CN114897193A - Airplane structure maintenance decision method and decision system based on man-in-the-loop - Google Patents
Airplane structure maintenance decision method and decision system based on man-in-the-loop Download PDFInfo
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- CN114897193A CN114897193A CN202210450388.XA CN202210450388A CN114897193A CN 114897193 A CN114897193 A CN 114897193A CN 202210450388 A CN202210450388 A CN 202210450388A CN 114897193 A CN114897193 A CN 114897193A
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Abstract
The invention discloses a human-in-loop-based aircraft structure maintenance decision method and a decision system, which comprises the following steps of obtaining damage inspection data of an aircraft structure of a target case; extracting case characteristics of the target case and analyzing case characteristic attribute weights based on a maintenance decision model algorithm library and a maintenance case library; obtaining a source case similar to the target case according to the case characteristic attribute weight, and outputting an optimal maintenance decision of the target case; and outputting a decision conclusion through man-machine interaction according to the optimal maintenance decision. The method and the system can accurately generate the maintenance decision result in an express way aiming at the damage inspection data of the airplane structure of the target case.
Description
Technical Field
The invention relates to an airplane structure maintenance decision method and a decision system based on a man-in-the-loop, and belongs to the technical field of airplane structure maintenance.
Background
The timely and accurate damage measurement and evaluation and the efficient repair scheme formulation are the core contents for solving the civil aircraft structure repair problem. Foreign civil aircraft represented by Boeing and air passengers are used as efficient repair schemes, and a support platform covering 'damage monitoring, assessment, reporting and repair scheme management' is established. The research on the aspects of structure repair evaluation and verification is started in China, but most of research results are in the level of theoretical analysis and simulation verification, and a reliable and feasible hyper-manual structure repair scheme making technology is formed without starting from specific factors such as structure design and use characteristics, maintenance resources and technical capability constraints. In the research of civil aircraft structure cases, some attributes need to be selected as comprehensively as possible for different types of civil aircraft according to the characteristics of the structure and the function of the civil aircraft, the information of manufacturers and the information of models, so as to fully embody the characteristics of the civil aircraft cases. For highly complex and specialized aircraft, redundancy of attributes, correlation, and the like are inevitable. Therefore, it is necessary to select a proper method to extract valid features from a plurality of attributes.
The traditional maintenance decision is directly related to the ability and experience of people, and the selected maintenance attribute characteristics and decision algorithm are relatively fixed in practice, so that the rapid improvement of the maintenance decision capability of the airplane structure is restricted. Meanwhile, maintenance decision of the aircraft structure needs to comprehensively consider global reasonable maintenance opportunity, consider maintenance modes of resources and capabilities, balance damage conditions and quick recovery measures of maintenance capabilities, and establish an optimal maintenance resource guarantee and technical state recovery system under complex damage conditions, so that an intelligent decision method suitable for aircraft structure maintenance needs to be developed urgently, damage inspection data of the aircraft structure is quickly and accurately checked, and maintenance decision of the damage structure is given, so that the integrity of the actual operation of the aircraft, the task reliability and the availability of the whole aircraft are effectively improved.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a human-in-loop-based aircraft structure maintenance decision method and a human-in-loop-based aircraft structure maintenance decision system, which can accurately generate a maintenance decision result in an express way according to damage inspection data of an aircraft structure of a target case.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in one aspect, the invention discloses a method for deciding maintenance of an aircraft structure based on a human-in-loop, comprising the following steps,
acquiring damage inspection data of an airplane structure of a target case;
extracting case characteristics of the target case and analyzing case characteristic attribute weights based on a maintenance decision model algorithm library and a maintenance case library according to damage inspection data of the airplane structure of the target case;
obtaining a source case similar to the target case according to the case characteristic attribute weight;
outputting an optimal maintenance decision of the target case according to the source case;
outputting a decision conclusion through man-machine interaction according to the optimal maintenance decision; if the optimal maintenance decision is correct, a decision conclusion is directly output; and if the optimal maintenance decision is wrong, manually verifying the decision conclusion of the target case and updating an optimal maintenance decision model algorithm library and a maintenance case library.
Further, the case characteristics of the target case include, but are not limited to, ATA site number, damage site structure type, damage cause, damage type, damage size, inspection method, repair type.
Furthermore, the case characteristics are selected by adopting a dimensionality reduction algorithm such as an analytic hierarchy process or a principal component analysis method.
Further, based on the maintenance decision model algorithm library and the maintenance case library, the case characteristic attribute weight is determined through an entropy weight method.
Further, according to the case characteristic attribute weight, calculating case similarity by adopting a weighted K-nearest neighbor search algorithm based on Euclidean distance to obtain a source case similar to the target case.
And further, outputting the optimal maintenance decision of the target case based on the similar case transfer learning according to the source case.
Further, when the optimal maintenance decision output by the man-machine interaction module is wrong,
if the target case is a new case, manually inputting a decision conclusion of the target case to form a new maintenance decision, and entering a maintenance case library as a newly added maintenance case; simultaneously triggering a case self-learning algorithm to perform case self-learning, updating case characteristics and case characteristic attribute weights, and optimizing a maintenance decision model algorithm library;
if the target case is not a new case, manually revising the decision conclusion of the target case and updating a maintenance case base; and simultaneously triggering a case self-learning algorithm to perform case self-learning and optimizing a maintenance decision model algorithm library.
On the other hand, the invention discloses a decision-making system of an airplane structure maintenance decision-making method based on a man-in-the-loop, which comprises a data acquisition module, an analysis data module, a maintenance decision model algorithm library, a maintenance case library, a source case module, an optimal maintenance decision model and a man-machine interaction module,
the data acquisition module is used for acquiring damage inspection data of the airplane structure of the target case;
the data analysis module is used for extracting case characteristics of the target case and analyzing case characteristic attribute weights according to damage inspection data of the airplane structure of the target case and based on a maintenance decision model algorithm library and a maintenance case library;
the maintenance case library is used for storing maintenance cases of the aircraft structure;
the maintenance decision model algorithm library is used for storing maintenance decision model algorithms of the aircraft structure;
the source case module is used for obtaining a source case similar to the target case according to the case characteristic attribute weight;
the optimal maintenance decision model is used for outputting an optimal maintenance decision of a target case according to the source case;
the human-computer interaction module is used for outputting a decision conclusion according to the optimal maintenance decision; if the optimal maintenance decision is correct, directly outputting a decision conclusion; and if the optimal maintenance decision is wrong, manually verifying the decision conclusion of the target case and updating the optimal maintenance decision model algorithm library and the maintenance case library.
Compared with the prior art, the invention has the following beneficial effects:
the method is based on a maintenance case base and a maintenance decision model algorithm base, case characteristics of the target cases are extracted, multi-dimensional case information is deeply mined through common characteristics among the cases, and source cases similar to the target cases are determined, so that the optimal maintenance decision of the target cases is obtained.
The invention also adopts a man-machine interaction module, and the optimal maintenance decision is fed back based on a man-in-loop method, so that a maintenance decision conclusion can be more accurately obtained; meanwhile, the maintenance case base and the maintenance decision model algorithm base are updated and optimized, so that a maintenance decision conclusion can be obtained more quickly after a new target case is input.
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FIG. 1 is a flow chart of a human-in-loop based aircraft structure repair decision method.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
This embodiment 1 provides a method for deciding maintenance of an aircraft structure based on human-in-the-loop, comprising the following steps,
the method comprises the following steps: damage inspection data of the aircraft structure of the target case is obtained.
Step two: and extracting case characteristics of the target case and analyzing case characteristic attribute weights based on a maintenance decision model algorithm library and a maintenance case library according to damage inspection data of the airplane structure of the target case.
Specifically, the type of case characteristics of the target case influencing the maintenance decision can be selected from aircraft structure historical repair cases such as ATA (advanced technology attachment) part numbers, damage part structure types, damage causes, damage types, damage sizes, inspection modes, repair types and the like according to engineering experience on one hand, and on the other hand, main case characteristics influencing the aircraft structure maintenance decision can be selected from the characteristics in the repair cases by adopting a dimensionality reduction algorithm such as an analytic hierarchy process, a principal component analysis method and the like.
And based on the maintenance case base, determining the case characteristic attribute weight after extracting the case characteristics of the target case. In order to avoid the influence of human subjectivity on the case characteristic attribute weight, the entropy weight method is adopted to determine the case characteristic attribute weight.
Firstly, standardizing a case characteristic attribute matrix, wherein a specific expression is as follows:
wherein, y ij Representing the normalized case characteristic attribute matrix, x ij Attribute matrix, y, representing case characteristics ij And x ij All are m rows and n columns of matrix, m represents the row number of the case characteristic attribute matrix, n represents the column number of the case characteristic attribute matrix, i represents the row coordinate of the case characteristic attribute matrix, j represents the column coordinate of the case characteristic attribute matrix, x i The ith characteristic attribute value of the source case x is represented.
Secondly, the information entropy E of each case characteristic attribute is obtained j The specific expression is as follows,
wherein E is j Entropy of information representing the characteristic attribute of the j-th case, k being an intermediate parameter, p ij Represents a pair y ij And carrying out normalization processing on the matrix.
And finally, determining the attribute weight of each case feature:
0≤ω j ≤1
wherein, ω is j The weight of the characteristic attribute of the jth case is shown.
Step three: and calculating case similarity by adopting a weighted K-nearest neighbor retrieval algorithm based on the Euclidean distance according to the case characteristic attribute weight to obtain a source case similar to the target case.
The method comprises the following specific steps:
weighted Euclidean distance D (X, Y) of
Wherein x is i An i-th characteristic attribute value representing a source case x, i ═ 1,2, 3.., n; y is i The ith characteristic attribute value of the target case y is represented;n represents the number of case feature attributes; omega j A weight representing a characteristic attribute of the jth case example; d (x) i ,y i ) 2 Representing the Euclidean distance, s, between the target case and the source case i Representing the ith feature on an n-dimensional case feature space.
The similarity S (X, Y) between the target case and the source case is:
S(X,Y)＝1/(1+D(X,Y))
step four: and outputting the optimal maintenance decision of the target case according to the source case.
Specifically, the source cases can be divided into similar type cases and similar type cases according to different types, and the optimal maintenance decision of the target case is obtained by information transfer learning of the source cases under the condition of considering similarity and matching degree.
Step five: and outputting a decision conclusion through man-machine interaction according to the optimal maintenance decision.
Specifically, the optimal maintenance decision obtained through transfer learning takes the maintenance decision information feedback of a person in a loop as a trigger condition, and a maintenance decision conclusion is verified by adopting manual input. And if the optimal maintenance decision is correct, directly outputting a maintenance decision conclusion of the damaged structure.
Step six: responding to the error of the optimal maintenance decision output by the human-computer interaction module:
if the target case is a new case, manually inputting a decision conclusion of the target case to form a new maintenance decision, and entering a maintenance case library as a newly added maintenance case; simultaneously triggering a case self-learning algorithm to perform case self-learning, updating case characteristics and case characteristic attribute weights, and optimizing a maintenance decision model algorithm library;
if the target case is not a new case, manually revising the decision conclusion of the target case and updating the maintenance case base; and simultaneously triggering a case self-learning algorithm to perform case self-learning and optimizing a maintenance decision model algorithm library.
And seventhly, inputting the target case after the updating optimization is completed, and repeating the steps.
Example 2
This embodiment 2 discloses a decision system based on the aircraft structure maintenance decision method of embodiment 1, which comprises a data acquisition module, an analysis data module, a maintenance decision model algorithm library, a maintenance case library, a source case module, an optimal maintenance decision model and a human-computer interaction module,
the system comprises a data acquisition module, a data analysis module and a data processing module, wherein the data acquisition module is used for acquiring damage inspection data of an airplane structure of a target case;
the data analysis module is used for extracting case characteristics of the target case and analyzing case characteristic attribute weights according to damage inspection data of the airplane structure of the target case and based on the maintenance decision model algorithm library and the maintenance case library;
the maintenance case library is used for storing maintenance cases of the airplane structure;
the maintenance decision model algorithm library is used for storing maintenance decision model algorithms of the airplane structure;
the source case module is used for obtaining a source case similar to the target case according to the case characteristic attribute weight;
the optimal maintenance decision model is used for outputting an optimal maintenance decision of the target case according to the source case;
the human-computer interaction module is used for outputting a decision conclusion according to the optimal maintenance decision; if the optimal maintenance decision is correct, directly outputting a decision conclusion; and if the optimal maintenance decision is wrong, manually verifying the decision conclusion of the target case and updating the optimal maintenance decision model algorithm library and the maintenance case library.
The technical concept of the invention is that aiming at the input damage inspection data of the airplane structure, the optimal maintenance decision is quickly generated, the optimal maintenance decision is verified by adopting a human-computer interaction module, the whole decision system is fed back, updated and optimized, and the decision conclusion can be given out more quickly and accurately by iterative use.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A method for deciding the maintenance of airplane structure based on man-in-the-loop includes such steps as providing a decision-making unit,
acquiring damage inspection data of an airplane structure of a target case;
extracting case characteristics of the target case and analyzing case characteristic attribute weights based on a maintenance decision model algorithm library and a maintenance case library according to damage inspection data of the airplane structure of the target case;
obtaining a source case similar to the target case according to the case characteristic attribute weight;
outputting an optimal maintenance decision of the target case according to the source case;
outputting a decision conclusion through man-machine interaction according to the optimal maintenance decision; if the optimal maintenance decision is correct, a decision conclusion is directly output; and if the optimal maintenance decision is wrong, manually verifying the decision conclusion of the target case and updating an optimal maintenance decision model algorithm library and a maintenance case library.
2. The human-in-loop based aircraft structure repair decision method as claimed in claim 1, wherein the case characteristics of the target case include, but are not limited to, ATA site number, damage site structure type, damage cause, damage type, damage size, inspection mode, repair type.
3. The human-in-loop-based aircraft structure maintenance decision method as claimed in claim 1, wherein the case characteristics are selected using a dimensionality reduction algorithm such as an analytic hierarchy process or a principal component analysis process.
4. The human-in-loop-based aircraft structure maintenance decision method as claimed in claim 1, wherein the case characteristic attribute weight is determined by an entropy weight method based on a maintenance decision model algorithm library and a maintenance case library.
5. The human-in-loop-based aircraft structure maintenance decision method as claimed in claim 1, wherein case similarity is calculated by using a weighted K-nearest neighbor search algorithm based on Euclidean distance according to the case characteristic attribute weight to obtain a source case similar to a target case.
6. The human-in-loop-based aircraft structure maintenance decision method as claimed in claim 1, wherein the optimal maintenance decision of the target case is output based on similar case transfer learning according to the source case.
7. The human-in-loop based aircraft structure maintenance decision method as claimed in claim 1, wherein in response to an error in the optimal maintenance decision output by the human-machine interaction module,
if the target case is a new case, manually inputting a decision conclusion of the target case to form a new maintenance decision, and entering a maintenance case library as a newly added maintenance case; simultaneously triggering a case self-learning algorithm to perform case self-learning, updating case characteristics and case characteristic attribute weights, and optimizing a maintenance decision model algorithm library;
if the target case is not a new case, manually revising the decision conclusion of the target case and updating a maintenance case base; and simultaneously triggering a case self-learning algorithm to perform case self-learning and optimizing a maintenance decision model algorithm library.
8. The decision-making system for the human-in-loop-based aircraft structure maintenance decision-making method according to any one of claims 1 to 7, comprising a data acquisition module, an analysis data module, a maintenance decision model algorithm library, a maintenance case library, a source case module, an optimal maintenance decision model and a human-computer interaction module,
the data acquisition module is used for acquiring damage inspection data of the airplane structure of the target case;
the data analysis module is used for extracting case characteristics of the target case and analyzing case characteristic attribute weights according to damage inspection data of the airplane structure of the target case and based on a maintenance decision model algorithm library and a maintenance case library;
the maintenance case library is used for storing maintenance cases of the aircraft structure;
the maintenance decision model algorithm library is used for storing maintenance decision model algorithms of the aircraft structure;
the source case module is used for obtaining a source case similar to the target case according to the case characteristic attribute weight;
the optimal maintenance decision model is used for outputting an optimal maintenance decision of a target case according to the source case;
the human-computer interaction module is used for outputting a decision conclusion according to the optimal maintenance decision; if the optimal maintenance decision is correct, directly outputting a decision conclusion; and if the optimal maintenance decision is wrong, manually verifying the decision conclusion of the target case and updating the optimal maintenance decision model algorithm library and the maintenance case library.
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| CN115730803A (en) * | 2022-11-29 | 2023-03-03 | 保银信科信息技术(湖北)有限公司 | Building maintenance progress intelligent monitoring management method based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115730803A (en) * | 2022-11-29 | 2023-03-03 | 保银信科信息技术(湖北)有限公司 | Building maintenance progress intelligent monitoring management method based on artificial intelligence |
| CN115730803B (en) * | 2022-11-29 | 2024-03-22 | 陈杰 | Intelligent monitoring and management method for maintenance progress of building house based on artificial intelligence |
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