The invention relates to the technical field of communication
machine room
energy conservation, in particular to a temperature energy-saving control method and
system for a communication
machine room, and the method comprises the steps: obtaining multi-region temperature and
humidity and equipment load data in the
machine room, inputting a pre-training model to obtain a cabinet heat dissipation
risk level, judging an environment state through combination with an
incidence matrix, entering an
operation mode if a requirement is satisfied, and entering a
control mode if a requirement is satisfied. If not, a local
temperature control adjusting mechanism is triggered; the method comprises the following steps: calculating a
refrigeration efficiency ratio according to the
air volume of ventilation equipment, air conditioner
power consumption, real-time
electricity price and the like, generating a candidate scheme set by using a dynamic weight multi-target optimization
algorithm in combination with factors such as equipment life, and selecting a target scheme through virtual
verification; and according to air conditioner
waste heat and cabinet heat dissipation and storage heat, a
heat energy distribution strategy is dynamically generated by using a deep Q network
reinforcement learning algorithm. Therefore, the problems that in the prior art, energy is wasted, cost is high, heat dissipation requirements of all areas are difficult to accurately adapt, the service life and stability of equipment are affected, and fault risks are increased are solved.