CN115639793A - Process route optimization method, device, and storage medium based on digital twins - Google Patents

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

Process route optimization method, device, and storage medium based on digital twins

technical field

This application relates to the technical field of process planning route, in particular, to a digital twin-based process route optimization method and device, storage medium and electronic device

Background technique

The process route is a technical document describing the operation sequence of material processing and component assembly, and it is a sequence of multiple processes. A process is an action or a series of actions performed by production operators or machinery and equipment in order to complete specified tasks. It is the most basic processing operation method for processing materials and assembling products, and is directly related to location information such as work centers and external suppliers. The data is the basic unit of the process route. For example, an assembly line is a process route, and this assembly line contains many processes.

The existing process route does not carry out the process route twinning simulation, and directly uses the process route determined at will for real production, so that it will not be able to achieve the best state and the cost of changing the process route after investment is huge.

Correspondingly, there is a need in the art for a new process route optimization scheme to solve the above problems.

Contents of the invention

The present application aims to solve the above technical problems, that is, to solve, the present application provides a digital twin-based process route optimization method and device, storage medium and electronic device.

In the first aspect, the present application provides a digital twin-based process route optimization method, the optimization method includes:

Build multiple process route models of the factory space model in the digital twin;

Obtain the production target parameters of each of the process route models, wherein the production target parameters include material master data, target production capacity, standard working hours and effective daily working hours;

calculating a predicted capacity for each of said routing models;

According to the difference between the target production capacity and the corresponding predicted production capacity, the corresponding process route is adjusted to obtain an optimized process route.

In a technical solution of the above-mentioned digital twin-based process route optimization method, the multiple process route models of the factory space model constructed in the digital twin body include:

Build a process route model of a multi-level process structure, the process structure includes the process structure from factory to workshop level, from workshop to line body level and from line body to station level;

Wherein, the process route model includes one or more process structures, the workshop includes one or more, the line body includes one or more lines, each line body includes a single line body or a multi-line body, and a multi-line body includes Incoming line body and imported line body, each single line body includes one or more processes, each multi-line body includes one or more processes, each process includes one or more stations, and the stations include Single station, serial station and/or parallel station.

In a technical solution of the above-mentioned digital twin-based process route optimization method, the acquisition of the production target parameters of each of the process route models includes:

Acquire the production target parameters of each of the process route models according to the production tact and actual market demand, and input the production target parameters into the process performance model in the digital twin, wherein the process performance model Used to calculate the forecast capacity of the routing model.

In a technical solution of the above digital twin-based process route optimization method, the calculation of the predicted production capacity of each of the process route models includes:

Calculate the duration of each single process of each of the process route models according to the standard man-hours in the production target parameters;

Obtain the process structure of each of the process route models initially set by the user, and determine the total time required to complete each of the process route models according to the process structure;

Calculate the predicted production capacity of each said process route model by dividing the daily effective working hours by the total time required to complete each said process route model.

In one technical solution of the above-mentioned process route optimization method based on digital twins, the process structure of each process route model initially set by the user is obtained, and the process structure required to complete each process route model is determined according to the process structure. Total time spent, including:

When determining the total time required to complete each of the process route models, if the time spent at the merging point of the merging line body is longer than the time consuming time of the merging line body to the merging point, then it is determined to complete each When the total time required by the process route model is calculated, the time taken from the imported line body to the import point is updated to the time spent at the import point of the imported line body.

In one technical solution of the above digital twin-based process route optimization method, the corresponding process route is adjusted according to the difference between the target production capacity and the corresponding predicted production capacity, and an optimized process route is obtained, including:

If the difference between the target production capacity and the predicted production capacity is less than a preset threshold, the process route is regarded as an optimized process route.

In one technical solution of the above digital twin-based process route optimization method, the corresponding process route is adjusted according to the difference between the target production capacity and the corresponding predicted production capacity to obtain an optimized process route, including:

If the difference between the target production capacity and the predicted production capacity is greater than a preset threshold, optimize the process structure of each of the process route models initially set by the user, and adjust the single process time of the process route;

Calculate the predicted capacity of the process route model after the optimized process structure, and obtain the difference between the target capacity and the predicted capacity of the process route model after the optimized process structure;

If the difference between the target capacity and the predicted capacity of the process route model after optimizing the process structure is greater than the preset threshold, continue to optimize until the target capacity is met and the difference between the predicted capacity of the process route model after the optimized process structure is less than the preset threshold , then the process route after optimizing the process structure multiple times is used as the optimized process route.

In a second aspect, the present invention provides a digital twin-based process route optimization device, the optimization device comprising:

Building blocks for building multiple process route models of the factory space model in the digital twin;

An acquisition module, configured to acquire production target parameters of each of the process route models, wherein the production target parameters include material master data, target production capacity, standard working hours and daily effective working hours;

Calculation module, used to calculate the predicted production capacity of each said process route model;

An optimization module, configured to adjust a corresponding process route according to the difference between the target production capacity and the corresponding predicted production capacity, so as to obtain an optimized process route.

In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium includes a stored program, wherein, when the program is running, the optimization method described in the first aspect of the present application is executed.

In a fourth aspect, the present application provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the optimization described in the first aspect of the present application through the computer program. method.

The above-mentioned one or more technical solutions of the present application have at least one or more of the following beneficial effects:

In implementing the technical scheme of the present application, a digital twin-based process route optimization method is proposed, which aims at constructing multiple process route models of the factory space model in the digital twin body; obtaining each of the process route models The production target parameters, wherein, the production target parameters include material master data, target production capacity, standard working hours and daily effective working hours; calculate the predicted production capacity of each of the process route models; according to the target production capacity and the corresponding Predict the difference in production capacity and adjust the corresponding process route to obtain an optimized process route. This optimization method uses digital twin technology to simulate the process flow and calculate production efficiency, helping factories compare different process route design schemes, discover the optimization space of the process route, prevent and control foreseeable risks in advance, reduce trial and error costs, and improve production efficiency.

Description of 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 technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, In other words, other drawings can also be obtained from these drawings without paying creative labor.

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 structural diagram of a process route model in a workshop constructed in a data twin according to an embodiment of the present application;

FIG. 3 is a schematic flowchart of the main steps of step S102 according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of the main steps of step S104 according to another embodiment of the present application;

FIG. 5 is a schematic block diagram of the main structure of a digital twin-based process route optimization device according to an embodiment of the present application;

FIG. 6 is a schematic block diagram of a main structure of an electronic device according to an embodiment of the present application.

Detailed ways

In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is an embodiment of a part of the application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

It should be noted that the terms "first" and "second" in the description and claims of the present application and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed instead, may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

Referring to accompanying drawing 1, Fig. 1 is a flow chart of main steps of a digital twin-based process route optimization method according to an embodiment of the present invention. As shown in FIG. 1 , the digital twin-based process route optimization method in the embodiment of the present invention mainly includes the following steps S101 - S104.

Step S101: Construct multiple process route models of the factory space model in the digital twin.

In this embodiment, digital twin technology, as a key trend of intelligent manufacturing, is more and more widely used in the production process. Under the guidance of digital twin technology, the traditional process design has gradually evolved into an intelligent and digital three-dimensional process design, and a new manufacturing model based on the process model has emerged. Digital twin technology provides technical support for product life cycle management, physical space and virtual space information transmission, data sharing, and guidance and prediction of processing processes, and promotes the progress of intelligent manufacturing. Digital Twin (Digital Twin) is a conceptual system for the interaction between the physical world and digital space. The Digital Twin is also continuously deepening and developing in the field of intelligent manufacturing. Before constructing multiple process route models of the factory space model in the Digital Twin, it is necessary to first Obtain the layout of the physical space of the factory, and construct multiple process route models of the factory space model in the digital twin according to the layout of the physical space of the factory. model etc.

Step S102: Obtain the production target parameters of each of the process route models, wherein the production target parameters include material master data, target production capacity, standard working hours and effective daily working hours.

In this embodiment, after constructing multiple process route models of the factory space model in the data twin, the production target parameters of each process route model are obtained, such as obtaining material master data, target production capacity, standard working hours and daily effective work Production target parameters such as duration and other parameters.

Step S103: Calculate the predicted production capacity of each of the process route models.

In this embodiment, the predicted production capacity of each process route model is calculated according to the multiple processes of each process route model and the time required after each production is completed and the daily effective working hours.

Step S104: According to the difference between the target production capacity and the corresponding predicted production capacity, adjust the corresponding process route to obtain an optimized process route.

In this embodiment, according to the difference between the target production capacity and the predicted production capacity, it is determined whether the process route is suitable. If it is suitable, it will be used in practice. If it is not suitable, the process route will be optimized to obtain an optimized process route.

Step S101-step S104 will be further described below.

In an implementation manner of the embodiment of the present invention, the step S101 may further include the following steps Step S1011:

Step S1011: constructing a process route model of a multi-level process structure,

Wherein, the process route model includes one or more process structures, the workshop includes one or more, the line body includes one or more lines, each line body includes a single line body or a multi-line body, and a multi-line body includes Incoming line body and imported line body, each single line body includes one or more processes, each multi-line body includes one or more processes, each process includes one or more stations, and the stations include Single station, serial station and/or parallel station.

In a specific example, as shown in Figure 2, in order to construct multiple process route models from factory to workshop, from workshop to line body and from line body to workstation in the data twin according to the layout of the physical air conditioner of the factory A process route model, the process route model can include multiple process structures, and one of the process structures is used as an example to illustrate. In this process route model, a workshop includes three lines: the first line body L1, the second line body L2 and the third line body L3, among which, the first line body L1 and the second line body L2 are incoming lines, and the third line body Body L3 is the imported line body. The first line body L1, the second line body L2 and the third line body L3 all include multiple processes. In Figure 2, the arrows indicate the direction of the line bodies and the relationship between the line bodies, and the dotted line part represents the process. The circles represent work stations. The first line body L1 includes four processes of l11, l12, l13 and l14, the second line body L2 includes three processes of l21, l22 and L23, and the third line body includes l31, l32, l33, l34, There are six processes in l35 and l36, among which, the process contained in the L2 line body is integrated between the l33 process and the l34 process, and the process contained in the L1 line body is integrated between the l34 process and the l35 process, and each process includes one or more A station includes a single station, a serial station and/or a parallel station. For example, the first process l11 and the third process l13 of the first line body L1 shown in FIG. Station.

In an implementation manner of the embodiment of the present invention, the step S102 may further include the following step S1021:

Step S1021: Obtain the production target parameters of each of the process route models according to the production tact and actual market demand, and input the production target parameters into the process efficiency model in the digital twin, wherein the The process performance model is used to calculate the predicted capacity of the process route model.

In a specific example, production takt, also known as customer demand cycle and production lead time, refers to the ratio of total effective production time to customer demand quantity within a certain period of time, which is the necessary market time for a customer to demand a product. Example: Assuming that there is only one regular shift every day, there are a total of 8 hours (480 minutes). Subtract 30 minutes for lunch, 30 minutes for breaks, 10 minutes for shift changes and 10 minutes for basic maintenance checks. Then available working time = 480-30-30-10-10 = 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's demand. The production takt is actually a kind of target time, which changes with the demand quantity and the effective working time of the demand period, and is artificially formulated. The tempo reflects the adjustment of demand to production. If the demand is relatively stable, the required tempo is also relatively stable. When the demand changes, the tempo will also change accordingly. If the demand decreases, the tempo will become longer, and vice versa. become shorter. Set the production target parameters of each of the process route models according to the production tact and actual market demand, and input the production target parameters into the process performance model in the digital twin, wherein the process performance model Used to calculate the forecast capacity of the routing 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-step S1033:

Step S1031: Calculate the duration of each single process of each of the process route models according to the standard working hours in the production target parameters;

Step S1032: Obtain the process structure of each of the process route models initially set by the user, and determine the total time required to complete each of the process route models according to the process structure;

Step S1033: Calculate the predicted production capacity of each of the process route models as the daily effective working hours divided by the total time required to complete each of the process route models.

Continuing with the above example, calculate the duration of each single process in the process route model shown in Figure 2 according to the standard man-hours in the production target parameters, such as the respective single processes of l11-l14, l21-l23 and l31-l36 Duration, to obtain the process structure of the process route model initially set by the user, such as which stations are selected for production in the l11 serial station process, l13 serial station process, and l34 serial station process, so as to base on the process set by the user The structure determines the total time required to complete the process route model, and obtains the predicted capacity of each process route model based on the daily effective working hours and the total time required to complete each process route model.

In an implementation manner of the embodiment of the present invention, the step S1032 may further include the following step S10321:

Step S10321: When determining the total time required to complete each of the process route models, if the time spent at the merging point of the merging line body is longer than the time consuming time of the merging line body to the merging point, then When the total time required to complete each process route model is determined, the time taken from the imported line body to the import point is updated to the time spent at the import point of the imported line body.

Continuing the above example, the first line body L1 and the second line body L2 shown in Fig. 2 are the incoming line body, and the third line body L3 is the imported line body, and it is determined to complete each said process route model. The duration is the total duration of the line l31-l36. If there is no line body on the l31-l36 line, the total duration of l31+l32+l33+l34+l35+l36 is to complete the process route For the total duration of the model, if there is an incoming line body on the l31-l36 line body, it is necessary to consider the relationship between the time length from the incoming line body to the incoming point and the time length before the incoming point on l31-l36.

For example, in Figure 2, the time taken when the second line body L2 merges into the third line body L3 is the total time of l21+l22+l23, and the import point is after the process l33 of the third line body L3, if the total time of l21+l22+l23 >l31+l32+l33 total time, when determining the total time required for the process route model, the total time l31+l32+l33 takes the total time l21+l22+l23, the first line L1 in Figure 2 The time spent when entering the third line body L3 is the total time of l11+l12+l13+l14, the import point is after the process l34 of the third line body L3, if the total time of l11+l12+l13+l14>l31+l32+ The total time of l33+l34, because the total time of l21+l22+l23>the total time of l31+l32+l33, so the total time of l21+l22+l23 taken by l31+l32+l33 here is l11+l12+l13 The total time of +l14 and the total time of l21+l22+l23+l34, if the total time of l11+l12+l13+l14>the total time of l21+l22+l23+l34, the time needed to determine the process route model When the total time is taken, the total time of l31+l32+l33+l34 is taken as the total time of l11+l12+l13+l14, and the total time needed to determine the process route model is l11+l12+l13+l14+l35+l36 total time.

When comparing the time of the confluence point of the second line body L2 with the time of the confluence point of the third line body L3, if the total elapsed time of l21+l22+l23 is less than the total elapsed time of l31+l32+l33, then the process route model needs to be determined For the total time used, the total time of l31+l32+l33 is taken as the total time of l31+l32+l33, and when comparing the time of the entry point of the first line body L1 with the time of the entry point of the third line body L3, If the total time of l11+l12+l13+l14 is less than the total time of l31+l32+l33+l34, when determining the total time required by the process route model, the total time of l31+l32+l33+l34 is taken as l31+ For the total time of l32+l33+l34, the total time required to determine the process route model is the total time of l31+l32+l33+l34+l35+l36.

In a specific example, the total time required for the process route model is determined, and the daily effective working hours are also determined, then the predicted production capacity of the process route model=the daily effective working time/the total time required for the process route model.

In an implementation manner of the embodiment of the present invention, the step S104 may further include the following step S1041:

If the difference between the target production capacity and the predicted production capacity is less than a preset threshold, the process route is regarded as an optimized process route.

In a specific example, the target production capacity and the predicted production capacity are compared, and if the difference between the target production capacity and the predicted production capacity is less than a preset threshold, then this process route can be used as an optimized process route and can be put into practical application to produce products , which can not only reduce trial and error costs, but also improve production efficiency.

In an implementation of the embodiment of the present invention, as shown in Figure 4, the step S104 may further include the following steps S1041'-step S1043':

Step S1041': If the difference between the target production capacity and the predicted production capacity is greater than a preset threshold, optimize the process structure of each of the process route models initially set by the user, and adjust the single process time of the process route;

Step S1042': Calculate the predicted production capacity of the process route model after the optimized process structure, and obtain the difference between the target production capacity and the predicted capacity of the process route model after the optimized process structure;

Step S1043': If the difference between the target production capacity and the predicted production capacity of the process route model after the optimized process structure is greater than the preset threshold, continue optimization until the difference between the target capacity and the predicted capacity of the process route model after the optimized process structure is met is less than the preset threshold, the process route after optimizing the process structure multiple times is used as the optimized process route.

In a specific example, if the difference between the target production capacity and the predicted production capacity is greater than a preset threshold, optimize the initial process structure of the process route model, and adjust the single process time of the process route in the optimized process structure, Calculate the predicted production capacity after optimizing the process structure again. If the difference between the target production capacity and the predicted production capacity is less than the preset threshold, use the optimized process structure as the optimized process route; if the target production capacity and the predicted production capacity are not satisfied If the difference in production capacity is less than the preset threshold, then continue to optimize until the difference between the target production capacity and the predicted production capacity of the process route model after optimizing the process structure is less than the preset threshold, and the process route after multiple optimized process structures will be used as the optimization Routing.

In the process of multiple optimizations, it is possible that the difference between the target production capacity and the predicted production capacity of the process route model after the optimized process structure will never be less than the preset threshold, then the maximum optimization times will be satisfied. The bottleneck process of the process route in the process route model is displayed, and adjustment suggestions are given.

Based on the above step S101-step S104, the present invention proposes a digital twin-based process route optimization method, which aims to construct multiple process route models of the factory space model in the digital twin; obtain each process route respectively The production target parameters of the route model, wherein the production target parameters include material master data, target production capacity, standard working hours and daily effective working hours; respectively calculate the predicted production capacity of each of the process route models; according to the target production capacity and corresponding The difference of the predicted production capacity is adjusted to obtain an optimized process route by adjusting the corresponding process route. This optimization method uses digital twin technology to simulate the process flow and calculate production efficiency, helping factories compare different process route design schemes, discover the optimization space of the process route, prevent and control foreseeable risks in advance, reduce trial and error costs, and improve production efficiency.

Further, the present application also provides a digital twin-based process route optimization device.

Referring to accompanying drawing 5, Fig. 5 is a main structural block diagram of a digital twin-based process route optimization device 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 construction module 11 , an acquisition module 12 , a calculation module 13 and an optimization module 14 . In some embodiments, one or more of the construction module 11 , the acquisition module 12 , the calculation module 13 and the optimization module 14 can be combined together into one module. In some embodiments, the construction module 11 can be configured to construct multiple process route models of the factory space model in the digital twin; the acquisition module 12 can be configured to obtain the production target parameters of each of the process route models, wherein, The production target parameters include material master data, target production capacity, standard man-hours and daily effective working hours; the calculation module 13 can be configured to calculate the predicted production capacity of each of the process route models; the optimization module 14 can be configured to The difference between the target production capacity and the corresponding predicted production capacity is adjusted to obtain an optimized process route by adjusting the corresponding process route.

In one embodiment, the description of the specific implementation functions may refer to the descriptions in step S101-step S104.

Those skilled in the art can understand that all or part of the process in the method of the above-mentioned embodiment of the present application can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable In the storage medium, when the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be realized. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable Storage media excludes electrical carrier signals and telecommunication signals.

Further, the present application also provides a computer-readable storage medium. In an embodiment of a computer-readable storage medium according to the present application, the computer-readable storage medium may be configured to store a program for executing the digital twin-based process route optimization method of the above method embodiment, the program may be loaded by a processor and run to implement the above-mentioned digital twin-based routing optimization method. For ease of description, only the parts related to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The computer-readable storage medium may be a memory device formed by various electronic devices. Optionally, the computer-readable storage medium in this embodiment of the present application is a non-transitory computer-readable storage medium.

Further, the present application also provides an electronic device. In an embodiment of an electronic device according to the present application, as shown in FIG. 6 , the electronic device includes a processor and a memory, and the memory can be configured to store a program for executing the digital twin-based process route optimization method of the above method embodiment, processing The device may be configured to execute the program in the memory, the program includes but not limited to the program for executing the digital twin-based process route optimization method of the above method embodiment. For ease of description, only the parts related to the embodiments of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The electronic device may be a control device device formed including various electronic devices.

Further, it should be understood that since the setting of each module is only to illustrate the functional units of the device of the present application, the physical device corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or Part of a combination of software and hardware. Therefore, the number of each module in the figure is only illustrative.

Those skilled in the art can understand that each module in the device can be split or combined adaptively. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present application, therefore, the technical solutions after splitting or merging will all fall within the protection scope of the present application.

The above description is only the preferred embodiment of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present application, some improvements and modifications can also be made. These improvements and modifications are also It should be regarded as the protection scope of this 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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