US 2026/0099708 A1 · BIG DATA INTELLIGENT SELECTION DESIGN METHOD FOR ROCKBURST-PREVENTION HYDRAULIC SUPPORTS IN ROCKBURST ROADWAYS

Abstract

The present invention provides a big data intelligent selection design method for rockburst-prevention hydraulic supports in rockburst roadways. In the method, firstly, data on geological conditions, rock mechanical properties, mine face layout, as well as usage performance and maintenance records of previous rockburst-prevention hydraulic supports is collected from different mining areas and historical records. Then, a machine learning method and a statistical analysis method are used to identify key factors affecting the performance of the rockburst-prevention hydraulic supports from an integrated dataset, thereby providing an intelligent rockburst-prevention hydraulic support selection system. The present invention can improve the selection accuracy and efficiency of the rockburst-prevention hydraulic supports and ensure safety. Through in-depth analysis of a large amount of geological data, mine face conditions, and historical rockburst-prevention hydraulic support usage, the most suitable rockburst-prevention hydraulic support selection suggestion is provided for roadways.

A big data intelligent selection design method for rockburst-prevention hydraulic supports in rockburst roadways, comprising the following steps:

Step 1:
performing data collection;
Step 2:
based on a neural network model, establishing a training sample for intelligent selection of the rockburst-prevention hydraulic supports;
Step 2.1:
selecting the neural network model suitable for selection of the rockburst-prevention hydraulic supports, wherein the neural network model selects an MLP neural network model;
and
Step 2.2:
defining a basic structure of the neural network model;
Step 2.2.1:
determining an input layer and a size thereof, wherein
calculation is performed by using the geomechanical characterization parameters of the rockburst mining face as the input layer of the neural network model, expressed as:
X
m
=
[
σ
c
,
K
,
E
,
φ
,
P
0
,
h
0
,
n
,
P
1
,
h
1
,
L
0
,
B
,
T
,
h
2
,
h
3
]
⁢
and
⁢
a
m
0
=
X
m
,
wherein
a
m
0
represents the input layer of a mth training sample of the neural network model;
Xm represents a feature vector of the mth training sample;
Step 2.2.2:
determining a number of intermediate layers, wherein
it is set that L layers in total exist in the neural network model, thus L−1 intermediate layers exist;
Step 2.2.3:
determining a number of neurons in each layer,
N
h
l
=
M
(
α
*
(
N
i
+
N
o
)
,
wherein Nhl is a number of neurons in a lth layer;
No is a number of neurons in an output layer;
Ni is a number of neurons in the input layer;
M is a number of samples;
α is an arbitrary variable, l≤L−1;
Step 2.2.4:
systematically constructing an intermediate layer model, wherein starting with the geomechanical characterization parameters of the rockburst mining face, after a linear transformation, processing is performed through an activation function to obtain new data of a next layer, a layer-to-layer transmission is performed in this manner, and finally the key parameters for the selection of the rockburst-prevention hydraulic supports for the rockburst roadways are reflected, as shown in the following formula:
W
l
=
[
w
11
l
…
W
1
⁢
N
h
l
l
⋮
⋱
⋮
W
N
h
l
-
1
⁢
1
l
…
W
N
h
l
-
1
⁢
N
h
l
l
]
,
z
m
l
=
f
⁡
(
W
l
·
a
m
(
l
-
1
)
+
b
l
)
=
[
z
m
1
l
⁢
…
⁢
z
m
N
h
l
l
]
,
and
⁢
a
m
l
=
tanh
⁢
(
z
m
l
)
=
e
z
m
l
-
e
-
z
m
l
e
z
m
l
+
e
-
z
m
l
,
wherein, Wl represents a weight matrix of the lth layer of the neural network model;
bl represents a bias vector of the lth layer of the neural network model:
a
m
(
l
-
1
)
represents an output result of the mth training sample passing through a (l−1)th layer of the neural network model:
z
m
l
represents a result obtained after the linear transformation of the mth training sample in the (l−1)th layer of the neural network model:
z
m
N
h
l
l
⁢
is
⁢
a
⁢
(
N
h
l
)
th component in
z
m
l
;
a
m
l
represents a result obtained by performing a transformation on zml by the activation function;
Step 2.2.5:
setting configuration of the output layer of the neural network model, wherein by using the key parameters for the selection of the rockburst-prevention hydraulic supports in the rockburst roadways as the output layer, the initial support force, the working resistance, and the support intensity of the rockburst-prevention hydraulic supports are predicted, and calculation of the output layer is represented by the following formula:
z
m
L
=
f
⁡
(
W
l
·
a
m
(
L
-
1
)
+
b
L
)
=
[
z
m
1
L
z
m
2
L
z
m
3
L
]
T
,
a ReLU activation function is used to acquire predicted values of the key parameters for the selection of the rockburst-prevention hydraulic supports in the rockburst roadways, with a specific formula as follows:
F
cm
=
ReLU
⁢
(
z
m
1
L
)
=
{
z
m
1
L
,
z
m
1
L
≥
0
0
,
z
m
1
L
≤
0
}
,
R
wm
=
ReLU
⁢
(
z
m
2
L
)
=
{
z
m
2
L
,
z
m
2
L
≥
0
0
,
z
m
2
L
≤
0
}
,
and
S
m
=
ReLU
⁢
(
z
m
1
L
)
=
{
z
m
1
L
,
z
m
1
L
≥
0
0
,
z
m
1
L
≤
0
}
,
wherein Fcm, Rwm and Sm represent predicted key parameter values for the selection of the rockburst-prevention hydraulic supports in the rockburst roadways, namely the initial support force, the working resistance and the support intensity of the rockburst-prevention hydraulic supports;
Step 3:
optimizing parameter configuration of the neural network model;
and
Step 4:
achieving intelligent selection of the rockburst-prevention hydraulic supports according to a mapping relationship obtained by training known geomechanical characterization parameters of a rockburst mining face in Step 2-Step 3.

396 more elements

Big data intelligent selection design method for rockburst-prevention hydraulic supports in rockburst roadways

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Big data intelligent selection design method for rockburst-prevention hydraulic supports in rockburst roadways

Life of this patent

Abstract

Claims6