CN116431796A - Knowledge fusion-based railway inspection information analysis method and system - Google Patents
Knowledge fusion-based railway inspection information analysis method and system Download PDFInfo
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Abstract
The application discloses a method and a system for analyzing railway inspection information based on knowledge fusion. The method comprises the following steps: acquiring inquiry information about railway inspection; inquiring in a database by adopting a term inquiring mode and/or a relation inquiring mode according to the inquiring information to obtain an inquiring result about railway inspection, wherein the database comprises original railway inspection information and a knowledge graph of the railway inspection information; and analyzing and evaluating the railway inspection condition according to the inquiry result to obtain a railway inspection condition evaluation result. The method can automatically analyze a large amount of unstructured data in the railway inspection information, and can provide an intelligent management system platform for railway operation and maintenance management staff based on the knowledge graph of the railway inspection information by constructing the knowledge graph of the railway inspection information, so that the railway operation and maintenance management efficiency can be improved.
Description
Technical Field
The application relates to the technical field of railway operation and maintenance management, in particular to a method and a system for analyzing railway inspection information based on knowledge fusion.
Background
The existing railway inspection information analysis method comprises the steps of acquiring railway operation data in real time according to a monitoring task through a plurality of sub-monitoring networks, and sending the plurality of railway operation data to a main monitoring network after converging; and establishing a state evaluation method based on fuzzy analytic hierarchy process for evaluation. An automatic analysis method for a large amount of unstructured data in railway inspection information and an analysis scheme for the railway inspection information are lacked.
Disclosure of Invention
In order to solve the problems of an automatic analysis method for a large amount of unstructured data in railway inspection information and an analysis scheme for the railway inspection information, the application provides a method and a system for analyzing the railway inspection information based on knowledge fusion.
In a first aspect of the present application, a method for analyzing railway inspection information based on knowledge fusion is provided, including:
acquiring inquiry information about railway inspection;
inquiring in a database by adopting a term inquiring mode and/or a relation inquiring mode according to the inquiring information to obtain an inquiring result about railway inspection, wherein the database comprises original railway inspection information and a knowledge graph of the railway inspection information;
and analyzing and evaluating the railway inspection condition according to the inquiry result to obtain a railway inspection condition evaluation result.
In some embodiments, the construction of the knowledge graph of the railway inspection information includes:
analyzing and processing the existing railway inspection information to obtain a knowledge triplet of the railway inspection information, wherein the knowledge triplet comprises a knowledge relation triplet of the railway inspection information and a knowledge attribute triplet of the railway inspection information;
and constructing a knowledge graph of the railway inspection information based on the knowledge triplets of the railway inspection information.
In some embodiments, the step of obtaining a knowledge triplet of railway inspection information comprises:
carrying out structural processing on the existing railway inspection information, and extracting unstructured data in the existing railway inspection information;
performing word segmentation and part-of-speech tagging on the unstructured data;
analyzing the knowledge relation between each word in the unstructured data to obtain a knowledge relation triplet of the railway inspection information;
and based on the unstructured data, acquiring the knowledge attribute triplets of the railway inspection information by using a network information wrapper.
In some embodiments, the knowledge base based on the knowledge base of the railway inspection information constructs a knowledge graph of the railway inspection information, comprising:
denoising the knowledge triples based on regular expressions of the knowledge triples of the railway inspection information;
automatically selecting a de-noised knowledge triplet by adopting a pre-designed pairing algorithm;
performing similarity processing on the automatically selected knowledge triples to determine similarity relation;
and constructing a knowledge graph of the railway inspection information according to the knowledge triples and the corresponding similarity relations of the knowledge triples.
In some embodiments, the rules for similarity processing the knowledge triples include:
if the automatically selected two denoised knowledge triples comprise a denoised knowledge attribute triplet of the railway inspection information and a denoised knowledge relation triplet of the railway inspection information, performing similarity calculation on a first knowledge element of the two denoised knowledge triples;
and if the automatically selected two denoised knowledge triples comprise the two denoised knowledge relation triples of the railway inspection information, performing similarity calculation on the third knowledge element of the selected knowledge relation triples of the first denoised railway inspection information and the first knowledge element of the selected knowledge relation triples of the second denoised railway inspection information.
In some embodiments, an edit distance algorithm is used to perform similarity calculations on automatically selected knowledge triples.
In some embodiments, any two automatically selected knowledge triples t are calculated using the following calculation formula i ＝(s i ,p i ,o i ) And t j ＝(s j ,p j ,o j ) Semantic distance f under relationship r r (t i ,t j )：
f r (t i ,t j )＝||αM ri ·t i -βM rj ·t j ||；
Wherein s is i ,p i ,o i Respectively represent t i Subject, predicate and object of (a); s is(s) j ,p j ,o j Respectively represent t j Subject, predicate and object of (a); m is M ri 、M rj Respectively the relation r to t i 、t j Alpha is the head coefficient and beta is the tail coefficient.
In some embodiments, the railway inspection condition evaluation result includes a single day railway inspection composite score and/or a railway inspection composite score over a set period of time.
In some embodiments, the single day railway patrol composite score P d The method is calculated by adopting the following calculation formula:
wherein d is a date number; vector X 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 And respectively representing the vehicle running state, the operation day vehicle overhaul condition, the vehicle abnormal state, the infrastructure service state, the infrastructure overhaul condition, the infrastructure monitoring alarm condition, the equipment running state, the equipment overhaul condition and the equipment damage condition in the query result.
In some embodiments, the integrated score for the railway inspection within the set time period is calculated using the following calculation formula:
wherein P is IJ Comprehensively grading the railway inspection in the time period from day I to day J; p (P) d Comprehensively grading for single-day railway inspection; d is a date number; max { P d I is not less than d is not less than J, and P is each time period from day I to day J d Maximum value of (2), min { P d I is not less than d is not less than J, and P is each time period from day I to day J d Is set to be a minimum value of (c),
for each P in the period from day I to day J d And M is the total number of times of single-day patrol comprehensive scores in the period from day I to day J.
In some embodiments, the query results are presented in the form of a graph.
In a second aspect of the present application, a system for analyzing railway inspection information based on knowledge fusion is provided, including:
the storage module is used for storing the original railway inspection information, the triplets of the railway inspection information and the knowledge graph of the railway inspection information into the database;
the query module is used for acquiring query information about railway inspection, and querying in a database by adopting an entry query mode and/or a relation query mode according to the query information to obtain a query result about railway inspection;
and the processing module is used for analyzing and evaluating the railway inspection situation according to the query result to obtain a railway inspection situation evaluation result.
In a third aspect of the present application, a computer readable storage medium is provided, which stores a computer program executable by one or more processors for implementing a method for analyzing railroad patrol information based on knowledge fusion as described above.
In a fourth aspect of the present application, there is provided a computer program product which, when run on a processor, performs a method of analysis of railroad patrol information based on knowledge fusion as described above.
In a fifth aspect of the present application, there is provided an electronic device, including a memory and a processor, where the memory stores a computer program, the memory and the processor are communicatively connected to each other, and the computer program, when executed by the processor, performs the method for analyzing railroad patrol information based on knowledge fusion as described above.
Compared with the prior art, the technical scheme of the application has the following advantages or beneficial effects: the method can automatically analyze a large amount of unstructured data in the railway inspection information, and can provide an intelligent management system platform for railway operation and maintenance management staff based on the knowledge graph of the railway inspection information by constructing the knowledge graph of the railway inspection information, so that the railway operation and maintenance management efficiency can be improved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the drawings provided without inventive effort to a person of ordinary skill in the art.
Fig. 1 is a flowchart of a method for analyzing railway inspection information based on knowledge fusion according to an embodiment of the present application;
fig. 2 is a flowchart of knowledge graph construction of railway inspection information provided in an embodiment of the present application;
fig. 3 is a schematic diagram of a system architecture of a railway inspection information analysis system based on knowledge fusion according to an embodiment of the present application;
fig. 4 is a connection block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following will describe embodiments of the present application in detail with reference to the drawings and examples, thereby how to apply technical means to the present application to solve technical problems, and realizing processes achieving corresponding technical effects can be fully understood and implemented accordingly. The embodiments and the features in the embodiments can be combined with each other on the premise of no conflict, and the formed technical schemes are all within the protection scope of the application.
Example 1
Fig. 1 is a flowchart of a method for analyzing railway inspection information based on knowledge fusion according to an embodiment of the present application, and as shown in fig. 1, the method provided in the embodiment includes:
step S11, acquiring inquiry information about railway inspection;
step S12, inquiring in a database by adopting an entry inquiry mode and/or a relation inquiry mode according to the inquiry information to obtain an inquiry result about railway inspection, wherein the database comprises original railway inspection information and a knowledge graph of the railway inspection information;
and step S13, analyzing and evaluating the railway inspection condition according to the query result to obtain a railway inspection condition evaluation result.
In some embodiments, fig. 2 is a flowchart of knowledge graph construction of railway inspection information provided in the embodiments of the present application, where, as shown in fig. 2, the construction of the knowledge graph of railway inspection information includes:
and S21, analyzing and processing the existing railway inspection information to obtain a knowledge triplet of the railway inspection information, wherein the knowledge triplet comprises a knowledge relation triplet of the railway inspection information and a knowledge attribute triplet of the railway inspection information.
Specifically, the obtaining of the knowledge triples includes the following steps:
(1) And carrying out structural processing on the existing railway inspection information, and extracting unstructured data in the existing original railway inspection information.
The railway inspection information comprises a large amount of unstructured data, and the railway knowledge information obtained from the unstructured data is rich in a large amount of punctuation marks and letters, such as English links, data sources or sources and other noise information; digits are also redundant information if it is not intended to relate to data such as a date address that may contain digits. Therefore, the original railway inspection information needs to be subjected to structural processing, and noise information in the original railway inspection information is removed.
(2) And performing word segmentation and part-of-speech tagging on the unstructured data.
Specifically, a word segmentation tool is used for sentence-by-sentence word segmentation of sentences in the unstructured data, and the position of each word is partitioned; the components of each word that act in the sentence, such as subject, predicate, object, etc., are labeled according to the part of speech of each word.
(3) And analyzing the knowledge relation between each word in the unstructured data marked with the part of speech to obtain a knowledge relation triplet of the railway inspection information.
Specifically, dependency syntactic analysis is carried out on the unstructured data marked with the part of speech by using a dependency syntactic analysis algorithm, knowledge relations among words are obtained, and a knowledge relation triplet of railway inspection information is obtained.
(4) And based on the unstructured data, acquiring the knowledge attribute triplets of the railway inspection information by using a network information wrapper.
(5) And outputting and storing the obtained knowledge relation triplets of the railway inspection information and the obtained knowledge attribute triplets of the railway inspection information.
And S22, constructing a knowledge graph of the railway inspection information based on the knowledge triplets of the railway inspection information.
Specifically, the knowledge triplets based on the railway inspection information construct a knowledge graph of the railway inspection information, including:
(1) And denoising the knowledge triples based on the regular expression of the railway inspection information knowledge triples.
After the knowledge triples of the railway inspection information are basically formed, most of noise information is removed, but impurities such as redundant punctuation marks and the like may exist in part of the knowledge triples of the railway inspection information. Therefore, before further acquiring the knowledge triples of the optimal railway inspection information, the knowledge triples of the railway inspection information need to be subjected to noise removal again.
(2) And the preset pairing algorithm is adopted, so that the triplets can be automatically linked, and the knowledge triplets after noise removal are automatically selected.
(3) And carrying out similarity processing on the automatically selected knowledge triples, and determining a similarity relation.
Preferably, the semantic similarity of the automatically selected knowledge triples is calculated by adopting an edit distance algorithm, and whether the two selected knowledge triples are similar or not is judged according to a preset threshold value so as to determine the similarity relation between the two selected knowledge triples.
The types of the knowledge triples automatically selected each time may be the same or different, so that rules for performing similarity processing on the knowledge triples are designed, including:
if the automatically selected two denoised knowledge triples comprise a denoised knowledge attribute triplet of the railway inspection information and a denoised knowledge relation triplet of the railway inspection information, performing similarity calculation on a first knowledge element of the two denoised knowledge triples;
if the automatically selected two noise-reduced knowledge triples comprise two noise-reduced knowledge triples of the railway inspection information, performing similarity calculation on a third knowledge element of the selected first noise-reduced knowledge triples of the railway inspection information and a first knowledge element of the selected second noise-reduced knowledge triples of the railway inspection information.
Specifically, any two automatically selected knowledge triples t are calculated by adopting the following calculation formula i ＝(s i ,p i ,o i ) And t j ＝(s j ,p j ,o j ) Semantic distance f under relationship r r (t i ,t j )：
f r (t i ,t j )＝||αM ri ·t i -βM rj ·t j ||；
Wherein s is i ,p i ,o i Respectively represent t i Subject, predicate and object of (a); s is(s) j ,p j ,o j Respectively represent t j Subject, predicate and object of (a); m is M ri 、M rj Respectively the relation r to t i 、t j Alpha is the head coefficient and beta is the tail coefficient.
(4) And constructing a knowledge graph of the railway inspection information according to the knowledge triples and the corresponding similarity relations of the knowledge triples.
In some embodiments, the query results are presented in the form of a graph.
In some embodiments, the query result and/or the railway inspection condition evaluation result are output in a two-dimensional and/or three-dimensional manner.
In some embodiments, the railway inspection condition evaluation result includes a single day railway inspection composite score and/or a railway inspection composite score over a set period of time.
Preferably, the single-day railway inspection comprehensive score P d The method is calculated by adopting the following calculation formula:
wherein d is a date number; vector X 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 Respectively representing the running state of the vehicle and the overhaul condition of the vehicle on the operation day in the query resultThe system comprises a vehicle abnormal state, an infrastructure service state, an infrastructure maintenance condition, an infrastructure monitoring alarm condition, an equipment running state, an equipment maintenance condition and an equipment damage condition.
The comprehensive score of the railway inspection within the set time period is calculated by the following calculation formula:
wherein P is IJ Comprehensively grading the railway inspection in the time period from day I to day J; p (P) d Comprehensively grading for single-day railway inspection; d is a date number; max { P d I is not less than d is not less than J, and P is each time period from day I to day J d Maximum value of (2), min { P d I is not less than d is not less than J, and P is each time period from day I to day J d Is set to be a minimum value of (c),
for each P in the period from day I to day J d And M is the total number of times of single-day patrol comprehensive scores in the time from day I to day J.
Example two
The present embodiment provides a system 200 for analyzing information of railway inspection based on knowledge fusion, fig. 3 is a schematic diagram of a system architecture of the system 200 for analyzing information of railway inspection based on knowledge fusion provided in the embodiment of the present application, as shown in fig. 3, the system 200 of the present embodiment includes:
the acquisition module 210 is configured to acquire original railway inspection information.
The storage module 220 is configured to store the original railway inspection information, the triplets of the railway inspection information, and the knowledge graph of the railway inspection information into a database. The triplets of the railway inspection information comprise knowledge relation triplets of the railway inspection information and knowledge attribute triplets of the railway inspection information.
The query module 230 is configured to obtain query information about railway inspection, query in a database according to the query information by adopting a term query mode and/or a relationship query mode, and obtain a query result about railway inspection;
in particular, the query module 230 provides semantic searches using a search engine. The entry query mode is as follows: acquiring inquiry information about railway inspection; and matching in the storage module 230 according to the entry in the query information to obtain a query result about railway inspection. The relation inquiry mode is as follows: by constructing an ontology, the ontology may be extracted from the storage module 230 and then manually modified; the corresponding data mapping and matching between the data stored in the storage module 230 realize the integration of the data and the semantic association of the data, and the constructed ontology and the knowledge graph obtained by the integration of the ontology can be utilized to analyze the natural language, so that the natural language query is directly converted into SQL (Structured Query Language ) to query the database, and the query result is given, which can be given in a graph mode.
And the processing module 240 is used for analyzing and evaluating the railway inspection condition according to the query result to obtain a railway inspection condition evaluation result.
And the display module 250 is used for displaying the query result and the railway inspection condition evaluation result.
In some embodiments, the railway inspection condition evaluation result includes a single day railway inspection composite score and/or a railway inspection composite score over a set period of time.
Preferably, the single-day railway inspection comprehensive score P d The method is calculated by adopting the following calculation formula:
wherein d is a date number; vector X 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 Respectively representing the vehicle running state, the vehicle maintenance condition on the operation day, the abnormal state of the vehicle, the service state of the infrastructure, the maintenance condition of the infrastructure, the monitoring alarm condition of the infrastructure, the running state of equipment and the equipment in the query resultAnd (5) overhauling conditions and equipment damage conditions.
The comprehensive score of the railway inspection within the set time period is calculated by the following calculation formula:
wherein P is IJ Comprehensively grading the railway inspection in the time period from day I to day J; p (P) d Comprehensively grading for single-day railway inspection; d is a date number; max { P d I is not less than d is not less than J, and P is each time period from day I to day J d Maximum value of (2), min { P d I is not less than d is not less than J, and P is each time from day I to day J d Is set to be a minimum value of (c),
for each P in the period from day I to day J d And M is the total number of times of single-day patrol comprehensive scores in the period from day I to day J.
In some embodiments, the processing module 240 is further configured to analyze existing railway inspection information to obtain a knowledge triplet of the railway inspection information; based on the unstructured data, acquiring a knowledge attribute triplet of the railway inspection information by using a network information wrapper; the knowledge triplets comprise knowledge relation triplets of railway inspection information and knowledge attribute triplets of railway inspection information.
Specifically, the obtaining of the knowledge triples includes the following steps:
(1) And carrying out structural processing on the existing railway inspection information, and extracting unstructured data in the existing railway inspection information.
The railway inspection information comprises a large amount of unstructured data, and the railway knowledge information obtained from the unstructured data is rich in a large amount of punctuation marks and letters, such as English links, data sources or sources and the like; digits are also redundant information if it is not intended to relate to data such as a date address that may contain digits. Therefore, before further acquiring the knowledge triples of the optimal railway inspection information, the knowledge triples of the railway inspection information need to be subjected to noise removal again.
(2) And performing word segmentation and part-of-speech tagging on the unstructured data.
Specifically, a word segmentation tool is used for sentence-by-sentence word segmentation of sentences in the unstructured data, and the position of each word is partitioned; the components of each word that act in the sentence, such as subject, predicate, object, etc., are labeled according to the part of speech of each word.
(3) And analyzing the knowledge relation between each word in the unstructured data marked with the part of speech to obtain a knowledge relation triplet of the railway inspection information.
Specifically, dependency syntactic analysis is carried out on the unstructured data marked with the part of speech by using a dependency syntactic analysis algorithm, knowledge relations among words are obtained, and a knowledge relation triplet of railway inspection information is obtained.
(4) And based on the unstructured data, acquiring the knowledge attribute triplets of the railway inspection information by using a network information wrapper.
(5) And outputting the obtained knowledge relation triplets of the railway inspection information and the obtained knowledge attribute triplets of the railway inspection information to the storage module 220.
Specifically, the knowledge triplets based on the railway inspection information construct a knowledge graph of the railway inspection information, including:
(1) And denoising the knowledge triples based on the regular expression of the railway inspection information knowledge triples.
After the knowledge triples of the railway inspection information are basically formed, most of noise information is removed, but impurities such as redundant punctuation marks and the like may exist in part of the knowledge triples of the railway inspection information. Therefore, before further acquiring the knowledge triples of the optimal railway inspection information, the noise removal processing work needs to be performed again on the knowledge triples of the railway inspection information.
(2) And the preset pairing algorithm is adopted, so that the triplets can be automatically linked, and the knowledge triplets after noise removal are automatically selected.
(3) And carrying out similarity processing on the automatically selected knowledge triples, and determining a similarity relation.
Preferably, the semantic similarity of the automatically selected knowledge triples is calculated by adopting an edit distance algorithm, and whether the two selected knowledge triples are similar or not is judged according to a preset threshold value so as to determine the similarity relation between the two selected knowledge triples.
The types of the knowledge triples automatically selected each time may be the same or different, so that rules for performing similarity processing on the knowledge triples are designed, including:
if the automatically selected two triples comprise a knowledge attribute triplet of the denoised railway inspection information and a knowledge relation triplet of the denoised railway inspection information, performing similarity calculation on a first knowledge element of the two denoised knowledge triples;
and if the automatically selected two denoised knowledge triples comprise the two denoised knowledge relation triples of the railway inspection information, performing similarity calculation on the third knowledge element of the selected knowledge relation triples of the first denoised railway inspection information and the first knowledge element of the selected knowledge relation triples of the second denoised railway inspection information.
Specifically, any two knowledge triples t automatically selected are calculated by adopting the following calculation formula i ＝(s i ,p i ,o i ) And t j ＝(s j ,p j ,o j ) Semantic distance f under relationship r r (t i ,t j )：
f r (t i ,t j )＝||αM ri ·t i -βM rj ·t j ||
Wherein s is i ,p i ,o i Respectively represent t i Subject, predicate and object of (a); s is(s) j ,p j ,o j Respectively represent t j Subject, predicate and object of (a); m is M ri 、M rj Respectively the relation r to t i 、t j Alpha is the head coefficient and beta is the tail coefficient.
(4) And constructing a knowledge graph of the railway inspection information according to the knowledge triples and the corresponding similarity relations of the knowledge triples.
In some embodiments, the system further includes an output module 260, where the output module 260 is configured to output the query result and/or the railway inspection condition evaluation result in a two-dimensional and/or three-dimensional manner.
Example III
The present embodiment provides a computer readable storage medium storing a computer program executable by one or more processors for implementing a method for analyzing railroad patrol information based on knowledge fusion as described above.
The computer-readable storage medium may also include, among other things, computer programs, data files, data structures, etc., alone or in combination. The computer readable storage medium or computer program may be specifically designed and understood by those skilled in the art of computer software, or the computer readable storage medium may be well known and available to those skilled in the art of computer software. Examples of the computer readable storage medium include: magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CDROM discs and DVDs; magneto-optical media, such as optical disks; and hardware means, specifically configured to store and execute computer programs, such as read-only memory (ROM), random Access Memory (RAM), flash memory; or a server, app application mall, etc. Examples of computer programs include machine code (e.g., code produced by a compiler) and files containing higher level code that may be executed by the computer using an interpreter. The described hardware devices may be configured to act as one or more software modules to perform the operations and methods described above, and vice versa. In addition, the computer readable storage medium may be distributed among networked computer systems, and the program code or computer program may be stored and executed in a decentralized manner.
Example IV
The present embodiment provides a computer program product which, when run on a processor, performs the method of analysis of railway patrol information based on knowledge fusion as described above.
Example five
The present embodiment provides an electronic device 100, fig. 4 is a block diagram of connection of the electronic device provided in the embodiment of the present application, and as shown in fig. 4, the electronic device 100 includes a processor 101, a memory 102, a multimedia component 103, an input/output (I/O) interface 104, and a communication component 105.
The processor 101 is configured to perform all or part of the steps in the embodiments of the method for analyzing railroad patrol information based on knowledge fusion as described above. The memory 102 is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.
The processor 101 may be an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), digital signal processor (Digital Signal Processor, DSP), digital signal processing device (Digital Signal Processing Device, DSPD), programmable logic device (Programmable Logic Device, PLD), field programmable gate array (Field Programmable Gate Array, FPGA), controller, microcontroller, microprocessor or other electronic component implementation for performing the methods as in the method embodiments described above.
The Memory 102 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The multimedia component 103 may include a screen, which may be a touch screen, and an audio component for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in a memory or transmitted through a communication component. The audio assembly further comprises at least one speaker for outputting audio signals.
The I/O interface 104 provides an interface between the one or more processors 101 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons.
The communication component 105 is used for wired or wireless communication between the electronic device 100 and other devices. The wired communication comprises communication through a network port, a serial port and the like; the wireless communication includes: wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, 5G, or a combination of one or more of them. The corresponding communication component 105 may thus comprise: wi-Fi module, bluetooth module, NFC module.
It should be further understood that the methods or systems disclosed in the embodiments provided herein may be implemented in other manners. The above-described method or system embodiments are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and apparatuses according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, a computer program segment, or a portion of a computer program, which comprises one or more computer programs for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures, and in fact may be executed substantially concurrently, or in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer programs.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, apparatus or device comprising such elements; if any, the terms "first," "second," etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of features indicated or implicitly indicating the precedence of features indicated; in the description of the present application, unless otherwise indicated, the terms "plurality", "multiple" and "multiple" mean at least two; if the description is to a server, it should be noted that the server may be an independent physical server or terminal, or may be a server cluster formed by a plurality of physical servers, or may be a cloud server capable of providing basic cloud computing services such as a cloud server, a cloud database, a cloud storage, a CDN, and the like; in this application, if an intelligent terminal or a mobile device is described, it should be noted that the intelligent terminal or the mobile device may be a mobile phone, a tablet computer, a smart watch, a netbook, a wearable electronic device, a personal digital assistant (Personal Digital Assistant, PDA), an augmented Reality device (Augmented Reality, AR), a Virtual Reality device (VR), an intelligent television, an intelligent sound device, a personal computer (Personal Computer, PC), etc., but the present application is not limited thereto.
Finally it is pointed out that in the description of the present specification, the terms "one embodiment," "some embodiments," "example," "one example" or "some examples" and the like refer to particular features, structures, materials or characteristics described in connection with the embodiment or example as being included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been illustrated and described above, it should be understood that the above-described embodiments are illustrative only and are not intended to limit the present application to the details of the embodiments employed to facilitate the understanding of the present application. Any person skilled in the art to which this application pertains will be able to make any modifications and variations in form and detail of implementation without departing from the spirit and scope of the disclosure, but the scope of protection of this application shall be subject to the scope of the claims that follow.
Claims (15)
1. The railway inspection information analysis method based on knowledge fusion is characterized by comprising the following steps of:
acquiring inquiry information about railway inspection;
inquiring in a database by adopting a term inquiring mode and/or a relation inquiring mode according to the inquiring information to obtain an inquiring result about railway inspection, wherein the database comprises original railway inspection information and a knowledge graph of the railway inspection information;
and analyzing and evaluating the railway inspection condition according to the inquiry result to obtain a railway inspection condition evaluation result.
2. The method of claim 1, wherein the constructing of the knowledge graph of the railway inspection information comprises:
analyzing and processing the existing railway inspection information to obtain a knowledge triplet of the railway inspection information, wherein the knowledge triplet comprises a knowledge relation triplet of the railway inspection information and a knowledge attribute triplet of the railway inspection information;
and constructing a knowledge graph of the railway inspection information based on the knowledge triplets of the railway inspection information.
3. The method of claim 2, wherein the step of obtaining a knowledge triplet of railroad patrol information comprises:
carrying out structural processing on the existing railway inspection information, and extracting unstructured data in the existing railway inspection information;
performing word segmentation and part-of-speech tagging on the unstructured data;
analyzing the knowledge relation between each word in the unstructured data to obtain a knowledge relation triplet of the railway inspection information;
and based on the unstructured data, acquiring the knowledge attribute triplets of the railway inspection information by using a network information wrapper.
4. The method of claim 2, wherein the knowledge triplets based on the railway patrol information construct a knowledge graph of railway patrol information, comprising:
denoising the knowledge triples based on regular expressions of the knowledge triples of the railway inspection information;
automatically selecting a de-noised knowledge triplet by adopting a preset pairing algorithm;
performing similarity processing on the automatically selected knowledge triples to determine similarity relation;
and constructing a knowledge graph of the railway inspection information according to the knowledge triples and the corresponding similarity relations of the knowledge triples.
5. The method of claim 4, wherein the rules for similarity processing the knowledge triples comprise:
if the automatically selected two denoised knowledge triples comprise a denoised knowledge attribute triplet of the railway inspection information and a denoised knowledge relation triplet of the railway inspection information, performing similarity calculation on a first knowledge element of the two denoised knowledge triples;
and if the automatically selected two denoised knowledge triples comprise the two denoised knowledge relation triples of the railway inspection information, performing similarity calculation on the third knowledge element of the selected knowledge relation triples of the first denoised railway inspection information and the first knowledge element of the selected knowledge relation triples of the second denoised railway inspection information.
6. The method of claim 5, wherein the similarity calculation is performed on automatically selected knowledge triples using an edit distance algorithm.
7. The method of claim 6, wherein any two automatically selected knowledge triples t are calculated using the following calculation formula i ＝(s i ,p i ,o i ) And t j ＝(s j ,p j ,o j ) Semantic distance f under relationship r r (t i ,t j )：
f r (t i ,t j )＝||αM ri ·t i -βM rj ·t j ||；
Wherein s is i ,p i ,o i Respectively represent t i Subject, predicate and object of (a); s is(s) j ,p j ,o j Respectively represent t j Subject, predicate and object of (a); m is M ri 、M rj Respectively the relation r to t i 、t j Alpha is the head coefficient and beta is the tail coefficient.
8. The method of claim 1, wherein the railway inspection condition evaluation result comprises a single day railway inspection integrated score and/or a railway inspection integrated score over a set period of time.
9. The method of claim 8, wherein the single day railway patrol composite score P d The method is calculated by adopting the following calculation formula:
wherein d is a date number; vector X 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 And respectively representing the vehicle running state, the operation day vehicle overhaul condition, the vehicle abnormal state, the infrastructure service state, the infrastructure overhaul condition, the infrastructure monitoring alarm condition, the equipment running state, the equipment overhaul condition and the equipment damage condition in the query result.
10. The method of claim 9, wherein the integrated score for the railroad patrol during the set period of time is calculated using the following calculation formula:
wherein P is IJ Comprehensively grading the railway inspection in the time period from day I to day J; p (P) d Comprehensively grading for single-day railway inspection; d is a date number; max { P d I is not less than d is not less than J, and P is each time period from day I to day J d Maximum value of (2), min { P d I is not less than d is not less than J, and P is each time period from day I to day J d Is set to be a minimum value of (c),
for each P in the range of I day to J day d And M is the total number of times of single-day patrol comprehensive scores in the period from day I to day J.
11. The method of claim 1, wherein the query results are presented in a chart.
12. The railway inspection information analysis system based on knowledge fusion is characterized by comprising:
the storage module is used for storing the original railway inspection information, the triplets of the railway inspection information and the knowledge graph of the railway inspection information into the database;
the query module is used for acquiring query information about railway inspection, and querying in a database by adopting an entry query mode and/or a relation query mode according to the query information to obtain a query result about railway inspection;
and the processing module is used for analyzing and evaluating the railway inspection situation according to the query result to obtain a railway inspection situation evaluation result.
13. A computer readable storage medium storing a computer program executable by one or more processors for implementing the knowledge fusion based railway patrol information analysis method according to any one of claims 1 to 11.
14. A computer program product, characterized in that it when run on a processor performs the method for analysis of railway patrol information based on knowledge fusion according to any one of claims 1-11.
15. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, the memory and the processor are in communication connection with each other, and the computer program, when executed by the processor, performs the method for analyzing railway patrol information based on knowledge fusion according to any one of claims 1 to 11.
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