Cloud-edge collaborative data management method and device for flexible manufacturing workshop

Abstract

The present disclosure provides a cloud edge collaborative data management method of a flexible manufacturing workshop, comprising: based on an edge computing node, standardizing source data of the flexible manufacturing workshop to generate an abstract data model; according to a preset production task requirement, instantiating the abstract data model to generate an object database; through a cloud platform, collaboratively analyzing the actual production task requirement and the object database to generate a data dynamic adaptation strategy; and according to the data dynamic adaptation strategy, maintaining and updating the object database. The method of the present disclosure meets the fusion and scheduling requirements of multi-source heterogeneous data in the flexible manufacturing workshop scenario, relieves the cloud computing pressure, forms a virtuous cycle of data acquisition, processing, feedback and optimization, realizes the production optimization of the flexible manufacturing workshop, and improves the perception, decision-making and adaptation ability of the flexible manufacturing workshop under complex data disturbance.

Description

Technical Field

[0001] This disclosure relates to the field of big data, specifically to a cloud-edge collaborative data management method, apparatus, electronic device, computer-readable storage medium, and program product for flexible manufacturing workshops. Background Technology

[0002] Existing manufacturing workshops suffer from numerous proprietary data protocols for equipment, and the massive amounts of heterogeneous data generated during production severely restrict interoperability between devices, leading to a dual dilemma of data silos and data processing delays. Current data management methods are clearly insufficiently adaptable to highly dynamic and flexible manufacturing environments. They fail to establish deep connections between the diverse sources and structures of the massive amounts of data generated by changes in product types or processes under dynamic production conditions, lacking the ability to adapt to changing data conditions. Sending all data generated by equipment to a unified data center in related technologies leads to bandwidth and data processing latency issues.

[0003] The information disclosed in the Background section of this disclosure is intended only to enhance the understanding of the general background of this disclosure and should not be construed as an admission or any implication that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] In view of the above issues, this disclosure provides cloud-edge collaborative data management methods, apparatus, equipment, media, and program products for flexible manufacturing workshops.

[0005] According to the first aspect of this disclosure, a cloud-edge collaborative data management method for a flexible manufacturing workshop is provided, comprising: standardizing source data of the flexible manufacturing workshop based on edge computing nodes to generate an abstract data model;

[0006] The abstract data model is instantiated according to the preset production task requirements to generate an object database;

[0007] The cloud platform is used to collaboratively analyze the actual production task requirements and the object database to generate a dynamic data adaptation strategy.

[0008] The object database is maintained and updated according to the data dynamic adaptation strategy.

[0009] According to embodiments of this disclosure, the abstract data model includes: a data structure module, a data operation module, and a data constraint module;

[0010] The process of standardizing source data from the flexible manufacturing workshop through edge computing nodes to generate an abstract data model includes:

[0011] Obtain the source data of the equipment units in the flexible manufacturing workshop;

[0012] The source data is converted to a standard format according to a standard industry protocol to generate data elements.

[0013] The attributes of the data elements are standardized and described to generate the data structure module; the data structure module is used to characterize the type, content, and properties of the data elements.

[0014] The interaction rules of the data elements are standardized and described to generate the data operation module; the data operation module is used to characterize the operation rules and control rules of the data elements.

[0015] The relationships between the data elements are standardized and described to generate the data constraint module; the data constraint module is used to characterize the association and constraint control relationships between the data elements.

[0016] According to embodiments of this disclosure, the standard format conversion of the source data according to a standard industry protocol includes:

[0017] The source data is converted into a standardized JSON format according to the standard industry protocol.

[0018] The object database according to embodiments of this disclosure includes: a behavior database, a rule database, and a relationship database;

[0019] The abstract data model is instantiated according to the preset production task requirements to generate an object database, including:

[0020] According to the preset production task requirements, the data structure module of the abstract data model is configured with data application methods to generate the behavior database; wherein, the data application methods include: data acquisition methods, data storage methods and data encapsulation methods;

[0021] Based on the preset production task requirements, production rules are configured for the data operation module of the abstract data model to generate the rule database; wherein, the production rules include: process standard rules, quality rules and safety control rules;

[0022] Based on the preset production task requirements, the data constraint module of the abstract data model is configured with data association relationships to generate the relational database; the association relationships include: data retrieval logic and signal touch logic.

[0023] According to embodiments of this disclosure, a cloud platform is used to collaboratively analyze actual production task requirements and the object database to generate a dynamic data adaptation strategy, including:

[0024] If the cloud platform determines that the object database cannot meet the actual production task requirements, a dynamic data adaptation strategy is generated based on the actual production task requirements; wherein, the dynamic data adaptation strategy includes: data deletion instructions, data creation instructions, and data modification instructions.

[0025] According to embodiments of this disclosure, after maintaining and updating the object database, the process further includes: using the maintained and updated object database to guide the flexible manufacturing workshop in fulfilling the actual production task requirements.

[0026] A second aspect of this disclosure provides a cloud-edge collaborative data management device for a flexible manufacturing workshop, comprising:

[0027] The standardization module is used to standardize the source data of the flexible manufacturing workshop based on edge computing nodes and generate an abstract data model.

[0028] The instantiation module is used to instantiate the abstract data model according to the preset production task requirements and generate an object database;

[0029] The collaborative analysis module is used to perform collaborative analysis of actual production task requirements and the object database through a cloud platform, generating dynamic data adaptation strategies; and

[0030] The update module is used to maintain and update the object database according to the data dynamic adaptation strategy.

[0031] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the cloud-edge collaborative data management method for flexible manufacturing workshops described above.

[0032] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the cloud-edge collaborative data management method for the flexible manufacturing workshop described above.

[0033] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the cloud-edge collaborative data management method for flexible manufacturing workshops described above.

[0034] The cloud-edge collaborative data management method, device, equipment, medium, and program product for flexible manufacturing workshops disclosed herein have at least the following beneficial effects: meeting the fusion and scheduling needs of multi-source heterogeneous data in flexible manufacturing workshop scenarios, alleviating cloud computing pressure, forming a virtuous cycle of data collection, processing, feedback, and optimization, realizing production optimization in flexible manufacturing workshops, and improving the perception, decision-making, and adaptability of flexible manufacturing workshops under complex data disturbances. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0037] Figure 1 This diagram illustrates an application scenario of a cloud-edge collaborative data management method for a flexible manufacturing workshop according to embodiments of the present disclosure.

[0038] Figure 2 A flowchart illustrating a cloud-edge collaborative data management method for a flexible manufacturing workshop according to an embodiment of the present disclosure is shown schematically.

[0039] Figure 3 A flowchart illustrating the generation of an abstract data model according to an embodiment of the present disclosure is shown schematically;

[0040] Figure 4 This schematically illustrates a cloud-edge collaborative data management architecture according to an embodiment of the present disclosure;

[0041] Figure 5 This schematically illustrates a structural block diagram of a cloud-edge collaborative data management device for a flexible manufacturing workshop according to an embodiment of the present disclosure; and

[0042] Figure 6 A block diagram of an electronic device suitable for implementing a cloud-edge collaborative data management method in a flexible manufacturing workshop, according to an embodiment of the present disclosure, is illustrated schematically. Detailed Implementation

[0043] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0044] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0045] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0046] When using expressions such as "at least one of A, B, and C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). When using expressions such as "at least one of A, B, or C," the expression should generally be interpreted in accordance with the meaning commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, or C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0047] This disclosure provides a cloud-edge collaborative data management method for flexible manufacturing workshops. The method standardizes and instantiates source data from the flexible manufacturing workshop using edge computing nodes to generate an object database. It then uses a cloud platform to collaboratively analyze actual production task requirements and the object database, completing the maintenance and updates of the object database. This disclosure achieves systematic management of heterogeneous data within the flexible manufacturing workshop through cloud-edge collaborative data management, saving cloud computing power and enabling efficient coordination of data management tasks across the cloud, edge, and device terminals.

[0048] Figure 1The diagram schematically illustrates an application scenario of a cloud-edge collaborative data management method for a flexible manufacturing workshop according to embodiments of the present disclosure. It should be noted that... Figure 1 The examples shown are merely application examples to which the embodiments of this disclosure can be applied, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0049] like Figure 1 As shown, application scenario 100 according to this embodiment may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as a medium for providing a communication link between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0050] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0051] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0052] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0053] It should be noted that the cloud-edge collaborative data management method for flexible manufacturing workshops provided in this disclosure embodiment can generally be executed by server 105. Correspondingly, the cloud-edge collaborative data management device for flexible manufacturing workshops provided in this disclosure embodiment can generally be located in server 105. The cloud-edge collaborative data management method for flexible manufacturing workshops provided in this disclosure embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the cloud-edge collaborative data management device for flexible manufacturing workshops provided in this disclosure embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0054] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0055] The following will be based on Figure 1 The described scene, through Figures 2-6 The cloud-edge collaborative data management method for a flexible manufacturing workshop according to the disclosed embodiments is described in detail.

[0056] Figure 2 A flowchart illustrating a cloud-edge collaborative data management method for a flexible manufacturing workshop according to an embodiment of the present disclosure is shown.

[0057] like Figure 2 As shown, the cloud-edge collaborative data management method for flexible manufacturing workshops in this embodiment includes operations S210 to S230:

[0058] In operation S210, source data from the flexible manufacturing workshop is standardized based on edge computing nodes to generate an abstract data model.

[0059] According to embodiments of this disclosure, an edge computing node can be a computer capable of data collection, data storage, and data analysis. The edge computing node can be located at the equipment unit end in a flexible manufacturing workshop to control the data acquisition behavior implemented by the equipment end, and simultaneously collect and process the data generated by the equipment end in the flexible manufacturing workshop. This edge computing node has edge data perception and processing capabilities. For example, CNC machine tools, industrial robots, AGVs, etc., in a flexible manufacturing workshop are data sources and execution terminals. The edge computing node is located on the execution terminal side, collects the source data uploaded by the aforementioned execution terminals, and performs standardization processing on the source data.

[0060] The above method uses edge computing nodes to standardize source data and generate a forward data model. Essentially, it pushes data processing capabilities down to a position closer to the data generation end, which can significantly reduce latency, save bandwidth, and improve data security. It can solve the problem that traditional source data processing methods cannot meet the needs of fast, real-time, and multi-source fusion processing.

[0061] The standardized data processing method for edge computing nodes disclosed herein can establish corresponding principles and standards for the information expression, classification, and location of complex multi-source heterogeneous source data, making it simpler, more structured, and standardized. This enables the source data information to be understandable, comparable, and shareable, providing a foundation for semantic interoperability between heterogeneous systems.

[0062] In operation S220, the abstract data model is instantiated according to the preset production task requirements to generate an object database.

[0063] According to embodiments of this disclosure, relevant documents for preset production task requirements and specific parameter data required for production are input into the abstract data model constructed in operation S210, and an object database is instantiated to generate it. By combining the abstract model with the relevant documents and parameters for specific production task data requirements through instantiation, the production task requirements and the structured abstract data model are combined. This allows the large amount of data generated in the flexible manufacturing workshop to be consistent with the facts of the objective world. In the form of an instantiated object database, it is convenient to instantiate and reuse data within the flexible manufacturing workshop.

[0064] When operating the S230, the cloud platform is used to collaboratively analyze the actual production task requirements and object database to generate dynamic data adaptation strategies.

[0065] When operating S240, the object database is maintained and updated according to the data dynamic adaptation strategy.

[0066] The cloud platform can be equipped with large-scale deep analysis models, including neural networks, deep learning modules, and convolutional neural networks. By training these models, the platform can perform collaborative reasoning analysis on actual production task requirements and the object database, generating dynamic data adaptation strategies. Based on these strategies, the object database is maintained and updated. The updated object database can then be used for management and scheduling of equipment in edge computing nodes and flexible manufacturing workshops.

[0067] According to embodiments of this disclosure, operations S210, S220, and S240 can be completed in edge computing nodes, while operation S230 can be completed on a cloud platform. The cloud-edge collaborative data management method for flexible manufacturing workshops disclosed herein allows processed data to be provided to the cloud for analysis via edge computing nodes. The cloud then updates the data in the edge computing nodes according to the strategies generated after analysis, thus achieving data optimization through cloud-edge collaboration.

[0068] By using collaborative data management across cloud and edge computing nodes, the amount of data transmission can be greatly reduced. At the same time, the computing power of edge computing nodes can be fully utilized to guide the equipment in flexible manufacturing workshops to respond to flexible production tasks in a timely and accurate manner. The actual production tasks that change flexibly can be reasonably allocated among the cloud, edge and equipment sides, giving full play to the advantages of each side and achieving optimal data management efficiency and user experience.

[0069] Figure 3 A flowchart illustrating the generation of an abstract data model according to an embodiment of the present disclosure is shown schematically.

[0070] According to embodiments of this disclosure, the abstract data model in operation S210 includes: a data structure module, a data operation module, and a data constraint module. For example... Figure 3 As shown, in operation S210, the source data of the flexible manufacturing workshop is standardized based on the edge computing node to generate an abstract data model, including the following operations S211 to S215.

[0071] In operation S211, source data of equipment units in the flexible manufacturing workshop is acquired.

[0072] In the embodiments of this disclosure, the source data is heterogeneous data generated by production elements such as "people, machines, materials, methods, and environment" from multiple equipment units in a flexible manufacturing workshop, such as order, production plan, process, product, tooling, and material data in CNC machine tool equipment units or dimensional inspection equipment units.

[0073] In operation S212, the source data is converted to a standard format according to the standard industrial protocol to generate data elements.

[0074] In the embodiments of this disclosure, the standard industrial protocol can be one or more of the following: Ethernet communication protocol (Modbus TCP), OPCUA communication protocol, CAN / CANopen, and EtherNet / IP. For example, for equipment units such as CNC machine tools, PLCs, and sensors in flexible workshops with diverse communication protocols, the edge computing node side achieves protocol unification at the data acquisition layer by integrating industrial communication protocols such as Modbus TCP and OPCUA.

[0075] Specifically, source data can be converted into a standardized JSON format according to standard industrial protocols. Converting source data into a parsable standard format can solve the problem of data silos in flexible manufacturing workshops, where heterogeneous data from various equipment units are different and cannot be shared or compatible, thus facilitating the processing and analysis of heterogeneous data.

[0076] In operation S213, the attributes of data elements are standardized and described to generate a data structure module. The data structure module is used to represent the type, content, and properties of data elements.

[0077] According to embodiments of this disclosure, data elements themselves possess specific types, contents, and properties, such as the machine tool's rotational speed and cutting force in a CNC machining system, and the AGV's running speed and trajectory planning in a transportation system. Based on the characteristics inherent in the data elements, a standardized description is performed at the attribute level to generate data structure modules that characterize the data element's type, content, and properties, facilitating structured retrieval and retrieval of the data elements.

[0078] In operation S214, the interaction rules of data elements are standardized and described to generate a data operation module. The data operation module is used to represent the operation rules and control rules of data elements.

[0079] According to embodiments of this disclosure, in the operation of production elements in a flexible manufacturing workshop, necessary standards and specifications must be followed to ensure product quality meets standards, such as process operation rules, processing standards, and quality standards. Therefore, it is necessary to standardize the description of data elements through a data operation module based on the above interaction rules.

[0080] In operation S215, the relationship between data elements is standardized and described to generate a data constraint module; the data constraint module is used to characterize the association and constraint control relationship between data elements.

[0081] According to the embodiments of this disclosure, in the production process of a flexible manufacturing workshop, there are numerous and dynamic production demands that vary greatly, and it is necessary to efficiently handle various events in the workshop. Different data elements have interactions and relationships, such as the control relationship between alarm commands and emergency stop commands. Therefore, it is necessary to standardize the data elements in the relational dimension to generate data constraint modules to support parallel data interaction and control interaction.

[0082] By standardizing data elements at the attribute, interaction rule, and relation levels, an abstract data model is generated, which includes data structure modules, data operation modules, and data constraint modules. This makes heterogeneous and disordered data more orderly, facilitating processing and achieving the goal of rapid analysis and processing.

[0083] According to embodiments of this disclosure, the object database in operation S220 may include: a behavior database, a rule database, and a relational database.

[0084] In operation S220, the abstract data model is instantiated according to the preset production task requirements to generate an object database, including the following operations S221 to S223:

[0085] In operation S221, the data application method is configured for the data structure module of the abstract data model according to the preset production task requirements, and a behavioral database is generated.

[0086] In embodiments of this disclosure, data application methods include: data acquisition methods, data storage methods, and data encapsulation methods. Specific data acquisition methods, data storage methods, and data encapsulation methods from preset production tasks are configured in an abstract data model to generate a behavioral database. This database is then used by the system to drive device units to perform data processing tasks such as data acquisition, storage, and encapsulation.

[0087] In operation S222, production rules are configured for the data operation module of the abstract data model according to the preset production task requirements, and a rule database is generated.

[0088] In the embodiments of this disclosure, the production rules include: process standard rules, quality rules, and safety control rules. Based on preset production task requirements, the production rules are configured in an abstract data model to generate a rule database, which can be used for background verification and detection logic in flexible manufacturing workshops for quality inspection, process compliance judgment, and other aspects.

[0089] In operation S223, data association relationships are configured for the data constraint module of the abstract data model according to the preset production task requirements, and a relational database is generated.

[0090] In the embodiments of this disclosure, the associated relationships include: data retrieval logic and signal touch control logic. The data retrieval logic and signal touch control logic from the preset production task requirements are configured in the data constraint module to generate associated relationships. This allows the backend to leverage the interrelated data retrieval logic and signal touch control logic in the relational database to achieve efficient associated retrieval and collaborative analysis. For example, if the system retrieves a "workpiece in place" signal based on the data retrieval logic, it triggers the processing program. Alternatively, based on the signal touch control logic, it issues a machine unit stop command according to the "alarm threshold" rule.

[0091] In the embodiments of this disclosure, specific instances of preset production task requirements are configured in an abstract database model to generate an object database including a behavioral database, a rule database, and a relational database. By loading facts from the preset production task requirements, an instantiated object database is generated, encompassing data elements, data sets, and related constraints and interactions generated during the manufacturing process. This facilitates data retrieval and supports decision analysis.

[0092] The core advantage of instantiating an abstract data model into an object database in this embodiment lies in achieving efficient reuse and flexible expansion of instances while maintaining interface consistency, through a "definition specification - filling in details" pattern that separates general logic from concrete implementations. Instantiating an abstract data model trades a small amount of abstract definition for a large amount of concrete implementation flexibility, which is key to building a scalable and maintainable data management system.

[0093] The abstract data model defines general data structure modules, data operation modules, and data constraints. Instantiation only requires passing in the specific type or parameters of the pre-defined production task requirements, avoiding repetitive writing of similar logic and significantly reducing code volume. The object database, based on the same abstract model, can be instantiated into various specific forms (such as behavioral databases, rule databases, and relational databases), facilitating smooth evolution of the system as flexible manufacturing needs change without requiring a complete architecture refactoring. The internal implementation details of production task requirements are instantiated in the object database. Subsequent changes in the manufacturing workshop's production tasks only require modifications to the underlying storage or algorithm, without affecting external calls, achieving high cohesion and low coupling, and reducing data management and maintenance costs.

[0094] According to embodiments of this disclosure, in operation S230, the actual production task requirements and the object database are collaboratively analyzed through a cloud platform to generate a dynamic data adaptation strategy. This can be achieved through the following steps: If the cloud platform determines that the object database cannot meet the actual production task requirements, then a dynamic data adaptation strategy is generated based on those requirements. The dynamic data adaptation strategy includes: data deletion instructions, data creation instructions, and data modification instructions. The generation of the dynamic data adaptation strategy can be achieved through deep analysis models such as neural networks, deep learning modules, and convolutional neural networks mounted on the cloud platform. These models are trained based on the actual production task requirements. The cloud platform performs global training optimization based on the actual production task requirements and distributes the data adaptation strategy accordingly, enabling remote management and scientific scheduling.

[0095] By analyzing actual production task requirements to generate dynamic data adaptation strategies, loading or changing object databases, the system enables rapid configuration and response of data storage structures, database modules, and production processes.

[0096] According to embodiments of this disclosure, if the actual production task requirements and the object database are collaboratively analyzed through a cloud platform, and it is determined that the object database can meet the actual production task requirements, then the generated data dynamic adaptation strategy is to execute the actual production task by directly calling the data in the object database.

[0097] In the embodiments disclosed herein, a cloud-edge collaborative analysis method is used to analyze the actual production task requirements and object database through a cloud platform to generate a dynamic data adaptation strategy, thereby enabling rapid configuration and response of production tasks within the flexible manufacturing workshop.

[0098] Figure 4 The cloud-edge collaborative data management architecture according to an embodiment of the present disclosure is illustrated schematically.

[0099] like Figure 4 As shown, source data from equipment units such as sensors and CNC machine tools are standardized and instantiated through edge computing nodes to generate an object database. Based on the cloud platform, collaborative analysis of actual production task requirements and the object database generates dynamic data adaptation strategies, achieving cloud-edge collaborative data management. Edge computing nodes are responsible for data standardization and transformation, including data cleaning and fusion. The cloud platform is responsible for deep learning analysis of the data processed by the edge computing nodes to generate dynamic adaptation strategies, enabling timely responses to changes in the production plan of the flexible manufacturing workshop and rapid adjustments to data retrieval strategies based on the dynamic data adaptation strategies. The edge handles data processing for equipment units, while the cloud handles global data analysis. This collaborative division of labor based on the dynamic production plan of the flexible manufacturing workshop saves cloud computing power and achieves efficient coordination of data management tasks across the cloud, edge, and equipment.

[0100] The cloud-edge collaborative data management method for flexible manufacturing workshops proposed in this disclosure meets the needs of fusion and scheduling of multi-source heterogeneous data in flexible manufacturing workshop scenarios, alleviates cloud computing pressure, forms a virtuous cycle of data collection, processing, feedback and optimization, realizes production optimization in flexible manufacturing workshops, and improves the perception, decision-making and adaptability of flexible manufacturing workshops under complex data disturbances.

[0101] Based on the aforementioned cloud-edge collaborative data management method for flexible manufacturing workshops, this disclosure also provides a cloud-edge collaborative data management device for flexible manufacturing workshops. The following will be combined with... Figure 5 The device is described in detail.

[0102] Figure 5 A schematic block diagram of a cloud-edge collaborative data management device for a flexible manufacturing workshop according to an embodiment of the present disclosure is shown.

[0103] like Figure 5 As shown, the cloud-edge collaborative data management device 300 for the flexible manufacturing workshop in this embodiment includes a standardization module 310, an instantiation module 320, a collaborative analysis module 330, and an update module 340.

[0104] The standardization module 310 is used to standardize the source data of the flexible manufacturing workshop based on edge computing nodes to generate an abstract data model. In one embodiment, the standardization module 310 can be used to perform the operation S210 described above, which will not be repeated here.

[0105] The instantiation module 320 is used to instantiate the abstract data model according to the preset production task requirements, thereby generating an object database. In one embodiment, the instantiation module 320 can be used to perform the operation S220 described above, which will not be repeated here.

[0106] The collaborative analysis module 330 is used to perform collaborative analysis of actual production task requirements and object databases through a cloud platform to generate dynamic data adaptation strategies. In one embodiment, the collaborative analysis module 330 can be used to execute the operation S230 described above, which will not be repeated here.

[0107] The update module 340 is used to maintain and update the object database according to the data dynamic adaptation strategy. In one embodiment, the update module 340 can be used to perform the operation S240 described above, which will not be repeated here.

[0108] According to embodiments of this disclosure, any plurality of modules among the standardization module 310, instantiation module 320, collaborative analysis module 330, and update module 340 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the standardization module 310, instantiation module 320, collaborative analysis module 330, and update module 340 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the standardization module 310, instantiation module 320, collaborative analysis module 330, and update module 340 may be implemented at least partially as a computer program module that can perform corresponding functions when the computer program module is run.

[0109] Figure 6 A block diagram of an electronic device suitable for implementing a cloud-edge collaborative data management method in a flexible manufacturing workshop, according to an embodiment of the present disclosure, is illustrated schematically.

[0110] like Figure 6As shown, an electronic device 400 according to an embodiment of the present disclosure includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage portion 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0111] RAM 403 stores various programs and data required for the operation of electronic device 400. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 402 and / or RAM 403. It should be noted that the programs may also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0112] According to embodiments of this disclosure, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to a bus 404. The electronic device 400 may also include one or more of the following components connected to the I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0113] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0114] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 402 and / or RAM 403 and / or one or more memories other than ROM 402 and RAM 403 described above.

[0115] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the cloud-edge collaborative data management method for flexible manufacturing workshops provided in embodiments of this disclosure.

[0116] When the computer program is executed by the processor 401, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0117] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 409, and / or installed from removable medium 411. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0118] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by processor 401, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0119] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0121] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0122] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A cloud-edge collaborative data management method for flexible manufacturing workshops, comprising: The source data of the flexible manufacturing workshop is standardized based on edge computing nodes to generate an abstract data model; The abstract data model is instantiated according to the preset production task requirements to generate an object database; The cloud platform is used to collaboratively analyze the actual production task requirements and the object database to generate a dynamic data adaptation strategy. The object database is maintained and updated according to the data dynamic adaptation strategy.

2. The method according to claim 1, wherein, The abstract data model includes: a data structure module, a data operation module, and a data constraint module; The process of standardizing source data from the flexible manufacturing workshop through edge computing nodes to generate an abstract data model includes: Obtain the source data of the equipment units in the flexible manufacturing workshop; The source data is converted to a standard format according to a standard industry protocol to generate data elements. The attributes of the data elements are standardized and described to generate the data structure module; the data structure module is used to characterize the type, content, and properties of the data elements. The interaction rules of the data elements are standardized and described to generate the data operation module; the data operation module is used to characterize the operation rules and control rules of the data elements. The relationships between the data elements are standardized and described to generate the data constraint module; the data constraint module is used to characterize the association and constraint control relationships between the data elements.

3. The method according to claim 2, wherein, The standard format conversion of the source data according to standard industry protocols includes: The source data is converted into a standardized JSON format according to the standard industry protocol.

4. The method according to claim 2, wherein, The object database includes: a behavior database, a rule database, and a relational database; The abstract data model is instantiated according to the preset production task requirements to generate an object database, including: According to the preset production task requirements, the data structure module of the abstract data model is configured with data application methods to generate the behavior database; wherein, the data application methods include: data acquisition methods, data storage methods, and data encapsulation methods; Based on the preset production task requirements, production rules are configured for the data operation module of the abstract data model to generate the rule database; wherein, the production rules include: process standard rules, quality rules and safety control rules; Based on the preset production task requirements, the data constraint module of the abstract data model is configured with data association relationships to generate the relational database; the association relationships include: data retrieval logic and signal touch logic.

5. The method according to claim 1, wherein, By collaboratively analyzing the actual production task requirements and the object database through a cloud platform, a dynamic data adaptation strategy is generated, including: If the cloud platform determines that the object database cannot meet the actual production task requirements, a dynamic data adaptation strategy is generated based on the actual production task requirements; wherein, the dynamic data adaptation strategy includes: data deletion instructions, data creation instructions, and data modification instructions.

6. The method according to claim 1, wherein, After maintaining and updating the object database, the process also includes: using the maintained and updated object database to guide the flexible manufacturing workshop in completing the actual production task requirements.

7. A cloud-edge collaborative data management device for a flexible manufacturing workshop, comprising: The standardization module is used to standardize the source data of the flexible manufacturing workshop based on edge computing nodes and generate an abstract data model. The instantiation module is used to instantiate the abstract data model according to the preset production task requirements and generate an object database; The collaborative analysis module is used to perform collaborative analysis of actual production task requirements and the object database through a cloud platform, generating dynamic data adaptation strategies; and The update module is used to maintain and update the object database according to the data dynamic adaptation strategy.

8. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 6.

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