A machine learning-based inverse design method for dissipative soliton resonance lasers

Abstract

The present invention discloses a method for reverse design of a dissipative soliton resonant laser based on machine learning. The method comprises the following steps: 1) establishing two sets of associated data sets, one for training an SVM and the other for training a BP neural network. The SVM is used to predict the convergence of the dissipative soliton resonant pulse, and the other for predicting the laser output time domain and spectral waveform. 2) initializing a PSO algorithm particle swarm, with each particle's position parameter corresponding to a set of random laser cavity parameters. The SVM model is pre-input to filter out particles that meet the convergence criteria and reuse them as the initial particle swarm. 3) Iteratively updating the particle position information and inputting it into the BP neural network to predict the pulse time domain and spectral waveform information. When the mean square error between the predicted waveform and the set target waveform is lower than a set value, a set of optimal laser cavity parameters is output, thus achieving laser reverse design. The present invention effectively solves the problem of difficult design of dissipative soliton resonant fiber lasers under conditions of low absolute net cavity dispersion.

Description

Technical Field

[0001] The present invention belongs to the fields of laser technology and artificial intelligence, and specifically relates to a method for predicting the pulse characteristics of a Figure-9 dissipative soliton resonant fiber laser and reverse designing the laser based on machine learning. Background Art

[0002] High-energy passively mode-locked fiber lasers have been widely used in fields such as fiber-optic communications, fiber-optic sensing, materials processing, medical diagnostics, defense and military, precision measurement, and scientific research. Dissipative soliton resonant pulses, due to peak power clamping, exhibit linear growth in pulse energy and duration with increasing pump power, while maintaining a constant peak power without wave splitting. This makes them an effective means of achieving high-energy single pulses. Such pulses can exist in both normal and anomalous dispersion fiber laser cavities, but the region of stable mode locking shrinks significantly as the absolute net cavity dispersion decreases. Generally, low net cavity dispersion implies relatively high peak power, which, to some extent, limits the output of high-peak-power dissipative soliton resonant pulses. Furthermore, the pulse temporal domain exhibits a rectangular structure in the low-absolute normal and anomalous dispersion regions, with a rectangular structure with a narrow central peak, and a double-peaked structure in the spectrum, respectively. Therefore, studying the characteristics of dissipative soliton resonant pulses in the low-dispersion range and designing targeted mode-locked fiber lasers is of great significance for achieving high-energy, high-peak-power pulse output and studying complex pulse dynamics.

[0003] At present, in the numerical simulation of mode-locked fiber lasers, it is usually necessary to use the split-step Fourier method (SSFM) to solve the generalized nonlinear Schrödinger equation (GNLSE) to obtain the mapping relationship between the output pulse characteristics and the input cavity parameters, which requires traversing the laser cavity parameter space to be determined. Obviously, the higher the dimension of the traversed cavity parameter space, the more universal the conclusion obtained. However, when the cavity parameter dimension exceeds three dimensions, the traditional laser solution method using SSFM to solve GNLSE will consume a lot of computing resources and simulation time, resulting in low computational efficiency. In addition, when the pulse characteristics are known, the traditional simulation method cannot obtain the corresponding cavity parameters through inverse calculation, which has great limitations.

[0004] In recent years, machine learning technology has been gradually applied to the numerical simulation of mode-locked lasers. By learning large-scale data, it can efficiently establish a mapping relationship between pulse characteristics and cavity parameters, thereby significantly reducing the calculation time and avoiding the tedious calculation process of traditional methods. Machine learning combined with optimization algorithms has also played a key role in the reverse design of lasers. However, existing numerical studies on pulse characteristics and pulse dynamics using machine learning mainly focus on relatively low-energy traditional solitons, dispersion-managed solitons, and dissipative soliton lasers. There are still relatively few numerical studies on high-energy, high-peak-power dissipative soliton resonant lasers, especially for 8- or 9-shaped cavity lasers with low absolute net cavity dispersion and complex cavity structures. Summary of the Invention

[0005] To address these issues, the present invention provides a machine learning-based reverse design method for dissipative soliton resonant lasers with low absolute cavity dispersion. Combining machine learning with optimization algorithms, this method effectively predicts the time-domain and frequency-domain waveforms of dissipative soliton resonant pulses within a high-dimensional parameter space, investigates pulse stability regions, and investigates how pulse characteristic parameters vary with cavity parameters. Furthermore, this method enables reverse design of dissipative soliton resonant pulse lasers.

[0006] The core of this invention is to use machine learning algorithms to pre-judge pulse convergence and train a model that can simultaneously predict the dissipative soliton resonance pulse waveform information in the normal and anomalous dispersion regions. Finally, combined with a selected optimization algorithm, the reverse design of the low absolute net cavity dispersion Figure-9 laser is achieved. The specific process is as follows: Figure 1 , the corresponding specific technical solutions are as follows:

[0007] 1. Construct a correlation dataset of laser cavity parameters and their corresponding pulse characteristics.

[0008] for Figure 2 The dissipative soliton resonant fiber laser shown in Figure-9 (which can also be a dissipative soliton resonant laser of other structures) first uses the traditional method of SSFM combined with GNLSE to simulate the transmission of the light beam in the laser cavity, and then constructs a related data set containing a large number of laser cavity parameters and their corresponding pulse convergence and pulse characteristics (including time domain and spectral waveform information). Among them, the laser cavity parameters and their corresponding pulse convergence labels are used as samples to train the SVM model to pre-determine whether the laser is stable in the dissipative soliton resonant pulse state. Subsequently, under the convergence condition, the laser cavity parameters and the corresponding pulse waveform information are used as samples to train the BP neural network to predict the time domain and spectral waveform of the pulse.

[0009] The present invention only focuses on the characteristics of strongly stable dissipative soliton resonance pulses. Other pulse types, unstable dissipative soliton resonance pulse states, and pulse non-convergence states are all classified as non-convergence of dissipative soliton resonance pulses.

[0010] To ensure the accuracy of the predicted waveform, the present invention employs an adaptive sampling method, taking into account the steep time-domain edges of rectangular dissipative soliton resonant pulses and the fact that, under anomalous dispersion conditions, the pulse exhibits a narrow peak in the time domain situated on a wide rectangular base, with low-intensity sidelobes between two main peaks. Ignoring high-order dispersion and high-order nonlinearities, the pulse's time-domain and spectral waveforms are typically symmetric about the simulation window, i.e., about T = 0s and ω = 0THz, respectively. Therefore, only half of the time-domain and frequency-domain data can be collected, saving computation time while ensuring that the central peak region of the pulse under anomalous dispersion conditions is effectively captured.

[0011] 2. Train the support vector machine (SVM) model to pre-judge the convergence of the dissipative soliton resonant pulse.

[0012] Under conditions of low absolute net cavity dispersion, the region where dissipative soliton resonant pulses can exist stably shrinks significantly. Therefore, it is particularly important to pre-determine whether the laser has converged to a stable dissipative soliton resonant pulse state to save computing resources and time.

[0013] Preferably, SVM, as a commonly used binary classification model in machine learning, is particularly suitable for solving classification problems in high-dimensional parameter spaces. In the present invention, by using the laser cavity parameters in the sample used to train the SVM model established in step 1 as input and the convergence of the dissipative soliton resonant pulse (convergence or non-convergence) as the label output of the classification problem, the SVM model can find the optimal hyperplane based on the training data and separate different convergence categories, demonstrating strong generalization ability.

[0014] The kernel function determines the quality of SVM model training. Therefore, to address nonlinear issues under multi-parameter conditions, this paper comprehensively compares the characteristics of linear kernels, polynomial kernels, radial basis function (RBF) kernels, and sigmoid kernels, and adopts the RBF kernel function, which can be mapped to infinite dimensions, to capture the nonlinear relationship between cavity parameters and convergence. In addition, during the model training process, cross-validation and grid search techniques are used to optimize the SVM hyperparameters.

[0015] 3. Train a back-propagation (BP) neural network that can map laser cavity parameters to pulse temporal and spectral waveforms.

[0016] After completing the SVM model training in step 2, the present invention further verifies the convergence of the samples used to train the BP neural network in step 1 using the SVM model to ensure that all samples converge to the dissipative soliton resonant pulse state. Subsequently, the sample data set is divided into a training set, a validation set, and a test set at 70%, 15%, and 15% respectively. The training set is used to train the BP neural network, the validation set is used for model optimization, and the test set is used for final verification, thereby establishing a BP neural network model capable of efficiently predicting the time domain and spectral waveforms of dissipative soliton resonant pulses.

[0017] The input layer nodes of the BP neural network correspond to the laser cavity parameters. The number of hidden layers and nodes is optimized using cross-validation. The output layer nodes contain the sampling point intensities, maximum sampling time, and spectral range of the pulse time and spectral waveforms. For example, if the number of sampling points in the time and frequency domains is 128, respectively, the number of output layer nodes is 258. During training, the mean squared error (MSE) is used as the loss function to optimize the cavity parameters.

[0018] 4. BP neural network combined with particle swarm optimization (PSO) algorithm to complete the reverse design of dissipative soliton resonant fiber laser.

[0019] The reverse design of the present invention is to search for the global optimal solution of laser cavity parameters based on the given target time domain and spectral waveforms by using the PSO algorithm to optimize the BP neural network output, and then reversely deduce the optimal set of laser cavity parameters.

[0020] First, the particle swarm is randomly initialized, and the position parameters of each particle (corresponding to a set of random cavity parameters) are input into the SVM model trained by step 2 to determine the pulse convergence, and the particles that meet the pulse convergence conditions are screened out as the initial particle swarm. Subsequently, the position parameters of the converged particles are sequentially input into the BP neural network trained by step 3 to complete the prediction of multiple sets of pulse time domain and spectral waveforms. Furthermore, the PSO algorithm is used to globally optimize the particle swarm, gradually adjust the laser cavity parameters and input them into the BP neural network for pulse waveform prediction, until the pulse waveform predicted by the BP neural network is closest to the given target pulse waveform, that is, the MSE value is minimized. At this point, the PSO algorithm stops iterating and outputs a set of optimal laser cavity parameters, thereby realizing the reverse design of the dissipative soliton fiber laser.

[0021] The advantages of the present invention are as follows:

[0022] 1. Based on a machine learning algorithm, this method can quickly and accurately predict the convergence, temporal and spectral waveform characteristics of dissipative soliton resonant pulses in a high-dimensional cavity parameter space, as well as the variation of pulse parameters with cavity parameters. Compared with traditional laser solution methods based on SSFM for GNLSE, this method significantly improves computational efficiency and saves computing resources.

[0023] 2. This paper, based on machine learning, investigates dissipative soliton resonant lasers under conditions of low absolute net cavity dispersion. As the net cavity dispersion transitions from normal to anomalous dispersion, the time-domain waveform of the dissipative soliton resonant pulse changes from a rectangular shape to a rectangular shape with a central peak, while the spectrum transitions from a single peak to a double peak structure. Using a trained BP neural network, the dynamic variation of pulse characteristics with cavity parameters can be rapidly studied.

[0024] 3. Using the PSO algorithm to optimize the trained BP neural network can quickly realize the reverse design of lasers with multi-dimensional cavity parameters, effectively solving the problem of narrow convergence region and relatively difficult design of dissipative soliton resonant lasers under low absolute net cavity dispersion conditions, greatly saving development time and R&D costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Design a flow chart for the overall system.

[0026] Figure 2 Figure-9 is the schematic diagram of the dissipative soliton resonant fiber laser.

[0027] Figure 3 Schematic diagram of pulse time domain and spectral waveform sampling.

[0028] Figure 4 Figure-9 is the flow chart of the reverse algorithm for dissipative soliton resonance fiber laser. DETAILED DESCRIPTION

[0029] The following is combined with Figure 1 The present invention is described in further detail. The examples given are only used to explain the present invention but not to limit the scope of the present invention.

[0030] 1. The present invention first uses the traditional laser simulation method combining SSFM and GNLSE to Figure 2 The dissipative soliton resonant laser shown in Figure 9 is simulated to obtain the convergence, time domain, and spectral waveform information of the dissipative soliton resonant pulse under conditions of multidimensional laser cavity parameter variation. The convergence label is used to train the SVM model, while the corresponding time domain and spectral waveform information under convergence conditions is used for subsequent BP neural network training.

[0031] Figure 2Figure-9 is the schematic diagram of the dissipative soliton resonant fiber laser. The laser cavity includes a pump source 1, a wavelength division multiplexer (WDM) 2, an erbium-doped fiber (EDF) 3, a non-reciprocal phase shifter (PS) 4, a dispersion-compensating fiber (DCF) 5, a single-mode fiber (SMF) 6, a coupler (OC) 7, a tunable filter (TF) 8, a single-mode fiber (SMF) 9, a dispersion delay line (DDL) 10, and a reflector 11.

[0032] There are 8 adjustable cavity parameters involved in the present invention, namely the length of EDF (L EDF ) and gain saturation energy (E sat )、DCF length (L DCF ), SMF 9 length (L SMF ), PS phase shift The coupling ratio (k) of OC, the bandwidth (Δλ) of TF and the dispersion (Δβ2) of DDL are defined. Each parameter has an upper and lower limit range, and the value of an input variable in a single sample is a random number within its definition domain, which is randomly generated by a computer program. Finally, 5000 samples were prepared for training the SVM and BP neural network models. In order to study the characteristics of dissipative soliton resonant pulses under low absolute net cavity dispersion conditions, the appropriate fiber length and Δβ2 range were selected before simulation to ensure that the net cavity dispersion was kept between -1.5 and 1.5 ps. 2 Conduct research within the scope.

[0033] In the simulation, the traditional laser simulation method of SSFM combined with GNLSE is used to simulate the transmission of the light field in the laser cavity, and the maximum number of cycles is set to 3000. If within 3000 cycles, the relative energy change of the pulses in two adjacent cycles has already been reduced to 10 -9 If the pulse intensity curves in both the time and frequency domains are smooth, the dissipative soliton resonance pulse is considered to have converged and is marked as 1. Conversely, if the pulse does not meet the above conditions, it is considered to have not converged and is marked as -1. The convergence flag is used for subsequent SVM model training.

[0034] Based on the steep pulse edge characteristics of the rectangular dissipative soliton resonance pulse and the unique time-frequency domain characteristics of the pulse in the anomalous dispersion region, the present invention adopts an adaptive sampling method for sampling. The pulse waveform is normalized before sampling to eliminate the influence of the peak intensity on the neural network. Subsequently, the waveform gradient is calculated on the time domain waveform to determine the areas of the rising edge, peak and falling edge of the pulse, and sampling points are added in these areas; for the spectral waveform, the gradient is calculated using the same method, focusing on sampling areas where the frequency components change dramatically. In order to speed up the calculation and better reflect the waveform information at the peak, half of the waveform is sampled, such as Figure 3 As shown, the red area is the key sampling area. The sampling time and frequency range correspond to the maximum intensity It,max and I f,max Reduce to I t,max / 200 and I f,max / 200, the corresponding horizontal coordinate value is recorded as ΔT max and Δf max To accurately restore the pulse waveform, ΔT max and Δf max The combined time domain and spectral sampling intensity values ​​are used as sample points of the BP neural network.

[0035] 2. The present invention divides the laser cavity parameters obtained in step 1 and their corresponding pulse convergence data sets into training and test sets in a ratio of 80:20, where 80% of the data is used for model training and 20% of the data is used for model verification. Each sample contains a set of laser cavity parameters (L EDF 、E sat , L DCF , L SMF 、 k, Δλ and Δβ2) and the convergence label (1 or -1) of the dissipative soliton resonant pulse.

[0036] Subsequently, the SVM model was trained using the training set and optimized by maximizing the distance between the support vector and the hyperplane, achieving accurate classification of convergent labels. During training, the RBF kernel function was used to address the nonlinear characteristics of the sample data. A grid search technique combined with cross-validation was used to fine-tune the RBF kernel function. The specific tuning parameters are the kernel parameter σ and the penalty parameter C. σ controls the width of the kernel function, while C balances the fit of the training set with the generalization ability of the model.

[0037] After the SVM model is trained, its performance is evaluated using the test data. Evaluation metrics include accuracy, precision, recall, and F1-score. The closer these metrics are to 1, the better the SVM model performance, ensuring the model's reliability and generalization ability in determining the convergence of dissipative soliton resonance pulses.

[0038] 3. After completing the SVM model training in step 2, the present invention uses this model to further verify the sample convergence of the BP neural network used to train the BP neural network in step 1, ensuring that all 5000 samples can converge to the dissipative soliton resonant pulse state. Subsequently, the samples are divided into a training set, a validation set, and a test set at a ratio of 70%, 15%, and 15%. Each sample includes a set of laser cavity parameters (L EDF 、E sat , L DCF , L SMF 、 k, Δλ and Δβ2) and their corresponding pulse time domain and spectral waveform sampling point intensity, sampling time range ΔTmax and frequency range Δf max .

[0039] Subsequently, the BP neural network was trained using the training set. By learning the input laser cavity parameters, the mapping relationship between the laser cavity parameters and the pulse time domain and spectral waveforms was learned. The input layer nodes of the BP neural network corresponded to the eight laser cavity parameters. The hidden layer was used to capture the nonlinear relationship between the cavity parameters and the pulse waveform. Each layer used the ReLU activation function to alleviate the problem of vanishing gradients during training. The output layer contained the pulse time domain and spectral waveform information obtained through adaptive sampling. The number of nodes in the output layer was L = m + n + 2, where m and n were the number of sampling points in the time domain and frequency domain waveforms, respectively.

[0040] During the training process, the mean square error (MSE) is used as the Loss function. Since the prediction accuracy of both the time domain and the spectral waveform needs to be considered at the same time, the Loss function is the sum of the two Loss functions, that is:

[0041]

[0042] Among them, MSE time is the MSE of the time domain waveform, MSE freq is the MSE of the spectral waveform, y t,i is the intensity value of the i-th time domain sampling point, is the corresponding target waveform intensity value, y f,i is the intensity value of the i-th frequency domain sampling point, is the corresponding target spectral intensity value. In addition, the optimization function selects the Adam optimizer to adaptively adjust the learning rate, and the initial learning rate is set to 0.001.

[0043] After completing the model training, the regression graphs of the training set, validation set, and test set were drawn to visually evaluate the consistency between the model output results and the target value, and the correlation coefficient R was used. 2 To quantify the model performance. 2 The closer it is to 1, the better the model training effect. In addition, within the defined range of cavity parameter variations, multiple groups of cavity parameter combinations can be randomly selected for prediction and further verified using traditional numerical methods.

[0044] 4. The present invention combines the trained BP neural network with the PSO algorithm to achieve the reverse design of the dissipative soliton resonant fiber laser. Compared with the commonly used genetic algorithm (GA), the PSO algorithm is faster and more accurate because it does not have the "crossover" and "mutation" operations of GA. The reverse algorithm flow chart is as follows: Figure 4 shown.

[0045] (1) Initialize the particle swarm in the PSO algorithm, including the number of particles, particle position, velocity, learning factor, inertia factor, and maximum number of iterations.

[0046] In the initialization phase of the PSO algorithm, the present invention introduces the SVM model to screen the randomly generated particle swarm to ensure that the position parameters (L EDF 、E sat , L DCF , L SMF 、 k, Δλ, and Δβ2) satisfy the convergence conditions for dissipative soliton resonant pulses. This operation can significantly reduce the computational overhead caused by invalid particles, narrow the search space, and improve the calculation speed.

[0047] The particle velocity v controls the particle's moving direction and step size in the parameter space. The initial value is set to 0 or randomly generated within a certain range. The learning factors c1 and c2 are set to the empirical value 2. The inertia factor w is a non-negative number and adopts a linear decreasing strategy, as shown in formula (2), where w max and w min are the maximum and minimum inertia weights, which are set to 0.9 and 0.4 respectively, to ensure the global exploration capability in the early stage of the search and enhance the local convergence capability in the later stage, thereby improving the accuracy and convergence speed of the global optimal solution. max is the maximum number of iterations, and t is the current number of iterations.

[0048]

[0049] (2) Determine the BP neural network structure and input the position of each particle into the trained BP neural network in sequence, thereby obtaining the sampling points of the pulse time domain and frequency domain, and restoring the time domain and spectral waveforms.

[0050] (3) Calculate the fitness function f(x j ), that is, calculate the MSE value:

[0051]

[0052] Here, j represents the jth particle and ranges from 1 to N; n and m represent the number of sampling points in the time domain and spectrum, respectively. This operation improves the physical accuracy and robustness of the search results.

[0053] (4) After each iteration, determine whether the termination condition is reached. Termination condition A: Global optimal fitness function maxf(x j ) is less than the preset target accuracy value. Termination condition B: The number of iterations is greater than the set maximum number of iterations. If the above termination conditions A or B are met, the global optimal particle position G is output best,d , otherwise update the individual optimal position Pbest,d and the global optimal position G best,d , update the position and velocity of each particle according to formula (4). is the speed after the tth iteration in the d-dimensional parameter space, is the position after the tth iteration in the d-dimensional parameter space, and r1 and r1 are independent random numbers between (0,1).

[0054]

[0055] Subsequently, the updated particle swarm data is re-input into the BP neural network, and steps 2-4 are repeated until the set termination condition is met. Through dynamic adjustment and intelligent search, this invention effectively balances the performance of global search and local convergence, ensuring the rapid discovery of cavity parameter combinations that meet design requirements, providing strong guidance and reference value for experimental design.

[0056] While specific embodiments of the present invention have been disclosed for illustrative purposes, intended to facilitate understanding and implementation of the present invention, those skilled in the art will appreciate that various substitutions, variations, and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the disclosure of the preferred embodiments, and the scope of protection claimed in the present invention shall be determined by the scope of the claims.

Claims

1. A method for inverse design of a dissipative soliton resonant laser based on machine learning, comprising the following steps: 1) establishing a correlation data set, the correlation data set comprising: (1) a first correlation data set of laser cavity parameters and their corresponding dissipative soliton resonance pulse convergence labels; (2) a second correlation data set of laser cavity parameters and pulse time domain and spectral waveform information under convergence conditions; 2) using samples of the first correlation data set to train an SVM model to obtain a classification model that can predict the convergence of dissipative soliton resonance pulses, which is used to pre-determine whether the laser converges to a stable dissipative soliton resonance pulse state; 3) using the samples in the second correlation data set to train a BP neural network to obtain a BP neural network capable of mapping laser cavity parameters with pulse time domain and spectral waveforms; 4) Initializing the particle swarm in the PSO algorithm, setting the position parameters of each particle to a set of random laser cavity parameters, and inputting them into the classification model trained in step 2) to predict the pulse convergence corresponding to the output, and screening out the particles that meet the convergence conditions as the initial particle swarm of the optimization algorithm; 5) Use the PSO optimization algorithm to perform global optimization on the particle swarm, iteratively adjust and update the laser cavity parameters, and input each set of updated laser cavity parameters into the BP neural network trained in step 3) in turn to output the predicted pulse waveform information; when the pulse waveform predicted by the BP neural network and the set target pulse waveform meet the preset conditions, Stop the iteration and output a set of optimal laser cavity parameters; 6) Designing a dissipative soliton fiber laser based on a set of optimal laser cavity parameters obtained in step 5).

2. The method according to claim 1, characterized in that SSFM is used to solve the transmission of the GNLSE simulated beam in the laser and establish the aforementioned associated data set; combined with the characteristics of dissipative soliton resonant pulses under low absolute net cavity dispersion conditions, the waveform is sampled using an adaptive sampling method to ensure data density in key areas. Since the time domain and frequency domain waveforms are symmetrical about the time and frequency axes, respectively, computing resources are saved by collecting half of the time domain and frequency domain data.

3. The method according to claim 1 or 2, characterized in that When the first associated data set is used to train the SVM model, the laser cavity parameters in the sample are input into the SVM model, and the SVM is supervised and trained in combination with the label information to obtain a classification model that can predict the convergence of dissipative soliton resonance pulses.

4. The method according to claim 1 or 2, characterized in that When the second associated data set is used to train the BP neural network, the laser cavity parameters in the sample are used as BP neural network input, and the time domain and spectral waveform data of the sample are used as output to establish a BP model for predicting pulse waveforms.

5. The method according to claim 4, characterized in that The input layer nodes of the BP neural network correspond to the laser cavity parameters, and the number of hidden layers and nodes is tuned according to the cross-validation method; the output layer nodes contain the sampling point intensity, maximum sampling time and spectrum range of the pulse time domain and spectral waveform; during the training process, the mean square error is used as the loss function to optimize the laser cavity parameters, and Adam is used as the optimizer.

6. The method according to claim 1, wherein The preset condition is that the mean square error between the pulse waveform predicted and output by the BP neural network and the target pulse waveform is minimized.

7. The method according to claim 1, characterized in that The dissipative soliton resonant laser is a Figure-9 dissipative soliton resonant fiber laser, a semiconductor laser or a solid laser.

8. A server, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program comprises instructions for executing the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images