CN111324779A - Interlocking logical relationship visualization information processing method based on knowledge graph - Google Patents
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Abstract
The invention relates to a knowledge graph-based interlocking logic relationship visualization information processing method, which reads an interlocking logic expression relationship stored in a text file through a python language, extracts a three-tuple module, wherein triplets are variables, relationships among the variables and the variables, and stores the triplets in a graphic database to form an interlocking logic relationship database for querying the interlocking variables and the logic relationships. Compared with the prior art, the knowledge graph technology is used in the railway signal industry for the first time and is applied to the visual expression of the interlocking logic relationship in the interlocking system for the first time, and reference is provided for the interlocking system and the application in other aspects of the railway signal industry.
Description
Technical Field
The invention relates to a computer interlocking system of rail transit, in particular to an interlocking logical relationship visualization information processing method based on a knowledge graph.
Background
The interlocking logical relationship is the most core and the most key part in the computer interlocking system, and no visual and visual expression form exists for the complex interlocking relationship in the world, so that the understandability and the transparency of the interlocking relationship are reduced. The existing interlocking logic relationship query mode is to search and analyze the logic expression of interlocking variables in a text file, and cannot comprehensively and conveniently query the logic relationship between the interlocking variables. The knowledge graph is an important branch of an artificial intelligence technology, is a data structure based on a graph, accords with a human thinking mode, and is the most effective expression mode in a relational network. The mutual relation between the entity knowledge and the entity knowledge is described through a visualization technology. Other industries in which knowledge maps have been widely used, for example, chinese patent publication No. CN110727777A discloses a method, an apparatus, a computer device and a storage medium for managing knowledge maps, the method comprising: a management interface for displaying the knowledge graph, wherein the management interface comprises a plurality of operation options for a knowledge graph database; when the trigger operation of a target operation option in the operation options is detected, determining a target operation statement corresponding to the target operation option; and executing the target operation statement in the knowledge map database to obtain an execution result, and visualizing data in the knowledge map database, so that a user can manage the knowledge map database only by simple operation without knowing the operation language of the database, and the management difficulty of the knowledge map is reduced.
However, the method has not been applied to the railway signal industry and the interlocking system, and the logical relationship in the interlocking system is relatively complex, so how to effectively apply the knowledge graph to the interlocking system to effectively realize the visualization of the logical relationship becomes a technical problem to be solved at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an interlocking logical relationship visualization information processing method based on a knowledge graph.
The purpose of the invention can be realized by the following technical scheme:
a knowledge graph-based interlocking logic relationship visualization information processing method comprises the steps of reading an interlocking logic expression relationship stored in a text file through a python language, extracting a three-tuple module, wherein triplets are variables, relationships among the variables and the variables, and storing the triplets in a graphic database to form an interlocking logic relationship database for querying the interlocking variables and the logic relationships.
Preferably, the extracting triplets specifically include: and on the basis of the interlocking data BOOL packet of each station, reading related files in the BOOL packet of the station to extract the logical relationship between the interlocking variables and the interlocking variables.
Preferably, the extraction of the interlocking variables is specifically as follows:
and extracting different variables according to different variable attributes, and finally combining to generate a total csv file for batch import into a graph database.
Preferably, the extracting of the logical relationship between the interlocking variables includes extracting the relationship between the variables and the attributes thereof, extracting the relationship between different variables, and generating a variable relationship file.
Preferably, the extracting of the relationship between the variable and the attribute thereof is specifically:
the attribute of the variable can be displayed in the variable entity or exist in the form of another entity, and can be visually displayed by establishing the relationship.
Preferably, the extracting of the relationship between the different variables specifically comprises:
three relations of AND, OR and equal exist among different variables, and the relations of AND, OR and equal among different variables can be visually expressed through relation extraction;
for a complex logic expression, an expression containing an AND, OR and bracket relation is decomposed into a plurality of OR expressions in a polynomial expansion mode, and each OR expression is a relation of a plurality of variable AND.
Preferably, the generation of the variable relation file specifically includes:
after the variable relation is extracted, a plurality of relation CSV files are generated and used for being imported into the graph database in batches.
Preferably, the graphic database adopts a Neo4j graph database.
Preferably, the batch import of Neo4j graph databases is specifically as follows:
and importing the variable CSV files and the relation CSV files extracted in the last step into the Neo4j database in batches through a python command.
Preferably, the query of the interlocking variables and the logical relationship is specifically as follows:
the interlocking logical relationship of a single variable may be looked up in the Neo4j graph database, or all variables involved in a logical relationship may be looked up.
Compared with the prior art, the invention has the following advantages:
1. the knowledge graph technology is used in the railway signal industry for the first time and is applied to the visual expression of the interlocking logic relationship in the interlocking system for the first time, and reference is provided for the application of the interlocking system and other aspects of the railway signal industry.
2. The interlocking logic variable and the logic relation are converted into a database form for the first time, a novel interlocking logic relation query method is provided, query efficiency and query comprehensiveness are improved, and the existing interlocking logic query mode through a 'search' function in a text file is too original.
3. The visual presentation form of the interlocking logic relationship is put forward for the first time, and a friendly and intuitive human-computer interface is provided for a user.
4. The existing station interlocking data packet is adopted, so that the universality is strong, the flexibility is high, and the large-area popularization of engineering is facilitated.
5. The complex interlocking expression comprising operators such as AND, OR, brackets and the like is converted into a display relation of two levels in a polynomial expansion mode, the first level is an OR relation, and the second level is an AND relation, so that the complexity of visualization of the logical expression relation is simplified.
Drawings
FIG. 1 is an exemplary diagram of an original interlocking logical relationship expression;
FIG. 2 is a schematic diagram illustrating an example of an interlocking logical relationship expression after polynomial expansion;
FIG. 3 is a flow chart of the operation of the present invention;
FIG. 4 is a display interface of the interlocking variables and the interlocking logical relationships imported into the graph database;
FIG. 5 is an interlocking variables query interface;
FIG. 6 is an interlocking logical relationship query interface.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Because different interlocking manufacturers have different expression forms and modes for interlocking variables and interlocking logical relations, the invention mainly aims to provide a visual idea of the interlocking logical relations, and particularly how to extract the interlocking variable and interlocking logical relation triplets, and different modes can be provided for different interlocking manufacturers.
As shown in fig. 3, the present invention reads the interlocking logical expression relationship stored in the text file through the python language, extracts the triplet (relationship between variables-variable) module, and stores the triplet in the Neo4j database to form the interlocking logical relationship database for the query of the interlocking variables and the logical relationship. The invention automatically extracts the interlocking logic relationship triple by a program on the basis of the interlocking data BOOL packet of each station and generates an interlocking logic relationship database belonging to the station.
The basic process comprises the following aspects:
1) triple extraction
The interlocking logical relationship has no structured or semi-structured data for use and exists only in the VTL file in text form. And extracting the logic relation between the interlocking variables by reading the related files in the BOOL data packet of the station.
(1) Variable extraction
Since different variables have different attributes, such as time variables, self-protected variables, secure input variables, secure output variables, communication variables, non-secure variables, etc. And extracting different variables according to different variable attributes, and finally combining to generate a total csv file for batch import into a graph database.
(2) Variable logical relationship extraction
The extraction of the logical relationship of the variables comprises the following aspects:
and extracting the relationship between the variable and the attribute of the variable. The attribute of the variable can be displayed in the variable entity, and can also exist in the form of other entities, so that the attribute can be displayed more intuitively. For example, the delay time of the time variable, for example, the specific positions of the security input variable and the security output variable on the physical board card, can be visually displayed by establishing the relationship.
And extracting the relation between different variables. The AND, or equal to three relations exist between different variables, and the AND, or equal relation between different variables can be visually expressed through relation extraction. For complex logic expressions, expressions containing AND, OR and bracket relations are decomposed into a plurality of OR expressions in a polynomial expansion mode, and each OR expression is a relation of a plurality of variable AND. For example:
the original expression of the IPSBDOWN variable is as follows:
IPSBDOWN＝(.N.IPB2-PERM1*(PERM1P2+
IPSBDOWN*.N.IPSBDOWNT*.N.SYSA-DI))
the expanded expression is:
IPSBDOWN＝.N.IPB2-PERM1*.N.IPSBDOWNT*.N.SYSA-DI*IPSBDOWN+.N.IPB2-PERM1*PERM1P2
the purpose of this operation is to transform a complex logical relationship into a simple and/or relationship, which facilitates intuitive presentation and simple implementation.
And generating a variable relation file. After the variable relation is extracted, a plurality of relation CSV files are generated and used for being imported into the graph database in batches.
2) Batch import of Neo4j graph database, as shown in fig. 4;
and importing the variable CSV files and the relation CSV files extracted in the last step into the Neo4j database in batches through a python command.
3) Query of interlocking logical relationships, as shown in FIGS. 5 and 6;
the interlocking logical relationship of a single variable may be looked up in the Neo4j graph database, or all variables involved in a logical relationship may be looked up.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
1. Variable and variable logical relationship extraction
Taking the company interlocking data as an example, the company interlocking data can be stored and viewed in a text form, and a plurality of related files in the BOOL data packet are extracted through the python language to obtain an interlocking variable list and related attributes of each variable.
The key operation of the interlocking variable relationship extraction is to convert the interlocking logic expression in the format of fig. 1 into the interlocking logic expression in the format of fig. 2, so that the logic expression comprising the and, or and bracket relationship can be simplified into the logic expression only having the and, or relationship, and the logic expression relationship is converted into a two-stage form, wherein the first stage is the or relationship of a plurality of intermediate variables, and the second stage is the and relationship of a plurality of variables, so that the complexity of the logic expression visualization is simplified.
2. Database batch import and viewing
The interlocking variables and the interlocking logical relations in the interlocking data of each station are very large in quantity, one logical relation is very time-consuming to import into the database, and the fast and automatic conversion from the BOOL data packet file to the database is realized by adopting a python language batch import mode. After the input, the variable list and the relation list can be visually checked.
The logical expression of the variable is directly inquired through the database inquiry statement, all the logical relations related to the variable can be displayed, and the method comprises the following steps:
(1) the path of excitation of the variable;
(2) a self-protection path for the change amount;
(3) the variable comes from that subsystem, that board, etc.;
(4) the variable participates in the information of other variable operations.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A knowledge graph-based interlocking logic relationship visualization information processing method is characterized in that the method reads an interlocking logic expression relationship stored in a text file through a python language, extracts a three-tuple module, wherein triplets are variables, relationships among the variables and the variables, and stores the triplets in a graphic database to form an interlocking logic relationship database for querying the interlocking variables and the logic relationships.
2. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 1, wherein the extracting triples specifically include: and on the basis of the interlocking data BOOL packet of each station, reading related files in the BOOL packet of the station to extract the logical relationship between the interlocking variables and the interlocking variables.
3. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 2, wherein the extraction of the interlocking variables specifically comprises:
and extracting different variables according to different variable attributes, and finally combining to generate a total csv file for batch import into a graph database.
4. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 2, wherein the extraction of the logical relationship between the interlocking variables comprises the extraction of the relationship between the variables and the attributes thereof, the extraction of the relationship between different variables, and the generation of a variable relationship file.
5. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 4, wherein the variable and self attribute relationship extraction specifically comprises:
the attribute of the variable can be displayed in the variable entity or exist in the form of another entity, and can be visually displayed by establishing the relationship.
6. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 4, wherein the relationship extraction between different variables is specifically as follows:
three relations of AND, OR and equal exist among different variables, and the relations of AND, OR and equal among different variables can be visually expressed through relation extraction;
for a complex logic expression, an expression containing an AND, OR and bracket relation is decomposed into a plurality of OR expressions in a polynomial expansion mode, and each OR expression is a relation of a plurality of variable AND.
7. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 4, wherein the generation of the variable relationship file specifically comprises:
after the variable relation is extracted, a plurality of relation CSV files are generated and used for being imported into the graph database in batches.
8. The method for knowledge-graph-based interlocking logical relationship visualization information processing according to claim 1, wherein the graph database is a Neo4j graph database.
9. The method for processing the visual information of the interlocking logical relationship based on the knowledge graph as claimed in claim 8, wherein the batch import of the Neo4j graph database specifically comprises:
and importing the variable CSV files and the relation CSV files extracted in the last step into the Neo4j database in batches through a python command.
10. The knowledge-graph-based interlocking logical relationship visualization information processing method according to claim 8, wherein the interlocking variable and logical relationship query specifically comprises:
the interlocking logical relationship of a single variable may be looked up in the Neo4j graph database, or all variables involved in a logical relationship may be looked up.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113268455A (en) * | 2021-04-26 | 2021-08-17 | 卡斯柯信号(成都)有限公司 | Boolean logic-based automatic configuration method and system for interlocking data |
| CN113276914A (en) * | 2021-06-08 | 2021-08-20 | 中国铁道科学研究院集团有限公司通信信号研究所 | Method and device for automatically generating computer interlocking data based on station yard shape structure |
| CN113807078A (en) * | 2021-10-09 | 2021-12-17 | 杭州路信科技有限公司 | Signal interlocking system control method and device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108170727A (en) * | 2017-12-12 | 2018-06-15 | 交控科技股份有限公司 | A kind of automatic processing method and device for interlocking data |
| CN110597999A (en) * | 2019-08-01 | 2019-12-20 | 湖北工业大学 | Judicial case knowledge graph construction method of dependency syntactic analysis relation extraction model |
-
2020
- 2020-02-28 CN CN202010129272.7A patent/CN111324779A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108170727A (en) * | 2017-12-12 | 2018-06-15 | 交控科技股份有限公司 | A kind of automatic processing method and device for interlocking data |
| CN110597999A (en) * | 2019-08-01 | 2019-12-20 | 湖北工业大学 | Judicial case knowledge graph construction method of dependency syntactic analysis relation extraction model |
Non-Patent Citations (2)
| Title |
|---|
| 林瑜筠 等, 中国铁道出版社 * |
| 王洋: "基于铁路领域的知识图谱研究与实现", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113268455A (en) * | 2021-04-26 | 2021-08-17 | 卡斯柯信号(成都)有限公司 | Boolean logic-based automatic configuration method and system for interlocking data |
| CN113268455B (en) * | 2021-04-26 | 2022-07-26 | 卡斯柯信号(成都)有限公司 | Boolean logic-based automatic configuration method and system for interlocking data |
| CN113276914A (en) * | 2021-06-08 | 2021-08-20 | 中国铁道科学研究院集团有限公司通信信号研究所 | Method and device for automatically generating computer interlocking data based on station yard shape structure |
| CN113807078A (en) * | 2021-10-09 | 2021-12-17 | 杭州路信科技有限公司 | Signal interlocking system control method and device, electronic equipment and storage medium |
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