High-power fiber laser stable output control method and system

Abstract

The present invention relates to the field of laser control technology, and in particular to a method and system for controlling the stable output of a high-power fiber laser. The method comprises the following steps: real-time monitoring of a fiber laser multimodal sensing signal based on a fiber sensor array; dynamic fiber sensing change analysis of the fiber laser multimodal sensing signal to generate dynamic fiber sensing change characteristics; laser output phase distribution analysis of the dynamic fiber sensing change characteristics, and real-time power feedback adjustment to construct a dynamic beam power feedback adjustment strategy; overheating power imbalance analysis based on the fiber laser multimodal sensing signal, and local temperature control power fine-tuning to construct a local temperature control power fine-tuning strategy; sensor position calculation of the fiber sensor array one by one, and nonlinear optical effect analysis to generate nonlinear optical effect characteristics. The present invention realizes efficient and stable laser output control.

Description

Technical Field

[0001] The present invention relates to the field of laser control technology, and in particular to a method and system for controlling the stable output of a high-power fiber laser. Background Art

[0002] As one of the widely used laser sources in modern industry, high-power fiber lasers have played an important role in many fields such as material processing, medical treatment, and laser communication due to their high efficiency, high-power output, and excellent beam quality. Especially in high-power application scenarios such as metal cutting, welding, and marking, the stable output of high-power fiber lasers is directly related to work efficiency and processing quality. However, in practical applications, due to the complexity of the working environment of fiber lasers and the influence of factors such as temperature fluctuations, power supply fluctuations, and fiber loss during operation, the output power of fiber lasers often fluctuates, attenuates, or becomes unstable. These problems will not only affect the performance of the laser, but may also lead to reduced processing accuracy, reduced production efficiency, and even damage to the laser itself.

[0003] Traditional high-power fiber laser stable output control methods usually rely on a single feedback control mechanism, such as power closed-loop control or temperature compensation control. Although these methods can improve the laser output stability to a certain extent, due to the lag in response to external environmental factors and internal working conditions, they often have disadvantages such as low control accuracy, slow response speed, and poor stability. Especially in the case of high-power output, with the increase in power demand and the accumulation of fiber loss, the stability problem of laser output becomes more prominent. Therefore, in order to meet the high requirements for laser output stability in modern industrial production, a new, efficient, and intelligent fiber laser stable output control method is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a high-power fiber laser stable output control method and system to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a high-power fiber laser stable output control method, comprising the following steps:

[0006] Step S1: real-time monitoring of a fiber laser multimodal sensing signal based on a fiber sensor array; performing dynamic fiber sensing change analysis on the fiber laser multimodal sensing signal to generate a dynamic fiber sensing change feature;

[0007] Step S2: Analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and construct a dynamic beam power feedback adjustment strategy;

[0008] Step S3: performing overheat power imbalance analysis based on the fiber laser multimodal sensing signal, and performing local temperature control power fine-tuning, thereby constructing a local temperature control power fine-tuning strategy;

[0009] Step S4: calculating the position of each sensor in the optical fiber sensor array, and performing nonlinear optical effect analysis to generate nonlinear optical effect characteristics;

[0010] Step S5: predicting the long-term distortion trend of the nonlinear optical effect characteristics and performing beam output distortion compensation to obtain a beam distortion compensation strategy;

[0011] Step S6: Coordinated control optimization is performed on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and dynamic migration optimization is performed to construct an intelligent laser control optimization model.

[0012] The present invention can obtain the state information of the fiber laser in time by real-time monitoring of the multimodal sensing signal of the fiber laser, and provide basic data for subsequent control and adjustment. The dynamic fiber sensing change analysis is helpful to generate the dynamic fiber sensing change characteristics, provide an in-depth understanding of the state change of the fiber laser, and lay the foundation for the formulation of the control strategy. The laser output phase distribution analysis combined with the real-time power feedback adjustment can maintain the phase stability of the beam output and improve the laser output quality and stability. The construction of the dynamic beam power feedback adjustment strategy is helpful to achieve the precise adjustment of the beam power and maintain the stability and consistency of the laser output power. The overheating power imbalance analysis combined with the local temperature control power fine-tuning strategy can effectively solve the power imbalance problem caused by the excessively high local temperature of the fiber laser and improve the output power balance. The construction of the local temperature control power fine-tuning strategy is helpful to maintain the stable operating temperature of each part of the fiber laser, reduce the power imbalance, and improve the output efficiency and stability of the laser. Qualitative, sensor position calculation and nonlinear optical effect analysis are helpful to understand the structure and characteristics of the fiber sensor array, and provide a basis for subsequent control strategies. The generated nonlinear optical effect characteristics can help identify and correct nonlinear distortion in the optical system, and improve the stability and quality of the beam output. Long-term operation distortion trend prediction and beam output distortion compensation are helpful to discover the distortion trend of the beam output in advance, and take compensation measures to maintain stable output quality. The beam distortion compensation strategy can effectively improve the quality and stability of the beam output, and improve the long-term operation effect and reliability of the laser. The combination of collaborative control optimization and dynamic migration optimization can realize the collaborative optimization of dynamic beam power feedback, local temperature control power fine-tuning and beam distortion compensation strategy, improve the overall performance of the laser control system, and build an intelligent laser control optimization model to help improve the stability, output efficiency and quality of the laser, and achieve the best effect of stable output control of high-power fiber lasers.

[0013] Preferably, step S1 comprises the following steps:

[0014] Step S11: real-time monitoring of the multi-modal sensing signal of the optical fiber laser based on the optical fiber sensor array;

[0015] Step S12: performing digital conversion processing on the fiber laser multimodal sensing signal to generate real-time fiber multimodal monitoring parameters;

[0016] Step S13: performing filtering and noise reduction processing on the real-time optical fiber multimodal monitoring parameters to generate filtering optimized optical fiber monitoring parameters;

[0017] Step S14: Performing dynamic optical fiber sensing change analysis on the filter optimization optical fiber monitoring parameters to generate dynamic optical fiber sensing change characteristics.

[0018] The present invention can timely acquire the multimodal sensing signal data of the fiber laser by real-time monitoring of the multimodal sensing signal of the fiber laser, and provide basic information for subsequent processing. The digital conversion and processing of the multimodal sensing signal helps to convert the signal into a digital form that can be processed by a computer, and improves the accuracy and efficiency of signal processing. The generation of real-time fiber multimodal monitoring parameters can provide more intuitive and easy-to-analyze data, and help to deeply understand the state of the fiber laser. The filtering and noise reduction processing can effectively remove the noise and interference in the signal, and improve the accuracy and stability of the monitoring parameters. The generation of filtering and optimizing the fiber monitoring parameters helps to provide more reliable and stable monitoring data, and provides a better basis for subsequent analysis and control. The dynamic fiber sensing change analysis combined with the optimization of the monitoring parameters helps to more accurately understand the sensing changes of the fiber laser, and provides a more accurate basis for the formulation of the subsequent control strategy. The generated dynamic fiber sensing change characteristics can help to identify and understand the state change mode of the fiber laser, and provide guidance for the optimization and improvement of the control system.

[0019] Preferably, the specific steps of step S14 are:

[0020] Perform laser micro-vibration identification on the filter-optimized optical fiber monitoring parameters and extract laser micro-vibration data;

[0021] Performing light intensity calculation on the filter-optimized optical fiber monitoring parameters to obtain light intensity values;

[0022] Performing time-series intensity variation evolution on the light intensity value, thereby generating a laser time-series intensity variation value;

[0023] Comprehensively evaluate the beam quality based on the laser time-series intensity variation value and laser micro-vibration data to generate a quantitative evaluation value of the beam quality;

[0024] Extract laser power output parameters based on filtering and optimizing fiber monitoring parameters;

[0025] Perform multi-time point power fluctuation mining on laser power output parameters to generate multi-time point laser power fluctuation features;

[0026] Dynamic fiber optic sensing change analysis is performed on the laser power fluctuation characteristics at multiple time points and the quantitative evaluation values ​​of the beam quality to generate dynamic fiber optic sensing change characteristics.

[0027] The present invention can understand the micro-vibration of the laser by identifying and extracting laser micro-vibration data, which is helpful to evaluate the stability and performance change of the laser. The calculated light intensity value can provide important information about the intensity of the laser output light, provide basic data for subsequent analysis, and help evaluate the output quality of the laser. The generation of the time-series intensity change value can reveal the change trend of the laser output intensity over time, and provide an important basis for further analysis of the beam quality. By comprehensively evaluating the time-series intensity change and micro-vibration data of the laser, the quality of the beam can be quantitatively evaluated to provide guidance for further optimization of control. Extracting the laser power output parameter is helpful to understand the power output of the laser and provide key data support for analyzing the performance of the laser. By mining the fluctuation of the laser power at multiple time points, the variation law of the laser power can be deeply understood, and a reference can be provided for the stability of the control system. By dynamically analyzing the laser power fluctuation characteristics and the beam quality evaluation value, the sensor changes of the fiber laser can be comprehensively evaluated, and more accurate data support can be provided for output control.

[0028] Preferably, the specific steps of step S2 are:

[0029] Step S21: performing laser output phase distribution analysis on dynamic optical fiber sensing change characteristics to generate dynamic laser phase distribution data;

[0030] Step S22: performing stress disturbance identification on the dynamic laser phase distribution data to obtain dynamic laser stress disturbance characteristics;

[0031] Step S23: mining the influence of beam power distribution stability based on the dynamic laser stress disturbance characteristics, thereby generating beam power distribution stability characteristics;

[0032] Step S24: Perform real-time power feedback adjustment according to the stability characteristics of the beam power distribution and construct a dynamic beam power feedback adjustment strategy.

[0033] By analyzing the dynamic optical fiber sensing change characteristics, the present invention can understand the phase distribution of the laser and provide important data support for subsequent adjustment strategies. Identifying and extracting the stress disturbance characteristics of the dynamic laser is helpful to understand the external disturbance of the laser system and provide a more accurate analysis basis for stable output. By exploring the influence of stress disturbance on the beam power distribution, the beam power distribution stability characteristics can be generated to help evaluate the stability of the beam power. Real-time feedback adjustment based on the power distribution stability characteristics can help maintain the stability of the beam power and improve the quality and efficiency of the laser output. Constructing a dynamic beam power feedback adjustment strategy can realize real-time adjustment of the laser output and improve the response speed and stability of the system.

[0034] Preferably, step S3 specifically comprises the following steps:

[0035] Step S31: performing laser temperature change analysis on the filter optimization optical fiber monitoring parameters to generate laser temperature change characteristics;

[0036] Step S32: performing discrete fitting of time series fluctuations on the laser temperature variation characteristics to construct a laser temperature fluctuation curve;

[0037] Step S33: fitting the laser surface temperature distribution to the laser temperature fluctuation curve to construct the surface temperature distribution field;

[0038] Step S34: performing local overheat analysis on the surface temperature distribution field to generate a local overheat area;

[0039] Step S35: performing overheat power imbalance analysis on the local overheat area, thereby generating laser overheat power imbalance data;

[0040] Step S36: Perform local temperature control power fine-tuning based on the laser overheat power imbalance data, thereby constructing a local temperature control power fine-tuning strategy.

[0041] The present invention can understand the performance changes of fiber lasers at different temperatures through laser temperature change analysis, providing an important reference for subsequent control. Generating laser temperature change characteristics is helpful to identify the law and trend of laser temperature change, providing data support for temperature control. By constructing a laser temperature fluctuation curve, the fluctuation of laser temperature can be presented more clearly, providing a basis for subsequent analysis. Time series fluctuation discrete fitting is helpful for more detailed modeling and analysis of temperature changes, improving the accuracy of temperature control. Constructing a surface temperature distribution field can help understand the distribution of laser temperature on the surface, providing a basis for local temperature control. Fitting the laser surface temperature distribution is helpful for a more comprehensive understanding of the laser temperature, providing a basis for temperature management. The strategy provides support. Local overheating analysis can identify overheating areas in the laser, help locate problems and carry out targeted processing. Generating local overheating area information helps to draw attention and take measures to avoid problems caused by laser overheating. Overheating power imbalance analysis helps to evaluate the power imbalance of the laser in different areas and provide a basis for power management and adjustment. Generating laser overheating power imbalance data can help optimize the power output balance of the laser and improve performance stability. The local temperature control power fine-tuning strategy can achieve fine control of the local temperature of the laser and improve the output stability and performance. Constructing a local temperature control power fine-tuning strategy helps to optimize the temperature management plan of the laser and ensure long-term stable output of the laser.

[0042] Preferably, the specific steps of step S4 are:

[0043] Step S41: Calculating the position of each sensor in the optical fiber sensor array one by one to generate the position coordinates of each sensor;

[0044] Step S42: performing light spatial distribution analysis on the multi-modal sensing signal of the optical fiber laser according to the position coordinates of each sensor to extract light spatial distribution feature data;

[0045] Step S43: performing laser scattering path identification on the light spatial distribution characteristic data, thereby obtaining laser scattering path distribution data;

[0046] Step S44: performing nonlinear optical effect analysis on the laser scattering path distribution data to generate nonlinear optical effect characteristics.

[0047] The present invention helps to establish the spatial structure of the optical fiber sensor array by generating the position coordinates of each sensor, providing a basis for subsequent analysis. The position calculation of each sensor can accurately determine the position of each sensor, which helps to accurately capture sensor data. Through the analysis of the spatial distribution of light, the distribution of the multimodal sensing signal of the optical fiber laser in space can be deeply understood, providing important information for signal processing. Extracting the characteristic data of the spatial distribution of light helps to identify the law and characteristics of light propagation, providing a basis for subsequent analysis and optimization. Laser scattering path identification can help determine the scattering path in the laser, which helps to understand the propagation of the laser inside the device. Obtaining the laser scattering path distribution data helps to analyze the optical characteristics inside the laser, providing a basis for performance optimization and adjustment. Through the nonlinear optical effect analysis, the nonlinear optical phenomenon in the laser can be deeply studied, providing a reference for the optimization of optical performance. Generating nonlinear optical effect characteristics helps to understand the complex optical characteristics in the laser, providing important information for further improvement and control.

[0048] Preferably, the specific steps of step S5 are:

[0049] Step S51: performing nonlinear distortion analysis on the nonlinear optical effect characteristics to obtain the nonlinear distortion characteristics of the laser;

[0050] Step S52: performing long-term distortion trend prediction on the nonlinear distortion characteristics of the laser to generate nonlinear distortion trend prediction data;

[0051] Step S53: performing distortion trend gain calculation on the nonlinear distortion trend prediction data to generate a nonlinear distortion trend gain parameter;

[0052] Step S54: performing beam output distortion compensation on the fiber laser multi-modal sensing signal according to the nonlinear distortion trend gain parameter, thereby obtaining a beam distortion compensation strategy.

[0053] The present invention performs nonlinear distortion analysis through nonlinear optical effect characteristics, can deeply understand the nonlinear distortion characteristics in the laser, and provides important insights into the performance of the optical system. Obtaining the nonlinear distortion characteristics of the laser is helpful to identify potential sources of distortion, and provides a basis for subsequent control and optimization. Long-term operation distortion trend prediction is performed based on the nonlinear distortion characteristics of the laser, which can help predict the distortion change trend of the laser in long-term operation, and help set maintenance plans and performance expectations. Generating nonlinear distortion trend prediction data is helpful to timely discover the change trend of laser performance, and provide early warning and management basis for system operation. Distortion trend gain calculation is performed on the nonlinear distortion trend prediction data, which is helpful to determine the change rate and amplitude of the distortion trend, and provides important parameters for subsequent control. Generating nonlinear distortion trend gain parameters can help optimize the distortion compensation strategy, improve system stability and output quality, and perform beam output distortion compensation on the multimodal sensing signal of the optical fiber laser according to the nonlinear distortion trend gain parameters, which can effectively reduce beam distortion and improve the output signal quality. Obtaining the beam distortion compensation strategy can help optimize the output performance of the laser, and achieve more stable and high-quality output, which meets the requirements of high-power optical fiber lasers.

[0054] Preferably, the specific steps of step S6 are:

[0055] Step S61: performing collaborative control optimization on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy to construct a collaborative control optimization engine;

[0056] Step S62: performing laser output control processing on the fiber laser based on the collaborative control optimization engine, and collecting real-time control response data;

[0057] Step S63: performing adaptive control feedback learning on the real-time control response data to obtain adaptive feedback learning data;

[0058] Step S64: Perform dynamic migration optimization based on adaptive feedback learning data to build an intelligent laser control optimization model.

[0059] The present invention performs collaborative control optimization on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, constructs a collaborative control optimization engine, and realizes the synergy of multiple control strategies. The construction of the collaborative control optimization engine can improve the stability and quality of the fiber laser output, ensure the reliability and performance of the system under different working conditions, and perform laser output control processing on the fiber laser based on the collaborative control optimization engine, which can effectively adjust the power and quality of the beam and realize the required output control. The collection of real-time control response data is helpful to monitor the actual operation of the system and provide data support for subsequent control adjustment and optimization. The real-time control response data is adaptively controlled. The control strategy can be adjusted according to the actual feedback data to improve the adaptability and stability of the system. The adaptive feedback learning data is helpful to improve the control performance of the system in continuous learning and adjustment, and improve the output quality and stability. Dynamic migration optimization is performed based on the adaptive feedback learning data to construct an intelligent laser control optimization model, which can realize higher-level intelligent control and optimization. The construction of the intelligent laser control optimization model can further improve the control accuracy and efficiency of the system and ensure the stable output control of the high-power fiber laser.

[0060] In this specification, a high-power fiber laser stable output control system is provided, which is used to execute the high-power fiber laser stable output control method as described above, including:

[0061] A dynamic optical fiber sensing module is used to monitor the multi-modal sensing signal of the optical fiber laser in real time based on the optical fiber sensor array; and to perform dynamic optical fiber sensing change analysis on the multi-modal sensing signal of the optical fiber laser to generate dynamic optical fiber sensing change characteristics;

[0062] The power feedback adjustment module is used to analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and build a dynamic beam power feedback adjustment strategy;

[0063] The local temperature control module is used to analyze overheating power imbalance based on the multi-modal sensing signal of the fiber laser and perform local temperature control power fine-tuning, thereby building a local temperature control power fine-tuning strategy;

[0064] The nonlinear optical effect module is used to calculate the position of each sensor in the optical fiber sensor array, perform nonlinear optical effect analysis, and generate nonlinear optical effect characteristics;

[0065] The nonlinear compensation module is used to predict the long-term distortion trend of the nonlinear optical effect characteristics and to compensate for the beam output distortion, thereby obtaining a beam distortion compensation strategy;

[0066] The intelligent collaborative control model is used to perform collaborative control optimization of the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and perform dynamic migration optimization to build an intelligent laser control optimization model.

[0067] The present invention provides real-time beam characteristic data by real-time monitoring of the multi-modal sensing signal of the fiber laser, generates dynamic fiber sensing change characteristics through dynamic fiber sensing change analysis, and provides key data support for subsequent control strategies. Real-time power feedback regulation is based on the dynamic fiber sensing change characteristics, which is used for laser output phase distribution analysis and regulation, and a dynamic beam power feedback regulation strategy is constructed, which can adjust the beam power in real time to ensure output stability and consistency. Through the local temperature control power fine-tuning module, overheating power imbalance analysis is performed according to the multi-modal sensing signal of the fiber laser, and local temperature control fine-tuning is achieved. A local temperature control power fine-tuning strategy is constructed, which helps to solve the local temperature influence on the laser. The influence of nonlinear optical effects on the performance of optical devices is improved, the stability and quality of the output are improved, and the analysis of nonlinear optical effects at each sensor position helps to understand the working principle and characteristics of the sensor array, generate nonlinear optical effect characteristics, which can be used for subsequent distortion compensation and system performance optimization, predict the long-term operation distortion trend of the beam output, and make real-time adjustments through the beam distortion compensation strategy to effectively deal with the influence of nonlinear optical effects on system performance, improve the output quality and stability, coordinately optimize the control strategies of each module, improve the overall performance and stability of the system, build an intelligent laser control optimization model, realize dynamic migration optimization, and provide a higher level of intelligent support for the stable output control of high-power fiber lasers. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of the steps of a high-power fiber laser stable output control method of the present invention;

[0069] Figure 2 Detailed implementation flow chart of step S1;

[0070] Figure 3 Detailed implementation flow chart of step S2;

[0071] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0073] The present application example provides a high-power fiber laser stable output control method and system. The execution subject of the high-power fiber laser stable output control method and system includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0074] See also Figures 1 to 4 The present invention provides a high-power fiber laser stable output control method, the high-power fiber laser stable output control method comprises the following steps:

[0075] Step S1: real-time monitoring of a fiber laser multimodal sensing signal based on a fiber sensor array; performing dynamic fiber sensing change analysis on the fiber laser multimodal sensing signal to generate a dynamic fiber sensing change feature;

[0076] Step S2: Analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and construct a dynamic beam power feedback adjustment strategy;

[0077] Step S3: performing overheat power imbalance analysis based on the fiber laser multimodal sensing signal, and performing local temperature control power fine-tuning, thereby constructing a local temperature control power fine-tuning strategy;

[0078] Step S4: calculating the position of each sensor in the optical fiber sensor array, and performing nonlinear optical effect analysis to generate nonlinear optical effect characteristics;

[0079] Step S5: predicting the long-term distortion trend of the nonlinear optical effect characteristics and performing beam output distortion compensation to obtain a beam distortion compensation strategy;

[0080] Step S6: Coordinated control optimization is performed on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and dynamic migration optimization is performed to construct an intelligent laser control optimization model.

[0081] The present invention can obtain the state information of the fiber laser in time by real-time monitoring of the multimodal sensing signal of the fiber laser, and provide basic data for subsequent control and adjustment. The dynamic fiber sensing change analysis is helpful to generate the dynamic fiber sensing change characteristics, provide an in-depth understanding of the state change of the fiber laser, and lay the foundation for the formulation of the control strategy. The laser output phase distribution analysis combined with the real-time power feedback adjustment can maintain the phase stability of the beam output and improve the laser output quality and stability. The construction of the dynamic beam power feedback adjustment strategy is helpful to achieve the precise adjustment of the beam power and maintain the stability and consistency of the laser output power. The overheating power imbalance analysis combined with the local temperature control power fine-tuning strategy can effectively solve the power imbalance problem caused by the excessively high local temperature of the fiber laser and improve the output power balance. The construction of the local temperature control power fine-tuning strategy is helpful to maintain the stable operating temperature of each part of the fiber laser, reduce the power imbalance, and improve the output efficiency and stability of the laser. Qualitative, sensor position calculation and nonlinear optical effect analysis are helpful to understand the structure and characteristics of the fiber sensor array, and provide a basis for subsequent control strategies. The generated nonlinear optical effect characteristics can help identify and correct nonlinear distortion in the optical system, and improve the stability and quality of the beam output. Long-term operation distortion trend prediction and beam output distortion compensation are helpful to discover the distortion trend of the beam output in advance, and take compensation measures to maintain stable output quality. The beam distortion compensation strategy can effectively improve the quality and stability of the beam output, and improve the long-term operation effect and reliability of the laser. The combination of collaborative control optimization and dynamic migration optimization can realize the collaborative optimization of dynamic beam power feedback, local temperature control power fine-tuning and beam distortion compensation strategy, improve the overall performance of the laser control system, and build an intelligent laser control optimization model to help improve the stability, output efficiency and quality of the laser, and achieve the best effect of stable output control of high-power fiber lasers.

[0082] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a high-power fiber laser stable output control method of the present invention. In this example, the steps of the high-power fiber laser stable output control method include:

[0083] Step S1: real-time monitoring of a fiber laser multimodal sensing signal based on a fiber sensor array; performing dynamic fiber sensing change analysis on the fiber laser multimodal sensing signal to generate a dynamic fiber sensing change feature;

[0084] In this embodiment, the layout of the fiber optic sensor array is designed, which usually includes a variety of sensors (such as temperature, pressure and strain sensors). According to actual needs, the appropriate type and quantity of sensors are selected to comprehensively monitor the operating status of the laser. The sensors are evenly arranged at key positions of the fiber optic laser to ensure that all important areas of the laser are covered. The sensors are connected to the data acquisition system through optical fibers or other appropriate connection methods to ensure that the signals can be transmitted smoothly. Suitable data acquisition hardware, such as a high-speed data acquisition card (DAQ), is selected to support real-time signal acquisition. The hardware needs to have a high sampling rate and multi-channel input capabilities to process signals from different sensors. Data acquisition software is developed or configured to ensure that multi-modal signals from fiber optic sensors can be monitored and recorded in real time. The software should have data preprocessing, real-time display and alarm functions to facilitate operators to monitor the laser status, start the data acquisition system, and monitor the multi-modal sensing signals of the fiber optic laser in real time. These signals may include multiple parameters such as light intensity, temperature, pressure and strain. Ensure that the data acquisition system can obtain signal data at a high frequency (such as hundreds of times per second). Store the collected multimodal signal data in the database to form a time series data set. The data should include information such as timestamp, signal value and sensor status for subsequent analysis. Preprocess the real-time collected signal data, including denoising, normalization and missing value processing. Wavelet transform or Kalman filtering and other technologies can be used to remove high-frequency noise to ensure the accuracy and reliability of the data. Select appropriate dynamic change analysis methods, such as time series analysis, spectrum analysis or signal feature extraction technology. These methods can identify key change patterns and trends in signals. Use the selected analysis method to perform dynamic change analysis on the preprocessed signal data and extract dynamic fiber optic sensing change characteristics. These characteristics may include signal change amplitude, frequency component, volatility, etc. Use data visualization tools (such as Matplotlib or Tableau) to display the dynamic change characteristics in the form of charts, so that analysts can intuitively understand the data change trend.

[0085] Step S2: Analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and construct a dynamic beam power feedback adjustment strategy;

[0086] In this embodiment, in the previous step, dynamic fiber optic sensing change characteristics have been extracted from the fiber optic sensor array. These characteristic data include key parameters such as signal amplitude, frequency, and phase change. Ensure that these data have been stored and are ready for subsequent analysis. Use a phase analysis model, such as Fourier transform or phase recovery algorithm, to analyze the phase distribution of the laser output. These methods can effectively extract phase information from the signal and identify the phase change pattern. Preprocess the collected dynamic fiber optic sensing signal, including denoising and standardization. Use wavelet transform or Kalman filtering and other techniques to remove unnecessary high-frequency noise to ensure the accuracy of phase analysis. Apply Fourier transform to convert the time domain signal into a frequency domain signal to extract phase information. Analyze the phase distribution characteristics in the spectrum to identify phase differences and fluctuations in the laser output. Design a real-time power feedback adjustment strategy based on the phase distribution analysis results. The strategy should include a real-time monitoring system that can quickly adjust the output power of the laser according to phase changes. rate to maintain the stability of the beam, determine the control parameters of feedback adjustment, such as target phase, phase tolerance and power adjustment amplitude, which are used to guide the adaptive adjustment of the control system when phase deviation occurs. Through the real-time data acquisition system, the phase and power state of the laser output are continuously monitored to ensure that the system can obtain signal data at a high frequency (such as hundreds of times per second) and feed it back to the control system in real time, implement feedback control algorithms (such as PID control or fuzzy control), and automatically adjust the output power of the laser according to the real-time monitored phase changes and the set target phase. This process should take into account the dynamic characteristics of the system to achieve fast and smooth power adjustment. Through data visualization tools (such as real-time monitoring dashboards), observe the phase and power changes after power adjustment, compare the states before and after adjustment, evaluate the effectiveness of the feedback adjustment strategy, and continuously optimize the power feedback adjustment strategy based on real-time monitoring results. If the phase adjustment is found to be insufficient or excessive, adjust the control parameters in time to improve the response speed and stability of the system.

[0087] Step S3: performing overheat power imbalance analysis based on the fiber laser multimodal sensing signal, and performing local temperature control power fine-tuning, thereby constructing a local temperature control power fine-tuning strategy;

[0088] In this embodiment, multimodal sensor signal data of the fiber laser are obtained. These data include information such as temperature, power, pressure and phase. It is ensured that these data are pre-processed, noise is removed and the format is unified for subsequent analysis. Statistical analysis methods and thermal models are used to evaluate the power imbalance of the laser. Regression analysis, heat conduction model or thermal imaging technology can be used to identify and quantify the temperature changes of various parts of the laser. The collected temperature and power data are used to establish a thermal model to analyze the temperature distribution of the laser under different working conditions. Through mathematical modeling, hot spots that may cause overheating are identified, and areas with excessive power input, which lead to local overheating, are identified. The degree of power imbalance is determined by monitoring the temperature data of different sensors, and the areas that need to be fine-tuned are marked. Based on the results of the imbalance analysis, a local temperature control power fine-tuning strategy is designed. The goal is to reduce the temperature and prevent overheating by adjusting the power input in specific areas of the laser. Determine the control parameters for fine-tuning, including target temperature, temperature tolerance and power adjustment range. Set corresponding power adjustment strategies according to the thermal characteristics of different areas to ensure the effectiveness of temperature control. Configure a real-time monitoring system to continuously track the temperature changes in various areas of the laser. Once the temperature of a certain area is detected to exceed the set threshold, the system will immediately feedback to the control system. Based on the real-time monitored temperature data, use feedback control algorithms (such as PID control) to automatically adjust the local power input. For overheated areas, reduce the power output; for areas with normal temperatures, keep the power stable. Use data visualization tools to monitor the temperature changes after fine-tuning, analyze the effect of power fine-tuning after implementation, ensure that the temperature falls back to a safe range, and observe the overall performance changes of the laser. Based on the monitoring results, continuously optimize the local temperature control power fine-tuning strategy. If it is found that the adjustment in some areas is insufficient or excessive, the control parameters should be adjusted in time to ensure the stability of the laser under different environmental conditions.

[0089] Step S4: calculating the position of each sensor in the optical fiber sensor array, and performing nonlinear optical effect analysis to generate nonlinear optical effect characteristics;

[0090] In this embodiment, according to the specific application requirements, a suitable sensor type and its layout model are selected, which usually includes a linear array, a two-dimensional array or a three-dimensional array, to ensure that the sensor can cover the target area and conduct effective monitoring, determine a coordinate system, set the origin position, and the origin can be a sensor position in the array to facilitate subsequent calculations. The Cartesian coordinate system or the polar coordinate system is used to describe the sensor position. According to the set spacing and arrangement, the position coordinates of each sensor are calculated one by one, and the position information of each sensor is recorded in the database to form a complete coordinate list for subsequent analysis and processing. According to the characteristics of the fiber laser, a suitable nonlinear optical effect analysis model is selected. Commonly used methods include nonlinear wave The real-time signal data obtained from the sensor array are prepared to be input into the nonlinear optical effect analysis model using dynamic equations, nonlinear optical property simulation or finite element analysis-based methods. These input data usually include the light intensity, phase, frequency and other related parameters of the optical fiber. The selected nonlinear optical effect analysis model is run to simulate the propagation process of light in the optical fiber and calculate the nonlinear effects at different positions, such as self-focusing, optical Kerr effect and nonlinear response of light intensity. These calculations usually involve complex mathematical operations and numerical simulations. The nonlinear optical effect characteristics are extracted from the analysis results, such as nonlinear refractive index, change in light intensity distribution, phase difference, etc. These characteristics provide an important basis for subsequent optical performance optimization and system design.

[0091] Step S5: predicting the long-term distortion trend of the nonlinear optical effect characteristics and performing beam output distortion compensation to obtain a beam distortion compensation strategy;

[0092] In this embodiment, nonlinear optical effect characteristic data are obtained from the previous steps. These data include the phase, amplitude, frequency and other related parameters of the light beam. The data are cleaned and preprocessed to facilitate subsequent trend prediction. The nonlinear optical effect characteristic data are organized into a time series format to ensure that each data point contains a timestamp. This data set will be used to predict the long-term distortion trend and should cover multiple time periods to reflect the operating characteristics of the laser. A suitable time series analysis model is used, such as an autoregressive integrated moving average model (ARIMA), a long short-term memory network (LSTM) or other machine learning regression models. These models can capture trends and periodic changes in time series data. The organized data set is divided into a training set and a validation set. The training set is used to train the selected prediction model, and the model parameters are optimized to improve the prediction accuracy. The performance of the model is evaluated by methods such as cross-validation to ensure its generalization ability on new data. The trained model is used to predict future nonlinear optical effect characteristics and generate long-term distortion trend data. These prediction data will help identify possible distortion problems in the future so that compensation measures can be taken in a timely manner. Based on the predicted distortion trend data, a beam output distortion compensation strategy is designed. The strategy should include a real-time monitoring and feedback mechanism that can quickly adjust the output of the laser according to the predicted distortion trend, determine the control parameters of the compensation strategy, such as the target output power, phase adjustment range, and compensation response time. These parameters will guide the system's compensation decision when distortion occurs. Configure a real-time monitoring system to continuously track the output status of the laser, including light intensity, phase, and distortion. Once distortion is detected, the system will immediately feedback to the control unit and use feedback control algorithms (such as PID control or fuzzy control) to automatically adjust the output parameters of the laser according to the real-time monitored distortion and predicted data. This process should take into account the dynamic characteristics of the system to achieve fast and smooth adjustment. Through data visualization tools, monitor the effect of beam output distortion compensation, analyze the status before and after compensation, ensure that the output distortion is effectively controlled, and record the compensation effect data for subsequent analysis. According to the monitoring results, continuously optimize the beam distortion compensation strategy. If it is found that the compensation is insufficient or excessive in some cases, adjust the control parameters in time to ensure the stability and performance of the system under different operating conditions.

[0093] Step S6: Coordinated control optimization is performed on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and dynamic migration optimization is performed to construct an intelligent laser control optimization model.

[0094] In this embodiment, the existing control strategies are reviewed, including the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, the role of each strategy in laser control and their mutual influence are analyzed, the synergistic relationship between the strategies is identified, and the optimization goals are determined, such as improving the stability of laser output, reducing power imbalance, reducing distortion, etc. These goals are clarified to guide the subsequent optimization process, and multi-objective optimization algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO) or deep learning optimization models, are used to coordinate the interactions between different control strategies. These algorithms can effectively handle multiple goals. The trade-offs between the three strategies are solved and the optimal solution is found. A comprehensive control optimization model is established to integrate the input and output of the three strategies. The model should be able to accept data from the real-time monitoring system, evaluate the current laser state, and make comprehensive adjustments according to the set optimization goals. The real-time monitoring data is input into the collaborative control optimization model, including key parameters such as beam power, temperature, and distortion. Ensure that the data is preprocessed and remove noise and outliers to improve the accuracy of the model. Run the collaborative control optimization model to automatically adjust the dynamic beam power, local temperature control, and distortion compensation strategies. The optimization algorithm should dynamically adjust the control parameters according to the real-time data to To achieve the best laser output performance, a dynamic migration optimization model is constructed based on real-time monitoring data and historical data. The model should be able to adaptively adjust the control strategy according to the changes in the operating status of the laser to cope with different working environments and conditions. Through continuous learning and feedback, the optimization model can adaptively adjust the strategy. For example, when the ambient temperature changes, the load changes, or other external conditions change, the model can automatically adjust the control parameters to ensure that the laser maintains stable operation. Use data visualization tools to monitor the optimized laser performance, analyze the output stability, power change, and distortion. By comparing the status before and after optimization, verify the effectiveness of collaborative control optimization and dynamic migration optimization. Establish a feedback mechanism to feed back the optimization results and monitoring data to the model for continuous learning and optimization. According to the evaluation results, the model parameters are continuously adjusted to improve the adaptability and stability of the control strategy. According to the monitoring results and feedback information, the collaborative control optimization model is continuously iterated and improved. The optimization process should include regular performance evaluation and model updates to adapt to new operating conditions and technological developments. Record key decisions, parameter changes, and performance evaluation results in the optimization process, and generate detailed reports. These documents will provide references for subsequent research and development and help the team understand the key factors in the optimization process.

[0095] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0096] Step S11: real-time monitoring of the multi-modal sensing signal of the optical fiber laser based on the optical fiber sensor array;

[0097] Step S12: performing digital conversion processing on the fiber laser multimodal sensing signal to generate real-time fiber multimodal monitoring parameters;

[0098] Step S13: performing filtering and noise reduction processing on the real-time optical fiber multimodal monitoring parameters to generate filtering optimized optical fiber monitoring parameters;

[0099] Step S14: Performing dynamic optical fiber sensing change analysis on the filter optimization optical fiber monitoring parameters to generate dynamic optical fiber sensing change characteristics.

[0100] In this embodiment, suitable types of fiber optic sensors are selected, including fiber grating sensors, fiber interferometers, and fiber optic sensor arrays. These sensors can monitor various physical quantities such as temperature, pressure, and displacement in real time under different environmental conditions. The fiber optic sensor array is arranged in the area to be monitored to ensure that it can cover all key monitoring points. The sensor layout should take into account the optical properties of the optical fiber and the environmental impact to optimize the signal acquisition effect. An appropriate data acquisition system is configured, and the fiber optic sensor array is connected to the signal processing unit. A high-performance data acquisition card is used to ensure that the sensor signal can be collected at a high frequency (such as thousands of times per second). Through the data acquisition system, the multi-modal sensing signal of the fiber laser is obtained in real time. During the monitoring process, it should be maintained The stability and integrity of the signal ensure that the signal fluctuations caused by environmental changes can be captured in time. A high-precision analog-to-digital converter (ADC) is used to convert the analog sensor signal into a digital signal. The appropriate sampling frequency and resolution are selected to ensure that the converted signal can accurately reflect the characteristics of the original signal. During the digitization process, the converted signal is stored in a data management system in a certain format (such as CSV or JSON). A timestamp is set for subsequent data analysis and tracking. According to the digitized signal, the fiber optic multimodal monitoring parameters such as amplitude, frequency, phase and harmonic components are calculated in real time. These parameters can provide real-time feedback on environmental changes. The calculated real-time fiber optic multimodal monitoring parameters are stored in the database for subsequent Analysis and processing, select the appropriate filter type, such as low-pass filter, high-pass filter, Kalman filter or wavelet transform filter. These algorithms can effectively eliminate the noise in the sensor signal and retain the useful signal. According to the signal characteristics of real-time monitoring, dynamically adjust the filter parameters to adapt to the noise characteristics under different environmental conditions. For example, in a high-noise environment, the stopband attenuation of the filter can be increased to enhance the clarity of the signal. Filter the real-time fiber optic multimodal monitoring parameters. When executing the filtering algorithm, ensure that the filtering process of each parameter can effectively reduce the noise while retaining the detailed information of the signal. Record the results of the filtering process as the filtering optimized fiber optic monitoring parameters and store them in the database for subsequent analysis and use. Select Suitable dynamic analysis algorithms, such as time domain analysis, frequency domain analysis or wavelet transform analysis, can identify the changing trends and characteristics in the signal, help understand the dynamic behavior monitored by the fiber optic sensor, and extract features of the filter-optimized fiber optic monitoring parameters, including mean, standard deviation, maximum value, minimum value and rate of change. These features will be used for subsequent dynamic analysis. The selected analysis algorithm is used to perform dynamic change analysis on the filter-optimized fiber optic monitoring parameters. During the analysis process, attention is paid to the changing trends, periodicity and emergencies of the signal to capture key dynamic information. Based on the results of the dynamic change analysis, dynamic fiber optic sensing change features are generated to describe the changes that occur during the monitoring process. These features will provide a basis for subsequent decision support and application.

[0101] In this embodiment, the specific steps of step S14 are:

[0102] Perform laser micro-vibration identification on the filter-optimized optical fiber monitoring parameters and extract laser micro-vibration data;

[0103] Performing light intensity calculation on the filter-optimized optical fiber monitoring parameters to obtain light intensity values;

[0104] Performing time-series intensity variation evolution on the light intensity value, thereby generating a laser time-series intensity variation value;

[0105] Comprehensively evaluate the beam quality based on the laser time-series intensity variation value and laser micro-vibration data to generate a quantitative evaluation value of the beam quality;

[0106] Extract laser power output parameters based on filtering and optimizing fiber monitoring parameters;

[0107] Perform multi-time point power fluctuation mining on laser power output parameters to generate multi-time point laser power fluctuation features;

[0108] Dynamic fiber optic sensing change analysis is performed on the laser power fluctuation characteristics at multiple time points and the quantitative evaluation values ​​of the beam quality to generate dynamic fiber optic sensing change characteristics.

[0109] In this embodiment, the filter-optimized optical fiber monitoring parameters are further cleaned to ensure that the data is free of outliers and noise. This step may involve using time domain filtering or wavelet transform technology to remove high-frequency noise to ensure the accuracy of subsequent micro-vibration identification. A suitable micro-vibration identification algorithm is selected, such as short-time Fourier transform (STFT), Hilbert-Huang transform (HHT) or a time series analysis method based on machine learning. These algorithms can effectively extract micro-vibration features from the signal. The selected algorithm is applied to analyze the optical fiber monitoring parameters to extract features related to laser micro-vibration, such as amplitude, frequency and phase information. These features are recorded to form a laser micro-vibration data set. According to the signal characteristics, a suitable light intensity calculation model is selected, such as using a transmission The response characteristics of the sensor are used to calculate the light intensity. Usually, the linear relationship between the light intensity and the output signal of the optical fiber sensor is used. The processed filtered optimized optical fiber monitoring parameters are input into the calculation model to generate real-time light intensity values. This step usually requires multiple calculations and averaging to ensure the stability and accuracy of the results. Time series analysis methods such as moving average, exponential smoothing or autoregressive moving average (ARMA) model are used to analyze the changes in light intensity values ​​over time. Time series analysis of light intensity values ​​is performed to generate laser time series intensity change values. This process will reveal the evolution trend of light intensity in different time periods, help identify potential change patterns, and determine the evaluation indicators of beam quality, such as beam divergence angle, beam contrast and M² factor. These indicators It can effectively reflect the quality of the laser beam. Based on the laser time-series intensity change value and micro-vibration data, a comprehensive evaluation model is constructed. The weighted average method or multivariate regression analysis can be used to integrate different indicators into a quantitative evaluation value of the beam quality. The extracted time-series intensity change value and micro-vibration data are input into the evaluation model to generate a quantitative evaluation value of the beam quality. The results are recorded for subsequent analysis. A suitable power calculation model is selected. It is usually based on the relationship between the output characteristics of the fiber laser and the monitoring parameters. The fiber monitoring parameters are optimized by filtering, the laser power output parameters are calculated, and the results are recorded. This step needs to ensure the accuracy of the calculation method for subsequent analysis. Multi-point power fluctuation mining of the laser power output parameters is performed to generate multi-point laser power. The fluctuation characteristics adopt fluctuation analysis algorithms, such as standard deviation analysis, volatility calculation or wavelet analysis, to explore the fluctuation characteristics of power output parameters at different time points, perform fluctuation analysis on laser power output parameters, and identify the power fluctuation characteristics at multiple time points. These characteristics will reflect the stability and changes of the laser under different operating conditions. Select a suitable dynamic analysis algorithm, such as dynamic time warping (DTW), time series clustering or machine learning model. These methods can identify the relationship between fluctuation characteristics and beam quality. Use the selected dynamic analysis algorithm to analyze the laser power fluctuation characteristics at multiple time points and the quantitative evaluation value of beam quality. By comparing historical data, identify dynamic sensing change characteristics, such as the relationship between power fluctuations and environmental factors.

[0110] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0111] Step S21: performing laser output phase distribution analysis on dynamic optical fiber sensing change characteristics to generate dynamic laser phase distribution data;

[0112] Step S22: performing stress disturbance identification on the dynamic laser phase distribution data to obtain dynamic laser stress disturbance characteristics;

[0113] Step S23: mining the influence of beam power distribution stability based on the dynamic laser stress disturbance characteristics, thereby generating beam power distribution stability characteristics;

[0114] Step S24: Perform real-time power feedback adjustment according to the stability characteristics of the beam power distribution and construct a dynamic beam power feedback adjustment strategy.

[0115] In this embodiment, parameters related to laser output, such as power, frequency, and vibration, are extracted from the previous dynamic fiber optic sensing change characteristics. These parameters will be used for phase distribution analysis. The extracted features are preprocessed, including denoising, smoothing, and normalization, to ensure the accuracy and comparability of the data. Moving average or wavelet transform and other techniques can be used to process the data to remove high-frequency noise and retain the main features of the signal. A suitable phase analysis algorithm is selected, such as Fourier transform, coherent interferometry, or Hilbert transform. These methods can effectively identify the phase distribution characteristics of the laser output signal. The preprocessed dynamic fiber optic sensing change characteristics are input into the selected algorithm to generate laser output phase distribution data. Transformation can convert the time domain signal into the frequency domain, analyze the phase information of each frequency component, store the calculated dynamic laser phase distribution data in the database, provide data support for subsequent stress disturbance identification, select appropriate stress disturbance identification algorithms, such as transform domain analysis, time domain analysis or machine learning methods, which can identify abnormal changes in phase distribution and infer possible stress disturbances. The dynamic laser phase distribution data is input into the selected stress disturbance identification algorithm to analyze the abnormal patterns appearing in the data, which may involve comparing the phase distribution under normal conditions with the current data, identifying significant change areas, and extracting dynamic laser stress disturbance features from the identification results, such as phase jumps, amplitude Abnormalities, etc. These features will help in the subsequent stability analysis of the beam power distribution. Select appropriate stability analysis methods, such as principal component analysis (PCA), regression analysis or machine learning models. These methods can effectively mine the impact of dynamic laser stress disturbance characteristics on beam power distribution. Combine the dynamic laser stress disturbance characteristics with the beam power distribution data, and apply the selected analysis algorithm for mining. During the analysis process, determine the degree of influence of each feature on the stability of the beam power, generate the stability characteristics of the beam power distribution, and record the changes in the beam power under different stress states. These features will provide a basis for the subsequent power feedback adjustment strategy. According to the stability characteristics of the beam power distribution, design real-time power feedback adjustment. Strategy: The strategy should include three links: detection, analysis and adjustment. It should ensure that adjustments can be made quickly when an unstable state is detected. A real-time monitoring system should be configured to ensure that the laser output power and beam quality parameters can be monitored in real time and data feedback is timely. The system should have high-frequency sampling capabilities to ensure response to rapidly changing states. When the beam power distribution is detected to be unstable, the output power and other related parameters of the laser (such as modulation frequency, beam shape, etc.) are automatically adjusted according to the preset feedback adjustment strategy to stabilize the beam output. The effectiveness of the feedback adjustment strategy is evaluated by continuously monitoring the adjusted beam quality and power distribution. According to the evaluation results, the adjustment strategy is optimized to improve the stability and performance of the system.

[0116] In this embodiment, refer to Figure 4, is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0117] Step S31: performing laser temperature change analysis on the filter optimization optical fiber monitoring parameters to generate laser temperature change characteristics;

[0118] Step S32: performing discrete fitting of time series fluctuations on the laser temperature variation characteristics to construct a laser temperature fluctuation curve;

[0119] Step S33: fitting the laser surface temperature distribution to the laser temperature fluctuation curve to construct the surface temperature distribution field;

[0120] Step S34: performing local overheat analysis on the surface temperature distribution field to generate a local overheat area;

[0121] Step S35: performing overheat power imbalance analysis on the local overheat area, thereby generating laser overheat power imbalance data;

[0122] Step S36: Perform local temperature control power fine-tuning based on the laser overheat power imbalance data, thereby constructing a local temperature control power fine-tuning strategy.

[0123] In this embodiment, temperature-related indicators are extracted from the filter-optimized optical fiber monitoring parameters, such as the operating temperature, ambient temperature, and power output of the laser, to ensure the timeliness and accuracy of the data for subsequent analysis. Suitable temperature change analysis methods are selected, such as linear regression, time series analysis, or Kalman filtering. These methods can effectively capture the trend and fluctuation characteristics of temperature changes. The extracted temperature-related data are input into the selected model for analysis to generate laser temperature change characteristics. These characteristics may include the amplitude, frequency, and fluctuation rate of temperature changes. These characteristics are recorded for subsequent use. Suitable fitting algorithms are selected, such as polynomial fitting, spline fitting, or Fourier analysis. These methods can effectively convert time series temperature change characteristics into The laser temperature fluctuation curve is transformed into a visual fluctuation curve, and the laser temperature change characteristics are discretely fitted to generate the laser temperature fluctuation curve. In this process, attention should be paid to the fitting accuracy to ensure that the fluctuation curve can truly reflect the trend of temperature change. The generated laser temperature fluctuation curve is saved in the database, and a visual chart is generated for subsequent analysis and display. A suitable temperature distribution fitting model is selected, such as a Gaussian distribution model, a heat conduction model, or a finite element analysis (FEA) method. These models can effectively describe the temperature distribution of the laser surface. The laser temperature fluctuation curve is input into the selected model for temperature distribution fitting to generate the temperature distribution field of the laser surface. This process will take into account the temperature changes at different positions to ensure the accuracy of the distribution field. Select a suitable overheating analysis algorithm, such as threshold segmentation method, cluster analysis or anomaly detection algorithm. These methods can effectively identify overheating areas in the temperature distribution field, perform local overheating analysis on the generated surface temperature distribution field, identify areas exceeding the threshold by setting a temperature threshold, and mark them as local overheating areas. Select a suitable power imbalance analysis method. Commonly used methods include power allocation model and energy conservation model. These methods help analyze the power distribution of the laser in the local overheating area. Combined with the data of the local overheating area, perform overheating power imbalance analysis. By calculating the relationship between the power output and the surface temperature of each area, identify the power imbalance area, record the analysis results as laser overheating power imbalance data, and store them. , for subsequent analysis and application, according to the laser overheating power imbalance data, design a local temperature control power fine-tuning strategy, the strategy should include temperature monitoring, power regulation and feedback mechanism to ensure timely adjustment when overheating is identified, configure a real-time monitoring system to monitor the temperature and power output of the laser, the system should have high-frequency sampling capability to ensure rapid response to temperature changes, when the local area of ​​the laser is monitored to be overheated, according to the preset fine-tuning strategy, automatically adjust the power output and cooling system of the area to reduce the temperature and reach a stable state, by continuously monitoring the temperature and power after fine-tuning, evaluate the effectiveness of the local temperature control strategy, according to the evaluation results, continuously optimize the fine-tuning strategy to improve the stability and performance of the laser.

[0124] In this embodiment, step S4 includes the following steps:

[0125] Step S41: Calculating the position of each sensor in the optical fiber sensor array one by one to generate the position coordinates of each sensor;

[0126] Step S42: performing light spatial distribution analysis on the multi-modal sensing signal of the optical fiber laser according to the position coordinates of each sensor to extract light spatial distribution feature data;

[0127] Step S43: performing laser scattering path identification on the light spatial distribution characteristic data, thereby obtaining laser scattering path distribution data;

[0128] Step S44: performing nonlinear optical effect analysis on the laser scattering path distribution data to generate nonlinear optical effect characteristics.

[0129] In this embodiment, the model and layout of the fiber optic sensor array are determined, which is usually a linear, two-dimensional or three-dimensional array. A suitable layout is selected according to application requirements, such as uniform distribution or dense layout in a specific area. A coordinate system (such as a Cartesian coordinate system or a polar coordinate system) is set, and the origin position is defined. The origin can be any sensor position in the array to facilitate subsequent calculations. According to the layout and spacing of the sensors, the position coordinates of each sensor are calculated one by one, and the position coordinates of each sensor are stored in a database to form a coordinate list for subsequent spatial distribution analysis and other processing. Multimodal sensing signals are collected from the fiber laser. These signals usually include light intensity, phase and frequency information in different modes. A suitable spatial distribution analysis method is selected, such as two-dimensional or three-dimensional interpolation analysis, principal component analysis (PCA) or beam propagation model. The beam propagation model is particularly suitable for processing the distribution of light in space. The position coordinates of each sensor are used to map the collected multimodal sensing signals to the corresponding spatial positions. The interpolation method is used to generate a spatial distribution map of the light in the entire sensor array. Important features such as light intensity distribution, scattering angle and modal characteristics are extracted from the spatial distribution map. These features will be used for subsequent The subsequent scattering path identification adopts laser scattering path identification methods, such as ray tracing or algorithms based on image processing. These methods can effectively identify the scattering path of light in the medium. Through the ray tracing algorithm, the propagation process of light in the medium is simulated, and the scattering and reflection paths of light are traced. This process usually needs to consider the refractive index, absorption coefficient and scattering characteristics of the medium. The identified laser scattering path is recorded as scattering path distribution data, including information such as the starting point, end point and path length of the path. An appropriate nonlinear optical effect analysis method is selected, such as nonlinear refractive index analysis, self-focusing model or optical Kerr effect model. These models can describe the nonlinear behavior of light at different intensities. The laser scattering path distribution data is input into the nonlinear optical effect analysis model. According to different light intensities and medium characteristics, the influence of nonlinear effects on light propagation is calculated. The nonlinear optical effect characteristics are extracted from the analysis results, such as nonlinear refractive index changes, changes in light intensity distribution and self-focusing phenomena. These characteristics will provide a basis for subsequent optical performance optimization. The generated nonlinear optical effect characteristic data is recorded in the database, and a visual chart is generated for subsequent analysis and reference.

[0130] In this embodiment, step S5 includes the following steps:

[0131] Step S51: performing nonlinear distortion analysis on the nonlinear optical effect characteristics to obtain the nonlinear distortion characteristics of the laser;

[0132] Step S52: performing long-term distortion trend prediction on the nonlinear distortion characteristics of the laser to generate nonlinear distortion trend prediction data;

[0133] Step S53: performing distortion trend gain calculation on the nonlinear distortion trend prediction data to generate a nonlinear distortion trend gain parameter;

[0134] Step S54: performing beam output distortion compensation on the fiber laser multi-modal sensing signal according to the nonlinear distortion trend gain parameter, thereby obtaining a beam distortion compensation strategy.

[0135] In this embodiment, nonlinear optical theory and distortion analysis methods, such as Taylor expansion method and polynomial fitting, are used to describe the nonlinear distortion of laser signals. These methods can help identify the nonlinear components in the laser output signal. The output signal is collected from the laser, and denoising and standardization are performed to ensure the accuracy of the signal. Wavelet transform or Kalman filtering and other technologies can be used to remove high-frequency noise. The processed laser output signal is input into the selected nonlinear distortion analysis model. By fitting the signal, the nonlinear distortion characteristics are identified, including the distortion amplitude, frequency component and phase difference, etc. The analysis results are recorded to form the laser nonlinear distortion characteristic data for subsequent trends. The prediction and compensation strategies provide a basis, using time series prediction models, such as the autoregressive integrated moving average model (ARIMA), long short-term memory network (LSTM) or regression analysis model, which can capture the changing trend of nonlinear distortion characteristics. Use the recorded nonlinear distortion feature data to build a training data set. The data set should contain timestamps and corresponding distortion feature values ​​so that the model can learn the dynamic behavior of the time series. Input the training data into the selected prediction model for training, adjust the model parameters to optimize the prediction accuracy, and use the cross-validation method to evaluate the performance of the model and avoid overfitting. Use the trained model to test the future nonlinear distortion features. Prediction, generate long-term distortion trend prediction data, these data will be used for subsequent distortion trend gain calculation, use linear gain calculation or nonlinear gain model to evaluate the impact of changes in distortion trend on signal quality, can use signal intensity ratio (SIR) or signal-to-noise ratio (SNR) as the basis for gain calculation, input nonlinear distortion trend prediction data into the gain calculation model, calculate the gain parameters at each time point, this process needs to consider the relationship between the current signal state and the predicted value, the calculated nonlinear distortion trend gain parameters are recorded in the database for subsequent analysis and application, based on the nonlinear distortion trend gain parameters, design the beam output distortion compensation strategy, The compensation algorithm should include real-time monitoring, gain application and feedback mechanism to ensure timely adjustment when distortion occurs. A real-time monitoring system should be configured to ensure that the output signal and corresponding gain parameters of the fiber laser can be obtained in real time. The system should have high-frequency sampling capability to respond to signal changes in a timely manner. When the beam output distortion is detected, the output power and other related parameters of the laser are automatically adjusted according to the nonlinear distortion trend gain parameters to compensate for the distortion. This process can be achieved by optimizing the feedback control algorithm. By continuously monitoring the output of the compensated beam, the effectiveness of the compensation strategy is evaluated. According to the evaluation results, the compensation strategy is continuously optimized to improve the stability and performance of the laser.

[0136] In this embodiment, step S6 includes the following steps:

[0137] Step S61: performing collaborative control optimization on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy to construct a collaborative control optimization engine;

[0138] Step S62: performing laser output control processing on the fiber laser based on the collaborative control optimization engine, and collecting real-time control response data;

[0139] Step S63: performing adaptive control feedback learning on the real-time control response data to obtain adaptive feedback learning data;

[0140] Step S64: Perform dynamic migration optimization based on adaptive feedback learning data to build an intelligent laser control optimization model.

[0141] In this embodiment, the respective functions and interrelationships of the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy, and the beam distortion compensation strategy are analyzed in detail to ensure that the role of each strategy in laser control and their mutual influence are understood. Multi-objective optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) or ant colony optimization (ACO), are used to optimize the collaborative control strategy. These algorithms can effectively handle the trade-offs between multiple objectives. A collaborative control optimization engine is designed to integrate the input and output of the three strategies. The engine should be able to evaluate the current laser state in real time and perform comprehensive optimization based on the set objectives (such as power, temperature, and distortion). Combined adjustment, determine performance evaluation indicators such as response time, stability and efficiency for quantitative analysis during the optimization process, set target values ​​to guide the optimization process, apply the output of the optimization engine to the laser, adjust the laser output power, temperature control parameters and compensation settings in real time, ensure that all control strategies are coordinated to achieve the best laser output performance, configure a high-frequency data acquisition system, monitor the output status of the laser in real time, including power, temperature and distortion, etc., ensure that the data acquisition system can process large amounts of data and respond quickly, store the real-time control response data in the database, and form a complete data set for subsequent analysis. The data should be Including information such as timestamp, control parameters and laser status, using adaptive control algorithms such as model predictive control (MPC), reinforcement learning (RL) or adaptive neural fuzzy control (ANFIS), these algorithms can learn and optimize control strategies in real-time feedback, identify the effectiveness of control strategies by analyzing the collected real-time control response data, compare the current output state with the target state, calculate the error and adjust the control parameters, record the process of adaptive feedback learning, including learning rate, adjustment amplitude and strategy changes, so as to facilitate subsequent optimization and model improvement, and apply the adaptive feedback learning data to build a dynamic migration optimization model, which should be able to dynamically adjust the control strategy according to different operating conditions and environmental changes. The constructed intelligent laser control optimization model is verified by historical data and real-time data, the model prediction results are compared with the actual output, the accuracy and stability of the model are evaluated, the model is iteratively optimized according to the verification results, and the model parameters are adjusted to improve the prediction accuracy and control effect, to ensure that the model can adapt to different working environments and laser states, and the actual effect of the intelligent laser control optimization model is evaluated by continuously monitoring the laser output. According to the evaluation results, necessary adjustments and improvements are made to ensure the optimal performance of the laser under different conditions.

[0142] In this embodiment, a high-power fiber laser stable output control system is provided, which is used to execute the high-power fiber laser stable output control method as described above, including:

[0143] A dynamic optical fiber sensing module is used to monitor the multi-modal sensing signal of the optical fiber laser in real time based on the optical fiber sensor array; and to perform dynamic optical fiber sensing change analysis on the multi-modal sensing signal of the optical fiber laser to generate dynamic optical fiber sensing change characteristics;

[0144] The power feedback adjustment module is used to analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and build a dynamic beam power feedback adjustment strategy;

[0145] The local temperature control module is used to analyze overheating power imbalance based on the multi-modal sensing signal of the fiber laser and perform local temperature control power fine-tuning, thereby building a local temperature control power fine-tuning strategy;

[0146] The nonlinear optical effect module is used to calculate the position of each sensor in the optical fiber sensor array, perform nonlinear optical effect analysis, and generate nonlinear optical effect characteristics;

[0147] The nonlinear compensation module is used to predict the long-term distortion trend of the nonlinear optical effect characteristics and to compensate for the beam output distortion, thereby obtaining a beam distortion compensation strategy;

[0148] The intelligent collaborative control model is used to perform collaborative control optimization of the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and perform dynamic migration optimization to build an intelligent laser control optimization model.

[0149] The present invention provides real-time beam characteristic data by real-time monitoring of the multi-modal sensing signal of the fiber laser, generates dynamic fiber sensing change characteristics through dynamic fiber sensing change analysis, and provides key data support for subsequent control strategies. Real-time power feedback regulation is based on the dynamic fiber sensing change characteristics, which is used for laser output phase distribution analysis and regulation, and a dynamic beam power feedback regulation strategy is constructed, which can adjust the beam power in real time to ensure output stability and consistency. Through the local temperature control power fine-tuning module, overheating power imbalance analysis is performed according to the multi-modal sensing signal of the fiber laser, and local temperature control fine-tuning is achieved. A local temperature control power fine-tuning strategy is constructed, which helps to solve the local temperature influence on the laser. The influence of nonlinear optical effects on the performance of optical devices is improved, the stability and quality of the output are improved, and the analysis of nonlinear optical effects at each sensor position helps to understand the working principle and characteristics of the sensor array, generate nonlinear optical effect characteristics, which can be used for subsequent distortion compensation and system performance optimization, predict the long-term operation distortion trend of the beam output, and make real-time adjustments through the beam distortion compensation strategy to effectively deal with the influence of nonlinear optical effects on system performance, improve the output quality and stability, coordinately optimize the control strategies of each module, improve the overall performance and stability of the system, build an intelligent laser control optimization model, realize dynamic migration optimization, and provide a higher level of intelligent support for the stable output control of high-power fiber lasers.

[0150] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0151] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A high-power fiber laser stable output control method, characterized in that: The following steps are involved: Step S1: real-time monitoring of the multi-modal sensing signal of the optical fiber laser based on the optical fiber sensor array; Performing dynamic optical fiber sensing change analysis on the optical fiber laser multimodal sensing signal to generate dynamic optical fiber sensing change characteristics; Step S2: Analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and construct a dynamic beam power feedback adjustment strategy; Step S3: performing overheat power imbalance analysis based on the fiber laser multimodal sensing signal, and performing local temperature control power fine-tuning, thereby constructing a local temperature control power fine-tuning strategy; Step S4: calculating the position of each sensor in the optical fiber sensor array, and performing nonlinear optical effect analysis to generate nonlinear optical effect characteristics; Step S5: predicting the long-term distortion trend of the nonlinear optical effect characteristics and performing beam output distortion compensation to obtain a beam distortion compensation strategy; Step S6: Coordinated control optimization is performed on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and dynamic migration optimization is performed to construct an intelligent laser control optimization model.

2. The high power fiber laser stable output control method according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: real-time monitoring of the multi-modal sensing signal of the optical fiber laser based on the optical fiber sensor array; Step S12: performing digital conversion processing on the fiber laser multimodal sensing signal to generate real-time fiber multimodal monitoring parameters; Step S13: performing filtering and noise reduction processing on the real-time optical fiber multimodal monitoring parameters to generate filtering optimized optical fiber monitoring parameters; Step S14: Performing dynamic optical fiber sensing change analysis on the filter optimization optical fiber monitoring parameters to generate dynamic optical fiber sensing change characteristics.

3. The high power fiber laser stable output control method according to claim 2, characterized in that: The specific steps of step S14 are: Perform laser micro-vibration identification on the filter-optimized optical fiber monitoring parameters and extract laser micro-vibration data; Performing light intensity calculation on the filter-optimized optical fiber monitoring parameters to obtain light intensity values; Performing time-series intensity variation evolution on the light intensity value, thereby generating a laser time-series intensity variation value; Comprehensively evaluate the beam quality based on the laser time-series intensity variation value and laser micro-vibration data to generate a quantitative evaluation value of the beam quality; Extract laser power output parameters based on filtering and optimizing fiber monitoring parameters; Perform multi-time point power fluctuation mining on laser power output parameters to generate multi-time point laser power fluctuation features; Dynamic fiber optic sensing change analysis is performed on the laser power fluctuation characteristics at multiple time points and the quantitative evaluation values ​​of the beam quality to generate dynamic fiber optic sensing change characteristics.

4. The high power fiber laser stable output control method according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing laser output phase distribution analysis on dynamic optical fiber sensing change characteristics to generate dynamic laser phase distribution data; Step S22: performing stress disturbance identification on the dynamic laser phase distribution data to obtain dynamic laser stress disturbance characteristics; Step S23: mining the influence of beam power distribution stability based on the dynamic laser stress disturbance characteristics, thereby generating beam power distribution stability characteristics; Step S24: Perform real-time power feedback adjustment according to the stability characteristics of the beam power distribution and construct a dynamic beam power feedback adjustment strategy.

5. The high power fiber laser stable output control method according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing laser temperature change analysis on the filter optimization optical fiber monitoring parameters to generate laser temperature change characteristics; Step S32: performing discrete fitting of time series fluctuations on the laser temperature variation characteristics to construct a laser temperature fluctuation curve; Step S33: fitting the laser surface temperature distribution to the laser temperature fluctuation curve to construct the surface temperature distribution field; Step S34: performing local overheat analysis on the surface temperature distribution field to generate a local overheat area; Step S35: performing overheat power imbalance analysis on the local overheat area, thereby generating laser overheat power imbalance data; Step S36: Perform local temperature control power fine-tuning based on the laser overheat power imbalance data, thereby constructing a local temperature control power fine-tuning strategy.

6. The high power fiber laser stable output control method according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Calculating the position of each sensor in the optical fiber sensor array one by one to generate the position coordinates of each sensor; Step S42: performing light spatial distribution analysis on the multi-modal sensing signal of the optical fiber laser according to the position coordinates of each sensor to extract light spatial distribution feature data; Step S43: performing laser scattering path identification on the light spatial distribution characteristic data, thereby obtaining laser scattering path distribution data; Step S44: performing nonlinear optical effect analysis on the laser scattering path distribution data to generate nonlinear optical effect characteristics.

7. The high power fiber laser stable output control method according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing nonlinear distortion analysis on the nonlinear optical effect characteristics to obtain the nonlinear distortion characteristics of the laser; Step S52: performing long-term distortion trend prediction on the nonlinear distortion characteristics of the laser to generate nonlinear distortion trend prediction data; Step S53: performing distortion trend gain calculation on the nonlinear distortion trend prediction data to generate a nonlinear distortion trend gain parameter; Step S54: performing beam output distortion compensation on the fiber laser multi-modal sensing signal according to the nonlinear distortion trend gain parameter, thereby obtaining a beam distortion compensation strategy.

8. The method according to claim 1, characterized in that The specific steps of step S6 are: Step S61: performing collaborative control optimization on the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy to construct a collaborative control optimization engine; Step S62: performing laser output control processing on the fiber laser based on the collaborative control optimization engine, and collecting real-time control response data; Step S63: performing adaptive control feedback learning on the real-time control response data to obtain adaptive feedback learning data; Step S64: Perform dynamic migration optimization based on adaptive feedback learning data to build an intelligent laser control optimization model.

9. A high-power fiber laser stable output control system, characterized in that: The method for controlling the stable output of a high-power fiber laser according to claim 1 comprises: A dynamic optical fiber sensing module is used to monitor the multi-modal sensing signal of the optical fiber laser in real time based on the optical fiber sensor array; and to perform dynamic optical fiber sensing change analysis on the multi-modal sensing signal of the optical fiber laser to generate dynamic optical fiber sensing change characteristics; The power feedback adjustment module is used to analyze the laser output phase distribution of the dynamic optical fiber sensing change characteristics, perform real-time power feedback adjustment, and build a dynamic beam power feedback adjustment strategy; The local temperature control module is used to analyze overheating power imbalance based on the multi-modal sensing signal of the fiber laser and perform local temperature control power fine-tuning, thereby building a local temperature control power fine-tuning strategy; The nonlinear optical effect module is used to calculate the position of each sensor in the optical fiber sensor array, perform nonlinear optical effect analysis, and generate nonlinear optical effect characteristics; The nonlinear compensation module is used to predict the long-term distortion trend of the nonlinear optical effect characteristics and to compensate for the beam output distortion, thereby obtaining a beam distortion compensation strategy; The intelligent collaborative control model is used to perform collaborative control optimization of the dynamic beam power feedback adjustment strategy, the local temperature control power fine-tuning strategy and the beam distortion compensation strategy, and perform dynamic migration optimization to build an intelligent laser control optimization model.

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