CN115186674A - Aviation failure case management method, device, equipment and storage medium - Google Patents
Aviation failure case management method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN115186674A CN115186674A CN202210694787.0A CN202210694787A CN115186674A CN 115186674 A CN115186674 A CN 115186674A CN 202210694787 A CN202210694787 A CN 202210694787A CN 115186674 A CN115186674 A CN 115186674A
- Authority
- CN
- China
- Prior art keywords
- failure
- knowledge network
- semantic
- information
- initial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims description 37
- 238000000034 method Methods 0.000 claims abstract description 37
- 238000004458 analytical method Methods 0.000 claims description 19
- 230000015654 memory Effects 0.000 claims description 17
- 238000005065 mining Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000005457 optimization Methods 0.000 abstract description 9
- 230000010354 integration Effects 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 15
- 238000004364 calculation method Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000007797 corrosion Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013515 script Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000019580 granularity Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/30—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/43—Querying
- G06F16/432—Query formulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
- G06F16/45—Clustering; Classification
Abstract
The application discloses a method, a device, equipment and a storage medium for managing aviation failure cases, wherein redundant information is eliminated by extracting and analyzing the information of the failure cases, failure semantic units are obtained, an initial failure knowledge network is constructed according to the relation between the failure semantic units, and the efficient integration combination of the failure semantic units and the effective connection between the failure information are realized; the failure rule is obtained through the initial failure knowledge network, the failure rule can reflect the logic relation between internal entities of the initial failure knowledge network, the failure rule is mapped back to the initial failure knowledge network for optimization, the optimized knowledge network improves the accuracy of association between failure information, the query and execution path with the lowest time complexity or space complexity can be found out, and effective management of failure cases is achieved.
Description
Technical Field
The application relates to the field of aviation management, in particular to an aviation failure case management method, device, equipment and storage medium.
Background
The aeronautical machinery has a complex structure, and information interaction exists among all module units, so that the fault phenomenon and the fault type are diversified. Due to close connection among the devices, many-to-many mapping relation exists between the fault phenomenon and the fault reason, and various failure cases and fault data show high relevance. Most of the descriptions of the faults are in a non-digital form or a simple judgment result, and the fault types, such as fracture, fatigue, corrosion and the like, are usually described in natural language. After the equipment fails, a worker describes the equipment according to a fault phenomenon, records fault time, fault location, a fault occurrence unit or module and final processing measures of the fault, and the information is recorded on a fault tracking table mainly in a text form of natural language, is irregular, non-uniform and incomplete and cannot be associated with information such as failure map information, failure data tables and the like representing the same failure case.
The conventional aviation failure case information management system is simple in structure and high in management difficulty of failure cases.
Disclosure of Invention
The application mainly aims to provide a method, a device, equipment and a storage medium for managing aviation failure cases, and aims to solve the technical problem of high difficulty in managing failure cases.
In order to achieve the above object, the present application provides an aviation failure case management method, including:
extracting multi-granularity semantic information of the failure case to be managed to obtain failure information;
performing category analysis on the failure information to obtain a failure semantic unit;
formulating a failure relation according to the failure semantic unit to obtain an initial failure knowledge network;
acquiring a failure rule according to the initial failure knowledge network;
and updating the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network.
Optionally, the step of formulating failure relationships according to the failure semantic units to obtain an initial failure knowledge network includes:
formulating the failure relation according to the logic relation among the failure semantic units;
and constructing the initial failure knowledge network by taking the failure semantic units as nodes and the failure relations as edges.
Optionally, the step of obtaining the failure rule according to the initial failure knowledge network includes:
traversing the nodes in the initial failure knowledge network, and extracting the rule relation among the nodes through an N-gram algorithm to form the failure rule; wherein N is a positive integer.
Optionally, the step of performing category analysis on the failure information to obtain a failure semantic unit includes:
carrying out synonym and near synonym identification on the failure information to obtain failure semantics;
disambiguating the failure semantics to obtain disambiguated failure semantics;
performing semantic analysis on the disambiguated failure semantics to obtain the same category failure semantics;
and fusing the same category failure semantics to obtain the failure semantic unit.
Optionally, the step of extracting the multi-granularity semantic information of the failure case to be managed to obtain the failure information includes:
and extracting a failure entity, a failure place and failure time of the failure case to be managed to obtain the failure information.
Optionally, after the step of optimizing the initial failure knowledge network based on the failure rule and obtaining the failure case knowledge network, the method further includes:
receiving query information;
performing semantic identification on the query information to obtain a semantic element;
and querying the semantic elements based on the failure case knowledge network to obtain a query result.
Optionally, the step of querying the semantic element based on the failure case knowledge network to obtain a query result includes:
retrieving the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive retrieval result;
matching the semantic elements based on failure rules in the failure case knowledge network to obtain an active mining result;
and obtaining the query result according to the passive retrieval result and the active mining result.
In addition, to achieve the above object, the present application further provides an aviation failure case management device, including:
the failure information acquisition module is used for extracting multi-granularity semantic information of a failure case to be managed to acquire failure information;
the failure semantic unit acquisition module is used for carrying out category analysis on the failure information to obtain a failure semantic unit;
the initial failure network acquisition module is used for formulating failure relations according to the failure semantic units to obtain an initial failure knowledge network;
the failure rule acquisition module is used for acquiring a failure rule according to the initial failure knowledge network;
and the failure case knowledge network acquisition module is used for updating the initial failure knowledge network based on the failure rule to acquire the failure case knowledge network.
In addition, to achieve the above object, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.
In addition, to achieve the above object, the present application further provides a computer readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.
The beneficial effect that this application can realize.
According to the aviation failure case management method, device, equipment and storage medium, failure information is obtained by extracting multi-granularity semantic information of failure cases to be managed; performing category analysis on the failure information to obtain a failure semantic unit; formulating a failure relation according to the failure semantic unit to obtain an initial failure knowledge network; acquiring a failure rule according to the initial failure knowledge network; and updating the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network. The method comprises the steps of extracting information and analyzing categories of failure cases, eliminating redundant information, obtaining failure semantic units, constructing an initial failure knowledge network according to the relationship among the failure semantic units, and realizing efficient integrated combination of the failure semantic units and effective connection among failure information; the failure rule is obtained through the initial failure knowledge network, the failure rule can reflect the logic relation between internal entities of the initial failure knowledge network, the failure rule is mapped back to the initial failure knowledge network for optimization, the optimized knowledge network improves the accuracy of association between failure information, the query and execution path with the lowest time complexity or space complexity can be found out, and effective management of failure cases is achieved.
Drawings
FIG. 1 is a schematic diagram of a computer device in a hardware operating environment according to an embodiment of the present application;
fig. 2 is a schematic flowchart of an aviation failure case management method according to an embodiment of the present application;
fig. 3 is a functional module schematic diagram of an aviation failure case management device according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The main solution of the embodiment of the application is as follows: the aviation failure case management method, device, equipment and storage medium are provided, wherein multi-granularity semantic information extraction is carried out on failure cases to be managed to obtain failure information; performing category analysis on the failure information to obtain a failure semantic unit; formulating a failure relation according to the failure semantic unit to obtain an initial failure knowledge network; acquiring a failure rule according to the initial failure knowledge network; and updating the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network.
In the prior art, the aeronautical machinery has a complex structure, and information interaction exists among all module units, so that the fault phenomenon and the fault type are diversified. Due to the close relation among the devices, a many-to-many mapping relation exists between the fault phenomenon and the fault reason, and various failure cases and fault data have high relevance. Most of the descriptions of the faults are in a non-digital form or a simple judgment result, and the fault types, such as fracture, fatigue, corrosion and the like, are usually described in natural language. After the equipment fails, a worker describes the equipment according to a failure phenomenon, records failure time, failure location, failure occurrence units or modules and final processing measures of the failure, and the information is mainly recorded on a failure tracking table in a text form of natural language, is irregular, non-uniform and incomplete and cannot be associated with failure map information, failure data sheets and other information representing the same failure case.
The conventional aviation failure case information management system is simple in structure, high in management difficulty of failure cases and low in accuracy of results of related query of the failure cases in the system.
Therefore, the method provides a solution, redundant information is eliminated by extracting and analyzing the category of failure cases, failure semantic units are obtained, an initial failure knowledge network is constructed according to the relationship among the failure semantic units, and efficient integration combination of the failure semantic units and effective connection among failure information are realized; meanwhile, the failure rule is obtained through the initial failure knowledge network, the failure rule can reflect the logic relationship between internal entities of the initial failure knowledge network, the failure rule is mapped back to the initial failure knowledge network for optimization, the optimized knowledge network improves the accuracy of association between failure information, an inquiry and execution way with the lowest time complexity or space complexity can be found out, and effective management of failure cases is achieved. Meanwhile, the obtained failure case knowledge network can be used for inquiring the failure cases, and the passive management inquiry of the failure cases is realized by converting the input inquiry information into semantic elements and combining nodes, relations, attributes and the like in the failure case knowledge network for retrieval; the semantic units are combined to form user rules, the user rules are matched with failure rules in a failure case knowledge network, the requirement intention is understood, active failure information mining is achieved, passive retrieval based on users and active mining based on the knowledge network are achieved, and accuracy of failure case query is improved.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a computer device in a hardware operating environment according to an embodiment of the present application.
As shown in fig. 1, the computer apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of a computer device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an electronic program.
In the computer device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the computer device of the present invention may be disposed in the computer device, and the computer device invokes the aviation failure case management apparatus stored in the memory 1005 through the processor 1001, and executes the aviation failure case management method provided in the embodiment of the present application.
Referring to fig. 2, based on the hardware device of the foregoing embodiment, an embodiment of the present application provides an aviation failure case management method, including:
s10, extracting multi-granularity semantic information of the failure case to be managed to obtain failure information;
in the concrete implementation process, failure refers to the phenomenon that a mechanical product loses its specified functions due to the effects of factors such as stress, time, temperature, medium, and operation errors during the service process, and generally includes: complete loss of function, decline of function, and failure to ensure working safety even though the prescribed function can be completed. The failure case refers to an event in which a failure phenomenon occurs. The failure cases do not usually show mutual connection, and the failure cases need to be managed and analyzed to prevent the repeated occurrence of similar accidents. Semantic information extraction with multiple granularities is carried out on the failure cases to be managed, and failure information is obtained.
As an optional implementation manner, the step of extracting the multi-granularity semantic information of the failure case to be managed to obtain the failure information includes: and extracting a failure entity, a failure place and failure time of the failure case to be managed to obtain the failure information.
In the specific implementation process, the failure information is obtained by extracting the multi-granularity semantic information such as the failure entity, the failure location, the failure time and the like of the failure case to be managed, and the semantic mapping representation of the data is realized. The failure entity refers to a subject in which a failure phenomenon occurs, the failure location refers to a location in which the failure phenomenon occurs, and the failure time refers to a time in which the failure phenomenon occurs.
S20, performing category analysis on the failure information to obtain a failure semantic unit;
in the specific implementation process, the failure information is converted into the failure semantic unit, so that failure knowledge mining and fusion of the same type information of the failure information are realized.
As an optional implementation manner, the step of performing category analysis on the failure information to obtain a failure semantic unit includes: carrying out synonym and near synonym identification on the failure information to obtain failure semantics; disambiguating the failure semantics to obtain disambiguated failure semantics; performing semantic analysis on the disambiguated failure semantics to obtain the same category failure semantics; and fusing the failure semantics of the same category to obtain the failure semantic unit.
In the specific implementation process, failure category analysis is carried out on failure information, wherein the failure category analysis comprises synonym and near synonym identification, ambiguity elimination, semantic analysis and same category fusion, and the multimode failure information is converted into a failure semantic unit; the semantic analysis comprises the part of speech analysis of failure semantics and the like.
S30, establishing a failure relation according to the failure semantic unit to obtain an initial failure knowledge network;
in the specific implementation process, the failure relation refers to the relationship which can describe failure case information with rich semantic connotations and can formulate the relation logic among failure semantic units, and the failure relation can optimize the failure semantic units; the initial failure network obtained according to the failure semantic unit and the failure relation thereof can realize the storage of failure case information and the association of failure knowledge, and the initial failure knowledge network can realize the query of internal nodes of the knowledge network by retrieving information such as entity names, relation names, entity attributes and the like.
As an optional implementation manner, the step of formulating failure relationships according to the failure semantic units to obtain an initial failure knowledge network includes: formulating the failure relation according to the logic relation among the failure semantic units; and constructing the initial failure knowledge network by taking the failure semantic units as nodes and the failure relations as edges.
In the specific implementation process, a failure relation of input requirements for failure fault setting and optimization is formed through similarity calculation and equivalent failure relation replacement, and the uniqueness and integrity of the relation are met; the failure relation can optimize the failure semantic unit, namely embedding the associated failure relation combination words, calculating the similarity, unifying the equivalent relation nouns, complementing the missing relation and the like. The failure relation can realize the efficient integrated combination of failure semantic units, and meets the semantic construction foundation of the failure semantic map. And constructing the initial failure knowledge network by taking the graph network structure as a form carrier of the initial failure knowledge network, taking the failure semantic unit as a node of the graph network and taking the failure relation as an edge of the graph network.
In the embodiment, failure semantic units converted from multi-modal failure case contents are combined in a graph network mode, the failure semantic units are used as nodes, failure relations are used as edges, redundancy of high-association repeated information in a relational database can be avoided, all failure semantic units are effectively connected together, the failure semantic units are composed of failure information, and effective association among the failure information is further realized.
S40, acquiring a failure rule according to the initial failure knowledge network;
in a specific implementation process, the failure rule refers to a relationship between nodes in the initial failure knowledge network, and the failure rule can be used for updating and optimizing the initial failure knowledge network.
As an optional implementation manner, the step of obtaining the failure rule according to the initial failure knowledge network includes: traversing the nodes in the initial failure knowledge network, and extracting the rule relation among the nodes through an N-gram algorithm to form the failure rule; wherein N is a positive integer.
In the specific implementation process, the N-gram is a language model commonly used in large vocabulary continuous speech recognition, and is essentially an algorithm based on a statistical language model, frequency statistics is performed on each formed byte segment, filtering is performed according to a preset threshold value, a key gram list is formed, that is, a vector feature space of the text is formed, and each gram in the list is a feature vector dimension. Traversing each node in the initial failure knowledge network, wherein a selection range among the nodes is realized by an N-gram algorithm, N can be specified according to an actual situation, and in the embodiment, N =3 is taken as an example: and when the node is a 3-gram, taking the node as a starting point, taking any other node as an end point, and forming a failure rule among the nodes, wherein the number of the nodes passing by the route is not more than the range defined by 3.
And S50, updating the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network.
In the specific implementation process, the failure rule is mapped back to the initial failure knowledge network for optimization of the network, so that the accuracy of association between failure information is improved, the query and execution path with the lowest time complexity or space complexity can be found out, and the effective management of the failure case is realized. The optimization method comprises the following steps: the failure rule is used as an optimization object, and a method based on rule combination is utilized to combine a large number of repeated calculations, so that the calculation times are reduced, the calculation time is reduced on the whole, and the efficiency is improved; and then, a rule calculation module with high cost is replaced by a rule calculation module with low cost by using a rule cost replacement-based method, so that optimization is realized, and the calculation efficiency is improved. And the knowledge network is optimized through the failure rules among the nodes, so that the high-efficiency management of the knowledge network on failure cases is realized.
As an optional implementation manner, after the step of optimizing the initial failure knowledge network based on the failure rule and obtaining the failure case knowledge network, the method further includes: receiving query information; performing semantic identification on the query information to obtain a semantic element; and querying the semantic elements based on the failure case knowledge network to obtain a query result.
In a specific implementation, the failure case knowledge network can be used to query relevant failure cases. The query information comprises the forms of voice, text, images and the like, semantic recognition is carried out on the query information, failure semantics are extracted, the query information is converted into semantic elements, and the failure case knowledge network queries the semantic elements to obtain query results.
As an optional implementation manner, the step of querying the semantic element based on the failure case knowledge network to obtain a query result includes: retrieving the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive retrieval result; matching the semantic elements based on failure rules in the failure case knowledge network to obtain an active mining result; and obtaining the query result according to the passive retrieval result and the active mining result.
In the specific implementation process, the semantic elements are combined with nodes, relations, attributes and the like in the failure case knowledge network for retrieval, so that passive management query of failure cases is realized, and passive retrieval results are obtained; meanwhile, combining semantic units to form user rules, matching the user rules with failure rules in a failure case knowledge network, understanding the requirement intention, realizing active failure information mining and obtaining active mining results; and outputting the passive retrieval result and the active mining result together as a query result of the failure case. The query method realizes passive retrieval based on users and active mining based on a knowledge network, and improves the accuracy of querying the failure cases.
It should be understood that the above is only an example, and the technical solution of the present application is not limited in any way, and those skilled in the art can make the setting based on the actual application, and the setting is not limited herein.
According to the embodiment, redundant information is eliminated by extracting information and analyzing the type of the failure case, failure semantic units are obtained, an initial failure knowledge network is constructed according to the relation among the failure semantic units, and efficient integration combination of the failure semantic units and effective connection among the failure information are realized; meanwhile, the failure rule is obtained through the initial failure knowledge network, the failure rule can reflect the logic relation between internal entities of the initial failure knowledge network, the failure rule is mapped back to the initial failure knowledge network for optimization, the optimized knowledge network improves the accuracy of association between failure information, the query and execution path with the lowest time complexity or space complexity can be found out, and the effective management of the failure case is realized. Meanwhile, the obtained failure case knowledge network can be used for inquiring the failure cases, and the passive management inquiry of the failure cases is realized by converting the input inquiry information into semantic elements and combining nodes, relations, attributes and the like in the failure case knowledge network for retrieval; the semantic units are combined to form user rules, the user rules are matched with failure rules in a failure case knowledge network, the requirement intention is understood, active failure information mining is achieved, passive retrieval based on users and active mining based on the knowledge network are achieved, and accuracy of failure case query is improved.
Referring to fig. 3, based on the same inventive concept, an embodiment of the present application further provides an aviation failure case management apparatus, including:
the failure information acquisition module is used for extracting multi-granularity semantic information of a failure case to be managed to acquire failure information;
the failure semantic unit acquisition module is used for carrying out category analysis on the failure information to obtain a failure semantic unit;
the initial failure network acquisition module is used for formulating failure relation according to the failure semantic unit to obtain an initial failure knowledge network;
the failure rule acquisition module is used for acquiring a failure rule according to the initial failure knowledge network;
and the failure case knowledge network acquisition module is used for updating the initial failure knowledge network based on the failure rule to acquire the failure case knowledge network.
It should be noted that, in the embodiment, each module in the aviation failure case management apparatus corresponds to each step in the aviation failure case management method in the foregoing embodiment one to one, so that the specific implementation of the embodiment may refer to the implementation of the aviation failure case management method, and details are not described here.
Furthermore, in an embodiment, an embodiment of the present application further provides a computer device, which includes a processor, a memory, and a computer program stored in the memory, and when the computer program is executed by the processor, the steps of the method in the foregoing embodiments are implemented.
Furthermore, in an embodiment, the present application further provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the steps of the method in the foregoing embodiments.
In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories. The computer may be a variety of computing devices including intelligent terminals and servers.
In some embodiments, executable instructions may be written in any form of programming language (including compiled or interpreted languages), in the form of programs, software modules, scripts or code, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
By way of example, executable instructions may, but need not, correspond to files in a file system, and may be stored in a portion of a file that holds other programs or data, such as in one or more scripts in a hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).
By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a multimedia terminal (e.g., a mobile phone, a computer, a television receiver, or a network device) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. An aviation failure case management method is characterized by comprising the following steps:
extracting multi-granularity semantic information of the failure case to be managed to obtain failure information;
performing category analysis on the failure information to obtain a failure semantic unit;
formulating a failure relation according to the failure semantic unit to obtain an initial failure knowledge network;
acquiring a failure rule according to the initial failure knowledge network;
and updating the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network.
2. The aviation failure case management method according to claim 1, wherein the step of formulating a failure relationship based on the failure semantic unit to obtain an initial failure knowledge network comprises:
formulating the failure relation according to the logic relation among the failure semantic units;
and constructing the initial failure knowledge network by taking the failure semantic units as nodes and the failure relations as edges.
3. The aviation failure case management method of claim 1, wherein the step of obtaining failure rules based on the initial failure knowledge network comprises:
traversing the nodes in the initial failure knowledge network, and extracting the rule relation among the nodes through an N-gram algorithm to form the failure rule; wherein N is a positive integer.
4. The aviation failure case management method according to claim 1, wherein the step of performing category analysis on the failure information to obtain a failure semantic unit comprises:
carrying out synonym and near synonym identification on the failure information to obtain failure semantics;
disambiguating the failure semantics to obtain disambiguated failure semantics;
performing semantic analysis on the disambiguated failure semantics to obtain the same category failure semantics;
and fusing the failure semantics of the same category to obtain the failure semantic unit.
5. The aviation failure case management method according to claim 1, wherein the step of extracting the multi-granularity semantic information of the failure case to be managed to obtain the failure information comprises:
and extracting a failure entity, a failure place and failure time of the failure case to be managed to obtain the failure information.
6. The aviation failure case management method according to claim 1, wherein after the step of optimizing the initial failure knowledge network based on the failure rules to obtain a failure case knowledge network, the method further comprises:
receiving query information;
performing semantic identification on the query information to obtain a semantic element;
and querying the semantic elements based on the failure case knowledge network to obtain a query result.
7. The aviation failure case management method according to claim 6, wherein the step of querying the semantic elements based on the failure case knowledge network to obtain query results comprises:
retrieving the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive retrieval result;
matching the semantic elements based on failure rules in the failure case knowledge network to obtain an active mining result;
and obtaining the query result according to the passive retrieval result and the active mining result.
8. An aviation failure case management apparatus, comprising:
the failure information acquisition module is used for extracting multi-granularity semantic information of a failure case to be managed to acquire failure information;
the failure semantic unit acquisition module is used for carrying out category analysis on the failure information to obtain a failure semantic unit;
the initial failure network acquisition module is used for formulating failure relations according to the failure semantic units to obtain an initial failure knowledge network;
the failure rule acquisition module is used for acquiring a failure rule according to the initial failure knowledge network;
and the failure case knowledge network acquisition module is used for updating the initial failure knowledge network based on the failure rule to acquire the failure case knowledge network.
9. A computer arrangement, characterized in that the computer arrangement comprises a memory in which a computer program is stored and a processor which executes the computer program for implementing the method as claimed in any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program, which, when executed by a processor, performs the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210694787.0A CN115186674A (en) | 2022-06-20 | 2022-06-20 | Aviation failure case management method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210694787.0A CN115186674A (en) | 2022-06-20 | 2022-06-20 | Aviation failure case management method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115186674A true CN115186674A (en) | 2022-10-14 |
Family
ID=83512831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210694787.0A Pending CN115186674A (en) | 2022-06-20 | 2022-06-20 | Aviation failure case management method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115186674A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391906A (en) * | 2017-06-19 | 2017-11-24 | 华南理工大学 | Health diet knowledge network construction method based on neutral net and collection of illustrative plates structure |
| US20190018904A1 (en) * | 2017-07-14 | 2019-01-17 | Phylot Inc. | Method and system for identifying and discovering relationships between disparate datasets from multiple sources |
| CN110413873A (en) * | 2019-06-11 | 2019-11-05 | 北京邮电大学 | A kind of knowledge network construction method, device and electronic equipment |
| CN111241260A (en) * | 2020-01-08 | 2020-06-05 | 平安科技（深圳）有限公司 | Data processing method, device and equipment based on human-computer interaction and storage medium |
| CN114153928A (en) * | 2021-12-03 | 2022-03-08 | 中国电信股份有限公司 | Method, system, equipment and medium for constructing urban geographic semantic knowledge network |
-
2022
- 2022-06-20 CN CN202210694787.0A patent/CN115186674A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391906A (en) * | 2017-06-19 | 2017-11-24 | 华南理工大学 | Health diet knowledge network construction method based on neutral net and collection of illustrative plates structure |
| US20190018904A1 (en) * | 2017-07-14 | 2019-01-17 | Phylot Inc. | Method and system for identifying and discovering relationships between disparate datasets from multiple sources |
| CN110413873A (en) * | 2019-06-11 | 2019-11-05 | 北京邮电大学 | A kind of knowledge network construction method, device and electronic equipment |
| CN111241260A (en) * | 2020-01-08 | 2020-06-05 | 平安科技（深圳）有限公司 | Data processing method, device and equipment based on human-computer interaction and storage medium |
| CN114153928A (en) * | 2021-12-03 | 2022-03-08 | 中国电信股份有限公司 | Method, system, equipment and medium for constructing urban geographic semantic knowledge network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110795455B (en) | Dependency analysis method, electronic device, computer apparatus, and readable storage medium | |
| CN109062952B (en) | Data query method and device and electronic equipment | |
| EP3080721B1 (en) | Query techniques and ranking results for knowledge-based matching | |
| US9684709B2 (en) | Building features and indexing for knowledge-based matching | |
| CN109241068B (en) | Method and device for comparing foreground and background data and terminal equipment | |
| CN111309760A (en) | Data retrieval method, system, device and storage medium | |
| CN116383238B (en) | Data virtualization system, method, device, equipment and medium based on graph structure | |
| CN108415998B (en) | Application dependency relationship updating method, terminal, device and storage medium | |
| CN112860727A (en) | Data query method, device, equipment and medium based on big data query engine | |
| CN105302827B (en) | A kind of searching method and equipment of event | |
| CN111708867A (en) | Question and answer query method, device and equipment applied to electric power operation and inspection | |
| CN112000692B (en) | Page query feedback method and device, computer equipment and readable storage medium | |
| CN116483850A (en) | Data processing method, device, equipment and medium | |
| CN112000690B (en) | Method and device for analyzing structured operation statement | |
| CN110866007B (en) | Information management method, system and computer equipment for big data application and table | |
| CN112905600A (en) | Data query method and device, storage medium and electronic equipment | |
| JP5422589B2 (en) | Module analysis system, module analysis method, and module analysis program | |
| CN115186674A (en) | Aviation failure case management method, device, equipment and storage medium | |
| CN116010461A (en) | Data blood relationship analysis method and device, storage medium and electronic equipment | |
| CN113868138A (en) | Method, system, equipment and storage medium for acquiring test data | |
| CN113312540A (en) | Information processing method, device, equipment, system and readable storage medium | |
| CN113742359A (en) | Method and device for inquiring presence, electronic equipment and storage medium | |
| CN112527880B (en) | Method, device, equipment and medium for collecting metadata information of big data cluster | |
| CN112182177A (en) | User problem processing method and device, electronic equipment and storage medium | |
| CN111859969B (en) | Data analysis method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |