Intelligent production line design system and method integrating data acquisition and equipment scheduling

Abstract

The invention relates to the technical field of industrial automation, and discloses an intelligent production line design system and method integrating data acquisition and equipment scheduling, and a production line construction module of the system supports a webpage end to drag and assemble a three-dimensional model and multi-person collaborative editing; the data adaptation module realizes data interaction with an external system; the process planning module is used for graphically configuring a process route; the equipment control module visually configures an equipment instruction and supports debugging; the connection management module configures parameters and realizes communication through the gateway; the gateway management module deploys a protocol application and issues configuration; and the data acquisition and scheduling module is responsible for data acquisition, uploading and dynamic scheduling. An integrated intelligent production line design and operation scheme is formed, the problems that a traditional production line is difficult to design, low in scheduling efficiency, high in cost and the like are effectively solved, the construction period of the intelligent production line is remarkably shortened, the labor and construction cost is reduced, the production efficiency is comprehensively improved, and the flexibility and integration of the production line are enhanced.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and more specifically to an intelligent production line design method that integrates data acquisition and equipment scheduling. Background Technology

[0002] Currently, to adapt to the demands of new-quality productivity development and promote the intelligent transformation and upgrading of the manufacturing industry, my country is accelerating the deep integration of industrial automation and digitalization. Intelligent production lines, as the core carrier of modern manufacturing, have achieved a leap from traditional automation to autonomous sensing, real-time decision-making, and dynamic optimization by integrating advanced technologies such as the Internet of Things, artificial intelligence, and big data analytics. Although automated production lines have been widely adopted and applied in the automotive, electronics, and food processing industries, the following problems still exist:

[0003] 1. Production line design relying on 2D drawings results in low visualization, high production difficulty, and high modification costs.

[0004] 2. It requires cross-domain technical capabilities such as automation control (e.g., PLC programming), industrial communication (OPC UA / Profinet), and cloud computing. Traditional manufacturing enterprises generally lack relevant technologies and talents, and need to invest a lot of resources in personnel training and knowledge updates.

[0005] 3. The integration challenges are prominent. Intelligent production lines need to integrate various business systems, automated equipment from different manufacturers, and a large number of sensors, resulting in huge upfront investment. Summary of the Invention

[0006] In view of this, in order to solve the shortcomings of the existing technology, such as difficulty in production line design, low scheduling efficiency and high cost, the present invention provides an intelligent production line design system and method that integrates data acquisition and equipment scheduling, providing an end-to-end intelligent production line solution that supports three-dimensional visualization collaborative design, plug-and-play multi-source equipment and data-driven intelligent scheduling.

[0007] In a first aspect, the present invention provides an intelligent production line design system integrating data acquisition and equipment scheduling, comprising:

[0008] The production line construction module, based on a lightweight web-based visual design tool, allows users to assemble production line equipment models into 3D production line models by dragging and dropping, and provides multi-user collaborative editing functionality.

[0009] The data adaptation module is used to configure production line-related data models and enable automated data interaction with external systems.

[0010] The process planning module is used to configure process routes and generate logical relationships between processes through a graphical interface;

[0011] The equipment control module is used to configure the equipment execution instructions and sequences in a visual manner, adapting to different types of processing equipment with the same function, and supporting real-time debugging and verification;

[0012] The connection management module is used to configure device connection parameters on the system side and enable device communication through the smart gateway;

[0013] The gateway management module is used to deploy protocol applications and distribute configurations to smart gateways;

[0014] The data acquisition and scheduling module is used to periodically collect device data and upload it to the cloud server, while simultaneously sending control commands to the smart gateway to achieve dynamic device scheduling.

[0015] The intelligent production line design system integrating data acquisition and equipment scheduling provided in this invention provides a lightweight web-based visual design tool for the production line construction module. This tool allows for rapid assembly of 3D production line models via drag-and-drop, and supports multi-user collaborative editing, significantly reducing the difficulty of production line design and improving design efficiency and flexibility. It is also highly intuitive and easy to modify. The data adaptation module enables automated data interaction with external systems, breaking down data barriers and promoting efficient information flow. The process planning module configures process routes through a graphical interface, clearly generating logical relationships between processes and simplifying the process design process. The equipment control module configures equipment execution commands in a visual manner. The command and sequence system can adapt to different types of processing equipment with the same function and supports real-time debugging and verification, reducing the difficulty of equipment adaptation and the cost of secondary development. The connection management module uniformly configures the device connection parameters on the system side, and combined with the gateway management module to deploy and configure the protocol application of the smart gateway, it realizes the rapid communication connection of the devices and reduces the tedious work of configuring each device one by one in the traditional way. The data acquisition and scheduling module collects device data at regular intervals, uploads it to the cloud, and issues control commands to realize dynamic scheduling, ensuring real-time monitoring and intelligent adjustment of the production process. Ultimately, it comprehensively improves production efficiency, reduces labor and construction costs, and enhances the flexibility and integration of the production line.

[0016] In one optional implementation, the web-based visual design tool includes:

[0017] A model resource library for storing standardized 3D models of equipment;

[0018] The graphical editing area allows users to select equipment models from the model resource library by dragging and dropping, and adjust model parameters and spatial positions to build digital production line models.

[0019] The bidirectional conversion unit is used to automatically convert between 3D models and CAD drawings.

[0020] This invention provides users with a rich and standardized model foundation by storing standardized 3D equipment models in a web-based visual design tool and storing them in a model resource library. The graphical editing area allows users to select models and adjust parameters and spatial positions by dragging and dropping to build digital production line models, significantly reducing the technical threshold for production line design. The operation is intuitive and convenient, improving design efficiency. The bidirectional conversion unit enables automatic conversion between 3D models and CAD drawings, which can seamlessly connect with traditional engineering design processes and give full play to the advantages of 3D models in visualization and collaborative editing. This reduces data conversion costs between different design stages, enhances the flexibility and compatibility of production line design, and helps enterprises quickly and efficiently complete the construction of digital production line models.

[0021] In one optional implementation, the smart gateway includes:

[0022] Protocol adaptation layer, supporting multiple industrial protocols, used to adapt to device communication with different protocols;

[0023] Edge computing engine is used to process device data in real time and respond to control commands.

[0024] The protocol adaptation layer of this invention supports multiple industrial protocols and can flexibly adapt to device communication with different protocols, effectively solving the connection problem between different brands and types of equipment and breaking down the barriers to device interconnection. Meanwhile, the edge computing engine can process device data and respond to control commands in real time, reducing the latency of data transmission to the cloud for further processing, improving device response speed and data processing efficiency, ensuring the timeliness and accuracy of equipment control during production, and reducing dependence on cloud computing resources, thus providing strong support for the efficient and stable operation of the production line.

[0025] In one optional implementation, the process planning module includes:

[0026] The process node library provides standard machining process units;

[0027] Connection logic components are used to establish sequential relationships between processes;

[0028] A process analysis engine is used to automatically generate a sequence of processing steps.

[0029] The process planning module of this invention provides standardized processing unit through a process node library, offering users a unified and standardized foundation for configuring process routes, reducing the arbitrariness and repetitive work in process definition; the connection logic component supports the intuitive establishment of pre- and post-process relationships, making the logical organization of process routes clearer and the operation more convenient, reducing the complexity of process configuration; the process parsing engine can automatically generate processing step sequences based on the configured process relationships, ensuring the continuity and accuracy of the processing flow, avoiding omissions that may occur when manually organizing steps, and overall simplifying the process route configuration process, improving the efficiency and standardization of process design, and helping the production line quickly form an executable processing plan.

[0030] In one optional implementation, the device control module includes:

[0031] Instruction flowchart editor, used for visually arranging device instructions;

[0032] A real-time debugging interface is provided to support the verification of command execution effects.

[0033] In this embodiment of the invention, the instruction flowchart editor of the equipment control module supports the visual arrangement of equipment instructions, allowing users to intuitively configure equipment execution instructions and sequences without relying on complex coding. This significantly reduces the difficulty of adapting to different types of processing equipment with the same function, and improves the efficiency and flexibility of instruction configuration. The real-time debugging interface can immediately verify the execution effect after the instruction configuration is completed, making it easy to quickly discover and correct configuration problems, shorten the equipment debugging cycle, and reduce the risk of production interruption caused by improper instruction configuration. Overall, it enhances the convenience and reliability of equipment control, and helps the production line quickly achieve flexible control and efficient operation of equipment.

[0034] In one optional implementation, the data acquisition and scheduling module includes:

[0035] The data analysis unit is used to analyze equipment usage efficiency and energy consumption based on the collected equipment operation data, and to display the results in real time and clearly through visual dashboards and digital twins;

[0036] The scheduling unit receives data from the cloud server based on integrated data and preset rules / algorithms, generates automated scheduling and dynamic adjustment and optimization control instructions for production tasks, and sends them to the smart gateway to realize equipment scheduling.

[0037] The data analysis unit of this invention analyzes the collected equipment operation data to accurately grasp the equipment utilization efficiency and energy consumption. It then uses a visual dashboard and digital twin to provide a real-time, clear display, allowing production managers to intuitively understand the production line's operating status and providing data support for production decisions. The scheduling unit receives production task automation scheduling and dynamic adjustment optimization instructions generated by the cloud server based on integrated data and preset rules / algorithms, and sends them to the intelligent gateway to achieve equipment scheduling. This not only eliminates reliance on manual experience and improves scheduling efficiency, but also flexibly responds to changes in the production process, ensuring efficient and flexible production line operation, thereby improving overall production efficiency and reducing energy costs.

[0038] Secondly, an intelligent production line design method integrating data acquisition and equipment scheduling includes the following steps:

[0039] Build a digital model of the production line using web-based visual design tools, select equipment models from the model resource library and drag them into the graphical editing area, adjust the model parameters and positions to complete the assembly;

[0040] Configure production line-related data models to enable automated data interaction with external systems;

[0041] The product processing route can be configured visually through a graphical interface. Process elements can be selected and connected to form a process flow diagram, and the logical relationships between processes can be parsed and obtained.

[0042] The system visualizes and schedules the processing and execution instructions and sequence for the equipment, and performs real-time debugging and verification.

[0043] Configure device connection parameters, establish communication through the smart gateway, and deploy and manage the smart gateway.

[0044] The system periodically collects device data and uploads it to the cloud server, while simultaneously sending control commands to the smart gateway to achieve dynamic device scheduling.

[0045] The method provided in this invention enables rapid construction of a digital production line model via drag-and-drop using a web-based visual tool, simplifying the production line design process, improving design efficiency and flexibility, and supporting intuitive adjustments. Configuring the production line data model allows for automated data interaction with external systems, breaking down information silos and promoting efficient production data flow. A graphical interface allows for configuring process routes and parsing process logic, making process design clearer and more convenient, reducing the complexity of process planning. Visually arranging equipment execution instructions and enabling real-time debugging and verification adapts to different types of equipment with the same function, reducing the technical barriers and debugging costs associated with equipment adaptation. Configuring equipment connection parameters on the system side and establishing communication through a smart gateway, combined with gateway deployment and management, avoids the tedious process of configuring each device individually, enabling rapid device interconnection. Regularly collecting equipment data, uploading it to the cloud, and issuing control commands for dynamic scheduling ensures real-time monitoring and intelligent adjustment of production status, improving the efficiency of production scheduling and the flexibility to respond to changes. Overall, this approach forms an integrated solution encompassing production line design, data interaction, process configuration, equipment control, IoT deployment, and dynamic scheduling. It significantly shortens the construction cycle of smart production lines, reduces implementation costs, and comprehensively improves production efficiency and flexibility.

[0046] In one optional implementation, the step of visually arranging the processing execution instructions and sequence for the device and performing real-time debugging and verification includes:

[0047] The process route is formed by connecting process nodes through a 2D canvas, and the equipment execution instruction flowchart is arranged for each process step. The debugging mode is started to verify the instruction execution effect.

[0048] This invention connects process nodes on a 2D canvas to form a process route, intuitively and clearly outlining the relationships between various processes in the production flow, providing a clear logical foundation for the arrangement of equipment execution instructions. Simultaneously, it creates a flowchart of equipment execution instructions for each step, visually clarifying the specific instructions and sequence of equipment operation. This allows for adaptation to different brands and models of the same type of processing equipment without complex coding, significantly reducing the technical threshold and operational difficulty of equipment instruction configuration. The debugging mode enables immediate verification of instruction execution effects, facilitating rapid identification and correction of configuration problems, reducing production interruptions or efficiency losses due to instruction errors, significantly improving the accuracy of equipment instruction configuration and debugging efficiency, and ensuring the smoothness and flexibility of the production line process.

[0049] In one optional implementation, the deployment and management of the smart gateway includes: configuring a protocol adaptation component to adapt to communication between devices using different protocols, and distributing device configuration data.

[0050] This invention, through the deployment and management of intelligent gateways, utilizes a protocol adaptation component to flexibly adapt to device communication protocols of different types. This effectively solves the connection problems caused by differences in communication protocols among devices of different brands and models, breaking down barriers to device interconnection and achieving efficient integration of various devices. Simultaneously, by sending device configuration data to the intelligent gateway, there is no need for tedious system address configuration on each device, significantly reducing the configuration workload when dealing with a large number of devices, lowering manual operation costs and error probability, accelerating the speed of device access to the system, and ensuring that production line equipment can quickly and stably establish communication connections with the system, laying a reliable foundation for subsequent data collection and intelligent scheduling.

[0051] In one optional implementation, the step of periodically collecting device data and uploading it to a cloud server, while simultaneously sending control commands to the smart gateway to achieve dynamic device scheduling, includes:

[0052] Based on the collected equipment operation data, analyze equipment usage efficiency and energy consumption, and display them in real time and clearly through visual dashboards and digital twins;

[0053] The collected equipment operation data is sent to the cloud service, and the cloud server generates automated scheduling and dynamic adjustment and optimization control instructions for production tasks based on the integrated data and preset rules / algorithms. These instructions are then sent to the smart gateway to achieve dynamic equipment scheduling.

[0054] This invention collects equipment operation data periodically and analyzes equipment efficiency and energy consumption based on the data. Simultaneously, it utilizes visual dashboards and digital twins to achieve real-time, clear display, allowing production managers to intuitively and promptly grasp the operating status of production line equipment. This provides data support for production decisions, helps identify inefficient processes and energy consumption issues in equipment operation, and facilitates timely optimization. The collected equipment operation data is uploaded to a cloud server, enabling the cloud to integrate multi-dimensional data and combine preset rules / algorithms to generate automated scheduling and dynamically optimized control commands for production tasks. These commands are then sent to a smart gateway for dynamic equipment scheduling. This eliminates reliance on manual experience, improving the efficiency and accuracy of production scheduling. It also allows for flexible responses to order changes and equipment malfunctions during production, ensuring the production line always operates efficiently and collaboratively. This ultimately improves overall production efficiency, reduces energy costs, and enhances the flexibility and adaptability of the production line. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0056] Figure 1 This is a structural block diagram of an intelligent production line design system integrating data acquisition and equipment scheduling according to an embodiment of the present invention;

[0057] Figure 2 This is a flowchart illustrating an intelligent production line design method that integrates data acquisition and equipment scheduling.

[0058] Figure 3 This is a specific example diagram of dragging and dropping to build a production line according to an embodiment of the present invention;

[0059] Figure 4 This is an example diagram of a process route configuration according to an embodiment of the present invention;

[0060] Figure 5 This is an example diagram of device control instruction arrangement according to an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0062] This invention provides an intelligent production line design system that integrates data acquisition and equipment scheduling.

[0063] like Figure 1 As shown, the system includes:

[0064] Production line construction module 10 is based on a lightweight web-based visual design tool, which allows users to assemble production line equipment models into 3D production line models by dragging and dropping, and provides multi-user collaborative editing functions.

[0065] Specifically, the lightweight 3D modeling tool based on WebGL in this invention can simplify the production process of production line design and support rapid modification and iteration, and includes:

[0066] The model resource library stores standardized 3D equipment models, providing users with a rich and standardized model foundation. The graphical editing area allows users to select equipment models from the resource library via drag-and-drop, adjusting model parameters and spatial positions to build digital production line models. This significantly lowers the technical barrier to production line design, offering intuitive and convenient operation and improving design efficiency. The bidirectional conversion unit enables automatic conversion between 3D models and CAD drawings. 3D production line models and traditional CAD drawings can be mutually converted; CAD drawings are automatically converted to 3D models, and 3D models can be exported as CAD drawings with a single click. This seamlessly integrates with traditional engineering design processes while fully leveraging the advantages of 3D models in visualization and collaborative editing. It reduces data conversion costs between different design stages, enhances the flexibility and compatibility of production line design, and overall helps enterprises quickly and efficiently build digital production line models.

[0067] The data adaptation module 20 is used to configure production line-related data models to achieve automated data interaction with external systems. The production line-related data models include order models, product models, and warehouse models. The system can automatically complete data docking with third-party systems (such as ERP / MES / WMS systems) based on the data models, which can break down data barriers, reduce data fragmentation and duplicate entry, and improve the efficiency of production line information flow.

[0068] Process planning module 30 is used to configure process routes and generate process logic relationships through a graphical interface, simplifying the process design process; this module includes:

[0069] The process node library provides standard processing process units, reducing the arbitrariness and repetitive work in process definition; the connection logic component establishes the sequential relationship between processes, making the process route logic clearer and more convenient, and reducing configuration complexity; the process parsing engine automatically generates processing step sequences, ensuring process continuity and accuracy, avoiding human error, and improving process design efficiency and standardization.

[0070] Equipment control module 40 is used to configure equipment execution commands and sequences in a visual manner, adapting to different types of processing equipment with the same function, and supporting real-time debugging and verification; this module includes:

[0071] The instruction flowchart editor is used to visually arrange device instructions, allowing users to intuitively configure device instructions and sequences without complex coding, reducing the difficulty of device adaptation and the cost of secondary development.

[0072] The real-time debugging interface is used to support the verification of command execution effects, quickly identify and correct problems, shorten the debugging cycle, reduce the risk of production interruption, and enhance the convenience and reliability of equipment control.

[0073] The connection management module 50 is used to configure device connection parameters on the system side, enabling device communication through the smart gateway. These connection parameters include the device IP address, port number, and communication protocol. Based on this configuration information, the system can connect and communicate with the device for data acquisition and device control. The smart gateway includes a protocol adaptation layer and an edge computing engine. The protocol adaptation layer supports multiple industrial protocols to adapt to device communication using different protocols, solving connectivity challenges for different devices, breaking down interoperability barriers, improving device response and data processing efficiency, and reducing reliance on cloud computing resources. The edge computing engine processes device data in real time and responds to control commands.

[0074] The gateway management module 60 is used to deploy protocol applications and distribute configurations to the smart gateway. The specific configuration is based on the production line design. It installs protocol applications on the gateway for communication between devices with different protocols, and distributes device orchestration and connection configuration data for device connection.

[0075] The data acquisition and scheduling module 70 is used to periodically collect device data and upload it to the cloud server, while simultaneously sending control commands to the smart gateway to achieve dynamic device scheduling. It includes:

[0076] The data analysis unit is used to analyze equipment usage efficiency and energy consumption based on the collected equipment operation data, and to display this information in real time and clearly through visual dashboards and digital twins. The digital twin method displays the equipment integration status and progress, making the process more intuitive and providing data support for production decisions.

[0077] The scheduling unit receives data and preset rules / algorithms from the cloud server, generates automated scheduling and dynamic adjustment and optimization control instructions for production tasks, and sends them to the smart gateway to realize equipment scheduling. This not only eliminates the reliance on human experience and improves scheduling efficiency, but also flexibly responds to changes in the production process, ensuring efficient and flexible operation of the production line, thereby improving overall production efficiency and reducing energy costs.

[0078] Among them, cloud servers, based on integrated data and preset rules / algorithms, can be combined with the following production scenario examples:

[0079] 1. Order Priority Rules: The system has a preset "urgent orders priority scheduling" rule. When both urgent and regular orders are received at the same time, the algorithm automatically identifies the order priority tags and prioritizes the allocation of equipment resources to urgent orders and adjusts the production sequence.

[0080] 2. Equipment load balancing algorithm: Based on real-time collected equipment operation data, the algorithm calculates the current load rate of each equipment (such as the number of processing tasks and remaining capacity). When the load rate of a certain equipment exceeds 80%, the subsequent tasks are automatically assigned to equipment with the same function whose load rate is less than 50%, so as to avoid equipment overload.

[0081] 3. Process connection optimization rules: Based on the process logic relationship analyzed by the process planning module, the algorithm presets the rule of "starting the preparation of the subsequent process when the completion rate of the preceding process reaches 90%". For example, after the part cutting process is 90% completed, the welding equipment preheating command is automatically triggered to shorten the waiting time for process connection.

[0082] The system provided by this invention can flexibly adapt to various brands and models of intelligent devices, PLCs, instruments, AGVs, etc. It provides an end-to-end integrated solution from 3D production line design, process configuration, equipment instruction arrangement to data acquisition and scheduling control, reducing the risk of repeated investment and integration between multiple systems and multiple suppliers, shortening the construction cycle of intelligent production lines, and reducing the total cost of ownership.

[0083] This embodiment also provides an intelligent production line design method that integrates data acquisition and equipment scheduling. For example... Figure 2 As shown, the process includes the following steps:

[0084] Step S1: Construct a digital model of the production line using a web-based visual design tool. Select equipment models from the model resource library and drag them into the graphical editing area. Adjust model parameters and positions to complete assembly. The 3D modeling tool can be implemented using a 3D designer. The 3D designer page consists of a model resource library, toolbar, attribute configuration bar, and a central 3D canvas, as shown below. Figure 3 As shown, users can select the 3D model of the equipment on the left and drag it into the canvas. They can adjust the size and orientation of the model. By adding all the models in the production line according to their coordinate positions, a 3D model of the production line can be formed. It supports macroscopic display of the overall production line and microscopic equipment layout, which greatly reduces the difficulty of production line design, improves design efficiency and flexibility, and is highly intuitive and easy to modify. Multiple people can edit on the same production line at the same time, and the modifications made by collaborators can be synchronized in real time, which can significantly improve the efficiency of production line design.

[0085] Step S2 involves configuring the production line-related data model to achieve automated data interaction with external systems. This breaks down data barriers between different systems, avoids the tedious process of manually entering data repeatedly, promotes the efficient flow of production information, enables the production line to seamlessly collaborate with external systems, and improves the overall efficiency of production data utilization.

[0086] Step S3 involves visually configuring the product processing route through a graphical interface, selecting process elements and connecting them to form a process flow diagram, and parsing to obtain the logical relationships between the processes; this is achieved through a 2D canvas, such as... Figure 4As shown, users select the production line processes as needed, drag the processes into the canvas, and then connect the processes with lines to form a process route map. The system will parse this process route map to obtain the preceding and following processes for each process. In this way, when the system processes the production line automatically, it can clearly know the current processing process and the next processing process, that is, the specific steps of processing, clearly presenting the logical relationship between processes, making the process design process simpler and easier to understand, reducing the reliance on professional technicians. At the same time, the process logic relationship obtained by the system provides a clear process basis for subsequent equipment instruction arrangement and production scheduling, reducing omissions in process design.

[0087] Step S4 involves visually arranging the processing execution instructions and sequence for the equipment, and performing real-time debugging and verification; specifically, such as... Figure 5 As shown, after arranging the process route, the user can select the process step and arrange the processing instructions of the equipment in the form of a flowchart on the right canvas. After the arrangement is completed, debugging can be performed immediately to quickly verify whether the configuration meets expectations. It can adapt to different types of processing equipment with the same function without complex coding, which reduces the technical difficulty of equipment control configuration. The real-time debugging function can quickly find and correct instruction problems, ensuring the accuracy of equipment operation, shortening the production line debugging cycle, and improving the flexibility and reliability of equipment control.

[0088] Step S5: Configure device connection parameters, establish communication through the smart gateway, deploy and manage the smart gateway, configure protocol adaptation components to adapt to communication between devices with different protocols, and send device configuration data. This avoids the tedious work of configuring each device individually, solves the communication problem between devices with different protocols, speeds up the speed of device access to the system, reduces the error rate of manual configuration, and provides a stable connection guarantee for device data acquisition and control command issuance.

[0089] Step S6: Periodically collect device data and upload it to the cloud server, while simultaneously sending control commands to the smart gateway to achieve dynamic device scheduling.

[0090] Specifically, the system analyzes equipment usage efficiency and energy consumption based on collected equipment operation data, and displays this data in real-time and clearly through visual dashboards and digital twins. The collected equipment operation data is sent to cloud services, and the cloud server, based on integrated data and preset rules / algorithms, generates automated scheduling and dynamic adjustment and optimization control instructions for production tasks. These instructions are then sent to the smart gateway to achieve dynamic equipment scheduling. This allows managers to monitor equipment operating status in real time, perform data analysis and optimization, and the cloud server can intelligently schedule and dynamically adjust based on real-time data and preset rules. This eliminates reliance on manual experience, improves the efficiency of production scheduling and the flexibility to respond to changes, and ensures that the production line is always operating at high efficiency.

[0091] The method provided by this invention forms an integrated solution through the coordinated operation of each step, from production line design, data interaction, process configuration, equipment control, IoT deployment to dynamic scheduling. This significantly shortens the construction cycle of intelligent production lines, reduces implementation costs, and comprehensively improves production efficiency and flexibility.

[0092] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An intelligent production line design system integrating data acquisition and equipment scheduling, characterized in that, include: The production line construction module, based on a lightweight web-based visual design tool, allows users to assemble production line equipment models into 3D production line models by dragging and dropping, and provides multi-user collaborative editing functionality. The data adaptation module is used to configure production line-related data models and enable automated data interaction with external systems. The process planning module is used to configure process routes and generate logical relationships between processes through a graphical interface; The equipment control module is used to configure the equipment execution instructions and sequences in a visual manner, adapting to different types of processing equipment with the same function, and supporting real-time debugging and verification; The connection management module is used to configure device connection parameters on the system side and enable device communication through the smart gateway; The gateway management module is used to deploy protocol applications and distribute configurations to smart gateways; The data acquisition and scheduling module is used to periodically collect device data and upload it to the cloud server, while simultaneously issuing control commands to achieve dynamic scheduling.

2. The system according to claim 1, characterized in that, The web-based visual design tools include: A model resource library for storing standardized 3D models of equipment; The graphical editing area allows users to select equipment models from the model resource library by dragging and dropping, and adjust model parameters and spatial positions to build digital production line models. The bidirectional conversion unit is used to automatically convert between 3D models and CAD drawings.

3. The system according to claim 1, characterized in that, The smart gateway includes: Protocol adaptation layer, supporting multiple industrial protocols, used to adapt to device communication with different protocols; Edge computing engine is used to process device data in real time and respond to control commands.

4. The system according to claim 1, characterized in that, The process planning module includes: The process node library provides standard machining process units; Connection logic components are used to establish sequential relationships between processes; A process analysis engine is used to automatically generate a sequence of processing steps.

5. The system according to claim 1, characterized in that, The device control module includes: Instruction flowchart editor, used for visually arranging device instructions; A real-time debugging interface is provided to support the verification of command execution effects.

6. The system according to claim 1 or 3, characterized in that, The data acquisition and scheduling module includes: The data analysis unit is used to analyze equipment usage efficiency and energy consumption based on the collected equipment operation data, and to display the results in real time and clearly through visual dashboards and digital twins; The scheduling unit receives data from the cloud server based on integrated data and preset rules / algorithms, generates automated scheduling and dynamic adjustment and optimization control instructions for production tasks, and sends them to the smart gateway to realize equipment scheduling.

7. An intelligent production line design method integrating data acquisition and equipment scheduling, characterized in that, Includes the following steps: Build a digital model of the production line using web-based visual design tools, select equipment models from the model resource library and drag them into the graphical editing area, adjust the model parameters and positions to complete the assembly; Configure production line-related data models to enable automated data interaction with external systems; The product processing route can be configured visually through a graphical interface. Process elements can be selected and connected to form a process flow diagram, and the logical relationships between processes can be parsed and obtained. The system visualizes and schedules the processing and execution instructions and sequence for the equipment, and performs real-time debugging and verification. Configure device connection parameters, establish communication through the smart gateway, and deploy and manage the smart gateway. The system periodically collects device data and uploads it to the cloud server, while simultaneously issuing control commands to achieve dynamic scheduling.

8. The method according to claim 7, characterized in that, The method of visually arranging processing and execution instructions and sequences for the device, and performing real-time debugging and verification, includes: The process route is formed by connecting process nodes through a 2D canvas, and the equipment execution instruction flowchart is arranged for each process step. The debugging mode is started to verify the instruction execution effect.

9. The method according to claim 7, characterized in that, The deployment and management of the smart gateway includes: configuring protocol adaptation components to adapt to communication between devices with different protocols, and sending device configuration data.

10. The method according to claim 7 or 9, characterized in that, The process of periodically collecting device data and uploading it to the cloud server, while simultaneously sending control commands to the smart gateway to achieve dynamic device scheduling, includes: Based on the collected equipment operation data, analyze equipment usage efficiency and energy consumption, and display them in real time and clearly through visual dashboards and digital twins; The collected equipment operation data is sent to the cloud service, and the cloud server generates automated scheduling and dynamic adjustment and optimization control instructions for production tasks based on the integrated data and preset rules / algorithms. These instructions are then sent to the smart gateway to achieve dynamic equipment scheduling.

Images