Abstract: The present disclosure provides a Q-leaming based model-free control method for an indoor thermal environment of an aged care building and belongs to the technical field of building environment control. According to the present disclosure, the monitored indoor temperatures of individual users and the heart rate and systolic pressure data of the aged are used as input data to a constructed Q-learning model, thus outputting a running control policy for a heating, ventilation and air conditioning system in the corresponding building. As a result, the control efficiency of the indoor temperature and the energy efficiency of the heating, ventilation and air conditioning system are improved. Compared with a traditional control model, the reinforced learning method based on the Q-leaming theory can realize more accurate prediction on the cardiovascular health risk of the aged and can create a dynamic indoor thermal environment more suitable for the physical health of the aged.

Abstract: The present invention provides a method for preparing a high molecular polymer powder material comprising the following steps: adding 20 parts of a high molecular powder material and 0.1 to 10 parts of carbon black into a mixing tank, and performing high-speed stirring for the first time to obtain a high molecular carbon black mixed powder material; adding the high molecular carbon black mixed powder material, 0.1 to 1 part of a flow aid, and 80 parts of a polymer powder material into the powder mixing tank; and performing high-speed stirring for a second time followed by sieving to obtain the high molecular polymer powder material.

Abstract: The present disclosure discloses a dispatching method and system for an electric vehicle battery swapping station. The dispatching method includes: acquiring battery data of electric vehicles and demand of battery swapping of users; determining total charging power and average charging power of a battery swapping station; determining a battery swapping price; determining an objective function of optimized dispatching of the electric vehicle battery swapping station according to the battery swapping price and the battery data; optimizing, according to a constraint condition and the objective function, a charging state of batteries in the battery swapping station and the demand of the battery swapping to obtain an optimized charging state of the batteries in the battery swapping station and optimized demand of the battery swapping; and performing the optimized dispatching on the electric vehicle battery swapping station.