CN110502786A - The twin processing method of number, device, system and equipment for production line - Google Patents
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Abstract
This application discloses the twin processing method of number, device, system and the equipment for production line, this method carries out simulation analysis to production task including the use of production line number is twin, and it is twin to generate implementation strategy number；It is twin that the data-optimized implementation strategy number is currently executed according to production line history execution data and production line.In the scheme of the embodiment of the present invention, it is twin using the twin generation implementation strategy number of production line number, and it is twin currently to execute data-optimized implementation strategy number according to production line history execution data and production line, in production implementation procedure, without artificial intervention, by the twin determination of number and optimization implementation strategy, the quality for being not need to rely on administrative staff is horizontal.
Description
Technical Field
The present application relates to the field of information processing technologies, and in particular, to a digital twin processing method, apparatus, system, and device for a production line.
Background
An intelligent factory is a new stage of modern factory informatization development, and information management and service are enhanced by using the technology of the Internet of things and the equipment monitoring technology on the basis of a digital factory; the production flow is clearly mastered, the controllability of the production process is improved, the production line data are timely and correctly collected, and the production planning and the production progress are reasonable.
However, the production process of a manufacturing enterprise is very complex in practice, and involves different production processes, equipment, labor, storage, transportation, packaging and other elements from raw materials, semi-finished products to finished products, historical conditions and real-time conditions of related elements, and also involves the link conditions between related elements. The existing intelligent factory can only acquire real-time data through the technology of the Internet of things, and then is mastered by managers and carries out production scheduling according to experience, which depends heavily on the quality level of the managers.
Disclosure of Invention
In view of the above problems, embodiments of the present invention provide a digital twin processing method, apparatus, system and device for a production line, which can solve the technical problems mentioned in the background section above.
A digital twinning processing method for a production line according to an embodiment of the present invention includes: carrying out simulation analysis on the production task by utilizing a production line digital twin to generate an execution strategy digital twin; optimizing the execution strategy digital twin based on production line historical execution data and production line current execution data.
A digital twin processing apparatus for a production line according to an embodiment of the present invention includes: the generating module is used for carrying out simulation analysis on the production tasks by utilizing the production line digital twin to generate an execution strategy digital twin; and the optimization module is used for optimizing the execution strategy digital twin according to the historical execution data of the production line and the current execution data of the production line.
A production line digital twinning processing system according to an embodiment of the present invention includes: a production task generating device, a production line, and the digital twin processing device; the production planning device is used for receiving a production order and generating a production task according to the production order; the digital twin processing device is used for carrying out simulation analysis on the production tasks by utilizing the production line digital twin to generate an execution strategy digital twin; the production line is used for carrying out actual production execution according to the execution strategy digital twin generated by the digital twin processing device.
A computer apparatus according to an embodiment of the present invention includes: a processor; and a memory having executable instructions stored thereon; wherein the processor is configured to execute the executable instructions to implement the aforementioned digital twinning processing method for a production line.
A computer storage medium according to an embodiment of the invention has stored thereon a computer program comprising executable instructions which, when executed by a processor, implement the aforementioned digital twinning processing method for a production line.
As can be seen from the above description, in the solution of the embodiment of the present invention, the execution policy digital twin is generated by using the production line digital twin, and the execution policy digital twin can be optimally executed according to the production line historical execution data and the production line current execution data.
Drawings
FIG. 1 is a flow chart of a digital twinning process for a production line in accordance with an embodiment of the present invention;
FIG. 2 is a schematic view of a digital twin processing apparatus for a production line according to an embodiment of the present invention;
FIG. 3 is a schematic view of a production line digital twinning processing system in accordance with an embodiment of the present invention;
FIG. 4 is a business flow diagram of a production line digital twin processing system in accordance with an embodiment of the present invention;
FIG. 5 is a diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and thereby implement the subject matter described herein, and are not intended to limit the 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 described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with respect to some examples may also be combined in other examples.
As used herein, the term "include" and its variants mean open-ended terms in the sense of "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," and the like may refer to different or the same object. Other definitions, whether explicit or implicit, may be included below. The definition of a term is consistent throughout the specification unless the context clearly dictates otherwise.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. 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 that depart from 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 the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Fig. 1 shows a flowchart of a digital twinning processing method for a production line according to an embodiment of the present application, where the method 100 may include the following steps:
and S102, carrying out simulation analysis on the production task by utilizing the production line digital twin to generate an execution strategy digital twin.
In the embodiment of the application, the production line digital twin is a digital twin model representing physical equipment and production personnel at each station of the production line, and can track the state of the physical equipment, including but not limited to data of efficiency, logic state, maintenance, load and fault of the equipment, and can also track the state of the production personnel, including but not limited to data of efficiency, attendance, times of operating relevant physical equipment, and the like.
In the embodiment of the application, the production line digital twin can be used for carrying out simulation analysis operation on the production task, and the execution strategy digital twin suitable for completing the production task is generated by adjusting the parameters of the production line digital twin model.
And S104, optimizing the execution strategy digital twin according to the historical execution data of the production line and the current execution data of the production line.
In the embodiment of the application, after the production line actually produces according to the execution strategy provided by the execution strategy digital twin, a large amount of historical execution data is recorded, and in the current actual production process, the production line records the current execution data through sensors, RFID, GPS and the like installed at each station and physical equipment and the like of each station. The historical production line execution data and the current production line execution data are sent to the execution strategy digital twin to be executed virtually, and the simulation degree of the execution strategy digital twin is improved through the comparison analysis of the virtual execution of the execution strategy digital twin and the actual execution of the production line and continuous iteration. The more actual production times, the higher the fidelity of the implementation strategy digital twin. The execution data in the embodiments of the present application includes, but is not limited to, data relating to individual workstations, physical equipment, and production personnel, and may include, for example, job workload, job overstock, number of equipment failures, equipment efficiency, material quality, material supply, production personnel efficiency, and the like.
In an embodiment of the present application, the method 100 may further include the following steps:
and S106, determining the execution risk analysis result of the production task based on the optimized execution strategy digital twin.
In the embodiment of the application, the execution strategy digital twin performs data processing processes such as fusion and analysis on the acquired current execution data of the production line, comprehensive analysis is performed on the execution risk of the production task by adopting a hierarchical analysis and fuzzy evaluation method based on the processed data, an execution risk comprehensive evaluation analysis system is established so as to analyze the execution risk of the production task and perform execution risk monitoring and early warning by using the established execution risk comprehensive evaluation analysis system, and an execution risk analysis result corresponding to the production task is obtained. The execution risk comprehensive evaluation analysis system can provide execution risk analysis results including various execution risk indexes, and provides risk early 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: performing data preprocessing on the current production line to obtain an execution risk index; determining a weight of an execution risk indicator based on the execution risk indicator; and calculating the execution risk index of the optimized execution strategy digital twin according to the weight of the execution risk index, evaluating and analyzing the execution risk index, and determining 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, so that the weight of the index is determined by using the execution risk index obtained after the preprocessing, and a plurality of execution risk indexes are calculated by using the weight. In an embodiment of the present application, currently executed data of a production line is preprocessed to obtain executed risk indexes, weights of the indexes are determined by an index evaluation analysis method, where the weights are W1, W2, W3, W4, and W5, an executed risk index of a production task is calculated according to the determined weights, and an executed risk index evaluation analysis result of the production task is obtained by performing evaluation analysis on the calculated executed risk index according to a statistical multi-index analysis method.
As can be seen from the above description, in the solution of the embodiment of the present invention, the execution policy digital twin is generated by using the production line digital twin, and the execution policy digital twin can be optimally executed according to the production line historical execution data and the production line current execution data.
Fig. 2 shows a schematic diagram of a digital twin processing apparatus for a production line according to an embodiment of the present application, and the apparatus 200 shown in fig. 2 may be implemented by software, hardware or a combination of software and hardware. The embodiment of the apparatus 200 is substantially similar to the embodiment of the method and therefore is described in a relatively simple manner, where relevant reference is made to 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 configured to perform simulation analysis on the production task by using the production line digital twin to generate an execution strategy digital twin. The optimization module 204 is configured to optimize the execution strategy digital twin based on the production line historical execution data and the production line current execution data.
In one aspect, the apparatus 200 may further include a determination module. The determining module is used for determining the execution risk analysis result of the production task based on the optimized execution strategy digital twin.
In another aspect, the determination module may include a preprocessing unit, a first determination unit, and a second determination unit. The preprocessing unit is used for preprocessing the current execution data of the production line to obtain an execution risk index. The first determination unit is configured to determine a weight of the execution risk indicator based on the execution risk indicator. The second determining unit is used for calculating the execution risk index of the optimized execution strategy digital twin according to the weight of the execution risk index, evaluating and analyzing the execution risk index and determining the execution risk analysis result of the production task.
Referring to fig. 3, fig. 3 is a schematic view of a production line digital twin processing system according to an embodiment of the present invention, and the system 300 may include a production planning device, a production line, and a digital twin processing device according to the above-described embodiments of the device.
The production planning device is used for receiving a production order and generating a production task according to the production order. The production Planning device may be, for example, an ERP (Enterprise Resource Planning) device, and arranges a production task and issues the production task to the digital twin processing device after the production Planning device receives the production order. And after receiving the production task issued by the production planning device, the digital twin processing device can judge according to the production task, and if the digital twin processing device has an effective historical execution strategy twin of the production task, the production line can execute actual production according to the effective historical execution strategy twin. And if the digital twin processing device does not store the effective historical execution strategy twin of the production task, performing simulation analysis on the production task by using the production line digital twin to generate the execution strategy digital twin of the production task, and performing actual production by using the production line according to the generated execution strategy digital twin.
In an embodiment of the application, the production line is further configured to send the production line historical execution data and the production line current execution data to the digital twin processing device, and the digital twin processing device is further configured to optimize the execution strategy for digital twin based on the production line historical execution data and the production line current execution data. In the embodiment of the application, after the production line actually produces according to the execution strategy provided by the execution strategy digital twin, a large amount of historical execution data is recorded, and in the current actual production process, the production line records the current execution data through sensors, RFID, GPS and the like installed at each station and physical equipment and the like of each station. The historical production line execution data and the current production line execution data are sent to the execution strategy digital twin to be executed virtually, and the simulation degree of the execution strategy digital twin is improved through the comparison analysis of the virtual execution of the execution strategy digital twin and the actual execution of the production line and continuous iteration. The more actual production times, the higher the fidelity of the implementation strategy digital twin. The execution data in the embodiments of the present application includes, but is not limited to, data relating to individual workstations, physical equipment, and production personnel, and may include, for example, job workload, job overstock, number of equipment failures, equipment efficiency, material quality, material supply, production personnel efficiency, and the like.
In another application embodiment, the digital twin processing device is further configured to determine an 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 the application, the execution strategy digital twin performs data processing processes such as fusion and analysis on the acquired current execution data of the production line, comprehensive analysis is performed on the execution risk of the production task by adopting a hierarchical analysis and fuzzy evaluation method based on the processed data, an execution risk comprehensive evaluation analysis system is established so as to analyze the execution risk of the production task and perform execution risk monitoring and early warning by using the established execution risk comprehensive evaluation analysis system, and an execution risk analysis result corresponding to the production task is obtained. The execution risk comprehensive evaluation analysis system can provide execution risk analysis results including various execution risk indexes, and provides risk early warning and execution strategy improvement guidance for the actual production process of the production line.
In another embodiment of the application, the production planning means is further adapted to receive an actual production execution result of the production line and/or a virtual production execution result of the digital twin processing means. In the embodiment of the application, the production line performs actual production execution and sends an actual production execution result to the production planning device, the digital twin processing device performs virtual production execution and sends a virtual production execution result to the production planning device, and the production planning device can monitor, count and manage the actual production execution result and/or the virtual production execution result.
With continued reference to fig. 4, an embodiment of a method applied to a production line digital twin processing system is further provided in the present application, fig. 4 is a business flow diagram of the embodiment of the present application, and 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.
And S406, the digital twin processing device utilizes the production line digital twin to perform simulation analysis on the production task to generate the execution strategy digital twin.
And S408, the digital twin processing device performs virtual production execution according to the execution strategy digital twin.
And S410, the production line carries out actual production execution according to the execution strategy provided by the production task and the execution strategy digital twin.
And S412, the production line collects the current execution data of the production line, and sends the historical execution data of the production line and the current execution data of the production line to the digital twin processing device.
And S414, the digital twin processing device carries out digital twin according to the production line historical execution data and the production line current execution data optimization execution strategy.
And S416, the digital twin processing device determines the execution risk analysis result of the production task based on the optimized execution strategy digital twin.
And S418, the production line adjusts the execution strategy according to the execution risk analysis result.
An embodiment of the present application further provides a computer device, please refer to fig. 5, and fig. 5 is a schematic diagram of an embodiment of a computer device according to the embodiment of the present application. As shown in fig. 5, for convenience of illustration, only the portions related to the embodiments of the present application are shown, and the technical details are not disclosed, please refer to the method portion of the embodiments of the present application.
As shown in fig. 5, the computer device 500 may include a processor 502 and a memory 504, wherein the memory 504 has stored thereon executable instructions that, when executed, cause the processor 502 to perform the method shown in the embodiment of fig. 1.
As shown in FIG. 5, computer device 500 may also include a bus 506 that couples various system components, including the processor 502 and memory 504. Bus 506 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, 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. Such media can be any available media that is accessible by computer device 500 and includes 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, the storage system 512 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 506 by one or more data media interfaces. Memory 504 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of the above-described fig. 1 embodiment of the invention.
A program/utility 514 having a set (at least one) of program modules 516 may be stored, for example, in memory 504, such program modules 516 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 516 generally perform the functions and/or methodologies described above in connection with the FIG. 1 embodiment of the present invention.
The computer device 500 may also communicate with one or more external devices 522 (e.g., keyboard, pointing device, display 524, etc.), with one or more devices that enable a user to interact with the computer device 500, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 500 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 518. Moreover, computer device 500 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via network adapter 520. As shown in FIG. 5, the network adapter 520 communicates with the other modules of the computer device 500 via the bus 506. It should be appreciated that although not shown in the figures, 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 systems, among others.
The processor 502 executes various functional applications and data processing by executing programs stored in the memory 504, for example, to implement the methods shown in the above-described embodiments.
Embodiments of the present application also provide a computer storage medium having stored thereon a computer program comprising executable instructions that, when executed by a processor, implement embodiments of the aforementioned embodiments of the method for digital twinning processing of a production line.
The computer storage media of this embodiment may include Random Access Memory (RAM)508, and/or cache memory 510, and/or storage system 512 of memory 504 in the embodiment illustrated in fig. 5 and described above.
With the development of technology, the propagation path of computer programs is no longer limited to tangible media, and the computer programs can be directly downloaded from a network or acquired by other methods. Thus, the computer storage media in the present embodiments may include not only tangible media, but also intangible media.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present 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 present 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, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus, and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The detailed description set forth above in connection with the appended drawings describes exemplary embodiments but does not represent all embodiments that may be practiced or fall within the scope of the claims. The term "exemplary" used throughout this specification means "serving as an example, instance, or illustration," and does not mean "preferred" or "advantageous" 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 previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the scope of the disclosure. Thus, the present 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. A digital twinning process for a production line, comprising:
carrying out simulation analysis on the production task by utilizing a production line digital twin to generate an execution strategy digital twin;
optimizing the execution strategy digital twin based on production line historical execution data and production line current execution data.
2. The method of claim 1, further comprising:
and determining an execution risk analysis result of the production task based on the optimized execution strategy digital twin.
3. The method of claim 2, wherein determining the execution risk analysis result for the production task based on the optimized execution strategy digital twin comprises:
performing data preprocessing on the current production line to obtain an execution risk index;
determining a weight of an execution risk indicator based on the execution risk indicator;
and calculating the execution risk index of the optimized execution strategy digital twin according to the weight of the execution risk index, evaluating and analyzing the execution risk index, and determining the execution risk analysis result of the production task.
4. A digital twinning processing apparatus for a production line, comprising:
the generating module is used for carrying out simulation analysis on the production tasks by utilizing the production line digital twin to generate an execution strategy digital twin;
and the optimization module is used for optimizing the execution strategy digital twin according to the historical execution data of the production line and the current execution data of the production line.
5. The apparatus of claim 4, further comprising:
and the determining module is used for determining the execution risk analysis result of the production task based on the optimized execution strategy digital twin.
6. The apparatus of claim 5, wherein the determining means specifically comprises:
the preprocessing unit is used for preprocessing the current execution data of the production line to obtain an execution risk index;
a first determining unit for determining a weight of an execution risk indicator based on the execution risk indicator;
and the second determining unit is used for calculating the execution risk index of the optimized execution strategy digital twin according to the weight of the execution risk index, evaluating and analyzing the execution risk index and determining the execution risk analysis result of the production task.
7. A production line digital twinning processing system, comprising: a production planning apparatus, a production line, and a digital twinning processing apparatus as claimed in any one of claims 4 to 6; wherein,
the production planning device is used for receiving a production order and generating a production task according to the production order;
the digital twin processing device is used for carrying out simulation analysis on the production tasks by utilizing the production line digital twin to generate an execution strategy digital twin;
and the production line is used for performing actual production execution according to the production task and the execution strategy digital twin.
8. The system of claim 7, wherein,
the production line is also used for sending production line historical execution data and production line current execution data to the digital twin processing device;
the digital twin processing device is also used for optimizing the execution strategy digital twin according to the production line historical execution data and the production line current execution data.
9. A computer device, comprising:
a processor; and
a memory having executable instructions stored thereon;
wherein the processor is configured to execute the executable instructions to implement the method of any of claims 1-3.
10. A computer storage medium having stored thereon a computer program comprising executable instructions which, when executed by a processor, carry out the method of any one of claims 1-3.
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