CN115639793A - Process route optimization method and device based on digital twinning and storage medium - Google Patents
Process route optimization method and device based on digital twinning and storage medium Download PDFInfo
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Abstract
The application discloses a process route optimization method and device based on digital twins and a storage medium, and relates to the technical field of process planning routes, wherein the process route optimization method based on the digital twins comprises the following steps: constructing a plurality of process route models of a factory space model in the digital twin; obtaining production target parameters of each process route model, wherein the production target parameters comprise material main data, target capacity, standard working hours and daily effective working duration; calculating the predicted capacity of each process route model; and adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route. The optimization method simulates the process flow and calculates the production efficiency through a digital twin technology, helps a factory to compare different process route design schemes, finds the optimization space of the process route, prevents and controls foreseeable risks in advance, reduces the trial and error cost and improves the production efficiency.
Description
Technical Field
The present application relates to the technical field of process planning routes, and in particular, to a process route optimization method and apparatus, a storage medium, and an electronic apparatus based on digital twinning
Background
The process route is a technical file for describing the operation sequence of material processing and part assembly, and is a sequence of a plurality of working procedures. The process is an action or a series of actions performed by production operators or machine equipment to complete a specified task, is the most basic processing operation mode for processing materials and assembling products, is data directly related to position information of a work center, an outsourced supplier and the like, and is a basic unit forming a process route. For example, a flow line is a process line, and the flow line includes a plurality of processes.
The existing process route does not carry out twin simulation of the process route, and the process route which is randomly determined is directly used for real investment production, so that the problems that the optimal state cannot be achieved and the cost for changing the process route after investment is huge are brought.
Accordingly, there is a need in the art for a new process route optimization solution to address the above problems.
Disclosure of Invention
The present application is directed to solving the above technical problem, that is, to providing a process route optimization method and apparatus based on digital twinning, a storage medium, and an electronic apparatus.
In a first aspect, the present application provides a method for digital twin-based process route optimization, the method comprising:
constructing a plurality of process route models of a factory space model in the digital twin;
acquiring production target parameters of each process route model, wherein the production target parameters comprise material master data, target capacity, standard working hours and effective working hours per day;
calculating the predicted capacity of each process route model;
and adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route.
In an embodiment of the above method for optimizing a process route based on a digital twin, the building of multiple process route models of a plant space model in a digital twin includes:
constructing a process route model of a multi-level process structure, wherein the process structure comprises process structures from a factory to a workshop level, from the workshop to a line level and from the line to a station level;
the process route model comprises one or more process structures, the workshop comprises one or more process structures, the line bodies comprise one or more lines, each line body comprises a single line body or a plurality of line bodies, the multiple line bodies comprise an afflux line body and an afflux line body, each single line body comprises one or more processes, each multi line body comprises one or more processes, each process comprises one or more stations, and each station comprises a single station, a serial station and/or a parallel station.
In one embodiment of the above method for optimizing a process route based on a digital twin, the obtaining of the production target parameter of each process route model includes:
and respectively acquiring a production target parameter of each process route model according to a production beat and an actual market demand, and respectively inputting the production target parameters into the process efficiency model in the digital twin, wherein the process efficiency model is used for calculating the predicted capacity of the process route model.
In one embodiment of the above method for optimizing a process route based on a digital twin, the calculating the predicted capacity of each process route model includes:
calculating the time duration of each single process of each process route model according to the standard man-hour in the production target parameters;
acquiring a process structure of each process route model initially set by a user, and determining the total time required for completing each process route model according to the process structure;
and calculating the predicted capacity of each process route model as the effective working time per day divided by the total time required for completing each process route model.
In one technical solution of the above digital twin-based process route optimization method, the obtaining a process structure of each process route model initially set by a user, and determining a total time required for completing each process route model according to the process structure includes:
and when the total time duration needed by each process route model is determined to be used, if the time duration needed by the convergence point of the convergence line body is longer than the time duration needed by the convergence line body to the convergence point, the time duration needed by each process route model to be used is determined to be used, and the time duration needed by the convergence line body to the convergence point is updated to the time duration needed by the convergence point of the convergence line body.
In one technical solution of the above method for optimizing a process route based on a digital twin, the adjusting a corresponding process route and obtaining an optimized process route according to a difference between the target capacity and the corresponding predicted capacity includes:
and if the difference value between the target capacity and the predicted capacity is smaller than a preset threshold value, taking the process route as an optimized process route.
In one embodiment of the above method for optimizing a process route based on a digital twin, the adjusting a corresponding process route according to a difference between the target capacity and the corresponding predicted capacity to obtain an optimized process route includes:
if the difference value between the target capacity and the predicted capacity is larger than a preset threshold value, optimizing the process structure of each process route model initially set by a user, and adjusting the single process time of the process route;
calculating the predicted capacity of the process route model after the process structure is optimized, and obtaining the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized;
and if the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized is larger than a preset threshold value, continuing to optimize until the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized is smaller than the preset threshold value, and taking the process route after the process structure is optimized for multiple times as an optimized process route.
In a second aspect, the present invention provides a digital twin-based process route optimisation device comprising:
the system comprises a construction module, a simulation module and a simulation module, wherein the construction module is used for constructing a plurality of process route models of a factory space model in a digital twin;
the acquisition module is used for acquiring production target parameters of each process route model, wherein the production target parameters comprise material master data, target productivity, standard working hours and daily effective working duration;
the calculation module is used for calculating the predicted capacity of each process route model;
and the optimization module is used for adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route.
In a third aspect, the present application provides a computer-readable storage medium comprising a stored program, wherein the program when executed performs the optimization method of the first aspect of the present application.
In a fourth aspect, the present application provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to execute the optimization method of the first aspect of the present application by the computer program.
One or more technical schemes of this application, have at least following one or more beneficial results:
in the technical scheme of the application, a process route optimization method based on digital twins is provided, and the optimization method aims at constructing a plurality of process route models of a factory space model in the digital twins; acquiring production target parameters of each process route model, wherein the production target parameters comprise material master data, target capacity, standard working hours and effective working hours per day; calculating the predicted capacity of each process route model; and adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route. The optimization method simulates the process flow and calculates the production efficiency through a digital twin technology, helps a factory to compare different process route design schemes, finds the optimization space of the process route, prevents and controls foreseeable risks in advance, reduces the trial-and-error cost and improves the production efficiency.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of the main steps of a digital twin-based process route optimization method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a process route model within a plant constructed in data twins in accordance with an embodiment of the present application;
FIG. 3 is a flow chart illustrating the main steps of step S102 according to an embodiment of the present application;
fig. 4 is a schematic flow chart of main steps of step S104 according to another embodiment of the present application;
FIG. 5 is a block diagram illustrating the principal components of a digital twin based process route optimizer in accordance with an embodiment of the present application;
fig. 6 is a main block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be implemented in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to FIG. 1, FIG. 1 is a flow chart of the main steps of a digital twin-based process route optimization method according to one embodiment of the present invention. As shown in fig. 1, the method for optimizing a process route based on digital twinning in the embodiment of the present invention mainly includes the following steps S101 to S104.
Step S101: a plurality of process route models of a plant space model are constructed in the digital twin.
In the embodiment, the digital twinning technology is applied more and more widely in the process production as a key trend of intelligent manufacturing. Under the guidance of a digital twin technology, the traditional process design gradually evolves into an intelligent and digital three-dimensional process design, and a novel manufacturing mode taking a process model as a manufacturing basis is brought forward. The digital twin technology provides technical support for management of the whole life cycle of the product, information transmission of physical space and virtual space, data sharing and guidance and prediction of the processing process, and promotes the progress of intelligent manufacturing. Digital twins (Digital twins) are conceptual systems of interaction of a physical world and a Digital space, the Digital twins are also under continuous development in the aspect of intelligent manufacturing industry, before a plurality of process route models of a factory space model are constructed in the Digital twins, the layout of the factory physical space needs to be obtained, and the plurality of process route models of the factory space model are constructed in the Digital twins according to the layout of the factory physical space, wherein the process route models can be process route models of shaft parts, process route models of manufacturing washing machine products and the like.
Step S102: and acquiring production target parameters of each process route model, wherein the production target parameters comprise material master data, target capacity, standard working hours and daily effective working duration.
In this embodiment, after a plurality of process route models of the plant space model are constructed in the data twin, production target parameters of each process route model, such as parameters of material master data, target productivity, standard working hours, daily effective working hours, and the like, are acquired.
Step S103: and calculating the predicted capacity of each process route model.
In this embodiment, the predicted capacity of each process route model is calculated according to the multiple processes of each process route model, the time required after production is completed, and the effective working time per day.
Step S104: and adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route.
In this embodiment, whether the process route is suitable or not is obtained according to the difference between the target capacity and the predicted capacity, if so, the process route is actually applied, and if not, the process route is optimized, so that an optimized process route is obtained.
The following further describes steps S101 to S104.
In one implementation manner of the embodiment of the present invention, the step S101 may further include the following step S1011:
step S1011: constructing a process route model of a multi-level process structure,
the process route model comprises one or more process structures, the workshop comprises one or more process structures, the line bodies comprise one or more lines, each line body comprises a single line body or a plurality of line bodies, the multiple line bodies comprise an afflux line body and an afflux line body, each single line body comprises one or more processes, each multi line body comprises one or more processes, each process comprises one or more stations, and each station comprises a single station, a serial station and/or a parallel station.
In a specific example, as shown in fig. 2, to construct one process route model of a plurality of process route models from a factory to a workshop, from the workshop to a line and from the line to a workstation in a data twin according to a layout of a physical air conditioner of the factory, the process route model may include a plurality of process structures, and one of the process structures is illustrated below. In the process route model, a workshop comprises three line bodies including a first line body L1, a second line body L2 and a third line body L3, wherein the first line body L1 and the second line body L2 are converged line bodies, the third line body L3 is converged line bodies, the first line body L1, the second line body L2 and the third line body L3 comprise a plurality of processes, in the figure 2, arrows represent the relation between line body directions and line bodies, dotted lines represent processes, circles represent stations, the first line body L1 comprises four processes including L11, L12, L13 and L14, the second line body L2 comprises three processes including L21, L22 and L23, the third line body comprises six processes including L31, L32, L33, L34, L35 and L36, wherein the processes included in the line bodies are fused between the L33 process and the L34 process, the processes included in the L2 are fused between the L34 process and the L35 process, each process comprises one or more than one station, a serial station and/or more than one station, and/or more than one station. For example, the first process L11 and the third process L13 of the first line L1 shown in fig. 2 from top to bottom are a plurality of serial stations, the second process L12 is a plurality of parallel stations, and the fourth process L14 is a single station.
In an implementation manner of the embodiment of the present invention, the step S102 may further include the following step S1021:
step S1021: and respectively acquiring the production target parameters of each process route model according to the production rhythm and the actual market demand, and respectively inputting the production target parameters into the process efficiency model in the digital twin, wherein the process efficiency model is used for calculating the predicted capacity of the process route model.
In one specific example, the production tact is also called as a customer demand cycle, a production interval time, and means a ratio of a total effective production time to a quantity of customer demand in a certain time period, which is a time necessary for a market that a customer demands a product. Examples are as follows: there were 8 hours (480 minutes) in total for one and only one day shift per day. Minus 30 minutes lunch, 30 minutes rest, 10 minutes commute and 10 minutes basic maintenance checks. Then the available working time =480-30-30-10 =400 minutes. When the customer demand is 400 pieces per day, the production time of each part should be controlled within one minute to ensure the customer demand. The production beat is actually a target time, is changed along with the change of the required quantity and the effective working time of the required period, and is artificially established. The tempo reflects the demand to production adjustment, if the demand is stable, the demanded tempo is stable, and when the demand changes, the tempo changes, if the demand decreases, the tempo becomes longer, otherwise, the tempo becomes shorter. And setting a production target parameter of each process route model according to the production rhythm and the actual market demand, and respectively inputting the production target parameter into the process efficiency model in the digital twin, wherein the process efficiency model is used for calculating the predicted capacity of the process route model.
In an implementation manner of the embodiment of the present invention, as shown in fig. 3, the step S103 may further include the following steps S1031 to S1033:
step S1031: calculating the time duration of each single process of each process route model according to the standard man-hour in the production target parameters;
step S1032: acquiring a process structure of each process route model initially set by a user, and determining the total time required for completing each process route model according to the process structure;
step S1033: and calculating the predicted capacity of each process route model as the effective daily working time divided by the total time required for completing each process route model.
Continuing with the above example, the time duration of each single process in the process route model shown in fig. 2 is calculated according to the standard man-hours in the production target parameters, for example, the time durations of the single processes in l11-l14, l21-l23, and l31-l36, the process structure of the process route model initially set by the user is obtained, for example, which stations are selected for production in the l11 serial station process, the l13 serial station process, and the l34 serial station process is used to determine the total time duration required for completing the process route model according to the process structure set by the user, and the predicted capacity of each process route model is obtained according to the daily effective work duration and the total time duration required for completing each process route model.
In one implementation manner of the embodiment of the present invention, the step S1032 may further include the following step S10321:
step S10321: when the total time length needed by the completion of each process route model is determined, if the time length of the merging point of the merging line body is longer than the time length of the merging point of the merged line body, the time length of the merging point of the merged line body is updated to the time length of the merging point of the merging line body when the total time length needed by the completion of each process route model is determined.
Continuing with the above example, the first line L1 and the second line L2 shown in fig. 2 are merged lines, the third line L3 is a merged line, the total duration on the line L31-L36, which is the total duration required for completing each process route model, is determined, if there is no merged line on the line L31-L36, the total duration of the line L31+ L32+ L33+ L34+ L35+ L36 is the total duration for completing the process route model, and if there is a merged line on the line L31-L36, the relationship between the duration of the merged line at the merging point and the duration before the merging point on the line L31-L36 needs to be considered.
For example, if the total time of L21+ L22+ L23 when the second line L2 is merged into the third line L3 in fig. 2 is L21+ L22+ L23, and if the total time of L21+ L22+ L23 is > L31+ L32+ L33, then when the total time required for the process route model is determined, the total time of L31+ L32+ L33 is L21+ L22+ L23, the total time of L11+ L12+ L13+ L14 when the first line L1 is merged into the third line L3 in fig. 2 is L11+ L12+ L14, the total time of L21+ L22+ L34 when the entry point is merged into the third line L3 is L31, and if the total time of L11+ L12+ L14 is > L31+ L32+ L33+ L34, the total time of L21+ L22+ L14 is L21+ L32+ L33+ L34, and the total time of L21+ L22+ L23+ L14 is L12+ L13+ L14, then the total time of L21+ L11+ L22+ L13+ L14 is equal to L13+ L14, and the total time of L21+ L22+ L14 is equal to the total time when the total time + L21+ L22+ L11+ L12+ L22+ L14+ L13+ L14, and the total time is determined, and the total time is equal to 21+ L14, and the total time + L14, then the total time is equal to the total time + L21+ L22+ L14, and the total time + L22+ L14.
When the total time of the second line L2 and the third line L3 is compared, if the total time of L21+ L22+ L23 is less than the total time of L31+ L32+ L33, when the total time of L31+ L32+ L33 is determined to be the total time required for the process route model, the total time of L31+ L32+ L33 is taken, when the total time of L11+ L12+ L13+ L14 is less than the total time of L31+ L32+ L33+ L34, when the total time of L11+ L32+ L13+ L14 is determined to be the total time required for the process route model, the total time of L31+ L32+ L33+ L34 is taken, the total time of L31+ L32+ L33+ L34 is taken, and the total time required for the process route model is L31+ L32+ L34+ L33+ L34, the total time required for the process route model is determined to be the total time of L31+ L32+ L33+ L34+ L33+ L34+ L36.
In one specific example, the total time required by the process route model is determined, the effective daily operating time is also determined, and the predicted capacity of the process route model = effective daily operating time/total time required by the process route model.
In one implementation manner of the embodiment of the present invention, the step S104 may further include the following step S1041:
and if the difference value between the target capacity and the predicted capacity is smaller than a preset threshold value, taking the process route as an optimized process route.
In one specific example, the target capacity and the predicted capacity are compared, and if the difference between the target capacity and the predicted capacity is smaller than a preset threshold, the process route is used as an optimized process route and can be put into practical application to produce products, so that the trial-and-error cost can be reduced, and the production efficiency can be improved.
In one implementation of the embodiment of the present invention, as shown in fig. 4, the step S104 may further include the following steps S1041 '-S1043':
step S1041': if the difference value between the target capacity and the predicted capacity is larger than a preset threshold value, optimizing the process structure of each process route model initially set by a user, and adjusting the single process time of the process route;
step S1042': calculating the predicted capacity of the process route model after the process structure is optimized, and obtaining the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized;
step S1043': and if the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized is larger than a preset threshold value, continuing the optimization until the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized is smaller than the preset threshold value, and taking the process route after the process structure is optimized for multiple times as an optimized process route.
In a specific example, if the difference between the target capacity and the predicted capacity is greater than a preset threshold, optimizing the initially set process structure of the process route model, and when a single process of a process route in the optimized process structure is adjusted, calculating the predicted capacity after the process structure is optimized again, and if the difference between the target capacity and the predicted capacity is less than the preset threshold, taking the optimized process structure as the optimized process route; and if the difference between the target capacity and the predicted capacity is not met and is smaller than a preset threshold, continuing optimization until the difference between the target capacity and the predicted capacity of the process route model after the process structure is optimized is smaller than the preset threshold, and taking the process route after the process structure is optimized for multiple times as an optimized process route.
In the process of multiple optimization, if the difference between the target capacity and the predicted capacity of the process route model after the process structure is optimized is never smaller than the preset threshold, displaying the bottleneck process of the process route model after the process structure is optimized for the last time, which meets the maximum optimization times, and giving an adjustment suggestion.
Based on the steps S101 to S104, the invention provides a process route optimization method based on digital twins, which aims to construct a plurality of process route models of a factory space model in the digital twins; respectively obtaining production target parameters of each process route model, wherein the production target parameters comprise material main data, target capacity, standard working hours and daily effective working duration; respectively calculating the predicted capacity of each process route model; and adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route. The optimization method simulates the process flow and calculates the production efficiency through a digital twin technology, helps a factory to compare different process route design schemes, finds the optimization space of the process route, prevents and controls foreseeable risks in advance, reduces the trial and error cost and improves the production efficiency.
Further, the application also provides a process route optimization device based on the digital twin.
Referring to fig. 5, fig. 5 is a main structural block diagram of a digital twin-based process route optimization apparatus according to an embodiment of the present application. As shown in fig. 5, the digital twin-based process route optimization device in the embodiment of the present application mainly includes a building module 11, an obtaining module 12, a calculating module 13, and an optimizing module 14. In some embodiments, one or more of the building module 11, the obtaining module 12, the calculating module 13, and the optimizing module 14 may be combined together into one module. In some embodiments the construction module 11 may be configured to construct a plurality of process route models of the plant space model in the digital twin; the acquisition module 12 may be configured to acquire production target parameters of each of the process route models, wherein the production target parameters include material master data, target capacity, standard working hours, and effective daily working hours; the calculation module 13 may be configured to calculate a predicted capacity for each of the process route models; the optimization module 14 can be configured to adjust the corresponding process route according to the difference between the target capacity and the corresponding predicted capacity, so as to obtain an optimized process route.
In one embodiment, the description of the specific implementation function may be described with reference to steps S101 to S104.
It will be understood by those skilled in the art that all or part of the flow of the method implemented by the present application may also be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the above-mentioned method embodiments when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
Further, the present application also provides a computer-readable storage medium. In one computer-readable storage medium embodiment according to the present application, a computer-readable storage medium may be configured to store a program for executing the digital twin-based process route optimization method of the above-described method embodiment, which may be loaded and executed by a processor to implement the above-described digital twin-based process route optimization method. For convenience of explanation, only the parts related to the embodiments of the present application are shown, and details of the specific technology are not disclosed. The computer readable storage medium may be a memory device formed by various electronic devices, and optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.
Further, the application also provides an electronic device. In an embodiment of the electronic device according to the present application, as shown in fig. 6, the electronic device comprises a processor and a memory, the memory may be configured to store a program for executing the digital twin based process route optimization method of the above-mentioned method embodiment, and the processor may be configured to execute a program in the memory, the program including but not limited to a program for executing the digital twin based process route optimization method of the above-mentioned method embodiment. For convenience of explanation, only the portions related to the embodiments of the present application are shown, and specific technical details are not disclosed, please refer to the method portion of the embodiments of the present application. The electronic device may be a control device apparatus formed including various electronic apparatuses.
Further, it should be understood that, since the setting of each module is only for explaining the functional units of the apparatus of the present application, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, the number of individual modules in the figures is merely illustrative.
Those skilled in the art will appreciate that the various modules in the apparatus may be adaptively split or combined. Such splitting or merging of specific modules does not cause the technical solutions to deviate from the principle of the present application, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present application.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (10)
1. A process route optimization method based on digital twinning is characterized by comprising the following steps:
constructing a plurality of process route models of a factory space model in the digital twin;
acquiring production target parameters of each process route model, wherein the production target parameters comprise material master data, target capacity, standard working hours and effective working hours per day;
calculating the predicted capacity of each process route model;
and adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route.
2. The method of claim 1, wherein constructing a plurality of process route models of a plant space model in a digital twin includes:
constructing a process route model of a multi-level process structure, wherein the process structure comprises process structures from a factory to a workshop level, from the workshop to a line level and from the line to a station level;
the process route model comprises one or more process structures, the workshop comprises one or more process structures, the line bodies comprise one or more lines, each line body comprises a single line body or a plurality of line bodies, the multiple line bodies comprise an afflux line body and an afflux line body, each single line body comprises one or more processes, each multi line body comprises one or more processes, each process comprises one or more stations, and each station comprises a single station, a serial station and/or a parallel station.
3. The method as claimed in claim 2, wherein said obtaining the production target parameter of each of the process route models comprises:
and respectively acquiring the production target parameters of each process route model according to the production rhythm and the actual market demand, and respectively inputting the production target parameters into the process efficiency model in the digital twin, wherein the process efficiency model is used for calculating the predicted capacity of the process route model.
4. The method as claimed in claim 3, wherein said calculating the predicted capacity of each of the process route models comprises:
calculating the time duration of each single process of each process route model according to the standard man-hour in the production target parameters;
acquiring a process structure of each process route model initially set by a user, and determining the total time required for completing each process route model according to the process structure;
and calculating the predicted capacity of each process route model as the effective daily working time divided by the total time required for completing each process route model.
5. The method of claim 4, wherein the obtaining of the process structure of each process route model initially set by a user and the determining of the total time required to complete each process route model according to the process structure comprises:
when the total time length needed by the completion of each process route model is determined, if the time length of the merging point of the merging line body is longer than the time length of the merging point of the merged line body, the time length of the merging point of the merged line body is updated to the time length of the merging point of the merging line body when the total time length needed by the completion of each process route model is determined.
6. The method of claim 5, wherein adjusting the corresponding process route and obtaining the optimized process route according to the difference between the target capacity and the corresponding predicted capacity comprises:
and if the difference value between the target capacity and the predicted capacity is smaller than a preset threshold value, taking the process route as an optimized process route.
7. The method of claim 5, wherein adjusting the corresponding process route to obtain an optimized process route according to the difference between the target capacity and the corresponding predicted capacity comprises:
if the difference value between the target capacity and the predicted capacity is larger than a preset threshold value, optimizing the process structure of each process route model initially set by a user, and adjusting the single process time of the process route;
calculating the predicted capacity of the process route model after the process structure is optimized, and obtaining the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized;
and if the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized is larger than a preset threshold value, continuing to optimize until the difference value between the target capacity and the predicted capacity of the process route model after the process structure is optimized is smaller than the preset threshold value, and taking the process route after the process structure is optimized for multiple times as an optimized process route.
8. A digital twinning based process route optimization device, comprising:
the system comprises a construction module, a simulation module and a simulation module, wherein the construction module is used for constructing a plurality of process route models of a factory space model in a digital twin;
the acquisition module is used for acquiring production target parameters of each process route model, wherein the production target parameters comprise material master data, target productivity, standard working hours and daily effective working duration;
the calculation module is used for calculating the predicted capacity of each process route model;
and the optimization module is used for adjusting the corresponding process route according to the difference value between the target capacity and the corresponding predicted capacity so as to obtain an optimized process route.
9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.
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