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Marine environment data fusion method and system based on attention mechanism

A marine environment and data fusion technology, applied in the field of data processing, can solve the problems that the model does not highlight the role of key information, contains noise, and the observation data is sparse, so as to achieve the scaling of space and channel feature weights, alleviate the influence of noise, and enhance high frequency information effect

Active Publication Date: 2022-04-12
OCEAN UNIV OF CHINA
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Problems solved by technology

The characteristics of the above data fusion tasks lead to the following shortcomings in the current deep learning-based marine environmental data fusion method: 1) Due to the characteristics of the satellite itself and the sensitivity to sampling conditions, the observation data is very sparse and may contain noise, which makes the model learning difficult to map
Therefore, only relying on deeper networks or deep features cannot better complete the task of ocean data reconstruction
3) The features extracted by each layer contain key information and supplementary information, and the model does not highlight the role of key information

Method used

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  • Marine environment data fusion method and system based on attention mechanism
  • Marine environment data fusion method and system based on attention mechanism
  • Marine environment data fusion method and system based on attention mechanism

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Embodiment 1

[0062] combine figure 1 , the marine environment data fusion system based on the attention mechanism of the present invention includes a multi-source data enhancement module and a multi-layer feature combination neural network based on the attention mechanism,

[0063] 1. Multi-source data enhancement module

[0064] Accurate and rich spatio-temporal context information is the key to training effective models. Due to the low spatial-temporal coverage of observational data, it is difficult to train an effective model by directly using it as the input of the neural network. Correspondingly, the result obtained by the optimal interpolation method is regular daily gridded data, but the accuracy and resolution of small-scale areas are low. Therefore, the method for constructing a time-space continuous data sequence in the present invention is to combine observation data and optimal interpolation data to construct a time-space continuous input data sequence for neural network mod...

Embodiment 2

[0117] combine Figure 4 , a marine environment data fusion method based on attention mechanism, including the following steps:

[0118] Step 1. Construct a marine environment input data sequence with continuous spatiotemporal distribution: combine the observation data with the optimal interpolation data, fill in the vacant areas of the observation data with the optimal interpolation data, obtain spatially continuous data, and use the spatially continuous data Construct a fixed-length time-continuous data sequence to obtain multi-source data enhanced data sequence;

[0119] Step 2. Construct a multi-layer feature combination neural network based on attention mechanism:

[0120] Such as figure 1 As shown, the neural network includes an initial feature extraction layer, a deep feature interaction part and a fusion reconstruction layer, and the data sequence output by the multi-source data enhancement module is sequentially input into the initial feature extraction layer, the ...

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Abstract

The invention belongs to the technical field of data processing, and discloses a marine environment data fusion method and system based on an attention mechanism, and the system comprises a multi-source data enhancement module which is used for constructing a time-space continuous data sequence, and a multilayer feature combination neural network which is used for outputting fusion data and is based on the attention mechanism. The multilayer feature combination neural network based on the attention mechanism comprises an initial feature extraction layer, a deep feature interaction part and a fusion reconstruction layer, the deep feature interaction part comprises N groups of multilayer feature combination modules, and each multilayer feature combination module comprises M residual units, a feature splicing layer, a fusion convolution layer and a channel attention module. The multi-layer feature combination module combines information in different layers of features through global jump connection and local jump connection, and the input of each multi-layer feature combination module is the output of the previous module. According to the invention, feature information is fused, and key information of features is highlighted in combination with an attention mechanism.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a marine environment data fusion method and system based on an attention mechanism. Background technique [0002] Time-space continuous marine environment monitoring observation data is the basis for understanding the ocean. Accurate ocean observation data can be obtained through remote sensing observation technology, but the data is relatively discrete and cannot meet the needs of practical applications in the ocean field. How to obtain temporal and spatial continuous marine monitoring data through observation data, that is, the method of marine environmental data fusion, is a key step in the utilization of marine environmental monitoring and observation data. [0003] Early researchers mainly used physical model-driven schemes. This kind of method usually achieves better results by parameterizing the mathematical and physical equations of complex internal o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/05G06V10/40G06V10/77G06V10/774G06V10/80G06V10/82G06K9/62G06N3/04G06N3/08
Inventor 黄磊张科魏志强安辰
Owner OCEAN UNIV OF CHINA
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