High-reuse extensible production configuration method based on discrete industry

Abstract

The application discloses a kind of based on discrete industry high reuse scalable production configuration method, specifically includes: based on the standard activity of discrete industry establishes basic class data;Based on production activity and basic class data, obtain general class data;Based on general class data and operation requirement inheritance and extension, obtain each business node of production activity;Through the configuration each business node, obtain product manufacturing model.The application proposes a kind of based on discrete industry high reuse scalable production configuration method, both can satisfy the functional needs of the multiple business scenarios of discrete industry, can greatly improve the application requirements of multiple business scenarios, while can reduce custom development workload, improve product delivery efficiency.

Description

Technical Field

[0001] This invention relates to the field of information technology construction and production in discrete manufacturing industries, specifically to a highly reusable and scalable production configuration method based on discrete industries. Background Technology

[0002] Currently, the application of MES systems has become a trend across various industries in China. In discrete manufacturing, products are composed of numerous parts, each with its own independent processing and assembly process. Therefore, the overall production process is discrete, with manufactured parts undergoing component assembly and final assembly to become finished products. MES systems in discrete manufacturing demonstrate the characteristics of diversified, multi-variety, small-scale, and flexible manufacturing in terms of process flow and production organization. Furthermore, with the development of software technology, various business systems are emerging. Due to the different needs of discrete manufacturing industries, the business processes included in different business systems also vary.

[0003] Under current technology, the business processes contained in various business systems face a high degree of customization. That is, when software products face different production processes and business flow, a new business process needs to be added. Developers need to redevelop the business process according to the actual production process. If a new product, a different process, or a new production line is added, or if abnormal situations are encountered, the software needs to be adjusted again, or new functional business processes need to be developed to meet the actual application.

[0004] For example, to meet the business requirements of an assembly industry, sequential business activities (Step 1, Step 2, Step 3, Step 4) are needed. Developers must develop corresponding software functions according to the activity sequence to achieve practical application. Conversely, another business requirement might require six scrambled activities (Step 3, Step 2, Step 1, Step 4, Step 1, Step 5) or activities involving rework, repair, skipping steps, and rework. In such cases, developers need to develop corresponding program functions for different activity scenarios. Currently, there is an urgent need in discrete manufacturing scenarios for a configuration method that can improve development efficiency and cover various business process models in discrete industries, specifically including business process models for normal production, rework production, repair production, and rework production. This would improve overall development efficiency and facilitate breakthroughs in the subsequent expansion and promotion of software products in discrete manufacturing industries. Summary of the Invention

[0005] To overcome the shortcomings of the above technologies, this invention provides a highly reusable and scalable production configuration method for discrete industries. This method not only meets the functional needs of multiple business scenarios in discrete industries, but also greatly improves the application requirements of various business scenarios. At the same time, it can reduce the workload of customized development and improve product delivery efficiency.

[0006] The technical solution adopted by this invention to overcome its technical problems is: a highly reusable and scalable production configuration method based on discrete industries, characterized in that it specifically includes: establishing basic class data based on standard activities of discrete industries; obtaining general class data based on production activities and basic class data; inheriting and extending the general class data and operational requirements to obtain each business node of the production activities; and obtaining the product manufacturing model by configuring each business node.

[0007] Furthermore, the basic data includes at least manufacturing data, process data, and operational data derived from discrete industry standard activities.

[0008] Furthermore, the job-type data inherits from and extends the process-type data.

[0009] Single inheritance of job-related data in discrete industries has been implemented.

[0010] Furthermore, the general data class abstracts and encapsulates manufacturing data, process data, and job data.

[0011] Furthermore, the general data also includes operation data and standard data, which inherit from process data and job data respectively, and are extended based on operation requirements.

[0012] It enables multiple inheritance of operational and standard data in discrete industries.

[0013] Furthermore, the general data also includes process data and quality data, which inherit from the standard data and are extended based on operational requirements.

[0014] This allows different classes of process data and quality data in discrete industries to inherit from the same class.

[0015] Furthermore, the process of inheriting and extending the general class data and operational requirements to obtain each business node of the production activity specifically includes inheriting and extending the general class data according to the operational requirements of each business node of the production activity, thereby encapsulating it into business node class data corresponding to the business node.

[0016] Furthermore, the step of obtaining the product manufacturing model by configuring each business node specifically includes: establishing the connection relationship between each business node according to production activities, thereby obtaining the product manufacturing model.

[0017] Furthermore, it also includes calling the product manufacturing model for actual production. Specifically, if the actual business activities are different from the product manufacturing model, the product manufacturing model is inherited and extended for actual production; if the business processes change in the actual business activities, the product manufacturing model is extended through process transformation and the same business activities as the product manufacturing model are inherited.

[0018] Polymorphism in business activities is achieved through inheritance and extension.

[0019] The beneficial effects of this invention are:

[0020] By streamlining business processes, an abstract and unified business model is created. Diverse production activities are encapsulated and inherited to form configurable and scalable digital models, simulating the multi-faceted business processes of on-site production. This allows for the rapid implementation of production process functions to meet actual operational requirements. Business activities within the production process are abstracted into nodes, parameters, and standard requirements, including encapsulation of business processes, user interfaces, and functional logic. Each node can be reused and extended to meet the functional needs of various production processes. This also improves overall customization development efficiency, paving the way for breakthroughs in the subsequent expansion and promotion of MES products in discrete manufacturing industries. Attached Figure Description

[0021] Figure 1 This is a flowchart of an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the production model according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of the basic class data in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of basic class data inheritance in an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of general class data in an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram illustrating the polymorphism of an embodiment of the present invention;

[0027] Figure 7 This is a general 3D assembly production flow chart for embodiments of the present invention;

[0028] Figure 8 This is a schematic diagram of the production process of the 3D printing assembly process according to an embodiment of the present invention;

[0029] Figure 9 This is a schematic diagram of the process step parameter configuration according to an embodiment of the present invention;

[0030] Figure 10This is a schematic diagram of exception handling in an embodiment of the present invention. Detailed Implementation

[0031] Before describing the highly reusable and scalable production configuration method for discrete industries according to the present invention, some technical terms will be explained first:

[0032] Discrete industries primarily refer to a large category of machining enterprises. Their basic production characteristic is the machining of workpieces by machines, followed by the assembly of different workpieces into products with a specific function. Because the relationship between machines and workpieces is separate in this production process, it is called a discrete industry. Typical discrete industries include: machinery manufacturing, electronics, aerospace manufacturing, and automobile manufacturing. These enterprises engage in both make-to-order and make-to-stock production; they also handle both batch production and small-batch production.

[0033] MES: A shop floor-oriented management information system located between the upper-level planning and management system and the lower-level industrial control system. It provides operators / managers with information on plan execution, tracking, and the current status of all resources (people, equipment, materials, customer needs, etc.).

[0034] The MES optimizes and manages the entire production process, from order placement to product completion, through information transmission. When real-time events occur in the factory, the MES can react promptly, report them, and guide and process them using current, accurate data. This rapid response to changes in status enables the MES to reduce non-value-added activities within the enterprise, effectively guiding the factory's production operations, thereby improving the factory's on-time delivery capabilities, material flow performance, and return on investment.

[0035] A business process is a series of activities jointly completed by different people to achieve a specific value goal. These activities are not only strictly sequential, but their content, methods, and responsibilities must also be clearly arranged and defined to enable the transfer of tasks between different roles. The transfer between activities can span a considerable distance in time and space. A business process is a work process composed of orderly process nodes and execution methods.

[0036] Business modeling: Describing the objects and elements involved in enterprise management and business, as well as their attributes, behaviors and relationships, in the form of software models. Business modeling emphasizes understanding, designing and structuring enterprise information systems in a systematic way.

[0037] Abstraction: Abstraction is the process of extracting common, essential characteristics from numerous things while discarding their non-essential features. Specifically, abstraction is the method by which people, based on practice, process rich sensory materials by refining them, eliminating the dross and retaining the essence, moving from the specific to the general, and from the superficial to the profound, to form thought forms such as concepts, judgments, and inferences, in order to reflect the essence and laws of things.

[0038] Scalability: From a software engineering perspective, software development requires at least the development, testing, and maintenance stages. The purpose of scalability is to enable the software to be used in real-world scenarios with minimal or no code modifications. This also means stability, and only stable programs can bring value and a good user experience.

[0039] To facilitate a better understanding of the present invention by those skilled in the art, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following are merely exemplary and do not limit the scope of protection of the present invention.

[0040] like Figure 1 As shown in the flowchart of the highly reusable and scalable production configuration method for discrete industries described in this embodiment, it includes:

[0041] S1. Establish basic data based on discrete industry standard activities.

[0042] First, a production model is obtained by abstracting and encapsulating discrete industry business activities.

[0043] Obtain a general process model including business requirements, activity nodes, operating standards, quality requirements, and process requirements to be developed. Furthermore, employ the 5M1E analysis method to comprehensively consider the six elements of MES technology surrounding manufacturing enterprise on-site management: People, Machines, Materials, Methods, Environment, and Measurement. Each element is explained in detail below.

[0044] a) Man: The operator's understanding of quality, skill level, physical condition, etc.;

[0045] b) Machine: The accuracy and maintenance status of machinery, equipment, and measuring instruments;

[0046] c) Material: The composition, physical properties, and chemical properties of the material;

[0047] d) Method: This includes production processes, equipment selection, operating procedures, etc.

[0048] e) Measurement: This mainly refers to whether the methods used in the measurement are standard and correct;

[0049] f) Environment: Temperature, humidity, lighting, and cleanliness conditions of the workplace.

[0050] Based on the above factors, the following can be established: Figure 2 The production model shown is used as a basis for object-oriented design in this discrete industry production model.

[0051] And based on the discrete industry production model, it organizes and abstracts encapsulates such as Figure 3 The general production process shown yields basic data.

[0052] The basic data includes at least manufacturing data (Class Area), process data (Class Process), and operational data (Class Step). Figure 2 For example, the manufacturing data (Class Area) includes parameters such as the manufacturing front-end area, manufacturing back-end area, manufacturing line edge area, manufacturing packaging area, and production warehousing area. The process data (Class Process) includes parameters such as batch production, customized production, production rework, production rework, production repair, production skipping steps, and production modification. The manufacturing data (Class Area) and process data (Class Process) are static data. The work data (Class Step) includes parameters such as Assembly 1, Assembly 2, Assembly 3, ..., Packaging. The work data (Class Step) is obtained by inheriting from and extending the process data (Class Process), and is dynamically configurable data, such as... Figure 4 The diagram illustrates single inheritance in [the context of inheritance].

[0053] S2, general data is obtained based on production activities and basic data.

[0054] Abstract and encapsulate manufacturing data (Class Area), process data (Class Process), and job data (Class Step) into... Figure 5 The general class data shown is the Class Abstract.

[0055] S3 inherits and extends from general data and operational requirements to obtain various business nodes of production activities.

[0056] Operational requirements include operational data and standard data. Operational data (Class Oper) and standard data (Class Standard) inherit from and extend process data (Class Process) and job data (Class Step), respectively. Based on the operational requirements of each business node in the production activity, the corresponding class data for each business node is obtained. Operational requirements also include process data (Class Techno) and quality data (Class Quality), which inherit from and extend standard data (Class Standard), such as... Figure 4 A diagram illustrating multiple inheritance and inheritance of the same class by different classes.

[0057] S4 obtains the product manufacturing model by configuring each business node.

[0058] Based on the production process, the class data corresponding to each business node is encapsulated into an object, which serves as the product manufacturing model.

[0059] S5 calls upon the product manufacturing model to conduct actual production.

[0060] Manufacturing companies typically have their production planning department issue production order plans to the workshops based on sales orders. Workshop managers then create production work orders, and the workshops execute material requisition, processing, and warehousing according to the production plan. Upon warehousing, finished products must be returned to the production order to monitor the production order progress. After the plan is executed, in some implementations, two abnormal situations may occur: one is a change in the sales order, requiring the production order system to transmit change information to the MES (Manufacturing Execution System). If the production order differs from the product manufacturing model, the product manufacturing model is inherited and expanded based on the changed production order for actual production.

[0061] Another approach is to manually adjust the production plan within the MES system based on actual production conditions. For orders that have already been executed, when the production plan is in progress and production flow is already underway, users need to manually pause or close the relevant batches in the system. Therefore, in actual business activities, if the business process changes, the product manufacturing model is extended through process transformation, inheriting the same business activities as the product manufacturing model. For example... Figure 6 As shown, the product manufacturing model starts from Star and executes Step 1, Step 2, ..., until Step 6 ends. Taking rework, repair, and rework as examples in actual production: In the case of rework, the product manufacturing model is inherited, and RWStep1 and RWStep2 are extended based on it. After rework is completed, it returns to Step 3 to continue the original production, thus realizing the rework process in actual production.

[0062] for Figure 6 The repair shown inherits from the product manufacturing model and extends StepR5 based on it. After completing StepR5, it returns to Step5 to continue production, thus realizing the repair process in actual production.

[0063] for Figure 6 The rework shown inherits from the product manufacturing model and configures Step 5 to return to Step 4, then continues execution after completion. This realizes the rework process in actual production.

[0064] It enables the configuration, inheritance, and extension of product manufacturing models, achieving polymorphism for different business scenarios and realizing different production processing capabilities.

[0065] The following is based on Figure 7 Taking the 3D assembly production activity shown as an example, the basic data based on discrete industry standard activities is abstracted and encapsulated into general data. Based on this general data and operational requirements, it is encapsulated into various business nodes of the process route, such as... Figure 8 The corresponding production process shown includes prefabrication, main unit assembly, component scanning head, component calibration instrument, component relay box, component base, matching assembly, and packaging business nodes, such as... Figure 9 As shown, the operation of each business node is configured, with the process sequence numbers being 1, 1, 1, 1, 1, 1, 2, 3, including both parallel and sequential flows, thus completing the establishment of the product manufacturing model. The left side shows the business flow corresponding to the process route, and the right side shows the activity parameter configuration for process 10007.

[0066] It should be noted that these parameters can be configured and managed according to the process requirements of each product. For aspects that differ based on business characteristics, the parameter items for the activity can be configured and managed. Examples include formulas, coordinates, and inspection items.

[0067] In some implementations, the user interface used for configuration is generic, while the business logic code is developed according to different business processes, such as normal production, rework production, rework production, repair production, and exception handling. Different Class Processes are logically categorized according to different business processes.

[0068] Some implementations also include exception handling and multi-scenario applications. For example... Figures 4-6 The options shown are "return," "repair," and "rework." The logic for each step (Step) is implemented through inheritance and extension of its classes and methods.

[0069] Anomaly handling refers to the processing of abnormalities occurring during the production process, such as those related to quality, processes, materials, or equipment. Currently, anomaly handling in discrete manufacturing processes is primarily manifested at the process level, and secondarily in anomaly feedback forms.

[0070] like Figure 10 The abnormality handling shown, in some embodiments, involves addressing quality abnormalities in the third-generation intraocular scanner, requiring rework. If rework is necessary during abnormality handling, the rework process is followed before production continues. The rework step leads to optical assembly, inheriting the process flow from the steps following the optical assembly of the intraocular scanner.

[0071] It should be noted that the process configuration can be flexibly configured and encapsulated according to the actual needs of the scenario. The overall processing will be combined with the abnormal handling process nodes, such as the judgment of a dedicated person, such as quality, process and production.

[0072] The above description only outlines the basic principles and preferred embodiments of the present invention. Those skilled in the art can make many changes and modifications based on the above description, and these changes and modifications should fall within the protection scope of the present invention.

Claims

1. A high-reuse extensible production configuration method based on discrete industries, characterized in that, Specifically comprising: establishing basic class data based on discrete industry standard activities; obtaining general class data based on production activities and the basic class data; inheriting and extending each business node of the production activities based on the general class data and operation requirements; the operation requirements further comprising process class data and quality class data, the process class data and the quality class data respectively inheriting the standard class data and being extended based on the operation requirements. obtaining a product manufacturing model by configuring each business node; the basic class data at least comprising manufacturing class data, process class data and job class data abstracted based on discrete industry standard activities; the job class data inheriting the process class data and being extended; the general class data abstracting the manufacturing class data, the process class data and the job class data and being encapsulated; the product manufacturing model being obtained by configuring each business node, specifically comprising: establishing a connection relationship of each business node according to the production activities, thereby obtaining the product manufacturing model; the process class data at least comprising production rework, production rework and production repair; calling the product manufacturing model for actual production, specifically comprising: if an exception occurs in the production process in the actual business activities, the product manufacturing model is extended through a flow conversion, and the same business activities as the product manufacturing model are inherited; wherein the flow conversion comprises adding any one of a production rework flow, a production rework flow or a production repair flow to the product manufacturing model; the operation requirements further comprising operation class data and standard class data, the operation class data and the standard class data respectively multiple-inheriting the process class data and the job class data and being extended based on the operation requirements; the operation requirements further comprising process class data and quality class data, the process class data and the quality class data respectively inheriting the standard class data and being extended based on the operation requirements.

2. The high-reuse, scalable production configuration method based on discrete industries as claimed in claim 1, wherein, further comprising calling the product manufacturing model for actual production, further comprising: if the actual business activities are different from the product manufacturing model, the product manufacturing model is inherited and extended for actual production.

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