Programmable waveform modulated high power fiber laser system

Abstract

The application discloses a programmable waveform modulation high-power fiber laser system and particularly relates to the high-power fiber laser field, which is used for solving the power monitoring and control instability problem caused by aliasing distortion at a high pulse repetition frequency, and is characterized in that micro random time jitter is implanted around a preprocessing link of the programmable waveform modulation high-power fiber laser, sampling is scattered and coupled with pulse rhythm through time scale and power sequence synchronous mapping, and the source is blocked to break the return pseudo-wave fluctuation; an abstract evaluation mechanism fuses energy domain and time domain features, a single determination identifier is used to drive adaptive migration of a sampling frequency, the monitoring link is continuously kept in a coprime fraction ratio operation interval, and the aliasing distortion root cause is eliminated; a sampling label and the determination identifier are jointly used to trigger a feedforward gain matrix, a monitoring evaluation adjustment compensation closed loop is formed, power residual vibration is reduced in real time, stimulated scattering and thermal lens chain amplification are inhibited, and the beam quality is stabilized and the energy is smoothly distributed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of high-power fiber lasers, and more particularly, to a programmable waveform modulation high-power fiber laser system. BACKGROUND

[0002] In the programmable waveform modulation high-power fiber laser architecture, the pulse repetition frequency often climbs to the order of megahertz to meet the dual demands of energy density and waveform flexibility in high-speed working conditions such as steel plate welding and additive manufacturing. If the power monitoring link uses full-bandwidth sampling, the analog-to-digital conversion unit will be forced to maintain a high sampling rate, thereby introducing a huge data throughput and heat dissipation burden. Engineering practice usually relies on band-limited sampling, which only intercepts the characteristic frequency band of the power envelope, and then assists in stabilizing the output with a feedback loop; this strategy performs stably in the low-to-moderate modulation frequency range.

[0003] As the modulation frequency continues to increase, the sampling rate and the pulse repetition frequency gradually approach an integer multiple relationship, and the discrete sampling sequence folds the power pulsation originally located in the high-frequency region to the low-frequency region, forming aliasing distortion. The feedback algorithm misjudges the folded component as a real slow drift and issues a reverse correction instruction, and the power oscillation is amplified. The oscillation amplification not only triggers the stimulated scattering threshold in advance, amplifies the spectral noise, but also leads to the enhancement of the thermal lens effect in the fiber core and the imbalance of the processing melt depth, which seriously damages the consistency of high-precision processing.

[0004] To solve the above problems, a technical scheme is provided. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide a programmable waveform modulation high-power fiber laser system, which implants a small random time jitter around the preprocessing link of the programmable waveform modulation high-power fiber laser, disperses the sampling and pulse rhythm coupling through the synchronous mapping of the time scale and the power sequence, and blocks the folded pseudo-oscillation from the source; the abstract evaluation mechanism fuses the energy domain and time domain features to identify the driving sampling frequency adaptive migration with a single judgment, so that the monitoring link continuously operates in the coprime fractional ratio operating interval, and the aliasing distortion is eliminated; the sampling label and the judgment identification jointly trigger the feedforward gain matrix to form a monitoring evaluation adjustment compensation closed loop, which reduces the power residual vibration in real time, suppresses the stimulated scattering and thermal lens chain amplification, and maintains the stability of the beam quality and the smooth distribution of the energy, thereby solving the problems raised in the background art.

[0006] To achieve the above-mentioned purposes, the present application provides the following technical scheme:

[0007] A programmable waveform modulation high-power fiber laser system, comprising:

[0008] a jitter injection module, a feature evaluation module, a frequency adjustment module, and a power stabilization module.

[0009] Jitter injection module: injects a small random time jitter into the power monitoring branch and records a time stamp sequence, and outputs a synchronous alignment data stream after synchronously capturing a power sample sequence;

[0010] Feature evaluation module: inputs the synchronous alignment data stream into a sparse reconstruction model to separate the turn-back energy component, extracts the sampling and pulse coupling features to perform abstract evaluation, judges the decoupling degree of the sampling link and the pulse link, and obtains a qualified identification;

[0011] Frequency adjustment module: reads the qualified identification, adjusts the real-time sampling frequency to a fractional ratio coprime with the pulse repetition frequency, and synchronously updates the sampling frequency label;

[0012] Power stabilization module: sets a feedforward gain matrix according to the sampling frequency label and the qualified indication, and implements correction on the pump driving signal to maintain the output power residual vibration within a set upper limit.

[0013] In a preferred embodiment, the jitter injection module includes the following:

[0014] In the power monitoring branch, an independent small random time offset is introduced for each sampling period to disturb the fixed correspondence between the sampling time and the pulse time. The small random time offset is generated by uniform distribution. At the same time, the actual sampling time of each sampling point is recorded, which is the sum of the nominal sampling time and the small random time offset. The nominal sampling time is the product of the sampling point number and the sampling period. Then, at each actual sampling time, a high-speed analog-to-digital converter is used to collect the instantaneous value of the laser output power, forming a power sample sequence. Finally, the actual sampling time and the corresponding power instantaneous value are paired to generate a synchronous alignment data stream.

[0015] In a preferred embodiment, the feature evaluation module includes the following:

[0016] The power sample sequence in the synchronous alignment data stream is sparsely decomposed using a dictionary learning method to separate the real power component and the turn-back energy component, from which the sampling and pulse coupling features, including the turn-back energy amplitude and the jitter dispersion rate, are extracted.

[0017] In a preferred embodiment, the feature evaluation module further includes the following:

[0018] By collecting the amplitude sequence of the turn-back energy rate and the dispersion sequence of the jitter in the sliding time window, a joint probability density field is constructed, the Wasserstein distance between the reference decoupling distribution is calculated by using the optimal transport algorithm, the minimum transport potential sequence is obtained, and then the multi-scale empirical mode decomposition is performed to extract the representative intrinsic components. After the radial normalization kernel mapping, the adaptive weight aggregation local entropy is used to generate the coupling strength identifier to judge the decoupling degree of the sampling link and the pulse link, and the qualified identifier is obtained.

[0019] In a preferred embodiment, the feature evaluation module further comprises the following:

[0020] The influence degree of aliasing distortion is quantified by performing fast Fourier transform on the turn-back energy component and calculating the turn-back energy rate amplitude by combining the adaptive frequency band selection method based on spectral entropy.

[0021] In a preferred embodiment, the feature evaluation module further comprises the following:

[0022] The deviation between the actual sampling time and the nominal sampling time is calculated, and the randomness of the random time jitter is quantified by calculating the jitter dispersion rate using the mutual information entropy method.

[0023] In a preferred embodiment, the frequency adjustment module comprises the following:

[0024] When the qualified identifier is qualified, the sampling frequency adjustment process is started; the current pulse repetition frequency and the current sampling frequency are obtained, the greatest common divisor of the current pulse repetition frequency and the current sampling frequency is calculated, if the greatest common divisor is 1, the current sampling frequency is kept unchanged; if the greatest common divisor is greater than 1, the adjustment step value is set, the target sampling frequency is adjusted by iteration, until the greatest common divisor of the target sampling frequency and the pulse repetition frequency is 1, while ensuring that the target sampling frequency is within the range of the hardware sampling rate.

[0025] In a preferred embodiment, the frequency adjustment module further comprises the following:

[0026] The frequency adjustment instruction containing the target sampling frequency is generated and sent to the sampling clock generator; the sampling clock generator adjusts the internal clock frequency according to the frequency adjustment instruction, so that the real-time sampling frequency matches the target sampling frequency; the adjusted sampling frequency is written into the sampling frequency label and stored in the register.

[0027] In a preferred embodiment, the power stabilization module comprises the following:

[0028] Read the sampling frequency label and the qualified identification from the register, obtain the adjusted sampling frequency and the coupling state of the sampling frequency and the pulse repetition frequency; when the qualified identification indicates that the coupling characteristic index is less than 1, adjust the reference feedforward gain matrix according to the ratio of the sampling frequency to the pulse repetition frequency, combine the preset reference ratio and the adjustment coefficient, and generate a new feedforward gain matrix.

[0029] In a preferred embodiment, the power stabilization module further comprises the following:

[0030] The original pump drive signal is corrected using the new feedforward gain matrix to generate a corrected pump drive signal, which is sent to the pump source controller to adjust the output power of the pump source; the real-time value of the laser output power is continuously monitored, and the difference between the target power value and the real-time value is calculated as the output power residual vibration; when the absolute value of the output power residual vibration exceeds the preset upper limit, a proportional-integral-derivative control algorithm is used to dynamically adjust the feedforward gain matrix until the absolute value of the output power residual vibration is less than or equal to the preset upper limit.

[0031] The technical effects and advantages of the programmable waveform modulation high-power fiber laser system of the present application are as follows:

[0032] The present application implants a small random time jitter around the preprocessing link of the programmable waveform modulation high-power fiber laser, and breaks the coupling of sampling and pulse rhythm through time scale and power sequence synchronization mapping, thereby blocking the return pseudo-wave from the source. The abstract evaluation mechanism fuses the energy domain and time domain features to drive the sampling frequency adaptive migration with a single decision identifier, so that the monitoring link is continuously in the operation interval of the coprime fraction ratio, and the alias distortion root cause is eliminated. The sampling label and the decision identifier jointly trigger the feedforward gain matrix to form a monitoring evaluation adjustment compensation closed loop, which reduces the power residual vibration in real time, suppresses the stimulated scattering and thermal lens chain amplification, and maintains the stability of the beam quality and the smooth distribution of the energy. Further, the present application improves the consistency of the molten pool depth, makes it difficult for spatter and micro-cracks to accumulate, expands the overall processing window width, and enables the equipment to maintain accurate output and high reliability for a long time under high repetition frequency operation, thereby providing higher flexibility and quality predictability for industrial precision manufacturing. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The structure diagram of the programmable waveform modulation high-power fiber laser system of the present application is shown in the figure;

[0034] Figure 2 The flowchart of the abstract evaluation of the programmable waveform modulation high-power fiber laser system of the present application is shown in the figure. DETAILED DESCRIPTION

[0035] Clearly, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all the other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0036] Embodiment 1 Figure 1 The application provides a programmable waveform modulation high-power fiber laser system, which comprises a jitter injection module, a feature evaluation module, a frequency adjustment module and a power stabilization module.

[0037] The jitter injection module injects a micro random time jitter into a power monitoring branch and records a time stamp sequence, and outputs a synchronous alignment data stream after synchronously capturing a power sample sequence.

[0038] The feature evaluation module inputs the synchronous alignment data stream into a sparse reconstruction model to separate a turn-back energy component, extracts a sample and pulse coupling feature to perform abstract evaluation, judges a decoupling degree of a sample link and a pulse link, and obtains a qualified identification.

[0039] The frequency adjustment module reads the qualified identification, adjusts a real-time sampling frequency to a fraction ratio which is prime to a pulse repetition frequency, and synchronously updates a sampling frequency label.

[0040] The power stabilization module sets a feedforward gain matrix according to the sampling frequency label and the qualified identification, implements correction on a pump driving signal, and maintains output power residual vibration in a set upper limit.

[0041] In the field of modern industrial precision manufacturing, such as steel plate welding and additive manufacturing, programmable waveform modulation high-power fiber laser systems have become the core technology support because they can provide high energy density and flexible waveform control. Such systems meet the dual needs of processing speed and precision by raising the pulse repetition frequency (PRF) to the megahertz (MHz) level. However, the challenge brought by high PRF is the implementation of power monitoring. The traditional full-bandwidth sampling method requires the analog-to-digital conversion (ADC) unit to operate at an extremely high sampling rate, resulting in a sharp increase in data throughput and exacerbating heat dissipation problems. Therefore, the engineering practice often uses band-limited sampling technology, which only intercepts the characteristic frequency band of the power envelope and stabilizes the output through a feedback loop. However, when the modulation frequency approaches or exceeds an integer multiple of the sampling rate, the discrete sampling sequence will fold the high-frequency power pulsation back to the low-frequency region, forming aliasing distortion. This distortion is misjudged by the feedback algorithm as a slow drift, triggering power oscillation amplification, and then triggering stimulated scattering and enhanced thermal lens effect, ultimately destroying the processing consistency. To solve this problem, the present invention proposes a programmable waveform modulation high-power fiber laser system, in which the jitter injection module breaks the coupling between sampling and pulse rhythm from the source by injecting a small random time jitter.

[0042] The jitter injection module includes the following:

[0043] S1.1, inject a small random time jitter:

[0044] In the sampling clock of the power monitoring branch, an independent random time offset is introduced for each sampling period to disturb the fixed correspondence between the sampling time and the pulse time.

[0045] First, the reciprocal of the sampling frequency is determined as the sampling period. For example, if the sampling frequency is set to one million times per second, the sampling period is 100 nanoseconds. Next, a random time offset is generated for each sampling point, with the amplitude range set to 1% to 5% of the sampling period. Taking the sampling period of 100 nanoseconds as an example, if the proportion coefficient is selected as 0.03, the range of the random time offset is from -3 nanoseconds to +3 nanoseconds. The value of the random time offset is generated by uniform distribution, that is, any value within the specified range is randomly selected with equal probability. Finally, the actual sampling time of each sampling point is determined by the nominal sampling time plus this random time offset, where the nominal sampling time is the sampling point number multiplied by the sampling period.

[0046] The purpose of introducing the micro-amplitude random time jitter is to break the integer multiple relationship between the sampling rate and the pulse repetition frequency, thereby preventing aliasing distortion caused by high-frequency power pulsation folding back to the low-frequency region. This perturbation method can block the data distortion caused by fixed coupling from the source, providing a more accurate original data basis for subsequent processing. At the same time, the amplitude of the random time offset is limited to a micro-amplitude range, ensuring that it does not significantly affect the sampling accuracy, and effectively decouples the correlation between the sampling time and the pulse rhythm.

[0047] S1.2, record the time stamp sequence:

[0048] Record the actual sampling time of each sampling point to generate a time stamp sequence, ensuring accurate correspondence between time information and power data.

[0049] First, calculate the nominal sampling time of each sampling point, which is the sampling point number multiplied by the sampling period. For example, if the sampling period is 100 nanoseconds, the nominal sampling time of the tenth sampling point is 1000 nanoseconds. Then, add the random time offset of each sampling point to its nominal sampling time to get the actual sampling time. For example, if the random time offset of the tenth sampling point is 2 nanoseconds, its actual sampling time is 1002 nanoseconds. The actual sampling times of all sampling points are recorded in order to form a time stamp sequence. The time stamp sequence needs to be recorded using a high-precision time counter, with a time resolution better than the amplitude of the random time offset, such as a resolution of 1 nanosecond or higher.

[0050] S1.3, capture the power sample sequence:

[0051] On the basis of injecting micro-amplitude random time jitter, use a high-speed analog-to-digital converter to collect the instantaneous value of the laser output power at each actual sampling time, forming a power sample sequence.

[0052] According to each actual sampling time recorded in the time stamp sequence, trigger the analog-to-digital converter to perform power sampling. For example, if the actual sampling time of a certain sampling point is 1002 nanoseconds, collect the instantaneous value of the laser output power at this time. The power instantaneous values of all sampling points are recorded in order to generate a power sample sequence consistent with the length of the time stamp sequence. The sampling accuracy of the analog-to-digital converter needs to meet the requirements of power monitoring, and can accurately reflect the characteristic changes of the power envelope, such as using a 12-bit or higher precision converter.

[0053] S1.4, output the synchronized alignment data stream:

[0054] Integrate the time stamp sequence and the power sample sequence to generate a synchronized alignment data stream containing the sampling time and the corresponding power value, which is used as the input for the subsequent steps.

[0055] Each actual sampling time in the timestamp sequence is paired with the power instantaneous value in the corresponding power sampling sequence to form a data pair. For example, if the actual sampling time of a certain sampling point is 1002 nanoseconds and the corresponding power instantaneous value is a certain value, a data pair containing this time and this power value is generated. All data pairs are arranged in the sampling order to form a synchronous alignment data stream and stored in a digital format for subsequent processing, such as a two-dimensional array or a data file.

[0056] In the application scenario of high pulse repetition frequency, aliasing distortion is easily generated by band-limited sampling due to the integer multiple relationship between the sampling rate and the pulse repetition frequency, which leads to the deviation of the power monitoring data from the true value and affects the stability of the laser system. The jitter injection module reduces the influence of aliasing distortion by injecting a small random time jitter, which disturbs the fixed coupling between the sampling time and the pulse time, and provides a cleaner and more reliable data basis for subsequent processing steps.

[0057] The jitter injection module generates a synchronous alignment data stream by injecting a small random time jitter in the power monitoring branch and recording a timestamp sequence, capturing a power sampling sequence. The synchronous alignment data stream breaks the fixed coupling between the sampling time and the pulse rhythm, providing a basis for reducing aliasing distortion for subsequent processing. The task of the feature evaluation module is to receive the synchronous alignment data stream, analyze the characteristics therein, separate the return energy component, extract the sampling and pulse coupling features, and generate a coupling strength identifier, which is used to guide the subsequent sampling frequency adjustment to ensure the stability of the processing process.

[0058] The feature evaluation module includes the following:

[0059] S2.1, input the synchronous alignment data stream into the sparse reconstruction model:

[0060] The synchronous alignment data stream contains actual sampling times and corresponding power instantaneous values. First, the synchronous alignment data stream is input into the sparse reconstruction model to decompose the power sampling sequence into real power components and return energy components.

[0061] The sparse reconstruction model uses a dictionary learning method to represent the power sampling sequence using a sparse combination of a set of basis functions. The basis functions are divided into two categories: the first category of basis functions represents the low-frequency changes of the real power envelope, and the second category of basis functions represents the high-frequency fluctuations caused by aliasing distortion. The decomposition process is achieved by optimizing the objective function, and the design purpose of the optimization objective function is to separate the return energy component, while using a sparse penalty parameter to control the sparsity of the decomposition result, ensuring the separation accuracy of the real power component and the return energy component.

[0062] For example, the implementation can be as follows:

[0063] The input data is the synchronous alignment data stream generated by the jitter injection module where is the actual sampling time, is the corresponding power instantaneous value, is the sampling point number. Using a sparse reconstruction method based on dictionary learning, the power sampling sequence is decomposed into a sparse combination of basis functions. The basis functions include low-frequency components of the true power envelope and high-frequency components caused by aliasing. By optimizing the following objective function, the aliasing energy component is separated: ;

[0064] : true power low-frequency basis function, representing the slowly varying component of the power envelope.

[0065] : aliasing high-frequency basis function, representing the high-frequency pulsation caused by aliasing distortion.

[0066] : sparse coefficient of the true power component.

[0067] : sparse coefficient of the aliasing energy component.

[0068] : sparsity penalty parameter, controlling the sparsity degree of decomposition.

[0069] After optimization, the aliasing energy component is , and the true power component is .

[0070] The sparse reconstruction model can effectively separate the components of different frequencies in the power sampling sequence, and is suitable for processing signals containing high-frequency aliasing distortion. By separating the aliasing energy component from the true power component, the impact of aliasing distortion can be more accurately identified and quantified. This separation provides a reliable data basis for subsequent feature extraction, improving the accuracy of analysis.

[0071] S2.2-1, Extracting sampling and pulse coupling features - aliasing energy rate amplitude:

[0072] After separating the aliasing energy component, the next step is to extract the aliasing energy rate amplitude in the sampling and pulse coupling features, which is used to quantify the energy intensity of the aliasing energy component. First, perform a fast Fourier transform on the aliasing energy component to obtain the frequency domain spectrum. Then, use an adaptive frequency band selection method based on spectral entropy to dynamically determine the aliasing frequency band. The specific process is as follows: calculate the normalized power density of the frequency domain spectrum, then calculate the spectral entropy according to the normalized power density to evaluate the uniformity of the frequency domain spectrum distribution; detect the significant change points of the frequency domain spectrum distribution by the change rate of the spectral entropy, determine the boundary of the aliasing frequency band; finally, accumulate the frequency domain spectrum energy within the adaptively determined aliasing frequency band to obtain the aliasing energy rate amplitude.

[0073] For example, the amplitude of the return energy rate can be obtained as follows:

[0074] Definition: Return energy rate amplitude Quantitative return energy components The energy intensity reflects the degree of influence of alias distortion.

[0075] Calculation method:

[0076] Reversed energy components Perform a Fast Fourier Transform (FFT) to obtain the frequency domain spectrum. .

[0077] An adaptive frequency band selection method based on spectral entropy is introduced to dynamically determine the foldback frequency band.

[0078] Specific steps:

[0079] 1. Calculate the spectral power density: For Calculate the power spectral density and normalize it to a probability distribution: ;

[0080] Normalized power spectral density represents the probability distribution of energy in the frequency domain.

[0081] 2. Calculate spectral entropy: based on Calculate spectral entropy : ;

[0082] Spectral entropy reflects the uniformity of the spectrum distribution.

[0083] 3. Calculate the rate of change of spectral entropy: for By frequency Differentiate to obtain the rate of change In practice, the difference approximation is used: ;

[0084] Spectral entropy change rate, used to detect significant changes in the spectral distribution.

[0085] 4. Determine the boundary of the return frequency band: find Local extrema, detect their zero crossover points (i.e. (Location) and confirm the power spectrum Significant peaks exist near these points, and the foldback frequency band is defined as follows: .

[0086] : Adaptively determined foldback frequency band.

[0087] 5. Verify and adjust the frequency band: calculate the energy proportion in the frequency band If the proportion is lower than the threshold (e.g. 80%), expand the frequency band range, repeat steps 3 and 4 until the requirements are met.

[0088] : The total energy in the aliasing frequency band.

[0089] Calculate the aliasing energy amplitude: accumulate the spectral energy in the adaptively determined frequency band: ;

[0090] : The aliasing energy amplitude, representing the intensity of the aliasing energy.

[0091] The aliasing energy amplitude is a parameter that quantifies the energy of the pseudo-power fluctuation caused by aliasing distortion in high-power fiber laser systems. When the sampling rate is close to an integer multiple of the pulse repetition frequency, the power pulsation in the high-frequency region will be folded back to the low-frequency region, forming aliasing distortion. The size of this parameter directly reflects the severity of aliasing distortion: when the amplitude is large, more energy is folded back to the low-frequency region, and aliasing distortion is more significant, which may lead to misjudgment of power changes in the feedback system, causing power oscillation and affecting output stability; when the amplitude is small, the folded energy is less, and the impact of aliasing distortion is smaller, and the system can more accurately monitor and control the power output, thereby maintaining the stability of the machining process. Therefore, the aliasing energy amplitude is an important indicator for evaluating the coupling degree of sampling and pulses, and its size is closely related to the stability of the system and the quality of the machining.

[0092] S2.2-2, Extract the sampling and pulse coupling feature - jitter dispersion rate:

[0093] Another part of extracting the sampling and pulse coupling feature is to calculate the jitter dispersion rate, which is used to quantify the dispersion degree of the sampling time distribution caused by random time jitter. The processing process first calculates the deviation of the actual sampling time and the nominal sampling time, and then uses the mutual information entropy method for analysis. The specific steps are: obtain the probability density function of the deviation by kernel density estimation method, and then calculate the entropy value according to the probability density function, which is defined as the jitter dispersion rate.

[0094] The jitter dispersion rate is a parameter for measuring the influence of random time jitter on the dispersion degree of sampling time distribution in a high-power fiber laser system. By injecting a small random time jitter, the fixed relationship between the sampling time and the pulse time can be broken, thereby reducing the influence of aliasing distortion. The size of this parameter reflects the randomness and effectiveness of the random time jitter: when the dispersion rate is large, the deviation distribution of the sampling time is more dispersed, the randomness is stronger, the fixed relationship between the sampling and the pulse can be effectively decoupled, and the aliasing distortion is significantly reduced; when the dispersion rate is small, the deviation distribution of the sampling time is more concentrated, the randomness is insufficient, and the coupling between the sampling and the pulse may not be fully eliminated, resulting in the existence of aliasing distortion. The jitter dispersion rate quantifies the effect of jitter injection, providing a basis for stable power monitoring of the system at high pulse repetition frequency.

[0095] S2.3, performing abstract evaluation (as shown in Figure 2

[0096] After extracting the amplitude of the turn-back rate and the jitter dispersion rate, an abstract evaluation is performed to generate a coupling strength identifier, the purpose of which is to evaluate the coupling degree between the sampling link and the pulse link based on the amplitude of the turn-back rate and the jitter dispersion rate. The specific processing process includes the following sub-steps:

[0097] The amplitude of the turn-back rate sequence and the jitter dispersion rate sequence are collected in the sliding time window to construct a joint probability density field; the Wasserstein distance between the joint probability density field and the reference decoupling distribution is calculated using the optimal transport algorithm to obtain a minimum transport potential energy sequence; the representative intrinsic components are extracted by performing multi-scale empirical mode decomposition on the minimum transport potential energy sequence; the representative intrinsic components are mapped to the zero-mean symmetric domain through radial normalization kernel; finally, the information entropy aggregation method is used to aggregate the local entropy with adaptive weights to generate the coupling strength identifier, and the qualified identifier is written when the coupling strength identifier is less than 1.

[0098] The size of the coupling strength identifier reflects the coupling degree between the sampling link and the pulse link in the high-power fiber laser system and its influence on power monitoring. When the coupling strength identifier value is small, it indicates that the sampling link and the pulse link tend to be decoupled, the influence of aliasing distortion is small, and the system can more accurately monitor the power output, thereby maintaining the stability of the machining process. On the contrary, when the coupling strength identifier value is large, the coupling degree between the sampling link and the pulse link is enhanced, the influence of aliasing distortion is intensified, and it may lead to misjudgment of power change in the feedback system, causing power oscillation and reducing the output stability. Therefore, the size of the coupling strength identifier is an important indicator for evaluating the power monitoring accuracy and stability of the system at high pulse repetition frequency, and the smaller the value is, the better the system performance is.

[0099] ​The abstract evaluation quantifies the coupling relationship between the amplitude of the return energy and the dispersion rate of the jitter by using the joint probability density field and the optimal transport algorithm. The Wasserstein distance can effectively measure the difference between the joint probability density field and the reference decoupling distribution, and accurately reflect the coupling strength. The multi-scale empirical mode decomposition extracts the representative intrinsic component, and the information entropy aggregation method further improves the robustness and accuracy of the evaluation. This multi-level analysis ensures the reliability of the coupling strength identification, providing a scientific basis for subsequent sampling frequency adjustment.

[0100] The feature evaluation module separates the return energy component by inputting the synchronous alignment data stream into the sparse reconstruction model, accurately identifies and quantifies the pseudo-power fluctuation caused by aliasing distortion; by extracting the amplitude of the return energy and the dispersion rate of the jitter, the influence degree of aliasing distortion and the characteristics of random time jitter are quantified respectively, and the coupling state of sampling and pulse is evaluated comprehensively; the abstract evaluation part uses joint probability density field, optimal transport algorithm, multi-scale empirical mode decomposition and information entropy aggregation to generate coupling strength identification, accurately representing the decoupling degree of sampling link and pulse link. The effect of the feature evaluation module is to significantly improve the performance of the high-power fiber laser system under high-speed working conditions: separating the return energy component eliminates the interference of pseudo-power fluctuation on monitoring, coupling feature extraction provides a clear basis for sampling frequency optimization, and the coupling strength identification generated by abstract evaluation directly guides frequency adjustment, so that the system realizes stable power output and monitoring under high pulse repetition frequency, ensuring the high consistency and reliability of the processing process. Especially the technical features of abstract evaluation, through multi-dimensional analysis and entropy aggregation, accurately capture the dynamic changes of coupling, significantly reduce the risk of power distortion caused by insufficient sampling, and optimize the stability of the overall system.

[0101] The feature evaluation module separates the return energy component by using the sparse reconstruction model, extracts the coupling features and generates qualified identification. However, under the high-speed working condition of dynamic pulse repetition frequency, only relying on the foregoing measures is not enough to completely eliminate aliasing distortion, and real-time optimization and adjustment of the sampling frequency become the key to maintaining the stability of the monitoring link and the consistency of the processing. The frequency adjustment module aims to realize the continuous decoupling of the sampling link and the pulse link by dynamically adjusting the sampling frequency to a fraction ratio that is coprime with the pulse repetition frequency, and to eliminate aliasing distortion from the root.

[0102] The frequency adjustment module includes the following:

[0103] S3.1, read the qualified identification:

[0104] The qualified mark is obtained from the characteristic evaluation module to confirm whether the coupling degree of the sampling frequency and the pulse repetition frequency meets the adjustment condition. If the qualified mark is "qualified", that is, the coupling strength mark is less than 1, the sampling frequency adjustment process is entered; if the qualified mark is not obtained, or the coupling strength mark is greater than or equal to 1, the current sampling frequency is maintained unchanged, and the subsequent adjustment operation is not performed.

[0105] The existence of the qualified mark is a necessary condition for triggering the sampling frequency adjustment process, because it is ensured that the sampling frequency adjustment is only performed when the coupling degree of the sampling link and the pulse link reaches an acceptable range, thereby avoiding invalid or unnecessary frequency changes. In the programmable waveform modulation high-power fiber laser system, the characteristic evaluation module generates a coupling strength mark by analyzing the synchronous alignment data stream, and writes a qualified mark when the coupling strength mark is less than 1, indicating that the aliasing distortion has been significantly reduced, and the coupling degree of the sampling frequency and the pulse repetition frequency is not enough to cause serious power return pseudo-fluctuation. At this time, the qualified mark indicates that the system has the condition to enter the sampling frequency adjustment process, and by adjusting the sampling frequency to a fraction ratio coprime with the pulse repetition frequency, the potential integer multiple relationship is further eliminated, and the occurrence of aliasing distortion is fundamentally eliminated. This mechanism not only improves the pertinence and efficiency of the adjustment, but also ensures the power monitoring accuracy and processing stability of the system at high pulse repetition frequency.

[0106] S3.2, calculate the coprime fraction ratio sampling frequency:

[0107] To avoid the formation of an integer multiple relationship between the sampling frequency and the pulse repetition frequency, a sampling frequency coprime with the current pulse repetition frequency is calculated. The specific calculation process is as follows:

[0108] First, the current pulse repetition frequency and the current sampling frequency are obtained, and then the greatest common divisor of the two is determined. If the greatest common divisor is 1, it indicates that the current sampling frequency is already coprime with the pulse repetition frequency, and no further adjustment is needed. If the greatest common divisor is greater than 1, the sampling frequency needs to be adjusted. For this purpose, a step adjustment value is set, for example, 10 kHz, and the target sampling frequency is initially set to the current sampling frequency plus the step adjustment value. Then, the target sampling frequency is iteratively adjusted, and each time the target sampling frequency is increased or decreased by the step adjustment value, and the greatest common divisor of the target sampling frequency and the pulse repetition frequency is recalculated until the greatest common divisor is 1. At the same time, it is necessary to ensure that the target sampling frequency is within the range of the hardware sampling rate, so as to ensure that the adjusted sampling frequency is within the range of the system hardware capability.

[0109] The sampling frequency is calculated to be coprime with the pulse repetition frequency, ensuring that there is no common divisor greater than 1, fundamentally eliminating alias distortion caused by integer multiple relationships. The target sampling frequency is calculated using an iterative adjustment method, which is simple and efficient, and can quickly determine the appropriate sampling frequency value. In addition, by considering the limitations of the hardware sampling rate range, the feasibility and practicality of the adjustment results are ensured, avoiding invalid adjustments that exceed the hardware capabilities.

[0110] S3.3, adjusting the real-time sampling frequency:

[0111] According to the calculated target sampling frequency, a frequency adjustment instruction is generated. The frequency adjustment instruction contains the specific value of the target sampling frequency and is sent to the sampling clock generator. After receiving the frequency adjustment instruction, the sampling clock generator adjusts its internal clock frequency to make the real-time sampling frequency accurately match the target sampling frequency, thereby completing the real-time update of the sampling frequency.

[0112] Adjusting the real-time sampling frequency to the target value coprime with the pulse repetition frequency ensures that the sampling sequence will not produce aliasing false fluctuations due to integer multiple relationships. This adjustment significantly improves the accuracy of power monitoring, avoiding the phenomenon of high-frequency power pulsation being incorrectly aliased to the low-frequency region, thereby improving the measurement accuracy and reliability of the system.

[0113] S3.4, synchronously updating the sampling frequency label:

[0114] The adjusted sampling frequency is written in digital format to the sampling frequency label and stored in the register. The stored sampling frequency label can be directly read and used by subsequent steps, ensuring that all parts of the system can operate based on the latest sampling frequency data.

[0115] The frequency adjustment module calculates and adjusts the real-time sampling frequency to be coprime with the pulse repetition frequency by reading the qualified identifier, completely eliminating the occurrence of alias distortion. The adjusted sampling frequency ensures that the sampling sequence will not alias high-frequency power pulsation to the low-frequency region due to integer multiple relationships, thereby significantly improving the accuracy of power monitoring. At the same time, the synchronously updated sampling frequency label provides an accurate frequency reference for subsequent closed-loop power feedback, enabling the system to optimize the pump drive signal based on the latest sampling conditions, ultimately achieving stable maintenance of the output power.

[0116] In the frequency adjustment module, the programmable waveform modulation high-power fiber laser system adjusts the sampling frequency to form a coprime fractional ratio with the pulse repetition frequency by reading the qualified identifier, and synchronously updates the sampling frequency label, thereby eliminating the root cause of alias distortion. However, simply adjusting the sampling frequency cannot completely eliminate the influence of fluctuations in power monitoring. In order to further ensure the stability of the output power, the power stabilization module introduces a real-time correction mechanism for the pump driving signal, uses the sampling frequency label and the qualified identifier, and accurately adjusts the pump driving signal by setting the feedforward gain matrix, finally realizes the goal of maintaining the output power residual vibration within the set upper limit.

[0117] The power stabilization module includes the following:

[0118] S4.1, read the sampling frequency label and the qualified identifier:

[0119] The sampling frequency label is read from the register to obtain the specific value of the sampling frequency adjusted by the frequency adjustment module. At the same time, the qualified identifier written by the feature evaluation module is read to confirm whether the coupling degree between the sampling frequency and the pulse repetition frequency meets the preset requirements, i.e., whether the coupling feature index is less than 1.

[0120] S4.2, set the feedforward gain matrix:

[0121] The feedforward gain matrix is a gain coefficient matrix used to adjust the pump driving signal, and its dimension is determined by the number of control channels of the pump source. The process of setting the feedforward gain matrix includes the following steps:

[0122] First, according to the qualified identifier, it is judged whether the feedforward gain matrix needs to be adjusted; if the qualified identifier indicates that the coupling degree between the sampling frequency and the pulse repetition frequency meets the requirements, i.e., the coupling feature index is less than 1, then the ratio of the current sampling frequency to the pulse repetition frequency is calculated; next, the difference between the calculated ratio and the pre-set reference ratio is obtained; then, according to the above difference, the reference feedforward gain matrix is adjusted to generate a new feedforward gain matrix, in order to compensate for the dynamic influence of the sampling frequency change on power monitoring.

[0123] The purpose of setting the feedforward gain matrix is to compensate for the interference of sampling frequency change on power monitoring by dynamically adjusting the gain of the pump driving signal. This method ensures that the output power of the pump source can be consistent with the target power level. The adjustment of the feedforward gain matrix is based on the current system state, which can quickly respond to the change of the sampling frequency, improve the control stability and response speed of the system, and reduce the power fluctuations that may be caused by feedback delay.

[0124] S4.3, implement correction on the pump driving signal:

[0125] According to the set feedforward gain matrix, the original pump driving signal is modified to generate a modified pump driving signal. Specifically, the feedforward gain matrix is applied to the original pump driving signal to generate a modified pump driving signal that can reflect the influence of the sampling frequency change by adjusting the amplitude or phase of the signal. Subsequently, the modified pump driving signal is sent to the pump source controller to adjust the output power of the pump source to match the target power level.

[0126] The purpose of modifying the pump driving signal is to quickly compensate for power fluctuations caused by sampling frequency changes by adjusting the driving signal of the pump source in real time. This modification mechanism can effectively suppress power instability caused by sampling frequency adjustment, improve the power control accuracy of the system at high pulse repetition frequency, and provide protection for stable laser output.

[0127] The pump source is the energy supply unit of the laser, usually composed of high-power semiconductor lasers (LD) or fiber lasers. Its main function is to provide excitation energy for the gain medium (usually doped fiber) to realize the amplification and generation of laser. Specifically, the pump source converts electrical energy into optical energy, injects it into the doped fiber, excites the rare earth ions (such as erbium, ytterbium, etc.) in it to high energy levels, and then produces laser output through the stimulated radiation process. In high-power fiber lasers, the performance of the pump source directly affects the efficiency and output power of the laser system.

[0128] The pump source controller is a device for precise management of the pump source, responsible for regulating its output power, wavelength, and temperature, etc. key parameters to ensure the stability and reliability of the laser system. Its main functions include the following aspects:

[0129] Power regulation: by adjusting the current or voltage of the pump source, the output power of the laser is accurately controlled, thereby indirectly adjusting the output power of the laser.

[0130] Temperature management: real-time monitoring and adjustment of the working temperature of the pump source to avoid overheating damage to the device life and performance.

[0131] Wavelength stabilization: through fine adjustment of temperature or current, the wavelength of the pump source is matched with the absorption peak of the gain medium, improving the energy conversion efficiency.

[0132] Safety protection: integrated with overcurrent, overvoltage, and overtemperature protection mechanisms to ensure the safe operation of the pump source and the entire system.

[0133] In high-power fiber lasers, the pump source provides the energy basis, and the pump source controller ensures the stability and output consistency of the system through real-time regulation. Especially in the programmable waveform modulation scenario, the pump source controller dynamically adjusts the pump source output according to the feedback signal to maintain the stability of the laser power and the beam quality, meeting the high-precision application requirements. Both are indispensable and jointly determine the performance of the laser system.

[0134] S4.4, maintain the output power residual vibration within the set upper limit:

[0135] The real-time value of the output power of the laser is continuously monitored, and the real-time value is compared with the preset target power value, and the difference between the two, i.e. the output power residual vibration, is calculated. If the absolute value of the output power residual vibration exceeds the preset upper limit, a proportional-integral-derivative control algorithm is used to gradually adjust the feedforward gain matrix to reduce the absolute value of the output power residual vibration until it is less than or equal to the preset upper limit. The specific adjustment process includes: determining the adjustment direction and amplitude according to the size and change trend of the output power residual vibration, and iteratively updating the feedforward gain matrix until the residual vibration control requirement is met.

[0136] The purpose of maintaining the output power residual vibration within the set upper limit is to monitor and adjust the output power in real time through a closed-loop feedback mechanism to prevent the residual vibration from being too large to cause amplification of stimulated scattering or thermal lens effect. This method can effectively maintain the stability of the beam quality and the uniformity of the energy distribution, providing reliable optical performance guarantee for high-precision machining.

[0137] The power stabilization module sets the feedforward gain matrix by reading the sampling frequency label and the qualified identification, implements real-time correction on the pump driving signal, and uses a closed-loop feedback mechanism to ensure that the output power residual vibration is maintained within the set upper limit. This technical logic significantly improves the power control accuracy of programmable waveform modulation high-power fiber lasers at high pulse repetition frequency, effectively suppresses the effects of aliasing distortion and power fluctuations, and provides stable beam quality and energy distribution for high-precision machining.

[0138] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0139] It should be noted that the system of the present application can be deployed on the device itself to realize embedded application, or run on PC or other terminal with user interface, so as to meet various hardware environments and use requirements.

[0140] It is apparent that for the person skilled in the art many changes and modifications can be made to the embodiments described above without departing from the spirit and scope of the application. It is therefore intended that such changes and modifications be included within the scope of the application which is defined only by the following claims.

[0141] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended that the scope of the application be limited to the particular forms described. Unless defined otherwise, all technical and scientific terms and any acronyms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this application belongs. Any methods and materials similar or equivalent to those described herein can be used in the practice of the present application. It is intended that the definition of the term "comprising" or "comprises" as used in this specification is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. It is intended that the term "consisting of" as used in this specification is taken to specify the presence of stated features, integers, steps or components and precludes the presence or addition of one or more other features, integers, steps, components or groups thereof. It is intended that the term "consisting essentially of as used in this specification is taken to specify the presence of stated features, integers, steps or components as well as other non-specified features, integers, steps, components or groups thereof which do not materially affect the basic and novel characteristics of the specific embodiments described herein.

[0142] The above description is only specific embodiments of the present application, and the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, and all these changes and replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A programmable waveform modulation high-power fiber laser system, characterized in that, include: Jitter injection module, feature evaluation module, frequency adjustment module, and power stabilization module; Jitter injection module: Injects micro-amplitude random time jitter into the power monitoring branch and records the timestamp sequence. Simultaneously, it captures the power sampling sequence and outputs a synchronized data stream. Feature evaluation module: Input the synchronously aligned data stream into the sparse reconstruction model, separate the reflected energy components, extract the sampling and pulse coupling features, perform abstract evaluation, determine the degree of decoupling between the sampling link and the pulse link, and obtain a qualified label; Frequency adjustment module: Reads the qualified flag, adjusts the real-time sampling frequency to a fractional ratio that is coprime to the pulse repetition frequency, and updates the sampling frequency label synchronously; Power stabilization module: Based on the sampling frequency label and the qualified mark, the feedforward gain matrix is ​​set to correct the pump drive signal so that the output power residual oscillation is maintained within the set upper limit.

2. The programmable waveform modulation high-power fiber laser system according to claim 1, characterized in that, The jitter injection module includes the following: In the power monitoring branch, an independent micro-amplitude random time offset is introduced for each sampling period to disrupt the fixed correspondence between the sampling time and the pulse time. The micro-amplitude random time offset is generated through uniform distribution. At the same time, the actual sampling time of each sampling point is recorded. The actual sampling time is the sum of the nominal sampling time and the micro-amplitude random time offset. The nominal sampling time is the product of the sampling point number and the sampling period. Then, at each actual sampling time, a high-speed analog-to-digital converter is used to acquire the instantaneous value of the laser output power to form a power sampling sequence. Finally, the actual sampling time is paired with the corresponding instantaneous power value to generate a synchronized data stream.

3. The programmable waveform modulation high-power fiber laser system according to claim 2, characterized in that, The feature evaluation module includes the following: The power sampling sequence in the synchronized data stream is sparsely decomposed using a dictionary learning method to separate the true power component and the reflected energy component. The sampling and pulse coupling features, including the reflected energy amplitude and jitter dispersion, are then extracted from them.

4. The programmable waveform modulation high-power fiber laser system according to claim 3, characterized in that, The feature evaluation module also includes the following: By collecting the return energy rate amplitude sequence and jitter dispersion rate sequence within a sliding time window, a joint probability density field is constructed. The Wasserstein distance with the reference decoupled distribution is calculated using the optimal transmission algorithm to obtain the minimum transmission potential energy sequence. Then, multi-scale empirical mode decomposition is performed on it to extract representative eigencomponents. After radial normalization kernel mapping, local entropy is aggregated using adaptive weights to generate coupling strength identifiers. The degree of decoupling between the sampling link and the pulse link is judged to obtain a qualified identifier.

5. A programmable waveform modulation high-power fiber laser system according to claim 3, characterized in that, The feature evaluation module also includes the following: The influence of alias distortion is quantified by performing a fast Fourier transform on the returned energy component and combining it with an adaptive frequency band selection method based on spectral entropy to calculate the returned energy rate amplitude.

6. A programmable waveform modulation high-power fiber laser system according to claim 3, characterized in that, The feature evaluation module also includes the following: The deviation between the actual sampling time and the nominal sampling time is calculated, and the jitter dispersion rate is calculated using the mutual information entropy method to quantify the randomness of random time jitter.

7. A programmable waveform modulation high-power fiber laser system according to claim 4, characterized in that, The frequency adjustment module includes the following: When the pass / fail indicator is passed, the sampling frequency adjustment process is initiated; the current pulse repetition frequency and the current sampling frequency are obtained, and the greatest common divisor of the current pulse repetition frequency and the current sampling frequency is calculated. If the greatest common divisor is 1, the current sampling frequency is kept unchanged. If the greatest common divisor is greater than 1, set an adjustment step value and iteratively adjust the target sampling frequency until the greatest common divisor of the target sampling frequency and the pulse repetition frequency is 1, while ensuring that the target sampling frequency is within the hardware sampling rate range.

8. A programmable waveform modulation high-power fiber laser system according to claim 7, characterized in that, The frequency adjustment module also includes the following: A frequency adjustment instruction containing the target sampling frequency is generated and sent to the sampling clock generator; the sampling clock generator adjusts its internal clock frequency according to the frequency adjustment instruction to make the real-time sampling frequency match the target sampling frequency; the adjusted sampling frequency is written to the sampling frequency tag and stored in a register.

9. A programmable waveform modulation high-power fiber laser system according to claim 8, characterized in that, The power stabilization module includes the following: Read the sampling frequency label and the pass / fail flag from the register to obtain the adjusted sampling frequency and the coupling status between the sampling frequency and the pulse repetition frequency; When the coupling characteristic index is less than 1, the reference feedforward gain matrix is ​​adjusted based on the ratio of the sampling frequency to the pulse repetition frequency, combined with the preset reference ratio and adjustment coefficient, to generate a new feedforward gain matrix.

10. A programmable waveform modulation high-power fiber laser system according to claim 9, characterized in that, The power stabilization module also includes the following: The original pump drive signal is corrected using a new feedforward gain matrix, a corrected pump drive signal is generated, and sent to the pump source controller to adjust the output power of the pump source. The real-time value of the laser output power is continuously monitored, and the difference between it and the target power value is calculated as the output power residual oscillation. When the absolute value of the output power residual oscillation exceeds the preset upper limit, the feedforward gain matrix is ​​dynamically adjusted using a proportional-integral-derivative control algorithm until the absolute value of the output power residual oscillation is less than or equal to the preset upper limit.

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