CN116501294A - Demand extraction and modeling method for discrete manufacturing system - Google Patents
Demand extraction and modeling method for discrete manufacturing system Download PDFInfo
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- 238000003058 natural language processing Methods 0.000 claims description 7
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Abstract
The invention discloses a demand extraction and modeling method for a discrete manufacturing system, which relates to the field of discrete manufacturing and comprises the following steps: step 1, dividing the requirement into a functional requirement and a non-functional requirement; step 2, establishing an original demand document according to functional requirements, and analyzing and extracting the original demand document through a structured demand extraction technology; step 3, constructing domain knowledge through a knowledge graph by the nonfunctional requirements to complete the requirements; and 4, establishing a resource-process-product demand model corresponding to the discrete manufacturing system. The invention designs a resource-process-product-based demand extraction technology and a demand complementation method, establishes a rapid demand extraction and modeling method, solves the problem of rapid demand change, improves the operability and feasibility of the modeling method, is simple and universal, and reduces the labor cost.
Description
Technical Field
The invention relates to the field of discrete manufacturing, in particular to a demand extraction and modeling method for a discrete manufacturing system.
Background
Analysis, extraction and modeling of existing system requirements are implemented based on the architecture of the requirements engineering. The demand engineering uses a structured demand extraction process to provide a framework of demand description, a demand extraction technology and demand clues possibly changed in the future for the demand extraction process, and then uses a systematic demand modeling process to provide predefined semantic interpretation and semantic constraint for a demand model, so as to ensure that the meaning of demand information is accurately understood semantically. The traditional research mode is that a client describes a requirement to build a requirement task book, and then a developer forms corresponding system software codes after requirement analysis and extraction. The unified modeling language UML is a modeling tool for object-oriented design, can be used for explaining, visualizing and documenting products, has wide modeling capability, models product design characteristics by the system modeling language SysML, creates meta-models and icon-based visual symbols on the basis of UML, and develops the unified demand modeling language URML. In the prior art, modeling structures (including relations, objects, methods, attributes and the like) in a demand document are extracted by using methods such as natural language processing, machine learning and the like, then a heuristic rule is used for establishing a model generator to synthesize the model structures and the relations, the model structures and the relations are stored in an XML format, so that a framework from the demand document to UML is established, XML is imported into a UML modeling tool, and finally a UML-based demand model is generated; there is also a hierarchical demand modeling technique that automatically generates a sequential function graph to describe a demand by importing the demand from a REQIF file based on a semi-formal graph model and a combined specification that support hierarchical modeling, using a hierarchical dependency graph and a causal matrix.
However, the prior art lacks a technology for directly and quickly constructing a demand model, cannot quickly re-analyze and model a changed demand, has long time consumption and poor flexibility, lacks a structured demand extraction method aiming at the characteristics of a discrete manufacturing system, cannot realize the identification and extraction of key functional elements in the system demand, lacks a demand complement method aiming at the characteristics of the discrete manufacturing system, and cannot establish a complete system demand model under the condition of incomplete user demand.
Therefore, those skilled in the art are working to develop a demand extraction and modeling method for a discrete manufacturing system, which can integrate demand extraction and demand complementation technologies and directly construct a demand model based on resource-process-product.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the technical problem to be solved by the present invention is how to establish a structured demand extraction method and how to implement a demand complement method.
To achieve the above object, the present invention provides a demand extraction and modeling method for a discrete manufacturing system, the method comprising the steps of:
step 1, dividing the requirement into a functional requirement and a non-functional requirement;
step 2, establishing an original demand document according to functional requirements, and analyzing and extracting the original demand document by a structural demand extraction technology;
step 3, constructing domain knowledge through a knowledge graph by the nonfunctional requirements to complete the requirements;
and 4, establishing a resource-process-product demand model corresponding to the discrete manufacturing system.
Further, the step 2 further includes:
step 2.1, preprocessing the original demand document to obtain a text demand template;
step 2.2, processing the text demand template to obtain a process flow list and a judgment logic dictionary;
step 2.3, matching the process flow list with a structured process flow function template, matching the judgment logic dictionary with a judgment logic description template, and extracting a process execution sequence and a logic jump condition from the judgment logic dictionary;
and 2.4, obtaining key elements in the production process of the discrete manufacturing system by extracting the process flow function template, and storing the key elements into an information base.
Further, the step 2.1 further includes: and carrying out natural language processing on the original demand document, extracting corresponding information according to a text demand template rule table, and converting the corresponding information into the text demand template.
Further, in the natural language processing process, keywords are obtained from priori knowledge and project information, and the keywords mainly relate to technological process information.
Further, the processing the text class requirement template includes: and obtaining key information and extracting logic through text segmentation.
Further, the key elements include: resources, processes, products.
Further, the step 3 further includes:
step 3.1, constructing the domain knowledge according to project information, priori knowledge and expert experience;
step 3.2, constructing a knowledge graph;
and 3.3, carrying out demand complement through the query and reasoning functions of the reasoning engine.
Further, the knowledge graph employs an RDF storage system to store the domain knowledge and build a network.
Further, the constructing a knowledge graph includes: and according to the knowledge graph and the characteristics of the RDF storage system, forming corresponding triples by taking resources, processes and products as predicates, and establishing resource, process and product relation graphs of different discrete manufacturing systems, thereby constructing the knowledge graph corresponding to the different discrete manufacturing systems and describing the domain knowledge related to the different discrete manufacturing systems.
Further, the RDF storage system uses the SPARQL language to query and infer the domain knowledge.
Compared with the prior art, the invention has at least the following beneficial technical effects:
1. the method solves the problem of rapid change of the requirements, can realize rapid analysis, extraction and modeling of the requirements for a discrete manufacturing system, improves the operability and feasibility of the modeling method, is simple and universal, and reduces the labor cost;
2. the description mode of the model elements is unified, the structured demand elements can be accurately and rapidly extracted, and meanwhile, demand change is supported;
3. the query and reasoning capability of the demand model is given, the modification of domain knowledge and demand is provided, and the completeness and expansibility of the demand model are improved.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
FIG. 1 is a technical roadmap of a preferred embodiment of the invention;
FIG. 2 is a flow chart of a structured need extraction technique according to a preferred embodiment of the present invention;
FIG. 3 is a process flow function template of a preferred embodiment of the present invention;
FIG. 4 is a decision logic description template of a preferred embodiment of the present invention;
FIG. 5 is a diagram of a meta model of a knowledge graph in accordance with a preferred embodiment of the invention.
Detailed Description
The following description of the preferred embodiments of the present invention refers to the accompanying drawings, which make the technical contents thereof more clear and easy to understand. The present invention may be embodied in many different forms of embodiments and the scope of the present invention is not limited to only the embodiments described herein.
In the drawings, like structural elements are referred to by like reference numerals and components having similar structure or function are referred to by like reference numerals. The dimensions and thickness of each component shown in the drawings are arbitrarily shown, and the present invention is not limited to the dimensions and thickness of each component. The thickness of the components is exaggerated in some places in the drawings for clarity of illustration.
The embodiment provides a demand extraction and modeling method for a discrete manufacturing system, which describes essential elements in the production process of the discrete manufacturing system, namely describes a process, a product and a resource based on a resource-process-product model. The resource refers to an entity in the production process, and comprises various machine equipment such as a mechanical arm, a conveyor belt, a tray and the like, and states and information of the machine equipment; the process refers to the process flow involved in the production process; the product refers to a finished product produced by a specific process flow on different resources and can be divided into a raw material, a primary product, a secondary product and a final product. In the demand model, three parts of resources, processes and products are closely connected, and different discrete manufacturing systems can construct a complete resource-process-product demand model according to the system demands. A technical roadmap of a demand extraction and modeling method for a discrete manufacturing system is shown in fig. 1.
From the needs, the needs are divided into functional needs and non-functional needs. Functional requirements refer to the functions and acts that the software must implement. Non-functional requirements generally refer to performance and constraint requirements, and are implicitly present in actual manufacturing. The functional requirements can be directly proposed by users or task books, the analysis and extraction can be directly carried out through the designed structured requirement extraction technology, the functional requirements are not required to depend on domain knowledge completion, the knowledge graph is built, and the requirement completion is carried out through query and reasoning functions.
The demand modeling method can accurately complete the extraction and the demand fusion of the demand information, provide the modification of domain knowledge and demand, solve the problem of rapid change of the demand, and improve the efficiency and reduce the cost.
As shown in FIG. 2, the structured requirements extraction technique designed provides a requirements templating framework for document input. The method comprises the steps of providing a process-based text demand template for preprocessing an original demand document, obtaining key information through text segmentation, extracting logic, and finally storing a resource-process-product model-based structured demand model element, and specifically comprises the following steps:
step 1, performing natural language processing on an original demand document, and extracting corresponding information according to a text demand template rule table, as shown in table 1, to convert the corresponding information into a text demand template. The key words are obtained from priori knowledge and project information in the natural language processing process, and mainly relate to process flow information.
And 2, obtaining information such as a process flow list, a judgment logic dictionary and the like through a text segmentation and information storage algorithm of a text demand-oriented template.
And step 3, matching the process flow list with the structured process flow function template, and matching the judgment logic dictionary with the judgment logic description template, so as to extract the process execution sequence and the logic jump condition. The process flow function template is shown in fig. 3, and the judgment logic description template is shown in fig. 4.
By extracting the functional templates of the process flow, key elements in the production process of the discrete manufacturing system can be obtained: and storing the resources, the process and the products into an information base, and providing basic element information for demand modeling. The extraction of the structural demand model elements and the process flow logic is not limited to one algorithm, and various data processing modes can be adopted for data extraction, analysis and integration.
TABLE 1 text class requirement template rule List
The adopted demand complement method uses a knowledge graph to construct domain knowledge so as to complement non-functional demands. The knowledge graph uses an RDF storage system to store domain knowledge and build a network. RDF storage systems use the structure of triples to store information, each triplet consisting of three elements, subject, predicate and object. Meanwhile, RDF storage systems use the SPARQL language for query and reasoning domain knowledge. The constructed knowledge graph provides a meta model based on resource-process-product, as shown in fig. 5, and specifically comprises the following steps:
and 1, constructing domain knowledge according to project information, priori knowledge, expert experience and the like.
And 2, constructing corresponding triples by taking resources, processes and products as predicates according to the characteristics of the knowledge graph and the RDF storage system, and establishing resource, process and product relation graphs of different discrete manufacturing systems, thereby constructing the knowledge graph corresponding to the system and describing the domain knowledge related to the system.
And 3, completely supplementing the requirements through the query and reasoning functions of the reasoning engine.
The description information of the discrete manufacturing system on the process, the product and the resource is obtained, the requirement model elements are complemented, and the resource-process-product requirement model of the corresponding system is established. The construction and storage of domain knowledge are not limited to knowledge graphs, and other structured knowledge construction and storage methods can be adopted. Meanwhile, the knowledge query and reasoning function is not limited to the SPARQL language, and can be realized by adopting various programming modes and languages.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention without requiring creative effort by one of ordinary skill in the art. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. A method of demand extraction and modeling for a discrete manufacturing system, the method comprising the steps of:
step 1, dividing the requirement into a functional requirement and a non-functional requirement;
step 2, establishing an original demand document according to functional requirements, and analyzing and extracting the original demand document by a structural demand extraction technology;
step 3, constructing domain knowledge through a knowledge graph by the nonfunctional requirements to complete the requirements;
and 4, establishing a resource-process-product demand model corresponding to the discrete manufacturing system.
2. The discrete manufacturing system oriented demand extraction and modeling method of claim 1, wherein step 2 further comprises:
step 2.1, preprocessing the original demand document to obtain a text demand template;
step 2.2, processing the text demand template to obtain a process flow list and a judgment logic dictionary;
step 2.3, matching the process flow list with a structured process flow function template, matching the judgment logic dictionary with a judgment logic description template, and extracting a process execution sequence and a logic jump condition from the judgment logic dictionary;
and 2.4, obtaining key elements in the production process of the discrete manufacturing system by extracting the process flow function template, and storing the key elements into an information base.
3. The discrete manufacturing system oriented demand extraction and modeling method of claim 2, wherein said step 2.1 further comprises: and carrying out natural language processing on the original demand document, extracting corresponding information according to a text demand template rule table, and converting the corresponding information into the text demand template.
4. A discrete manufacturing system oriented demand extraction and modeling method as claimed in claim 3 wherein the keywords are derived from a priori knowledge, project information during the natural language processing.
5. The discrete manufacturing system oriented demand extraction and modeling method of claim 2, wherein said processing said textual class demand template comprises: and obtaining key information and extracting logic through text segmentation.
6. The discrete manufacturing system oriented demand extraction and modeling method of claim 2, wherein the key elements comprise: resources, processes, products.
7. The discrete manufacturing system oriented demand extraction and modeling method of claim 1, wherein said step 3 further comprises:
step 3.1, constructing the domain knowledge according to project information, priori knowledge and expert experience;
step 3.2, constructing a knowledge graph;
and 3.3, carrying out demand complement through the query and reasoning functions of the reasoning engine.
8. The discrete manufacturing system oriented demand extraction and modeling method of claim 7 wherein the knowledge graph employs an RDF storage system to store the domain knowledge and build a network.
9. The discrete manufacturing system oriented demand extraction and modeling method of claim 8, wherein the constructing a knowledge graph comprises: and according to the knowledge graph and the characteristics of the RDF storage system, forming corresponding triples by taking resources, processes and products as predicates, and establishing resource, process and product relation graphs of different discrete manufacturing systems, thereby constructing the knowledge graph corresponding to the different discrete manufacturing systems and describing the domain knowledge related to the different discrete manufacturing systems.
10. The discrete manufacturing system oriented demand extraction and modeling method of claim 8 wherein the RDF storage system uses SPARQL language to query and infer the domain knowledge.
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