Light control method and device based on illuminance clustering and support vector machine

Abstract

The embodiment of the invention provides a light control method and device based on illuminance clustering and a support vector machine. The method comprises the following steps: acquiring illuminance, time, sound intensity and infrared signals, clustering the illuminance by adopting a K-means algorithm, and mapping the time, the sound intensity, the infrared signals and the clustered illuminanceto a high-dimensional space by adopting a Gaussian kernel function to obtain a high-dimensional space feature vector; and taking the high-dimensional spatial feature vector as an input, and training the model by using a sequence minimum optimization algorithm SMO in a support vector machine to obtain an effective support vector machine model; and predicting the high-dimensional spatial data acquired in real time by adopting an effective support vector machine model, and sending a turn-on or turn-off instruction to the lamp bank according to a prediction result. According to the invention, thelight can be turned on or turned off in proper occasions and time, manual inspection on the turning on or turning off of the light is not needed, the economic cost is saved, and the application rangeis wider.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of lighting control, in particular to a lighting control method and device based on illuminance clustering and support vector machines. Background technique [0002] Indoor lighting power consumption is one of the important reasons for power consumption. In order to save energy and improve convenience, automatic sensor lighting has been widely used in modern buildings. [0003] Among them, the old-fashioned automatic sensing lights mostly use sound sensors and light sensors. When the user returns home at night, the sound sensor detects peoples footsteps and then turns on the lights in the corridor. However, the disadvantage of this method is that the switching threshold of the sound sensor is difficult to control. If the threshold of the sound is lowered, the occasional sound from the building next door, the barking of a child in the community, the barking of a dog, etc. may cause...

AI Technical Summary

Problems solved by technology

The sound sensor can only judge whether there is a person through the threshold of the sound, and the infrared sensor can only detect people by detecting changes in temperature. If a person stays still, neither the sound sensor nor the infrared sensor can detect people.

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