CN111324779A - Visual information processing method of interlocking logical relationship based on knowledge graph - Google Patents

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

Translated fromChinese

基于知识图谱的联锁逻辑关系可视化信息处理方法Visual information processing method of interlocking logical relationship based on knowledge graph

技术领域technical field

本发明涉及一种轨道交通的计算机联锁系统，尤其是涉及一种基于知识图谱的联锁逻辑关系可视化信息处理方法。The invention relates to a computer interlocking system for rail transit, in particular to a visual information processing method for interlocking logical relations based on a knowledge graph.

背景技术Background technique

联锁逻辑关系是计算机联锁系统中最核心和最关键的部分，在业界对复杂的联锁关系一直没有一个直观、可视化的表现形式，这也降低了联锁关系的可理解性和透明度。现有的联锁逻辑关系查询方式是在文本文件中对联锁变量的逻辑表达式进行搜索和分析，无法全面和方便的查询联锁变量之间的逻辑关系。知识图谱是人工智能技术的一个重要分支，是一种基于图的数据结构，符合人的思维模式，是关系网络中最有效的表示方式。它通过可视化的技术描述实体知识及实体知识之间的相互关系。知识图谱已广泛应用其他行业，例如中国专利公开号CN110727777A公开了一种知识图谱的管理方法、装置、计算机设备和存储介质，所述方法包括：展示知识图谱的管理界面，所述管理界面包括对知识图谱数据库的多个操作选项；当检测到对多个所述操作选项中的目标操作选项的触发操作后，确定所述目标操作选项对应的目标操作语句；在所述知识图谱数据库中执行所述目标操作语句，得到执行结果，将知识图谱数据库中的数据进行可视化，用户不需要了解数据库操作语言，只需要简单操作即可对知识图谱数据库进行管理，降低了知识图谱的管理难度。The interlocking logic relationship is the most core and critical part of the computer interlocking system. There has never been an intuitive and visual representation of the complex interlocking relationship in the industry, which also reduces the comprehensibility and transparency of the interlocking relationship. The existing interlocking logical relationship query method is to search and analyze the logical expressions of the interlocking variables in the text file, which cannot comprehensively and conveniently query the logical relationship between the interlocking variables. Knowledge graph is an important branch of artificial intelligence technology. It is a graph-based data structure that conforms to human thinking mode and is the most effective representation in relational networks. It describes entity knowledge and the relationship between entity knowledge through visual technology. Knowledge graphs have been widely used in other industries. For example, Chinese Patent Publication No. CN110727777A discloses a knowledge graph management method, device, computer equipment and storage medium. Multiple operation options of the knowledge graph database; after detecting a trigger operation on the target operation option in the multiple operation options, determine the target operation statement corresponding to the target operation option; execute all the operation options in the knowledge graph database The target operation statement is described, the execution result is obtained, and the data in the knowledge graph database is visualized. The user does not need to understand the database operation language, and only needs a simple operation to manage the knowledge graph database, which reduces the management difficulty of the knowledge graph.

但在铁路信号行业以及联锁系统中还未开始应用，且联锁系统中的逻辑关系较为复杂，因此如何将知识图谱有效应用在联锁系统从而有效地实现逻辑关系的可视化，成为当下需要解决的技术问题。However, it has not yet been applied in the railway signal industry and the interlocking system, and the logical relationship in the interlocking system is relatively complex. Therefore, how to effectively apply the knowledge graph to the interlocking system to effectively realize the visualization of the logical relationship has become a problem that needs to be solved at present. technical issues.

发明内容SUMMARY OF THE INVENTION

本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种基于知识图谱的联锁逻辑关系可视化信息处理方法。The purpose of the present invention is to provide a visual information processing method of interlocking logical relationship based on knowledge graph in order to overcome the above-mentioned defects of the prior art.

本发明的目的可以通过以下技术方案来实现：The object of the present invention can be realized through the following technical solutions:

一种基于知识图谱的联锁逻辑关系可视化信息处理方法，该方法通过python语言读取储存于文本文件中的联锁逻辑表达关系，提取三元组模块，其中三元组为变量、变量之间关系和变量，并将三元组存储于图形数据库中，形成了联锁逻辑关系图数据库，用于联锁变量及逻辑关系查询。A method for visual information processing of interlocking logical relations based on knowledge graph, the method reads interlocking logical expression relations stored in text files through python language, and extracts triplet modules, wherein triplet is a variable and a variable between variables. relationship and variables, and store the triples in the graph database to form an interlocking logical relation graph database, which is used for interlocking variables and logical relation queries.

优选的，所述的提取三元组具体为：以每个车站的联锁数据BOOL包为基础，通过对车站的BOOL数据包中相关文件的读取，提取联锁变量和联锁变量之间的逻辑关系。Preferably, the extraction triplet is specifically: based on the interlocking data BOOL package of each station, by reading the relevant files in the BOOL data package of the station, extracting the interlocking variable and the interlocking variable logical relationship.

优选的，所述的联锁变量的提取具体为：Preferably, the extraction of the interlocking variable is specifically:

将不同的变量按照不同的变量属性进行提取，最后合并生成一个总的csv文件，用于批量导入图数据库中。Different variables are extracted according to different variable attributes, and finally merged to generate a total csv file for batch import into the graph database.

优选的，所述的联锁变量之间的逻辑关系提取包括变量与本身属性关系提取、不同变量之间的关系提取、以及变量关系文件的生成。Preferably, the extraction of the logical relationship between the interlocking variables includes extraction of the relationship between the variable and its own attributes, extraction of the relationship between different variables, and generation of a variable relationship file.

优选的，所述的变量与本身属性关系提取具体为：Preferably, the extraction of the relationship between the variable and its own attributes is as follows:

所述的变量的属性可显示在变量实体中，或者以另外实体的形式存在，通过建立关系可直观的显示出来。The attributes of the variable can be displayed in the variable entity, or exist in the form of another entity, and can be displayed visually by establishing a relationship.

优选的，所述的不同变量之间的关系提取具体为：Preferably, the extraction of the relationship between the different variables is specifically:

不同变量之间存在着与、或、等于三种关系，通过关系提取，可直观的表现不同变量之间与、或、等于的关系；There are and, or, and equal relationships between different variables. Through relationship extraction, the relationship between and, or, and equal to different variables can be intuitively expressed;

对于复杂的逻辑表达式，采用多项式展开的方式，将包含与、或及括号关系的表达式，拆解为多个或表达式，每个或表达式中是多个变量相与的关系。For complex logical expressions, the polynomial expansion method is used to decompose the expressions containing AND, OR and parentheses into multiple OR expressions, and each OR expression is an AND relationship of multiple variables.

优选的，所述的变量关系文件的生成具体为：Preferably, the generation of the variable relationship file is specifically:

通过变量关系的提取后，生成多个关系CSV文件，用于批量导入图数据库中。After the variable relationship is extracted, multiple relationship CSV files are generated for batch import into the graph database.

优选的，所述的图形数据库采用Neo4j图数据库。Preferably, the graph database adopts Neo4j graph database.

优选的，所述的Neo4j图数据库的批量导入具体为：Preferably, the batch import of the Neo4j graph database is specifically:

将上一步提取出来的变量CSV文件和关系CSV文件，通过python命令批量导入到Neo4j图数据库中。Import the variable CSV file and relational CSV file extracted in the previous step into the Neo4j graph database in batches through python commands.

优选的，所述的联锁变量及逻辑关系查询具体为：Preferably, the interlocking variable and logical relationship query is specifically:

在Neo4j图数据库中可查询某个单独变量的联锁逻辑关系，或者也可查询某个逻辑关系涉及到的所有变量。In the Neo4j graph database, the interlocking logical relationship of a single variable can be queried, or all variables involved in a logical relationship can be queried.

与现有技术相比，本发明具有以下优点：Compared with the prior art, the present invention has the following advantages:

1、知识图谱技术首次在铁路信号行业使用，并首次应用于联锁系统中联锁逻辑关系的可视化表达，为联锁系统、铁路信号行业其他方面的应用提供了借鉴。1. The knowledge graph technology was used in the railway signal industry for the first time, and was applied to the visual expression of the interlocking logical relationship in the interlocking system for the first time, which provided a reference for the application of the interlocking system and other aspects of the railway signal industry.

2、首次将联锁逻辑变量和逻辑关系转换成数据库形式，提供了一种新型的联锁逻辑关系查询方法，提高查询效率和查询的全面性，现有通过文本文件内‘搜索’功能的的联锁逻辑查询方式太过原始。2. For the first time, the interlocking logical variables and logical relationships are converted into database form, which provides a new query method for interlocking logical relationships, which improves the query efficiency and the comprehensiveness of the query. The interlocking logic query method is too primitive.

3、首次提出了联锁逻辑关系可视化的表现形式，为用户提供了友好和直观的人机界面。3. For the first time, a visual representation of interlocking logic relationship is proposed, which provides users with a friendly and intuitive man-machine interface.

4、采用既有的车站联锁数据包，通用性强，灵活度高，利于工程化的大面积推广。4. Using the existing station interlocking data package, it has strong versatility and high flexibility, which is conducive to the large-scale promotion of engineering.

5、将包含与，或，括号等运算符的复杂联锁表达式，采用多项式展开的形式，转换成两个层级的显示关系，第一个层级是或关系，第二个层级是与关系，简化了逻辑表达关系可视化的复杂度。5. Convert the complex interlocking expressions containing operators such as AND, OR, and parentheses into a two-level display relationship in the form of polynomial expansion. The first level is an OR relationship, and the second level is an AND relationship. Simplifies the complexity of visualization of logical expression relationships.

附图说明Description of drawings

图1原联锁逻辑关系表达式举例示意图；Figure 1 is a schematic diagram of an example of the original interlocking logical relationship expression;

图2采用多项式展开后的联锁逻辑关系表达式举例示意图；Fig. 2 adopts the example schematic diagram of the interlocking logic relation expression after polynomial expansion;

图3为本发明的工作流程图；Fig. 3 is the working flow chart of the present invention;

图4为联锁变量和联锁逻辑关系导入图数据库后的显示界面；Fig. 4 is the display interface after the interlocking variable and the interlocking logical relationship are imported into the graph database;

图5为联锁变量查询界面；Figure 5 is the interlock variable query interface;

图6为联锁逻辑关系查询界面。Figure 6 is the interlocking logical relationship query interface.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都应属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

由于不同联锁厂家对于联锁变量和联锁逻辑关系的表现形式和方式都不尽相同，本发明的主要目的是提供一种将联锁逻辑关系可视化的思路，具体如何提取联锁变量和联锁逻辑关系三元组，针对不同的联锁厂家会有不同的方式。Since different interlocking manufacturers have different expressions and ways of interlocking variables and interlocking logical relationships, the main purpose of the present invention is to provide a way of visualizing the interlocking logical relationship, specifically how to extract interlocking variables and interlocking logic relationships. The three-tuple of lock logical relationship will have different methods for different interlocking manufacturers.

如图3所示，本发明是通过python语言读取储存于文本文件中的联锁逻辑表达关系，提取三元组(变量-变量之间关系-变量)模块，并将三元组存储于Neo4j图数据库中，形成了联锁逻辑关系图数据库，用于联锁变量及逻辑关系查询。本发明以每个车站的联锁数据BOOL包为基础，通过程序自动提取联锁逻辑关系三元组，并生成属于该站的联锁逻辑关系图数据库。As shown in Figure 3, the present invention reads the interlocking logic expression relationship stored in the text file through the python language, extracts the triple (variable-variable relationship-variable) module, and stores the triple in Neo4j In the graph database, an interlocking logical relation graph database is formed, which is used for interlocking variables and logical relation query. Based on the interlocking data BOOL package of each station, the invention automatically extracts the interlocking logical relation triples through the program, and generates the interlocking logical relation graph database belonging to the station.

基本过程包括如下几方面：The basic process includes the following aspects:

1)三元组提取1) Triple Extraction

联锁逻辑关系没有结构化或者半结构化数据供使用，只存在于文本形式的VTL文件中。通过对车站的BOOL数据包中相关文件的读取，提取联锁变量和联锁变量之间的逻辑关系。The interlocking logical relationship has no structured or semi-structured data for use, and only exists in the VTL file in text form. By reading the relevant files in the BOOL data package of the station, extract the interlocking variables and the logical relationship between the interlocking variables.

(1)变量提取(1) Variable extraction

由于不同变量具有不同的属性，比如时间变量，自保变量，安全输入变量，安全输出变量，通信变量，非安全变量等。将不同的变量按照不同的变量属性进行提取，最后合并生成一个总的csv文件，用于批量导入图数据库中。Because different variables have different properties, such as time variables, self-preserving variables, safety input variables, safety output variables, communication variables, non-safety variables, etc. Different variables are extracted according to different variable attributes, and finally merged to generate a total csv file for batch import into the graph database.

(2)变量逻辑关系提取(2) Extraction of variable logical relationship

变量逻辑关系提取包括如下几个方面：The extraction of variable logical relationship includes the following aspects:

变量与本身属性关系提取。变量的属性不但可以显示在变量实体中，也可以以另外实体的形式存在，可以更直观的展示出来。例如时间变量的延时时间，例如安全输入和安全输出变量在物理板卡上的具体位置，都可以通过建立关系，直观的显示出来。Extract the relationship between variables and their attributes. The attributes of variables can not only be displayed in the variable entity, but also exist in the form of other entities, which can be displayed more intuitively. For example, the delay time of time variables, such as the specific positions of safety input and safety output variables on the physical board, can be visually displayed by establishing a relationship.

不同变量之间的关系提取。不同变量之间存在着与，或，等于三种关系，通过关系提取，可以直观的表现不同变量之间与，或，等于的关系。对于复杂的逻辑表达式，采用多项式展开的方式，将包含与，或及括号关系的表达式，拆解为多个或表达式，每个或表达式中是多个变量相与的关系。例如：Extraction of relationships between different variables. There are and, or, equal to three relationships between different variables. Through relationship extraction, the and, or, equal relationship between different variables can be intuitively expressed. For complex logical expressions, the polynomial expansion method is used to decompose the expressions containing AND, OR and parentheses into multiple OR expressions, and each OR expression is an AND relationship of multiple variables. E.g:

IPSBDOWN变量的原表达式如下：The original expression of the IPSBDOWN variable is as follows:

IPSBDOWN＝(.N.IPB2-PERM1*(PERM1P2+IPSBDOWN=(.N.IPB2-PERM1*(PERM1P2+

IPSBDOWN*.N.IPSBDOWNT*.N.SYSA-DI))IPSBDOWN*.N.IPSBDOWN*.N.SYSA-DI))

展开后表达式为：The expanded expression is:

IPSBDOWN＝.N.IPB2-PERM1*.N.IPSBDOWNT*.N.SYSA-DI*IPSBDOWN+.N.IPB2-PERM1*PERM1P2IPSBDOWN=.N.IPB2-PERM1*.N.IPSBDOWNT*.N.SYSA-DI*IPSBDOWN+.N.IPB2-PERM1*PERM1P2

这种操作的目的是将复杂的逻辑关系转变为简单的与、或关系，便于直观的展示和简单的实现。The purpose of this operation is to transform the complex logical relationship into a simple AND or OR relationship, which is convenient for intuitive display and simple implementation.

变量关系文件的生成。通过变量关系的提取后，生成多个关系CSV文件，用于批量导入图数据库中。Generation of variable relationship files. After the variable relationship is extracted, multiple relationship CSV files are generated for batch import into the graph database.

2)Neo4j图数据库的批量导入，如图4所示；2) Batch import of Neo4j graph database, as shown in Figure 4;

将上一步提取出来的变量CSV文件和关系CSV文件，通过python命令批量导入到Neo4j图数据库中。Import the variable CSV file and relational CSV file extracted in the previous step into the Neo4j graph database in batches through python commands.

3)联锁逻辑关系的查询，如图5和6所示；3) Query of interlocking logical relationship, as shown in Figures 5 and 6;

在Neo4j图数据库中可以查询某个单独变量的联锁逻辑关系，或者也可以查询某个逻辑关系涉及到的所有变量。In the Neo4j graph database, you can query the interlocking logical relationship of a single variable, or you can query all variables involved in a logical relationship.

具体实施例specific embodiment

1.变量和变量逻辑关系提取1. Variable and variable logical relationship extraction

以本公司联锁数据为例，本公司联锁数据可以用文本形式存储和查看，通过python语言对BOOL数据包中相关的多个文件进行提取，获取联锁变量列表和每个变量的相关属性。Taking our company's interlocking data as an example, our company's interlocking data can be stored and viewed in text form, and multiple files related to the BOOL data package can be extracted through the python language to obtain a list of interlocking variables and the relevant attributes of each variable. .

联锁变量关系提取的关键操作是将图1格式的联锁逻辑表达式转换成图2格式的联锁逻辑表达式，这样就可以将包括与、或及括号关系的逻辑表达式简化为只有与、或关系的逻辑表达式，从而将逻辑表达关系转换成两级形式，第一级是多个中间变量的或关系，第二级是多个变量的与关系，简化了逻辑表达可视化的复杂度。The key operation of extracting the interlocking variable relationship is to convert the interlocking logic expression in the format of Figure 1 into the interlocking logic expression in the format of Figure 2, so that the logic expression including AND, OR, and parentheses can be simplified to only AND. , or the logical expression of the relationship, thereby converting the logical expression relationship into a two-level form, the first level is the OR relationship of multiple intermediate variables, and the second level is the AND relationship of multiple variables, which simplifies the complexity of logical expression visualization .

2.数据库批量导入和查看2. Database batch import and view

每个车站联锁数据中包括的联锁变量和联锁逻辑关系数量非常大，一条一条的将逻辑关系导入数据库非常的耗时，采用python语言批量导入的方式，实现了快速、自动化的从BOOL数据包文件到数据库的转换。导入后可以直观的查看变量列表和关系列表。The number of interlocking variables and interlocking logical relationships included in the interlocking data of each station is very large. It is very time-consuming to import the logical relationships one by one into the database. The method of batch importing in python language realizes fast and automatic conversion from BOOL Data package file to database conversion. After importing, you can view the variable list and relationship list intuitively.

通过数据库查询语句直接查询变量的逻辑表达式，可以将与变量相关的所有逻辑关系全部展示出来，包括：By directly querying the logical expressions of variables through database query statements, all logical relationships related to variables can be displayed, including:

(1)该变量的励磁之路；(1) The excitation path of the variable;

(2)改变量的自保之路；(2) The self-preservation way of changing the quantity;

(3)该变量来自于那个子系统，那个板卡等；(3) The variable comes from which subsystem, which board, etc.;

(4)该变量参与其他变量运算的信息。(4) Information that the variable participates in the operation of other variables.

以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited to this. Any person skilled in the art can easily think of various equivalents within the technical scope disclosed by the present invention. Modifications or substitutions should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (10)

Translated fromChinese

1.一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，该方法通过python语言读取储存于文本文件中的联锁逻辑表达关系，提取三元组模块，其中三元组为变量、变量之间关系和变量，并将三元组存储于图形数据库中，形成了联锁逻辑关系图数据库，用于联锁变量及逻辑关系查询。1. a method for visualizing information processing of interlocking logical relations based on knowledge graph, is characterized in that, this method reads the interlocking logical expression relation stored in the text file by python language, extracts triplet module, wherein triplet For variables, the relationship between variables and variables, the triples are stored in the graph database, and the interlocking logic relation graph database is formed, which is used for interlocking variables and logical relation query.2.根据权利要求1所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的提取三元组具体为：以每个车站的联锁数据BOOL包为基础，通过对车站的BOOL数据包中相关文件的读取，提取联锁变量和联锁变量之间的逻辑关系。2. a kind of visual information processing method of interlocking logical relationship based on knowledge graph according to claim 1, is characterized in that, described extraction triplet is specifically: based on the interlocking data BOOL bag of each station , by reading the relevant files in the BOOL data package of the station, extract the interlocking variables and the logical relationship between the interlocking variables.3.根据权利要求2所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的联锁变量的提取具体为：3. a kind of visual information processing method of interlocking logical relationship based on knowledge graph according to claim 2, is characterized in that, the extraction of described interlocking variable is specifically:将不同的变量按照不同的变量属性进行提取，最后合并生成一个总的csv文件，用于批量导入图数据库中。Different variables are extracted according to different variable attributes, and finally merged to generate a total csv file for batch import into the graph database.4.根据权利要求2所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的联锁变量之间的逻辑关系提取包括变量与本身属性关系提取、不同变量之间的关系提取、以及变量关系文件的生成。4. The method for visualizing information processing of interlocking logical relationships based on knowledge graph according to claim 2, wherein the extraction of the logical relationship between the interlocking variables comprises the extraction of the relationship between variables and their own attributes, the extraction of different variables The relationship between the extraction, and the generation of the variable relationship file.5.根据权利要求4所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的变量与本身属性关系提取具体为：5. a kind of visual information processing method of interlocking logical relationship based on knowledge graph according to claim 4, is characterized in that, described variable and own attribute relation extraction is specifically:所述的变量的属性可显示在变量实体中，或者以另外实体的形式存在，通过建立关系可直观的显示出来。The attributes of the variable can be displayed in the variable entity, or exist in the form of another entity, and can be displayed visually by establishing a relationship.6.根据权利要求4所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的不同变量之间的关系提取具体为：6. a kind of visual information processing method of interlocking logical relationship based on knowledge graph according to claim 4, is characterized in that, the relation extraction between described different variables is specifically:不同变量之间存在着与、或、等于三种关系，通过关系提取，可直观的表现不同变量之间与、或、等于的关系；There are and, or, and equal relationships between different variables. Through relationship extraction, the relationship between and, or, and equal to different variables can be intuitively expressed;对于复杂的逻辑表达式，采用多项式展开的方式，将包含与、或及括号关系的表达式，拆解为多个或表达式，每个或表达式中是多个变量相与的关系。For complex logical expressions, the polynomial expansion method is used to decompose the expressions containing AND, OR and parentheses into multiple OR expressions, and each OR expression is an AND relationship of multiple variables.7.根据权利要求4所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的变量关系文件的生成具体为：7. a kind of visualization information processing method of interlocking logical relationship based on knowledge graph according to claim 4, is characterized in that, the generation of described variable relationship file is specifically:通过变量关系的提取后，生成多个关系CSV文件，用于批量导入图数据库中。After the variable relationship is extracted, multiple relationship CSV files are generated for batch import into the graph database.8.根据权利要求1所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的图形数据库采用Neo4j图数据库。8 . The method for visualizing information processing of interlocking logical relationships based on knowledge graph according to claim 1 , wherein the graph database adopts Neo4j graph database. 9 .9.根据权利要求8所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的Neo4j图数据库的批量导入具体为：9. a kind of visualization information processing method of interlocking logical relationship based on knowledge graph according to claim 8, is characterized in that, the batch import of described Neo4j graph database is specifically:将上一步提取出来的变量CSV文件和关系CSV文件，通过python命令批量导入到Neo4j图数据库中。Import the variable CSV file and relational CSV file extracted in the previous step into the Neo4j graph database in batches through python commands.10.根据权利要求8所述的一种基于知识图谱的联锁逻辑关系可视化信息处理方法，其特征在于，所述的联锁变量及逻辑关系查询具体为：10. A method for visualizing information processing of interlocking logical relationships based on knowledge graphs according to claim 8, wherein the interlocking variables and logical relationship queries are specifically:在Neo4j图数据库中可查询某个单独变量的联锁逻辑关系，或者也可查询某个逻辑关系涉及到的所有变量。In the Neo4j graph database, the interlocking logical relationship of a single variable can be queried, or all variables involved in a logical relationship can be queried.

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