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Natural language-based airworthiness instruction problem feature extraction

A natural language and instruction technology, applied in the field of extraction of airworthiness instruction problem features, can solve problems such as manual discovery and positioning of airworthiness reference information, and achieve high accuracy results

Pending Publication Date: 2020-12-22
中国民用航空上海航空器适航审定中心
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  • Claims
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AI Technical Summary

Problems solved by technology

In the case of large-scale data, reviewers cannot manually discover and locate the truly valuable airworthiness reference information related to current activities, resulting in a large amount of valuable airworthiness auxiliary information that cannot provide sufficient information support for reviewers in the review work

Method used

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  • Natural language-based airworthiness instruction problem feature extraction
  • Natural language-based airworthiness instruction problem feature extraction
  • Natural language-based airworthiness instruction problem feature extraction

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Embodiment Construction

[0052] The present invention will be further described below in conjunction with the accompanying drawings, but not as a limitation of the present invention.

[0053] The present invention proposes a method for extracting the characteristics of airworthiness instructions based on natural language. First, it detects (Detect) all potential overlapping sentence clusters in the sentences described in the text, and then sorts the found sentence clusters and selects (Select) a given The number of sentence clusters, and finally extract (Extract) two-word phrases from the selected sentence clusters as feature descriptions.

[0054] In general, the goal of community detection is to detect node clusters from a complex network, which can overlap with each other. To discover overlapping communities, LMF (local maxima of fitness, community detection algorithm) starts from different seeds and greedily probes the members of each community. These seeds are randomly selected and have not been...

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Abstract

The invention relates to the technical field of airworthiness certification, in particular to natural language-based airworthiness instruction problem feature extraction, which comprises the followingsteps of: extracting problem description chapters behind an airworthiness instruction, and carrying out text data preprocessing; detecting overlapped sentence clusters; selecting a given number of sentence clusters; extracting feature descriptors. The method for extracting the features by detecting the overlapped sentence clusters and directly selecting the phrases from the text description has higher accuracy. Meanwhile, the method has better performance in the aspect of time consumption compared with a comparison method selected in the prior art; key design features, expressed by the airworthiness instruction text, of aircraft products can also be found in feature extraction actually for airworthiness instructions.

Description

technical field [0001] The invention relates to the technical field of airworthiness certification, in particular to the extraction of problem features of airworthiness instructions based on natural language. Background technique [0002] Extracting design features and safety trends that affect safety during the entire life cycle of aircraft operation is also the main way to carry out airworthiness review activities, that is, the "listening mode" of airworthiness certification. The typical application of the monitoring mode is for various minor deviations and unsafe information recorded and reported in the process of design, manufacture and use. Usually, a large amount of descriptive information about various deviations and changes will be accumulated in the process of aircraft design, manufacture and use. The sources, uses, and descriptions of this information vary, and the unsafe design features of aircraft contained therein are often not obvious. In the case of large-sc...

Claims

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Application Information

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IPC IPC(8): G06F40/284G06F40/247G06F40/289G06F40/216G06F16/35G06K9/62
CPCG06F40/284G06F40/247G06F40/289G06F40/216G06F16/35G06F18/22
Inventor 朱玉屏蔡喁申岳刘春
Owner 中国民用航空上海航空器适航审定中心
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