control system, enabling it to maintain stable performance in the face of continuously changing power demands. By introducing the cosine term, the control system can actively respond to periodic interference. By adjusting the coefficient and angular frequency of the cosine term, effective suppression of specific frequency interference can be achieved, thereby further improving the system's anti-interference ability and control accuracy.

[0062] The various coefficients in the formula (such as proportional coefficient, integral coefficient, differential coefficient, etc.) can be adjusted according to actual needs. This flexible adjustability enables the control system to easily adapt to different application scenarios and working conditions to achieve control effects.

[0063] In a preferred embodiment of the present invention, the acquired wavelength data, power data and related data in the control process are stored; a multi-mode interactive interface is provided, and through an intelligent assistant, real-time responses to user needs are provided, and multi-user collaboration and remote access are allowed, which may include: In the embodiment of the present invention, a suitable database system (such as MySQL, PostgreSQL, MongoDB, etc.) is selected to store wavelength data, power data and related data in the control process. Design a data table structure, including field names, data types, constraints, etc., to store wavelength data (such as timestamps, wavelength values, deviations, etc.), power data (such as timestamps, power values, deviations, etc.) and control data (such as control signal values, control time, etc.). Develop a data storage interface to write real-time collected data into the database and ensure the consistency and integrity of the data. Analyze user needs and determine the functions that the interactive interface needs to provide, such as data viewing, data analysis, remote control, etc. Design a prototype of the interactive interface, including layout, controls, interactive processes, etc., to meet the needs and operating habits of different users. Use front-end technologies (such as HTML, CSS, JavaScript, etc.) and back-end frameworks (such as Flask, Django, etc.) to develop a multi-mode interactive interface.

[0064] Integrate natural language processing (NLP) technologies, such as speech recognition and text analysis, to understand and parse user voice or text instructions. Develop intent recognition mechanisms to map user instructions to specific operations or query requests and respond to user needs in real time. Use machine learning technology to allow the intelligent assistant to continuously learn and optimize responses to user needs, improving the accuracy and efficiency of interactions. Implement user authentication mechanisms, such as username / password login, OAuth, etc., to ensure that only authorized users can access the system. Design a permission management system to control the viewing, modification, and deletion permissions of different users for data. Develop a data synchronization mechanism to ensure that multiple users can share and view the latest wavelength data, power data, and control data in real time. Implement multi-user collaboration functions, such as annotations, tags, discussions, etc., so that users can jointly analyze and solve problems. Configure the server and network environment to allow users to remotely access the system via the Internet and perform secure data transmission. Perform detailed tests on each function of the system to ensure that functions such as data storage, interactive interface, intelligent assistant, multi-user collaboration, and remote access work properly. Perform performance tests on the system, including response time, number of concurrent users, data transmission speed, etc., to ensure that the system can meet the needs of actual application scenarios.

[0065] In a preferred embodiment of the present invention, real-time monitoring of data processing speed, storage capacity, key energy consumption indicators, automatic execution of optimization solutions and adjustment of related system configurations, including processor allocation, memory management, and prediction of system life and failure points may include: Analyze the collected real-time data, calculate the average value, peak value, and volatility statistics of each key indicator, and obtain the statistical analysis results of the key indicators; Based on the statistical analysis results of key indicators, performance bottlenecks, resource waste or potential failure points are identified, and a performance evaluation report is obtained, including a list of problems and priority ranking; According to the performance evaluation report, formulate optimization plans for processor allocation and memory management system configuration to obtain the optimized system configuration status; Based on the optimized system configuration status, historical data sets, and real-time data sets, the prediction model is used to predict the system life, and potential failure points are predicted based on the statistical analysis results of key indicators and performance evaluation reports.

[0066] In the embodiment of the present invention, key indicators such as data processing speed, storage capacity, energy consumption, etc. that need to be monitored in real time are clearly defined. Monitoring agents are deployed at key nodes of the system to ensure that the required indicator data can be captured in real time.

[0067] A secure data transmission channel is established to send the collected real-time data to the central data processing unit for storage.

[0068] For example, use the Prometheus monitoring tool to collect server CPU usage, memory usage, disk IO and other indicator data, and visualize them through Grafana.

[0069] Clean, denoise and format the collected real-time data. Calculate the average, peak, volatility and other statistical quantities of each key indicator to identify outliers and trends. Generate a performance evaluation report based on the statistical analysis results, list potential performance bottlenecks, resource waste and failure points, and sort them by priority.

[0070] Establish a strategy library containing various optimization strategies, such as dynamic processor allocation strategy, memory compression strategy, etc.

[0071] According to the problem types identified in the performance evaluation report, the most appropriate optimization solution is matched from the strategy library.

[0072] Verify the effectiveness of the optimization plan in a sandbox environment or test environment, and implement it in the production environment after confirmation. After implementing the optimization plan, continue to monitor system performance to ensure that the optimization effect meets expectations.

[0073] For example, for the memory waste problem identified in the performance evaluation report, select a memory compression strategy from the strategy library for implementation. By adjusting the memory management configuration parameters, reduce unnecessary memory usage and improve the overall system performance. Integrate the optimized system configuration status, historical data sets, and real-time data sets. Use machine learning technology to build a prediction model, such as a prediction model based on time series. Use the trained prediction model to predict the system life, and combine the statistical analysis results of key indicators and the performance evaluation report to predict potential failure points. Display the prediction results to the user in a visual form so that the user can take appropriate measures in advance to extend the system life or prevent failures.

[0074] For example, we use TensorFlow to build an LSTM (Long Short-Term Memory) neural network model to predict the remaining life of the server. At the same time, we combine the statistical analysis results of key indicators such as CPU usage and memory occupancy to predict possible hardware failure points, such as hard disk damage and memory leaks. We display the prediction results through the Grafana dashboard to remind users to take maintenance measures in a timely manner.

[0075] Regularly collect user feedback on optimization effects and prediction results. Based on user feedback and actual monitoring results, continuously optimize prediction models and optimization strategies to improve prediction accuracy and optimization effects. Iterate and improve the entire process of real-time monitoring, performance evaluation, optimization plan formulation and implementation, system life and fault point prediction to adapt to the ever-changing system environment and business needs.

[0076] For example, we regularly communicate with users to understand their satisfaction with the optimization solutions and prediction results. Based on user feedback, we adjust the optimization strategy parameters or improve the prediction model structure to improve system performance and prediction accuracy. At the same time, we pay attention to the latest technological developments in the industry and continuously introduce new optimization methods and prediction technologies to improve the effectiveness of the overall solution.

[0077] By monitoring the data processing speed in real time and combining it with the statistical analysis results, bottlenecks in the data processing process, such as insufficient CPU or memory resources, can be discovered in a timely manner. The automatic execution of the optimization plan can dynamically adjust the processor allocation and memory management strategy to ensure that data processing tasks are efficiently allocated resources, thereby improving the overall data processing efficiency. Real-time monitoring of storage capacity usage can predict the growth trend of storage capacity and avoid data overflow or waste of storage resources. Based on the historical data and real-time data of storage capacity usage, the prediction model can be used to reasonably plan the storage expansion plan to ensure the stability and scalability of the storage system. Real-time monitoring of key energy consumption indicators, such as server power consumption, helps to discover energy consumption anomalies and energy-saving potential. The optimization plan can adjust the system configuration, such as reducing the processor frequency during non-critical periods or shutting down idle devices, to reduce unnecessary energy consumption, thereby reducing operating costs. Identifying potential failure points through performance evaluation reports and taking preventive measures in advance, such as replacing aging hardware or optimizing software configuration, can reduce the probability of system failures. The application of prediction models can predict system life and help plan hardware update cycles. Automated execution of optimization plans reduces the need for manual intervention, reduces operation and maintenance costs, and improves response speed. The automatic generation of performance evaluation reports and the prioritization of problem lists enable the operation and maintenance team to quickly locate and handle key issues, improving overall operation and maintenance efficiency. Continuous optimization of system performance and timely prediction and handling of fault points can ensure that users have a smoother and more stable experience when using the system. Reducing system failures and downtime improves system availability, thereby enhancing user satisfaction.

[0078] In another preferred embodiment of the present invention, the system life is predicted using a prediction model based on the optimized system configuration state, historical data set, and real-time data set, and potential failure points are predicted based on the statistical analysis results of key indicators and performance evaluation reports, which may include: Integrate the optimized system configuration status, historical data set and real-time data set to obtain an integrated data set; Conduct statistical analysis of key indicators on the integrated data set and generate key indicator statistical analysis results; Based on the statistical analysis results and performance evaluation report, time series analysis is determined as the forecasting model; Using historical data sets to train the time series analysis model, and adjusting the parameters of the time series analysis model to obtain a trained time series analysis model; Input the real-time data set into the trained time series analysis model to predict the remaining life of the system or the performance trend in the future to generate a system life prediction report, including prediction results, confidence intervals and prediction trend graphs; Predict potential failure points in the system based on statistical analysis results of key indicators, performance evaluation reports, and trained time series analysis models.

[0079] In an embodiment of the present invention, the optimized system configuration state data is collected, which includes key configuration information such as processor allocation and memory management strategy. Next, the historical data set is integrated, which usually includes system performance data in the past period of time, such as processing speed, storage capacity usage, energy consumption, etc. Then, the real-time data set is collected, which reflects the current operating state of the system. Finally, the three parts of data (optimized configuration state, historical data set, real-time data set) are integrated to form a data set that comprehensively reflects the system state. The integrated data set is statistically analyzed to calculate the average value, standard deviation, peak value, volatility and other statistics of each key indicator. Through these statistics, the overall performance of the system and the distribution of each indicator can be preliminarily understood. According to the statistical analysis results and the performance evaluation report, a suitable time series analysis model is selected as the prediction model. The time series analysis model can capture the law of data changes over time and is suitable for predicting system life and performance trends. The time series analysis model includes ARIMA (autoregressive integrated moving average model). The selected time series analysis model is trained using the historical data set. The model parameters are adjusted by cross-validation, grid search and other technologies to find the model configuration. Evaluate the performance of the model to ensure that it has a good fit on the training set. Input the real-time data set into the trained time series analysis model to predict system performance in the future. Based on the prediction results, generate a system life prediction report, including the predicted remaining life, confidence interval, and prediction trend chart. This information helps plan system updates or maintenance plans in advance.

[0080] Combine the statistical analysis results of key indicators, performance evaluation reports, and trained time series analysis models to identify potential failure points in the system. By analyzing abnormal fluctuations in key indicators, exceeding thresholds, and future performance trends predicted by the model, it is possible to predict which components or modules may fail.

[0081] Identifying potential failure points in advance helps take preventive measures in time to avoid system crashes or data loss.

[0082] Suppose there is a large data center that needs to predict the lifespan and potential failure points of its server system. Collect the optimized configuration status of the server, such as CPU allocation strategy, memory usage strategy, etc. Integrate historical data from the past year, including key indicators such as server processing speed, storage capacity, and energy consumption.

[0083] Collect the current operating status data of the server in real time. Perform statistical analysis on the integrated data and find that the CPU usage of a certain server is continuously high and fluctuates greatly. Based on the statistical analysis results, select the ARIMA model as the prediction model because it performs well in processing time series data. Use historical data sets to train the ARIMA model and find the best parameter configuration through grid search. Input real-time data into the trained ARIMA model to predict the performance trend of the server in the next 6 months. Based on the prediction results, generate a system life prediction report showing that the server may reach a performance bottleneck in 8 months. Combining the statistical analysis results with the ARIMA model prediction, it is found that the hard disk read and write speed of a certain server has dropped abnormally, and the predicted trend continues to deteriorate. Identify this hard disk as a potential failure point and plan to replace the hard disk in advance to avoid data loss or system crash.

[0084] By integrating the optimized system configuration status, historical data sets, and real-time data sets, a more comprehensive and accurate data foundation can be obtained, thereby improving the accuracy of the prediction model. The time series analysis model can capture the changing patterns of data over time, making the prediction of system life and potential failure points more accurate. By predicting the remaining life of the system, the trend of system performance degradation can be discovered in time, and corresponding maintenance measures can be taken to extend the service life of the system. This helps to reduce the frequency of system replacement, reduce cost expenditure, and improve system stability. Combining the statistical analysis results of key indicators, performance evaluation reports, and trained time series analysis models, potential failure points in the system can be accurately predicted. This enables the operation and maintenance team to take preventive measures before failure occurs, avoid system crashes or data loss, and ensure the continuous and stable operation of the system. Based on the results of the prediction model, the system's resource configuration, such as processor allocation and memory management strategies, can be adjusted to meet performance requirements in the future. This helps to avoid resource waste and shortages, ensure efficient use of system resources, and improve overall performance. Through automated prediction and report generation, the need for manual analysis and intervention can be reduced, and the work efficiency of the operation and maintenance team can be improved.

[0085] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system, characterized by: include: A data acquisition module, used to obtain wavelength data and power data of multi-wavelength tunable ultrafast fiber lasers in real time; A data processing module is used to receive and process the acquired wavelength data and power data, perform data analysis, filtering and outlier detection, and obtain data analysis results; An intelligent control module is used to automatically adjust the parameters of the multi-wavelength tunable ultrafast fiber laser according to the data processing and analysis results to control the wavelength and power; A data management module is used to store the acquired wavelength data, power data and related data in the control process; Intelligent interaction module, which is used to provide a multi-modal interactive interface, respond to user needs in real time through an intelligent assistant, and allow multi-user collaboration and remote access; The self-optimization module is used to monitor key indicators of data processing speed, storage capacity, and energy consumption in real time, automatically execute optimization plans and adjust related system configurations, including processor allocation, memory management, and predict system life and failures.

2. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 1 is characterized in that: Receive and process the acquired wavelength data and power data, perform data analysis, filtering and outlier detection, and obtain data analysis results, including: Receiving wavelength data and power data, and sorting the data to obtain sorted data; Analyze the sorted data and extract key information, including wavelength distribution and power change trend; Filtering the extracted key information to obtain filtered data, and comparing the filtered data with a preset calibration standard value, adjusting the data value through linear transformation to obtain calibrated data; The calibrated data is analyzed and outliers are detected to identify and process abnormal points or outliers in the data and obtain data analysis results.

3. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 2, characterized in that: Analyze and detect outliers on the calibrated data, identify and process outliers or outliers in the data, and obtain data analysis results, including: Initialize the parameters of the artificial bee colony according to the characteristics of the calibrated data, including the colony size, number of iterations, and search space range; Define a fitness function to measure whether a data point is an outlier, and simulate hired bees to randomly select data points in the search space; Calculate the fitness value of the data point, and determine whether the corresponding data point is a potential outlier based on the size of the fitness value; The scout bees search for potential outliers based on the potential outliers provided by the employed bees. During the search, they update the fitness values ​​of the data points and randomly search new areas through the scout bees. Repeat the stages of employed bees, observer bees, and scout bees. In each iteration, the fitness value of the data point and the list of outliers are updated until the preset number of iterations is reached and the list of outliers is determined. According to the outlier list, outliers in the calibrated data are processed, including deletion, correction, or retention and annotation; Analyze the remaining data except the outliers, including calculating statistics and drawing data distribution graphs to obtain data analysis results.

4. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 3, characterized in that: The fitness value calculation formula of a data point is: ; in, Indicates The fitness value of each data point; , , , , represents the weight coefficient; Indicates data points; Indicates The wavelength value of each data point; Represents the mean wavelength of all data points in the data set; represents the wavelength standard deviation of all data points in the data set; Indicates The power value of each data point; Represents the power mean of all data points in the data set; represents the standard deviation of the power of all data points in the data set; Indicates The difference between the skewness of a data point and the skewness of the data set; Indicates The difference between the kurtosis of a data point and the kurtosis of the data set; It is The local density of the data points.

5. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 4, characterized in that: According to the data processing and analysis results, the parameters of the multi-wavelength tunable ultrafast fiber laser are automatically adjusted to control the wavelength and power, including: Receive and interpret the analysis results from the data processing module, including the current status of wavelength and power, deviations from target values, and detected anomalies; According to the analysis results, a control plan is formulated. If the wavelength deviates from the target value, the wavelength tuning mechanism of the laser is adjusted; if the power decreases, the pump power is increased or the optical path is optimized; According to the control scheme, calculate the adjusted parameter values, including current, voltage, temperature, and grating position; The parameter values ​​are sent to the laser control system, which automatically adjusts the corresponding parameters according to the instruction content to achieve wavelength and power regulation.

6. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 5, characterized in that: According to the analysis results, a control plan is formulated. If the wavelength deviates from the target value, the wavelength tuning mechanism of the laser is adjusted; If the power drops, increase the pump power or optimize the optical path, including: Parse the analysis results of the data processing module to obtain the wavelength deviation and power deviation, and initialize the proportional coefficient, integral coefficient and differential coefficient for wavelength control and power control respectively; Calculating a proportional control signal and an integral control signal of the wavelength deviation, and calculating a rate of change of the wavelength deviation to obtain a differential control signal; Adding the proportional control signal, the integral control signal and the differential control signal to obtain a total control signal for wavelength control; Determining parameters for adjusting the wavelength tuning mechanism, including current, voltage or grating position, based on the calculated wavelength control total control signal; If the wavelength deviates from the target value, the wavelength tuning mechanism is adjusted according to the control signal, and the proportional, integral and differential control signals of the power control are calculated, and the proportional, integral and differential control signals are added to obtain the total control signal of the power control; According to the total control signal of power control, it is determined to increase the pump power or optimize the optical path parameters. If the power decreases, the pump power is increased or the optical path parameters are adjusted according to the value of the control signal.

7. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 6, characterized in that: The calculation formula of the total control signal for wavelength control is: ; in, Indicates time The total control signal for wavelength control when represents the time-varying proportionality coefficient; Represents the logarithm of the natural base; Indicates time; Indicates time delay; represents the integral coefficient; represents the integral variable; represents the attenuation coefficient; represents the differential coefficient; represents the coefficient of the nonlinear term; represents the system of higher-order derivative terms; Indicates wavelength deviation Small changes in Indicates time Small changes in Represents the time variable A small change in is an integral variable used to calculate the integral control signal of the wavelength deviation.

8. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 7, characterized in that: The calculation formula of the total control signal for power control is: ; in, Indicates time The total control signal for power control when Indicates at time Delayed The power deviation after , Represent the first integral coefficient and the second integral coefficient respectively; , Respectively represent the attenuation coefficient of the first integral term and the attenuation coefficient of the second integral term; Indicates power deviation; Indicates power deviation The differential of Indicates the influence coefficient of power deviation; represents the coefficient of the cosine term; represents the angular frequency of the cosine term.

9. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 8, characterized in that: Real-time monitoring of key indicators of data processing speed, storage capacity, and energy consumption, automatically executing optimization plans and adjusting related system configurations, including processor allocation and memory management, and predicting system life and failure points, including: Analyze the collected real-time data, calculate the average value, peak value, and volatility statistics of each key indicator, and obtain the statistical analysis results of the key indicators; Based on the statistical analysis results of key indicators, performance bottlenecks, resource waste or potential failure points are identified, and a performance evaluation report is obtained, including a list of problems and priority ranking; According to the performance evaluation report, formulate optimization plans for processor allocation and memory management system configuration to obtain the optimized system configuration status; Based on the optimized system configuration status, historical data sets, and real-time data sets, the prediction model is used to predict the system life, and potential failure points are predicted based on the statistical analysis results of key indicators and performance evaluation reports.

10. The multi-wavelength tunable ultrafast fiber laser wavelength and power data intelligent control system according to claim 9, characterized in that: Based on the optimized system configuration status, historical data sets, and real-time data sets, the prediction model is used to predict the system lifespan, and based on the statistical analysis results of key indicators and performance evaluation reports, potential failure points are predicted, including: Integrate the optimized system configuration status, historical data set and real-time data set to obtain an integrated data set; Conduct statistical analysis of key indicators on the integrated data set and generate key indicator statistical analysis results; Based on the statistical analysis results and performance evaluation report, time series analysis is determined as the forecasting model; Using historical data sets to train the time series analysis model, and adjusting the parameters of the time series analysis model to obtain a trained time series analysis model; Input the real-time data set into the trained time series analysis model to predict the remaining life of the system or the performance trend in the future to generate a system life prediction report, including prediction results, confidence intervals and prediction trend graphs; Predict potential failure points in the system based on statistical analysis results of key indicators, performance evaluation reports, and trained time series analysis models.

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