CN113010696A - Engineering field knowledge graph construction method based on metadata model - Google Patents
Engineering field knowledge graph construction method based on metadata model Download PDFInfo
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Abstract
The invention discloses a construction method of an engineering field knowledge graph based on a metadata model, which comprises the following steps: summarizing various basic data in the engineering project to construct a service data source; constructing a knowledge graph body layer based on various basic data in a service data source, wherein the knowledge graph body layer comprises a body, body attributes and body relations; constructing a metadata layer based on the knowledge graph body layer, wherein the metadata layer comprises a plurality of metadata models; constructing a data task layer, including a data extraction task, a data cleaning task and a data storage task; constructing a knowledge graph storage layer to store the knowledge graph constructed by the data task layer; and constructing a knowledge graph application layer, wherein the knowledge graph application layer comprises a data acquisition interface, a data billboard and a data blood margin display module. The invention has the advantages that: based on the metadata model, a checking, tracing and synchronizing method for the data in the engineering field is provided, and the standardized construction, management and application of the knowledge map are efficiently realized.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a construction method of an engineering field knowledge graph based on a metadata model.
Background
When the knowledge graph in the engineering field is constructed, a large amount of basic data related to engineering services need to be processed. The business data has the characteristics of various and heterogeneous sources, uncertain correctness, irregular updating along with enterprise business and the like. The following difficulties exist when the conventional technical means is adopted to construct the knowledge map in the engineering field:
when a knowledge graph body layer is constructed, multi-source heterogeneous business basic data needs to be sorted and cleaned, and the correctness and completeness of the data in the knowledge graph are difficult to ensure;
when business data are updated, updating of a plurality of ontology data is involved, the knowledge synchronization process of the knowledge graph is complicated, and the condition of knowledge lag exists to a certain extent;
in addition, when data in the knowledge graph needs to be used in business, the reading process of the graph data is not friendly to business operators, and the access efficiency and the data safety are difficult to guarantee.
In order to solve the difficulties in construction of the knowledge graph in the engineering field, the invention provides a checking, tracing and synchronizing method for data in the engineering field based on a metadata model, and standardized construction, management and application of the knowledge graph are efficiently realized.
Disclosure of Invention
The invention aims to provide a construction method of the knowledge graph in the engineering field based on the metadata model according to the defects of the prior art, and the method provides a checking, tracing and synchronizing method of the data in the engineering field based on the metadata model, so as to efficiently realize the standardized construction of the knowledge graph.
The purpose of the invention is realized by the following technical scheme:
a construction method of an engineering field knowledge graph based on a metadata model is characterized by comprising the following steps:
(1) summarizing various basic data in the engineering project to construct a service data source;
(2) constructing a knowledge graph body layer based on various basic data in the service data source, wherein the knowledge graph body layer comprises a body, body attributes and body relations;
(3) constructing a metadata layer based on the knowledge graph body layer to define a mapping relation between the business data source and the knowledge graph body layer, wherein the metadata layer comprises a plurality of metadata models, and each metadata model corresponds to different application scenes;
(4) constructing a data task layer, wherein the data task layer comprises a data extraction task, a data cleaning task and a data storage task;
(5) constructing a knowledge graph storage layer to store the knowledge graph constructed by the data task layer;
(6) the component knowledge graph application layer comprises a data acquisition interface, a data billboard and a data blood margin display module.
The basic data comprises survey data, monitoring data, construction data and design data.
The metadata model comprises a plurality of fields which are related in business logic, the fields have unified data specification standards, and the data specification standards comprise field naming standards, field descriptions, field data type specifications, data storage positions and data blood margins; the fields have a declaration of the corresponding data processing method.
The field data type specification is used for describing a standard data storage type of the field, the standard data storage type comprises a basic data type and an aggregate data type, the basic data type is int, float or string, and the aggregate data type is a list, a hash table or a tuple.
The data context is the interdependence between the fields.
The data extraction task, the data cleaning task and the data storage task in the data task layer respectively have respective data processing methods.
The data processing method of the data extraction task is to extract source data from the business data source in a full or incremental manner, and carry out field splitting, null value processing and repeated data processing on the source data;
the data processing method of the data cleaning task is to convert the source data processed by the data extraction task into a format conforming to the metadata model standard;
the data processing method of the data storage task comprises the steps of carrying out data range verification, data uniqueness and consistency verification and data updating on the source data cleaned by the data cleaning task, and then storing the source data into the knowledge graph.
The knowledge map storage layer is composed of one or more types of a map database, a relational database, a non-relational database, a local server or a cloud server.
The data acquisition interface encapsulates a uniform data reading mode for different storage positions in the knowledge graph storage layer, and can read data of corresponding storage positions of the authorities in the knowledge graph storage layer according to the authorities of users.
The invention has the advantages that: based on the metadata model, a checking, tracing and synchronizing method for the data in the engineering field is provided, the standardized construction, management and application of the knowledge graph are efficiently realized, and the method is suitable for the construction, management and application of knowledge graphs in various engineering fields such as civil engineering, bridges, tunnels and the like.
Drawings
FIG. 1 is a schematic flow chart of a method for building an engineering domain knowledge graph based on a metadata model according to the present invention;
FIG. 2 is a diagram of a data context showing a metadata model and dependency relationships of fields therein in a directed graph manner according to the present invention;
fig. 3 is a schematic diagram of a tunnel engineering knowledge-graph body layer in the invention.
Detailed Description
The features of the present invention and other related features are described in further detail below by way of example in conjunction with the following drawings to facilitate understanding by those skilled in the art:
example (b): as shown in fig. 1 and 2, this embodiment takes the construction of a knowledge graph in the field of tunnel engineering as an example, and specifically relates to a construction method of a knowledge graph in the field of engineering based on a metadata model, where the construction method includes the following steps:
(1) and (4) sorting and summarizing various basic data in the tunnel engineering project, including but not limited to basic data such as survey data, monitoring data, construction data and design data, and forming a service data source in the field of tunnel engineering.
(2) Constructing a knowledge graph body layer in the engineering field based on various basic data in a service data source, wherein the knowledge graph body layer comprises a body, body attributes and body relations; the knowledge map body layer is required to accurately and comprehensively cover various data of tunnel engineering.
Three bodies of a tunnel interval, a tunnel segment and a disease in a tunnel engineering knowledge map body layer are taken as examples for explanation. The body attribute of the tunnel section comprises a belonging line, a section name, opening time, an assembling form and the like; the body attributes of the tunnel segment comprise an affiliated section, a ring number, a coordinate, a buried depth, a deformation measuring value, the existence of diseases and the like; the ontology attributes of the disease comprise disease type, discovery time, severity, discovery position and the like. The ontology, the ontology properties and the ontology relationship between them are shown in fig. 3.
(3) And constructing a metadata layer of the knowledge graph based on the knowledge graph body layer so as to define the mapping relation between the service data source and the knowledge graph body layer. In this embodiment, a plurality of metadata models are established for different application scenarios of tunnel engineering, such as design, construction, monitoring, and the like, and the metadata models jointly form a metadata layer of a knowledge graph in the field of tunnel engineering.
The metadata model comprises a plurality of fields related in business logic, and defines a uniform data specification standard for each field, including the standard definitions of metadata such as field naming specification, field description, field data type specification, data storage position, data blood margin and the like, and the declaration of a data processing method corresponding to the field.
The field data type specification describes a standard data storage type of a field, the standard data storage type comprises a basic data type and an aggregate data type, the basic data type is int, float, string and the like, and the aggregate data type is a list, a hash table, a tuple and the like; further, a field type may be another metadata model or a collection of metadata models. If the data type of a field is a collection type, such as the following list, the elements in the list may be another metadata model in addition to the basic data type, for example:
metadata model 1
Field 1: int
Field 2: string
Field 3: list [ int ]
}
Metadata model 2
Field 1: list [ metadata model 1]
Field 2: int
}
Wherein, the data consanguinity records other fields depended by the field so as to trace the source when the data is updated and analyzed. Each field may have multiple fields that are dependent, and may also be dependent on multiple fields. The depended fields may be other fields in the same metadata model, or may be fields in other metadata models.
Taking the metadata model of the measured value of the tunnel engineering segment as an example, the metadata model includes a plurality of fields such as segment ring number, segment coordinates, measured value of deformation, monitoring time, and whether there is danger. The definition of naming specification, field description, data type, data storage location and data relationship of each field in the metadata model is shown in the following table:
| field(s) | Name of field | Description of field | Data type | Data storage location | Dependency field |
| ring_num | Number of pipe piece | Ring number of tunnel segment | int | MySQL.Ring | / |
| location | Duct piece coordinate | Baidu map coordinates of duct piece | [float, float] | MySQL.Ring | / |
| value | Value of deformation measurement | Segment deformation value in mm | float | MySQL.Value | / |
| time | Monitoring time | Monitoring time of certain measured value | date | MySQL.Value | / |
| is_danger | Whether it is dangerous | Measured value>60mm is at risk | bool | MySQL.Ring | value |
The segment Ring number, the segment coordinates and the fields of whether danger exists in the metadata model of the segment measurement Value are stored in the Ring table of the MySQL database, and the fields of the deformation measurement Value and the monitoring time are stored in the Value table of the MySQL database. The value of the risk field is determined by the size of the deformation measurement, so that the deformation measurement field is present in the dependency field of this field.
(4) And constructing a data task layer, wherein the data task layer is a set of a series of metadata model-oriented data processing methods and is a concrete implementation of the data processing methods in the metadata model. The data task layer mainly comprises a data extraction task, a data cleaning task and a data storage task. Each task has its own data processing method to achieve its task purpose. Wherein:
the data extraction task aims at extracting basic data from a business data source, and the main functions comprise: extracting basic data from a business data source in a full or incremental mode, and carrying out operations such as field splitting, null value processing, repeated data processing and the like on the basic data according to requirements.
The purpose of the data cleansing task is to convert the underlying data in the business data source into a format that conforms to the metadata model standard. The main functions include: data format conversion, data sorting and indexing, new field calculation and the like.
And the purpose of the data storage task is to store the basic data in the cleaned service data source into the knowledge graph. The main functions include: data range verification, data uniqueness and consistency verification, data updating, data warehousing and the like.
The data processing method provided for each task defines the execution frequency, execution mode, execution order, and the like of each data processing task. The execution frequency describes the executable times of the task, such as only one time, multiple times and the like; the execution mode describes a specific execution method of a data processing task, such as active execution, periodic execution, execution after completion of a certain task, and the like; the execution sequence describes the priority and order of execution of the data processing tasks corresponding to each field.
Taking the tunnel engineering in this embodiment as an example, each field oriented to the metadata model defines a data task. If a monitoring item is newly added, the definition data extraction task extracts the monitoring item, and extracts the related information of the newly added monitoring item and the related measured value measuring point information of the tunnel segment from the generated database. The data cleaning task 'processing monitoring project' converts the extracted source data into a format meeting the definition of a metadata model, and finally the data storage task 'adding a monitoring project' stores the record into a database. The various data processing tasks together form a data task layer.
(5) And storing the multi-source heterogeneous basic data in the service data source into a database in a standardized format defined by a metadata model through a data task layer, and completing construction of a knowledge map storage layer. The knowledge map storage layer is composed of one or more types of a map database, a relational database, a non-relational database, a local server or a cloud server.
(6) And constructing a knowledge graph application layer based on the knowledge graph storage layer. The knowledge map application layer provides various data services of the knowledge map and the metadata layer thereof, and the functions comprise a data acquisition interface, a data billboard, a data blood margin display module and the like. Wherein:
the data acquisition interface encapsulates a uniform data reading mode facing different storage positions of the knowledge graph storage layer, and a user can conveniently acquire data in the knowledge graph; the data acquisition interface restricts the user authority and ensures that the user with the appointed authority can only read the specified data.
The data billboard shows a metadata model and data definition standards thereof in the knowledge graph and statistical information of various types of data in the knowledge graph in a chart form.
The data blood relationship display module displays the metadata model and the dependency relationship of each field in a directed graph mode, and a user can conveniently and quickly know the overall appearance of the constructed knowledge graph.
Taking the tunnel engineering in this embodiment as an example, a corresponding data reading interface is developed for a data application scenario in a service (for example, reading deformation monitoring data of a segment in a tunnel in the month), so as to provide a data service. The knowledge graph application layer ensures the safety of data while improving the reading efficiency. Based on the metadata model and the data in the knowledge graph storage layer, the metadata model and the data definition standard thereof as well as the statistical information of various data in the knowledge graph are displayed in a graph form. The data lineage shows the metadata model and the dependencies of the fields therein in a directed graph, as shown in FIG. 2.
Claims (9)
1. A construction method of an engineering field knowledge graph based on a metadata model is characterized by comprising the following steps:
(1) summarizing various basic data in the engineering project to construct a service data source;
(2) constructing a knowledge graph body layer based on various basic data in the service data source, wherein the knowledge graph body layer comprises a body, body attributes and body relations;
(3) constructing a metadata layer based on the knowledge graph body layer to define a mapping relation between the business data source and the knowledge graph body layer, wherein the metadata layer comprises a plurality of metadata models, and each metadata model corresponds to different application scenes;
(4) constructing a data task layer, wherein the data task layer comprises a data extraction task, a data cleaning task and a data storage task;
(5) constructing a knowledge graph storage layer to store the knowledge graph constructed by the data task layer;
(6) and constructing a knowledge graph application layer, wherein the knowledge graph application layer comprises a data acquisition interface, a data billboard and a data blood margin display module.
2. The method of claim 1, wherein the basic data comprises survey data, monitoring data, construction data, and design data.
3. The method according to claim 1, wherein the metadata model comprises a plurality of fields related in business logic, the fields have unified data specification standards, and the data specification standards include field naming standards, field descriptions, field data type specifications, data storage locations, and data bloodborders; the fields have a declaration of the corresponding data processing method.
4. The method of claim 3, wherein the field data type specification is used to describe a standard data storage type of the field, the standard data storage type includes a basic data type and an aggregate data type, the basic data type is int, float or string, and the aggregate data type is list, hash table or tuple.
5. The method of claim 3, wherein the data context is an interdependence between the fields.
6. The method as claimed in claim 3, wherein the data extraction task, the data cleaning task and the data storage task in the data task layer have respective data processing methods.
7. The method according to claim 6, wherein the data processing method of the data extraction task is to extract source data from the business data source in full or incremental manner, and perform field splitting, null processing and duplicate data processing on the source data;
the data processing method of the data cleaning task is to convert the source data processed by the data extraction task into a format conforming to the metadata model standard;
the data processing method of the data storage task comprises the steps of carrying out data range verification, data uniqueness and consistency verification and data updating on the source data cleaned by the data cleaning task, and then storing the source data into the knowledge graph.
8. The method of claim 1, wherein the knowledge-graph storage layer comprises one or more of a graph database, a relational database, a non-relational database, a local server, and a cloud server.
9. The method as claimed in claim 1, wherein the data acquisition interface encapsulates a uniform data reading manner for different storage locations in the knowledge graph storage layer, and the data acquisition interface can read data of the storage location corresponding to the authority in the knowledge graph storage layer according to the authority of a user.
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