A method and system for multi-parameter joint evaluation of laser source linewidth stability

Abstract

The application provides a kind of laser light source line width stability multi-parameter joint evaluation method and system, obtains the junction temperature parameter of laser, environmental sound spectrum data and optical interference original signal during power mutation;The energy accumulation operation is carried out to the environmental sound spectrum data, and acoustic characteristic energy value sequence is generated;The optical interference original signal is phase demodulation processing, and line width drift value sequence is generated;The junction temperature parameter, the acoustic characteristic energy value sequence and the line width drift value sequence are carried out frequency domain correlation operation, and joint transfer function matrix is generated;The amplitude response modulus of the matrix is calculated, and dynamic weight vector is obtained;The line width drift value sequence is Fourier transformed, and initial line width frequency domain distribution vector is obtained, combined with the dynamic weight vector, and multi-parameter joint evaluation value is calculated;The application realizes dynamic weighted comprehensive evaluation to laser light source line width stability, and improves the accuracy and robustness of evaluation.

Description

Technical Field

[0001] This application relates to the field of laser physics technology, and in particular to a method and system for joint evaluation of multiple parameters of linewidth stability of laser sources. Background Technology

[0002] In cutting-edge applications such as high-precision optical coherent communication, distributed fiber optic sensing, and precision laser measurement, the linewidth stability of the laser source determines the system's signal-to-noise ratio, coherence length, and measurement accuracy. Especially under dynamic conditions involving rapid power switching or sudden load changes, the source linewidth is prone to transient broadening or frequency drift due to thermal transients and mechanical vibrations, affecting system performance. Therefore, there is an urgent need for a technique that can accurately assess laser linewidth stability in real time during power surges, enabling multi-physics parameter collaborative analysis to support closed-loop control and reliability verification of high-performance lasers.

[0003] To address the aforementioned needs, the current mainstream approach is a linewidth stability assessment scheme based on junction temperature and spectrometer data fusion. This scheme monitors the laser heat sink temperature change using a high-speed temperature sensor and simultaneously acquires spectral broadening information of the optical heterodyne signal. It then utilizes the temporal correlation between these two factors to establish an empirical transfer model, thereby predicting the linewidth evolution trend during power step transitions. However, existing schemes have certain drawbacks. For example, they rely solely on unidirectional thermal and optical coupling modeling, failing to effectively incorporate the impact of environmental mechanical vibration, a key source of interference, leading to systematic biases in the prediction of linewidth transient overshoot. Furthermore, the linear regression model used is insufficient to characterize the nonlinear dynamic response characteristics under multi-physics excitation, and cannot distinguish the contribution of vibrations in different frequency bands to the linewidth impact. This results in insufficient sensitivity of the assessment results under high-frequency disturbance scenarios, making it difficult to meet the requirements for refined characterization of linewidth stability under high-dynamic conditions. Summary of the Invention

[0004] This application provides a multi-parameter joint evaluation method and system for laser source linewidth stability, which solves the problems in the prior art where there are deviations in the prediction of transient overshoot of linewidth and insufficient sensitivity in high-frequency disturbance scenarios, thus failing to meet the requirements for linewidth stability.

[0005] Firstly, this application provides a multi-parameter joint evaluation method for the linewidth stability of a laser source, including:

[0006] Acquire the junction temperature parameters of the laser, ambient acoustic spectrum data, and the raw optical interference signals during power surges;

[0007] The environmental acoustic spectrum data is subjected to an energy accumulation operation to generate an acoustic feature energy value sequence;

[0008] The original optical interference signal is subjected to phase demodulation processing to generate a linewidth drift value sequence;

[0009] The junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence are subjected to frequency domain correlation operation to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter, and the linewidth parameter;

[0010] Calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain the dynamic weight vector;

[0011] The linewidth drift value sequence is subjected to Fourier transform to obtain the initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, the multi-parameter joint evaluation value of the laser source linewidth stability during power mutation is calculated.

[0012] Optionally, an energy accumulation operation is performed on the environmental acoustic spectrum data to generate a sequence of acoustic feature energy values, including:

[0013] The sound pressure amplitude of each frequency point is extracted from the environmental sound spectrum data, and the frequency region where the sound pressure amplitude exceeds the preset threshold is taken as the target feature frequency band.

[0014] Based on a preset frequency interval, the target feature frequency band is discretely sampled to obtain a set of discrete frequency points;

[0015] Extract the time series of sound pressure amplitude corresponding to each discrete frequency point in the set of discrete frequency points from the environmental sound spectrum data;

[0016] The sound pressure amplitude time series is divided into multiple time units, and the sum of squares of the sound pressure amplitude at all discrete frequency points in each time unit is calculated. Each sum of squares of the sound pressure amplitude is used as the energy accumulation value of the corresponding time unit.

[0017] Based on the time unit order, all accumulated energy values ​​are sorted to obtain the acoustic feature energy value sequence.

[0018] Optionally, the original optical interference signal is subjected to phase demodulation processing to generate a linewidth drift value sequence, including:

[0019] Based on the photoelectric conversion principle, the original optical interference signal is transmitted to the photosensitive area of ​​the photoelectric converter to convert the original optical interference signal into a current signal.

[0020] The current signal is converted into a voltage signal waveform using the current-to-voltage conversion circuit inside the photoelectric converter.

[0021] Based on a preset sampling duration, the voltage signal waveform is discretely sampled to obtain a discrete electrical signal sequence;

[0022] Calculate the phase difference between adjacent sampling points in the discrete electrical signal sequence, and aggregate all phase difference values ​​to obtain a phase difference sequence;

[0023] The phase difference value in the phase difference value sequence is multiplied by a preset scaling factor to obtain multiple wavelength offsets;

[0024] Based on all wavelength offsets, the standard deviation is calculated, and the standard deviation is used as the linewidth drift value. According to the sampling time order, all linewidth drift values ​​are arranged to generate a linewidth drift value sequence.

[0025] Optionally, the junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence are subjected to frequency domain correlation operations to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter, and the linewidth parameter, including:

[0026] The junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence are subjected to discrete Fourier transform operations to obtain a frequency domain complex sequence characterizing the junction temperature parameter, a frequency domain complex sequence characterizing the acoustic parameter, and a frequency domain complex sequence characterizing the linewidth parameter.

[0027] Complex elements corresponding to the same frequency point are extracted from the frequency domain complex number sequences characterizing junction temperature parameters, the frequency domain complex number sequences characterizing acoustic parameters, and the frequency domain complex number sequences characterizing linewidth parameters, respectively, so as to calculate the cross-correlation value of the same frequency point. The cross-correlation value includes junction temperature acoustic cross-correlation value, junction temperature linewidth cross-correlation value, and acoustic linewidth cross-correlation value.

[0028] The autocorrelation value is calculated based on the cross-correlation value and the corresponding conjugate coefficient at the same frequency point. The autocorrelation value includes the junction temperature autocorrelation value, the acoustic autocorrelation value, and the linewidth autocorrelation value.

[0029] The normalized correlation coefficient is calculated based on the cross-correlation and autocorrelation values ​​at the same frequency point.

[0030] Based on the normalized correlation coefficient of each frequency point, a unit matrix is ​​constructed. By arranging the unit matrices of all frequency points, a joint transfer function matrix representing the relationship between junction temperature parameters, acoustic parameters, and linewidth parameters is obtained.

[0031] Optionally, the magnitude of the amplitude response of the joint transfer function matrix at each frequency point is calculated, and all magnitudes of the amplitude response are combined to obtain a dynamic weight vector, including:

[0032] Extract the main diagonal elements of the unit matrix at each frequency point from the joint transfer function matrix, and construct a unit test vector with the same dimension as the unit matrix based on the amplitude characteristic values ​​of the main diagonal elements.

[0033] The unit test vector and unit matrix at each frequency point are multiplied on the left to obtain multiple response vectors, and the magnitude portion of the response vector that is greater than a preset amplitude threshold is selected as the amplitude response magnitude.

[0034] Arrange all amplitude response magnitudes in ascending order of frequency to obtain the amplitude response sequence;

[0035] The amplitude response sequence is scaled to a preset weight range by linear interpolation to obtain a scaled amplitude response sequence, which is then used as a dynamic weight vector.

[0036] Optionally, based on the magnitude characteristic values ​​of the main diagonal elements, a unit test vector with the same dimension as the unit matrix is ​​constructed, including:

[0037] The magnitude characteristic value of each main diagonal element is calculated by summing the squares of the real and imaginary parts of each complex element in each main diagonal element.

[0038] Construct an initial zero-value vector based on the dimension of the matrix unit;

[0039] Based on the order of the main diagonal positions, the amplitude characteristic value is written into the corresponding component of the initial zero value vector to generate a unit test vector with the same dimension as the unit matrix.

[0040] Optionally, the linewidth drift value sequence is subjected to Fourier transform to obtain an initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, a multi-parameter joint evaluation value of the laser source linewidth stability during power abrupt changes is calculated, including:

[0041] And construct an orthogonal basis function set of the same length as the linewidth drift value sequence, the orthogonal basis function set including a cosine basis function sequence and a sine basis function sequence;

[0042] The linewidth drift value sequence is multiplied by the cosine basis function sequence and the sine basis function sequence to obtain the real part correlation value and the imaginary part correlation value.

[0043] Based on the real part correlation value and the imaginary part correlation value, a linewidth frequency domain complex vector is constructed, and the magnitude of each complex element in the linewidth frequency domain complex vector is arranged to obtain an initial linewidth frequency domain distribution vector;

[0044] When the total number of elements in the initial linewidth frequency domain distribution vector and the dynamic weight vector are not equal, the target vector with the smaller total number of elements is filled with zero elements until the total number of elements in the linewidth frequency domain distribution vector and the dynamic weight vector are equal, thus obtaining the filled linewidth frequency domain distribution vector and the filled dynamic weight vector.

[0045] The dot product operation is performed on the filled linewidth frequency domain distribution vector and the filled dynamic weight vector to obtain a weighted sum value, which is used as a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation.

[0046] Secondly, this application provides a multi-parameter joint evaluation system for the linewidth stability of a laser source, comprising:

[0047] The acquisition module is used to acquire the junction temperature parameters of the laser, ambient acoustic spectrum data, and the raw optical interference signal during power surges.

[0048] The accumulation module is used to perform energy accumulation operations on the environmental acoustic spectrum data to generate a sequence of acoustic feature energy values;

[0049] The demodulation module is used to perform phase demodulation processing on the original optical interference signal to generate a linewidth drift value sequence;

[0050] The correlation module is used to perform frequency domain correlation operations on the junction temperature parameter, the acoustic feature energy value sequence, and the linewidth drift value sequence to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter, and the linewidth parameter;

[0051] The calculation module is used to calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain a dynamic weight vector;

[0052] The transformation module is used to perform a Fourier transform on the linewidth drift value sequence to obtain an initial linewidth frequency domain distribution vector, and combine it with the dynamic weight vector to calculate a multi-parameter joint evaluation value of the laser source linewidth stability during power mutation.

[0053] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a multi-parameter joint evaluation method for linewidth stability of a laser source as described in the first aspect above.

[0054] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multi-parameter joint evaluation method for the linewidth stability of a laser source as described in the first aspect.

[0055] This application acquires the junction temperature parameters of a laser, ambient acoustic spectrum data, and the original optical interference signal during a power surge. It performs energy accumulation on the ambient acoustic spectrum data to generate an acoustic characteristic energy value sequence. It then performs phase demodulation processing on the original optical interference signal to generate a linewidth drift value sequence. Finally, it performs frequency domain correlation operations on the junction temperature parameters, the acoustic characteristic energy value sequence, and the linewidth drift value sequence to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameters, acoustic parameters, and linewidth parameters. The application calculates the amplitude response magnitude of the joint transfer function matrix at each frequency point and combines all amplitude response magnitudes to obtain a dynamic weight vector. Finally, it performs a Fourier transform on the linewidth drift value sequence to obtain an initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, it calculates a multi-parameter joint evaluation value of the laser source linewidth stability during a power surge. The technical solution provided in this application acquires the junction temperature parameters of a laser, ambient acoustic spectrum data, and the original optical interference signal during a power surge, providing a data foundation for subsequent multi-source signal fusion analysis. By transforming environmental acoustic spectrum data into feature sequences, the ability to characterize complex acoustic interference is enhanced. Phase demodulation of the original optical interference signal extracts the dynamic trajectory of linewidth changes during power abrupt changes, providing a high-time-resolution basis for linewidth response. A joint transfer function matrix is ​​constructed, overcoming the limitations of single parameters. The amplitude response modulus of the joint transfer function matrix at each frequency point is calculated, achieving adaptive weighting of the influence intensity of different frequency components. Fourier transform of the linewidth drift sequence effectively integrates multi-dimensional information from thermal, acoustic, and optical dimensions, improving the comprehensive discrimination capability for transient linewidth behavior under complex dynamic conditions. Furthermore, by extracting the main diagonal elements of the unit matrix at each frequency point of the joint transfer function matrix, constructing unit test vectors, and performing left multiplication, modulus exceeding a preset amplitude threshold is selected to form an amplitude response sequence; this sequence is then scaled using linear interpolation to generate a dynamic weight vector. It solves the problem of fixed weights in the uniform linear model, and can dynamically adjust the contribution weights of each frequency band according to the actual coupling strength at different frequencies. This improves the sensitivity of the evaluation results to high-frequency vibration interference and nonlinear thermal, acoustic and optical interaction effects, suppresses the interference of weakly correlated frequency band noise, and strengthens the representation of the dominant disturbance source in the final evaluation value.

[0056] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart of a multi-parameter joint evaluation method for linewidth stability of a laser source provided in this application;

[0059] Figure 2 A schematic diagram of a multi-parameter joint evaluation system for linewidth stability of a laser source provided in this application;

[0060] Figure 3 This is a schematic diagram of the structure of a computing device provided in this application. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0062] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] Driven by the urgent need for refined evaluation of linewidth stability, this application incorporates external acoustic interference into the evaluation system by simultaneously acquiring junction temperature parameters, ambient acoustic spectrum data, and optical interference signals. Multidimensional temporal characteristics are obtained through acoustic spectrum energy accumulation and phase demodulation. Frequency domain correlation operations are introduced to construct a joint transfer function matrix, revealing the interaction mechanism between thermal and vibrational excitation in the linewidth response. Adaptive weighting of key disturbance frequency bands is achieved by extracting the amplitude response modulus at each frequency point and generating a dynamic weight vector. Multi-parameter fusion evaluation is completed by combining the linewidth frequency domain distribution and dynamic weights, improving the accuracy and robustness of the characterization of linewidth transient behavior in complex dynamic environments.

[0065] Figure 1 This application provides a flowchart of a multi-parameter joint evaluation method for the linewidth stability of a laser source, as shown in the embodiments of this application. Figure 1 As shown, the method includes:

[0066] Step 101: Acquire the junction temperature parameters of the laser, ambient acoustic spectrum data, and the raw optical interference signal during power abrupt changes.

[0067] In this step, junction temperature parameter refers to the temperature parameter of the core junction region when the laser is operating. Ambient acoustic spectrum data refers to the spectral data of the sound signal in the operating environment of the laser. Optical interference raw signal refers to the raw optical signal obtained after the laser produces interference phenomena during power abrupt changes.

[0068] In this embodiment, the junction temperature parameters of the laser during operation are collected, the ambient sound spectrum data of the environment in which the laser is located are obtained, and the original optical interference signal generated by the laser during power abrupt changes is also collected.

[0069] Step 102: Perform an energy accumulation operation on the environmental sound spectrum data to generate an acoustic feature energy value sequence.

[0070] In this step, the acoustic characteristic energy value sequence refers to the energy accumulation value sequence calculated based on the sound pressure amplitude.

[0071] Step 103: Perform phase demodulation processing on the original optical interference signal to generate a linewidth drift value sequence.

[0072] In this step, the linewidth drift value sequence refers to the sequence calculated based on the phase difference value.

[0073] Step 104: Perform frequency domain correlation operations on the junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, acoustic parameter, and linewidth parameter.

[0074] In this step, the joint transfer function matrix refers to the matrix constructed based on the normalized correlation coefficient.

[0075] Step 105: Calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain the dynamic weight vector.

[0076] In this step, the magnitude of the amplitude response refers to the magnitude value extracted from the response vector of the joint transfer function matrix. The dynamic weight vector refers to the vector obtained by linear interpolation scaling based on the amplitude response sequence.

[0077] Step 106: Perform a Fourier transform on the linewidth drift value sequence to obtain the initial linewidth frequency domain distribution vector. Combine the dynamic weight vector to calculate the multi-parameter joint evaluation value of the laser source linewidth stability during power mutation.

[0078] In this step, the initial linewidth frequency domain distribution vector refers to the vector obtained after performing a Fourier transform on the linewidth drift value sequence. The multi-parameter joint evaluation value refers to the weighted sum obtained by performing a dot product operation on the filled linewidth frequency domain distribution vector and the filled dynamic weight vector.

[0079] In this embodiment, an orthogonal basis function set is constructed, and a dot product operation is performed between it and the linewidth drift value sequence to obtain the correlation value of the real and imaginary parts, so as to construct a linewidth frequency domain complex vector. Its magnitude is calculated and arranged to obtain an initial linewidth frequency domain distribution vector. After performing a dot product operation between it and the dynamic weight vector, a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation is obtained.

[0080] This application embodiment obtains multiple parameters of the laser and, through a series of processing and calculations, obtains a multi-parameter joint evaluation value, realizing a comprehensive and accurate evaluation of the linewidth stability of the laser source during power mutations. It combines the influence of junction temperature, acoustic and linewidth-related parameters, improving the reliability and comprehensiveness of the evaluation.

[0081] This application provides a specific embodiment. Step 102 involves performing an energy accumulation operation on the environmental sound spectrum data to generate an acoustic feature energy value sequence, specifically including the following steps:

[0082] Step 201: Extract the sound pressure amplitude of each frequency point from the environmental sound spectrum data, and take the frequency region where the sound pressure amplitude exceeds the preset threshold as the target feature frequency band.

[0083] In this step, sound pressure amplitude refers to the amplitude of sound pressure corresponding to each frequency point in the ambient sound spectrum data. Preset threshold refers to a pre-defined critical value used to filter sound pressure amplitudes. Frequency region refers to a frequency range consisting of multiple frequency points. Target characteristic frequency band refers to the frequency region related to laser linewidth stability selected from the ambient sound spectrum data.

[0084] In this embodiment of the application, the sound pressure amplitude corresponding to each frequency point is parsed from the environmental sound spectrum data, and then the frequency points whose sound pressure amplitude exceeds a preset threshold are selected, and the frequency region where these frequency points are located is determined as the target characteristic frequency band.

[0085] Step 202: Based on a preset frequency interval, perform discrete sampling on the target characteristic frequency band to obtain a set of discrete frequency points.

[0086] In this step, the preset frequency interval refers to the pre-defined frequency interval for discrete sampling within the target characteristic frequency band. The discrete frequency point set refers to the set of multiple frequency points obtained by sampling within the target characteristic frequency band at the preset frequency interval.

[0087] In this embodiment of the application, multiple discrete frequency points are selected within the frequency range of the target characteristic frequency band according to a preset frequency interval, and these frequency points are integrated to form a discrete frequency point set.

[0088] Step 203: Extract the time series of sound pressure amplitude corresponding to each discrete frequency point in the set of discrete frequency points from the environmental sound spectrum data.

[0089] In this step, the sound pressure amplitude time series refers to the sequence formed by arranging the sound pressure amplitude of each discrete frequency point at different time points in chronological order.

[0090] In this embodiment of the application, the sound pressure amplitude of each discrete frequency point at different times is extracted from the environmental sound spectrum data and arranged in chronological order to form a time series of sound pressure amplitude corresponding to each discrete frequency point.

[0091] Step 204: Divide the sound pressure amplitude time series into multiple time units, calculate the sum of squares of the sound pressure amplitude at all discrete frequency points in each time unit, and use the sum of squares of each sound pressure amplitude as the energy accumulation value of the corresponding time unit.

[0092] In this step, the cumulative energy value refers to the sum of the squares of the sound pressure amplitudes at all discrete frequency points within each time unit.

[0093] In this embodiment of the application, the sound pressure amplitude time series is divided into multiple time units according to a preset time length. The sound pressure amplitude of all discrete frequency points in each time unit is squared and then summed. The sum of squares is used as the energy accumulation value of the corresponding time unit.

[0094] Step 205: Sort all accumulated energy values ​​according to the time unit order to obtain the acoustic feature energy value sequence.

[0095] In this embodiment of the application, the cumulative energy values ​​corresponding to each time unit are arranged sequentially according to the order of time units from first to last, forming an acoustic feature energy value sequence.

[0096] This application embodiment extracts acoustic features related to laser linewidth stability from environmental acoustic spectrum data and eliminates irrelevant noise interference to obtain an acoustic feature energy value sequence that reflects the environmental acoustic characteristics, providing a reliable acoustic input for multi-parameter joint evaluation.

[0097] This application provides a specific embodiment. Step 103 involves performing phase demodulation processing on the original optical interference signal to generate a linewidth drift value sequence, specifically including the following steps:

[0098] Step 301: Based on the photoelectric conversion principle, the original optical interference signal is transmitted to the photosensitive area of ​​the photoelectric converter to convert the original optical interference signal into a current signal.

[0099] In this step, the photoelectric conversion principle refers to the physical principle of converting light signals into electrical signals. The photosensitive area of ​​the photoelectric converter refers to the specific region within the converter that can receive light signals and generate the photoelectric conversion effect. The current signal refers to the signal in the form of current that changes with the light signal, obtained through photoelectric conversion.

[0100] In this embodiment, the original optical interference signal is transmitted to the photosensitive area of ​​the photoelectric converter through the photoelectric conversion principle of photoelectric conductivity effect, so that the material in the photosensitive area generates a corresponding current change due to the change in light intensity, thereby obtaining a current signal.

[0101] Step 302: Use the current-to-voltage conversion circuit inside the photoelectric converter to convert the current signal into a voltage signal waveform.

[0102] In this step, the current-to-voltage conversion circuit refers to the electronic circuit used to convert a current signal into a voltage signal. The voltage signal waveform refers to the voltage curve that changes over time after the current signal has been converted.

[0103] In this embodiment, the current-to-voltage conversion circuit inside the photoelectric converter is used to generate a voltage drop when the current signal flows through the current-to-voltage conversion circuit, thereby obtaining a voltage signal waveform.

[0104] Step 303: Based on a preset sampling duration, the voltage signal waveform is discretely sampled to obtain a discrete electrical signal sequence.

[0105] In this step, the preset sampling duration refers to the pre-defined time interval for sampling the voltage signal waveform. The discrete electrical signal sequence refers to the discrete voltage value sequence obtained after sampling the voltage signal waveform according to the preset sampling duration.

[0106] In this embodiment of the application, the voltage values ​​of the voltage signal waveform at different time points are discretely acquired by the sampling circuit according to the preset sampling duration, and the acquired voltage values ​​are arranged in chronological order to obtain a discrete electrical signal sequence.

[0107] Step 304: Calculate the phase difference between adjacent sampling points in the discrete electrical signal sequence, and aggregate all phase difference values ​​to obtain a phase difference sequence.

[0108] In this step, the phase difference refers to the difference between the phase information of two adjacent sampling points in a discrete electrical signal sequence. The phase difference sequence refers to the sequence formed by arranging all phase differences in the time order of their corresponding sampling points.

[0109] In this embodiment of the application, phase information is extracted from each sampling point of the discrete electrical signal sequence by a phase extraction algorithm, the difference between the phase information of two adjacent sampling points is calculated, and the difference between the phase information is the phase difference value. Then, all phase difference values ​​are integrated according to the time order of the corresponding sampling points to form a phase difference value sequence.

[0110] Step 305: Multiply each phase difference in the phase difference sequence with a preset scaling factor to obtain multiple wavelength offsets.

[0111] In this step, the preset scaling factor refers to a pre-set proportional constant used to convert the phase difference value into a wavelength offset. The wavelength offset refers to the value reflecting the change in laser wavelength obtained by multiplying the phase difference value by the preset scaling factor.

[0112] In this embodiment of the application, each phase difference in the phase difference sequence is multiplied by a preset scaling factor to obtain the wavelength offset corresponding to each phase difference.

[0113] Step 306: Calculate the standard deviation based on all wavelength offsets, use the standard deviation as the linewidth drift value, and arrange all linewidth drift values ​​according to the sampling time order to generate a linewidth drift value sequence.

[0114] In this step, standard deviation refers to a statistic used to measure the dispersion of a set of wavelength offsets. Linewidth drift refers to the value that characterizes the change in laser linewidth by using the standard deviation of the wavelength offset.

[0115] In this embodiment, the average value of the wavelength offset is first calculated, then the average value of the squares of the differences between each wavelength offset and the average value is calculated, and then the square root of the average value is taken as the standard deviation. This standard deviation is used as the linewidth drift value, and then all the linewidth drift values ​​are arranged in the order of sampling time to generate a linewidth drift value sequence.

[0116] This application embodiment converts the original optical interference signal into a linewidth drift value sequence that reflects the changes in laser linewidth, providing linewidth-related data support for subsequent multi-parameter joint evaluation of laser linewidth stability.

[0117] This application provides a specific embodiment. Step 104 involves performing a frequency domain correlation operation on the junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter, and the linewidth parameter. This specifically includes the following steps:

[0118] Step 401: Perform Discrete Fourier Transform operations on the junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence to obtain a frequency domain complex sequence characterizing the junction temperature parameter, a frequency domain complex sequence characterizing the acoustic parameter, and a frequency domain complex sequence characterizing the linewidth parameter.

[0119] In this step, the frequency domain complex sequence characterizing the junction temperature parameter refers to the complex sequence obtained after performing a Discrete Fourier Transform on the junction temperature parameter. The frequency domain complex sequence characterizing the acoustic parameter refers to the complex sequence obtained after performing a Discrete Fourier Transform on the acoustic characteristic energy value sequence. The frequency domain complex sequence characterizing the linewidth parameter refers to the complex sequence obtained after performing a Discrete Fourier Transform on the linewidth drift value sequence.

[0120] In this embodiment of the application, the time-domain data of junction temperature parameters, the time-domain data of acoustic feature energy value sequence, and the time-domain data of linewidth drift value sequence are transformed by the discrete Fourier transform algorithm, and mapped from the time domain to the frequency domain to obtain a sequence composed of complex numbers, where each complex number contains a real part and an imaginary part, which correspond to the amplitude and phase characteristics of each parameter in the frequency domain, respectively.

[0121] Step 402: Extract the complex elements corresponding to the same frequency point from the frequency domain complex sequence characterizing the junction temperature parameter, the frequency domain complex sequence characterizing the acoustic parameter, and the frequency domain complex sequence characterizing the linewidth parameter, respectively, to calculate the cross-correlation value of the same frequency point. The cross-correlation value includes the junction temperature acoustic cross-correlation value, the junction temperature linewidth cross-correlation value, and the acoustic linewidth cross-correlation value.

[0122] In this step, the complex element refers to the complex number corresponding to each frequency point in the frequency domain complex sequence. The cross-correlation value refers to the product of the frequency domain complex elements of two different parameters at the same frequency point. The junction temperature acoustic cross-correlation value is the product of the complex elements of the frequency domain complex sequence characterizing the junction temperature parameter and the frequency domain complex sequence characterizing the acoustic parameter at the same frequency point. The junction temperature linewidth cross-correlation value is the product of the complex elements of the frequency domain complex sequence characterizing the junction temperature parameter and the frequency domain complex sequence characterizing the linewidth parameter at the same frequency point. The acoustic linewidth cross-correlation value is the product of the complex elements of the frequency domain complex sequence characterizing the acoustic parameter and the frequency domain complex sequence characterizing the linewidth parameter at the same frequency point.

[0123] In this embodiment, positions with the same frequency value are found from the frequency domain complex sequences characterizing junction temperature parameters, acoustic parameters, and linewidth parameters. The three complex elements corresponding to these positions are extracted, and the products of the complex elements of junction temperature parameters and acoustic parameters, the complex elements of junction temperature parameters and linewidth parameters, and the complex elements of acoustic parameters and linewidth parameters are calculated by complex multiplication operations to obtain three cross-correlation values, including the junction temperature-acoustic cross-correlation value, the junction temperature-linewidth cross-correlation value, and the acoustic linewidth cross-correlation value.

[0124] Step 403: Calculate the autocorrelation value based on the cross-correlation value and the corresponding conjugate coefficient at the same frequency point. The autocorrelation value includes the junction temperature autocorrelation value, the acoustic autocorrelation value, and the linewidth autocorrelation value.

[0125] In this step, the conjugate coefficient refers to the conjugate of a complex element. The autocorrelation value is the product of the cross-correlation value at the same frequency and its own conjugate coefficient at the same frequency.

[0126] In the embodiments of this application, the autocorrelation values ​​of the junction temperature parameter, the acoustic parameter, and the linewidth parameter are obtained by multiplying the cross-correlation value at the same frequency point with its own conjugate coefficient.

[0127] Step 404: Calculate the normalized correlation coefficient based on the cross-correlation and autocorrelation values ​​at the same frequency point.

[0128] In this step, the normalized correlation coefficient refers to the coefficient obtained after standardizing the cross-correlation values.

[0129] In the embodiments of this application, for the cross-correlation value and autocorrelation value at the same frequency point, the normalized correlation coefficient is calculated. The calculation formula is as follows: the normalized correlation coefficient between junction temperature and acoustics = the square root of the junction temperature-acoustic cross-correlation value / (junction temperature autocorrelation value × acoustic autocorrelation value); the normalized correlation coefficient between junction temperature and linewidth = the square root of the junction temperature-linewidth cross-correlation value / (junction temperature autocorrelation value × linewidth autocorrelation value); the normalized correlation coefficient between acoustics and linewidth = the square root of the acoustic linewidth cross-correlation value / (acoustic autocorrelation value × linewidth autocorrelation value).

[0130] Step 405: Construct a unit matrix based on the normalized correlation coefficient of each frequency point, arrange the unit matrices of all frequency points to obtain the joint transfer function matrix characterizing the relationship between junction temperature parameters, acoustic parameters and linewidth parameters.

[0131] In this step, the unit matrix refers to the matrix constructed from three normalized correlation coefficients at the same frequency point.

[0132] In this embodiment, the three normalized correlation coefficients at the same frequency point are filled into the matrix in a preset order to form the unit matrix of that frequency point. Then, the unit matrices of all frequency points are arranged in order from low to high frequency to form a joint transfer function matrix that characterizes the relationship between junction temperature parameters, acoustic parameters, and linewidth parameters.

[0133] This application embodiment performs frequency domain correlation operations on junction temperature parameters, acoustic characteristic energy value sequences, and linewidth drift value sequences to obtain frequency domain complex number sequences, cross-correlation values, autocorrelation values, and normalized correlation coefficients in sequence. Finally, a joint transfer function matrix is ​​constructed, realizing a quantitative representation of the correlation relationship between the three parameters in the frequency domain.

[0134] This application provides a specific embodiment, step 105, calculating the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combining all the magnitudes of the amplitude response to obtain a dynamic weight vector, specifically including the following steps:

[0135] Step 501: Extract the main diagonal elements of the unit matrix at each frequency point from the joint transfer function matrix, and construct a unit test vector with the same dimension as the unit matrix based on the amplitude characteristic values ​​of the main diagonal elements.

[0136] In this step, the main diagonal elements refer to the elements along the diagonal from the top left to the bottom right corner of the cell matrix. The magnitude characteristic value refers to the magnitude of the main diagonal elements. The cell test vector is a vector with the same dimension as the cell matrix, constructed based on the magnitude characteristic values ​​of the main diagonal elements.

[0137] In this embodiment, the element extraction technique is used to separate the unit matrix corresponding to each frequency point from the joint transfer function matrix. The elements on the main diagonal from the top left to the bottom right corner of these unit matrices are extracted, and the square root of the sum of the square of the real part and the square of the imaginary part is calculated to obtain the amplitude characteristic value. Then, a vector with the same dimension as the unit matrix, i.e., the unit test vector, is constructed based on these amplitude characteristic values.

[0138] Step 502: Perform a left multiplication operation on the unit test vector and the unit matrix at each frequency point to obtain multiple response vectors, and select the magnitude portion of the response vector that is greater than the preset amplitude threshold as the amplitude response magnitude.

[0139] In this step, the response vector refers to the vector obtained by left multiplying the unit test vector and the unit matrix. The preset amplitude threshold refers to a pre-set critical value used to filter the magnitude of the response vector.

[0140] In this embodiment, the unit test vector at each frequency point is left-multiplied by the corresponding unit matrix to obtain the response vector at that frequency point. The magnitude of the response vector is calculated, and the magnitude is compared with a preset amplitude threshold. The magnitude portion that is greater than the preset amplitude threshold is selected as the amplitude response magnitude.

[0141] Step 503: Arrange all amplitude response magnitudes in ascending order of frequency to obtain the amplitude response sequence.

[0142] In this step, the amplitude response sequence refers to the sequence of all amplitude response moduli arranged in ascending order of frequency.

[0143] In the embodiments of this application, the amplitude response magnitudes corresponding to each frequency point are arranged sequentially in order from low to high frequency to form an amplitude response sequence.

[0144] Step 504: Scale the amplitude response sequence to a preset weight range using linear interpolation to obtain a scaled amplitude response sequence, and use the scaled amplitude response sequence as a dynamic weight vector.

[0145] In this step, linear interpolation refers to the method of mapping numerical values ​​from one interval to another by establishing a linear functional relationship. The preset weight interval refers to a pre-defined interval used to standardize the range of values ​​for the dynamic weight vector. The scaled amplitude response sequence refers to the amplitude response sequence scaled to the preset weight interval through linear interpolation.

[0146] In this embodiment, linear interpolation is used to scale each value in the amplitude response sequence to a preset weight range, and the scaled amplitude response sequence is the dynamic weight vector.

[0147] This application embodiment extracts key elements from the joint transfer function matrix, constructs test vectors, performs calculations, filters, sorts, and scales them to obtain a dynamic weight vector that reflects the weight of each frequency point, providing a weight allocation basis that conforms to the actual correlation characteristics for multi-parameter joint evaluation.

[0148] This application provides a specific embodiment. Step 501 involves constructing a unit test vector with the same dimension as the unit matrix based on the magnitude characteristic values ​​of the main diagonal elements. This specifically includes the following steps:

[0149] Step 511: Calculate the magnitude characteristic value of each main diagonal element based on the sum of the squares of the real and imaginary parts of each complex element in each main diagonal element.

[0150] In the embodiments of this application, for each complex element in the main diagonal element, the square of its real part and the square of its imaginary part are calculated respectively, and then the two square values ​​are added together to obtain a sum. Then the square root of the sum is calculated, and this square root is used as the amplitude characteristic value of the main diagonal element.

[0151] Step 512: Construct an initial zero-value vector based on the dimension of the matrix unit.

[0152] In this step, the dimension of the matrix element refers to the number of rows or columns in the element matrix, and is a parameter used to determine the dimensions of the initial zero vector and the unit test vector.

[0153] In this embodiment of the application, a vector containing the same number of elements and all element values ​​being zero is created based on the number of rows or columns of the unit matrix. This vector is the initial zero value vector.

[0154] Step 513: According to the position order of the main diagonal, write the amplitude characteristic value into the corresponding component of the initial zero value vector to generate a unit test vector with the same dimension as the unit matrix.

[0155] In this step, the initial zero-value vector refers to a vector constructed based on the dimensions of the matrix units, in which all element values ​​are zero.

[0156] In this embodiment, according to the order of the positions of the main diagonal elements in the unit matrix, the magnitude characteristic value corresponding to each main diagonal element is sequentially filled into the component corresponding to that position in the initial zero value vector, thereby forming a vector with the same dimension as the unit matrix. This vector is the unit test vector.

[0157] This application embodiment calculates the magnitude characteristic value of the main diagonal elements, constructs an initial zero-value vector based on the matrix unit dimension and writes the magnitude characteristic value, and generates a unit test vector with the same dimension as the unit matrix. This provides an input vector for subsequent operations with the unit matrix to obtain the response vector, ensuring the reliability of the magnitude response modulus calculation.

[0158] This application provides a specific embodiment. Step 106 involves performing a Fourier transform on the linewidth drift value sequence to obtain an initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, a multi-parameter joint evaluation value of the laser source linewidth stability during power abrupt changes is calculated. This specifically includes the following steps:

[0159] Step 601: Construct an orthogonal basis function set of the same length as the linewidth drift value sequence, wherein the orthogonal basis function set includes a cosine basis function sequence and a sine basis function sequence.

[0160] In this step, an orthogonal basis function set refers to a function set consisting of two mutually orthogonal basis function sequences. A cosine basis function sequence refers to a sequence formed by arranging cosine functions in a certain frequency order. A sine basis function sequence refers to a sequence formed by arranging sine functions in a certain frequency order.

[0161] In this embodiment of the application, two sets of basis functions with the same length as the linewidth drift value sequence are generated by the function generation method. One set is a cosine basis function sequence formed by arranging cosine basis functions in sequence, and the other set is a sine basis function sequence formed by arranging sine basis functions in sequence. These two sets of sequences together constitute an orthogonal basis function set.

[0162] Step 602: Perform dot product operations between the linewidth drift value sequence and the cosine basis function sequence and the sine basis function sequence respectively to obtain the real part correlation value and the imaginary part correlation value.

[0163] In this step, the real part correlation value refers to the result of the dot product operation between the linewidth drift value sequence and the cosine basis function sequence. The imaginary part correlation value refers to the result of the dot product operation between the linewidth drift value sequence and the sine basis function sequence.

[0164] In this embodiment, the real part correlation value is obtained by multiplying each element in the linewidth drift value sequence with the corresponding element in the cosine basis function sequence and summing the results. The imaginary part correlation value is obtained by multiplying each element in the linewidth drift value sequence with the corresponding element in the sine basis function sequence and summing the results.

[0165] Step 603: Based on the real part correlation value and the imaginary part correlation value, construct a linewidth frequency domain complex vector, and arrange the magnitude of each complex element in the linewidth frequency domain complex vector to obtain an initial linewidth frequency domain distribution vector.

[0166] In this step, the linewidth frequency domain complex vector refers to the vector formed by arranging complex elements consisting of real and imaginary correlation values ​​in sequence.

[0167] In this embodiment, the real part correlation value is used as the real part and the imaginary part correlation value is used as the imaginary part to form complex elements. These complex elements are arranged in order to form a linewidth frequency domain complex vector. The sum of the square of the real part and the square of the imaginary part of each complex element in the linewidth frequency domain complex vector is calculated, and then the square root is taken to obtain the modulus. All modulus are then arranged in order to obtain the initial linewidth frequency domain distribution vector.

[0168] Step 604: When the total number of elements in the initial linewidth frequency domain distribution vector and the dynamic weight vector are not equal, the target vector with the smaller total number of elements is filled with zero elements until the total number of elements in the linewidth frequency domain distribution vector and the dynamic weight vector are equal, thus obtaining the filled linewidth frequency domain distribution vector and the filled dynamic weight vector.

[0169] In this step, the filled linewidth frequency domain distribution vector refers to the vector obtained by filling the initial linewidth frequency domain distribution vector with zero elements. The filled dynamic weight vector refers to the vector obtained by filling the dynamic weight vector with zero elements.

[0170] In this embodiment, the number of elements in the initial linewidth frequency domain distribution vector and the dynamic weight vector are compared. Zero elements are added to the tail of the vector with fewer elements so that the total number of elements in both vectors is the same, thus obtaining the filled linewidth frequency domain distribution vector and the filled dynamic weight vector, respectively.

[0171] Step 605: Perform a dot product operation on the filled linewidth frequency domain distribution vector and the filled dynamic weight vector to obtain a weighted sum value, and use the weighted sum value as a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation.

[0172] In this embodiment, the elements at corresponding positions in the filled linewidth frequency domain distribution vector and the filled dynamic weight vector are multiplied and summed to obtain a weighted sum value, which is the multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation.

[0173] This application embodiment constructs an orthogonal basis function group for frequency domain transformation, and obtains a multi-parameter joint evaluation value through dot product operation, vector filling and weighted summation, thereby realizing a comprehensive quantitative evaluation of linewidth stability and integrating the correlation information between linewidth drift and dynamic weights.

[0174] Figure 2 This application provides a schematic diagram of a multi-parameter joint evaluation system for laser source linewidth stability, as shown in the embodiment. Figure 2 As shown, the system includes:

[0175] The acquisition module 21 is used to acquire the junction temperature parameters of the laser, the ambient acoustic spectrum data, and the original optical interference signal during power abrupt changes.

[0176] Accumulation module 22 is used to perform energy accumulation operation on the environmental sound spectrum data to generate a sequence of acoustic feature energy values;

[0177] Demodulation module 23 is used to perform phase demodulation processing on the original optical interference signal to generate a linewidth drift value sequence;

[0178] The association module 24 is used to perform frequency domain association operations on the junction temperature parameter, the acoustic feature energy value sequence and the linewidth drift value sequence to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter and the linewidth parameter;

[0179] Calculation module 25 is used to calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain a dynamic weight vector;

[0180] The transformation module 26 is used to perform a Fourier transform on the linewidth drift value sequence to obtain an initial linewidth frequency domain distribution vector, and combine it with the dynamic weight vector to calculate a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation.

[0181] Figure 2 The aforementioned multi-parameter joint evaluation system for laser source linewidth stability can perform... Figure 1 The implementation principle and technical effects of the multi-parameter joint evaluation method for laser source linewidth stability described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the multi-parameter joint evaluation system for laser source linewidth stability in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0182] In one possible design, Figure 2 The laser source linewidth stability multi-parameter joint evaluation system of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0183] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0184] The processing component 32 is used for the above Figure 1 The embodiment describes a method for jointly evaluating the linewidth stability of a laser source using multiple parameters.

[0185] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0186] Storage component 31 is configured to store various types of data to support operations on the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Random Access Memory (RAM), Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0187] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0188] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0189] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0190] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a method for joint evaluation of multiple parameters related to the linewidth stability of a laser source.

[0191] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0192] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0193] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for jointly evaluating the linewidth stability of a laser source using multiple parameters, characterized in that, include: Acquire the junction temperature parameters of the laser, ambient acoustic spectrum data, and the raw optical interference signals during power surges; The environmental acoustic spectrum data is subjected to an energy accumulation operation to generate an acoustic feature energy value sequence; The original optical interference signal is subjected to phase demodulation processing to generate a linewidth drift value sequence; The junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence are subjected to frequency domain correlation operation to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter, and the linewidth parameter; Calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain the dynamic weight vector; The linewidth drift value sequence is subjected to Fourier transform to obtain the initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, the multi-parameter joint evaluation value of the laser source linewidth stability during power mutation is calculated. Calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain the dynamic weight vector, including: Extract the main diagonal elements of the unit matrix at each frequency point from the joint transfer function matrix, and construct a unit test vector with the same dimension as the unit matrix based on the amplitude characteristic values ​​of the main diagonal elements. The unit test vector and unit matrix at each frequency point are multiplied on the left to obtain multiple response vectors, and the magnitude portion of the response vector that is greater than a preset amplitude threshold is selected as the amplitude response magnitude. Arrange all amplitude response magnitudes in ascending order of frequency to obtain the amplitude response sequence; The amplitude response sequence is scaled to a preset weight range by linear interpolation to obtain a scaled amplitude response sequence, which is then used as a dynamic weight vector. The linewidth drift value sequence is subjected to Fourier transform to obtain the initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, a multi-parameter joint evaluation value of the laser source linewidth stability during power abrupt changes is calculated, including: Construct an orthogonal basis function set of the same length as the linewidth drift value sequence, wherein the orthogonal basis function set includes a cosine basis function sequence and a sine basis function sequence; The linewidth drift value sequence is multiplied by the cosine basis function sequence and the sine basis function sequence to obtain the real part correlation value and the imaginary part correlation value. Based on the real part correlation value and the imaginary part correlation value, a linewidth frequency domain complex vector is constructed, and the magnitude of each complex element in the linewidth frequency domain complex vector is arranged to obtain an initial linewidth frequency domain distribution vector; When the total number of elements in the initial linewidth frequency domain distribution vector and the dynamic weight vector are not equal, the target vector with the smaller total number of elements is filled with zero elements until the total number of elements in the linewidth frequency domain distribution vector and the dynamic weight vector are equal, thus obtaining the filled linewidth frequency domain distribution vector and the filled dynamic weight vector. The dot product operation is performed on the filled linewidth frequency domain distribution vector and the filled dynamic weight vector to obtain a weighted sum value, which is used as a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation.

2. The method according to claim 1, characterized in that, The environmental acoustic spectrum data is subjected to an energy accumulation operation to generate an acoustic feature energy value sequence, including: The sound pressure amplitude of each frequency point is extracted from the environmental sound spectrum data, and the frequency region where the sound pressure amplitude exceeds the preset threshold is taken as the target feature frequency band. Based on a preset frequency interval, the target feature frequency band is discretely sampled to obtain a set of discrete frequency points; Extract the time series of sound pressure amplitude corresponding to each discrete frequency point in the set of discrete frequency points from the environmental sound spectrum data; The sound pressure amplitude time series is divided into multiple time units, and the sum of squares of the sound pressure amplitude at all discrete frequency points in each time unit is calculated. Each sum of squares of the sound pressure amplitude is used as the energy accumulation value of the corresponding time unit. Based on the time unit order, all accumulated energy values ​​are sorted to obtain the acoustic feature energy value sequence.

3. The method according to claim 1, characterized in that, The original optical interference signal is subjected to phase demodulation processing to generate a linewidth drift value sequence, including: Based on the photoelectric conversion principle, the original optical interference signal is transmitted to the photosensitive area of ​​the photoelectric converter to convert the original optical interference signal into a current signal. The current signal is converted into a voltage signal waveform using the current-to-voltage conversion circuit inside the photoelectric converter. Based on a preset sampling duration, the voltage signal waveform is discretely sampled to obtain a discrete electrical signal sequence; Calculate the phase difference between adjacent sampling points in the discrete electrical signal sequence, and aggregate all phase difference values ​​to obtain a phase difference sequence; The phase difference value in the phase difference value sequence is multiplied by a preset scaling factor to obtain multiple wavelength offsets; Based on all wavelength offsets, the standard deviation is calculated, and the standard deviation is used as the linewidth drift value. According to the sampling time order, all linewidth drift values ​​are arranged to generate a linewidth drift value sequence.

4. The method according to claim 1, characterized in that, The junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence are subjected to frequency domain correlation operations to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, acoustic parameter, and linewidth parameter, including: The junction temperature parameter, the acoustic characteristic energy value sequence, and the linewidth drift value sequence are subjected to discrete Fourier transform operations to obtain a frequency domain complex sequence characterizing the junction temperature parameter, a frequency domain complex sequence characterizing the acoustic parameter, and a frequency domain complex sequence characterizing the linewidth parameter. Complex elements corresponding to the same frequency point are extracted from the frequency domain complex number sequences characterizing junction temperature parameters, the frequency domain complex number sequences characterizing acoustic parameters, and the frequency domain complex number sequences characterizing linewidth parameters, respectively, so as to calculate the cross-correlation value of the same frequency point. The cross-correlation value includes junction temperature acoustic cross-correlation value, junction temperature linewidth cross-correlation value, and acoustic linewidth cross-correlation value. The autocorrelation value is calculated based on the cross-correlation value and the corresponding conjugate coefficient at the same frequency point. The autocorrelation value includes the junction temperature autocorrelation value, the acoustic autocorrelation value, and the linewidth autocorrelation value. The normalized correlation coefficient is calculated based on the cross-correlation and autocorrelation values ​​at the same frequency point. Based on the normalized correlation coefficient of each frequency point, a unit matrix is ​​constructed. By arranging the unit matrices of all frequency points, a joint transfer function matrix representing the relationship between junction temperature parameters, acoustic parameters, and linewidth parameters is obtained.

5. The method according to claim 1, characterized in that, Based on the magnitude characteristic values ​​of the main diagonal elements, a unit test vector with the same dimension as the unit matrix is ​​constructed, including: The magnitude characteristic value of each main diagonal element is calculated by summing the squares of the real and imaginary parts of each complex element in each main diagonal element. Construct an initial zero-value vector based on the dimension of the matrix unit; Based on the order of the main diagonal positions, the amplitude characteristic value is written into the corresponding component of the initial zero value vector to generate a unit test vector with the same dimension as the unit matrix.

6. A multi-parameter joint evaluation system for linewidth stability of a laser source, characterized in that, include: The acquisition module is used to acquire the junction temperature parameters of the laser, ambient acoustic spectrum data, and the raw optical interference signal during power surges. The accumulation module is used to perform energy accumulation operations on the environmental acoustic spectrum data to generate a sequence of acoustic feature energy values; The demodulation module is used to perform phase demodulation processing on the original optical interference signal to generate a linewidth drift value sequence; The correlation module is used to perform frequency domain correlation operations on the junction temperature parameter, the acoustic feature energy value sequence, and the linewidth drift value sequence to generate a joint transfer function matrix characterizing the relationship between the junction temperature parameter, the acoustic parameter, and the linewidth parameter; The calculation module is used to calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain a dynamic weight vector; The transformation module is used to perform a Fourier transform on the linewidth drift value sequence to obtain an initial linewidth frequency domain distribution vector, and combine it with the dynamic weight vector to calculate a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation. Calculate the magnitude of the amplitude response of the joint transfer function matrix at each frequency point, and combine all the magnitudes of the amplitude response to obtain the dynamic weight vector, including: Extract the main diagonal elements of the unit matrix at each frequency point from the joint transfer function matrix, and construct a unit test vector with the same dimension as the unit matrix based on the amplitude characteristic values ​​of the main diagonal elements. The unit test vector and unit matrix at each frequency point are multiplied on the left to obtain multiple response vectors, and the magnitude portion of the response vector that is greater than a preset amplitude threshold is selected as the amplitude response magnitude. Arrange all amplitude response magnitudes in ascending order of frequency to obtain the amplitude response sequence; The amplitude response sequence is scaled to a preset weight range by linear interpolation to obtain a scaled amplitude response sequence, which is then used as a dynamic weight vector. The linewidth drift value sequence is subjected to Fourier transform to obtain the initial linewidth frequency domain distribution vector. Combined with the dynamic weight vector, a multi-parameter joint evaluation value of the laser source linewidth stability during power abrupt changes is calculated, including: Construct an orthogonal basis function set of the same length as the linewidth drift value sequence, wherein the orthogonal basis function set includes a cosine basis function sequence and a sine basis function sequence; The linewidth drift value sequence is multiplied by the cosine basis function sequence and the sine basis function sequence to obtain the real part correlation value and the imaginary part correlation value. Based on the real part correlation value and the imaginary part correlation value, a linewidth frequency domain complex vector is constructed, and the magnitude of each complex element in the linewidth frequency domain complex vector is arranged to obtain an initial linewidth frequency domain distribution vector; When the total number of elements in the initial linewidth frequency domain distribution vector and the dynamic weight vector are not equal, the target vector with the smaller total number of elements is filled with zero elements until the total number of elements in the linewidth frequency domain distribution vector and the dynamic weight vector are equal, thus obtaining the filled linewidth frequency domain distribution vector and the filled dynamic weight vector. The dot product operation is performed on the filled linewidth frequency domain distribution vector and the filled dynamic weight vector to obtain a weighted sum value, which is used as a multi-parameter joint evaluation value of the linewidth stability of the laser source during power mutation.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the multi-parameter joint evaluation method for linewidth stability of a laser source as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a multi-parameter joint evaluation method for linewidth stability of a laser source as described in any one of claims 1 to 5.

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