CN113064965A - Intelligent recommendation method for similar cases of civil aviation unplanned events based on deep learning - Google Patents

Intelligent recommendation method for similar cases of civil aviation unplanned events based on deep learning Download PDF

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CN113064965A
CN113064965A CN202110307398.3A CN202110307398A CN113064965A CN 113064965 A CN113064965 A CN 113064965A CN 202110307398 A CN202110307398 A CN 202110307398A CN 113064965 A CN113064965 A CN 113064965A
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王捷
周迪
左洪福
胡煜雯
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Nanjing University of Aeronautics and Astronautics
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Abstract

The invention discloses a deep learning-based intelligent recommendation method for similar cases of civil aviation unplanned events, which is used for obtaining the similarity between different cases and a target event based on a Doc2Vec deep learning algorithm, quickly positioning the optimal case and providing a reference for formulating emergency treatment measures of the target event. The deep learning-based intelligent recommendation method for similar cases of civil aviation unplanned events can accurately and quickly perform similar case matching on target events, and realizes quick emergency response of unplanned events of a civil aircraft system.

Description

Intelligent recommendation method for similar cases of civil aviation unplanned events based on deep learning
Technical Field
The invention belongs to the technical field of civil aviation safety, and particularly relates to an intelligent recommendation method for similar cases of an unplanned event of a civil aviation.
Background
Civil aviation safety is a permanent theme of the civil aviation industry, the civil aviation industry rises rapidly, and an airplane system is widely applied. Aviation maintenance decisions and airworthiness management also become key concerns for various civil aviation organizations. The quantity of civil aircraft equipment of each airline increases rapidly, the system complexity improves, and the factor that influences aircraft operation safety also increases by a wide margin. During the actual operation of an aircraft, various unplanned events are often accompanied. Although most of them are related to conventional problems, emergency response to unplanned events in aircraft operations is very necessary in order to avoid the development of unsafe conditions into serious safety problems caused by unplanned events. The emergency response of the unplanned event can timely and accurately correct the high-risk unsafe state, and the minimum casualties and economic losses are avoided.
Deep learning is a machine learning method simulating a human brain neural network mechanism, and achieves better effects in the image and vision fields. With the popularization and application of deep learning methods in Natural Language Processing (NLP), the solution of text analysis by deep learning algorithms has been widely studied. The deep learning method can dig out deep meanings of texts and improve the accuracy of extracting the key information of the texts. In the field of aviation safety, a deep learning algorithm is mature and applied to the aspects of potential safety hazards in the flight stage of an airplane, analysis of association relation between the potential safety hazards and accident reasons, analysis of major aviation accident reasons, analysis of reasons of aviation safety reports and the like. The research on intelligent recommendation of similar cases of civil aircraft unplanned events is less.
Disclosure of Invention
In order to solve the technical problems mentioned in the background technology, the invention provides an intelligent case recommendation method for the civil aviation unplanned events based on deep learning, so that the rapid emergency response of the civil aviation unplanned events is realized, and the continuous airworthiness time of the civil aviation is prolonged.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
the method for intelligently recommending the similar cases of the civil aviation unplanned events based on deep learning comprises the following steps:
(1) data collection: determining the starting time and the ending time of data collection, and collecting M fault event log reports about an aircraft engine in total of X airplanes with the model of Y of an airline company as sample data;
(2) data normalization: standardizing aviation terms involved in fault event log reports using a civil aviation dictionary; event information containing specific execution codes of an airline company is removed, and only M' pieces of standardized event information are reserved;
(3) preprocessing the standardized data;
(4) training the event text into a multi-dimensional vector by using a Doc2Vec model;
(5) calculating the similarity between the target event and the M' pieces of standardized event texts;
(6) and (4) taking the target event as a new case, putting the new case into a case library for similar case retrieval, and screening out a reference case similar to the new case.
Further, in step (3), the data preprocessing method is as follows:
(3a) event text information is arranged to form event standardized Chinese text corpora;
(3b) removing special characters which can influence the calculation speed and the test precision from the corpus;
(3c) removing irrelevant stop words which can influence the calculation of the text similarity in the corpus;
(3d) and performing word segmentation processing on the text by adopting a word segmentation tool.
Further, the specific process of step (4) is as follows:
(4a) training by adopting a PV-DM model, and randomly initializing two matrixes W and D of NxV dimension, wherein V represents the size of word traffic, N is the dimension of a set finally-desired word vector, and N is far smaller than V;
(4b) mapping each paragraph of the input corpus into a vector as a column vector of a matrix D; each word is mapped into a vector as a column vector of the matrix W;
(4c) for a piece of event information, a given sequence of words (w)j-k,wj-k+1,···wj+k) Total of C in the context1The word vector corresponding to each word is { wj-k,wj-k+1,···wj+kK is the window size, where C1K, word vector wj-k,wj-k+1,···wj+kThe dimension of is V1, for a given word wjDefine the paragraph vector of the sentence in which it is located as
Figure BDA0002988059500000031
(4d) Word vector of C words to be input corpus wj-k,wj-k+1,···wj+kThe sum is added with a paragraph vector representing the sentence
Figure BDA0002988059500000032
Then carrying out projection transformation and mapping to an N-dimensional combination vector h;
(4e) weighting and summing each combination vector h in W to obtain uj：
uj＝γj'h
Wherein, γj' is a weight used in weighted summation, and the dimension is V multiplied by 1, j is 1, 2. V;
Figure BDA0002988059500000033
is the word wjNon-regularized uniform probability of:
Figure BDA0002988059500000034
wherein U, b are parameters of a Softmax function;
(4f) u to be obtainedjAnd
Figure BDA0002988059500000035
prediction word w substituted into Softmax functionjProbability of occurrence:
Figure BDA0002988059500000036
(4g) obtaining an optimized objective function y by adopting a random gradient descent algorithm:
Figure BDA0002988059500000037
(4h) and (5) repeating the steps (4a) - (4g) to find the optimal word vector dimension N to obtain the trained Doc2Vec model.
Further, in step (5), cosine similarity is used to measure the similarity between each piece of event information in the event log report and all events before and after the event, and the similarity between the event information and other events is summed and averaged to obtain the similarity between the event and other events.
Further, the formula of the cosine similarity is as follows:
Figure BDA0002988059500000041
wherein sim (a, b) represents the similarity between two events,
Figure BDA0002988059500000042
a vectorized representation of the representation event a,
Figure BDA0002988059500000043
representing a vectorized representation of event b.
Adopt the beneficial effect that above-mentioned technical scheme brought:
the invention utilizes the characteristic recognition characteristic of deep learning to realize the rapid response of the unplanned event, assists in formulating the emergency disposal scheme of the unplanned event of the airplane system, and realizes the rapid positioning of the unplanned event of the airplane and the accurate elimination of the unplanned factors from the perspective of text semantic mining. The invention improves the emergency disposal efficiency of the civil aviation unplanned event and reduces the possibility of generating potential safety hazard by the airplane.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a Doc2Vec model (PV-DM model);
FIG. 3 is a diagram of a Doc2Vec model (PV-DBOW model);
FIG. 4 is a diagram illustrating the best word vector dimensions for Doc2Vec model training in an embodiment.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The diversity, complexity and subjectivity of manual judgment of the unplanned event increase the emergency response time of the unplanned event, and the untimely emergency response may cause serious safety problems such as casualties, airplane damage and the like. In order to solve the problem of emergency disposal efficiency of the civil aviation unplanned event, the invention designs an intelligent recommendation method for similar cases of the civil aviation unplanned event based on deep learning, which comprises the following steps as shown in figure 1:
step 1: data collection: determining the starting time and the ending time of data collection, and collecting M fault event log reports about an aircraft engine in total of X airplanes with the model of Y of an airline company as sample data;
step 2: data normalization: standardizing aviation terms involved in fault event log reports using a civil aviation dictionary; event information containing specific execution codes of an airline company is removed, and only M' pieces of standardized event information are reserved;
and step 3: preprocessing the standardized data;
and 4, step 4: training the event text into a multi-dimensional vector by using a Doc2Vec model;
and 5: calculating the similarity between the target event and the M' pieces of standardized event texts;
step 6: and (4) taking the target event as a new case, putting the new case into a case library for similar case retrieval, and screening out a reference case similar to the new case.
In this embodiment, specifically, in step 1, firstly, an aircraft with 10 models of a certain airline company as boeing 737 is collected through a civil aircraft maintenance operation control system and an airline mobile end platform in a manual entry manner, 142 fault event log reports about an aircraft engine from 10 months 2015 to 10 months 2020 are used as sample data, wherein each fault log relates to 18 attributes, of the 18 attributes, part of the attributes have no influence on a text similarity calculation result, and the attributes belong to unrelated attributes and are manually removed. And finally, selecting two attributes of ATA and event description as indexes for case retrieval from each piece of event information.
In the embodiment, specifically, in step 2, the aviation terms in the event description are standardized through the civil aviation dictionary, meanwhile, the event information containing the specific execution code of the airline company is manually rejected, and finally, only 134 standardized fault event logs are reserved.
In this embodiment, preferably, the data preprocessing method in step 3 is as follows:
(3a) the method comprises the following steps that (1) aviation company fault log information is formed into event standardized Chinese text corpora for experiments;
(3b) removing special characters which can influence the calculation speed and the test precision in the corpus, wherein the special characters comprise brackets, slashes and various punctuations;
(3c) the method for removing irrelevant stop words which influence the text similarity calculation in the corpus by using the Baidu stop vocabulary comprises the following steps: not only, but also, etc.;
(3d) and performing word segmentation on the text by adopting a jieba word segmentation tool.
Specifically, event description items in the fault event log are extracted, and special characters, stop words and word segmentation are removed. After the data preprocessing is finished, the processing result of each event is as follows: the 'unit reports that APU start is unsuccessful, FAULIT lamp is on' -the 'unit reports that APU start is unsuccessful, FAULIT lamp is on', and the sentence is divided into single words.
In this embodiment, preferably, the specific process of step 4 is as follows:
(4a) training by adopting a PV-DM model, and randomly initializing two matrixes W and D of NxV dimension, wherein V represents the size of word traffic, N is the dimension of a set finally-desired word vector, and N is far smaller than V;
(4b) mapping each paragraph of the input corpus into a vector as a column vector of a matrix D; each word is mapped into a vector as a column vector of the matrix W;
(4c) for a piece of event information, a given sequence of words (w)j-k,wj-k+1,···wj+k) Total of C in the context1The word vector corresponding to each word is { wj-k,wj-k+1,···wj+kK is the window size, where C1K, word vector wj-k,wj-k+1,···wj+kThe dimension of is V1, for a given word wjDefine the paragraph vector of the sentence in which it is located as
Figure BDA0002988059500000061
(4d) Word vector of C words to be input corpus wj-k,wj-k+1,···wj+kThe sum is added with a paragraph vector representing the sentence
Figure BDA0002988059500000062
Then carrying out projection transformation and mapping to an N-dimensional combination vector h;
(4e) weighting and summing each combination vector h in W to obtain uj：
uj＝γj'h
Wherein, γj' is a weight used in weighted summation, and the dimension is V multiplied by 1, j is 1, 2. V;
Figure BDA0002988059500000071
is the word wjNon-regularized uniform probability of:
Figure BDA0002988059500000072
wherein U, b are parameters of a Softmax function;
(4f) u to be obtainedjAnd
Figure BDA0002988059500000073
prediction word w substituted into Softmax functionjProbability of occurrence:
Figure BDA0002988059500000074
(4g) obtaining an optimized objective function y by adopting a random gradient descent algorithm:
Figure BDA0002988059500000075
(4h) and (5) repeating the steps (4a) - (4g) to find the optimal word vector dimension N to obtain the trained Doc2Vec model.
Specifically, the Doc2Vec model is shown in fig. 2(PV-DM model) and fig. 3(PV-DBOW model). In this embodiment, a genesis toolkit of python is used, the PV-DM model is adopted in the default Doc2Vec model algorithm, the optimal word vector dimension size is set to 400, as shown in FIG. 4, the window length Windows is set to 3, the minimum occurrence number of training words min-count is set to 2, the thread number Workers is set to 4, and the iteration number epoch in training is set to 15. On an Intel (R) core (TM) i7 processor, a well-trained Doc2Vec model is finally obtained.
In this embodiment, preferably, in step 5, cosine similarity is used to measure the similarity between each piece of event information in the event log report and all events before and after the event, and the similarity between the event information and other events is calculated by summing and averaging. The formula of the cosine similarity is as follows:
Figure BDA0002988059500000081
wherein sim (a, b) represents the similarity between two events,
Figure BDA0002988059500000082
a vectorized representation of the representation event a,
Figure BDA0002988059500000083
representing a vectorized representation of event b.
In this embodiment, specifically, the target event is put into a case library as a new case for similar case retrieval, and a similar case with similarity degree ranking 10 before the new case is selected, and the result is shown in table 1.
TABLE 1 Intelligent recommendation results for similar cases
Figure BDA0002988059500000084
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.

Claims (5)

1. The method for intelligently recommending the similar cases of the civil aviation unplanned events based on deep learning is characterized by comprising the following steps of:
(1) data collection: determining the starting time and the ending time of data collection, and collecting M fault event log reports about an aircraft engine in total of X airplanes with the model of Y of an airline company as sample data;
(2) data normalization: standardizing aviation terms involved in fault event log reports using a civil aviation dictionary; event information containing specific execution codes of an airline company is removed, and only M' pieces of standardized event information are reserved;
(3) preprocessing the standardized data;
(4) training the event text into a multi-dimensional vector by using a Doc2Vec model;
(5) calculating the similarity between the target event and the M' pieces of standardized event texts;
(6) and (4) taking the target event as a new case, putting the new case into a case library for similar case retrieval, and screening out a reference case similar to the new case.
2. The deep learning-based intelligent recommendation method for similar cases of civil aviation unplanned events according to claim 1, characterized in that in the step (3), the data preprocessing method is as follows:
(3a) event text information is arranged to form event standardized Chinese text corpora;
(3b) removing special characters which can influence the calculation speed and the test precision from the corpus;
(3c) removing irrelevant stop words which can influence the calculation of the text similarity in the corpus;
(3d) and performing word segmentation processing on the text by adopting a word segmentation tool.
3. The deep learning-based intelligent recommendation method for similar cases of civil aviation unplanned events according to claim 1, characterized in that the specific process of the step (4) is as follows:
(4a) training by adopting a PV-DM model, and randomly initializing two matrixes W and D of NxV dimension, wherein V represents the size of word traffic, N is the dimension of a set finally-desired word vector, and N is far smaller than V;
(4b) mapping each paragraph of the input corpus into a vector as a column vector of a matrix D; each word is mapped into a vector as a column vector of the matrix W;
(4c) for a piece of event information, a given sequence of words (w)j-k,wj-k+1,…wj+k) Total of C in the context1The word vector corresponding to each word is { wj-k,wj-k+1,…wj+kK is the window size, where C1K, word vector wj-k,wj-k+1,…wj+kThe dimension of is V1, for a given word wjDefine the paragraph vector of the sentence in which it is located as
Figure FDA0002988059490000021
(4d) Word vector of C words to be input corpus wj-k,wj-k+1,…wj+kThe sum is added with a paragraph vector representing the sentence
Figure FDA0002988059490000022
Then carrying out projection transformation and mapping to an N-dimensional combination vector h;
(4e) weighting and summing each combination vector h in W to obtain uj：
uj＝γj'h
Wherein, γj' is the weight used in weighted summation, and the dimension is V × 1, j is 1,2 … V;
Figure FDA0002988059490000023
is the word wjNon-regularized uniform probability of:
Figure FDA0002988059490000024
wherein U, b are parameters of a Softmax function;
(4f) u to be obtainedjAnd
Figure FDA0002988059490000025
prediction word w substituted into Softmax functionjProbability of occurrence:
Figure FDA0002988059490000026
(4g) obtaining an optimized objective function y by adopting a random gradient descent algorithm:
Figure FDA0002988059490000027
(4h) and (5) repeating the steps (4a) - (4g) to find the optimal word vector dimension N to obtain the trained Doc2Vec model.
4. The intelligent deep learning-based case-like recommendation method for unplanned civil aviation events according to claim 1, wherein in step (5), cosine similarity is used to measure the similarity between each piece of event information in the event log report and all events before and after the event, and the similarity between the event information and other events is summed and averaged to obtain the similarity between the event and other events.
5. The deep learning-based intelligent recommendation method for similar cases of civil aviation unplanned events according to claim 4, wherein the cosine similarity is expressed by the following formula:
Figure FDA0002988059490000031
wherein sim (a, b) represents the similarity between two events,
Figure FDA0002988059490000032
a vectorized representation of the representation event a,
Figure FDA0002988059490000033
representing a vectorized representation of event b.
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CN114943227A (en) * 2022-05-30 2022-08-26 中国银行股份有限公司 Matching method and device of user story and test case
CN117520484A (en) * 2024-01-04 2024-02-06 中国电子科技集团公司第十五研究所 Similar event retrieval method, system, equipment and medium based on big data semantics
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