Laser frequency adaptive locking and in-cavity matching method and system based on deep learning

The invention discloses a deep learning laser frequency adaptive locking and in-cavity matching method and system, and relates to the technical field of laser. A deep learning model is introduced, the spot mode category and the frequency locking state score of the laser are evaluated automatically and efficiently, and the laser direction and the laser frequency are adjusted according to the spot mode category and the frequency locking state score until the laser reaches a stable state, so that self-adaptive intelligent control of the laser frequency and the light beam in-cavity matching is realized, manual intervention is not needed, and the working efficiency is improved. The labor cost is reduced; the stability of the laser can be kept for a long time, and reliable technical support is provided for application of the laser in the fields of space communication, aerospace, precision measurement and the like. 本申请公开了一种深度学习激光频率自适应锁定与入腔匹配方法及系统，涉及激光技术领域。引入了深度学习模型，自动高效评估激光的光斑模式类别和锁频状态评分，并根据光斑模式类别和锁频状态评分，来调节激光指向和激光频