CN115309911A - MES and ERP information integration method based on knowledge graph - Google Patents

MES and ERP information integration method based on knowledge graph Download PDF

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CN115309911A
CN115309911A CN202210938328.2A CN202210938328A CN115309911A CN 115309911 A CN115309911 A CN 115309911A CN 202210938328 A CN202210938328 A CN 202210938328A CN 115309911 A CN115309911 A CN 115309911A
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data
knowledge
mes
model
source
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刘成明
周晓宇
李英豪
李翠霞
卫琳
陶永才
马正祥
刘晓亮
韩慧霞
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Tianzhu Science & Technology Co ltd
Zhengzhou University
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Zhengzhou University
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    • G—PHYSICS
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    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
    • G06F16/367—Ontology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F16/31—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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    • G06F16/95—Retrieval from the web
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    • G06F16/9566—URL specific, e.g. using aliases, detecting broken or misspelled links
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract

The invention relates to the field of manufacturing informatization, and particularly discloses a knowledge graph-based MES and ERP information integration method, which comprises the following steps: s1: using a local model to carry out unified modeling on heterogeneous data, and carrying out normalization on fields according to levels; s2: the multi-source heterogeneous data source is changed into a uniform ontology model through extraction, conversion and cleaning; s3: after the unified ontology model is passed, a more friendly interface can be exposed for upper-layer application or analysts; s4: through the modeling process, a uniform logic view of multi-source data is established on the application side, namely a graph model is constructed for all data from the perspective of an analyst; the invention can automatically extract the knowledge of mass data by utilizing the constructed resource knowledge map so as to solve the problems of information island and information isomerism between MES and ERP systems existing at the workshop bottom layer of a large number of manufacturing enterprises.

Description

MES and ERP information integration method based on knowledge graph
Technical Field
The invention relates to the technical field of manufacturing informatization, in particular to an MES and ERP information integration method based on a knowledge graph.
Background
In order to grasp the new requirements of the manufacturing industry under the current new scientific and technological revolution, industrial change and informatization large background, the method firmly and autonomously updates, deepens the innovation of the scientific and technological System, conforms to the development situation of the new technology of digital intelligent manufacturing, effectively fuses the advanced information technology and the development of the manufacturing industry, introduces an Enterprise Resource Planning (ERP) System and a Manufacturing Execution System (MES) for the respective enterprises to become excellent enterprises of digital intelligent manufacturing type, so as to quickly respond to the dynamic change of various internal and external factors, and enable the enterprises to have good agility and intelligence to meet the intense market competition requirements. However, along with the continuous deepening of enterprise information construction, financial information, purchasing information, production information, inventory information, sales information, quality information, equipment information and the like generated by each system are deeply learned to be integrated and different from each other. Therefore, enterprises are difficult to effectively monitor and manage production and operation activities, and a bottleneck restricting the rapid development of the enterprises is formed, so that the enterprises realize the importance of realizing the integration of field-level, MES and ERP integrated information, and the information island and the management gap of the enterprises are eliminated.
Currently used integration methods include direct integration and indirect integration, in which:
the direct integration means that no intermediate connection software exists between the ERP system software and the production MES system, and the ERP system software and the production MES system can directly carry out work related to data access through some connection. For the implementation of the method, the IP address, the login account number and the password in the MES system database and the like need to be designed in the ERP system, so that the ERP can directly access the MES to acquire the related data.
For direct integration, the designer must be able to have a familiar study of the two systems and their respective databases, and also to understand the types of transformation and transformation formulas for different data between the two systems, only to ensure the accuracy of the transformed data.
Indirect integration is different from direct integration, and the name implies that an intermediate file exists between two systems, and different data of the two systems are converted mutually through the intermediate file, so that sharing and interaction of data information are realized. Such intermediate files are many, such as Excel, TXT, XML, and the like.
For direct integration, the designer must be able to have a familiar study of the two systems and their respective databases, and also to understand the types of conversion and conversion formulas for different data between the two systems, only to ensure the accuracy of the converted data;
for indirect integration, the frequency of data conversion between the two is very frequent, and the conversion formula is relatively complex, so that the problem of low real-time response exists in practical application, and the correct format of the file must be ensured in the conversion process; the method has inflexible solution, needs to be integrated again by offline service once the data is changed or new data enters, and has huge cost.
Disclosure of Invention
The invention provides a knowledge graph-based MES and ERP information integration method, which solves the technical problems in the related technology.
According to one aspect of the invention, a knowledge graph-based MES and ERP information integration method is provided, which comprises the following steps:
s1: using a local model to carry out unified modeling on heterogeneous data, and carrying out normalization on fields according to levels;
s2: the multi-source heterogeneous data source is changed into a uniform ontology model through extraction, conversion and cleaning;
s3: after the unified ontology model is passed, a more friendly interface can be exposed for upper-layer application or analysts;
s4: through the modeling process, a uniform logic view of multi-source data is established on the application side, namely a graph model is constructed for all data from the perspective of an analyst.
Further, the method comprises the following steps: the knowledge-graph comprises: the system comprises a data source module for storing original data, a knowledge management module for modeling multi-source heterogeneous data, a knowledge storage module for converting the original data, a monitoring background management module and a knowledge application module for searching.
Further: the knowledge management module also maps different source data to a uniform knowledge model, and finally configures a fusion rule and a conflict resolution rule of knowledge.
Further: the knowledge storage module comprises HBase core storage, an Elasticissearch full-text index and a neo4j relation index, wherein the HBase stores complete data, the Elasticissearch full-text index is established by the Elasticissearch to facilitate searching of a user, and the neo4j relation index is established by the neo4 j.
Further: and a scheme of separating the metadata from the storage and querying is adopted to endow the knowledge graph with dynamic characteristics.
Further: the dynamic characteristics include the dynamics of the data model, the dynamics of model changes, the dynamics of fusion, and the dynamics of "event" data.
Further: also included are small scale data integration, high value density data integration, low value density data integration, and internet semi-structured data integration.
The invention has the beneficial effects that: the invention can automatically extract the knowledge of mass data by utilizing the established resource knowledge map so as to solve the problems of information islands existing at the bottom layer of the workshop of a large number of manufacturing enterprises and information isomerism existing between MES and ERP systems, decouples the model and the data by utilizing a dynamic knowledge map technology, adopts a flexible metadata management mode, and does not need to put the put-in-storage data again even if the metadata is changed.
Drawings
FIG. 1 is a schematic diagram of the process steps of the present invention;
FIG. 2 is a diagram of an information integration framework of the present invention;
FIG. 3 is a data storage architecture diagram of the present invention;
Detailed Description
The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and thereby implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as needed. In addition, features described with respect to some examples may also be combined in other examples.
Example one
As shown in fig. 1, a method for integrating MES and ERP information based on a knowledge graph includes the following steps:
s1: using a local model to carry out unified modeling on heterogeneous data, and carrying out normalization on fields according to levels;
s2: the multi-source heterogeneous data source is changed into a uniform ontology model through extraction, conversion and cleaning;
an ontology model is established through data extraction, and four types of ontologies are established, including entities, events, documents and relationships, wherein the entities include: the event is a person, an object, an organization and a position, has a time attribute and is regarded as a special relation for connecting an entity and an entity, and the entity and a document; the document refers specifically to unstructured documents such as attachments in various formats in mails, including but not limited to PDF documents, word documents, and video and audio in various formats; relationships are used to link the interrelationships between entities, entities and events, documents, etc., such as people-to-people relatives, people-to-goods ownership, and people-to-event dominant relationships.
When data rotation is carried out, heterogeneous data is extracted out of the 4 ontologies, then the 4 ontologies are converted into the same standard format, and certain tools (such as flash) are used for carrying out keyword addition, replacement, deletion, query and the like during cleaning
S3: after the unified ontology model is used, a more friendly interface can be exposed for upper-layer application or analysis personnel, so that convenient data operation is provided;
s4: through the modeling process, a uniform logic view of multi-source data is established on the application side, namely a graph model is established for all data from the perspective of an analyst, and the analyst does not need to pay attention to differences and storage details of a bottom data source and only needs to pay attention to how to analyze on the graph model.
Example two
As shown in fig. 2, the knowledge-graph comprises: the system comprises a data source module for storing original data, a knowledge management module for modeling multi-source heterogeneous data, a knowledge storage module for converting the original data, a monitoring background management module and a knowledge application module for searching.
A data source module: the raw data supports various types of data, such as structured data, data in RDBMS, NOSQL and MQ, and also can be various types of semi-structured data, such as various documents like HTML, PDF and TEXT, or multimedia data like audio and video. Meanwhile, the system also supports the configuration of URL and web page data crawled through the Internet.
A knowledge management module: the core of knowledge management is to establish a uniform model for multi-source heterogeneous data, map different source data to the uniform knowledge model, and finally configure a knowledge fusion rule and a conflict resolution rule to form a uniform knowledge system.
A knowledge storage module: in the core knowledge base, original data is converted into knowledge after offline or real-time ETL processing, and the knowledge is pulled through, fused and conflict solved with stock data in the base according to the configuration of a model, so that the knowledge is consumed by an upstream system.
A background management module: the system monitoring, alarming, log auditing, resource management and scheduling management are realized, and the collected data is subjected to statistical analysis so as to improve the operating efficiency of the whole dynamic knowledge graph.
A knowledge application module: the method supports global knowledge base joint search, map analysis, multi-dimensional analysis of knowledge, multi-person multi-machine collaborative analysis and tactical analysis, and supports customized analysis application of specific industries besides various general analysis means.
EXAMPLE III
As shown in fig. 3, it also includes small scale data integration, high value density data integration, low value density data integration, and internet semi-structured data integration
Small-scale data integration: the data is often a small-scale sample provided by a client, and various types of files can be directly uploaded through a foreground imort function and can be imported.
High-value density data integration: the data is key data provided by a client, and the data needs to be modeled by a service person according to requirements, and then is accessed into an ontology library through a background offline/real-time data stream.
Low-value density data integration: the data is usually 'event' data, the data volume is extremely large, the timeliness is certain, and regular House Keeping is needed. The current implementation mode is that the data is stored in an external OLAP type database, and an application layer performs adhoc query in a direct connection mode, so that valuable data in the data is selectively imported into an ontology library.
Internet semi-structured data integration: and (4) starting a background crawler by giving the URL, crawling a corresponding webpage into a knowledge base, and performing collaborative analysis with stock knowledge.
The embodiments of the present invention have been described with reference to the drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are only illustrative and not restrictive, and those skilled in the art can make many forms without departing from the spirit and scope of the present invention and the protection scope of the claims.

Claims (7)

1. A MES and ERP information integration method based on a knowledge graph is characterized by comprising the following steps:
s1: using a local model to carry out unified modeling on heterogeneous data, and carrying out normalization on fields according to levels;
s2: the multi-source heterogeneous data source is changed into a uniform ontology model through extraction, conversion and cleaning;
s3: after the unified ontology model is used, a more friendly interface can be exposed for upper-layer application or analysis personnel;
s4: through the modeling process, a uniform logic view of multi-source data is established on the application side, namely a graph model is constructed for all data from the perspective of an analyst.
2. The MES and ERP information integration method based on knowledge graph as claimed in claim 1, wherein the knowledge graph comprises: the system comprises a data source module for storing original data, a knowledge management module for modeling multi-source heterogeneous data, a knowledge storage module for converting the original data, a monitoring background management module and a knowledge application module for searching.
3. The method as claimed in claim 2, wherein the knowledge management module further comprises mapping different source data onto a unified knowledge model, and finally configuring fusion rules and conflict resolution rules of knowledge.
4. The MES and ERP information integration method based on knowledge graph as claimed in claim 2, wherein the knowledge storage module is composed of HBase core storage, elasticissearch full-text index, and neo4j relationship index, wherein HBase stores complete data, elasticissearch full-text index is established for facilitating user search, and neo4j relationship index is established.
5. The MES and ERP information integration method based on knowledge graph as claimed in claim 1, wherein metadata and storage are separated from query to give the knowledge graph dynamic characteristics.
6. The MES and ERP information integration method based on knowledge graph as claimed in claim 4, wherein the dynamic characteristics comprise the dynamics of data model, the dynamics of model change, the dynamics of fusion and the dynamics of "event" data.
7. The method for MES and ERP information integration based on knowledge-graphs as claimed in claim 1, further comprising small-scale data integration, high-value-density data integration, low-value-density data integration and Internet semi-structured data integration.
CN202210938328.2A 2022-08-05 2022-08-05 MES and ERP information integration method based on knowledge graph Pending CN115309911A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116894152A (en) * 2023-09-11 2023-10-17 山东唐和智能科技有限公司 Multisource data investigation and real-time analysis method

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116894152A (en) * 2023-09-11 2023-10-17 山东唐和智能科技有限公司 Multisource data investigation and real-time analysis method
CN116894152B (en) * 2023-09-11 2023-12-12 山东唐和智能科技有限公司 Multisource data investigation and real-time analysis method

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