US 12,223,042 B2
Method and system for leakage measurement error compensation based on cloud-edge collaborative computing
Ziran Wu, Wenzhou (CN); Guichu Wu, Wenzhou (CN); Zhenquan Lin, Wenzhou (CN); Juntao Yan, Wenzhou (CN); and Yuelei Sun, Wenzhou (CN)
Assigned to Wenzhou University, Wenzhou (CN); and Technology Institute of Wenzhou University in Yueqing, Wenzhou (CN)
Filed by Technology Institute of WenzhouUniversityinYueqing, Wenzhou (CN); and Wenzhou University, Wenzhou (CN)
Filed on May 31, 2022, as Appl. No. 17/828,933.
Claims priority of application No. 202110620709.1 (CN), filed on Jun. 3, 2021.
Prior Publication US 2022/0391504 A1, Dec. 8, 2022
Int. Cl. G06F 21/55 (2013.01); G06F 18/2413 (2023.01); G06N 7/02 (2006.01); G06N 20/20 (2019.01)
CPC G06F 21/556 (2013.01) [G06F 18/2414 (2023.01); G06N 7/023 (2013.01); G06N 20/20 (2019.01)] 5 Claims
OG exemplary drawing
 
1. A leakage measurement error compensation method based on cloud-edge collaborative computing, implemented on a communication network formed by interconnection between a leakage current edge monitoring terminal and a power consumption management cloud platform, and comprising the following steps:
monitoring, by the leakage current edge monitoring terminal, load voltage, load current and leakage current data, and sending the data to the power consumption management cloud platform;
iteratively training, by the power consumption management cloud platform, a pseudo-leakage compensation model by using the received data, updating pseudo-leakage model parameters, and feeding the pseudo-leakage model parameters back to the leakage current edge monitoring terminal; and
processing, by the leakage current edge monitoring terminal, the leakage current data according to the pseudo-leakage compensation model parameters received from the power consumption management cloud platform and by using the same pseudo-leakage compensation model as the power consumption management cloud platform, so as to eliminate the influence of a pseudo-leakage phenomenon in the leakage current data in real time;
wherein the pseudo-leakage compensation model is constructed by a Multiple Instance Regression (MIR) algorithm based on Robust Fuzzy Clustering (RFC), and center points and covariances of fuzzy members are gradually optimized in a training process until probabilistic members complete the convergence;
wherein an optimization formula of the fuzzy members is:

OG Complex Work Unit Math
wherein

OG Complex Work Unit Math
C represents a number of clusters; m>1, and represents a fuzzy weight parameter; xj represents a sample; ci represents a center of a cluster i; eik represents a kth characteristic vector of a covariance matrix

OG Complex Work Unit Math
of the cluster i; and vik represents a weight coefficient of a characteristic root in the kth characteristic vector.