CN118232155A - Solid mode-locked laser control method and system based on twin convolutional neural network - Google Patents

Solid mode-locked laser control method and system based on twin convolutional neural network Download PDF

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CN118232155A
CN118232155A CN202410641747.9A CN202410641747A CN118232155A CN 118232155 A CN118232155 A CN 118232155A CN 202410641747 A CN202410641747 A CN 202410641747A CN 118232155 A CN118232155 A CN 118232155A
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关晨
渠帅
王晓飞
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Abstract

The application relates to the technical field of laser pulse characteristic measurement, and provides a solid mode-locked laser control method and system based on a twin convolutional neural network, wherein the control method comprises the steps of training the twin convolutional neural network; acquiring one or more of a second time sequence signal, a second radio frequency spectrum signal or a second spectrum signal; converting the extracted features into a second transient feature sequence; inputting the second characteristic parameter into a second sub-network to obtain a second characteristic parameter; comparing to obtain Euclidean distance; determining that the real-time working state of the solid mode-locked laser does not comprise the stable mode-locked state in response to the Euclidean distance not being in the preset range; and adjusting the position of the X-cavity mirror assembly to enable the X-cavity mirror assembly to be in a stable mode locking state. The control method integrates the automatic analysis and adjustment functions of the convolutional neural network, can reduce manual intervention, improves the degree of automation and the stability, and reduces the maintenance cost and the labor cost. The output performance of the solid mode-locked laser can be continuously adjusted and optimized through real-time monitoring and adjustment.

Description

基于孪生卷积神经网络的固体锁模激光器控制方法及系统Solid-state mode-locked laser control method and system based on twin convolutional neural network

技术领域Technical Field

本申请涉及激光脉冲特性测量技术领域,尤其涉及一种基于孪生卷积神经网络的固体锁模激光器控制方法及系统。The present application relates to the technical field of laser pulse characteristic measurement, and in particular to a solid-state mode-locked laser control method and system based on a twin convolutional neural network.

背景技术Background technique

固体锁模激光光源在科学研究、医学诊疗、材料加工、通信技术和激光雷达等领域发挥着重要作用。其稳定的脉冲输出和高功率特性使其成为超快光学实验和精密加工的理想选择。在科学研究中,固体锁模激光器用于观测超快动态过程,如原子和分子的振动和电子行为。在医学领域,固体锁模激光器可用于眼科手术、皮肤治疗和癌症治疗,具有高效、精确的特点。此外,固体锁模激光器在材料加工中也发挥着重要作用,可用于微加工、激光打标和切割等领域。在通信技术中,固体锁模激光器可用于高速数据传输和光纤通信系统。其优点包括稳定的脉冲输出、高功率、优异的光束质量和紧凑的设计,使其在各种应用场景中具有广泛的适用性和可靠性。Solid-state mode-locked laser light sources play an important role in scientific research, medical diagnosis and treatment, material processing, communication technology, and lidar. Its stable pulse output and high power characteristics make it an ideal choice for ultrafast optical experiments and precision processing. In scientific research, solid-state mode-locked lasers are used to observe ultrafast dynamic processes such as the vibration and electronic behavior of atoms and molecules. In the medical field, solid-state mode-locked lasers can be used in ophthalmic surgery, skin treatment, and cancer treatment, with the characteristics of high efficiency and precision. In addition, solid-state mode-locked lasers also play an important role in material processing and can be used in fields such as micromachining, laser marking, and cutting. In communication technology, solid-state mode-locked lasers can be used in high-speed data transmission and fiber-optic communication systems. Its advantages include stable pulse output, high power, excellent beam quality, and compact design, which make it widely applicable and reliable in various application scenarios.

然而,固体锁模激光光源在设计和搭建过程时,根据实际需求或实验条件初步确定腔型和腔长的条件下,利用ABCD传输矩阵计算晶体和饱和吸收体位置的束腰半径/直径,选择确定合适曲率的腔镜,从而进一步确定稳区的位置和大小来指导实验,实际中会根据实验现象去人为手动的调节判断某个关键腔镜的位置。对于不同离子掺杂的晶体对应的锁模稳区范围不同,特别是发射截面较小的晶体,对准条件非常苛刻。如果腔镜位置未调节到达锁模稳区范围,整个锁模腔较难形成稳定的锁模激光输出甚至无法输出激光。However, during the design and construction of a solid-state mode-locked laser light source, the cavity type and cavity length are initially determined according to actual needs or experimental conditions, and the beam waist radius/diameter of the crystal and saturated absorber position is calculated using the ABCD transmission matrix, and the cavity mirror with a suitable curvature is selected to further determine the position and size of the stable zone to guide the experiment. In practice, the position of a key cavity mirror is manually adjusted based on the experimental phenomenon. The range of the mode-locked stable zone is different for crystals doped with different ions, especially for crystals with a small emission cross-section, and the alignment conditions are very harsh. If the cavity mirror position is not adjusted to reach the range of the mode-locked stable zone, it is difficult for the entire mode-locked cavity to form a stable mode-locked laser output or even unable to output laser.

因此,如何去调节判断关键腔镜的位置形成稳定的固体激光锁模激光输出是当前难以解决的技术难题。Therefore, how to adjust and determine the position of the key cavity mirror to form a stable solid-state laser mode-locked laser output is a technical problem that is difficult to solve at present.

发明内容Summary of the invention

本申请提供了一种基于孪生卷积神经网络的固体锁模激光器控制方法及系统,以解决固体锁模激光器搭建过程中调节关键腔镜达到锁模稳区范围较为困难的技术问题。The present application provides a solid-state mode-locked laser control method and system based on a twin convolutional neural network to solve the technical problem that it is difficult to adjust the key cavity mirror to reach the mode-locked stable range during the construction of the solid-state mode-locked laser.

本申请第一方面提供的基于孪生卷积神经网络的固体锁模激光器控制方法,包括:建立数据库和孪生卷积神经网络;其中,数据库包括第一瞬态特征序列以及与第一瞬态序列对应的第一特征参量,孪生卷积神经网络包括第一子网络和第二子网络;采用数据库训练孪生卷积神经网络;其中,第一子网络和第二子网络的训练方式相同;第一子网络的输入为第一瞬态特征序列,第一子网络的输出为第一特征参量,第一瞬态特征序列为固体锁模激光器在不同工作状态下的第一时序信号、第一射频谱信号或第一光谱信号中一种或多种经特征提取后得到,工作状态包括未出光状态、输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态中的一种;获取固体锁模激光器的第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种;其中,第二时序信号、第二射频谱信号或第二光谱信号分别为固体锁模激光器的实时时序信号、实时射频谱信号或实时光谱信号;将第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种经特征提取后转换为第二瞬态特征序列;将第二瞬态特征序列输入至第二子网络中,得到第二特征参量;对比第二特征参量与第一特征参量,得到欧氏距离;响应于欧氏距离不在预设范围内,确定固体锁模激光器的实时工作状态不包括稳定锁模状态;根据实时工作状态调整固体锁模激光器中X腔镜组件位置,使固体锁模激光器处于稳定锁模状态。The first aspect of the present application provides a solid-state mode-locked laser control method based on a twin convolutional neural network, comprising: establishing a database and a twin convolutional neural network; wherein the database comprises a first transient feature sequence and a first feature parameter corresponding to the first transient sequence, and the twin convolutional neural network comprises a first subnetwork and a second subnetwork; the twin convolutional neural network is trained using the database; wherein the first subnetwork and the second subnetwork are trained in the same manner; the input of the first subnetwork is the first transient feature sequence, the output of the first subnetwork is the first feature parameter, the first transient feature sequence is one or more of the first timing signal, the first radio frequency spectrum signal or the first spectrum signal of the solid-state mode-locked laser under different working states, obtained after feature extraction, and the working states include a non-light-emitting state, a continuous light output state, a Q-switched state, an incompletely mode-locked state and a stable mode-locked state. A method of the present invention comprises the following steps: obtaining a second timing signal, a second radio frequency spectrum signal or a second spectrum signal of a solid-state mode-locked laser; obtaining one or more of a second timing signal, a second radio frequency spectrum signal or a second spectrum signal of the solid-state mode-locked laser; wherein the second timing signal, the second radio frequency spectrum signal or the second spectrum signal are respectively real-time timing signals, real-time radio frequency spectrum signals or real-time spectrum signals of the solid-state mode-locked laser; converting one or more of the second timing signal, the second radio frequency spectrum signal or the second spectrum signal into a second transient feature sequence after feature extraction; inputting the second transient feature sequence into a second subnetwork to obtain a second feature parameter; comparing the second feature parameter with the first feature parameter to obtain a Euclidean distance; in response to the Euclidean distance being not within a preset range, determining that the real-time working state of the solid-state mode-locked laser does not include a stable mode-locked state; adjusting the position of an X-cavity mirror assembly in the solid-state mode-locked laser according to the real-time working state to put the solid-state mode-locked laser in a stable mode-locked state.

在一些可行的实现方式中,基于孪生卷积神经网络的固体锁模激光器控制方法还包括:响应于欧氏距离在预设范围内,确定固体锁模激光器的实时工作状态为稳定锁模状态。In some feasible implementations, the solid-state mode-locked laser control method based on a twin convolutional neural network further includes: in response to the Euclidean distance being within a preset range, determining that the real-time operating state of the solid-state mode-locked laser is a stable mode-locked state.

在一些可行的实现方式中,第一子网络和第二子网络均包括依次相连的第一卷积层、第一池化层、第二卷积层和第二池化层;其中,第一卷积层和第二卷积层均采用Relu激活函数。In some feasible implementations, the first sub-network and the second sub-network both include a first convolutional layer, a first pooling layer, a second convolutional layer, and a second pooling layer connected in sequence; wherein the first convolutional layer and the second convolutional layer both use a Relu activation function.

本申请第一方面提供的基于孪生卷积神经网络的固体锁模激光器控制方法,通过对输入的时序信号、射频谱或者光谱信号进行高精度的分析和识别,从而确定固体锁模激光器的工作状态和性能参数,实现精确调节关键腔镜位置状态。由于卷积神经网络的高效性和并行处理能力,可以实时地对固体锁模激光器的输出进行监测和调节,使其可以实现稳定的锁模状态,适应环境变化和工作条件的波动。该控制方法整合了卷积神经网络的自动化分析和调节功能,可以减少人工干预,提高系统的自动化程度和稳定性,降低了维护成本和人力成本。通过实时的监测和调节,可以不断调节优化固体锁模激光器的输出性能,使其达到最佳工作状态,提高能效和输出质量。The first aspect of the present application provides a solid-state mode-locked laser control method based on a twin convolutional neural network, which determines the working state and performance parameters of the solid-state mode-locked laser by performing high-precision analysis and identification on the input timing signal, radio frequency spectrum or spectrum signal, and realizes precise adjustment of the key cavity mirror position state. Due to the high efficiency and parallel processing capability of the convolutional neural network, the output of the solid-state mode-locked laser can be monitored and adjusted in real time, so that it can achieve a stable mode-locked state and adapt to environmental changes and fluctuations in working conditions. The control method integrates the automated analysis and adjustment functions of the convolutional neural network, which can reduce manual intervention, improve the automation and stability of the system, and reduce maintenance costs and labor costs. Through real-time monitoring and adjustment, the output performance of the solid-state mode-locked laser can be continuously adjusted and optimized to achieve the best working state, improve energy efficiency and output quality.

本申请第二方面提供的基于孪生卷积神经网络的固体锁模激光器控制系统,采用第一方面提供的基于孪生卷积神经网络的固体锁模激光器控制方法,基于孪生卷积神经网络的固体锁模激光器控制系统包括:泵浦源,被配置为产生光线;其中,光线中携带能量;X腔镜组件,设置在光线的光路上;激光器晶体,设置在X腔镜组件中,被配置为吸收能量并产生为激光;其中,X腔组件镜被配置为将光线反射至激光器晶体,接收并反射激光器晶体转换的激光;探测器,设置在X腔镜组件的输出光路上,被配置为接收X腔镜组件反射后的激光;数据采集卡,与探测器相连,被配置为采集激光中的数据;处理器,与数据采集卡相连,被配置为处理数据,确定并输出固体锁模激光器的实时工作状态;状态控制器,与处理器相连,被配置为响应于实时工作状态不包括稳定锁模状态,根据实时工作状态调节X腔镜组件的位置,使固体锁模激光器处于稳定锁模状态。The second aspect of the present application provides a solid-state mode-locked laser control system based on a twin convolutional neural network, which adopts the solid-state mode-locked laser control method based on a twin convolutional neural network provided in the first aspect. The solid-state mode-locked laser control system based on a twin convolutional neural network includes: a pump source, which is configured to generate light; wherein the light carries energy; an X-cavity mirror assembly, which is arranged on the optical path of the light; a laser crystal, which is arranged in the X-cavity mirror assembly, is configured to absorb energy and generate laser light; wherein the X-cavity mirror assembly is configured to reflect the light to the laser crystal, receive and reflect the laser converted by the laser crystal; a detector, which is arranged on the output optical path of the X-cavity mirror assembly, is configured to receive the laser reflected by the X-cavity mirror assembly; a data acquisition card, which is connected to the detector, is configured to collect data in the laser; a processor, which is connected to the data acquisition card, is configured to process data, determine and output the real-time working state of the solid-state mode-locked laser; a state controller, which is connected to the processor, is configured to adjust the position of the X-cavity mirror assembly according to the real-time working state in response to the real-time working state not including the stable mode-locked state, so that the solid-state mode-locked laser is in a stable mode-locked state.

在一些可行的实现方式中,X腔镜组件包括第一腔镜、第二腔镜、第三腔镜、半导体饱和吸收镜和输出镜;其中,激光器晶体设置在第一腔镜和第二腔镜之间。In some feasible implementations, the X-cavity mirror assembly includes a first cavity mirror, a second cavity mirror, a third cavity mirror, a semiconductor saturated absorption mirror and an output mirror; wherein the laser crystal is arranged between the first cavity mirror and the second cavity mirror.

在一些可行的实现方式中,根据实时工作状态调节X腔镜组件的位置包括:In some feasible implementations, adjusting the position of the X-cavity mirror assembly according to the real-time working status includes:

根据实时工作状态调节第二腔镜和输出镜的设置位置。The setting positions of the second cavity mirror and the output mirror are adjusted according to the real-time working status.

在一些可行的实现方式中,基于孪生卷积神经网络的固体锁模激光器控制系统还包括:第一透镜和第二透镜,第一透镜和第二透镜依次设置在泵浦源和第一腔镜之间。In some feasible implementations, the solid-state mode-locked laser control system based on the twin convolutional neural network also includes: a first lens and a second lens, and the first lens and the second lens are sequentially arranged between the pump source and the first cavity mirror.

在一些可行的实现方式中,数据采集卡包括第一采集模块和第二采集模块,其中,第一采集模块被配置为获取固体锁模激光器在不同工作状态下的第一时序信号、第一射频谱信号或第一光谱信号,工作状态包括未出光、输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态中的一种;第二采集模块被配置为获取固体锁模激光器的第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种;第二时序信号、第二射频谱信号或第二光谱信号分别为固体锁模激光器的实时时序信号、实时射频谱信号或实时光谱信号;处理器包括:建立模块,被配置为建立数据库和孪生卷积神经网络;其中,孪生卷积神经网络包括第一子网络和第二子网络;数据库包括第一瞬态特征参量,第一瞬态特征参量为第一时序信号、第一射频谱信号或第一光谱信号中的一种或多种经特征提取后得到;训练模块,被配置为利用数据库训练孪生卷积神经网络;其中,第一子网络和第二子网络的训练方式相同;第一子网络的输入为第一瞬态特征参量,第一子网络的输出为第一特征参量;第二子网络的输入为第二瞬态特征参量;第二瞬态特征参量为第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种经特征提取后得到,第二子网络的输出为第二特征参量;对比模块,被配置为对比第二特征参量与第一特征参量的欧氏距离;确定模块,被配置为响应于欧氏距离不在预设范围内,确定固体锁模激光器的实时工作状态不包括稳定锁模状态。In some feasible implementations, the data acquisition card includes a first acquisition module and a second acquisition module, wherein the first acquisition module is configured to acquire a first timing signal, a first radio frequency spectrum signal or a first spectrum signal of a solid-state mode-locked laser in different working states, and the working state includes one of no light output, continuous light output state, Q-switched state, incomplete mode-locked state and stable mode-locked state; the second acquisition module is configured to acquire one or more of a second timing signal, a second radio frequency spectrum signal or a second spectrum signal of the solid-state mode-locked laser; the second timing signal, the second radio frequency spectrum signal or the second spectrum signal are respectively a real-time timing signal, a real-time radio frequency spectrum signal or a real-time spectrum signal of the solid-state mode-locked laser; the processor includes: an establishment module, configured to establish a database and a twin convolutional neural network; wherein the twin convolutional neural network includes a first subnetwork and a second subnetwork; the database includes a first Transient characteristic parameter, the first transient characteristic parameter is obtained after feature extraction of one or more of the first timing signal, the first radio frequency spectrum signal or the first spectrum signal; the training module is configured to train the twin convolutional neural network using a database; wherein the training method of the first subnetwork and the second subnetwork is the same; the input of the first subnetwork is the first transient characteristic parameter, and the output of the first subnetwork is the first characteristic parameter; the input of the second subnetwork is the second transient characteristic parameter; the second transient characteristic parameter is obtained after feature extraction of one or more of the second timing signal, the second radio frequency spectrum signal or the second spectrum signal, and the output of the second subnetwork is the second characteristic parameter; the comparison module is configured to compare the Euclidean distance between the second characteristic parameter and the first characteristic parameter; the determination module is configured to determine that the real-time working state of the solid-state mode-locked laser does not include a stable mode-locked state in response to the Euclidean distance not being within a preset range.

在一些可行的实现方式中,确定模块,还被配置为响应于欧氏距离在预设范围内,确定固体锁模激光器的实时工作状态为稳定锁模状态。In some feasible implementations, the determination module is further configured to determine that the real-time operating state of the solid-state mode-locked laser is a stable mode-locked state in response to the Euclidean distance being within a preset range.

在一些可行的实现方式中,第一子网络和第二子网络均包括依次相连的第一卷积层、第一池化层、第二卷积层和第二池化层;其中,第一卷积层和第二卷积层均采用Relu激活函数。In some feasible implementations, the first sub-network and the second sub-network both include a first convolutional layer, a first pooling layer, a second convolutional layer, and a second pooling layer connected in sequence; wherein the first convolutional layer and the second convolutional layer both use a Relu activation function.

本申请第二方面提供的基于孪生卷积神经网络的固体锁模激光器控制系统采用第一方面提供的基于孪生卷积神经网络的固体锁模激光器控制方法,因此,其能够达到的有益技术效果可参见第一方面,在此不再赘述。The solid-state mode-locked laser control system based on a twin convolutional neural network provided in the second aspect of the present application adopts the solid-state mode-locked laser control method based on a twin convolutional neural network provided in the first aspect. Therefore, the beneficial technical effects that can be achieved can be referred to the first aspect and will not be repeated here.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

为了更清楚地说明本申请的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,显而易见地,对于本领域普通技术人员而言,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

图1是本申请实施例提供的一种基于孪生卷积神经网络的固体锁模激光器控制方法的流程示意图;FIG1 is a flow chart of a solid-state mode-locked laser control method based on a twin convolutional neural network provided in an embodiment of the present application;

图2是本申请实施例提供的一种孪生卷积神经网络的结构示意图;FIG2 is a schematic diagram of the structure of a twin convolutional neural network provided in an embodiment of the present application;

图3是本申请实施例提供的一种固体锁模激光器在不同状态下第一光谱信号、第一时序信号、第一射频谱信号的示意图;3 is a schematic diagram of a first spectrum signal, a first timing signal, and a first radio frequency spectrum signal of a solid-state mode-locked laser in different states provided by an embodiment of the present application;

图4是本申请实施例提供的一种第二光谱信号、第二时序信号、第二射频谱信号的示意图;4 is a schematic diagram of a second spectrum signal, a second timing signal, and a second radio frequency spectrum signal provided in an embodiment of the present application;

图5是本申请实施例提供的一种具体实现方式中的数据处理流程图;FIG5 is a data processing flow chart of a specific implementation method provided in an embodiment of the present application;

图6是本申请实施例提供的一种基于孪生卷积神经网络的固体锁模激光器控制系统的结构示意图。FIG6 is a schematic diagram of the structure of a solid-state mode-locked laser control system based on a twin convolutional neural network provided in an embodiment of the present application.

图示标记:Graphic marking:

100-基于孪生卷积神经网络的固体锁模激光器控制系统;10-泵浦源;20-X腔镜组件;21-第一腔镜;22-第二腔镜;23-第三腔镜;24-半导体饱和吸收镜;25-输出镜;30-激光器晶体;40-探测器;50-数据采集卡;60-处理器;70-状态控制器;80-第一透镜;90-第二透镜。100-solid-state mode-locked laser control system based on twin convolutional neural network; 10-pump source; 20-X cavity mirror assembly; 21-first cavity mirror; 22-second cavity mirror; 23-third cavity mirror; 24-semiconductor saturated absorption mirror; 25-output mirror; 30-laser crystal; 40-detector; 50-data acquisition card; 60-processor; 70-state controller; 80-first lens; 90-second lens.

具体实施方式Detailed ways

下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚地描述。显然,所描述的实施例是本申请的一部分实施例,而不是全部实施例。基于本申请的实施例,本领域普通技术人员在没有做出创造性劳动的前提下所得到的其他实施例,都属于本申请的保护范围。The technical solutions in the embodiments of the present application will be described clearly below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, other embodiments obtained by ordinary technicians in this field without making creative work all belong to the protection scope of the present application.

以下,术语“第一”、“第二”等仅用于描述目的,而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此,限定有“第一”、“第二”等的特征可以明示或者隐含地包括一个或者更多个该特征。在本申请的描述中,除非另有说明,“多个”的含义是两个或两个以上。In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

此外,本申请中,“上”、“下”、“内”、“外”等方位术语是相对于附图中的部件示意置放的方位来定义的,应当理解到,这些方向性术语是相对的概念,它们用于相对于的描述和澄清,其可以根据附图中部件所放置的方位的变化而相应地发生变化。In addition, in the present application, directional terms such as "upper", "lower", "inner" and "outer" are defined relative to the orientation of the components in the drawings. It should be understood that these directional terms are relative concepts. They are used for relative description and clarification, and they can change accordingly according to the changes in the orientation of the components in the drawings.

为解决上述技术问题,本申请实施例提供一种基于孪生卷积神经网络的固体锁模激光器控制方法。参见图1,本申请实施例提供的基于孪生卷积神经网络的固体锁模激光器控制方法应用于基于孪生卷积神经网络的固体锁模激光器控制系统中,该控制方法可以由以下步骤S100-步骤S800所实现。In order to solve the above technical problems, the embodiment of the present application provides a solid mode-locked laser control method based on a twin convolutional neural network. Referring to Figure 1, the solid mode-locked laser control method based on a twin convolutional neural network provided in the embodiment of the present application is applied to a solid mode-locked laser control system based on a twin convolutional neural network, and the control method can be implemented by the following steps S100-S800.

步骤S100:建立数据库和孪生卷积神经网络。Step S100: Establish a database and a twin convolutional neural network.

其中,步骤S100可以分别通过建立数据库和建立孪生卷积神经网络所实现,具体可以包括以下步骤S101和步骤S102。Among them, step S100 can be implemented by establishing a database and establishing a twin convolutional neural network, and can specifically include the following steps S101 and S102.

步骤S101:建立数据库。Step S101: Establish a database.

步骤S102:建立孪生卷积神经网络。Step S102: Establish a twin convolutional neural network.

其中,步骤S101和步骤S102没有固定的执行顺序,可以按照步骤S101至步骤S102的顺序执行,也可以按照步骤S102至步骤S101的顺序执行。There is no fixed execution order for step S101 and step S102, and they may be executed in the order of step S101 to step S102, or in the order of step S102 to step S101.

在一些可行的实现方式中,步骤S101可以由以下步骤S101a至步骤S101b所实现。In some feasible implementations, step S101 may be implemented by the following steps S101a to S101b.

具体的,孪生卷积神经网络为一维网络,输入的数据为一维数据。Specifically, the twin convolutional neural network is a one-dimensional network, and the input data is one-dimensional data.

步骤S101a:获取训练数据。Step S101a: Obtain training data.

其中,训练数据为固体锁模激光器在不同工作状态下的第一时序信号、第一射频谱信号或第一光谱信号中的一种或多种,以及与第一时序信号、第一射频谱信号或第一光谱信号中的一种或多种对应的第一特征参量。工作状态包括未出光状态、输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态中的一种。The training data is one or more of the first timing signal, the first radio frequency spectrum signal or the first spectrum signal of the solid state mode-locked laser in different working states, and the first characteristic parameter corresponding to one or more of the first timing signal, the first radio frequency spectrum signal or the first spectrum signal. The working state includes one of the state of no light output, the state of continuous light output, the Q-switched state, the state of incomplete mode-locking and the state of stable mode-locking.

具体的,输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态下的信号是固体锁模激光器的X腔镜组件处于稳区范围内时进行采集的,未出光状态下的信号是固体锁模激光器的X腔镜组件处于非稳区范围内时进行采集的。Specifically, the signals in the continuous light output state, Q-switched state, incomplete mode-locked state and stable mode-locked state are collected when the X-cavity mirror assembly of the solid mode-locked laser is in the stable range, and the signals in the non-light output state are collected when the X-cavity mirror assembly of the solid mode-locked laser is in the unstable range.

示例的,不同工作状态下可以分别记录10000组。For example, 10,000 groups can be recorded in different working states.

在获取训练数据之初,可以搭建好基于孪生卷积神经网络的固体锁模激光器控制系统,便于对采集到的数据进行处理和反馈。At the beginning of acquiring training data, a solid-state mode-locked laser control system based on a twin convolutional neural network can be built to facilitate the processing and feedback of the collected data.

步骤S101b:对训练数据进行特征提取,得到数据库。Step S101b: extract features from the training data to obtain a database.

具体的,对获取到的第一时序信号、第一射频谱信号或第一光谱信号中的一种或多种进行信号分帧做特征提取,得到第一瞬态特征序列,以建立数据库。其中,第一瞬态特征序列与第一特征参量是对应的。Specifically, one or more of the acquired first time series signal, the first radio frequency spectrum signal or the first spectrum signal is framed and feature extracted to obtain a first transient feature sequence to establish a database, wherein the first transient feature sequence corresponds to the first feature parameter.

其中,特征提取的原则本申请中不做具体限定。The principle of feature extraction is not specifically limited in this application.

示例的,对第一时序信号进行特征提取时,可以采用均方根、时域波形的峰度、偏度等原则。For example, when extracting features from the first time series signal, principles such as root mean square, kurtosis, and skewness of the time domain waveform may be used.

对第一射频谱信号进行特征提取时,可以采用频谱特征如功率谱密度、频率分布等原则。When extracting features from the first radio frequency spectrum signal, spectrum features such as power spectrum density, frequency distribution, etc. may be used.

对第一光谱信号进行特征提取时,可以采用峰值波长、波长范围、光谱宽度等原则。When extracting features from the first spectral signal, principles such as peak wavelength, wavelength range, and spectral width may be used.

本申请实施例不对特征提取进行限定,采用上述任意特征提取原则均可以,或者采用其他上述介绍以外的原则也可以。The embodiments of the present application do not limit feature extraction, and any of the above-mentioned feature extraction principles may be adopted, or other principles other than those introduced above may be adopted.

在一些可行的实现方式中,在对第一时序信号、第一射频谱信号或第一光谱信号中的一种或多种进行特征提取之前,可以对第一时序信号、第一射频谱信号或第一光谱信号中进行预处理,预处理可以包括去噪、归一化等操作。In some feasible implementations, before performing feature extraction on one or more of the first time series signal, the first radio frequency spectrum signal, or the first spectral signal, the first time series signal, the first radio frequency spectrum signal, or the first spectral signal may be preprocessed, and the preprocessing may include operations such as denoising and normalization.

步骤S200:采用数据库训练孪生卷积神经网络。Step S200: Use the database to train the twin convolutional neural network.

具体的,孪生卷积神经网络包括第一子网络和第二子网络,在训练孪生卷积神经网络的过程中,第一子网络和第二子网络的训练方式相同。其中,训练方式可以为任意训练方式,本申请实施例不对孪生卷积神经网络的训练方式做具体限定。Specifically, the twin convolutional neural network includes a first subnetwork and a second subnetwork. In the process of training the twin convolutional neural network, the training methods of the first subnetwork and the second subnetwork are the same. The training method can be any training method, and the embodiment of the present application does not specifically limit the training method of the twin convolutional neural network.

在训练完成后,第一子网络和第二子网络的输入均可以为第一瞬态特征序列,第一子网络和第二子网络的输出均可以为第一特征参量。After the training is completed, the inputs of the first sub-network and the second sub-network can both be the first transient feature sequence, and the outputs of the first sub-network and the second sub-network can both be the first feature parameter.

结合图2、图3和图4,第一子网络和第二子网络可以均包括相连接的第一卷积层、第一池化层、第二卷积层和第二池化层。其中,第一卷积层和第二卷积层均可以均采用Relu激活函数,且第一子网络和第二子网络的输入均为一维数据。In conjunction with Figures 2, 3 and 4, the first subnetwork and the second subnetwork may both include a first convolutional layer, a first pooling layer, a second convolutional layer and a second pooling layer connected to each other. The first convolutional layer and the second convolutional layer may both use a Relu activation function, and the inputs of the first subnetwork and the second subnetwork are both one-dimensional data.

步骤S300:获取固体锁模激光器的第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种。Step S300: obtaining one or more of a second timing signal, a second radio frequency spectrum signal or a second optical spectrum signal of a solid state mode-locked laser.

在训练好孪生卷积神经网络之后,进一步对固体锁模激光器的实时输出性能进行评估,采集实时信号,实时信号包括第二时序信号、第二射频谱信号或第二光谱信号,分别为固体锁模激光器的实时时序信号、实时射频谱信号和实时光谱信号。After training the twin convolutional neural network, the real-time output performance of the solid-state mode-locked laser is further evaluated by collecting real-time signals. The real-time signals include a second timing signal, a second radio frequency spectrum signal or a second spectral signal, which are respectively the real-time timing signal, the real-time radio frequency spectrum signal and the real-time spectral signal of the solid-state mode-locked laser.

步骤S400:将第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种经特征提取后转换为第二瞬态特征序列。Step S400: converting one or more of the second time series signal, the second radio frequency spectrum signal or the second optical spectrum signal into a second transient feature sequence after feature extraction.

在步骤S400中,对实时信号进行信号分帧做特征提取之前,可以对实时信号进行预处理操作,其中预处理可以包括去噪、归一化。这样能够保证实时信号的准确性,便于进行特征提取的同时减小测量误差。将预处理后的信号进行特征提取,转换为第二瞬态特征序列。In step S400, before the real-time signal is framed for feature extraction, the real-time signal may be preprocessed, wherein the preprocessing may include denoising and normalization. This ensures the accuracy of the real-time signal, facilitates feature extraction and reduces measurement errors. The preprocessed signal is feature extracted and converted into a second transient feature sequence.

其中,实时信号的特征提取原则与上述训练数据中特征提取原则相同,从而保证数据的统一性。The feature extraction principle of the real-time signal is the same as that of the above-mentioned training data, thereby ensuring the uniformity of the data.

示例的,第一时序信号采用均方根进行特征提取时,第二时序信号也采用均方根进行特征提取。For example, when the first time series signal uses the root mean square to extract features, the second time series signal also uses the root mean square to extract features.

步骤S500:将第二瞬态特征序列输入至第二子网络中,得到第二特征参量。Step S500: inputting the second transient feature sequence into the second sub-network to obtain a second feature parameter.

具体的,第二子网络是为了实时信号服务的,在采集到实时信号并对实时信号进行预处理和特征提取得到第二瞬态特征序列之后,输入至第二子网络,得到第二特征参量。也就是说,第二子网络的输入为第二瞬态特征序列,第二子网络的输出为第二特征参量。Specifically, the second sub-network serves the real-time signal. After the real-time signal is collected and pre-processed and feature extracted to obtain the second transient feature sequence, it is input into the second sub-network to obtain the second feature parameter. In other words, the input of the second sub-network is the second transient feature sequence, and the output of the second sub-network is the second feature parameter.

可以理解为,第一子网络是用于输出原始数据,第二子网络是用于输出实时数据,从而便于后续通过实时数据与原始数据的对比结果确定固体锁模激光器的实时工作状态。第一子网络和第二子网络形成训练和对比模型。It can be understood that the first sub-network is used to output raw data, and the second sub-network is used to output real-time data, so as to facilitate the subsequent determination of the real-time working state of the solid-state mode-locked laser by comparing the real-time data with the raw data. The first sub-network and the second sub-network form a training and comparison model.

其中,实时信号与第一时序信号、第一射频谱信号、第一光谱信号的具体预处理方法相同。Among them, the specific preprocessing method of the real-time signal is the same as the first time series signal, the first radio frequency spectrum signal, and the first optical spectrum signal.

步骤S600:对比第二特征参量与第一特征参量,得到欧氏距离。Step S600: Compare the second characteristic parameter with the first characteristic parameter to obtain the Euclidean distance.

对比第二特征参量和第一特征参量,得到欧氏距离,从而通过欧氏距离确定固体锁模激光器的实时工作状态。The second characteristic parameter is compared with the first characteristic parameter to obtain the Euclidean distance, and the real-time working state of the solid-state mode-locked laser is determined by the Euclidean distance.

具体的,将数据库中的数据与实时信号进行对比,比较两个子网络输出的特征参量,采用欧氏距离评估实时信号与对应的数据库各分帧之间的相似性,根据相似性评估的结果来调整孪生卷积神经网络,以进一步提高孪生卷积神经网络的性能和稳定性。根据欧氏距离可以确定当前固体锁模激光器工作在何种激光状态。Specifically, the data in the database are compared with the real-time signal, the characteristic parameters of the two sub-networks are compared, and the Euclidean distance is used to evaluate the similarity between the real-time signal and the corresponding database frames. The twin convolutional neural network is adjusted according to the results of the similarity evaluation to further improve the performance and stability of the twin convolutional neural network. The Euclidean distance can be used to determine the laser state in which the current solid-state mode-locked laser is working.

值得注意的是,若实时信号包括多种时,例如,采集到的实时信号包括第二时序信号和第二射频谱信号,那么在输出第二特征参量时,输出两个第二特征参量,分别为时序第二特征参量和射频第二特征参量。而在对比时,将时序第二特征参量与第一子网络输出的时序第一特征参量进行对比,得到时序欧氏距离,将射频第二特征参量与第一子网络输出的射频第一特征参量进行对比,得到射频欧氏距离。换言之,将实时信号与对应的第一子网络中的数据进行对比,从而保证网络的输出准确性。It is worth noting that if the real-time signal includes multiple types, for example, the collected real-time signal includes a second timing signal and a second radio frequency spectrum signal, then when the second characteristic parameter is output, two second characteristic parameters are output, namely the timing second characteristic parameter and the radio frequency second characteristic parameter. When comparing, the timing second characteristic parameter is compared with the timing first characteristic parameter output by the first sub-network to obtain the timing Euclidean distance, and the radio frequency second characteristic parameter is compared with the radio frequency first characteristic parameter output by the first sub-network to obtain the radio frequency Euclidean distance. In other words, the real-time signal is compared with the data in the corresponding first sub-network to ensure the output accuracy of the network.

步骤S700:响应于欧氏距离不在预设范围内,确定固体锁模激光器的实时工作状态不包括稳定锁模状态。Step S700: In response to the Euclidean distance not being within a preset range, determining that the real-time operating state of the solid state mode-locked laser does not include a stable mode-locked state.

若判断得到的欧氏距离不在预设范围内时,表示此时固体锁模激光器的实时工作状态并非是稳定锁模状态。其中,预设范围表征的是固体锁模激光器在稳定锁模状态下的欧氏距离的基准范围,预设范围的具体数值可以根据实际的固体锁模激光器的使用需求进行设定,本申请实施例不做具体限定。If the Euclidean distance is not within the preset range, it means that the real-time working state of the solid-state mode-locked laser is not a stable mode-locked state. The preset range represents the reference range of the Euclidean distance of the solid-state mode-locked laser in the stable mode-locked state. The specific value of the preset range can be set according to the actual use requirements of the solid-state mode-locked laser, and the embodiment of the present application does not make specific limitations.

步骤S800:根据实时工作状态调整固体锁模激光器中X腔镜组件位置,使固体锁模激光器处于稳定锁模状态。Step S800: adjusting the position of the X-cavity mirror assembly in the solid-state mode-locked laser according to the real-time working status, so that the solid-state mode-locked laser is in a stable mode-locked state.

在确定固体锁模激光器不在稳定锁模状态时,通过调整X腔镜组件的位置,从而使固体锁模激光器处于稳定锁模状态。When it is determined that the solid mode-locked laser is not in a stable mode-locked state, the position of the X-cavity mirror assembly is adjusted to put the solid mode-locked laser in a stable mode-locked state.

也就是说,当孪生卷积神经网络评估到固体锁模激光器处于未出光状态、输出连续光状态、调Q状态、未完全锁模状态这几种状态时,需要调节X腔镜组件中某一或者某两个关键腔镜的位置,使得固体锁模激光器处于位于稳定锁模区范围内。这样,固体锁模激光器可以达到稳定输出。That is to say, when the twin convolutional neural network evaluates that the solid-state mode-locked laser is in the state of no light output, continuous light output, Q-switched state, or incomplete mode-locked state, it is necessary to adjust the position of one or two key cavity mirrors in the X cavity mirror assembly so that the solid-state mode-locked laser is within the stable mode-locked region. In this way, the solid-state mode-locked laser can achieve stable output.

另外,在存在外界扰动等因素导致固体锁模激光器处于失调时,可以通过该控制方法实现自行反馈调节。In addition, when the solid-state mode-locked laser is out of adjustment due to external disturbances or other factors, this control method can be used to achieve self-feedback adjustment.

在一些可行的实现方式中,控制方法还包括步骤S900。In some feasible implementations, the control method further includes step S900.

步骤S900:响应于欧氏距离在预设范围,确定固体锁模激光器的实时工作状态为稳定锁模状态。Step S900: In response to the Euclidean distance being within a preset range, determining that the real-time working state of the solid state mode-locked laser is a stable mode-locked state.

此时,在判断固体锁模激光器处于稳定锁模状态时,不需要对X腔镜组件进行调整。At this time, when it is determined that the solid-state mode-locked laser is in a stable mode-locked state, there is no need to adjust the X-cavity mirror assembly.

具体的,本申请实施例提供的控制方法,通过对输入的时序信号、射频谱信号或者光谱信号进行高精度的分析和识别,从而确定固体锁模激光器的工作状态和性能参数,实现精确调节关键腔镜位置状态。由于卷积神经网络的高效性和并行处理能力,控制系统可以实时地对固体锁模激光器的输出进行监测和调节,使其可以实现稳定的锁模状态,适应环境变化和工作条件的波动。该控制方法整合了卷积神经网络的自动化分析和调节功能,可以减少人工干预,提高控制系统的自动化程度和稳定性,降低了维护成本和人力成本。通过实时的监测和调节,可以不断调节优化固体锁模激光器的输出性能,使其达到最佳工作状态,提高能效和输出质量。Specifically, the control method provided in the embodiment of the present application determines the working state and performance parameters of the solid-state mode-locked laser by performing high-precision analysis and identification on the input timing signal, radio frequency spectrum signal or spectrum signal, and realizes precise adjustment of the position state of the key cavity mirror. Due to the high efficiency and parallel processing capability of the convolutional neural network, the control system can monitor and adjust the output of the solid-state mode-locked laser in real time, so that it can achieve a stable mode-locked state and adapt to environmental changes and fluctuations in working conditions. The control method integrates the automated analysis and adjustment functions of the convolutional neural network, which can reduce manual intervention, improve the automation and stability of the control system, and reduce maintenance costs and labor costs. Through real-time monitoring and adjustment, the output performance of the solid-state mode-locked laser can be continuously adjusted and optimized to achieve the best working state, improve energy efficiency and output quality.

在一个具体的实现方式中,参见图5,本申请实施例提供的基于孪生卷积神经网络的固体锁模激光器控制方法可以由以下步骤S1至步骤S7所实现。In a specific implementation, referring to FIG5 , the solid-state mode-locked laser control method based on a twin convolutional neural network provided in an embodiment of the present application can be implemented by the following steps S1 to S7.

步骤S1:获取光谱、时序、射频谱信号。Step S1: Acquire spectrum, time series, and radio frequency spectrum signals.

该步骤可以对应上述步骤S300。This step may correspond to the above-mentioned step S300.

步骤S2:数据采集。Step S2: Data collection.

步骤S3:输入数据与处理,去噪、归一化等。Step S3: Input data and processing, denoising, normalization, etc.

该步骤可以为对实时信号的预处理过程。This step may be a preprocessing process for the real-time signal.

步骤S4:孪生卷积神经网络构建与训练。Step S4: Construction and training of twin convolutional neural network.

该步骤可以为上述步骤S100和步骤S200中的部分步骤。This step may be part of the above-mentioned steps S100 and S200.

值得注意的是,该实现方式中,之所以先执行步骤S300再执行步骤S100和步骤S200,可以将其理解为不断获取实时信号的过程,正是不断地获取实时信号,从而实现对非稳定锁模状态的实时调节。It is worth noting that in this implementation, the reason why step S300 is executed first and then step S100 and step S200 can be understood as a process of continuously acquiring real-time signals. It is precisely by continuously acquiring real-time signals that real-time adjustment of the unstable locking state is achieved.

步骤S5:特征比较。Step S5: Feature comparison.

步骤S6:相似度分析。Step S6: Similarity analysis.

步骤S5和步骤S6可以对应上述步骤S600。Step S5 and step S6 may correspond to the above-mentioned step S600.

步骤S7:控制信号生成,实时调控。Step S7: Generate control signal and adjust in real time.

该步骤可以对应上述步骤S800。This step may correspond to the above-mentioned step S800.

与前述控制方法的实施例对应的,本申请还提供一种基于孪生卷积神经网络的固体锁模激光器控制系统100的实施例。该基于孪生卷积神经网络的固体锁模激光器控制系统100采用上述实施例提供的基于孪生卷积神经网络的固体锁模激光器控制方法。Corresponding to the embodiment of the aforementioned control method, the present application also provides an embodiment of a solid-state mode-locked laser control system 100 based on a twin convolutional neural network. The solid-state mode-locked laser control system 100 based on a twin convolutional neural network adopts the solid-state mode-locked laser control method based on a twin convolutional neural network provided in the above embodiment.

参见图6,该基于孪生卷积神经网络的固体锁模激光器控制系统100包括泵浦源10、X腔镜组件20、激光器晶体30、探测器40、数据采集卡50、处理器60和状态控制器70。6 , the solid-state mode-locked laser control system 100 based on the twin convolutional neural network includes a pump source 10 , an X-cavity mirror assembly 20 , a laser crystal 30 , a detector 40 , a data acquisition card 50 , a processor 60 and a state controller 70 .

泵浦源10用于产生光线,光线中携带能量。具体的,泵浦源10用于提供能量激发激光器晶体30的原子或离子,从而使其处于更高的能级状态,准备发射受激辐射。The pump source 10 is used to generate light, which carries energy. Specifically, the pump source 10 is used to provide energy to excite atoms or ions in the laser crystal 30, so that the atoms or ions are in a higher energy state and are ready to emit stimulated radiation.

X腔镜组件20设置在光线的光路上,X腔镜组件20包括第一腔镜21、第二腔镜22、第三腔镜23、半导体饱和吸收镜24和输出镜25。The X-cavity mirror assembly 20 is disposed on the optical path of the light, and the X-cavity mirror assembly 20 includes a first cavity mirror 21 , a second cavity mirror 22 , a third cavity mirror 23 , a semiconductor saturated absorption mirror 24 and an output mirror 25 .

具体的,第一腔镜21、第二腔镜22、第三腔镜23、半导体饱和吸收镜24和输出镜25组成一个典型的X型锁模腔,基于ABCD传输矩阵去设计固体锁模激光器,搭建一个两臂等长的谐振腔,这样两个稳区之间没有间隔或者分裂,稳区范围相对较大。其中,半导体饱和吸收镜24是一种关键组件,用于实现和稳定锁模操作,半导体饱和吸收镜24能够通过其非线性吸收特性启动锁模。当激光强度较低时,半导体饱和吸收镜24表现为高吸收,使得激光腔内的噪声和不稳定成分被吸收。随着激光强度增加到一定阈值,半导体饱和吸收镜24的吸收率下降,透过率增加,从而促进了强度较高的脉冲形成,启动锁模过程。半导体饱和吸收镜24还可以帮助维持锁模状态的稳定性。由于其饱和吸收特性,使其能够在激光脉冲达到一定强度时减少吸收,允许高强度脉冲在腔内稳定传播。这种动态调节吸收的能力有助于抑制腔内的脉冲波动,保持脉冲的稳定性和一致性。Specifically, the first cavity mirror 21, the second cavity mirror 22, the third cavity mirror 23, the semiconductor saturated absorber mirror 24 and the output mirror 25 form a typical X-shaped mode-locked cavity. Based on the ABCD transmission matrix, a solid mode-locked laser is designed to build a resonant cavity with two arms of equal length, so that there is no interval or split between the two stable regions, and the stable region range is relatively large. Among them, the semiconductor saturated absorber mirror 24 is a key component for realizing and stabilizing the mode-locked operation. The semiconductor saturated absorber mirror 24 can start the mode-locked operation through its nonlinear absorption characteristics. When the laser intensity is low, the semiconductor saturated absorber mirror 24 exhibits high absorption, so that the noise and unstable components in the laser cavity are absorbed. As the laser intensity increases to a certain threshold, the absorption rate of the semiconductor saturated absorber mirror 24 decreases and the transmittance increases, thereby promoting the formation of pulses with higher intensity and starting the mode-locked process. The semiconductor saturated absorber mirror 24 can also help maintain the stability of the mode-locked state. Due to its saturated absorption characteristics, it can reduce absorption when the laser pulse reaches a certain intensity, allowing high-intensity pulses to propagate stably in the cavity. This ability to dynamically adjust absorption helps to suppress pulse fluctuations in the cavity and maintain the stability and consistency of the pulse.

激光器晶体30设置在X腔镜组件20中,激光器晶体30用于接收到的光线中的能量,并将其转换为激光。具体地,调节第一腔镜21和第二腔镜22以及激光器晶体30的位置,将激光器晶体30放置在计算的稳区范围中心附近。这样,激光器晶体30被激发产生的激光就可以直接发送至第一腔镜21和第二腔镜22,并通过第一腔镜21和第二腔镜22在X型锁模腔内反射。The laser crystal 30 is arranged in the X-cavity mirror assembly 20, and the laser crystal 30 is used to receive the energy in the light and convert it into laser. Specifically, the positions of the first cavity mirror 21, the second cavity mirror 22 and the laser crystal 30 are adjusted, and the laser crystal 30 is placed near the center of the calculated stable range. In this way, the laser generated by the laser crystal 30 being excited can be directly sent to the first cavity mirror 21 and the second cavity mirror 22, and reflected in the X-type mode-locked cavity through the first cavity mirror 21 and the second cavity mirror 22.

其中,激光器晶体30作为激光介质,能够吸收泵浦源10的能量,电子被激发到高能态。然后,通过受激辐射返回基态,发射出具有特定波长的光子,形成激光。X腔镜组件20的作用是形成光学谐振腔,通过反射光子,使它们在激光介质中多次通过,增加受激辐射的概率,进而放大光强。Among them, the laser crystal 30, as a laser medium, can absorb the energy of the pump source 10, and the electrons are excited to a high energy state. Then, they return to the ground state through stimulated radiation, emitting photons with a specific wavelength to form a laser. The function of the X-cavity mirror assembly 20 is to form an optical resonant cavity, which reflects photons and makes them pass through the laser medium multiple times, increasing the probability of stimulated radiation and thereby amplifying the light intensity.

探测器40设置在X腔镜组件20的输出光路上,用于接收X腔镜组件20反射后的激光。具体的,探测器40设置在输出镜25的输出光路上,从而便于接收输出镜25射出的激光。其中,探测器40接收到的激光中存在时序信号、射频谱信号和光谱信号,这些信号可以表征固体锁模激光器的工作状态。The detector 40 is arranged on the output optical path of the X-cavity mirror assembly 20, and is used to receive the laser reflected by the X-cavity mirror assembly 20. Specifically, the detector 40 is arranged on the output optical path of the output mirror 25, so as to facilitate receiving the laser emitted by the output mirror 25. Among them, the laser received by the detector 40 contains timing signals, radio frequency spectrum signals and spectrum signals, and these signals can characterize the working state of the solid state mode-locked laser.

数据采集卡50与探测器40相连,用于采集激光中的数据,这些数据即为时序信号、射频谱信号和光谱信号。The data acquisition card 50 is connected to the detector 40 and is used to collect data in the laser, which are timing signals, radio frequency spectrum signals and optical spectrum signals.

在一些可行的实现方式中,数据采集卡50包括第一采集模块和第二采集模块。其中,第一采集模块用于获取固体锁模激光器在不同工作状态下的第一时序信号、第一射频谱信号或第一光谱信号以及与第一时序信号、第一射频谱信号或第一光谱信号对应的第一特征参量;工作状态包括未出光状态、输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态中的一种。In some feasible implementations, the data acquisition card 50 includes a first acquisition module and a second acquisition module. The first acquisition module is used to acquire a first timing signal, a first radio frequency spectrum signal or a first spectrum signal of the solid state mode-locked laser in different working states and a first characteristic parameter corresponding to the first timing signal, the first radio frequency spectrum signal or the first spectrum signal; the working state includes one of a non-light-emitting state, a continuous light output state, a Q-switched state, an incomplete mode-locked state and a stable mode-locked state.

其中,输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态下的信号是固体锁模激光器的X腔镜组件处于稳区范围内时进行采集的,未出光状态下的信号是固体锁模激光器的X腔镜组件处于非稳区范围内时进行采集的。Among them, the signals in the continuous light output state, Q-switched state, incomplete mode-locked state and stable mode-locked state are collected when the X-cavity mirror assembly of the solid mode-locked laser is in the stable range, and the signals in the non-light output state are collected when the X-cavity mirror assembly of the solid mode-locked laser is in the unstable range.

第二采集模块被配置为获取固体锁模激光器的第二时序信号、第二射频谱信号或第二光谱信号;其中,第二时序信号、第二射频谱信号或第二光谱信号分别为固体锁模激光器的实时时序信号、实时射频谱信号或实时光谱信号。The second acquisition module is configured to acquire a second timing signal, a second radio frequency spectrum signal or a second spectrum signal of the solid mode-locked laser; wherein the second timing signal, the second radio frequency spectrum signal or the second spectrum signal is respectively a real-time timing signal, a real-time radio frequency spectrum signal or a real-time spectrum signal of the solid mode-locked laser.

也就是说,第一采集模块用于执行上述控制方法实施例中的步骤S101a。第二采集模块用于执行上述控制方法实施例中的步骤S300。That is, the first acquisition module is used to execute step S101a in the above control method embodiment. The second acquisition module is used to execute step S300 in the above control method embodiment.

处理器60与数据采集卡50相连,用于处理数据,并根据处理后的数据确定固体锁模激光器的实时工作状态,并将该实时工作状态发送给状态控制器70。The processor 60 is connected to the data acquisition card 50 and is used for processing data, determining the real-time working state of the solid-state mode-locked laser according to the processed data, and sending the real-time working state to the state controller 70 .

具体的,处理器60包括建立模块、训练模块、对比模块和确定模块。Specifically, the processor 60 includes a building module, a training module, a comparing module and a determining module.

建立模块用于建立孪生卷积神经网络。其中,孪生卷积神经网络包括第一子网络和第二子网络;也就是说,建立模块用于执行上述控制方法实施例中的步骤S102。The establishment module is used to establish a twin convolutional neural network. The twin convolutional neural network includes a first sub-network and a second sub-network; that is, the establishment module is used to execute step S102 in the above control method embodiment.

训练模块用于利用训练库训练孪生卷积神经网络;其中,第一子网络和第二子网络的训练方式相同;第一子网络的输入为第一瞬态特征参量,第一瞬态特征参量为第一时序信号、第一射频谱信号或第一光谱信号中的一种或多种经特征提取后得到;第一子网络的输出为第一特征参量;第二子网络的输入为第二瞬态特征参量;第二瞬态特征参量为第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种经特征提取后得到,第二子网络的输出为第二特征参量;也就是说,训练模块用于执行上述控制方法实施例中的步骤S200。The training module is used to train the twin convolutional neural network using the training library; wherein, the training method of the first subnetwork and the second subnetwork is the same; the input of the first subnetwork is a first transient characteristic parameter, and the first transient characteristic parameter is obtained after feature extraction of one or more of the first time series signal, the first radio frequency spectrum signal or the first spectrum signal; the output of the first subnetwork is the first characteristic parameter; the input of the second subnetwork is a second transient characteristic parameter; the second transient characteristic parameter is obtained after feature extraction of one or more of the second time series signal, the second radio frequency spectrum signal or the second spectrum signal, and the output of the second subnetwork is the second characteristic parameter; that is, the training module is used to execute step S200 in the above-mentioned control method embodiment.

具体的,第一子网络和第二子网络均可以包括依次相连的第一卷积层、第一池化层、第二卷积层和第二池化层;其中,第一卷积层和第二卷积层均采用Relu激活函数。Specifically, the first sub-network and the second sub-network may include a first convolutional layer, a first pooling layer, a second convolutional layer, and a second pooling layer connected in sequence; wherein the first convolutional layer and the second convolutional layer both use a Relu activation function.

对比模块用于对比第二特征参量与第一特征参量的欧氏距离;也就是说,对比模块用于执行上述控制方法实施例中的步骤S600。The comparison module is used to compare the Euclidean distance between the second characteristic parameter and the first characteristic parameter; that is, the comparison module is used to execute step S600 in the above control method embodiment.

确定模块用于响应于欧氏距离不在预设范围内,确定固体锁模激光器的实时工作状态不包括稳定锁模状态。也就是说,确定模块用于执行上述控制方法实施例中的步骤S700。The determination module is used to determine that the real-time working state of the solid state mode-locked laser does not include a stable mode-locked state in response to the Euclidean distance not being within a preset range. That is, the determination module is used to execute step S700 in the above control method embodiment.

确定模块还用于响应于欧氏距离在预设范围内,确定固体锁模激光器的实时工作状态为稳定锁模状态。也就是说,确定模块还用于执行上述控制方法实施例中的步骤S900。The determination module is also used to determine that the real-time working state of the solid state mode-locked laser is a stable mode-locked state in response to the Euclidean distance being within a preset range. That is, the determination module is also used to execute step S900 in the above control method embodiment.

状态控制器70的一端与处理器60相连,用于在响应于实时工作状态不包括稳定锁模状态时,根据实时工作状态调节X腔镜组件20中第二腔镜22和输出镜25的设置位置,从而使固体锁模激光器处于稳定锁模状态。One end of the state controller 70 is connected to the processor 60, and is used to adjust the setting positions of the second cavity mirror 22 and the output mirror 25 in the X-cavity mirror assembly 20 according to the real-time working state when the real-time working state does not include the stable mode-locked state, so as to put the solid-state mode-locked laser in a stable mode-locked state.

具体的,位置可以包括坐标、角度等参数。Specifically, the position may include parameters such as coordinates and angles.

状态控制器70的另一端与第二腔镜22和输出镜25相连,在基于孪生卷积神经网络的固体锁模激光器控制系统100判断出固体锁模激光器未处于稳定锁模状态时,可以自适应调节驱动电动的第二腔镜22和输出镜25,通过改变第二腔镜22和输出镜25的设置位置以使X形锁模腔处于稳定锁模区域范围,此时固体锁模激光器可以实现稳定输出,完成对固体锁模激光器的控制。The other end of the state controller 70 is connected to the second cavity mirror 22 and the output mirror 25. When the solid-state mode-locked laser control system 100 based on the twin convolutional neural network determines that the solid-state mode-locked laser is not in a stable mode-locked state, the second cavity mirror 22 and the output mirror 25 that are driven electrically can be adaptively adjusted. By changing the setting positions of the second cavity mirror 22 and the output mirror 25, the X-shaped mode-locked cavity is placed in a stable mode-locked region. At this time, the solid-state mode-locked laser can achieve stable output, thereby completing the control of the solid-state mode-locked laser.

继续参见图6,本申请实施例提供的基于孪生卷积神经网络的固体锁模激光器控制系统100还包括第一透镜80和第二透镜90,第一透镜80和第二透镜90依次设置在泵浦源10和第一腔镜21之间。通过设置第一透镜80和第二透镜90,能够更好的将光线透射至第一腔镜21。Continuing to refer to Fig. 6, the solid-state mode-locked laser control system 100 based on a twin convolutional neural network provided in an embodiment of the present application further includes a first lens 80 and a second lens 90, and the first lens 80 and the second lens 90 are sequentially arranged between the pump source 10 and the first cavity mirror 21. By arranging the first lens 80 and the second lens 90, light can be better transmitted to the first cavity mirror 21.

本申请实施例提供的基于孪生卷积神经网络的固体锁模激光器控制系统100中,孪生卷积神经网络可以对输入的时序信号、射频谱信号或者光谱信号进行高精度的分析和识别,从而确定固体锁模激光器的工作状态和性能参数,实现精确调节第二腔镜22和输出镜25的位置状态。由于孪生卷积神经网络的高效性和并行处理能力,基于孪生卷积神经网络的固体锁模激光器控制系统100可以实时地对固体锁模激光器的输出进行监测和调节,使其可以实现稳定的锁模状态,适应环境变化和工作条件的波动。孪生卷积神经网络可以根据实时收集到的数据动态地调整反馈至基于孪生卷积神经网络的固体锁模激光器控制系统100,使基于孪生卷积神经网络的固体锁模激光器控制系统100具有自适应性,保证输出稳定性和性能优异。本发明整合了孪生卷积神经网络的自动化分析和调节功能,可以减少人工干预,提高基于孪生卷积神经网络的固体锁模激光器控制系统100的自动化程度和稳定性,降低了维护成本和人力成本。通过实时的监测和调节,基于孪生卷积神经网络的固体锁模激光器控制系统100可以不断调节优化固体锁模激光器的输出性能,使其达到最佳工作状态,提高能效和输出质量。同时基于孪生卷积神经网络的固体锁模激光器控制系统100实时利用多维度-多角度激光数据库自适应调节固体锁模激光器的锁模脉冲输出,提高锁模稳定性和鲁棒性,从而改善固体锁模激光器在实现中的冗余操作。In the solid-state mode-locked laser control system 100 based on the twin convolutional neural network provided in the embodiment of the present application, the twin convolutional neural network can perform high-precision analysis and identification of the input timing signal, radio frequency spectrum signal or spectrum signal, so as to determine the working state and performance parameters of the solid-state mode-locked laser, and realize accurate adjustment of the position state of the second cavity mirror 22 and the output mirror 25. Due to the high efficiency and parallel processing capability of the twin convolutional neural network, the solid-state mode-locked laser control system 100 based on the twin convolutional neural network can monitor and adjust the output of the solid-state mode-locked laser in real time, so that it can achieve a stable mode-locked state and adapt to environmental changes and fluctuations in working conditions. The twin convolutional neural network can dynamically adjust the feedback to the solid-state mode-locked laser control system 100 based on the twin convolutional neural network according to the data collected in real time, so that the solid-state mode-locked laser control system 100 based on the twin convolutional neural network has adaptability, ensuring output stability and excellent performance. The present invention integrates the automated analysis and adjustment functions of the twin convolutional neural network, which can reduce manual intervention, improve the automation and stability of the solid-state mode-locked laser control system 100 based on the twin convolutional neural network, and reduce maintenance costs and labor costs. Through real-time monitoring and adjustment, the solid-state mode-locked laser control system 100 based on the twin convolutional neural network can continuously adjust and optimize the output performance of the solid-state mode-locked laser, so that it reaches the optimal working state, and improves energy efficiency and output quality. At the same time, the solid-state mode-locked laser control system 100 based on the twin convolutional neural network uses a multi-dimensional-multi-angle laser database in real time to adaptively adjust the mode-locked pulse output of the solid-state mode-locked laser, improve the stability and robustness of the mode-locked laser, and thus improve the redundant operation of the solid-state mode-locked laser in the implementation.

需要说明的是,本领域技术人员在考虑说明书及实践这里公开的申请后,将容易想到本申请的其它实施方案。本申请旨在涵盖本申请的任何变型、用途或者适应性变化,这些变型、用途或者适应性变化遵循本申请的一般性原理并包括本申请未公开的本技术领域中的公知常识或惯用技术手段。说明书和实施例仅被视为示例性的,本申请的真正范围由权利要求指出。It should be noted that those skilled in the art will easily think of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope of the present application is indicated by the claims.

应当理解的是,本申请并不局限于上面已经描述并在附图中示出的精确结构,并且可以在不脱离其范围进行各种修改和改变。本申请的范围仅由所附的权利要求来限制。It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims (10)

1.一种基于孪生卷积神经网络的固体锁模激光器控制方法,其特征在于,包括:1. A solid-state mode-locked laser control method based on a twin convolutional neural network, characterized by comprising: 建立数据库和孪生卷积神经网络;其中,所述数据库包括第一瞬态特征序列以及与所述第一瞬态序列对应的第一特征参量,所述孪生卷积神经网络包括第一子网络和第二子网络;Establishing a database and a twin convolutional neural network; wherein the database includes a first transient feature sequence and a first feature parameter corresponding to the first transient sequence, and the twin convolutional neural network includes a first subnetwork and a second subnetwork; 采用所述数据库训练所述孪生卷积神经网络;其中,所述第一子网络和所述第二子网络的训练方式相同;所述第一子网络的输入为所述第一瞬态特征序列,所述第一子网络的输出为所述第一特征参量,所述第一瞬态特征序列为固体锁模激光器在不同工作状态下的第一时序信号、第一射频谱信号或第一光谱信号中一种或多种经特征提取后得到,所述工作状态包括未出光状态、输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态中的一种;The twin convolutional neural network is trained using the database; wherein the first subnetwork and the second subnetwork are trained in the same manner; the input of the first subnetwork is the first transient feature sequence, the output of the first subnetwork is the first feature parameter, the first transient feature sequence is one or more of the first timing signal, the first radio frequency spectrum signal or the first spectrum signal of the solid-state mode-locked laser under different working states, obtained after feature extraction, and the working state includes one of a non-light-emitting state, a continuous light output state, a Q-switched state, an incompletely mode-locked state and a stable mode-locked state; 获取所述固体锁模激光器的第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种;其中,所述第二时序信号、所述第二射频谱信号或所述第二光谱信号分别为所述固体锁模激光器的实时时序信号、实时射频谱信号或实时光谱信号;Acquire one or more of a second timing signal, a second radio frequency spectrum signal or a second spectrum signal of the solid-state mode-locked laser; wherein the second timing signal, the second radio frequency spectrum signal or the second spectrum signal is respectively a real-time timing signal, a real-time radio frequency spectrum signal or a real-time spectrum signal of the solid-state mode-locked laser; 将所述第二时序信号、所述第二射频谱信号或所述第二光谱信号中的一种或多种经特征提取后转换为第二瞬态特征序列;Convert one or more of the second time series signal, the second radio frequency spectrum signal or the second optical spectrum signal into a second transient feature sequence after feature extraction; 将所述第二瞬态特征序列输入至所述第二子网络中,得到第二特征参量;Inputting the second transient feature sequence into the second sub-network to obtain a second feature parameter; 对比所述第二特征参量与所述第一特征参量,得到欧氏距离;Comparing the second characteristic parameter with the first characteristic parameter to obtain a Euclidean distance; 响应于所述欧氏距离不在预设范围内,确定所述固体锁模激光器的实时工作状态不包括所述稳定锁模状态;In response to the Euclidean distance not being within a preset range, determining that the real-time operating state of the solid-state mode-locked laser does not include the stable mode-locked state; 根据所述实时工作状态调整所述固体锁模激光器中X腔镜组件位置,使所述固体锁模激光器处于所述稳定锁模状态。The position of the X-cavity mirror assembly in the solid-state mode-locked laser is adjusted according to the real-time working state, so that the solid-state mode-locked laser is in the stable mode-locked state. 2.根据权利要求1所述的基于孪生卷积神经网络的固体锁模激光器控制方法,其特征在于,所述基于孪生卷积神经网络的固体锁模激光器控制方法还包括:2. The solid-state mode-locked laser control method based on a twin convolutional neural network according to claim 1, characterized in that the solid-state mode-locked laser control method based on a twin convolutional neural network further comprises: 响应于所述欧氏距离在所述预设范围内,确定所述固体锁模激光器的实时工作状态为所述稳定锁模状态。In response to the Euclidean distance being within the preset range, determining that the real-time operating state of the solid-state mode-locked laser is the stable mode-locked state. 3.根据权利要求1所述的基于孪生卷积神经网络的固体锁模激光器控制方法,其特征在于,3. The solid-state mode-locked laser control method based on twin convolutional neural network according to claim 1, characterized in that: 所述第一子网络和所述第二子网络均包括依次相连的第一卷积层、第一池化层、第二卷积层和第二池化层;其中,所述第一卷积层和所述第二卷积层均采用Relu激活函数。The first sub-network and the second sub-network both include a first convolutional layer, a first pooling layer, a second convolutional layer, and a second pooling layer connected in sequence; wherein the first convolutional layer and the second convolutional layer both use a Relu activation function. 4.一种基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,采用如权利要求1至3任一项所述的基于孪生卷积神经网络的固体锁模激光器控制方法,所述基于孪生卷积神经网络的固体锁模激光器控制系统包括:4. A solid-state mode-locked laser control system based on a twin convolutional neural network, characterized in that the solid-state mode-locked laser control method based on a twin convolutional neural network according to any one of claims 1 to 3 is adopted, and the solid-state mode-locked laser control system based on a twin convolutional neural network comprises: 泵浦源,被配置为产生光线;其中,所述光线中携带能量;A pump source is configured to generate light; wherein the light carries energy; X腔镜组件,设置在所述光线的光路上;An X-cavity mirror assembly is arranged on the optical path of the light; 激光器晶体,设置在所述X腔镜组件中,被配置为吸收所述能量并产生激光;其中,所述X腔组件镜被配置为将所述光线反射至所述激光器晶体,接收并反射所述激光器晶体转换的所述激光;A laser crystal, disposed in the X-cavity mirror assembly, is configured to absorb the energy and generate laser light; wherein the X-cavity mirror assembly is configured to reflect the light to the laser crystal, receive and reflect the laser light converted by the laser crystal; 探测器,设置在所述X腔镜组件的输出光路上,被配置为接收所述X腔镜组件反射后的所述激光;A detector, arranged on an output optical path of the X-ray cavity mirror assembly, configured to receive the laser reflected by the X-ray cavity mirror assembly; 数据采集卡,与所述探测器相连,被配置为采集所述激光中的数据;A data acquisition card, connected to the detector and configured to acquire data from the laser; 处理器,与所述数据采集卡相连,被配置为处理所述数据,确定并输出固体锁模激光器的实时工作状态;A processor, connected to the data acquisition card, configured to process the data, determine and output the real-time working state of the solid-state mode-locked laser; 状态控制器,与所述处理器相连,被配置为响应于所述实时工作状态不包括稳定锁模状态,根据所述实时工作状态调节所述X腔镜组件的位置,使所述固体锁模激光器处于所述稳定锁模状态。A state controller is connected to the processor and is configured to adjust the position of the X-cavity mirror assembly according to the real-time working state in response to the real-time working state not including the stable mode-locked state, so that the solid-state mode-locked laser is in the stable mode-locked state. 5.根据权利要求4所述的基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,5. The solid-state mode-locked laser control system based on twin convolutional neural network according to claim 4, characterized in that: 所述X腔镜组件包括第一腔镜、第二腔镜、第三腔镜、半导体饱和吸收镜和输出镜;其中,所述激光器晶体设置在所述第一腔镜和所述第二腔镜之间。The X-cavity mirror assembly comprises a first cavity mirror, a second cavity mirror, a third cavity mirror, a semiconductor saturated absorption mirror and an output mirror; wherein the laser crystal is arranged between the first cavity mirror and the second cavity mirror. 6.根据权利要求5所述的基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,6. The solid-state mode-locked laser control system based on twin convolutional neural network according to claim 5, characterized in that: 根据所述实时工作状态调节所述X腔镜组件的位置包括:Adjusting the position of the X-cavity mirror assembly according to the real-time working state includes: 根据所述实时工作状态调节所述第二腔镜和所述输出镜的设置位置。The setting positions of the second cavity mirror and the output mirror are adjusted according to the real-time working status. 7.根据权利要求5所述的基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,7. The solid-state mode-locked laser control system based on twin convolutional neural network according to claim 5, characterized in that: 所述基于孪生卷积神经网络的固体锁模激光器控制系统还包括:第一透镜和第二透镜,所述第一透镜和所述第二透镜依次设置在所述泵浦源和所述第一腔镜之间。The solid-state mode-locked laser control system based on a twin convolutional neural network also includes: a first lens and a second lens, wherein the first lens and the second lens are sequentially arranged between the pump source and the first cavity mirror. 8.根据权利要求4所述的基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,8. The solid-state mode-locked laser control system based on twin convolutional neural network according to claim 4, characterized in that: 所述数据采集卡包括第一采集模块和第二采集模块,其中,所述第一采集模块被配置为获取所述固体锁模激光器在不同工作状态下的第一时序信号、第一射频谱信号或第一光谱信号,所述工作状态包括未出光、输出连续光状态、调Q状态、未完全锁模状态和稳定锁模状态中的一种;所述第二采集模块被配置为获取所述固体锁模激光器的第二时序信号、第二射频谱信号或第二光谱信号中的一种或多种;所述第二时序信号、所述第二射频谱信号或所述第二光谱信号分别为所述固体锁模激光器的实时时序信号、实时射频谱信号或实时光谱信号;The data acquisition card includes a first acquisition module and a second acquisition module, wherein the first acquisition module is configured to acquire a first timing signal, a first radio frequency spectrum signal or a first spectrum signal of the solid-state mode-locked laser in different working states, wherein the working state includes one of no light output, continuous light output state, Q-switched state, incomplete mode-locked state and stable mode-locked state; the second acquisition module is configured to acquire one or more of a second timing signal, a second radio frequency spectrum signal or a second spectrum signal of the solid-state mode-locked laser; the second timing signal, the second radio frequency spectrum signal or the second spectrum signal are respectively a real-time timing signal, a real-time radio frequency spectrum signal or a real-time spectrum signal of the solid-state mode-locked laser; 所述处理器包括:The processor comprises: 建立模块,被配置为建立数据库和孪生卷积神经网络;其中,所述孪生卷积神经网络包括第一子网络和第二子网络;所述数据库包括第一瞬态特征参量,所述第一瞬态特征参量为所述第一时序信号、所述第一射频谱信号或所述第一光谱信号中的一种或多种经特征提取后得到;An establishment module is configured to establish a database and a twin convolutional neural network; wherein the twin convolutional neural network includes a first subnetwork and a second subnetwork; the database includes a first transient characteristic parameter, and the first transient characteristic parameter is obtained after feature extraction of one or more of the first time series signal, the first radio frequency spectrum signal, or the first spectrum signal; 训练模块,被配置为利用所述数据库训练所述孪生卷积神经网络;其中,所述第一子网络和所述第二子网络的训练方式相同;所述第一子网络的输入为所述第一瞬态特征参量,所述第一子网络的输出为所述第一特征参量;所述第二子网络的输入为第二瞬态特征参量;所述第二瞬态特征参量为所述第二时序信号、所述第二射频谱信号或所述第二光谱信号中的一种或多种经特征提取后得到,所述第二子网络的输出为第二特征参量;A training module is configured to train the twin convolutional neural network using the database; wherein the first subnetwork and the second subnetwork are trained in the same manner; the input of the first subnetwork is the first transient characteristic parameter, and the output of the first subnetwork is the first characteristic parameter; the input of the second subnetwork is the second transient characteristic parameter; the second transient characteristic parameter is obtained after feature extraction of one or more of the second time series signal, the second radio frequency spectrum signal, or the second spectrum signal, and the output of the second subnetwork is the second characteristic parameter; 对比模块,被配置为对比所述第二特征参量与所述第一特征参量的欧氏距离;A comparison module, configured to compare the Euclidean distance between the second feature parameter and the first feature parameter; 确定模块,被配置为响应于所述欧氏距离不在预设范围内,确定所述固体锁模激光器的实时工作状态不包括所述稳定锁模状态。The determination module is configured to determine, in response to the Euclidean distance not being within a preset range, that the real-time working state of the solid-state mode-locked laser does not include the stable mode-locked state. 9.根据权利要求8所述的基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,9. The solid-state mode-locked laser control system based on twin convolutional neural network according to claim 8, characterized in that: 所述确定模块,还被配置为响应于所述欧氏距离在所述预设范围内,确定所述固体锁模激光器的实时工作状态为所述稳定锁模状态。The determination module is further configured to determine, in response to the Euclidean distance being within the preset range, that the real-time operating state of the solid-state mode-locked laser is the stable mode-locked state. 10.根据权利要求8所述的基于孪生卷积神经网络的固体锁模激光器控制系统,其特征在于,10. The solid-state mode-locked laser control system based on twin convolutional neural network according to claim 8, characterized in that: 所述第一子网络和所述第二子网络均包括依次相连的第一卷积层、第一池化层、第二卷积层和第二池化层;其中,所述第一卷积层和所述第二卷积层均采用Relu激活函数。The first sub-network and the second sub-network both include a first convolutional layer, a first pooling layer, a second convolutional layer, and a second pooling layer connected in sequence; wherein the first convolutional layer and the second convolutional layer both use a Relu activation function.
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