Real-time data integration method for production management and control of ship group intelligent production line

Abstract

The invention provides a data real-time integration method and system for production management and control of a ship assembly intelligent production line. The method comprises the following steps: analyzing a manufacturing process of the assembly intelligent production line, carding production materials and process files of the assembly intelligent production line, and constructing an expandable assembly process semantic model by using an ontology technology; on the basis of the assembly process semantic model, constructing a mapping relationship between a semantic OWL model and a relational data model to obtain a data integration model; and on the basis of the data integration model, determining extraction, conversion and loading schemes of the whole data integration model, and realizing real-time integration of industrial software data by applying an flink CDC technology. According to the method, real-time integration of multi-source heterogeneous data of the small-assemblage intelligent production line can be realized, and dynamic integration retrieval is carried out on the data.

Description

Technical Field

[0001] The present invention relates to the field of intelligent production line technology, and in particular to a real-time data integration method for production control of a small-scale intelligent production line of a ship. Background Art

[0002] Since shipbuilding has the inherent characteristics of production-to-order, high personalization and complex manufacturing processes, the structure of shipbuilding panels is diverse. Each type of panel requires the formulation of corresponding process parameters, and the workshop manufacturing process information is complex. At the same time, the production-related information of the panel is currently stored in different industrial software. Since the data of these software systems are defined by different stakeholders, there are certain differences in the description of the same object, and there are serious information island problems between the information systems.

[0003] Faced with a production environment with complex manufacturing processes and multi-source heterogeneous information, the production factors in the small-scale intelligent production line lack a unified and standardized description, resulting in the production control system being unable to obtain the global information of the workshop in a timely and accurate manner, hindering the effective deployment of the production control system.

[0004] At the same time, in related research on data integration, most methods use the RDF series of languages ​​to achieve data integration and storage, which can only realize the retrieval of static data and cannot meet the management and control system's demand for real-time data feedback, resulting in the management and control system being unable to be deployed in actual industrial scenarios. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a real-time data integration method for production control of independent intelligent production lines of ship groups.

[0006] According to one aspect of the present invention, a real-time data integration method for production control of a small-scale intelligent production line of a ship is provided, comprising:

[0007] Analyze the manufacturing process of the small-scale intelligent production line, sort out the production data and process documents of the small-scale intelligent production line, and use ontology technology to build an extensible assembly process semantic model;

[0008] Constructing a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model;

[0009] Based on the data integration model, the extraction, conversion and loading scheme of the entire data integration model is determined, and the flink CDC technology is applied to realize the real-time integration of data from various industrial software.

[0010] Preferably, the manufacturing process of the analysis group-based intelligent production line is analyzed, the production data and process documents of the group-based intelligent production line are sorted out, and an extensible assembly process semantic model is constructed using ontology technology, including:

[0011] A recognized top-level ontology is obtained, wherein the top-level ontology includes: an occurrence body, an independent continuum, a universal dependency continuum, and a specific dependency continuum;

[0012] On the basis of the top-level ontology, construct an assembly process reference ontology;

[0013] constructing an assembly process application ontology and aligning it with the assembly process reference ontology;

[0014] The data table fields related to each entity are found in the database of each industrial software, and the data attributes of the entities in the assembly process application ontology are uniformly converted and integrated.

[0015] Preferably, the recognized top-level ontology includes: an occurrence body, an independent continuum, a general dependency continuum, and a specific dependency continuum, including:

[0016] Under the guidance of the IOF framework, following the classification logic of the top-level ontology BFO, entities are classified into occurrence entities, independent continuum entities, general dependency continuum entities, and specific dependency continuum entities.

[0017] Preferably, the assembly process reference ontology is constructed based on the top-level ontology, including:

[0018] Reuse the ontologies MASON and CHAMP and refer to the international standard IEC62264 to refine the concepts of the IOF-core ontology and add entities for term definitions;

[0019] Through the ontology MASON, CHAMP, and the international standard IEC62264, the added entities are processed by one or more of disambiguation, conflict coordination, and redundancy deletion to obtain entities that comply with the BFO classification logic definition;

[0020] Determine the object attributes between entities that obey the BFO classification logic and construct the assembly process reference ontology.

[0021] Preferably, the step of constructing the assembly process application ontology and aligning it with the assembly process reference ontology includes:

[0022] Identify the specific entities involved in the application of the assembly process;

[0023] According to the term definition of the assembly process reference ontology, the specific entity involved in the assembly process application is aligned with the assembly process reference ontology.

[0024] Preferably, the step of constructing a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model includes:

[0025] Construct one or more of the following mapping relationships:

[0026] - Each class in the Owl model (OWL model is the assembly process semantic model) is mapped to a table in the relational data model, and the table is named after the class, and a primary key is set for the table, and the primary key is named with the table name as a prefix plus "_id";

[0027] -If there is a direct or indirect inheritance relationship between classes, select a class on the inheritance link and convert it into a table in the relational data model according to user needs;

[0028] -Map the data attributes of a class and its XML model data type to the non-foreign key attribute fields of the table and their corresponding SQL data types;

[0029] - For object attributes with a 1:1 correspondence cardinality, the mapping of the object attributes is achieved by adding foreign keys that reference each other in the tables corresponding to the domain and range;

[0030] -For object attributes with a corresponding relationship cardinality of 1:n or n:1, add the corresponding foreign key in the corresponding table on the more end and reference the corresponding table on the less end to implement the mapping of the object attributes.

[0031] -For object attributes with a corresponding relationship cardinality of m:n, create an association table r3 to decompose this many-to-many m:n relationship into two one-to-many 1:n relationships, and use these two foreign keys as the joint primary key of this association table r3;

[0032] The Owl model is converted into the corresponding data integration model by using the mapping relationship, that is, the integrated data table is designed.

[0033] Preferably, based on the data integration model, the extraction, conversion and loading scheme of the entire data integration model is determined, and the flink CDC technology is applied to realize the real-time integration of data from various industrial software, including:

[0034] Obtaining a data integration solution through the alignment process of the specific entities involved in the assembly process application with the assembly process reference ontology, wherein the data integration solution includes one or more of extraction, conversion and loading;

[0035] Deploy Flink cluster and elastic search cluster on the server;

[0036] Start flink SQL, and use the flink SQL to create a table that is completely consistent with the table that needs to be integrated in the industrial software database, so as to synchronize the data of the database table corresponding to the terms expanded in the assembly process application ontology in the actual industrial software database, and realize data extraction;

[0037] Use flink SQL to create the integrated data table defined in the data integration model, and connect the created integrated data table to the elastic search cluster;

[0038] In Flink SQL, four SQL connection rules are used to write the data from each industrial software database into each integrated data table in the elastic search cluster according to the data integration solution;

[0039] After the data is integrated in the server of the elastic search cluster, the data stored in the elastic search index can be viewed through kibana, and when the data in the ERP and MES databases is modified, the data will be updated in real time.

[0040] According to a second aspect of the present invention, a real-time data integration system for production control of a small-scale intelligent production line of a ship is provided, comprising:

[0041] Ontology semantic model module: Analyze the manufacturing process of the small-scale intelligent production line, sort out the production data and process documents of the small-scale intelligent production line, and use ontology technology to build an extensible assembly process semantic model;

[0042] Data set integration model module: constructs a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model;

[0043] Real-time integration data module: Based on the data integration model, determine the extraction, conversion and loading scheme of the entire data integration model, and apply flink CDC technology to realize real-time integration of data from various industrial software.

[0044] According to a third aspect of the present invention, there is provided a terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can be used to execute any one of the methods described, or to run the system described, when executing the program.

[0045] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to execute any of the methods described above, or to run the system described above.

[0046] Compared with the prior art, the embodiments of the present invention have at least one of the following beneficial effects:

[0047] The real-time data integration method for production control of a small-scale intelligent production line of a ship in the embodiment of the present invention realizes the real-time integration of multi-source heterogeneous data of a small-scale intelligent production line, and overcomes the problem that data in the prior art can only be statically integrated and retrieved.

[0048] The real-time data integration method for production control of ship group-based intelligent production lines in the embodiment of the present invention adopts ontology-based semantic model construction technology to achieve the technical goal of semantic interoperability of multi-source heterogeneous data and solve the technical problem of inconsistent semantics of data interaction in group-based intelligent production lines.

[0049] The real-time data integration method for production control of a ship group-based intelligent production line in the embodiment of the present invention adopts the automatic mapping technology between the ontology model and the relational data model to achieve the technical purpose of multi-source database data integration and solve the technical problem of data integration model construction;

[0050] The real-time data integration method for production control of a ship group intelligent production line in the embodiment of the present invention adopts the flink CDC technology, which can achieve the technical purpose of real-time data integration and solve the technical problem that the data of the traditional data integration method cannot be updated in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0052] Figure 1 It is a flow chart of a real-time data integration method for production control of a ship group-based intelligent production line in one embodiment of the present invention;

[0053] Figure 2 is an ontology semantic model diagram in one embodiment of the present invention;

[0054] Figure 3 This is a diagram of data integration results in one embodiment of the present invention. DETAILED DESCRIPTION

[0055] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0056] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0057] In one embodiment of the present invention, a real-time data integration method for production control of a small ship group intelligent production line is provided. Figure 1 As shown, it mainly includes the following steps:

[0058] S1: Analyze the manufacturing process of the small-scale intelligent production line, sort out the production data and process documents of the small-scale intelligent production line, and use ontology technology to build an extensible assembly process semantic model;

[0059] The extensibility here means: specifically, the constructed ontology model (ie, semantic model) can be further supplemented with term definitions.

[0060] S2: On the basis of the assembly process semantic model constructed in S1, a mapping relationship between the semantic OWL model and the relational data model is constructed to obtain a data integration model;

[0061] S3: Based on the data integration model of S2, determine the extraction, conversion and loading scheme of the entire data integration model, and apply flink CDC technology to realize real-time integration of data from various industrial software.

[0062] The manufacturing process refers to one or more processes in the group coding and sorting, parts grinding, assembly, assembly welding, and quality inspection. The production materials refer to one or more materials in the group cutting layout, segmented detailed production design drawings, and equipment operation manuals. The process documents mainly refer to the group welding process specifications.

[0063] The above-mentioned embodiment realizes the real-time integration of multi-source heterogeneous data of a small group of independent intelligent production lines, and overcomes the problem that the data in the prior art can only be statically integrated and retrieved.

[0064] A semantic model is a language model, and its training is based on various features of the language, such as words, phrases, sentences, etc., and the relationship between them. The semantic model can be understood as a mathematical abstract model that can abstract various features of the language and establish the relationship between them. In addition, the semantic model can also be regarded as a correspondence relationship, which corresponds the relationship between language and semantics. This correspondence relationship can be established through objective facts of the language, thereby forming an effective language abstract mathematical modeling. A semantic model is a mathematical model trained based on language features and relationships. It can abstract the language and establish a correspondence between language and semantics. In a preferred implementation of the present invention, step S1 is implemented to analyze the manufacturing process of the small-scale intelligent production line, sort out the production materials and process documents of the small-scale intelligent production line, and use ontology technology to construct an extensible assembly process semantic model. Specifically, the following steps can be adopted:

[0065] S101. A top-down ontology construction method is used to construct a semantic model. Under the guidance of the IOF framework and following the classification logic of the top-level ontology BFO, entities are mainly classified into occurrence entities, independent continuum entities, general dependency continuum entities, and specific dependency continuum entities.

[0066] IOF is an international organization, full name Industrial Ontologies Foundry, whose purpose is to build a global unified industrial ontology model. The organization has proposed the top-level ontology of IOF-core. The top-level ontology needs to rely on experts in different fields to build their own lower-level domain ontology models on this basis.

[0067] S102. By reusing existing manufacturing process-related ontologies (MASON, CHAMP) and referring to the international standard IEC62264, the concepts of the IOF-core ontology (IOF-core ontology has been publicly released and does not need to be built by yourself) are further refined. By further disambiguating, coordinating contradictions, and removing redundancies from candidate definitions in reference materials such as MASON, CHAMP and the national standard IEC62264, a more refined definition that obeys the BFO classification logic is obtained, the object attributes between each entity are determined, and the assembly process reference ontology is constructed to ensure the scalability of the ontology.

[0068] In some specific embodiments, Figure 2As shown in the figure, on the basis of establishing the top-level ontology, the IOF-core ontology is added. For example, the more refined definition of the occurrence body that obeys the BFO classification logic is: process. The IOF-core ontology that further refines the occurrence body and process includes: state, process characteristics, events, planning process, manufacturing process, measurement process, business process, procurement business process, assembly process, etc.; the constructed assembly process reference ontology is: manufacturing resource status, discrete events, material separation, process scheduling, procedures, quality supervision and process supervision.

[0069] A more refined definition of independent continuum that obeys the BFO classification logic is material entity. The IOF-core ontology related to material entity is: product, resource, raw material, artifact, person, component, system, equipment and assembly. The assembly process reference ontology is further constructed as: worker, part, assembly system and processing system.

[0070] The IOF-core ontologies further related to the generic dependency continuum are: information content entity, action procedure, description specification, production plan, production target specification, and contract. The constructed assembly process reference ontology is: business process specification, process specification, design drawing, process flow, scheduling specification, resource order, material order, production plan, daily production plan, production order, purchase order.

[0071] The BFO entities further related to the specific dependency continuum are realizable entities, tendencies, and functions. The IOF-core entity is further constructed as: capability, and then the assembly process reference ontology is constructed as: equipment capability and equipment function.

[0072] S103. Use a bottom-up approach to further build the assembly process application ontology. By investigating the production process of a specific application scenario and interviewing workshop managers, the specific entities involved in process monitoring and scheduling in the workshop are determined. Then, according to the term definitions of the assembly process reference ontology, they are aligned with the assembly process reference ontology.

[0073] In some specific embodiments, by investigating the small-scale intelligent production line, collecting and investigating the existing process, material resources, production specifications of the manufacturing system, and the implicit knowledge of domain experts, the application layer entities for the small-scale intelligent production line are summarized and aligned with the assembly process reference ontology, such as Figure 2 As shown in the figure, the key vocabulary and inheritance relationship of the assembly process application ontology are shown as follows:

[0074] Among the entities inherited from the occurrence class, the main processes of the small group assembly workshop are: sorting, coding, grinding, assembly, and welding. They are the five processes with strict prior requirements in the small group assembly and welding task group.

[0075] Among the entities inherited from the independent continuum class, the main material entities of the small group assembly process are covered. For example, sorting and coding equipment, grinding equipment, welding equipment, and AGV equipment are subclasses of equipment. Control personnel are subclasses of workers. Ribs and longitudinal beams are subclasses of part entities. Small group components are subclasses of assemblies. Among them, small groups are subclasses of material artifacts, and are also the parent class of small group components, ribs, and longitudinal beams.

[0076] The entities inherited from the Generically Dependent Continuant class include the manufacturing specification documents that guide the group assembly process. For example, the process sequencing plan and worker shift plan are subclasses of the production plan, and the group assembly process drawing is a subclass of the design drawing.

[0077] In the entities that inherit from the Specifically Dependent Continuant class, some capabilities that describe substances are included, such as grinding equipment capabilities and welding equipment functions.

[0078] S104. Then, the data table fields related to each entity are searched in the database of each industrial software, and the multi-source heterogeneous data fields related to each entity are uniformly converted and integrated into the data attributes of the entity in the assembly process application ontology.

[0079] The above-mentioned embodiment adopts the semantic model construction technology based on ontology to achieve the technical purpose of semantic interoperability of multi-source heterogeneous data and solve the technical problem of inconsistent semantics of data interaction in small-scale intelligent production lines.

[0080] Based on the assembly process semantic model established in the above embodiment, in a preferred embodiment of the present invention, step S2 is implemented to construct a mapping relationship between the semantic OWL model and the relational data model to obtain a data integration model. The specific steps are as follows:

[0081] S201. Each class in Owl is mapped to a table in the relational data model, and the table is named after the class. A primary key is set for the table, and the primary key is named with the table name as a prefix plus "_id".

[0082] S202: If there is a direct or indirect inheritance relationship between classes, in order to avoid data redundancy, select a class on the inheritance link according to user needs and convert it into a table in the relational data model.

[0083] S203: Map the data attributes of a class and the data types of its XML model to the non-foreign key attribute fields of the table and their corresponding SQL data types.

[0084] S204. For object attributes with a corresponding relationship cardinality of 1:1, the mapping of the relationship attributes can be achieved by adding foreign keys that reference each other in the tables corresponding to the domain and range.

[0085] S205. For object attributes with a corresponding relationship cardinality of 1:n or n:1, a corresponding foreign key may be added to the corresponding table on the more (n) side, and the corresponding table on the less (1) side may be referenced to implement the mapping of such relationship attributes.

[0086] S206. For object attributes with a corresponding relationship cardinality of m:n, an association table (r3) can be created to decompose the many-to-many (m:n) relationship into two one-to-many (1:n) relationships, and these two foreign keys are used as the joint primary key of the association table.

[0087] In the above embodiments, by using the automatic mapping technology between the ontology model and the relational data model, the goal of multi-source database data integration can be achieved and the technical difficulties in the process of building the data integration model can be solved.

[0088] In a preferred embodiment of the present invention, step 3 is implemented to determine the extraction, conversion and loading scheme of the entire data integration model based on the data integration model of S2, and the flink CDC technology is applied to realize the real-time integration of data from various industrial software. The specific steps are as follows:

[0089] S301. Propose a data integration solution

[0090] By referring to the ontology alignment process in step S103.2, we know the specific sources of each data. Thus, we determine the original fields of the industrial software database corresponding to each field in the relational data model, the original data sources of each field, and the data conversion requirements. That is, we obtain the corresponding data integration ETL (extract, transform, load) solution, which provides a basis for building a real-time data integration platform and proposes a data integration solution.

[0091] S302, deploy the Flink cluster and elastic search cluster on the server, then start the flink SQLClient, and use flink SQL to create tables that are completely consistent with the tables that need to be integrated in the industrial software database, which are used to synchronize the data of these original database tables and realize data extraction. The original data table refers to: the database table corresponding to the expanded terms in the assembly process application ontology in the actual industrial database.

[0092] S303. Use flink SQL to create the integrated tables defined in the data relation model created in step S2, and connect them with elastic search.

[0093] S304. Using the four SQL connection rules in flink SQL, extract, transform and load the data from each industrial software database in S302 into each integrated table connected to elastic search in S303 according to the data integration solution.

[0094] S305. After the data is integrated in the elastic search server, the data stored in the elastic search index can be viewed, and when the data in the ERP, MES and other databases is modified, the data in the elastic search cluster will also be updated in real time.

[0095] In a specific embodiment, an open source data analysis and visualization platform Kibana is used to display the effect after the data is integrated in the elastic search server. Figure 3 As shown in the figure, the "table" in the upper left corner represents the "data integration table"; field analysis integrates the data distribution of each field in the data table, which is the built-in content of kibana; "query result" means the data result obtained by querying the SQL query statement in kibana, which represents: data stored in the elastic search index.

[0096] The above embodiment achieves the goal of real-time data integration by applying Flink CDC technology, and effectively solves the technical problem that data cannot be updated in real time in traditional data integration methods.

[0097] Based on the same inventive concept, the present invention provides a real-time data integration system for production control of a small-scale intelligent production line of a ship, comprising:

[0098] Ontology semantic model module: Analyze the manufacturing process of the small-scale intelligent production line, sort out the production data and process documents of the small-scale intelligent production line, and use ontology technology to build an extensible assembly process semantic model;

[0099] Data set integration model module: constructs a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model;

[0100] Real-time integration data module: Based on the data integration model, determine the extraction, conversion and loading scheme of the entire data integration model, and apply flink CDC technology to realize real-time integration of data from various industrial software.

[0101] The modules / units in the above examples of the present invention may specifically refer to the implementation technology of the steps corresponding to the real-time data integration method for production control of independent intelligent production lines of ship groups in the above embodiments, which will not be repeated here.

[0102] Based on the same inventive concept, in other embodiments of the present invention, a terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can be used to execute the above-mentioned method or to run the above-mentioned system when executing the program.

[0103] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above-mentioned computer programs, computer instructions, etc. can be partitioned and stored in one or more memories.

[0104] The processor is used to execute the computer program stored in the memory to implement the various steps in the method involved in the above embodiment. For details, please refer to the relevant description in the above method embodiment.

[0105] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0106] Based on the same inventive concept, in other embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, it can be used to execute the above method, or run the above system.

[0107] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also be present in a communication device as discrete components.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0111] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0112] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essence of the present invention. The above preferred features can be used in any combination without conflicting with each other.

Claims

1. A real-time data integration method for production control of a small-scale intelligent production line of a ship, characterized in that: include: Analyze the manufacturing process of the small-scale intelligent production line, sort out the production data and process documents of the small-scale intelligent production line, and use ontology technology to build an extensible assembly process semantic model; Constructing a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model; Based on the data integration model, the extraction, conversion and loading scheme of the entire data integration model is determined, and the flink CDC technology is applied to realize the real-time integration of data from various industrial software.

2. According to claim 1, a real-time data integration method for production control of a small-scale intelligent production line of a ship is characterized in that: The analysis of the manufacturing process of the small-scale intelligent production line, sorting out the production data and process documents of the small-scale intelligent production line, and using ontology technology to build an extensible assembly process semantic model, including: A recognized top-level ontology is obtained, wherein the top-level ontology includes: an occurrence body, an independent continuum, a universal dependency continuum, and a specific dependency continuum; On the basis of the top-level ontology, construct an assembly process reference ontology; constructing an assembly process application ontology and aligning it with the assembly process reference ontology; In the database of each industrial software, the data table fields related to each entity are searched and uniformly converted and integrated into the data attributes of the entity in the assembly process application ontology.

3. According to claim 2, a real-time data integration method for production control of a small-scale intelligent production line of a ship is characterized in that: The recognized top-level ontology includes: an occurrence body, an independent continuum, a general dependency continuum, and a specific dependency continuum, including: Under the guidance of the IOF framework, following the classification logic of the top-level ontology BFO, entities are classified into occurrence entities, independent continuum entities, general dependency continuum entities, and specific dependency continuum entities.

4. According to claim 2, a real-time data integration method for production control of a small-scale intelligent production line of a ship is characterized in that: The assembly process reference ontology is constructed based on the top-level ontology, including: Reuse the ontologies MASON and CHAMP and refer to the international standard IEC62264 to refine the concepts of the IOF-core ontology and add entities for term definitions; Through the ontology MASON, CHAMP, and the international standard IEC62264, the added entities are processed by one or more of disambiguation, conflict coordination, and redundancy deletion to obtain entities that comply with the BFO classification logic definition; Determine the object attributes between entities that obey the BFO classification logic and construct the assembly process reference ontology.

5. According to claim 2, a real-time data integration method for production control of a small-scale intelligent production line of a ship is characterized in that: The step of constructing the assembly process application ontology and aligning it with the assembly process reference ontology includes: Identify the specific entities involved in the application of the assembly process; According to the term definition of the assembly process reference ontology, the specific entity involved in the assembly process application is aligned with the assembly process reference ontology.

6. According to claim 1, a real-time data integration method for production control of a small-scale intelligent production line of a ship is characterized in that: The step of constructing a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model includes: Construct one or more of the following mapping relationships: - Each class in the assembly process semantic model is mapped to a table in the relational data model, and the table is named after the class, and a primary key is set for the table, and the primary key is named with the table name as a prefix plus "_id"; -If there is a direct or indirect inheritance relationship between classes, select a class on the inheritance link and convert it into a table in the relational data model according to user needs; -Map the data attributes of a class and its XML model data type to the non-foreign key attribute fields of the table and their corresponding SQL data types; - For object attributes with a 1:1 correspondence cardinality, the mapping of the object attributes is achieved by adding foreign keys that reference each other in the tables corresponding to the domain and range; -For object attributes with a corresponding relationship cardinality of 1:n or n:1, add a corresponding foreign key to the corresponding table on the more end and reference the corresponding table on the less end to implement the mapping of the object attributes; -For object attributes with a corresponding relationship cardinality of m:n, create an association table r3 to decompose this many-to-many m:n relationship into two one-to-many 1:n relationships, and use these two foreign keys as the joint primary key of this association table r3; The mapping relationship is used to convert the assembly process semantic model into the corresponding data integration model, that is, to design an integrated data table.

7. A real-time data integration method for production control of a small-scale intelligent production line of a ship according to claim 6, characterized in that: Based on the data integration model, the extraction, conversion and loading scheme of the entire data integration model is determined, and the flink CDC technology is applied to realize the real-time integration of data from various industrial software, including: Obtaining a data integration solution through an alignment process of specific entities involved in the assembly process application with the assembly process reference ontology, wherein the data integration solution includes one or more of extraction, conversion and loading; Deploy Flink cluster and elastic search cluster on the server; Start flink SQL, and use the flink SQL to create a table that is completely consistent with the table that needs to be integrated in the industrial software database, so as to synchronize the data of the database table corresponding to the terms expanded in the assembly process application ontology in the actual industrial software database, and realize data extraction; Use flink SQL to create the integrated data table defined in the data integration model, and connect the created integrated data table to the elastic search cluster; In Flink SQL, four SQL connection rules are used to write the data from each industrial software database into each integrated data table in the elastic search cluster according to the data integration solution; After the data is integrated in the server of the elastic search cluster, the data stored in the elastic search index can be viewed through the software kibana. When the data in the ERP and MES databases is modified, the data will also be updated in real time.

8. A real-time data integration system for production control of a small-scale intelligent production line of a ship, characterized in that: include: Ontology semantic model module: Analyze the manufacturing process of the small-scale intelligent production line, sort out the production data and process documents of the small-scale intelligent production line, and use ontology technology to build an extensible assembly process semantic model; Data set integration model module: constructs a mapping relationship between the assembly process semantic model and the relational data model to obtain a data integration model; Real-time integration data module: Based on the data integration model, determine the extraction, conversion and loading scheme of the entire data integration model, and apply flink CDC technology to realize real-time integration of data from various industrial software.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it can be used to execute the method described in any one of claims 1 to 7, or to run the system described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it can be used to execute the method described in any one of claims 1 to 7, or to run the system described in claim 8.

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