CN112667739A - Unified information model, digital twin and big data optimization method and production management system - Google Patents
Unified information model, digital twin and big data optimization method and production management system Download PDFInfo
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Abstract
The invention discloses a unified information model, which relates to the technical field of enterprise management, can construct one hundred percent close-fitting service of software to a production management process, forms a closed loop through perception, prediction and optimization to the production management process, and improves the product quality and economic benefit of enterprises, and the specific scheme is as follows: the system comprises a system integration perception management and control layer, a knowledge learning layer and a big data mining layer; the system integration perception management and control layer comprises an analysis layer, a business layer, a platform layer, a data layer and/or a basic resource layer; the analysis layer comprises a sales analysis unit, a purchase analysis unit, a production analysis unit, a receivable and paid analysis unit, an inventory analysis unit, a mold analysis unit, a quality control analysis unit, an internet of things monitoring analysis unit and/or an integrated analysis unit; and a unified information model is adopted to construct a digital twin body, so that each link of the production process is uniformly described and recognized in a digital space, and the change of the digital twin body is responded in real time.
Description
Technical Field
The invention relates to the technical field of enterprise management, in particular to a unified information model, a digital twin and big data optimization method and a production management system.
Background
The goal of the two-part integration is to realize the integration of industrial knowledge and production management processes accumulated by enterprises for many years and an information system, and in the past 30 years of information age (industrial 3.0 age), because the digital technology is constrained by algorithm and calculation force, information software (ERP, CRM, WMS and other information software) can only establish an information model to simulate a physical world as much as possible through the research on local historical data. And then the software is applied to realize the management of the digital world to the physical world. Although these software establish some local standardized industrial applications and solve some problems of digital management, it cannot make real-time reflection following physical world changes, and cannot perform comprehensive management on the production process of the whole product.
In the intelligent era, an information system is required to be capable of changing 100% synchronously with a physical system, so that a digital twin body is formed, the change of data of the physical system can be sensed in real time, knowledge learning is automatically carried out, the cognition of industrial technology, a business target and business logic are combined, an optimal business decision solution is formed through an algorithm and configuration rules, and a closed loop for controlling the production process through artificial intelligence is formed. In the closed loop, the data flow of the information system IT needs to be completely consistent with the operation flow of the physical system OT, so that the state of the physical system can be sensed in real time, the analysis and prediction are automatically performed, and then the physical system is adjusted in real time, which requires the information system to realize the leap from virtual simulation to virtual reality.
Currently, 90% of enterprises commonly use ERP, CRM, WMS and other informationized software. The method is characterized in that a server, an operating system and a configuration database are configured from the bottom layer, are vertically built and are quite closed, and are products of the information era. They are local optimization software systems developed by developers to simulate past production processes as much as possible. And for software stability, they are closed and cannot adapt to the individual and real-time changing requirements of each enterprise.
In order to adapt to the production management operation flow of each enterprise, an implementer can only establish another set of information system to develop various solutions, such as an MES system. The data of the respective information software is then interfaced periodically (approximately once to 3 times per day) via an interface or bus.
Therefore, IT cannot realize that real-time data of an enterprise is sent to correct people and system nodes in a correct manner at a correct time, so that fusion of an information system IT and a physical system OT is achieved, which is a difficult problem of industrialization and informatization fusion existing in the last 30 years.
Disclosure of Invention
In order to solve the technical problems, the invention provides a unified information model, a digital twin and big data optimization method and a production management system, which can construct close-fitting service of software to the production management process, form a closed loop through perception, prediction and optimization to the production management process, and improve the product quality and economic benefit of enterprises.
The invention is realized by the following technical scheme:
a unified information model comprises a system integration perception management and control layer, a knowledge learning layer and a big data mining layer;
the system integration perception management and control layer comprises an analysis layer, a business layer, a platform layer, a data layer and/or a basic resource layer;
the analysis layer comprises a sales analysis unit, a purchase analysis unit, a production analysis unit, a receivable and paid analysis unit, an inventory analysis unit, a mold analysis unit, a quality control analysis unit, an internet of things monitoring analysis unit and/or an integrated analysis unit;
the business layer comprises a safety management unit, a technical data unit, a sales management unit, a purchase management unit, a quality management unit, a warehouse management unit, a production management unit, an outsourcing management unit, a mold management unit, a bar code management unit, a receivable and paid management unit, an internet of things monitoring unit, a personnel information unit, a mobile application end unit and/or a production scene unit;
the platform layer comprises system settings, workflow services, journal services, messaging services, printing services, automatic planning services and/or interface services;
the basic resource layer comprises a local server, a cloud server and/or an internet of things server.
As a preferred scheme, the system further comprises an on-cloud sensing management and control system and an off-cloud sensing management and control system;
the cloud sensing management and control system comprises a general PAAS and a cloud safety protection layer, a system integration sensing and management and control layer, a digital twin and knowledge learning layer and a data mining and big data optimization layer;
the under-cloud perception management and control system comprises a network security layer, an edge management and control layer, an equipment management and control layer and a data acquisition layer.
As a preferred scheme, the model adopts a PostgreSQL database.
A digital twin and big data optimization method based on a unified information model comprises the following steps based on the unified information model:
s1: constructing a unified information model of the test bed, and uniformly describing the names of all links;
s2: constructing an interaction module to enable data information of different links and different services to be shared in real time;
s3: adopting uniform identification codes for each link of an enterprise, and constructing a digital twin scene data chain according to the time sequence of the production process;
s4: and comparing the digital twin production process scene data chain with historical data and real-time data respectively, and optimizing the resource allocation of an enterprise by adopting a big data scheduling model.
As a preferred scheme, in the S2 process, a unified API interface is used to construct the interaction module.
A gear metal machining production management system based on a unified information model comprises a personnel management module, an equipment management module, a material management module, a process management module, an environment management module and a detection management module.
As a preferred scheme, the management system comprises a plurality of signal receiving and transmitting ports, the signal receiving and transmitting ports comprise an upper computer and/or a mobile terminal, and the signal receiving and transmitting ports are connected through 5G signals.
In conclusion, the invention has the following beneficial effects:
(1) and a unified information model is adopted to construct a digital twin body, so that each link of the production process is uniformly described and recognized in a digital space, and the change of the digital twin body is responded in real time.
(2) On a unified data platform (industrial Internet PAAS platform), micro-service function modules with service logic and unified information models are developed, the modules are called, an informationized software integration system which can synchronously change along with the production management process of an enterprise is constructed, and the production process of the enterprise synchronously presents virtual mapping (digital twin) in the information system.
(3) The method has the advantages that all production processes are associated through unified identification in the information model, a digital twin production process scene data chain is formed, knowledge learning is automatically carried out, big data are accumulated, an operation standard is formed, the test bed can automatically match a corresponding process route according to input product structure data and the operation standard, a solution is output, and the production process and the product quality are controlled.
(4) The method comprises the steps of establishing various digital twin industrial application solutions for enterprises on an industrial internet platform through a unified API (application programming interface) in an information model, comprehensively sensing real-time data of the enterprises, people, machines, materials, methods, rings and measurement of various services through an industrial internet and a 5G (the third generation) network in the enterprises, carrying out big data analysis according to a big data scheduling model by combining historical data, transmitting an optimization scheme to various departments, workshops and workshop sections through a visualization system to guide operation, and accordingly forming closed-loop optimization of the big data from sensing to management and control on a factory production management process.
Drawings
FIG. 1 is a system framework diagram in an embodiment of the invention;
FIG. 2 is a schematic structural diagram in an embodiment of the present invention;
FIG. 3 is a first application view in an embodiment of the invention;
FIG. 4 is a diagram of a second application scenario in an embodiment of the present invention;
FIG. 5 is a diagram of a third application scenario in an embodiment of the present invention;
FIG. 6 is a model diagram of the overall architecture 2.0 in an embodiment of the invention.
Detailed Description
This specification and claims do not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect.
The terms in upper, lower, left, right and the like in the description and the claims are combined with the drawings to facilitate further explanation, so that the application is more convenient to understand and is not limited to the application.
The present invention will be described in further detail with reference to the accompanying drawings.
The test bed system utilizes a unified information model for the first time to realize the unified description of each link of product drawing numbers, engineering technology, process development, business management, production planning, product manufacturing and storage logistics, so that data circulation of each department, workshop and workshop section information system is free of obstacles and ambiguities, virtual mapping and digital twinning of the information system to the production management process of an enterprise are established, and the production process data of the enterprise are sensed in real time.
The test bed is used for decoupling various functions of enterprise resource management and control software and production process software by adopting a unified data platform, various micro-service general and customized function modules with business logic are developed by a unified information model technology, the modules are called on the unified platform to build an enterprise production process management and control integrated system, all production management functions of an enterprise are sensed and converged on the same platform, data sharing between different businesses is realized, real-time information of the production process of the enterprise is provided for production managers at different levels of the enterprise, and the production management efficiency of the enterprise is improved.
The test bed realizes the identification of a product information model to each production management link by adopting uniform identification codes for personnel, materials, equipment, storage, products and processes of enterprises, thereby automatically constructing a digital twin product production process scene data chain, automatically learning knowledge, accumulating big data, outputting an operation standard solution and controlling the production quality of the products in the production management process of the products.
The test bed can call various mechanism models and algorithm models through a uniform API (application programming interface) embedded in a uniform information model and an algorithm model, various solutions of enterprise production management are built on a uniform data platform, a digital twin body with an information system and a physical process completely fused is formed, enterprise, people, machines, materials, methods, rings and measurement data are sensed in real time through the industrial internet and a 5G network in an enterprise, a big data scheduling model is utilized, data mining is carried out on historical data and real-time data, operation standards and solutions are continuously optimized, the historical data and the real-time data are transmitted to relevant departments, workshops and workshop sections through an enterprise visualization system, operation is guided, a closed loop is formed, enterprise production resource configuration and production scheduling are continuously optimized, and enterprise benefits are improved.
On the basis of a unified information model, including a unified identification code and a unified API (application program interface) technology, the functions of various information isolated island software in the past information era are decoupled and broken on a unified database and an open source platform, universal and customized function modules of micro-services are developed, a production process control data integration system with a horizontal architecture is reconstructed according to business requirements and management targets of enterprises, and the complete fusion of an information system and a production management process is realized.
Verifying that the gear production process management and control integrated software system is constructed by using a unified information model technology,
1. virtual mapping and digital twinning of an information system in the enterprise production management process are realized, and the management cost is reduced by 10%;
2. the real-time sensing of production process data is realized, industrial knowledge learning and big data accumulation are automatically carried out, an operation standard is formed, production is guided, and the product quality is improved by 10%;
3. by means of big data mining, continuous optimization operation and closed loop construction, the gear production process is comprehensively controlled, enterprise resource allocation is optimized, and enterprise comprehensive benefits are increased by 15% -25%;
4. for the whole basic parts trade of follow-up, include: the intelligent technology transformation exploration implementation path in the industries of bearings, gears, springs, chains, fasteners, stamping, forging, casting and machining.
The test bed is mainly applied to the management and control of the gear metal processing production management process, and the process scene comprises the following steps: personnel management, equipment management, material management, process management, environment management and detection management.
That is to say, the test bed comprises all factory production management scenes of factory, man, machine, material, method, ring and test, and can play a demonstration role in the digital transformation of the whole metal processing industry. Because the method constructs a full-perception, full-connection and full-scene digital world through the deep application of a new generation of digital technology, and further optimizes and reconstructs the operation standard of the physical world through a big data technology, the management mode, the business mode and the business mode of the traditional enterprise are innovated and remodeled, and the era crossing from human brain management to artificial intelligence management and control enterprises is realized.
At present, most domestic enterprises finish the production process management requirements which cannot be met by informationized software by means of manual statistics, even though the MES system is developed by inputting manpower and material resources, due to an information isolated island, data butt joint of the ERP and the MES system can only be finished after work, and the result is the same as the EXEL table statistical result, so that most enterprises use manual statistics in a short time, and are more reliable without BUG.
The test bed firstly constructs the close-fitting service of software to the production management process through the test bed project, can sense the real-time data of the whole production management process from blank storage, material receiving and reporting and product detection in the whole factory through 5G connection, automatically performs data operation, outputs real-time human, machine, material, product, semi-finished product and statistical result, and immediately pushes the real-time data to all levels of production management departments through a visualization system.
Compared with the conventional method that a large number of statistical personnel are used for counting work reports filled by hundreds of workers every day, thousands of production data are input into a computer, the daily report can be printed after work, hundreds of thousands of data are gathered at the end of a month and are checked with warehouse material data repeatedly, and the management efficiency is improved by hundreds of times.
More importantly, when the artificial intelligence management and control physical system of the internet of things is introduced, enterprises must construct a digital twin body with information and physical system integrated in one hundred percent to realize self-perception, self-analysis, self-decision and self-execution of the artificial intelligence. In the practice of the FB-CL test bed, a digital twin scene data chain in the production process of a product is constructed through an information model, an identification code and an API interface, an enterprise informatization system automatically learns industrial knowledge, an enterprise KNOW HOW is automatically precipitated, and an operation standard is automatically formed, so that artificial intelligence is formed to analyze and predict the production process and the result of the product, the operation standard is continuously improved through a big data technology, and the operation is guided through a visualization system to control the quality of the product and optimize the resource allocation of the enterprise. The method has very practical intelligent transformation demonstration significance for a large number of part enterprises engaged in the metal processing industry.
The test bed is based on a unified information model technology on a unified data platform:
1. and establishing a system integration perception control layer (corresponding to the AII perception control layer), perceiving real-time state data of human, machine, material, law and environment in the production management process of the enterprise by constructing a digital twin body, and controlling the production process.
2. And establishing a knowledge learning layer (corresponding to the AII digital model layer), and performing knowledge learning, predictive analysis, big data accumulation and product quality control in the manufacturing process by constructing a digital twin production process scene data chain.
3. Establishing a big data mining layer (corresponding to an AII decision optimization layer), mining big data by learning and memorizing various historical digital twin data chains in the production management process of an enterprise, analyzing the historical data and real-time data sensed in the production process according to a big data scheduling model, outputting an optimized production plan and a resource allocation scheme, transmitting the real-time solutions to each corresponding department, workshop section and warehouse through each level of visualization systems of the enterprise, and adjusting the resource allocation and product quality control of the enterprise in real time, thereby forming closed-loop optimization of the big data and improving the product quality and economic benefit of the enterprise.
The current industrial software adopts five-layer architecture technology mainly originated from 90 s, and the method is to configure a server and an operating system from the lowest layer and establish a vertical five-layer architecture, which comprises the following steps: the method comprises the steps of an enterprise planning layer, a factory management layer, a flow control layer, an equipment control layer, a field layer, database preparation, equipment connection, data collection, model establishment and business logic realization, and basically is the application of vertically establishing the closure. If we look at the production site from the plane formed by the application fields, including the axes of the process, the quality, the equipment and the like, and the axes of the different processes, we can see a series of large and small chimney applications. Their problems are:
1. the data and the equipment state of the hierarchical closed production process are closed in professional information systems of all levels and are difficult to circulate.
2. Vertical solidification vertical architecture mode: (Server, operating System, database, application)
3. And (3) tight coupling design: data, algorithms and business logic, lack of function sharing and difficult development.
The unified relational database is adopted to realize database cluster operation and ensure the consistency of data in the system: our factory brain integration system (FB-CL) employs a PostgreSQL database, which is a relational database management system that keeps data in different tables instead of putting all data in one large repository, which increases speed and flexibility. Meanwhile, different table spaces can be established for all tables of the database, so that the database can be distributed on different servers when in operation, and database cluster operation is realized;
PostgreSQL adopts standard TSQL grammar and makes local adjustment, so that SQL sentences are simpler and the operation efficiency is higher, and an SQL efficiency testing tool is provided to improve the SQL operation efficiency; meanwhile, the PostgreSQL also has a plurality of plug-ins for maintaining tools, so that the database is safer, effective parameterization adjustment is carried out according to the use scene, the database is more smoothly operated, and the requirements of different businesses of different enterprises are met; moreover, the PostgreSQL is a free database which is open, so that the overall cost can be greatly reduced; the method supports various operating systems such as Ubuntu, Windows, Mac OS, Linux and the like, supports multithreading and fully utilizes CPU resources. The PostgreSQL supports dual-computer hot standby, so that the host computer and the standby computer database realize data synchronization operation, necessary disaster recovery is provided for the main server, and the loss of enterprises is reduced.
A unified data platform is adopted, a horizontal information framework composed of micro services is constructed, and cross-service information sharing is realized: the FB-CL test bed decouples various tightly coupled industrial software functions on a unified data platform, develops a micro-service general function and special function module comprising a unified information model and service logic, establishes a micro-service function model library, and associates and couples the model in the model library with historical data and other data of a main database. The state of the enterprise production process is continuously changed, and after the data are uploaded, the data are stored in the main database, so that the consistency of the data is ensured.
The FB-CL test bed calls various functional modules of micro-services on the platform, and develops application functions of enterprise sales management, purchase management, technical management, production management, warehousing management, workshop management, equipment management, material management, personnel management and the like according to business needs and targets of enterprises, and the application functions form the consistency of data inside the system through the association of a general model library and a main database, so that cross-business information sharing is realized.
A unified information model is adopted to construct a digital twin body, so that the fusion, perception, prediction and optimization production processes of an information system and a physical system are realized: the FB-CL test bed adopts a uniform information model, and comprises uniform identification codes and a uniform API interface to construct various application services to describe, recognize and respond to product design, processes and engineering, and various production links and elements such as man, machine, material, method, ring, test and the like in the product manufacturing process; we can see by this information model below: the identification of the information model is uniform, the internal rule of the information model is uniform, the information model includes all scenes in the production process in a uniform format, and the information model automatically senses the real-time data of a certain scene in the production process by executing different commands; in the production process, different production scenes sensed by the model can be associated to form a data chain through uniform identification.
Therefore, the digital twin body corresponding to the virtual and the real can be established in a specific production environment through the data model and the algorithm model in the unified information model and the called service interface.
For example, in the production process of a product, the FB-CL test bed connects the digital twin structures corresponding to the virtual and the real to form a digital twin scene data chain in the production process of the product, and the data chain is fed back to form a closed loop, so that the technical and technological knowledge in the production process of the product can be automatically learned, cognitively analyzed and predictively adjusted, and the production quality of the product is controlled.
The FB-CL test bed utilizes the technology of the industrial internet to perform function decoupling on the traditional industrial IT framework on a platform layer and develops a micro-service module comprising a unified information model and a business logic function. The enterprise information integration system built based on the horizontal framework has the advantages that various applications do not need to repeat vertical data acquisition, and enterprise data automatically sense enterprise production and management data through a data sensing layer formed by fusion of an information system and a physical system. That is to say, the FB-CL test bed utilizes the platform technology of the industrial internet to realize the reconstruction and the decoupling of the IT architecture of the production environment, and solves the application and development bottlenecks of the vertical architecture and the island.
The FB-CL test bed systematically senses and describes entities of the physical world through digital twin bodies built by a unified information model, so that various industrial application solutions are more easily developed, real-time data and historical data are analyzed through big data mining, production operation is optimized, and resource allocation capacity and economic benefit of enterprises are improved.
The test bed is built according to an AII overall framework 2.0 model, and adopts a three-layer structure of enterprises, edges and equipment. (as shown in FIG. 6)
The perception control part on the cloud comprises four layers:
1. general PAAS and cloud safety protection layer
2. System integrated sensing and control layer
3. Digital twin, knowledge learning layer
4. Data mining and big data optimization layer
In the production process of a product, digital twin structures corresponding to virtual and real are connected according to time sequence through a unified information model to form a digital twin scene data chain in the production process of the product, the data chain generated in the production process every day is taken as historical data, and big data analysis is carried out on the historical data and real-time data in the production management process according to a big data scheduling model, so that the technical process and human, machine and material resources in the production process of the product can be subjected to predictive analysis and optimization adjustment, and the product quality and resource allocation of enterprises are optimized.
The lower 4 layers of the cloud are respectively: the system comprises a network security layer, an edge management and control layer, an equipment management and control layer and a data acquisition layer (5G application).
1. Network security layer: the method is constructed according to an industrial internet system architecture 2.0 model and comprises two layers of safety protection, an enterprise sets a router, adopts an AR6300+400 router, is additionally provided with a double-master control software firewall, and two factories respectively adopt an AR6140-16G 4G router and a firewall.
2. An edge layer: the control cabinet (production line brain) of all automatic production lines of the test bed and all management departments of the factory are located on the edge layer, all the management departments of the factory are mutually operated with the enterprise layer factory brain to control the operation of the factory, and the edge layer production line brain is mutually operated with the enterprise layer factory brain to produce according to the order requirement.
3. Equipment layer: the equipment brain (control cabinet) of the equipment layer interoperates with the production line brain (control cabinet) to complete the manufacturing process in cooperation with various machines. And a part of equipment is provided with a real-time adjusting system, so that self-detection, self-analysis, self-decision and self-execution of the production process can be realized, processing data can be adjusted in real time, and the production quality of products is ensured.
4. A data acquisition layer: the method comprises the steps of establishing an enterprise 5G network, utilizing the 5G network to be flexible in deployment and large in coverage range, meeting requirements of industrial scenes on network bandwidth, time delay, reliability, connection range and the like, connecting human, machine, material, method, ring and measurement real-time data in the production process of a factory through the 5G network, and directly transmitting the production process real-time data such as 1500 workers in the whole factory, detection data of tens of thousands of products every day, material circulation data in the production process of gears, process processing data in the production process of the products, product packaging and warehousing data and the like to a production perception and control functional layer of a cloud enterprise to perform perception, prediction and optimization operation, thereby realizing quality improvement and efficiency improvement.
The adopted unified information model technology not only can enable the production process of an enterprise to be described, known and responded through a unified information model, but also can enable an information system to automatically sense, learn and master the production management process technology, accumulate data and further continuously optimize the production management process and the product quality of the enterprise through big data mining.
Adopting a unified data platform technology to integrate production, management, administration and business into the same platform
1. Establishing application function layers
On the general PAAS platform, an open source database and an open source data platform are adopted, a unified data model comprising a unified identification code and a unified API (application program interface) technology is adopted, the information software which is tightly coupled in the past is decoupled, an enterprise operation general function module and a special function module with service logic are developed, and a micro service module functional layer of a horizontal architecture is established.
2. Establishing a system integration layer
The FB-CL test bed calls various universal function modules and customized function modules developed by a unified information model technology on a unified data platform, establishes an algorithm and a matching rule according to the production management flow of an enterprise, constructs various solutions meeting the requirements of the enterprise, and builds a production management integrated system of the enterprise through a unified API (application program interface), so that all the solutions of the enterprise are integrated on the same data platform, and cross-business data sharing is realized.
3. Establishes an industrial internet (including a 5G network) inside an enterprise,
the FB-CL test bed is used for building the industrial internet in an enterprise
A. The FB-CL (factory brain) layer of the factory control system is comprehensively connected with the edge operation layer, and the FB-CL layer comprises all operation data of sales, purchase, technology, production, storage, financial departments, production workshops and workshop sections in the process of sensing enterprise management operation.
B. And (3) connecting a factory control system FB-CL (factory brain) with control systems of all automatic production lines of edge control, and sensing state data, equipment operation data and equipment production data of enterprise production equipment in real time.
C. A factory control system FB-CL (factory brain) is connected with 1500 multi-site operators, production scheduling personnel, product detection personnel and warehouse management personnel of an enterprise through a 5G network, and processes, quantity, quality and warehouse-in and warehouse-out data of a product processing process are sensed in real time.
Therefore, comprehensive connection and perception of man, machine, material, method, ring and measurement in the production management process of the enterprise are formed, data required by the enterprise can be transmitted to specific departments and production lines at specific time, and barrier-free flow of the data in the enterprise is realized.
(II) by utilizing a unified information model technology, a digital twin body is built, and the perception and cognition of the real-time data of the physical system by the information system are realized
Adopting a uniform information model comprising a uniform API interface and a uniform identification code to construct various application services to describe the characteristics of various elements such as human, machine, material, method, ring and the like in the product design, process and engineering and product manufacturing process; and the method responds to real-time change in the production process, constructs a digital twin body according to each link of production management of an enterprise, describes, cognizes and responds to the production process of the enterprise in a digital space, and forms virtual and real mapping. Because all business activities of an enterprise establish digital twin bodies of a unified information model, the information system can sense real-time data changes of all businesses of the enterprise. Constructing various application services to describe, recognize and respond to product design, process and engineering and various production links and elements such as man, machine, material, method, ring, measurement and the like in the product manufacturing process through a uniform information model comprising uniform identification codes and uniform API interfaces; from this information model of fig. 2 we can see that:
the identification of the information model is uniform;
the internal rules of the information model are uniform;
the information model includes all scenes in the production process in a uniform format;
the information model automatically senses real-time data of a certain scene in the production process by executing different commands;
as can be seen from fig. 3, the real-time status of the production site is described in the digital space by adopting a unified digital model technology, building services according to the production scene of the reporter through unified code identification and an API interface and using software to build virtual and real corresponding digital twins.
Production reporter solution
(1) An operator uses a mobile phone APP to report workers on site, and after submitting worker reporting data, field quality personnel carry out detection and confirmation on quantity and qualification conditions;
(2) the qualified product data is confirmed by a dispatcher mobile phone APP to finish work order reporting and circulation
(3) The unqualified product quantity is determined by a mobile phone APP of a quality inspector to determine the unqualified product quantity
All field data enter a production management and control integrated system through a mobile phone APP, and software senses real-time data of a production field through a digital twin body.
And thirdly, a scene data chain in the production process of the product is constructed by utilizing the uniform identification codes, so that the automatic learning and precipitation of industrial knowledge are realized, and the product quality is improved. From fig. 4 we can see that: the method is characterized in that unified identification codes are adopted for personnel, materials, equipment and processes of enterprises, and the product information model is recognized for each production management link, so that the production processes of the products are automatically associated according to time sequence in the production management process of the products, a digital twin production process scene data chain is established, feedback is established, a closed loop is formed, manufacturing knowledge learning is automatically carried out, various manufacturing KNOWHOWs in brains of production managers are accumulated and precipitated, operation standards are formed, artificial intelligence is established for analysis, the production process and results of the products are predicted, solutions are output, and the product quality is controlled.
(IV) building a perception and management control integrated system of various solutions of enterprise production management by utilizing a uniform API (application programming interface) technology to form a closed loop and continuously optimize enterprise resource configuration, as shown in FIG. 5
Various mechanism models and algorithm models are called through a uniform API (application programming interface) embedded in a uniform information model and an algorithm model, various solution integrated management and control platforms of enterprise production management are built, the fusion of an information system and a physical system is realized through an industrial internet (including a 5G network) in an enterprise, a digital twin body of each link of production management is built, and people, machines, materials, methods, rings and measurement data of the enterprise are sensed in real time.
The method has the advantages that real-time data and historical data of an enterprise are analyzed by using a big data technology, production operation standards are continuously improved, various optimization solutions are output, operation of departments, workshops and workshop sections is guided by using a visual system, closed loop from perception to management and control adjustment is realized, configuration capacity of various resources of the enterprise is improved, and production cost of the enterprise is reduced. And the economic benefit of enterprises is improved.
(V) data acquisition by 5G network (as shown in FIG. 6)
The method comprises the steps of establishing an enterprise 5G network on a data acquisition layer, utilizing the 5G network to be flexible in deployment and large in coverage range, meeting requirements of industrial scenes on network bandwidth, time delay, reliability, connection range and the like, connecting human, machine, material, method, ring and real-time data of factory production processes through the 5G network, and directly transmitting the real-time data of the production processes of 1500 workers, detection data of tens of thousands of products every day, material circulation data of a gear production process and process processing data of a product production process, product packaging and warehousing and the like to a cloud enterprise production perception and control functional layer for perception, prediction and optimization operation, so that quality improvement and efficiency improvement are realized.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (7)
1. A unified information model is characterized by comprising a system integration perception management and control layer, a knowledge learning layer and a big data mining layer;
the system integration perception management and control layer comprises an analysis layer, a business layer, a platform layer, a data layer and/or a basic resource layer;
the analysis layer comprises a sales analysis unit, a purchase analysis unit, a production analysis unit, a receivable and paid analysis unit, an inventory analysis unit, a mold analysis unit, a quality control analysis unit, an internet of things monitoring analysis unit and/or an integrated analysis unit;
the business layer comprises a safety management unit, a technical data unit, a sales management unit, a purchase management unit, a quality management unit, a warehouse management unit, a production management unit, an outsourcing management unit, a mold management unit, a bar code management unit, a receivable and paid management unit, an internet of things monitoring unit, a personnel information unit, a mobile application end unit and/or a production scene unit;
the platform layer comprises system settings, workflow services, journal services, messaging services, printing services, automatic planning services and/or interface services;
the basic resource layer comprises a local server, a cloud server and/or an internet of things server.
2. The unified information model according to claim 1, comprising an on-cloud perception management and control system and an off-cloud perception management and control system;
the cloud sensing management and control system comprises a general PAAS and a cloud safety protection layer, a system integration sensing and management and control layer, a digital twin and knowledge learning layer and a data mining and big data optimization layer;
the under-cloud perception management and control system comprises a network security layer, an edge management and control layer, an equipment management and control layer and a data acquisition layer.
3. The unified information model of claim 2, wherein the model employs a PostgreSQL database.
4. A digital twin and big data optimization method based on a unified information model, which is based on the unified information model of any one of claims 1 to 3, and is characterized by comprising the following steps:
s1: constructing a unified information model of the test bed, and uniformly describing the names of all links;
s2: constructing an interaction module to enable data information of different links and different services to be shared in real time;
s3: adopting uniform identification codes for each link of an enterprise, and constructing a digital twin scene data chain according to the time sequence of the production process;
s4: and comparing the digital twin production process scene data chain with historical data and real-time data respectively, and optimizing the resource allocation of an enterprise by adopting a big data scheduling model.
5. The unified information model based digital twin and big data optimization method according to claim 4, wherein in the S2 process, a unified API interface is used to construct an interaction module.
6. A gear metal processing production management system based on a unified information model, which is based on the unified information model of any one of claims 1 to 3, and is characterized by comprising a personnel management module, an equipment management module, a material management module, a process management module, an environment management module and a detection management module.
7. The gear metal working production management system based on the unified information model according to claim 6, wherein the management system comprises a plurality of signal transceiving ports, the signal transceiving ports comprise an upper computer and/or a mobile terminal, and the signal transceiving ports are connected through 5G signals.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113496360A (en) * | 2021-07-23 | 2021-10-12 | 鸿利达精密组件（中山）有限公司 | Injection molding production management method |
| CN113706093A (en) * | 2021-07-23 | 2021-11-26 | 联合智造(北京)科技发展有限公司 | Integrated management and control information model for metal processing production and operation process |
| CN114578770A (en) * | 2022-02-28 | 2022-06-03 | 安徽工程大学 | Digital twin formula intelligence production line system |
| CN114815759A (en) * | 2022-06-27 | 2022-07-29 | 广州力控元海信息科技有限公司 | Virtual-real fusion flexible production line variable control method and system |
| CN115981639A (en) * | 2023-01-19 | 2023-04-18 | 浙江高格软件股份有限公司 | Twin modeling method for data definition and relation based on meta-framework |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113496360A (en) * | 2021-07-23 | 2021-10-12 | 鸿利达精密组件（中山）有限公司 | Injection molding production management method |
| CN113706093A (en) * | 2021-07-23 | 2021-11-26 | 联合智造(北京)科技发展有限公司 | Integrated management and control information model for metal processing production and operation process |
| CN114578770A (en) * | 2022-02-28 | 2022-06-03 | 安徽工程大学 | Digital twin formula intelligence production line system |
| CN114815759A (en) * | 2022-06-27 | 2022-07-29 | 广州力控元海信息科技有限公司 | Virtual-real fusion flexible production line variable control method and system |
| CN114815759B (en) * | 2022-06-27 | 2022-09-20 | 广州力控元海信息科技有限公司 | Virtual-real fusion flexible production line variable control method and system |
| CN115981639A (en) * | 2023-01-19 | 2023-04-18 | 浙江高格软件股份有限公司 | Twin modeling method for data definition and relation based on meta-framework |
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