Production method based on edge computing, edge computing device, workstation and system

Abstract

Embodiments of the present disclosure disclose a production method based on edge computing, an edge computing device, a workstation, and a system. The production method based on edge computing is implemented by the edge computing device, and the method includes: collecting production data from workstations on a production line, where the production data collected from the workstations includes at least one of sensing data, image data, and production execution machine operation data of the workstations; inputting the production data collected from the workstations into a first analysis model for analysis, and adjusting the production process of the production line according to the analysis results.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of intelligent manufacturing technology, and more particularly, to a production method based on edge computing, an edge computing device, a workstation of a production line, and a production system based on edge computing. Background Art

[0002] In a traditional production line, workstations and production line beats are set according to production requirements. This production line layout method has problems of discrete and fixed workstations, resulting in inflexible production processes that are difficult to adjust. Summary of the Invention

[0003] An object of embodiments of the present disclosure is to provide a production method based on edge computing, an edge computing device, a workstation of a production line, and a production system based on edge computing to improve the flexibility of the production line.

[0004] According to a first aspect of the present disclosure, there is provided a production method based on edge computing, implemented by an edge computing device, the method including:

[0005] Collect production data from workstations of a production line, and the production data collected from the workstations includes at least one of sensing data, image data, and production execution machine operation data of the workstations;

[0006] Input the production data collected from the workstations into a first analysis model for analysis, and adjust the production process of the production line according to the analysis results.

[0007] Optionally, the method further includes: collecting production data from a manufacturing execution system, and the production data collected from the manufacturing execution system includes at least production target data and remaining material data;

[0008] Input the production data collected from the workstations and the production data collected from the manufacturing execution system into a second analysis model for analysis, and adjust the production beat of the production line according to the analysis results.

[0009] Optionally, the workstation includes a display device, and the method further includes:

[0010] Controlling the display device of the workstation to display the production data of the workstation and the production target data.

[0011] Optionally, the method further includes:

[0012] Receiving a remote login request from a client device, and feeding back the collected production data and analysis results to the client device.

[0013] Optionally, the method further includes:

[0014] Communicate with a cloud server to back up the collected production data and analysis results, and upgrade the software system of the edge computing device according to the upgrade instructions issued by the cloud server.

[0015] Optionally, collecting production data from the workstations on the production line includes:

[0016] Collect production data from the workstation by communicating with the agent-side firmware in the control device installed in the workstation.

[0017] According to a second aspect of the present disclosure, there is provided an edge computing device, including a communication device, a processor, and a memory;

[0018] The communication device is used to communicate with the workstations on the production line;

[0019] The memory stores computer instructions, and when the computer instructions are executed by the processor, the production method provided in the first aspect of the present disclosure is implemented.

[0020] Optionally, the edge computing device is implemented based on a Raspberry Pi platform.

[0021] According to a third aspect of the present disclosure, there is provided a workstation on a production line, including a production execution machine, sensors, industrial cameras, a communication device, and a control device;

[0022] The control device is installed with agent-side firmware corresponding to the edge computing device.

[0023] According to a fourth aspect of the present disclosure, there is provided a production system based on edge computing, including: the edge computing device provided in the second aspect of the present disclosure, and the workstation on the production line provided in the third aspect of the present disclosure.

[0024] The production method provided by the embodiments of the present disclosure can collect production data from the workstations on the production line and perform analysis, and adjust the production process of the production line according to the analysis results, thereby improving the flexibility of the production line.

[0025] Through the following detailed description of the exemplary embodiments of the present specification with reference to the accompanying drawings, the features and advantages of the embodiments of the present specification will become clear. Description of the Drawings

[0026] The drawings incorporated in the specification and constituting a part of the specification illustrate the embodiments of the present specification, and together with the description thereof are used to explain the principles of the embodiments of the present specification.

[0027] Figure 1 A schematic diagram of the production system showing the embodiments of the present disclosure;

[0028] Figure 2A schematic diagram of an edge computing device according to an embodiment of the present disclosure is shown;

[0029] Figure 3 A schematic diagram of a workstation according to an embodiment of the present disclosure is shown;

[0030] Figure 4 A flowchart of a production method based on edge computing according to an embodiment of the present disclosure is shown. Detailed Embodiments

[0031] Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.

[0032] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the embodiments of the present specification, their applications, or uses.

[0033] It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.

[0034] Figure 1 It is a schematic diagram of the composition structure of a production system to which an embodiment according to the present disclosure can be applied.

[0035] As Figure 1 shown, the production system 100 of this embodiment includes an MES system, an edge computing device, each workstation of the production line, and a client device.

[0036] The full English name of the MES system is manufacturing execution system, and the Chinese name is manufacturing execution system. The MES system can generally be used to help enterprises achieve production planning management, production process control, product quality management, workshop inventory management, project kanban management, etc., and improve the manufacturing execution ability of enterprises.

[0037] In this embodiment, the production line includes N workstations, and each workstation can communicate with the edge computing device. Refer to Figure 3 shown, the workstation includes a control device, a production execution machine, sensors, an industrial camera, and a display.

[0038] The production executor may include machines that directly perform production processing, such as fixtures, cutting tools, etc. For example, the production executor includes a robotic arm with multiple degrees of freedom.

[0039] The sensors may include temperature sensors, optical sensors, ranging sensors, etc.

[0040] The control device can control the operation of production execution machines, sensors, industrial cameras, and displays, and obtain data from the production execution machines, sensors, and industrial cameras respectively. For example, it obtains the operation data of the production execution machine from the production execution machine, obtains sensing data from the sensor, and obtains the image data of the workstation from the industrial camera. The image data of the workstation can be, for example, the image data of the product on the workstation. The control device is installed with a proxy-end firmware corresponding to the edge computing device to execute the instructions of the edge computing device. Specifically, the edge computing device realizes the control of the workstation through the interaction with the proxy-end firmware of the control device. The proxy-end firmware seals the communication interface to communicate with the production execution machine, sensor, industrial camera, and display respectively to control these devices in real time.

[0041] Edge computing refers to an open platform that integrates network, computing, storage, and application core capabilities on the side close to the object or data source, providing the nearest-end services nearby. Its application programs are initiated on the edge side, generating faster network service responses and meeting the basic needs of the industry in aspects such as real-time services, application intelligence, security, and privacy protection.

[0042] In an embodiment of the present disclosure, the edge computing device can be set on the production line side. Refer to Figure 2 As shown, the edge computing device can include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, and an input device 1600. The processor 1100 can include, but is not limited to, a central processing unit CPU, a microprocessor MCU, etc. The memory 1200 includes, for example, ROM (read-only memory), RAM (random access memory), non-volatile memory such as a hard disk, etc. The interface device 1300 includes, for example, various bus interfaces, such as a serial bus interface (including a USB interface), a parallel bus interface, etc. The communication device 1400 can perform wired or wireless communication, for example. The display device 1500 is, for example, a liquid crystal display screen, an LED display screen, a touch display screen, etc. The input device 1600 can include, for example, a touch screen, a keyboard, a mouse, etc.

[0043] The edge computing device includes a vision detection and analysis system. The edge computing device 1000 can be, for example, a device with learning and analysis capabilities based on the Raspberry Pi platform.

[0044] In this embodiment, the memory 1200 of the edge computing device 1000 is used to store instructions for controlling the processor 1100 to operate to implement or support the implementation of the edge computing-based production method according to any embodiment. Those skilled in the art can design the instructions according to the solutions disclosed in this specification. How the instructions control the processor to operate is well known in the art, so it will not be described in detail here.

[0045] Figure 2 The hardware configuration shown is only illustrative and is in no way intended to limit the present disclosure, its applications, or uses.

[0046] The client device is, for example, an electronic device such as a desktop computer, a tablet computer, a mobile phone, etc. installed with a client. The client device is used to log in to the edge computing device through the installed client, perform management and control work on the edge computing device, and obtain production data and analysis results from the edge computing device and display them to the user. In an embodiment of the present disclosure, the client device includes a processor, a memory, an interface device, a communication device, a display device, and an input device. The processor may include, but is not limited to, a central processing unit (CPU), a microcontroller unit (MCU), etc. The memory includes, for example, a read-only memory (ROM), a random access memory (RAM), a non-volatile memory such as a hard disk, etc. The interface device includes, for example, various bus interfaces, such as a serial bus interface (including a USB interface), a parallel bus interface, etc. The communication device can perform wired or wireless communication, for example. The display device is, for example, a liquid crystal display screen, an LED display screen, a touch display screen, etc. The input device may include, for example, a touch screen, a keyboard, a mouse, etc.

[0047] See Figure 4 As shown, the production method based on edge computing provided by the embodiment of the present disclosure is implemented by an edge computing device and includes the following steps:

[0048] S102. Collect production data from the workstations on the production line. The production data collected from the workstations includes at least one of the sensing data, image data, and production execution machine operation data of the workstations.

[0049] The sensing data may be data obtained by sensors provided at the workstations. The sensing data may at least include the temperature data of the environment where the production execution machine is located and the air humidity data of the environment where the production execution machine is located. The sensing data may also include the performance data of the products on the workstation.

[0050] The image data may be data obtained by industrial cameras provided at the workstations. The image data may at least include the image data of the workstations. The image data of the workstations may be, for example, the image data of the products on the workstation.

[0051] The production execution machine operation data at least includes status data. The status data is boolean data. For example, when the status data is "0", it indicates that the production execution machine is in a normal state, and when the status data is "1", it indicates that the production execution machine is in a fault state.

[0052] The production execution machine operation data may also include the temperature data of the production execution machine, the power consumption data of the production execution machine, and the running time of the production execution machine.

[0053] S104. Input the production data collected from the workstations into the first analysis model for analysis, and adjust the production process of the production line according to the analysis results.

[0054] That is to say, an algorithm model is provided in the edge computing device, which can process the production data collected from the workstations in real time and adjust the production process of the production line.

[0055] In specific implementation, the first analysis model includes a surface defect analysis sub-model and a performance analysis sub-model. Input the image data of the products at the workstation collected from the workstation into the surface defect analysis sub-model for analysis and prediction to detect whether there are surface defects in the products at the workstation, that is, to detect whether there are defects such as cracks, depressions, foreign objects, etc. in the products at the workstation, and obtain the surface defect analysis results. Input the sensing data of the products at the workstation collected from the workstation into the performance analysis sub-model for analysis and prediction to detect whether there are performance defects in the products at the workstation, and obtain the performance analysis results. Adjust the production process of the production line according to the obtained surface defect analysis results and performance analysis results. According to the embodiments of the present disclosure, production data can be collected from each workstation in real time, so that the production process of the production line can be adjusted in real time according to the collected production data, thereby realizing the control of the production process and solving the problem of discrete production processes of the existing production lines.

[0056] In one embodiment, the method further includes steps S202 and S204.

[0057] S202. Collect production data from the manufacturing execution system. The production data collected from the manufacturing execution system at least includes production target data and remaining material data.

[0058] S204. Input the production data collected from the workstations and the production data collected from the manufacturing execution system into the second analysis model for analysis, and adjust the production rhythm of the production line according to the analysis results.

[0059] That is to say, an algorithm model is provided in the edge computing device, which can process the production data collected from the workstations and the production data of the MES system in real time to adjust the process rhythm.

[0060] For example, input the temperature data of the production execution machine collected from the workstation, the power consumption data of the production execution machine, the temperature data of the environment where the production execution machine is located, the air humidity data of the environment where the production execution machine is located, and the production target data and remaining material data collected from the manufacturing execution system into the second analysis model for analysis, and adjust the production rhythm of the production line according to the analysis results. When the production target data is not completed, accelerate the production rhythm to avoid being unable to complete the production task on time. When the temperature of the production execution machine is too high or the running time is too long, slow down the production rhythm, so as to avoid the production execution machine from malfunctioning due to overloading.

[0061] The production method provided by the embodiments of the present disclosure can realize the self-adjusting ability of the production line rhythm based on edge computing, and solve the problems of fixed production rhythm after production and waste of work-in-process caused by production bottlenecks.

[0062] In one embodiment, the workstation includes a display device, and the method further includes: controlling the display device of the workstation to display the production data and production target data of the workstation.

[0063] In this embodiment, the production target data is real-time projected on the display device of the workstation, and the key performance indicators (KPI) of the production line can also be customized and displayed.

[0064] In this embodiment, when the production execution machine fails, the display device can also be controlled to display the fault information for warning. When the temperature of the production execution machine exceeds the temperature threshold, the display device can also be controlled to display the warning information for reminder. When the running time of the production execution machine exceeds the time threshold, the display device can also be controlled to display the warning information for reminder. And when the temperature of the environment where the production execution machine is located does not meet the preset conditions or / and the air humidity of the environment where the production execution machine is located does not meet the preset conditions, the display device can be controlled to display the warning information for reminder. In addition, when the remaining material data is insufficient, the display device can be controlled to display the warning information for reminder to replenish the materials.

[0065] In one embodiment, the edge computing device supports local area network login and wide area network cloud login. For example, the edge computing device supports the login of local area network users, and after logging in, the users can perform operations such as device management and permission management.

[0066] In one embodiment, the method further includes: receiving a remote login request from a client device and feeding back the collected production data and analysis results to the client device.

[0067] In this embodiment, the user can log in to the edge computing device through the client device, manage and control the edge computing device, and obtain production data and analysis results from the edge computing device and display them to the user.

[0068] The client can be a web terminal or a dedicated application APP (application). The client device can be a fixed device or a mobile terminal.

[0069] In one embodiment, the method further includes: communicating with a cloud server to back up the collected production data and analysis results, and upgrading the software system of the edge computing device according to the upgrade instructions issued by the cloud server.

[0070] In this embodiment, edge computing devices are connected to cloud servers to support external network and mobile terminal control and monitoring of production line operations. The cloud server can be integrated with other industrial Internet cloud technologies or information flow and logistics interfaces to support the sharing of data, reports, and analysis results in the cloud.

[0071] In one embodiment, collecting production data from workstations of a production line includes collecting the production data from the workstations by communicating with agent firmware installed in a control device of the workstations.

[0072] In one embodiment, the edge computing device is set up on the production line side.

[0073] The production method provided by the embodiments of the present disclosure is based on edge computing, collects data from the underlying equipment of the workstation, and rationally controls the underlying equipment of the workstation by combining the production information of the MES system and the self-learning algorithm model of the edge computing layer to form a deeper feedback link, thereby achieving the effect of increasing production capacity, improving quality control, and increasing the intelligence and automation level of the production line.

[0074] The production method provided by the embodiment of the present disclosure is based on edge computing and can achieve real-time data collection and analysis, real-time screen display, and assist in decision-making and early warning. It can solve the problems of manual entry, offline analysis, and large feedback delays in the original data analysis.

[0075] The production method provided by the embodiment of the present disclosure is based on edge computing and has the ability to collect and calculate all data in real time and to control equipment in real time, which is not available in the MES system.

[0076] The production method provided by the embodiment of the present disclosure is based on edge computing and can obtain more production detail data, which has positive significance for multi-dimensional quality control of products after processing and analysis.

[0077] The production method provided by the embodiments of the present disclosure is implemented based on edge computing. Through the user management and device control center capabilities carried by edge computing, devices can be remotely and online controlled and adjusted, and the flexibility of the production line is higher.

[0078] An embodiment of the present disclosure also provides an edge computing device, including a communication device, a processor, and a memory;

[0079] The communication device is used to communicate with the workstations on the production line;

[0080] Computer instructions are stored in the memory, and when the computer instructions are executed by the processor, the production method based on edge computing disclosed in any of the foregoing embodiments is implemented.

[0081] In one example, the edge computing device is implemented based on the Raspberry Pi platform.

[0082] In one example, the edge computing device can be implemented using a server. One server can carry the edge computing functions of multiple production lines, that is, one server serves as the edge computing device for multiple production lines. In this way, the hardware aggregation degree is higher, and data collection and analysis tend to be more normalized.

[0083] An embodiment of the present disclosure also provides a workstation on a production line, including a production execution machine, a sensor, an industrial camera, a communication device, and a control device. The communication device is used to communicate with the edge computing device. The control device is installed with proxy end firmware corresponding to the edge computing device.

[0084] An embodiment of the present disclosure also provides a production system based on edge computing, including the foregoing edge computing device and the foregoing workstation. In one example, the production system further includes a manufacturing execution system.

[0085] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the device and equipment embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0086] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be executed in a different order from that in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] Embodiments of this specification may be devices, methods, and / or computer program products. A computer program product may include a computer-readable storage medium having computer-readable program instructions thereon for causing a processor to implement various aspects of the embodiments of this specification.

[0088] A computer-readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device. A computer-readable storage medium may be, by way of example and not limitation, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device such as a punch card or raised structures in a groove having instructions stored thereon, and any suitable combination of the foregoing. The computer-readable storage medium as used herein is not to be construed as a transitory signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through an optical fiber cable), or electrical signals transmitted through a wire.

[0089] The computer-readable program instructions described herein may be downloaded to respective computing / processing devices from a computer-readable storage medium or may be downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0090] The computer program instructions for performing the operations of the embodiments of this specification may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it may be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the embodiments of this specification.

[0091] Aspects of the embodiments of this specification are described herein with reference to the flowcharts and / or block diagrams of methods, apparatus (devices), and computer program products according to the embodiments of this specification. It should be understood that each block of the flowcharts and / or block diagrams, and the combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0092] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine such that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is produced that implements the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other devices to work in a specific manner. Thus, the computer-readable medium storing the instructions includes a manufactured article that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0093] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, causing a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process such that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0094] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present specification. In this regard, each box in the flowchart or block diagram may represent a module, a segment of a program, or a part of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the boxes may occur in a different order than noted in the figures. For example, two consecutive boxes may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagrams and / or flowcharts, and combinations of boxes in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or acts, or by a combination of dedicated hardware and computer instructions. As is well known to those skilled in the art, implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.

[0095] The embodiments of the present specification have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art in the technical field without departing from the scope of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, the practical application, or the improvement of the technology in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein.

Claims

1. A production method based on edge computing, characterized in that, Implemented by an edge computing device, the method includes: Collecting production data from workstations on a production line, the production data collected from the workstations including sensor data and image data of the workstations, the image data including image data of products at the workstations, and the sensor data including performance data of the products at the workstations; inputting the production data collected from the workstations into a first analysis model for analysis, and adjusting the production process of the production line according to the analysis results; The first analysis model includes a surface defect analysis sub-model and a performance analysis sub-model. The production data collected from the workstation is input into the first analysis model for analysis, and the production process of the production line is adjusted according to the analysis results, including: Inputting the image data of the product on the workstation into the surface defect analysis sub-model to obtain a surface defect analysis result; Inputting the performance data of the product on the workstation into the performance analysis sub-model to obtain a performance analysis result; The production process of the production line is adjusted according to the surface defect analysis results and the performance analysis results.

2. The method according to claim 1, wherein The production data collected from the workstations further includes production execution machine operation data, and the method further includes: collecting production data from a manufacturing execution system, the production data collected from the manufacturing execution system including at least production target data and remaining material data; The sensor data of the workstation and the operation data of the production execution machine collected from the workstation, as well as the production data collected from the manufacturing execution system are input into the second analysis model for analysis, and the production rhythm of the production line is adjusted according to the analysis results.

3. The method according to claim 1, characterized in that, The workstation includes a display device, and the method further includes: The display device of the work station is controlled to display the production data of the work station and the production target data collected from the manufacturing execution system.

4. The method according to claim 1, characterized in that: The method further comprises: Receive a remote login request from a client device and feed back the collected production data and analysis results to the client device.

5. The method according to claim 1, characterized in that, The method further comprises: Communicate with the cloud server to back up the collected production data and analysis results, and upgrade the software system of the edge computing device according to the upgrade instructions issued by the cloud server.

6. The method according to claim 1, characterized in that The production data collected from the workstations of the production line includes: Production data is collected from the workstations by communicating with agent firmware installed in the control devices of the workstations.

7. An edge computing device, characterized in that, including a communication device, a processor, and a memory; The communication device is used to communicate with the workstations of the production line; The memory stores computer instructions, which, when executed by the processor, implement the production method according to any one of claims 1 to 6.

8. The edge computing device according to claim 7, characterized in that The edge computing device is implemented based on the Raspberry Pi platform.

9. A production system based on edge computing, comprising: An edge computing device according to any one of claims 7-8, and a workstation of a production line.

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