The invention discloses a
circadian rhythm influence degree detection method and
system, and belongs to the technical field of intelligent detection. The method comprises the following steps: acquiring outdoor lighting environment image data to construct a
data set, and performing
physical quantity calibration on the image data to obtain related physical quantities; calculating a CS value based on the related
physical quantity, and performing classification labeling on the
data set according to the CS value to obtain a
training set; building a deep convolutional
network model, and training the deep convolutional
network model based on the
training set to obtain an optimized classification model; and acquiring outdoor lighting environment image data in real time, inputting the outdoor lighting environment image data into the optimization classification model, and outputting a
circadian rhythm influence result. According to the method,
melatonin inhibition condition information is migrated to the visible light image, whether the outdoor lighting environment affects the
human body circadian rhythm or not is detected by using the image data through
deep learning, the operation complexity is greatly reduced, the detection speed is increased, the detection cost is reduced, meanwhile, the good detection precision is kept, and the detection efficiency is improved. And efficient intelligent detection is realized.