Patent ID: 11874429
Assignee: NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 1:
2. The high-temperature disaster forecast method based on the directed graph neural network according to claim 1, wherein in the step S1, a process of constructing the multidimensional time series sample set according to the time periodic characteristics of the meteorological elements comprises:
setting zt ∈R, which represents values of multivariate variables at a time stride t, where R represents a real number; wherein zt[i]∈R represents a value of a variable i at the time stride t, and a historical sequence of multivariate data at a given time length of p is as follows:

X={zt1[i],zt2[i], . . . ,ztp[i]}; 

wherein a label set is as follows:

Y={(yt11,yt12,yt13,yt14),(yt21,yt22,yt23,yt24), . . . ,(ytp1,ytp2,ytp3,ytp4)};

where, ytg1,ytg2,ytg3,ytg4respectively represent a probability of the no high-temperature disaster, a probability of the high-temperature yellow early warning, a probability of the high-temperature orange early warning and a probability of the high-temperature red early warning at a time g; and g=1, 2, 3, . . . ,p.