CN110502786A - Digital twin processing method, device, system and equipment for production line - Google Patents

Abstract

The application discloses a digital twin processing method, device, system and equipment for a production line, the method includes using the digital twin of the production line to simulate and analyze production tasks, generating a digital twin of an execution strategy; optimizing according to the historical execution data of the production line and the current execution data of the production line The executive strategy digital twin. In the solution of the embodiment of the present invention, the digital twin of the execution strategy is generated by using the digital twin of the production line, and the digital twin of the execution strategy can be optimized according to the historical execution data of the production line and the current execution data of the production line. Determining and optimizing execution strategies does not need to depend on the quality level of managers.

Description

Digital twin processing method, device, system and equipment for production line

technical field

The present application relates to the technical field of information processing, and in particular to a digital twin processing method, device, system and equipment for a production line.

Background technique

Smart factory is a new stage in the development of modern factory informatization. It is based on the digital factory, using Internet of Things technology and equipment monitoring technology to strengthen information management and services; clearly grasp the production process, improve the controllability of the production process, and instantly correct Accurately collect production line data, as well as reasonable production planning and production progress.

However, the production process of a manufacturing enterprise is actually very complicated. From raw materials, semi-finished products to finished products, it involves different production processes, equipment, labor, warehousing, transportation and packaging. The real-time situation also involves the link situation between related elements. Existing smart factories can only collect real-time data through the Internet of Things technology, and then master it by managers and conduct production scheduling based on experience, which depends heavily on the quality level of managers.

Contents of the invention

In view of the above problems, the embodiments of the present invention provide a digital twin processing method, device, system and equipment for a production line, which can solve the technical problems mentioned in the background technology section above.

According to the embodiment of the present invention, the digital twin processing method for the production line includes: using the digital twin of the production line to simulate and analyze the production tasks, and generating the digital twin of the execution strategy; optimizing the execution strategy according to the historical execution data of the production line and the current execution data of the production line digital twin.

According to the embodiment of the present invention, the digital twin processing device for the production line includes: a generation module, which is used to simulate and analyze the production tasks by using the digital twin of the production line, and generate a digital twin of the execution strategy; an optimization module, which is used to execute the data according to the history of the production line And the current execution data of the production line optimizes the digital twin of the execution strategy.

The production line digital twin processing system according to an embodiment of the present invention includes: a production task generation device, a production line, and the aforementioned digital twin processing device; wherein, the production planning device is used to receive a production order and generate a production order according to the production order Task; the digital twin processing device is used to simulate and analyze the production task using the digital twin of the production line to generate a digital twin of the execution strategy; the production line is used to perform actual production according to the digital twin of the execution strategy generated by the digital twin processing device implement.

A computer device according to an embodiment of the present invention includes: a processor; and a memory on which executable instructions are stored; wherein the processor is configured to execute the executable instructions to implement the aforementioned digital twin for a production line Approach.

A computer storage medium according to an embodiment of the present invention, on which a computer program is stored, the computer program includes executable instructions, and when the executable instructions are executed by a processor, the aforementioned digital twin processing method for a production line is implemented .

It can be seen from the above description that in the solution of the embodiment of the present invention, the digital twin of the execution strategy is generated by using the digital twin of the production line, and the digital twin of the execution strategy can be optimized according to the historical execution data of the production line and the current execution data of the production line. During the production execution process , without human intervention, the digital twin determines and optimizes the execution strategy, and does not need to rely on the quality level of managers.

Description of drawings

Fig. 1 is a flowchart of a digital twin processing method for a production line according to an embodiment of the present invention;

2 is a schematic diagram of a digital twin processing device for a production line according to an embodiment of the present invention;

3 is a schematic diagram of a production line digital twin processing system according to an embodiment of the present invention;

Fig. 4 is a business flow chart of a production line digital twin processing system according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a computer device according to an embodiment of the present invention.

Detailed ways

The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and realize the subject matter described herein, and is not intended to limit the protection scope, applicability or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as needed. For example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may also be combined in other examples.

As used herein, the term "comprising" and its variants represent open terms meaning "including but not limited to". The term "based on" means "based at least in part on". The terms "one embodiment" and "an embodiment" mean "at least one embodiment." The term "another embodiment" means "at least one other embodiment." The terms "first", "second", etc. may refer to different or the same object. The following may include other definitions, either express or implied. Unless the context clearly indicates otherwise, the definition of a term is consistent throughout the specification.

In the following description, for purposes of illustration rather than limitation, specific details, such as specific system architectures, interfaces, and techniques, are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.

FIG. 1 shows a flow chart of a digital twin processing method for a production line provided by an embodiment of the present application. The method 100 may include the following steps:

S102, using the digital twin of the production line to simulate and analyze the production task, and generate the digital twin of the execution strategy.

In the embodiment of this application, the digital twin of the production line is a digital twin model representing the physical equipment and production personnel at each station of the production line, which can track the state of the physical equipment, including but not limited to the efficiency, logical state, maintenance, maintenance, and load of the equipment. It can also track the status of production personnel, including but not limited to data such as efficiency, attendance, and the number of times related physical equipment is operated.

In the embodiment of this application, the digital twin of the production line can be used to simulate and analyze the production tasks, and the digital twin of the execution strategy suitable for completing the production task can be generated by adjusting the parameters of the digital twin model of the production line.

S104. Optimizing the digital twin of the execution strategy according to the historical execution data of the production line and the current execution data of the production line.

In the embodiment of this application, after the production line performs actual production according to the execution strategy provided by the digital twin of the execution strategy, a large amount of historical execution data is recorded. At the same time, in the current actual production process, the production line passes sensors, RFID, GPS, etc. and the physical equipment of each station record the current execution data. Both the historical execution data of the production line and the current execution data of the production line are sent to the digital twin of the execution strategy for virtual execution. By comparing and analyzing the virtual execution of the digital twin of the execution strategy and the actual execution of the production line, iteratively improves the fidelity of the digital twin of the execution strategy. The higher the number of actual production runs, the higher the fidelity of the digital twin of the execution strategy. In the embodiment of this application, the execution data includes but is not limited to data related to each station, physical equipment, and production personnel. For example, it may include post workload, post backlog, equipment failure times, equipment efficiency, raw material quality, raw material supply, Production staff efficiency, etc.

In the embodiment of the present application, the method 100 may also include the following steps:

S106. Based on the digital twin of the optimized execution strategy, determine an execution risk analysis result of the production task.

In the embodiment of this application, the digital twin of the execution strategy performs data processing such as fusion and analysis of the acquired current execution data of the production line, and uses hierarchical analysis and fuzzy evaluation methods to comprehensively analyze the execution risks of production tasks based on the processed data. Establish a comprehensive execution risk evaluation and analysis system to analyze the execution risk of production tasks and use the established execution risk comprehensive evaluation and analysis system to monitor and warn execution risks, and obtain the execution risk analysis results corresponding to production tasks. Based on the established execution risk comprehensive assessment and analysis system, it can provide execution risk analysis results including various execution risk indicators, and provide risk warning and execution strategy improvement guidance for the actual production process of the production line.

In an embodiment of the present application, step S106 includes: preprocessing the current execution data of the production line to obtain an execution risk index; determining the weight of the execution risk index based on the execution risk index; The execution risk index of the digital twin of the optimized execution strategy is evaluated and analyzed to determine the execution risk analysis result of the production task. In an embodiment of the present application, the current execution data of the production line is preprocessed to obtain the execution risk index, which is convenient for subsequent use of the processed execution risk index to determine the weight of the index and use the weight to calculate multiple execution risk indexes. In one embodiment of the present application, the current execution data of the production line is preprocessed to obtain the execution risk index, and the weight of each index is determined by the index evaluation and analysis method. If the weight is W1, W2, W3, W4, W5, then according to the determined The execution risk index of the production task is calculated according to the weight of the production task, and the calculated execution risk index is evaluated and analyzed according to the multi-index analysis method in statistics, and the execution risk analysis result of the production task is obtained.

It can be seen from the above description that in the solution of the embodiment of the present invention, the digital twin of the execution strategy is generated by using the digital twin of the production line, and the digital twin of the execution strategy can be optimized according to the historical execution data of the production line and the current execution data of the production line. During the production execution process , without human intervention, the digital twin determines and optimizes the execution strategy, and does not need to rely on the quality level of managers.

Fig. 2 shows a schematic diagram of a digital twin processing device for a production line according to an embodiment of the present application. The device 200 shown in Fig. 2 can be implemented by software, hardware or a combination of software and hardware. The embodiment of the device 200 is basically similar to the embodiment of the method, so the description is relatively simple, and for related parts, refer to the part of the description of the method embodiment.

As shown in FIG. 2 , the apparatus 200 may include a generation module 202 and an optimization module 204 . The generating module 202 is used to simulate and analyze the production tasks by using the digital twin of the production line, and generate the digital twin of the execution strategy. The optimization module 204 is used to optimize the digital twin of the execution strategy according to the historical execution data of the production line and the current execution data of the production line.

In one aspect, the apparatus 200 may further include a determining module. The determination module is used to determine the execution risk analysis result of the production task based on the digital twin of the optimized execution strategy.

In another aspect, the determining module may include a preprocessing unit, a first determining unit, and a second determining unit. The preprocessing unit is used for preprocessing the current execution data of the production line to obtain execution risk indicators. The first determining unit is configured to determine the weight of the execution risk index based on the execution risk index. The second determining unit is configured to calculate the execution risk index of the optimized execution strategy digital twin according to the weight of the execution risk index, evaluate and analyze the execution risk index, and determine the execution risk analysis result of the production task.

The embodiment of the present application also provides a production line digital twin processing system, please refer to FIG. 3, which is a schematic diagram of the production line digital twin processing system according to the embodiment of the present application. The system 300 may include a production planning device, a production line, and the above-mentioned device embodiment The digital twin processing device described above.

The production planning device is used for receiving production orders and generating production tasks according to the production orders. The production planning device may be, for example, an ERP (Enterprise Resource Planning, enterprise resource planning) device. After receiving a production order, the production planning device schedules production tasks and issues the production tasks to the digital twin processing device. After the digital twin processing device receives the production task issued by the production planning device, it can judge according to the production task. If the digital twin processing device has an effective historical execution strategy twin of the production task, the production line can execute the strategy twin according to the effective history. actual production. If the digital twin processing device does not store the effective historical execution strategy twin of the production task, then use the digital twin of the production line to simulate and analyze the production task, generate the execution strategy digital twin of the production task, and the production line performs actual execution according to the generated execution strategy digital twin. production.

In an embodiment of the application, the production line is also used to send the historical execution data of the production line and the current execution data of the production line to the digital twin processing device, and the digital twin processing device is also used to optimize the execution according to the historical execution data of the production line and the current execution data of the production line Strategic digital twins. In the embodiment of this application, after the production line performs actual production according to the execution strategy provided by the digital twin of the execution strategy, a large amount of historical execution data is recorded. At the same time, in the current actual production process, the production line passes sensors, RFID, GPS, etc. and the physical equipment of each station record the current execution data. Both the historical execution data of the production line and the current execution data of the production line are sent to the digital twin of the execution strategy for virtual execution. By comparing and analyzing the virtual execution of the digital twin of the execution strategy and the actual execution of the production line, iteratively improves the fidelity of the digital twin of the execution strategy. The higher the number of actual production runs, the higher the fidelity of the digital twin of the execution strategy. In the embodiment of this application, the execution data includes but is not limited to data related to each station, physical equipment, and production personnel. For example, it may include post workload, post backlog, equipment failure times, equipment efficiency, raw material quality, raw material supply, Production staff efficiency, etc.

In yet another application embodiment, the digital twin processing device is further configured to determine the execution risk analysis result of the production task based on the optimized execution strategy digital twin, and send the execution risk analysis result to the production line. In the embodiment of this application, the digital twin of the execution strategy performs data processing such as fusion and analysis of the acquired current execution data of the production line, and uses hierarchical analysis and fuzzy evaluation methods to comprehensively analyze the execution risks of production tasks based on the processed data. Establish a comprehensive execution risk evaluation and analysis system to analyze the execution risk of production tasks and use the established execution risk comprehensive evaluation and analysis system to monitor and warn execution risks, and obtain the execution risk analysis results corresponding to production tasks. Based on the established execution risk comprehensive assessment and analysis system, it can provide execution risk analysis results including various execution risk indicators, and provide risk warning and execution strategy improvement guidance for the actual production process of the production line.

In another application embodiment, the production planning device is also used to receive the actual production execution result of the production line and/or the virtual production execution result of the digital twin processing device. In this embodiment of the application, the production line performs actual production execution and sends the actual production execution results to the production planning device, and the digital twin processing device performs virtual production execution and sends the virtual production execution results to the production planning device. Monitor, count and manage production execution results and/or virtual production execution results.

Continuing to refer to FIG. 4, the embodiment of the present application also provides a method embodiment applied to the digital twin processing system of the production line. FIG. 4 is a business flow chart of the embodiment of the present application. The method 400 may include the following steps:

S402. The production planning device receives a production order.

S404. The production planning device generates a production task according to the production order.

S406. The digital twin processing device uses the digital twin of the production line to simulate and analyze the production task, and generates the digital twin of the execution strategy.

S408, the digital twin processing device performs virtual production execution according to the digital twin of the execution strategy.

S410, the production line performs actual production execution according to the execution strategy provided by the production task and the execution strategy digital twin.

S412, the production line collects the current execution data of the production line, and sends the production line historical execution data and the production line current execution data to the digital twin processing device.

S414, the digital twin processing device optimizes the execution strategy digital twin according to the historical execution data of the production line and the current execution data of the production line.

S416. The digital twin processing device determines an execution risk analysis result of the production task based on the optimized digital twin of the execution strategy.

S418, the production line adjusts the execution strategy according to the execution risk analysis result.

The embodiment of the present application further provides a computer device, please refer to FIG. 5 , which is a schematic diagram of an embodiment of the computer device in the embodiment of the present application. As shown in FIG. 5 , for ease of description, only the parts related to the embodiment of the present application are shown, and for technical details not disclosed, please refer to the method part of the embodiment of the present application.

As shown in FIG. 5 , a computer device 500 may include a processor 502 and a memory 504, where executable instructions are stored on the memory 504, wherein when executed, the executable instructions cause the processor 502 to perform the operations described in the embodiment of FIG. 1 . method shown.

As shown in FIG. 5 , computer device 500 may also include a bus 506 that connects various system components, including processor 502 and memory 504 . Bus 506 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. These architectures include, by way of example, but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect ( PCI) bus.

Computer device 500 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 500 and include both volatile and nonvolatile media, removable and non-removable media.

Memory 504 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 508 and/or cache memory 510 . The computer device 500 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 512 may be used to read and write to non-removable, non-volatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a disk drive for reading and writing to removable nonvolatile disks (e.g., "floppy disks") may be provided, as well as for removable nonvolatile optical disks (e.g., CD-ROM, DVD-ROM or other optical media) CD-ROM drive. In these cases, each drive may be connected to bus 506 through one or more data media interfaces. The memory 504 may include at least one program product having a set (eg, at least one) of program modules configured to perform the functions of the above-described embodiment of FIG. 1 of the present invention.

Program/utility 514 may be stored, for example, in memory 504 as a set (at least one) of program modules 516 including, but not limited to, an operating system, one or more application programs, other program modules, and program data , each or some combination of these examples may include implementations of network environments. The program module 516 generally executes the functions and/or methods described in the present invention in the above-mentioned embodiment in FIG. 1 .

The computer device 500 may also communicate with one or more external devices 522 (e.g., a keyboard, pointing device, display 524, etc.), and with one or more devices that enable a user to interact with the computer device 500, and/or with Any device (eg, network card, modem, etc.) that enables the computing device 500 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 518 . Moreover, the computer device 500 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 520 . As shown in FIG. 5 , network adapter 520 communicates with other modules of computer device 500 via bus 506 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with computer device 500, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.

The processor 502 executes various functional applications and data processing by running the programs stored in the memory 504 , for example, implementing the methods shown in the above-mentioned embodiments.

Embodiments of the present application also provide a computer storage medium on which a computer program is stored, the computer program includes executable instructions, and when the executable instructions are executed by a processor, the implementation of the above-mentioned embodiment for the production line Implementation in the digital twin processing method.

The computer storage medium in this embodiment may include a random access memory (RAM) 508 in the memory 504 in the embodiment shown in FIG. 5 , and/or a cache memory 510 , and/or a storage system 512 .

With the development of science and technology, the transmission channels of computer programs are no longer limited to tangible media, and can also be directly downloaded from the Internet or obtained in other ways. Therefore, the computer storage medium in this embodiment may include not only tangible media, but also intangible media.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, devices, or computer program products. Accordingly, embodiments of the invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

Embodiments of the present invention are described with reference to flowcharts and/or block diagrams of methods, apparatuses, and computer program products according to embodiments of the present invention. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor or processor of other programmable data processing terminal equipment to produce a machine such that instructions executed by the computer or processor of other programmable data processing terminal equipment Produce means for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

The specific implementation manner described above in conjunction with the accompanying drawings describes exemplary embodiments, but does not represent all embodiments that can be realized or fall within the protection scope of the claims. As used throughout this specification, the term "exemplary" means "serving as an example, instance, or illustration," and does not mean "preferred" or "advantaged" over other embodiments. The detailed description includes specific details for the purpose of providing an understanding of the described technology. However, the techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form in order to avoid obscuring the concepts of the described embodiments.

The above description of the present disclosure is provided to enable any person of ordinary skill in the art to make or use the present disclosure. Various modifications to this disclosure will be readily apparent to those skilled in the art, and the general principles defined herein can also be applied to other variants without departing from the scope of this disclosure. . Thus, the disclosure is not intended to be limited to the examples described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. Digital twin processing methods for production lines, including: Use the digital twin of the production line to simulate and analyze the production tasks, and generate a digital twin of the execution strategy; The digital twin of the execution strategy is optimized according to the historical execution data of the production line and the current execution data of the production line. 2. The method of claim 1, further comprising: Based on the digital twin of the optimized execution strategy, the execution risk analysis result of the production task is determined. 3. The method according to claim 2, wherein, the execution strategy digital twin based on the optimization determines the execution risk analysis result of the production task, comprising: Execute data preprocessing on the production line currently to obtain execution risk indicators; determining the weight of the execution risk index based on the execution risk index; Calculate the execution risk index of the digital twin of the optimized execution strategy according to the weight of the execution risk index, evaluate and analyze the execution risk index, and determine the execution risk analysis result of the production task. 4. Digital twin processing devices for production lines, including: The generation module is used to simulate and analyze the production tasks by using the digital twin of the production line, and generate the digital twin of the execution strategy; An optimization module, configured to optimize the digital twin of the execution strategy according to the historical execution data of the production line and the current execution data of the production line. 5. The device according to claim 4, further comprising: The determination module is configured to determine the execution risk analysis result of the production task based on the digital twin of the optimized execution strategy. 6. The device according to claim 5, wherein the determining module specifically comprises: A preprocessing unit, configured to preprocess the current execution data of the production line to obtain execution risk indicators; a first determining unit, configured to determine the weight of the execution risk index based on the execution risk index; The second determination unit is configured to calculate the execution risk index of the optimized execution strategy digital twin according to the weight of the execution risk index, evaluate and analyze the execution risk index, and determine the execution risk analysis result of the production task. 7. A production line digital twin processing system, comprising: a production planning device, a production line, and a digital twin processing device according to any one of claims 4-6; wherein, The production planning device is used to receive a production order and generate a production task according to the production order; The digital twin processing device is used to simulate and analyze the production task by using the digital twin of the production line to generate a digital twin of an execution strategy; The production line is used for actual production execution according to the production task and the digital twin of the execution strategy. 8. The system of claim 7, wherein, The production line is also used to send the historical execution data of the production line and the current execution data of the production line to the digital twin processing device; The digital twin processing device is further configured to optimize the execution strategy digital twin according to the historical execution data of the production line and the current execution data of the production line. 9. Computer equipment, including: processor; and a memory having executable instructions stored thereon; Wherein, the processor is configured to execute the executable instructions to implement the method according to any one of claims 1-3. 10. A computer storage medium, on which a computer program is stored, the computer program comprising executable instructions, when the executable instructions are executed by a processor, the method according to any one of claims 1-3 is implemented.