US 12,449,150 B1
Cooperative control optimization method suitable for near-field fan wall micromodule
Ligang Wang, Suzhou (CN); Depeng Li, Suzhou (CN); Tao Deng, Suzhou (CN); Wei Liu, Suzhou (CN); Yumao Lin, Suzhou (CN); Meng Li, Suzhou (CN); Qing Huang, Suzhou (CN); Yizhe Xu, Suzhou (CN); and Chengchu Yan, Suzhou (CN)
Assigned to CHINA CONSTRUCTION INDUSTRIAL & ENERGY ENGINEERING GROUP CO., LTD., Jiangsu (CN); and Nanjing Tech University, Jiangsu (CN)
Filed by CHINA CONSTRUCTION INDUSTRIAL & ENERGY ENGINEERING GROUP CO., LTD., Suzhou (CN); and Nanjing Tech University, Suzhou (CN)
Filed on Jun. 3, 2025, as Appl. No. 19/227,423.
Application 19/227,423 is a continuation of application No. PCT/CN2024/107623, filed on Jul. 25, 2024.
Claims priority of application No. 202410900768.8 (CN), filed on Jul. 5, 2024.
Int. Cl. F24F 11/46 (2018.01); F24F 11/63 (2018.01); H05K 7/20 (2006.01)
CPC F24F 11/46 (2018.01) [F24F 11/63 (2018.01); H05K 7/20745 (2013.01)] 7 Claims
OG exemplary drawing
 
1. A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps:
step 10: obtaining information technology (IT) system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and
step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model,
wherein in the step 20, the performing cooperative control on deployment of the virtual machines and the air-conditioning system by using the optimization method of combining the real-time joint optimization and the timed decoupling optimization comprises:
step 21: when a cloud platform receives a deployment request for a new virtual machine, performing joint optimization on a deployment location of the new virtual machine and a fan frequency of each of cooling terminals, to achieve a lowest overall power of servers and each of the cooling terminals under deployment of an individual virtual machine; and
step 22: at each of given moments, first performing whole migration on the virtual machines running on each of servers, so that a total power of the servers is the lowest;
then, performing optimization on a coil evaporation temperature and the fan frequency of each of the cooling terminals of the air-conditioning system, to achieve a lowest overall power of the air-conditioning system.