US 12,223,042 B2Granted

Method and system for leakage measurement error compensation based on cloud-edge collaborative computing

AssigneesWenzhou University, Technology Institute of Wenzhou University in Yueqing

InventorsZiran Wu, Guichu Wu, Zhenquan Lin, Juntao Yan, Yuelei Sun

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Abstract

A leakage measurement error compensation method based on cloud-edge collaborative computing is implemented on a communication network formed by an interconnection between a leakage current edge monitoring terminal and a power consumption management cloud platform. The method includes the following steps: iteratively training, by the power consumption management cloud platform, a pseudo-leakage compensation model by using the received leakage current data, continuously 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.

Claims5

Dependent claims

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:
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wherein
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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
∑
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and vik represents a weight coefficient of a characteristic root in the kth characteristic vector.

141 more elements

A leakage measurement error compensation system based on cloud-edge collaborative computing, comprising a leakage current edge monitoring terminal and a power consumption management cloud platform which are interconnected, wherein

the leakage current edge monitoring terminal is used to monitor leakage current data and send the leakage current data to the power consumption management cloud platform, and uses the same pseudo-leakage compensation model as the power consumption management cloud platform and processes the leakage current data according to the pseudo-leakage compensation model parameters received from the power consumption management cloud platform, so as to eliminate the influence of a pseudo-leakage phenomenon in the leakage current data;
and
the power consumption management cloud platform uses the received leakage current data to iteratively train a pseudo-leakage compensation model, updates pseudo-leakage model parameters, and feeds the pseudo-leakage model parameters back to the leakage current edge monitoring terminal;
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:
u
ij
F
=
[
∑
k
=
1
C
(
dist
ij
2
dist
kj
2
)
1
m
-
1
]
-
1
,
wherein
u
ij
F
∈
[
0
,
1
]
;
∑
i
=
1
C
u
ij
F
=
1
;
dist
ij
2
=
∑
k
=
1
d
+
1
v
ik
[
(
x
j
-
c
i
)
·
e
ik
]
2
;
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
∑
i
=
∑
j
=
1
N
(
u
ij
F
)
m
⁢
(
x
j
-
c
i
)
⁢
(
x
j
-
c
i
)
T
∑
j
=
1
N
(
u
ij
F
)
m
of the cluster i;
and vik represents a weight coefficient of a characteristic root in the kth characteristic vector.

126 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/828,933Filed 31 May 2022
Priority: CN 202110620709.13 Jun 2021
Published: US 2022/0391504 A18 Dec 2022
Granted: 11 Feb 2025
Term ends: Not estimatedThe source records don’t support an estimate.

Classification10 codes

G06F 21/556
Detecting local intrusion or implementing counter-measures: involving covert channels, i.e. data leakage between processes
G06F 18/214
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: generating training…
G06F 18/23
Analysing: clustering techniques
G06F 18/2414
Distances to cluster centroïds: smoothing the distance, e.g. radial basis function networks [RBFN]

Family

US 2022/0391504 A1Pre-grant publication · published 8 Dec 2022
US 12,223,042 B2This patent

From the US filing history. Foreign family members aren’t listed.

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