Patent AU-2021105964-A4 (AU)
Title: Self-adaptive Energy-saving Control Method and System for Smart Street Lamps Based on Deep Reinforcement Learning
Inventor: LIN SHAOFU
IPC: H04L29/08
Filed: 2021-08-19
Granted: 2021-10-28
Abstract: The invention relates to a smart lighting self-adaptive energy-saving control method 
and system based on deep reinforcement learning, which comprises the following steps: 
SI: collecting environmental state data by sensors in a sensing control layer and sending 
the environmental state data to a gateway in an edge computing layer; S2: after collecting 
the environmental state data, the edge computing layer gateway caches and processes the 
data through the micro server, and then sends the data to the data service layer through 
the gateway; S3: storing the environmental state data in the data service layer, and 
inputting the environmental state data into the deep reinforcement learning Q-network 
model trained in advance to obtain the optimal action decision sequence (1 on /0 off) of 
the first number of lighting devices; S4: after converting the 1/0 digital signal output by 
the model into the corresponding on/off control instruction in the data service layer, the 
control instruction is sent to the gateway of the edge computing layer, and the regulation 
information analyzed by the system is sent to the application program of the user layer; 
S5: after receiving the on/off control instruction, the edge computing layer gateway sends 
the instruction to the device controller in the sensing control layer, and the device 
controller realizes adaptive regulation and control of lighting equipment; S6: the terminal 
application layer is directly connected with the data service layer, and if the self-adaptive 
regulation result does not meet the lighting demand of the user, the user adjusts the switch 
through the application program, and the application program sends the user instruction 
to the server in the data service layer; S7: the server in the data service layer has set the 
priority of the user instruction to be greater than the algorithm output instruction, 
determines the final action instruction according to the received instruction, and executes 
S4-S6 again. According to the invention, dynamic sensing acquisition of environmental 
data is realized in the Internet of Things scene, and high-efficiency adaptive energy-
saving control of lighting equipment is realized under the condition of providing users 
with appropriate illumination intensity by adopting a deep reinforcement learning 
algorithm.
1/4 
FIGURES 
collecting environmental state data by sensors in a 
sensing control layer, and sending the environmental 
state data to a gateway in an edge computing layer 
after collecting the environmental state data, the edge 
computing layer gateway caches and processes the data 
through the micro server, and then sends the data to the data 
service layer through the gateway 
Storing the environmental state data in the data service layer, and 
inputting the environmental state data into the deep reinforcement 
learning Q-network model trained in advance to obtain the optimal 
action decision sequence of the first number of lighting devices 
application programs; Terminal 
apiainlayer sends the user' 
instruction to data service layer 
Does data service 
layer receive the 
user'instruction? 
Determining the f fral Determining the fal 
instruction as the output instruction as the user's 
instructionofalgorithm instruction 
User receives self-adaptive 
regulation information of 
system from application 
program in terminal 
applicationlayer 
Data service layer converts fmnal instruction 
into corresponding control instruction; The 
server sends control instruction to gateway and
sends the regulation information to the Is the user satisfied 
application program in terminal application with the regulation 
laver information? 
After receiving the on/off control instruction, the edge 
computing layer gateway sends the instruction to the No operation from the 
device controller in the sensing control layer, and the user.  
device controller realizes adaptive regulation and 
control of lighting equipment 
Figure I