Greenhouse parameter model construction and recovery method based on low-rank tensor

A recovery method and mathematical model technology, applied in the direction of constraint-based CAD, complex mathematical operations, special data processing applications, etc.

Inactive Publication Date: 2021-05-25
NANKAI UNIV
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Problems solved by technology

[0005] As low-rank supplementary information, full variational regularization has been widely used in tensor filling methods, but for WSNs data tensors with special structures, linear full variational methods will make the recovered tensors produce " "staircase effect"

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  • Greenhouse parameter model construction and recovery method based on low-rank tensor
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  • Greenhouse parameter model construction and recovery method based on low-rank tensor

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Embodiment Construction

[0070] The present invention will be further described below in conjunction with the accompanying drawings.

[0071] At present, most of the singular value decomposition methods are used to verify the low rank of each attribute data. However, for multi-attribute data, if you want to prove its low rank, you need to consider the tensor as a whole. Therefore, the present invention adopts the matrix Singular value decomposition of tensor singular value decomposition of similar structure to verify it. By performing tensor singular value decomposition on the tensor, the tensor tube rank can be obtained, and the tensor core norm is l of the tensor tube rank 1 The tightest convex relaxation under the norm. At the same time, Hu et al. also proved that the tensor nuclear norm is equivalent to the nuclear norm of the tensor's block cyclic transformation matrix. Based on the above proof, we use the method of singular value decomposition of the tensor's block cyclic matrix to verify Low-...

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Abstract

The invention discloses a greenhouse model construction and recovery method based on low rank tensor completion (LRTC). The method mainly aims at a large-scale multi-attribute wireless sensor network (WSNs) with slow environmental parameter change, and since greenhouse data tensors monitored by the network have inherent correlation: low rank and smoothness in time dimension, a tensor nuclear norm regularization (TNN) minimization model based on time smoothness is constructed, and the model is optimized and solved by adopting an alternating direction multiplier method (ADMM). By well balancing the low-rank performance and the time smoothness of the data tensors, the data recovery method provided by the invention is greatly improved in the aspects of stability and recovery precision.

Description

【Technical field】 [0001] The invention relates to a new method for constructing and restoring a greenhouse parameter model in a wireless sensor network based on low-rank tensor filling, and belongs to the field of wireless sensor network data reconstruction. 【Background technique】 [0002] Wireless sensor networks consisting of a large number of sensor nodes deployed in space play an important role in real-time applications, such as environmental monitoring, disaster management, healthcare monitoring, etc. Usually, sensor nodes regularly monitor and collect environmental parameters and transmit data to receivers through multi-hop routes, and then data processing is performed at the receivers. Since the sensor nodes have limited energy, and in the actual implementation of WSNs, the sensor nodes need to observe and collect the surrounding environment for a long time, so the frequency of charging or replacing the batteries in the sensor nodes becomes extremely high, so that it ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/20G06F17/16G06F111/04G06F119/08
CPCG06F17/16G06F30/20G06F2111/04G06F2119/08
Inventor 孙桂玲刘晓超王志红郑博文
Owner NANKAI UNIV
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