Non-contact indoor thermal environment control system and method based on reinforcement learning

A non-contact indoor thermal environment control system based on reinforcement learning, comprising an information collection unit, an information processing unit, an environment prediction unit, a voice broadcasting unit and a terminal control unit;

• wherein the information collection unit is used to collect indoor video information and indoor environmental information in real time;
• and the information collection unit comprises:
• an image acquisition module, comprising a camera, wherein the camera is used to collect the indoor video information;
• and
• an environmental detection module, comprising a temperature sensor and a humidity sensor, wherein the temperature sensor and the humidity sensor are used to collect the indoor environmental information, which includes temperature and humidity information;
• wherein the information processing unit, comprises a first processor, wherein the first processor is used to:
• obtain an indoor condition and a hot/cold posture of indoor personnel according to the indoor video information collected by the camera, and judge a hot/cold state of the indoor personnel according to the hot/cold posture of the indoor personnel;
• wherein the environment prediction unit, comprises a second processor, wherein the second processor is used to receive the indoor environmental information collected by the temperature sensor and the humidity sensor and the hot/cold state of the indoor personnel output by the first processor, and train a regulation strategy in a current environment by combining with a historical regulation strategy of a thermal environment and using a Q learning algorithm to obtain an optimal regulation strategy and output the optimal regulation strategy to the voice broadcasting unit;
• and
• wherein the voice broadcasting unit comprises a sound, and the terminal control unit comprises a controller;
• and the sound is used to:
• receive the optimal regulation strategy output by the second processor, and broadcast the optimal regulation strategy and receive a reply instruction of the indoor personnel;
• in response to the reply instruction of the indoor personnel being affirmative, output the optimal regulation strategy to the controller;
• in response to the reply instruction of the indoor personnel being negative, return the optimal regulation strategy to the second processor for retraining and updating the optimal regulation strategy;
• and in response to no reply instruction being received within a set time, output the optimal regulation strategy to the controller;
• and the controller is used to control an environmental temperature by adjusting an output level of an air conditioner responsive to implementing the optimal regulation strategy.