A system and method for online aging testing of high-power lasers

Abstract

This invention relates to the field of laser aging testing technology, and particularly to an online aging testing system and method for high-power lasers. The method includes: acquiring processed light intensity data of the laser using a light intensity sensor; specifically including: dynamically attenuating the output power of the laser to avoid measurement distortion caused by thermal saturation of the attenuator; constructing a spectral adaptation model to expand the spectral response range of the light intensity sensor based on the edge signals and cross-band correlation of the sensor's inherent spectral response; acquiring external temperature data of the laser using a temperature sensor to obtain the thermal conductivity, and calculating the internal temperature data of the laser using the thermal conductivity and external temperature data; preprocessing the acquired light intensity data and internal temperature data to form a laser parameter dataset, and transmitting it to a host computer; and obtaining the laser's optical decay process by analyzing the data in the laser parameter dataset.

Description

Technical Field

[0001] This invention relates to the field of laser aging testing technology, and in particular to an online aging testing system and method for high-power lasers. Background Technology

[0002] As a light source, the stability of laser output optical power directly affects communication quality in fiber optic communication. Optical attenuation leads to a decrease in optical power, which in turn affects the transmission distance and the signal-to-noise ratio at the receiver. Therefore, it is necessary to monitor and measure laser optical attenuation to ensure that its optical power output remains within the normal operating range, guaranteeing the stable operation of the communication system. By measuring laser optical attenuation, we can understand its performance change trends during use, thereby assessing its reliability and predicting its lifespan. For example, the optical performance of a COS laser tube may gradually decline during long-term use, leading to reduced output optical power and changes in spectral characteristics. Aging tests aim to simulate real-world usage conditions, accelerating the aging process of the laser tube to obtain information on its performance degradation in a short period, thereby assessing its reliability and lifespan.

[0003] Existing technologies typically use attenuators to reduce laser energy. This single, fixed attenuation method has poor stability, especially when the attenuator is saturated, leading to measurement distortion. Summary of the Invention

[0004] This invention extends the spectral response range of a light intensity sensor through an algorithm, taking into account the nonlinear relationship between the actual spectrum and the channel response. A nonlinear term is introduced into the relationship function, and the actual light intensity data is deduced when the attenuator saturates.

[0005] The technical solution proposed in this invention is: an online aging test method for high-power lasers, the method comprising:

[0006] The light intensity data of the laser is collected by a light intensity sensor after processing. Specifically, this includes: dynamically attenuating the output power of the laser to avoid measurement distortion caused by thermal saturation of the attenuator; and constructing a spectral adaptation model to expand the spectral response range of the light intensity sensor based on the edge signals and cross-band correlation of the sensor's inherent spectral response, thereby obtaining light intensity data that exceeds the original spectral response range of the light intensity sensor.

[0007] The external temperature data of the laser is collected by a temperature sensor to obtain the thermal conductivity. The internal temperature data of the laser is then calculated using the thermal conductivity and the external temperature data.

[0008] After preprocessing the acquired light intensity data and internal temperature data, a laser parameter dataset is constructed and transmitted to the host computer.

[0009] The optical decay process of the laser is obtained by analyzing the data in the laser parameter dataset.

[0010] Preferably, the dynamic attenuation of the laser's output power to avoid measurement distortion caused by thermal saturation of the attenuator includes:

[0011] The output power of the laser is controlled by laser pulse width modulation;

[0012] Construct a dynamic energy adjustment matrix to dynamically adjust the laser energy output by the laser;

[0013] Specifically, it includes the following steps;

[0014] Let the thermal saturation influence matrix be... The diagonal elements in the thermal saturation influence matrix represent the degree of influence of thermal saturation of the thermal attenuator on laser energy attenuation for different laser channels, while the off-diagonal elements represent the thermal saturation coupling effect between different laser channels. Indicates the number of laser channels;

[0015] Therefore, when the attenuator reaches thermal saturation, the laser energy after passing through the attenuator is:

[0016] ,in, These represent the laser energy after being affected by the thermal saturation of the attenuator and the initial laser energy, respectively.

[0017] Constructing a dynamic adjustment matrix ,in, Indicates the first step in the laser pulse width adjustment process. The laser in the first Energy adjustment ratio for each time slice Indicates the number of time slices;

[0018] The adjusted energy is: ,in, This represents the initial pulse width adjustment matrix for the laser.

[0019] Preferably, the construction of the spectral adaptation model, based on the edge signals and cross-band correlations of the sensor's inherent spectral response, expands the spectral response range of the light intensity sensor, including:

[0020] Construct the spectral correlation matrix;

[0021] A relational function is constructed for spectral inversion to calculate spectral values ​​that exceed the corresponding range of the sensor; the spectral adaptation model includes a spectral correlation matrix and a relational function.

[0022] The construction of the spectral correlation matrix includes:

[0023] Obtain the number of channels and the center wavelength of each channel to form a wavelength sequence. ;

[0024] Determine the range of the target spectrum to be inverted, the range of the target spectrum including wavelengths below the lowest center wavelength of the laser channel. and higher than the highest center wavelength of the laser channel Spectrum;

[0025] Obtain known standard spectral data with a wide spectral coverage from the database, and acquire spectral values ​​at the center wavelength of each channel and at multiple wavelengths within the target spectral range. Let the number of samples be... The standard spectral data at the corresponding wavelength is... ;

[0026] Calculate the correlation coefficient between the light intensity sensor corresponding to each channel and the spectral value at the sampling wavelength. ;in, Indicates the first The first channel in the The response value in this case. It is the first The average response value of each channel. Indicates the first The mean of the spectral values ​​at each sampling wavelength;

[0027] Using the correlation coefficient as an element, the spectral correlation matrix is ​​obtained. .

[0028] Preferably, the constructed relation function is used for spectral inversion to calculate spectral values ​​that exceed the corresponding range of the sensor, including:

[0029] Let the target spectrum be ,in, Indicates the first in the target spectral range Spectral values ​​at each wavelength;

[0030] Establish relational functions ,in , ,in These are elements in the spectral correlation matrix. Indicates standard spectral data in the first The value at each sampling wavelength;

[0031] Solving the relation function using the least squares method yields the following solution: ;

[0032] If we consider the nonlinear relationship between the actual spectrum and the channel response, we introduce a nonlinear term into the relationship function;

[0033] By fitting the relation function with a polynomial, a relation function in the form of a quadratic polynomial can be obtained:

[0034] ,in, Denotes undetermined coefficients. Indicates the first The response values ​​of each channel;

[0035] Using known standard spectral data and the corresponding sensor channel response values, the values ​​of the undetermined coefficients are calculated using a curve fitting algorithm.

[0036] By substituting the known edge spectral response values ​​of the light intensity sensor into the relational function, spectral values ​​that exceed the response range of the light intensity sensor are calculated.

[0037] Preferably, the construction of the spectral adaptation model, based on the edge signals and cross-band correlations of the sensor's inherent spectral response, to extend the spectral response range of the light intensity sensor, further includes:

[0038] A spectral adaptation model is constructed using a convolutional neural network, which takes low-spectral data as input and outputs high-spectral data; specifically including:

[0039] Acquire edge wavelength data within the spectral response range of the light intensity sensor, i.e., the lowest and highest response wavelengths within a band, to construct a low-spectral dataset. ;

[0040] Acquire wavelength data that exceeds the preset spectral response range of the light intensity sensor to construct a hyperspectral dataset. ;

[0041] The data in the low-spectral and high-spectral datasets are normalized to form a sample dataset;

[0042] A convolutional neural network is trained using a sample dataset. The trained network then outputs predicted hyperspectral data, representing the expanded spectral response range of the light intensity sensor within a specific wavelength band. ,in They represent the first The lowest and highest wavelengths of light intensity sensor response within a certain band, including the visible light band, infrared band, and ultraviolet band;

[0043] The convolutional neural network includes an input layer, a hidden layer, and an output layer. The number of neurons in the input layer matches the amount of data in the low-spectral dataset, and the neurons receive low-spectral data in one band. The number of neurons in the output layer matches the amount of data in the hyperspectral dataset.

[0044] The neural network is optimized using the Adam optimizer.

[0045] Preferably, the step of acquiring external temperature data of the laser through a temperature sensor, obtaining the thermal conductivity, and calculating the internal temperature data of the laser using the thermal conductivity and the external temperature data includes:

[0046] A three-dimensional transient thermal model was constructed, and the three-dimensional transient temperature field inside the laser was derived using the reverse heat conduction algorithm. The specific steps are as follows:

[0047] Obtain laser structure data and structural data of all internal components from the database; construct a 3D model of the laser based on the obtained laser structure data and structural data of all internal components;

[0048] Construct the governing equations for three-dimensional transient heat conduction:

[0049] ;in The thermal diffusivity;

[0050] The 3D model of the laser is discretized using the finite difference method, dividing the object into multiple solid units, each unit having a size of [missing information]. ; Indicates the length of the time step;

[0051] Discretize time and space using the central difference method to obtain:

[0052] ;

[0053] in, Indicates the first One time step, location Temperature at that location;

[0054] Set boundary conditions; based on the surface temperature measured by the temperature sensor and the boundary conditions, solve the three-dimensional transient heat conduction equation in the forward direction to obtain the temperature distribution inside the object;

[0055] The sensitivity of the temperature field to the parameters of a three-dimensional transient thermal model is calculated, and the parameters of the three-dimensional transient thermal model include laser surface material information and boundary conditions;

[0056] The model parameters are estimated using the laser's surface temperature and sensitivity measured by a temperature sensor, either through the least squares method or the gradient descent method.

[0057] Using the estimated model parameters, the three-dimensional transient heat conduction equation is solved by regression to obtain the temperature distribution inside the laser, i.e., the temperature field distribution.

[0058] Preferably, the step of obtaining the laser's optical decay process by analyzing data within the laser parameter dataset includes:

[0059] Light intensity data is extracted and arranged in chronological order to form a light intensity-time variation curve; the light intensity-time variation curve reflects the light decay trend of the laser.

[0060] Establish a model showing the relationship between various materials and chips inside the laser tube and the laser tube's optical decay, and set up a correction matrix;

[0061] Based on the correction matrix and the aging factor of the corresponding materials, the materials and chips that have the greatest impact on optical decay are analyzed; specifically, the following steps are included:

[0062] Assume the laser tube contains Different materials or chips, respectively used express;

[0063] Constructing the aging factor vector of materials ;in, Indicates material or chip The aging factor, the larger the value of the aging factor, the greater the impact of the aging degree of the material or chip on the performance of the laser tube;

[0064] Establish the relationship between laser output power and aging factor ;in, This represents the output power vector of the laser tube. ,in This indicates the number of power measurement points, i.e., the number of laser tubes; Indicates the first The output power of the laser tube measured at each measurement point; This represents the original output power, i.e., the theoretical output power before being affected by material aging. This represents the coefficient matrix of factors influencing the material aging factor on the output power.

[0065] Constructing the correction matrix ;in, Represents the identity matrix. This represents an adjustable coefficient used to adjust the correction magnitude; the correction matrix satisfies the following relationship: ;

[0066] When the output power of the laser tube is corrected to 100%, that is, the corresponding theoretical output power ,have ;

[0067] The corrected value is:

[0068] ;

[0069] By observing the coefficients of the terms related to different materials in the above formula, the magnitude of the absolute value of the coefficient reflects the extent to which the aging of the corresponding material affects the optical decay of the laser.

[0070] Preferably, the method includes constructing an edge computing resource call matrix, calling edge computing resources according to the computational requirements, and implementing real-time temperature-light intensity coupling analysis on the local FPGA. Specifically, this includes the following steps:

[0071] Acquire edge computing resource data, which includes CPU resources, memory resources, and storage resources;

[0072] Obtain the CPU resource utilization, memory resource utilization, and storage resource utilization of the host computer;

[0073] Constructing an edge computing resource call matrix ,in These represent the intensity of CPU, memory, and storage resource usage during low computational loads; This indicates the intensity of computational demands on CPU, memory, and storage resources. This indicates the intensity of CPU, memory, and storage resource usage during high computational demands.

[0074] The CPU resource utilization rate, memory resource utilization rate, and storage resource utilization rate of the host computer are used to determine the computational load of the received computing task, and the CPU, memory, and storage resources in the edge computing device are allocated according to the computational load.

[0075] By interacting with the monitoring module inside the FPGA or the operating system, the utilization rate of CPU, memory and storage resources and the progress of task execution can be monitored.

[0076] A high-power laser aging online testing system includes multiple acquisition modules, an acquisition control unit, a cooling unit, a host computer, and a cabinet. The acquisition modules, acquisition control unit, and cooling unit are all housed within the cabinet. The acquisition control unit is connected to the host computer. The cooling unit is used to reduce the internal temperature of the cabinet. Each acquisition module includes a laser lamp holder, a light intensity sensor, an attenuator, and a temperature sensor. The light intensity sensor is connected to the acquisition control unit. The system is used to execute the aforementioned high-power laser aging online testing method.

[0077] A computer-readable storage medium storing a computer program that is executed by a processor to implement the online aging test method for a high-power laser.

[0078] The beneficial effects of this invention are:

[0079] 1. This invention adjusts the laser intensity by dynamically attenuating the laser pulse width modulation, thereby achieving closed-loop control of optical power based on the existing fixed attenuation, which can avoid measurement distortion caused by thermal saturation of the attenuator in traditional solutions.

[0080] 2. This invention establishes a matrix that reflects the spectral correlation between the spectra of each channel and constructs a relational function to invert the spectrum beyond the response range of the light intensity sensor based on the known edge spectral response of the sensor. This expands the response range without replacing the sensor and can solve the problem of inaccurate measurement of actual light intensity when the attenuator is saturated. Attached Figure Description

[0081] Figure 1 This is a flowchart of an online aging test method for a high-power laser according to the present invention. Detailed Implementation

[0082] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0083] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0084] Example 1:

[0085] refer to Figure 1 The technical solution provided by this invention is: an online aging test method for high-power lasers, comprising the following steps:

[0086] Step 1: Collect processed laser light intensity data using a light intensity sensor; specifically, this includes: dynamically attenuating the laser's output power to avoid measurement distortion caused by thermal saturation of the attenuator; constructing a spectral adaptation model to expand the spectral response range of the light intensity sensor based on the sensor's inherent edge signals and cross-band correlation, thereby obtaining light intensity data beyond the original spectral response range of the light intensity sensor.

[0087] The dynamic attenuation of the laser's output power, used to avoid measurement distortion caused by thermal saturation of the attenuator, includes:

[0088] Step 1.1.1: Control the output power of the laser by laser pulse width modulation;

[0089] Step 1.1.2: Construct a dynamic energy adjustment matrix to dynamically adjust the laser output energy. This achieves closed-loop control of optical power based on the existing fixed attenuation via an attenuator, avoiding measurement distortion caused by thermal saturation of the attenuator.

[0090] Step 1.1.2 includes the following steps:

[0091] Let the thermal saturation influence matrix be... The diagonal elements in the thermal saturation influence matrix represent the degree of influence of thermal saturation of the thermal attenuator on laser energy attenuation for different laser channels, while the off-diagonal elements represent the thermal saturation coupling effect between different laser channels. Indicates the number of laser channels;

[0092] Therefore, when the attenuator reaches thermal saturation, the laser energy after passing through the attenuator is:

[0093] ,in, These represent the laser energy after being affected by the thermal saturation of the attenuator and the initial laser energy, respectively.

[0094] Constructing a dynamic adjustment matrix ,in, Indicates the first step in the laser pulse width adjustment process. The laser in the first Energy adjustment ratio for each time slice Indicates the number of time slices;

[0095] The adjusted energy is: ,in, This represents the initial pulse width adjustment matrix for the laser.

[0096] The project constructs a spectral adaptation model, which expands the spectral response range of the light intensity sensor based on the edge signals and cross-band correlations of the sensor's inherent spectral response.

[0097] For example, if the target range is 0.4μm - 0.8μm, and the sensor channel range is 0.5μm - 0.7μm, how can the channel range of the light intensity sensor be extended without replacing the light intensity sensor? This can be achieved through the following steps:

[0098] Step 1.2.1: Construct the spectral correlation matrix;

[0099] Step 1.2.2: Construct a relational function for spectral inversion to calculate and obtain spectral values ​​that exceed the corresponding range of the sensor; the spectral adaptation model includes a spectral correlation matrix and a relational function.

[0100] The process of constructing a spectral correlation matrix and establishing cross-band correlations includes the following steps:

[0101] Obtain the number of channels and the center wavelength of each channel to form a wavelength sequence. The term "channel" refers to the measurable wavelength range of a light intensity sensor, including the visible light band, near-infrared band, ultraviolet band, and far-infrared band, with each band corresponding to one channel. For example, a multispectral sensor with five channels has center wavelengths of 0.45 μm, 0.55 μm, 0.65 μm, 0.85 μm, and 1.2 μm, covering a wavelength range from visible light to near-infrared.

[0102] Determine the range of the target spectrum to be inverted, the range of the target spectrum including a certain wavelength below the lowest center wavelength of the laser channel. The sum of the wavelengths is higher than the highest center wavelength of the laser channel by a certain wavelength. The spectrum; that is, the edge response signal of the spectrum of the light intensity sensor is extended. The corresponding range of wavelengths.

[0103] Obtain known standard spectral data with a wide spectral coverage from the database, and acquire spectral values ​​at the center wavelength of each channel and at multiple wavelengths within the target spectral range. Let the number of samples be... The standard spectral data at the corresponding wavelength is... ;

[0104] Calculate the correlation coefficient between the light intensity sensor corresponding to each channel and the spectral value at the sampling wavelength. ;in, Indicates the first The first channel in the The response value in this case. It is the first The average response value of each channel. Indicates the first The mean of the spectral values ​​at each sampling wavelength;

[0105] Using the correlation coefficient as an element, the spectral correlation matrix is ​​obtained. .

[0106] The process of constructing a relational function for spectral inversion to calculate spectral values ​​exceeding the sensor's corresponding range includes the following steps:

[0107] Let the target spectrum be ,in, Indicates the first in the target spectral range Spectral values ​​at each wavelength;

[0108] Establish relational functions ,in , ,in These are elements in the spectral correlation matrix. Indicates standard spectral data in the first The value at each sampling wavelength;

[0109] Solving the relation function using the least squares method yields the following solution: ;

[0110] If we consider the nonlinear relationship between the actual spectrum and the channel response, we introduce a nonlinear term into the relationship function;

[0111] By fitting the relation function with a polynomial, a relation function in the form of a quadratic polynomial can be obtained:

[0112] ,in, Denotes undetermined coefficients. Indicates the first The response values ​​of each channel;

[0113] Using known standard spectral data and the corresponding sensor channel response values, the values ​​of the undetermined coefficients are calculated using a curve fitting algorithm.

[0114] By substituting the known edge spectral response values ​​of the light intensity sensor into the relational function, spectral values ​​that exceed the response range of the light intensity sensor are calculated.

[0115] Step 2: Collect external temperature data of the laser using a temperature sensor to obtain the thermal conductivity. Then, use the thermal conductivity and external temperature data to calculate the internal temperature data of the laser. This includes the following steps:

[0116] A three-dimensional transient thermal model was constructed, and the three-dimensional transient temperature field inside the laser was derived using the reverse heat conduction algorithm. The specific steps are as follows:

[0117] Obtain laser structure data and structural data of all internal components from the database; construct a 3D model of the laser based on the obtained laser structure data and structural data of all internal components;

[0118] Construct the governing equations for three-dimensional transient heat conduction:

[0119] ;in The thermal diffusivity;

[0120] The 3D model of the laser is discretized using the finite difference method, dividing the object into multiple solid units, each unit having a size of [missing information]. ; Indicates the length of the time step;

[0121] Discretize time and space using the central difference method to obtain:

[0122] ;

[0123] in, Indicates the first One time step, location Temperature at that location;

[0124] Set boundary conditions; based on the surface temperature measured by the temperature sensor and the boundary conditions, solve the three-dimensional transient heat conduction equation in the forward direction to obtain the temperature distribution inside the object;

[0125] The sensitivity of the temperature field to the parameters of a three-dimensional transient thermal model is calculated, and the parameters of the three-dimensional transient thermal model include laser surface material information and boundary conditions;

[0126] The model parameters are estimated using the laser's surface temperature and sensitivity measured by a temperature sensor, either through the least squares method or the gradient descent method.

[0127] Using the estimated model parameters, the three-dimensional transient heat conduction equation is solved by regression to obtain the temperature distribution inside the laser, i.e., the temperature field distribution.

[0128] Step 3: After preprocessing the acquired light intensity data and internal temperature data, a laser parameter dataset is constructed and transmitted to the host computer.

[0129] Step 4: Obtain the laser's optical decay process by analyzing the data in the laser parameter dataset. This includes the following steps:

[0130] Light intensity data is extracted and arranged in chronological order to form a light intensity-time variation curve; the light intensity-time variation curve reflects the light decay trend of the laser.

[0131] Establish a model showing the relationship between various materials and chips inside the laser tube and the laser tube's optical decay, and set up a correction matrix;

[0132] Based on the correction matrix and the aging factor of the corresponding materials, the materials and chips that have the greatest impact on optical decay are analyzed; specifically, the following steps are included:

[0133] Assume the laser tube contains Different materials or chips, respectively used express;

[0134] Constructing the aging factor vector of materials ;in, Indicates material or chip The aging factor, the larger the value of the aging factor, the greater the impact of the aging degree of the material or chip on the performance of the laser tube;

[0135] Establish the relationship between laser output power and aging factor ;in, This represents the output power vector of the laser tube. ,in This indicates the number of power measurement points, i.e., the number of laser tubes; Indicates the first The output power of the laser tube measured at each measurement point; This represents the original output power, i.e., the theoretical output power before being affected by material aging. This represents the coefficient matrix of factors influencing the material aging factor on the output power.

[0136] Constructing the correction matrix ;in, Represents the identity matrix. This represents an adjustable coefficient used to adjust the correction magnitude; the correction matrix satisfies the following relationship: ;

[0137] When the output power of the laser tube is corrected to 100%, that is, the corresponding theoretical output power ,have ;

[0138] The corrected value is:

[0139] ;

[0140] By observing the coefficients of the terms related to different materials in the above formula, the magnitude of the absolute value of the coefficient reflects the extent to which the aging of the corresponding material affects the optical decay of the laser.

[0141] For example, if a laser tube is composed of two materials, there are 3 measurement points;

[0142] The aging factor vector is , ; ;

[0143] set up , ;

[0144] The correction matrix is:

[0145] ;

[0146] ;

[0147] From the results of the above formula, it can be seen that the coefficient related to the first material is the largest. Therefore, it can be concluded that the aging of the first material has a greater impact on optical decay. Improvements can be made to the corresponding material to improve the lifespan of the laser.

[0148] Example 2:

[0149] The difference between Example 2 and Example 1 in terms of illumination intensity is:

[0150] 1. When expanding the spectral response range of the light intensity sensor, a sampling convolutional neural network is used to construct a spectral adaptation model, which specifically includes the following steps:

[0151] Acquire edge wavelength data within the spectral response range of the light intensity sensor, i.e., the lowest and highest response wavelengths within a band, to construct a low-spectral dataset. ;

[0152] Acquire wavelength data that exceeds the preset spectral response range of the light intensity sensor to construct a hyperspectral dataset. ;

[0153] The data in the low-spectral and high-spectral datasets are normalized to form a sample dataset;

[0154] A convolutional neural network is trained using a sample dataset. The trained network then outputs predicted hyperspectral data, representing the expanded spectral response range of the light intensity sensor within a specific wavelength band. ,in They represent the first The lowest and highest wavelengths of light intensity sensor response within a certain band, including the visible light band, infrared band, and ultraviolet band;

[0155] The convolutional neural network includes an input layer, a hidden layer, and an output layer. The number of neurons in the input layer matches the amount of data in the low-spectral dataset, and the neurons receive low-spectral data in one band. The number of neurons in the output layer matches the amount of data in the hyperspectral dataset.

[0156] The neural network is optimized using the Adam optimizer.

[0157] 2. Based on the four steps of Example 1, an additional step is added to improve technical efficiency, thereby reducing the impact of network bandwidth on computational efficiency during online measurement. Specifically:

[0158] Construct an edge computing resource mobilization matrix, and mobilize edge computing resources according to the computational requirements to implement real-time temperature-light intensity coupling analysis on the local FPGA. The specific steps include:

[0159] Acquire edge computing resource data, which includes CPU resources, memory resources, and storage resources;

[0160] Obtain the CPU resource utilization, memory resource utilization, and storage resource utilization of the host computer;

[0161] Constructing an edge computing resource call matrix ,in These represent the intensity of CPU, memory, and storage resource usage during low computational loads; This indicates the intensity of computational demands on CPU, memory, and storage resources. This indicates the intensity of CPU, memory, and storage resource usage during high computational demands.

[0162] The CPU resource utilization rate, memory resource utilization rate, and storage resource utilization rate of the host computer are used to determine the computational load of the received computing task, and the CPU, memory, and storage resources in the edge computing device are allocated according to the computational load.

[0163] By interacting with the monitoring module inside the FPGA or the operating system, the utilization rate of CPU, memory and storage resources and the progress of task execution can be monitored.

[0164] This invention also provides an online aging test system for high-power lasers, comprising multiple acquisition modules, an acquisition control unit, a cooling unit, a host computer, and a cabinet. The acquisition modules, acquisition control unit, and cooling unit are all housed within the cabinet. The acquisition control unit is connected to the host computer. The cooling unit is used to reduce the internal temperature of the cabinet. Each acquisition module includes a laser lamp holder, a light intensity sensor, an attenuator, and a temperature sensor. The light intensity sensor is connected to the acquisition control unit. The system is used to execute the aforementioned online aging test method for high-power lasers.

[0165] The present invention also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the online aging test method for a high-power laser.

[0166] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this invention. It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical cables, RF, etc., or any suitable combination thereof.

[0167] The flowcharts in the accompanying drawings illustrate the architecture, functionality, and operation that may be implemented according to various embodiments of the invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0168] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the stated principles, the implementation of the present invention may have any changes or modifications.

Claims

1. A method for on-line testing of high power laser aging, characterized in that, The method includes: The light intensity data of the laser is collected by a light intensity sensor after processing. Specifically, this includes: dynamically attenuating the output power of the laser to avoid measurement distortion caused by thermal saturation of the attenuator; and constructing a spectral adaptation model to expand the spectral response range of the light intensity sensor based on the edge signals and cross-band correlation of the sensor's inherent spectral response, thereby obtaining light intensity data that exceeds the original spectral response range of the light intensity sensor. The external temperature data of the laser is collected by a temperature sensor to obtain the thermal conductivity. The internal temperature data of the laser is then calculated using the thermal conductivity and the external temperature data. After preprocessing the acquired light intensity data and internal temperature data, a laser parameter dataset is constructed and transmitted to the host computer. The optical decay process of the laser is obtained by analyzing the data in the laser parameter dataset.

2. The method of claim 1, wherein, The dynamic attenuation of the laser's output power, used to avoid measurement distortion caused by thermal saturation of the attenuator, includes: The output power of the laser is controlled by laser pulse width modulation; Construct a dynamic energy adjustment matrix to dynamically adjust the laser energy output by the laser; Specifically, it includes the following steps; a thermal saturation influence matrix is set as an element of a diagonal line in the thermal saturation influence matrix represents a degree of influence of thermal attenuation piece thermal saturation corresponding to different laser channels on laser energy attenuation, a non-diagonal line element represents thermal saturation coupling influence between different laser channels, wherein represents a number of laser channels; Therefore, when the attenuator reaches thermal saturation, the laser energy after passing through the attenuator is: wherein, respectively represent the laser energy affected by the heat saturation of the attenuator and the initial laser energy. Constructing dynamic adjustment matrix wherein, represents the energy adjustment ratio of the laser in the time slice during the laser pulse width adjustment process, represents the number of time slices; The adjusted energy is: wherein, represents the laser initial pulse width adjustment matrix.

3. The method of claim 2, wherein the method further comprises: The construction of the spectral adaptation model, based on the edge signals and cross-band correlations of the sensor's inherent spectral response, expands the spectral response range of the light intensity sensor, including: Construct the spectral correlation matrix; A relational function is constructed for spectral inversion to calculate spectral values ​​that exceed the corresponding range of the sensor; the spectral adaptation model includes a spectral correlation matrix and a relational function. The construction of the spectral correlation matrix includes: Obtaining the number of channels and the center wavelength of each channel, and constituting a wavelength sequence ; determining a range of target spectra to be inverted, the range of target spectra including spectra lower than a lowest center wavelength of the laser channel and spectra higher than a highest center wavelength of the laser channel ​ Obtaining known standard spectral data with wide spectral coverage from a database, obtaining spectral values at a plurality of wavelengths in the central wavelength of each channel and the target spectral range, assuming the number of samples , the value of the standard spectral data at the corresponding wavelength is ; correlation coefficient between the light intensity sensor corresponding to each channel and the spectral value at the sampling wavelength ; wherein, represents the response value of the th channel in the th time, is the mean of the response value of the th channel, represents the mean of the spectral value at the th sampling wavelength; The correlation coefficient is obtained as an element of a spectral correlation matrix .

4. The method of claim 3, wherein the method further comprises: The constructed relational function is used for spectral inversion, calculating and obtaining spectral values ​​that exceed the corresponding range of the sensor, including: Let the target spectrum be wherein, denotes the spectral value at the th wavelength in the target spectral range; establishing a relation function , wherein , , wherein is an element in the spectral correlation matrix, denotes the value of the standard spectral data at the th sampling wavelength; The solution obtained by solving the relation function with the least square method is: ; If we consider the nonlinear relationship between the actual spectrum and the channel response, we introduce a nonlinear term into the relationship function; By fitting the relation function with a polynomial, a relation function in the form of a quadratic polynomial can be obtained: wherein, denotes a pending coefficient, denotes the response value of the channel. Using known standard spectral data and the corresponding sensor channel response values, the values ​​of the undetermined coefficients are calculated using a curve fitting algorithm. By substituting the known edge spectral response values ​​of the light intensity sensor into the relational function, spectral values ​​that exceed the response range of the light intensity sensor are calculated.

5. The method of claim 4, wherein the method further comprises: The construction of the spectral adaptation model, based on the edge signals and cross-band correlations of the sensor's inherent spectral response, expands the spectral response range of the light intensity sensor, and also includes: A spectral adaptation model is constructed using a convolutional neural network, which takes low-spectral data as input and outputs high-spectral data; specifically including: Obtaining edge wavelength data in the spectral response range of the illumination intensity sensor, i.e. the lowest and highest response wavelengths within a waveband, constitutes a low spectral data set ; Acquiring wavelength data beyond the preset spectral response range of the illumination intensity sensor to form a hyperspectral data set ; The data in the low-spectral and high-spectral datasets are normalized to form a sample dataset; The convolutional neural network is trained by using a sample data set, and the trained convolutional neural network is used to output predicted hyperspectral data, i.e. an extended spectral response range of the illumination intensity sensor in one waveband is wherein respectively represent the minimum wavelength and the maximum wavelength of the illumination intensity sensor response in the waveband, the waveband including a visible light waveband, an infrared waveband and an ultraviolet waveband; The convolutional neural network includes an input layer, a hidden layer, and an output layer. The number of neurons in the input layer matches the amount of data in the low-spectral dataset, and the neurons receive low-spectral data in one band. The number of neurons in the output layer matches the amount of data in the hyperspectral dataset. The neural network is optimized using the Adam optimizer.

6. The method of claim 5, wherein the method further comprises: The external temperature data of the laser is collected by the temperature sensor, the thermal conductivity is obtained, and the internal temperature data of the laser is calculated by using the thermal conductivity and the external temperature data, comprising: A three-dimensional transient thermal model is constructed, and a three-dimensional transient temperature field inside the laser is derived by using a reverse heat conduction algorithm; the specific steps are as follows: Obtain the laser structure data and the structure data of all internal components from the database; construct a three-dimensional model of the laser according to the obtained laser structure data and the structure data of all internal components; The control equation of three-dimensional transient heat conduction is constructed: ; wherein is the thermal diffusion coefficient; The three-dimensional model of the laser is discretized by finite difference method, and the object is divided into a plurality of cubic elements, each element having a size of ; denotes the length of a time step; Discretize time and space using central difference method to obtain: ； wherein, represents the temperature at position at time step . Set boundary conditions; according to the surface temperature measured by the temperature sensor and the boundary conditions, the three-dimensional transient heat conduction equation is solved to obtain the temperature distribution inside the object; Calculate the sensitivity of the temperature field to the parameters of the three-dimensional transient thermal model, the parameters of the three-dimensional transient thermal model including the surface material information of the laser and the boundary conditions; Use the surface temperature of the laser measured by the temperature sensor and the sensitivity to estimate the model parameters by least squares method or gradient descent method; Use the estimated model parameters to solve the three-dimensional transient heat conduction equation to obtain the temperature distribution inside the laser, i.e. the temperature field distribution.

7. The method of claim 6, wherein the method further comprises: The light decay process of the laser is obtained by analyzing the data in the laser parameter data set, comprising: Extract the light intensity data, arrange the measured light data in time sequence to form a light-time variation curve; the light decay trend of the laser is reflected by the light-time variation curve; Establish a relationship model between various materials and chips inside the laser tube and the light decay of the laser tube, and set up a correction matrix; Based on the correction matrix and the aging factor of the corresponding material, analyze the materials and chips that have the greatest impact on light decay; the specific steps are as follows: The laser tube comprises A different material or chip is represented by respectively; Constructing the aging factor vector of materials ;in, Indicates material or chip The aging factor, the larger the value of the aging factor, the greater the impact of the aging degree of the material or chip on the performance of the laser tube; establishing a relationship between the output power of the laser and an aging factor ; wherein represents an output power vector of the laser tube, wherein represents the number of power measurement points, i.e. the number of laser tubes; represents the output power of the laser tube measured at the th measurement point; represents the original output power, i.e. the theoretical output power without the influence of material aging; represents a factor coefficient matrix of the influence of the material aging factor on the output power; Constructing a correction matrix ; wherein, denotes an identity matrix, denotes an adjustable coefficient for adjusting the correction amplitude; the correction matrix satisfies the following relationship: ; When the output power of the laser tube is corrected to 100%, i.e. corresponds to the theoretical output power , there is ; The corrected value is: ； Observe the coefficients of the terms related to different materials in the above formula, and reflect the influence of the aging of the corresponding material on the light decay of the laser through the absolute value of the coefficient.

8. The method of claim 7, wherein the method further comprises: The edge computing resource calling matrix is constructed, the edge computing resources are called according to the calculation amount demand, and the real-time temperature-light intensity coupling analysis is realized in the local FPGA, comprising the following steps: Obtain edge computing resource data, including CPU resources, memory resources and storage resources; Obtain the CPU resource occupation rate, memory occupation rate and storage resource occupation rate of the host computer; Constructing an edge computing resource invocation matrix wherein respectively represent the invocation intensity of CPU, memory and storage resources at low computation; represent the invocation intensity of CPU, memory and storage resources at medium computation; represent the invocation intensity of CPU, memory and storage resources at high computation; Through the monitoring module inside the FPGA or the interaction of the operating system, monitor the usage rate of CPU, memory and storage resources and the task execution progress.

9. A high-power laser aging online test system, the test system comprising a plurality of acquisition modules, an acquisition control unit, a cooling unit, a host computer and a cabinet, the acquisition modules, the acquisition control unit and the cooling unit are all arranged in the cabinet, the acquisition control unit is in data connection with the host computer; the cooling unit is used to reduce the temperature inside the cabinet, the acquisition module comprises a laser lamp holder, an illumination intensity sensor, an attenuation sheet and a temperature sensor, the illumination intensity sensor is connected with the acquisition control unit, characterized in that, The system is used to execute the online aging test method of the high-power laser in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the online aging test method of the high-power laser in any one of claims 1-8.

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