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Visual analysis system and method for ocean multi-dimensional data

A multi-dimensional data and analysis method technology, applied in the field of visual analysis system for marine multi-dimensional data, can solve the problems of low performance of marine data system, incompatibility of platforms, inapplicability of streaming data, etc.

Inactive Publication Date: 2020-03-24
CENT SOUTH UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The existing visual analysis system for marine multi-dimensional data cleans data for a single type of static data, which cannot meet the real-time requirements of the data; separate processing of missing values ​​and abnormal points requires at least two scans of the data, which is not suitable for streaming data
[0005] At the same time, there are problems in the sharing service of marine data, such as low system performance, inability to interoperate between platforms, security and credibility cannot be guaranteed, and shared records cannot be traced back.

Method used

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  • Visual analysis system and method for ocean multi-dimensional data
  • Visual analysis system and method for ocean multi-dimensional data

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Experimental program
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Effect test

Embodiment 1

[0157] The data cleaning module 4 cleaning method provided by the present invention comprises:

[0158] (1) Data source evaluation: given n data sources in the sensor network monitoring area S={s 1 ,s 2 ,...,S n}, periodically comprehensively measure the reliability of each data source from three aspects: accuracy, completeness, and consistency.

[0159] (2) Data source selection: According to the reliability of the data source obtained in step (1) and the arbitrary precision requirements given by the user, some data sources are selected for data transmission through the Bernoulli uniform sampling algorithm.

[0160] (3) Data acquisition: Based on the data source selected in step (2), the acquired data is transmitted from the sensor node to the server via the sensor network end to realize real-time acquisition of the data stream.

[0161] (4) Data cleaning: For the obtained real-time data streams containing a large number of outliers and missing values, online and integrate...

Embodiment 2

[0164] Step (1) provided by the present invention specifically includes:

[0165] Step a: At time t, for n data sources S={s 1 ,s 2 ,...,S n}Information fusion of historical data at time t, t-1, ..., t-L, and analysis of the fused data based on ocean cube-S evidence theory and gray relational degree, to obtain t, t-1, ..., t-L The classification of the monitoring objects in the monitoring area of ​​the sensor network at all times C = {C 1 , C 2 ,...,C L}, and take it as the true value of the classification result.

[0166] Step b: Based on Ocean Cube-S Evidence Theory, according to a single data source s i Perception data at time t, t-1,..., t-L obtains the classification result of each data source for the monitoring object, denoted as C i ={c i1 , c i2 ,...,c iL}.

[0167] Step c: Single data source s i Classification result C i The proportion of the results consistent with the true value C in all historical data at time t, t-1, ..., t-L is expressed as the data ...

Embodiment 3

[0176] The shared module 8 sharing methods provided by the present invention include:

[0177] 1) The data owner broadcasts the description information of the released ocean data to all nodes on the data storage chain.

[0178] 2) All nodes on the chain choose whether to participate in this storage competition according to the filtering rules pre-configured by the administrator, allocate data storage rights, and compete to become data storage parties.

[0179] 3) After the data storage party is generated, according to the address of the data owner included in the description information released by the data owner, issue a storage request message to the data owner.

[0180] 4) After receiving the storage request information, the data owner will use a set number of signed tokens as the storage fee, and send the encrypted ocean data to the data storage party in a point-to-point manner through the SHA256 algorithm.

[0181] 5) After the data storage party completes the marine dat...

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Abstract

The invention belongs to the technical field of visual analysis of marine multi-dimensional data. The invention discloses a visual analysis system and method for ocean multi-dimensional data. The visual analysis system for ocean multi-dimensional data comprises an ocean data acquisition module, a main control module, a data correction module, a data cleaning module, a data conversion module, a visualization module, an analysis module, a sharing module, a cloud storage module and a display module. Through the data cleaning module, the problem that a batch processing mode data cleaning algorithmcannot be suitable for data streams is solved; a brand-new decentralized system architecture and a calculation normal form are adopted to design a marine data resource sharing application mode through a sharing module; based on a block chain technology, a marine data resource sharing block chain is constructed, a breakthrough is formed for existing problems, free release, autonomous discovery, flexible delivery, safe, controllable and comprehensive supervision of a transaction process of marine data are realized, and a solid guarantee is provided for safe sharing and transaction of marine bigdata.

Description

technical field [0001] The invention belongs to the technical field of visual analysis of marine multidimensional data, and in particular relates to a visual analysis system and method for marine multidimensional data. Background technique [0002] Oceanographic data are vast. It covers seabed topographic data, marine remote sensing data, ship survey data, buoy data, model assimilation data and many other aspects. These marine data have the characteristics of massive, multi-category, ambiguity, and spatial-temporal process. The original marine data cannot be directly used for analysis and mining, so the data must be cleaned, converted, and processed before mining the data. Choose preprocessing. Subsequent marine data mining, commonly used algorithms include regression algorithms, statistical analysis, cluster analysis, association rule mining, etc. Linked data mining can effectively discover the underlying laws of data; cluster analysis is a kind of unsupervised learning t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/26G06F16/28
CPCG06F16/26G06F16/283
Inventor 王雨思陈可道
Owner CENT SOUTH UNIV
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