On the analysis network of mobile machinery equipment, we will simplify the machinery and equipment with mobility and embedded technology to solve the problems of hydraulic mechanical properties of nonlinear filtering of coarse-grained service detection.
A crowd lightweight data-driven analysis model with fine-grained characteristics is proposed. The model includes the control protocol of embedded nodes in machinery equipment, the elimination mechanism of interference, the control protocol of transmission power, and the crowd data-driven mechanism.
We assume that the mobile embedded nodes are deployed in a mechanical device with a hop detection signal within the effective sensing radius. The node can sense the hydraulic real-time state of the mechanical equipment. In the characteristic analysis network of the dynamic mechanical equipment, the signal transmission power of the mobile embedded node can be adjusted adaptively according to the size of the crowd data. The effective sensing radius of the mobile embedded node is related to the residual energy of the node. Mobile embedded node adjusts its position and signal transmission power according to the residual energy. When the embedded node forwarding of swarm intelligence data meets the demand characteristic analysis of mechanical hydraulic, adjusting mobile embedded nodes of the perceived distance and signal transmit power, at the edge of the state, achieves lightweight data-driven service push.
Moving track of mobile embedded node is shown in Fig. 1. Among them, there are three kinds of critical points in mobile embedded node. The dotted circle in the graph is the effective sensing radius of the embedded node. When the residual energy of mobile embedded nodes is decreased and the crowd data is larger, the node can move from A to B in order to meet the requirement of the mechanical hydraulic characteristic detection. In order to achieve lightweight data-driven, the data size is reduced from the B position to the C position. Among them, A, B, C three positions can be driven by the crowd data to achieve adaptive switching.
Crowd data scale could be obtained by formula (1). Here, S
MC is the crowd data scale. D
i
is the mobile embedded device crowd forwarding data. \( {\overline{D}}_{\mathrm{MN}} \) is the average value of the hydraulic characteristics of the data in A, B, C three different locations. D
MC is the total data of crowd sensing. I
MN is the set of mobile crowd nodes. I
ME is the set of mechanical equipment.
$$ {S}_{\mathrm{MC}}=\frac{{\displaystyle \sum_{i\in {I}_{\mathrm{MN}}}\left({D}_i-{\overline{D}}_{\mathrm{MN}}\right)}}{\sqrt{{\displaystyle \sum_{i\in {I}_{\mathrm{MN}}\cap {I}_{\mathrm{ME}}}\left({D}_{\mathrm{MC}}\right)}}} $$
(1)
The transfer process of the three locations is as follows:
Here, T
ER denotes the residual energy threshold. T
PT denotes the signal transmit power threshold. These values could be obtained by formula (2).
$$ \left\{\begin{array}{l}{T}_{\mathrm{ER}}=\frac{{\displaystyle \sum_{i=1}{E}_i\cdot {S}_i(d)}}{M_N}\\ {}{T}_{\mathrm{PT}}=\frac{{\displaystyle \sum_{i=1}{T}_i\cdot {D}_i(d)}}{D_{\mathrm{ME}}}\end{array}\right. $$
(2)
Here, E
i
is the energy consumption of mobile embedded node. S
i
is the crowd data. M
N
is the number of mobile crowd nodes. T
i
is the signal sending power of mobile crowd node. D
i
is the sending data of mobile embedded node. D
ME is the hydraulic characteristic data of mechanical equipment.
For a number of mechanical equipment at the same time to detect and analyze the hydraulic characteristics, there is a need to set up a mobile mechanical equipment characteristic analysis network. The network is composed of multiple mobile embedded nodes. Each node has the crowd function. Topology is shown in Fig. 2.
When the node 3 is unable to detect and analyze the hydraulic characteristics of the mechanical equipment due to energy failure, the nodes are 1, 2, and 4 after moving to update the analysis network topology, as shown in Fig. 3. The solid circle for 1 node represents the analysis of network characteristics of hydraulic machinery and equipment 2 and 4.