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Analysis Method of Railway Accident Causes Based on Word Expansion LDA

A technology of accident causes and analysis methods, applied in semantic analysis, instruments, data processing applications, etc., can solve problems such as the decline of expert judgment ability and the impact of subjective accident analysis results

Active Publication Date: 2021-05-18
BEIJING JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

A large amount of repetitive work will reduce the judgment ability of experts, and the subjectivity of expert judgment will affect the results of accident analysis

Method used

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  • Analysis Method of Railway Accident Causes Based on Word Expansion LDA
  • Analysis Method of Railway Accident Causes Based on Word Expansion LDA
  • Analysis Method of Railway Accident Causes Based on Word Expansion LDA

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Experimental program
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Effect test

Embodiment 1

[0046] like figure 1 As shown, Embodiment 1 of the present invention provides a method for analyzing the cause of a railway accident, the method comprising the following steps:

[0047] Step S110: use TF-IDF to represent the railway accident text, construct a document vector space model, and generate document vectors;

[0048] Step S120: using the TextRank method to calculate the importance of words in the railway accident text;

[0049] Step S130: according to word importance and semantic similarity, weight the words that meet the semantic threshold, and train and generate word expansion LDA model;

[0050] Step S140: using the word expansion LDA model to extract the features of the railway accident text, and extracting the cause of the railway accident theme and feature items;

[0051] Step S150: use the SVM accident classification model to classify the text of the railway accident report, and determine the data set of the cause of the railway accident;

[0052] Step S160...

Embodiment 2

[0064] like figure 2 As shown, Embodiment 2 of the present invention provides a method for constructing a word-extended LDA topic model based on word importance and semantic similarity, which method includes the following process steps:

[0065] Step 1.1, use the TextRank method to calculate the importance of words in the document

[0066] Specifically, the given accident text is divided into complete sentences, each sentence is segmented, and stop words are removed, each sentence is represented as a set of phrases, a word graph is constructed, and then any co-occurrence relationship is used to construct any The edge between two words, only when two words co-occur in a fixed-length window, there is an edge between them, initialize the importance of all words, and calculate the importance of each word through multiple iterations, by setting The maximum number of iterations is to control the calculation, and the final iteration result is defined as the importance of words, and...

Embodiment 3

[0076] like image 3 As shown, the third embodiment of the present invention provides a text classification method based on two-level accident causes using the SVM accident classification model. The method includes the following steps:

[0077] Step 2.1, constructing the improved HFACS-RAs model.

[0078] The word expansion LDA topic model that the training of embodiment two generates carries out subject feature extraction to accident text, and each subject selects the top eight subject words of frequency ranking as the accident cause characteristic item, constitutes the accident cause characteristic space; From the implication of subject word It can identify the human factors and organizational classifications in current accidents, and based on the content extracted from the accident text features, an improved HFACS-RAs model is designed on the basis of the HFACS-RAs model, such as Figure 4 As shown, the "preconditions for unsafe behaviors" are further divided into "persona...

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Abstract

The invention provides a method for analyzing the cause of a railway accident based on word expansion LDA, and relates to the technical field of railway train operation safety analysis. The method is based on the text content of the railway accident report, and uses the word expansion LDA model to extract accident-related topics and subject words; Classify the subject feature items according to the human factors and system classification method HFACS to form an improved HFACS‑RAs model; use SVM to classify the text of the accident report to determine the accident cause data set; use chi-square test combined with unconstrained 0 / 1 Optimization realizes Bayesian network structure optimization; uses Logistic regression model to estimate CPT parameters of Bayesian network; determines the accident cause analysis model based on the improved Bayesian network, and calculates the key cause of the accident. The invention completes the feature extraction of accident causes based on the word-expanded LDA model, determines the factors that cause railway accidents and the degree of influence of the factors on the consequences of accidents, and is conducive to deepening the understanding of the accident process and taking measures to prevent recurrence of similar accidents.

Description

technical field [0001] The invention relates to the technical field of railway train operation safety analysis, in particular to a method for analyzing causes of railway accidents based on word expansion LDA. Background technique [0002] It is of great significance to analyze the human and organizational factors in railway accidents. After the accident, in order to take effective preventive measures to prevent similar accidents from happening again, it is very important to diagnose and locate the root cause of the accident. The analysis of the causes of accidents in my country's railway industry started relatively late. At present, for the research on the cause analysis method of railway accidents, or the application of the algorithm combining dissipative structure and entropy theory to study the evolution mechanism of high-speed railway operation accidents; or the application of behavioral safety "2-4" model to analyze the root, root, indirect, direct Causes and transmiss...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/289G06F40/30G06K9/62G06Q10/06G06Q50/30
CPCG06Q10/0635G06Q50/30G06F18/2411
Inventor 张国琛郑伟
Owner BEIJING JIAOTONG UNIV
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