A cloud edge-end architecture-based manufacturing collaboration system

Abstract

The application discloses a kind of manufacturing collaborative systems based on cloud edge architecture, it is related to the field of intelligent manufacturing.It includes cloud layer for global planning and analysis, production edge layer for real-time scheduling and execution, and basic equipment layer.Edge layer is deployed with multiple edge execution MES and internet-of-things interface gateway, rule engine and event listening mechanism are built-in edge execution MES, support event triggering, condition judgment and timing scheduling, and can run autonomously when disconnected, guarantee production continuity;Internet-of-things interface gateway is used to uniformly access devices of multiple industrial communication protocols, through protocol adaptation and device abstraction, realize standardized access to heterogeneous devices, reduce system integration complexity;Multiple edge execution MES realizes event-driven collaborative work through standardized interface and asynchronous message mechanism. The present application can realize the efficient cooperation of cloud centralized decision and edge autonomous execution, and further improve the real-time performance, reliability and scalability of distributed manufacturing system.

Description

Technical Field

[0001] This invention relates to the field of industrial software technology, and more specifically to a manufacturing collaboration system based on a cloud-edge-device architecture, particularly to distributed production execution, edge autonomous decision-making, and cross-node collaboration technologies in an intelligent manufacturing environment. Background Technology

[0002] With the deepening development of intelligent manufacturing, manufacturing enterprises are facing an urgent need for distributed production organization, intelligent decision-making, and system collaboration. Traditional centralized Manufacturing Execution Systems (MES) centrally deploy all production management functions on a central server, and the equipment and controllers on the production floor rely entirely on the instructions issued by the central server. This architecture has many limitations when facing large-scale distributed production scenarios.

[0003] On the one hand, centralized architectures are highly dependent on networks. If the central server fails or the network is interrupted, the entire production system will be paralyzed, unable to continue production tasks, leading to production interruptions and economic losses. On the other hand, all equipment data needs to be uploaded to the central server for processing before control commands are sent to field devices. Data transmission and processing latency is significant, making it difficult to meet the real-time response requirements of the production site. This latency issue is particularly prominent in precision manufacturing scenarios requiring millisecond-level response. Furthermore, manufacturing sites commonly suffer from heterogeneous equipment and diverse protocols. Equipment from different manufacturers uses different communication protocols, such as PLC S7, Modbus RTU, Modbus TCP, HART, and RS232. Traditional MES systems typically only support a limited number of protocols, requiring extensive custom development when integrating new equipment, resulting in high system integration complexity, high costs, and long cycles. Simultaneously, in distributed production environments, multiple production units need to collaborate. Traditional MES systems lack effective cross-node collaboration mechanisms, making data sharing and task coordination between production units difficult, hindering flexible production organization.

[0004] Therefore, there is an urgent need to develop a solution that can address the aforementioned technical problems. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a cloud-edge-device-based manufacturing collaboration system that overcomes or at least partially solves the above problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The manufacturing collaboration system based on a cloud-edge-device architecture provided in this embodiment of the invention includes: The cloud layer contains a cloud-based MES (Management Execution System). The edge layer, connected to the cloud layer, includes multiple edge execution management systems (MES). These multiple edge MES are connected to multiple edge PLCs via an IoT interface gateway. Each edge MES receives, parses, and locally executes production tasks from the cloud, achieving collaborative scheduling through the IoT interface gateway and making autonomous decisions when disconnected from the cloud. The IoT interface gateway supports multi-protocol device access. The multiple edge PLCs generate control commands based on the decision results of the edge MES. The device layer includes multiple edge IoT devices and a state machine, which are used to execute control commands and provide feedback on the execution status.

[0008] Preferably, the cloud layer also deploys a data middle platform and a business middle platform.

[0009] Preferably, the edge execution of MES includes: Event listeners are used to receive event notifications from cloud-based MES and other edge-executed MES. The rules engine, which includes a collaborative rule base, a rule parser, and a decision reasoning module, is used for local scheduling decisions and autonomous decisions when disconnected from the cloud. The data caching module is used to cache process flow data and equipment status data.

[0010] Preferably, the rule engine also includes an offline operation guarantee module, which automatically switches to local autonomous mode when an interruption in communication with the cloud is detected, and performs production scheduling based on local cached data in the data caching module and collaborative rule base in the rule engine.

[0011] Preferably, the offline operation guarantee module includes a communication status monitoring unit and a mode switching unit; The communication status monitoring unit is used to periodically send heartbeat messages to the cloud MES using a heartbeat detection mechanism, and to count the success rate and response time of the heartbeat response returned by the cloud MES. If no response is received for N consecutive heartbeat messages, or if the response times out, it is determined that the communication is interrupted. The mode switching unit is used to switch the operating mode of the edge-executed MES from "online mode" to "local autonomous mode" when communication is interrupted.

[0012] Preferably, the data caching module includes a local time-series database and a configuration data cache area. The local time-series database is used to store historical operating data of the equipment, and the configuration data cache area is used to store process recipes and equipment parameters.

[0013] Preferably, the edge execution MES also includes a process decoupling module, which separates the process flow definition from the execution code and manages it through a process metadata model. The process flow definition includes process parameters, process types, quality standards and time constraints.

[0014] Preferably, the edge execution MES also includes a data synchronization unit, which is used to synchronize data between the cloud layer and the edge layer using an incremental synchronization strategy, detect and handle data conflicts through a version number mechanism, and realize event-driven collaboration through an asynchronous message queue.

[0015] Preferably, the IoT interface gateway includes: Standardized interfaces are used to enable communication between cloud-based MES and edge-executed MES, as well as between edge-executed MES systems. The protocol adapter module supports access to multiple industrial protocols, including PLC S7, Modbus RTU, Modbus TCP, HART, and RS232. The data acquisition module is used to collect the status of the end-side devices in real time. The command issuing module is used to convert control commands into a protocol format that the target device can recognize.

[0016] Preferably, the protocol adaptation module includes a protocol driver configured for each communication protocol, and a unified device abstraction layer, which is used to abstract devices with different protocols into virtual device objects containing device identifiers, capability descriptions, and status attributes.

[0017] This invention provides a manufacturing collaboration system based on a cloud-edge architecture. By pushing computing power to the edge and empowering edge nodes with autonomous decision-making capabilities, it achieves efficient collaboration in a distributed environment while ensuring production continuity, thus meeting the needs of intelligent manufacturing for distributed deployment, real-time response, and flexible collaboration.

[0018] Compared with the prior art, the beneficial effects of the above-mentioned technical solutions provided by the embodiments of the present invention include at least the following: 1. This application achieves global production planning, resource allocation, and centralized data analysis by deploying a cloud-based MES, data platform, and business platform at the cloud layer. The cloud layer possesses powerful computing capabilities and massive data storage capacity, responsible for handling long-cycle optimization decisions and cross-factory resource coordination. Multiple edge execution MESs are deployed at the edge layer, each managing a specific production unit or workshop, receiving and parsing production tasks from the cloud layer, and executing local production scheduling. The edge execution MESs have built-in event listeners, rule engines, and data caching modules, possessing complete local execution capabilities. Connecting the cloud layer and edge layer via a dedicated cloud line ensures data transmission security and stability while combining global decision-making at the cloud with real-time execution at the edge, effectively solving the technical problems of strong network dependence and poor real-time performance inherent in traditional centralized architectures.

[0019] 2. The edge-executed MES of this application has autonomous decision-making capabilities, which can effectively solve the problem of complete paralysis of the production system when the traditional centralized architecture is interrupted by network or server failure, and significantly improve the reliability of the system and the continuity of production.

[0020] 3. The IoT interface gateway of this application is deployed at the edge layer, connecting the edge execution MES and the edge PLC. By uniformly deploying the IoT interface gateway at the edge, centralized access and protocol adaptation of field-programmable logic controllers (PLCs), CNC equipment, sensors, and other industrial equipment are achieved. This eliminates the need to deploy a complete edge execution MES (such as workshop MES, unit MES, etc.) on each edge device or production unit. The system can access and control the corresponding edge device resources on demand through the IoT interface gateway. This significantly reduces the number of software deployments and the operating load of edge computing nodes, lowering the overall system resource consumption and operational complexity. Furthermore, through a unified interface abstraction and protocol conversion mechanism, it improves the consistency and scalability of access for different types of devices, avoiding redundant development and configuration due to device differences. This enhances the deployment efficiency and operational stability of the manufacturing collaboration system in multi-production line, multi-device scenarios.

[0021] Meanwhile, the IoT interface gateway includes a protocol adaptation module, which supports access to multiple industrial communication protocols. This effectively solves the technical problems of difficult equipment integration and high custom development costs in traditional MES systems, significantly reduces the complexity of system integration, and improves the system's compatibility with equipment from multiple vendors.

[0022] 4. This application enables event-driven collaboration among multiple edge execution MESs through standardized interfaces of IoT interface gateways, thereby achieving loosely coupled collaboration between edge execution MESs. Each edge execution MES can independently complete local production tasks and respond quickly when receiving collaboration requests, forming a flexible and efficient distributed collaboration mode. This effectively solves the technical problem of weak cross-node collaboration capabilities in traditional MES systems and significantly improves the system's adaptability to complex and ever-changing production scenarios.

[0023] This application constructs a complete distributed manufacturing collaborative technology system through global decision-making at the cloud layer, real-time execution and autonomous decision-making at the edge layer, precise control at the device layer, and event-driven collaboration between edge-executed MES, providing an effective technical solution for large-scale distributed production in intelligent manufacturing environments. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 A schematic diagram of a cloud-edge-device-based manufacturing collaboration system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the edge layer structure provided in an embodiment of the present invention; Figure 3 The flowchart of collaborative control provided for embodiments of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1 This invention discloses a manufacturing collaboration system based on a cloud-edge-device architecture, aiming to solve the technical problems of low reliability, poor real-time performance, difficult equipment integration, and weak cross-node collaboration capabilities in existing centralized architectures. Specifically, it constructs a complete distributed manufacturing collaboration technology system through global decision-making at the cloud layer, real-time execution and autonomous decision-making at the edge layer, precise control at the device layer, and event-driven collaboration between edge-executed MES, providing an effective technical solution for large-scale distributed production in intelligent manufacturing environments.

[0028] In this embodiment, the manufacturing collaboration system based on a cloud-edge-device architecture has the following structure: Figure 1 As shown, it includes the cloud layer, edge layer, and device layer.

[0029] The cloud layer is equipped with computing and storage resources, and specifically deploys a cloud-based MES, a data platform, and a business platform. The edge layer is connected to the cloud layer via a dedicated cloud line and includes multiple edge execution MESs. These multiple edge execution MESs are connected to multiple edge PLCs through an IoT interface gateway. The equipment layer includes multiple end-side devices, among which the end-side devices are connected to the edge PLC. The end-side devices include IoT devices and state machines, which are used to execute control commands from the edge PLC and provide feedback on the execution results.

[0030] In one specific embodiment, the edge execution MES is used to receive, parse, and locally execute production tasks from the cloud, achieving collaborative scheduling through the IoT interface gateway, and making autonomous decisions when disconnected from the cloud; such as Figure 2 ,include: Event listeners are used to receive event notifications from cloud-based MES and other edge execution MES, such as process completion events and equipment status change events. They support publishing and subscribing to task completion events. When an edge execution MES completes its assigned production task, it publishes a task completion event. Other edge execution MES requiring subsequent collaboration receive notifications by subscribing to this event, triggering the execution of subsequent tasks. See the detailed process below. Figure 3 .

[0031] The rules engine includes a collaborative rule library, a rule parser, and a decision reasoning module. The collaborative rule library stores collaborative scheduling rules, the rule parser parses scheduling rules defined in a declarative configuration manner, and the decision reasoning module makes local scheduling decisions based on a combination of logic including event publishing, condition judgment, and timed triggering. When the connection with the cloud is lost, it makes autonomous decisions based on local cached data (process flow data and equipment status data in the cache module) to continue scheduling and executing production tasks without waiting for instructions from the cloud.

[0032] A data caching module is used to cache process flow data and equipment status data, including process data, equipment information, and historical records, to ensure that the required data can still be accessed in offline mode. In a preferred embodiment, the data caching module includes a local time-series database and a configuration data cache. The local time-series database stores historical equipment operating data, and the configuration data cache stores process recipes and equipment parameters. By configuring a local time-series database and a configuration cache at the edge, low-latency local access to equipment operating data and process parameters can be achieved, supporting real-time edge analysis, fault diagnosis, and process playback, improving the accuracy and response speed of local decision-making. In addition, local caching reduces frequent data interactions with the cloud, saves bandwidth, and ensures the availability of necessary data in offline situations, thereby enhancing the robustness and production continuity of the system and providing a complete data foundation for subsequent incremental synchronization.

[0033] In this application, edge autonomous decision-making capability can effectively solve the problem of complete paralysis of production system when traditional centralized architecture is interrupted by network or server failure, and significantly improve the reliability of system and production continuity.

[0034] In some implementations, the IoT interface gateway connects to the edge execution MES and the edge PLC, supporting multi-protocol device access; see reference Figure 2 ,include: Standardized interfaces provide communication guarantees between cloud-based MES and edge-executed MES, as well as between edge-executed MES; The protocol adaptation module supports access to multiple industrial protocols, including PLC S7, Modbus RTU, Modbus TCP, HART, and RS232, effectively solving the technical problems of difficult equipment integration and high custom development costs in traditional MES systems, significantly reducing the complexity of system integration, and improving the system's compatibility with equipment from multiple vendors. In this embodiment, the protocol adaptation module is configured with a dedicated protocol driver for each protocol and a unified device abstraction layer. The device abstraction layer is used to abstract devices with different protocols into virtual device objects containing device identifiers, capability descriptions, and status attributes. The virtual device objects can provide a unified access interface, facilitating consistent logic for management and scheduling by the upper-layer edge MES. The device abstraction design of this application can effectively shield the differences of underlying protocols, simplify the heterogeneous device access process, eliminate the coupling between device manufacturers and protocols, and enable the edge execution MES to access heterogeneous devices in a unified manner, improving system maintainability, scalability, and engineering deployment efficiency, and accelerating the network verification and deployment speed of new devices. The data acquisition module is used to collect the status of the end-side devices in real time, including collecting real-time status data from the end-side devices and uploading it to the edge execution MES via the edge PLC; The instruction issuing module receives control instructions from the edge execution MES transmitted via the edge PLC and converts them into a protocol format that can be recognized by the end-side devices.

[0035] The data acquisition and command issuance mechanism enables real-time closed-loop control from end-side devices to the edge MES: real-time acquisition ensures the timeliness of scheduling and control decisions, and command format conversion ensures the compatibility of control commands with lower-level devices, thereby improving control accuracy and execution reliability; centralized acquisition / issuance logic also facilitates the implementation of unified verification, authentication, and anomaly handling strategies, improving system security and ease of operation and maintenance, and reducing the software complexity and maintenance workload of field devices.

[0036] In this application, edge execution MESs collaborate via event-driven mechanisms through standardized interfaces of IoT gateways, supporting the publication and subscription of task completion events. Furthermore, in a preferred embodiment, the edge execution MES includes a data synchronization unit, used to synchronize data between the cloud and edge layers via a cloud dedicated line using an incremental synchronization strategy, detect and handle data conflicts through a version number mechanism, and achieve event-driven collaboration through an asynchronous message queue. The incremental synchronization strategy and version number conflict detection reduce network load and latency for data synchronization, ensuring cross-layer data consistency while reducing redundant transmissions; the asynchronous message queue enables decoupled communication between edge MESs, improving system throughput and concurrent processing capabilities, and ensuring that single-point delays or failures do not block the overall collaborative process, thereby enhancing system availability, scalability, and fault tolerance.

[0037] In this embodiment, the event-driven collaboration mechanism enables loosely coupled collaboration between edge-executed MES systems. Each edge-executed MES system can independently complete local production tasks and respond quickly when it receives a collaboration request, thus forming a flexible and efficient distributed collaboration mode. This effectively solves the technical problem of weak cross-node collaboration capability in traditional MES systems and significantly improves the system's adaptability to complex and ever-changing production scenarios.

[0038] This application achieves centralized access and protocol adaptation for field-programmable logic controllers (PLCs), CNC equipment, sensors, and other industrial equipment by uniformly deploying an IoT interface gateway at the edge. This eliminates the need to deploy complete edge execution management systems (MES) (such as workshop MES, unit MES, etc.) on each edge device or production unit. Instead, the system can access and control the corresponding edge device resources on demand through the IoT interface gateway. This significantly reduces the number of software deployments and the workload of edge computing nodes, lowering the overall system resource consumption and operational complexity. Furthermore, the unified interface abstraction and protocol conversion mechanism improves the consistency and scalability of access for different types of devices, avoiding redundant development and configuration due to device differences. This enhances the deployment efficiency and operational stability of the manufacturing collaboration system in multi-production line, multi-device scenarios.

[0039] In some implementations, multiple edge PLCs are connected to an IoT interface gateway to generate control commands based on the decision results of the MES executed at the edge.

[0040] This application achieves global production planning, resource allocation, and centralized data analysis by deploying a cloud-based MES, data platform, and business platform at the cloud layer. The cloud layer possesses powerful computing capabilities and massive data storage capacity, handling long-cycle optimization decisions and cross-factory resource coordination. Multiple edge-execution MESs are deployed at the edge layer, each managing a specific production unit or workshop, receiving and parsing production tasks from the cloud layer, and executing local production scheduling. The edge-execution MESs have built-in event listeners, rule engines, and data caching modules, providing complete local execution capabilities. Connecting the cloud and edge layers via a dedicated cloud line ensures data transmission security and stability while combining global cloud-based decision-making with real-time edge execution, effectively solving the technical problems of strong network dependence and poor real-time performance inherent in traditional centralized architectures.

[0041] Example 2 Based on Embodiment 1, the rule engine in this embodiment also includes an offline operation guarantee module, which is used to automatically switch to local autonomous mode when an interruption in communication with the cloud is detected, and to perform production scheduling based on the local cached data in the data caching module and the collaborative rule base in the rule engine.

[0042] Optionally, the offline operation guarantee module includes a communication status monitoring unit and a mode switching unit; The communication status monitoring unit is responsible for real-time monitoring of the communication status between the edge execution MES and the cloud layer. It employs a heartbeat detection mechanism, periodically (e.g., every 10 seconds) sending heartbeat messages to the cloud MES. Upon receiving the heartbeat message, the cloud MES returns an acknowledgment response. The communication status monitoring unit tracks the success rate and response time of the heartbeat responses. If no response is received for N consecutive heartbeat messages (e.g., 3 times), or if the response times out, it is considered a communication interruption. The mode switching unit automatically switches the operating mode of the edge-executed MES from "online mode" to "local autonomous mode" when a communication interruption is detected. In online mode, the edge-executed MES receives production tasks and scheduling instructions from the cloud-based MES and executes production scheduling according to the cloud instructions. In local autonomous mode, the edge-executed MES no longer relies on cloud instructions but continues to execute production scheduling based on local data in the collaborative rule base and data cache module.

[0043] By introducing the aforementioned offline operation assurance mechanism, the edge can maintain real-time control and continuous operation of critical production processes even in the event of network anomalies, cloud service unavailability, or cross-domain communication restrictions. This avoids the risk of production stoppages or anomalies caused by cloud-edge communication interruptions, thereby significantly improving the reliability and business continuity of the manufacturing collaboration system. Simultaneously, the offline operation assurance module supports the synchronization or back transmission of status data, execution results, and event information generated during local operation after communication is restored. This helps ensure data consistency and collaborative consistency between cloud-based global management and edge autonomous control, further enhancing the system's stable operation capabilities in complex industrial environments.

[0044] Example 3 Based on Example 1, the edge execution MES in this example also includes a process decoupling module, which is used to separate the process flow definition from the system execution code and manage it through the process metadata model.

[0045] The process decoupling module adopts a data-driven design concept, separating the definition of the process flow from the program code and storing and managing it in the form of data. The process metadata model defines the structured representation of the process flow, including key information such as process type, process parameters, quality standards, and time constraints.

[0046] The process type field identifies the category of the process. Different process types correspond to different technological characteristics and control requirements. The system calls the corresponding control logic module based on the process type. The process parameter field records the specific parameters required for process execution. Process parameters are stored in key-value pairs, where the key is the parameter name and the value is the parameter value, supporting multiple data types such as integer, floating-point, and string. The quality standard field defines the quality requirements of the process output. Quality standards are used to guide quality control during process execution and quality inspection after process completion. The time constraint field specifies the time requirements of the process. Time constraints are used for process scheduling and progress monitoring.

[0047] In this application, the process decoupling module provides configurable management of the process flow, supporting the creation, modification, and deletion of the process metadata model through a configuration interface or configuration file. When the process flow needs to be adjusted, engineers only need to modify the corresponding fields in the process metadata model, without modifying the system's execution code. The system reads the latest process metadata model at runtime and executes according to the new process requirements. This process decoupling design significantly improves the system's adaptability to process changes, shortens the new product introduction cycle, and reduces system maintenance costs.

[0048] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0049] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A manufacturing collaboration system based on a cloud-edge-device architecture, characterized in that, include: The cloud layer contains a cloud-based MES (Management Execution System). The edge layer, connected to the cloud layer, includes multiple edge execution management systems (MES). These multiple edge MES are connected to multiple edge PLCs through an IoT interface gateway. The edge MES are used to receive, parse, and execute production tasks from the cloud locally, and achieve collaborative scheduling through the IoT interface gateway, as well as make autonomous decisions when disconnected from the cloud. The IoT interface gateway supports multi-protocol device access. The edge PLC is used to generate control instructions based on the decision results of the edge-executed MES; The device layer includes multiple edge IoT devices and a state machine, which are used to execute control commands and provide feedback on the execution status.

2. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 1, characterized in that, The cloud layer also deploys a data middle platform and a business middle platform.

3. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 1, characterized in that, The edge execution of MES includes: Event listeners are used to receive event notifications from cloud-based MES and other edge-executed MES. The rules engine, which includes a collaborative rule base, a rule parser, and a decision reasoning module, is used for local scheduling decisions and autonomous decisions when disconnected from the cloud. The data caching module is used to cache process flow data and equipment status data.

4. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 3, characterized in that, The rule engine also includes an offline operation guarantee module, which automatically switches to local autonomous mode when an interruption in communication with the cloud is detected, and performs production scheduling based on local cached data in the data caching module and collaborative rule base in the rule engine.

5. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 4, characterized in that, The offline operation support module includes a communication status monitoring unit and a mode switching unit; The communication status monitoring unit is used to periodically send heartbeat messages to the cloud MES using a heartbeat detection mechanism, and to count the success rate and response time of the heartbeat response returned by the cloud MES. If no response is received for N consecutive heartbeat messages, or if the response times out, it is determined that the communication is interrupted. The mode switching unit is used to switch the operating mode of the edge-executed MES from "online mode" to "local autonomous mode" when communication is interrupted.

6. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 3, characterized in that, The data caching module includes a local time-series database and a configuration data cache area. The local time-series database is used to store historical operating data of the equipment, and the configuration data cache area is used to store process recipes and equipment parameters.

7. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 3, characterized in that, The edge execution MES also includes a process decoupling module, which separates the process flow definition from the execution code and manages it through a process metadata model. The process flow definition includes process parameters, process types, quality standards, and time constraints.

8. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 3, characterized in that, The edge execution MES also includes a data synchronization unit, which is used to synchronize data between the cloud layer and the edge layer using an incremental synchronization strategy, detect and handle data conflicts through a version number mechanism, and achieve event-driven collaboration through an asynchronous message queue.

9. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 1, characterized in that, The IoT interface gateway includes: Standardized interfaces are used to enable communication between cloud-based MES and edge-executed MES, as well as between edge-executed MES systems. The protocol adapter module supports access to multiple industrial protocols, including PLC S7, Modbus RTU, Modbus TCP, HART, and RS232. The data acquisition module is used to collect the status of the terminal devices in real time. The command issuing module is used to convert control commands into a protocol format that the target device can recognize.

10. The manufacturing collaboration system based on a cloud-edge-device architecture as described in claim 9, characterized in that, The protocol adaptation module includes a protocol driver configured for each communication protocol, and a unified device abstraction layer. The device abstraction layer is used to abstract devices with different protocols into virtual device objects containing device identifiers, capability descriptions, and status attributes.

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