Lighting control system and light regulation mapping device based on sleep efficiency factor
A lighting control system and efficiency technology, applied in lighting devices, energy-saving control technology, light sources, etc., can solve problems such as pertinence and limited effectiveness
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Embodiment 1
[0133] The human biological clock is the physiological and biochemical processes, morphological structures, and behaviors that change periodically in the human body with time. There are many kinds of biological clocks in the human body, and various physiological indicators of the human body, such as pulse, body temperature, blood pressure, physical strength, emotion, intelligence, etc., will change periodically with the change of day and night.
[0134] Such as figure 1As shown, at 2 o'clock in the morning, people's sleep reaches the maximum depth, at 4:30 in the morning, body temperature reaches the lowest, blood pressure rises fastest at 6:45 in the morning, and the secretion of melatonin at 7:30 in the morning Stop, at 8:30 in the morning, intestinal peristalsis occurs frequently, at 9:00 the secretion of testosterone reaches the highest, at 10:00 in the morning is the most clear-headed moment for people, and at 14:30 in the afternoon, people's limb activities match to In ...
Embodiment 2
[0261] Different from Embodiment 1, this embodiment increases the duration parameter of falling asleep in the input amount of the Elman neural network, and the falling asleep sign parameter in its training samples is obtained as follows:
[0262] Continuously detect the user's eye opening, and when it is found that the eye opening value is continuously less than (1-Δ%) times the eye opening value in the initial stage of falling asleep within a set period of time, the current time is used as the timing zero point of the duration of falling asleep , while discarding the sample records before the zero point, the Δ can be an integer between 5 and 10.
[0263] The rate of change of the user's eye opening k eo , the rate of change of eye-closed duration k ec , heart rate change rate k h , Body motion frequency change rate k b , body temperature change rate k p These five physical sign parameters are all calculated by moving average filtering, for example, for the rate of change ...
Embodiment 3
[0269] Different from Embodiment 1, in this embodiment, the system also includes a ready-to-sleep button. When the user is ready to fall asleep, press this button,
[0270] Define a sleep transition time t sl It is the length of time from when the button is pressed to when the eyes are continuously closed for more than 1 minute,
[0271] Add a falling asleep transition duration parameter in the output of the Elman neural network, and correspondingly increase a falling asleep transition duration evaluation value f in the light environment evaluation function 6 ,
[0272]
[0273] in,
[0274] In the formula, t slT1 and t slT2 are the thresholds of the two sleep transition times, respectively.
[0275] f 6 for t slT1 is the score function of the semi-trapezoidal distribution of the endpoint, and the lower the evaluation value is when the transition time of falling asleep is longer.
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