CN115186674B - Aviation failure case management method, device, equipment and storage medium - Google Patents
Aviation failure case management method, device, equipment and storage medium Download PDFInfo
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Abstract
The application discloses an aviation failure case management method, device, equipment and storage medium, which are characterized in that redundant information is eliminated by carrying out information extraction and category analysis on failure cases, 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; the failure rule is obtained through the initial failure knowledge network, the logic relationship between the entities in the initial failure knowledge network can be reflected by the failure rule, 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 inquiry and execution path with the lowest time complexity or space complexity can be found, and the effective management of failure cases is realized.
Description
Technical Field
The present application relates to the field of aviation management, and in particular, to a method, an apparatus, a device, and a storage medium for managing aviation failure cases.
Background
The aero-mechanical structure is complex, and information interaction exists among all the module units, so that the fault phenomenon and the diversity of fault types are caused. Because of the tight connection between the devices, the fault phenomenon and the fault cause have a mapping relation of many to many, and various failure cases and fault data show high relevance. Most of the description of faults is in a non-digital form or is a simple judgment result, and the fault types such as fracture, fatigue, corrosion and the like are usually described in natural language, but in practice, the same fault cause can cause multiple fault manifestations, and the same fault phenomenon can be caused by different fault causes. After the equipment fails, a worker describes the equipment according to the failure phenomenon, records the failure time, the failure place and the unit or module of the failure and the final processing measure, 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 related with failure map information, failure data form and other information representing the same failure case.
The existing 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 an aviation failure case management method, device, equipment and storage medium, 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 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;
Obtaining 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 the failure relation according to the failure semantic unit 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 unit as a node and the failure relation as an edge.
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 rule relations 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:
Identifying synonyms and paraphraseology for the failure information to obtain failure semantics;
disambiguating the failure semantics to obtain disambiguated failure semantics;
carrying out semantic analysis on the disambiguated failure semantics to obtain same-class failure semantics;
and fusing the same-category invalidation semantics to obtain the invalidation semantic unit.
Optionally, the step of extracting multi-granularity semantic information of the failure case to be managed to obtain failure information includes:
and extracting a failure entity, a failure place and failure time from 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 to obtain the failure case knowledge network, the method further includes:
receiving inquiry information;
carrying out semantic recognition on the query information to obtain semantic elements;
And inquiring the semantic element based on the failure case knowledge network to obtain an inquiry result.
Optionally, the step of querying the semantic element based on the failure case knowledge network to obtain a query result includes:
searching the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive search 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 search result and the active mining result.
In addition, in order 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 the failure case to be managed to obtain failure information;
the failure semantic unit acquisition module is used for carrying out category analysis on the failure information to acquire a failure semantic unit;
The initial failure network acquisition module is used for formulating a failure relation according to the failure semantic unit to acquire 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, where the memory stores a computer program, and the processor executes the computer program to implement the above method.
In addition, in order 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 application has the beneficial effects that can be realized.
According to the aviation failure case management method, device, equipment and storage medium provided by the embodiment of the application, failure information is obtained by extracting multi-granularity semantic information of the failure case 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; obtaining 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 relation among the failure semantic units, and realizing efficient integration combination of the failure semantic units and effective connection among the failure information; the failure rule is obtained through the initial failure knowledge network, the logic relationship between the entities in the initial failure knowledge network can be reflected by the failure rule, 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 inquiry and execution path with the lowest time complexity or space complexity can be found, and the effective management of failure cases is realized.
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 flow chart of an aviation failure case management method according to an embodiment of the present application;
fig. 3 is a schematic functional block diagram of an aviation failure case management device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The main solutions of the embodiments of the present application are: the method, the device, the equipment and the storage medium for managing the aviation failure cases are provided, and the failure information is obtained by extracting multi-granularity semantic information of the 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; obtaining 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 aero-mechanical structure is complex, and information interaction exists among all module units, so that the fault phenomenon and the diversity of fault types are caused. Because of the tight connection between the devices, the fault phenomenon and the fault cause have a mapping relation of many to many, and various failure cases and fault data show high relevance. Most of the description of faults is in a non-digital form or is a simple judgment result, and the fault types such as fracture, fatigue, corrosion and the like are usually described in natural language, but in practice, the same fault cause can cause multiple fault manifestations, and the same fault phenomenon can be caused by different fault causes. After the equipment fails, a worker describes the equipment according to the failure phenomenon, records the failure time, the failure place and the unit or module of the failure and the final processing measure, 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 related with failure map information, failure data form and other information representing the same failure case.
The existing 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 queries of the failure cases in the system.
Therefore, the application provides a solution, redundant information is eliminated by carrying out information extraction and category analysis on the failure cases, 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 logic relationship between the entities in the initial failure knowledge network can be reflected by the failure rule, the failure rule is mapped back to the initial failure knowledge network for optimization, the optimized knowledge network improves the accuracy of association between the failure information, the query and execution path with the lowest time complexity or space complexity can be found, and effective management of failure cases is realized. Meanwhile, the obtained failure case knowledge network can be used for inquiring the failure case, and the input inquiry information is converted into semantic elements, and the nodes, the relations, the attributes and the like in the failure case knowledge network are combined for searching, so that the passive management inquiry of the failure case is realized; the semantic units are combined to form the user rule, the user rule is matched with the failure rule in the failure case knowledge network, the requirement intention understanding is carried out, the active failure information mining is realized, the passive retrieval based on the user and the active mining based on the knowledge network are realized, and the accuracy of the failure case query is improved.
Referring to fig. 1, fig. 1 is a schematic diagram of a computer device structure of a hardware running environment according to an embodiment of the present application.
As shown in fig. 1, the computer device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further 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 high-speed random access Memory (Random Access Memory, RAM) Memory or a stable Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the architecture shown in fig. 1 is not limiting of a computer device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a data storage module, a network communication module, a user interface module, and an electronic program may be included in the memory 1005 as one type of storage medium.
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 application may be provided in the computer device, where 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 by 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 a failure case to be managed to obtain failure information;
In a specific implementation process, failure refers to a phenomenon that a mechanical product loses a specified function due to the effects of stress, time, temperature, medium, misoperation and other factors in the service process, and generally comprises: three conditions of complete loss of function, functional decline and incapacity of ensuring work safety although the specified functions can be completed. Failure cases refer to events in which a failure phenomenon occurs. The failure cases are not related, management analysis is needed for the failure cases, and the similar accidents are prevented from happening repeatedly. And extracting semantic information of a plurality of granularities from the failure case to be managed to obtain failure information.
As an optional implementation manner, the step of extracting multi-granularity semantic information of the failure case to be managed to obtain failure information includes: and extracting a failure entity, a failure place and failure time from the failure case to be managed to obtain the failure information.
In the specific implementation process, the failure information is obtained by extracting multi-granularity semantic information such as failure entity, failure place, failure time and the like of the failure case to be managed, so that semantic mapping representation of data is realized. The failure entity refers to a main body where a failure phenomenon occurs, the failure site refers to a site where the failure phenomenon occurs, and the failure time refers to the time when 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 knowledge mining and the fusion of the same type of information are realized through the conversion from the failure information to the failure semantic unit.
As an optional implementation manner, the step of performing category analysis on the failure information to obtain a failure semantic unit includes: identifying synonyms and paraphraseology for the failure information to obtain failure semantics; disambiguating the failure semantics to obtain disambiguated failure semantics; carrying out semantic analysis on the disambiguated failure semantics to obtain same-class failure semantics; and fusing the same-category invalidation semantics to obtain the invalidation semantic unit.
In the specific implementation process, failure category analysis is carried out on failure information, wherein the failure category analysis comprises synonym and paraphrasing recognition, disambiguation, semantic analysis and homonymy fusion, and the failure information is converted into a failure semantic unit from multi-mode; the semantic analysis includes part-of-speech analysis of the failing semantics, and the like.
S30, formulating 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 relation that failure case information with rich semantic connotation can be described, relation logic between failure semantic units can be formulated, and the failure relation can optimize the failure semantic units; according to the initial failure network obtained by the failure semantic unit and the failure relation thereof, the failure case information can be stored, the failure knowledge can be associated, and the initial failure knowledge network can search the information such as entity names, relation names, entity attributes and the like to inquire the nodes in the knowledge network.
As an optional implementation manner, the step of formulating the failure relation according to the failure semantic unit 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 unit as a node and the failure relation as an edge.
In the specific implementation process, through similarity calculation and equivalent failure relation replacement, a failure relation for failure fault setting and optimizing input requirements is formed, and the uniqueness and the integrity of the relation are met; the failure relation can optimize the failure semantic unit, namely, the related failure relation is embedded by combining words, similarity calculation is performed, noun unification is performed on equivalent relation, and missing relation is complemented. The failure relation can realize high-efficiency integrated combination of the failure semantic units, and meets the semantic construction foundation of the failure semantic graph. The graph network structure is used as a form carrier of an initial failure knowledge network, the failure semantic unit is used as a node of the graph network, the failure relation is used as an edge of the graph network, and the initial failure knowledge network is constructed.
According to the embodiment, the failure semantic units converted from the multi-mode failure case content are combined in the form of the graph network, the failure semantic units are taken as nodes, the failure relationship is taken as edges, redundancy of high-association repeated information in the relational database can be avoided, the failure semantic units are effectively connected together, the failure semantic units are composed of the failure information, and effective association among the failure information is further achieved.
S40, obtaining a failure rule according to the initial failure knowledge network;
In a specific implementation process, the failure rule refers to a relationship between each node 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 a failure rule according to the initial failure knowledge network includes: traversing the nodes in the initial failure knowledge network, and extracting rule relations among the nodes through an N-gram algorithm to form the failure rule; wherein N is a positive integer.
In the implementation process, the N-gram is a language model commonly used in large-vocabulary continuous speech recognition, is essentially an algorithm based on a statistical language model, performs frequency statistics on each formed byte segment, and filters according to a preset threshold value to form a key gram list, namely a vector feature space of the text, wherein each gram in the list is a feature vector dimension. Traversing each node in the initial failure knowledge network, realizing a selection range between the nodes by using an N-gram algorithm, wherein N can be specified along with actual conditions, and taking N=3 as an example in the embodiment: when the 3-gram is, the node is used as a starting point, other arbitrary nodes are used as end points, and the number of nodes passing through the path is not more than the range defined by 3, so that a failure rule among the nodes is formed.
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 rules are mapped back to the initial knowledge failure knowledge network to optimize the network, so that the accuracy of correlation between failure information is improved, the inquiry and execution path with the lowest time complexity or space complexity can be found out, and the effective management of failure cases is realized. The optimization method comprises the following steps: the failure rule is used as an optimization object, 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 as a whole, and the efficiency is improved; and then, a rule calculation module with low cost is used for replacing a rule calculation module with high cost by using a rule cost replacement based method, so that optimization is realized, and the calculation efficiency is improved. And optimizing the knowledge network through failure rules among the nodes, so that the knowledge network can efficiently manage failure cases.
As an optional implementation manner, after the step of optimizing the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network, the method further includes: receiving inquiry information; carrying out semantic recognition on the query information to obtain semantic elements; and inquiring the semantic element based on the failure case knowledge network to obtain an inquiry result.
In implementations, a failure case knowledge network can be used to query related failure cases. The query information comprises voice, text, image and other forms, semantic recognition is carried out on the query information, failure semantics are extracted, the query information is converted into semantic elements, the semantic elements are queried through a failure case knowledge network, and then a query result is obtained.
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: searching the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive search 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 search 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 to search, so that the passive management inquiry of the failure case is realized, and a passive search result is obtained; meanwhile, semantic units are combined to form user rules, the user rules are matched with failure rules in a failure case knowledge network, demand intention understanding is carried out, active failure information mining is achieved, and an active mining result is obtained; and outputting the passive search result and the active mining result together as a query result for the failure case. The query method realizes passive retrieval based on the user and active mining based on the knowledge network, and improves the accuracy of query on the failure cases.
It should be understood that the foregoing is merely illustrative, and the technical solution of the present application is not limited in any way, and those skilled in the art may set the technical solution as required in practical applications, and the present application is not limited herein.
Through the description, it is easy to find that in the embodiment, redundant information is removed by extracting information and analyzing categories of failure cases, failure semantic units are obtained, an initial failure knowledge network is built according to the relationship among the failure semantic units, and efficient integration combination of the failure semantic units and effective connection among the failure information are achieved; meanwhile, the failure rule is obtained through the initial failure knowledge network, the logic relationship between the entities in the initial failure knowledge network can be reflected by the failure rule, the failure rule is mapped back to the initial failure knowledge network for optimization, the optimized knowledge network improves the accuracy of association between the failure information, the query and execution path with the lowest time complexity or space complexity can be found, and effective management of failure cases is realized. Meanwhile, the obtained failure case knowledge network can be used for inquiring the failure case, and the input inquiry information is converted into semantic elements, and the nodes, the relations, the attributes and the like in the failure case knowledge network are combined for searching, so that the passive management inquiry of the failure case is realized; the semantic units are combined to form the user rule, the user rule is matched with the failure rule in the failure case knowledge network, the requirement intention understanding is carried out, the active failure information mining is realized, the passive retrieval based on the user and the active mining based on the knowledge network are realized, and the accuracy of the 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 the failure case to be managed to obtain failure information;
the failure semantic unit acquisition module is used for carrying out category analysis on the failure information to acquire a failure semantic unit;
The initial failure network acquisition module is used for formulating a failure relation according to the failure semantic unit to acquire 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, each module in the aviation failure case management apparatus in this embodiment corresponds to each step in the aviation failure case management method in the foregoing embodiment one by one, so specific implementation manners of this embodiment may refer to implementation manners of the foregoing aviation failure case management method, and will not be described herein again.
Furthermore, in an embodiment, an embodiment of the present application also provides a computer device, the device including a processor, a memory, and a computer program stored in the memory, which when executed by the processor, implements the steps of the method in the foregoing embodiment.
Furthermore, in an embodiment, an embodiment of the present application further provides a computer storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the method in the previous embodiment.
In some embodiments, the computer readable storage medium may be FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; but may be a variety of devices including one or any combination of the above memories. The computer may be a variety of computing devices including smart terminals and servers.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, such as in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) 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).
As an example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices located 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 one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a multimedia terminal device (which may be a mobile phone, a computer, a television receiver, or a network device, etc.) to perform the method according to the embodiments of the present application.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. An aviation failure case management method is characterized by comprising the following steps:
Extracting multi-granularity semantic information of 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;
Obtaining a failure rule according to the initial failure knowledge network; the failure rule refers to the relation among all nodes in the initial failure knowledge network;
Updating the initial failure knowledge network based on the failure rule, taking the failure rule as an optimization object, combining repeated calculation by using a rule combining-based method, and replacing a large-cost rule calculation module by using a rule cost replacing-based method to obtain a failure case knowledge network;
The step of optimizing the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network further comprises the following steps:
Receiving inquiry information; carrying out semantic recognition on the query information to obtain semantic elements; searching the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive search 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 search result and the active mining result.
2. The method of 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 unit as a node and the failure relation as an edge.
3. The method of claim 1, wherein the step of obtaining a failure rule based on the initial failure knowledge network comprises:
Traversing the nodes in the initial failure knowledge network, and extracting rule relations among the nodes through an N-gram algorithm to form the failure rule; wherein N is a positive integer.
4. The method of claim 1, wherein the step of performing a class analysis on the failure information to obtain a failure semantic unit comprises:
Identifying synonyms and paraphraseology for the failure information to obtain failure semantics;
disambiguating the failure semantics to obtain disambiguated failure semantics;
carrying out semantic analysis on the disambiguated failure semantics to obtain same-class failure semantics;
and fusing the same-category invalidation semantics to obtain the invalidation semantic unit.
5. The method for managing aviation failure cases according to claim 1, wherein the step of extracting multi-granularity semantic information of the failure cases to be managed to obtain failure information comprises:
and extracting a failure entity, a failure place and failure time from the failure case to be managed to obtain the failure information.
6. An aeronautical failure case management device, comprising:
the failure information acquisition module is used for extracting multi-granularity semantic information of the failure case to be managed to obtain failure information;
the failure semantic unit acquisition module is used for carrying out category analysis on the failure information to acquire a failure semantic unit;
The initial failure network acquisition module is used for formulating a failure relation according to the failure semantic unit to acquire an initial failure knowledge network;
The failure rule acquisition module is used for acquiring a failure rule according to the initial failure knowledge network; the failure rule refers to the relation among all nodes in the initial failure knowledge network;
The failure case knowledge network acquisition module is used for updating the initial failure knowledge network based on the failure rule, taking the failure rule as an optimization object, combining repeated calculation by using a rule combining-based method, and replacing a rule calculation module with a low cost by using a rule cost replacing-based method to obtain the failure case knowledge network;
The step of optimizing the initial failure knowledge network based on the failure rule to obtain a failure case knowledge network further comprises the following steps:
Receiving inquiry information; carrying out semantic recognition on the query information to obtain semantic elements; searching the semantic elements based on the failure semantic units in the failure case knowledge network to obtain a passive search 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 search result and the active mining result.
7. A computer device, characterized in that it comprises a memory in which a computer program is stored and a processor which executes the computer program, implementing the method according to any of claims 1-5.
8. A computer readable storage medium, having stored thereon a computer program, the computer program being executable by a processor to implement the method of any of claims 1-5.
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| CN202210694787.0A CN115186674B (en) | 2022-06-20 | Aviation failure case management method, device, equipment and storage medium |
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| CN202210694787.0A CN115186674B (en) | 2022-06-20 | Aviation failure case management method, device, equipment and storage medium |
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| 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 |
| 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 |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| 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 |
| 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 |
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