A digital twin workshop space-time deduction and verification method for mixed flow production
By constructing a digital twin model and process tree, the problem of difficulty in verifying production plans in mixed-flow production is solved, enabling rapid prediction and verification of production results and supporting high-frequency dynamic production decisions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIHANG UNIV
- Filing Date
- 2026-03-12
- Publication Date
- 2026-06-19
AI Technical Summary
In mixed-flow production, traditional production control methods are difficult to adapt to dynamic scenarios, leading to production congestion, resource waste, and order delivery delays, and making it impossible to quickly predict and verify production results.
Construct digital twin models of production equipment and processes, assemble digital twin models of mixed-flow production workshops, and realize spatiotemporal simulation and verification of digital twin workshops through production process tree and time window, simulating the production process and quickly verifying production plans.
It enables rapid prediction and verification of mixed-flow production results in the virtual world, providing high-frequency dynamic production decision support and avoiding production congestion and resource waste.
Smart Images
Figure CN122243310A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of digital twin and intelligent manufacturing technology, specifically relating to a method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production. Background Technology
[0002] The personalized market demands that the manufacturing industry possess flexible and customized capabilities. Against this backdrop, mixed-flow production has gradually become the mainstream production method for products such as automobiles, 3C electronics, and high-end equipment. Its core advantage lies in the ability to process multiple varieties and small batches of products concurrently on a single production line, enabling rapid response to fluctuations in market demand. However, mixed-flow production, influenced by order demands, may encounter complex problems such as frequent product changes, conflicts in equipment resource sharing, dynamic changes in material delivery paths, and uneven production rhythms. This makes traditional static planning-based production control methods difficult to adapt to real-time dynamic scenarios, and production control schemes are difficult to verify accurately in real time. It is also difficult to accurately predict and evaluate production results before executing production plans, easily leading to consequences such as production congestion, resource waste, and order delivery delays. The spatiotemporal simulation and verification method of digital twin workshops provides technical support for solving these problems. Summary of the Invention
[0003] To address the aforementioned technical problems, this invention proposes a method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production. This method comprises five steps: constructing a digital twin model of production equipment, constructing a digital twin model of the production process, assembling a digital twin model of the mixed-flow production workshop, constructing a production process tree, and performing spatiotemporal simulation and verification of the mixed-flow production digital twin workshop. This invention can effectively address the frequently changing production demands of mixed-flow production workshops and solve the problems of being unable to quickly predict mixed-flow production results and quickly verify production plans.
[0004] The technical problem solved by this invention is achieved through the following technical solution: a method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production, comprising the following steps:
[0005] Step 1: Digitally and formally describe the appearance, function, behavior, and operating mechanism of the production equipment, and construct a digital twin model of the production equipment;
[0006] Step 2: Digitally formalize the production process flow, the equipment that can realize each process, and the implementation process of each process on different equipment, and build a digital twin model of the production process;
[0007] Step 3: Based on the digital twin model of the production process and the digital twin model of the assembly production equipment according to production requirements, construct a digital twin model of the mixed-flow production workshop;
[0008] Step 4: Construct a production process tree based on the digital twin model of the production process and the digital twin model of the mixed-flow production workshop;
[0009] Step 5: Realize the spatiotemporal simulation and verification of the mixed-flow production digital twin workshop based on time windows.
[0010] The advantages of this invention compared to the prior art are:
[0011] By constructing digital twin models of mixed-flow production equipment and production processes, the production process in a mixed-flow production workshop can be simulated in a virtual world, displaying details of the production process from both temporal and spatial dimensions. By building a production process tree, idle time and computing power can be used to simulate and analyze the mixed-flow production process in advance. When production orders or processes change, the spatiotemporal extrapolation and verification method for digital twin workshops oriented towards mixed-flow production can achieve rapid extrapolation of future production processes and rapid verification of workshop capacity and production plans, thus providing effective support for production decisions and execution to meet high-frequency dynamic production demands. Attached Figure Description
[0012] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.
[0014] This invention discloses a method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production. The method includes six steps: system digital twin modeling, agent construction, flexible control implementation, configuring multiple decision-making strategies for the agent, setting strategy selection rules for the agent, and constructing a decision and flexible control knowledge base.
[0015] like Figure 1 As shown, the specific implementation method is as follows:
[0016] Step 1: Digitally and formally describe the appearance, function, behavior, and operating mechanism of the production equipment, and construct a digital twin model of the production equipment;
[0017] Step 2: Digitally formalize the production process flow, the equipment that can realize each process, and the implementation process of each process on different equipment, and build a digital twin model of the production process;
[0018] Step 3: Based on the digital twin model of the production process and the digital twin model of the assembly production equipment according to production requirements, construct a digital twin model of the mixed-flow production workshop;
[0019] Step 4: Construct a production process tree based on the digital twin model of the production process and the digital twin model of the mixed-flow production workshop;
[0020] Step 5: Realize the spatiotemporal simulation and verification of the mixed-flow production digital twin workshop based on time windows.
[0021] Furthermore, step 1 includes:
[0022] Step 1.1: The entire mixed-flow production workshop is broken down into the smallest functional units that cannot be further subdivided, with each functional unit having two categories: active functions and passive functions. For example, machine tools have active functions such as cutting, drilling, and punching, while shelves have passive functions such as carrying goods.
[0023] Step 1.2: Construct geometric models using SolidWorks and Unity3D software to recreate the spatial location and shape of equipment, storage facilities, and production lines. Faithfully describe the geometric features, spatial location, and operational posture of each functional unit. Geometric features include the shape, geometric structure, and dimensions of the functional unit; spatial location refers to the coordinates of the functional unit in the Cartesian coordinate system at a given moment; and operational posture refers to the angle of rotation of the functional unit around the x, y, and z axes of the spatial coordinate system relative to a reference posture at a given moment. The software used must have simulation and derivation capabilities.
[0024] Step 1.3: Based on the geometric model of the functional unit, incrementally digitize and formalize its operating mechanism, behavioral response, production function, and rules to form a digital twin model of the functional unit. The operating mechanism includes physical laws and the principles of functional unit function implementation; the behavioral response includes the rules governing the transition of the functional unit's operating state under different external inputs; the production function refers to the processing tasks that the functional unit can perform and the degree of their achievement; and the rules refer to the relationships, constraints, and derivations between parameters based on production data mining and expert experience input. The above digitization and formalization of the operating mechanism, behavioral response, production function, and rules can be implemented using different software and programming languages, but compatibility and interoperability between different software are required. By binding these digitized description files to the geometric model of the functional unit, a complete digital twin model of the functional unit is constructed.
[0025] Furthermore, step 2 includes:
[0026] Step 2.1: Construct processing procedure models based on tree diagrams for different product models, and connect all processing procedure models in series or parallel to form the main body of the final digital twin model of the production process. The processing procedure model is a precise digital description of the processing flow and processing operation details and requirements. The software used must be compatible with and interactive with the digital twin workshop simulation software.
[0027] Step 2.2: Based on semantic matching and string matching methods, select a set of functional units with corresponding processing capabilities for each process from the constructed functional unit digital twin model;
[0028] Step 2.3: With the goal of completing the process, construct production capacity evaluation indicators for different processes, conduct targeted evaluation of the production capacity of functional units that can realize the process, and add the quantified process completion capacity value as the core attribute of the equipment in the process to the processing process model;
[0029] Step 2.4: Based on historical operation data analysis, add production process details and constraints of different functional units to the model of each processing step as a supplement to the process completion capability;
[0030] Step 2.5: For processes or steps that require the collaborative operation of multiple functional units to complete, incrementally describe the equipment interaction logic and interaction constraints in the production process model, and add different combinations of functional units as a whole into the processing process model.
[0031] Furthermore, step 3 includes:
[0032] Step 3.1: Configure the digital twin model parameters of the equipment according to the current equipment composition and layout of the mixed-flow production workshop, including the position, orientation, and production tasks being performed by each functional unit;
[0033] Step 3.2: Initialize the production process digital twin model based on production needs and the current production process, and update the production process digital twin model for each work-in-process product.
[0034] Step 3.3: Integrate the digital twin models of each functional unit and the digital twin models of each product's manufacturing process to form a digital twin model of the mixed-flow production workshop.
[0035] Furthermore, step 4 includes:
[0036] Step 4.1: In the simulation environment, based on the current... The root node of the production process tree is constructed by monitoring the equipment status and task completion status of the mixed-flow production workshop at all times. ;
[0037] Step 4.2: Based on the digital twin model of the production process and the digital twin model of the mixed-flow production workshop, conduct spatiotemporal simulation of the digital twin workshop. Through accelerated simulation, at appropriate time intervals... Generate several new backbone nodes to Main node For a moment Status of each piece of equipment and task completion in the mixed-flow production workshop;
[0038] Step 4.3: During the expansion of the main node, introduce possible events such as changes in production tasks, priorities, equipment performance degradation, and changes in production plans to create new branch root nodes. The root node of the branch is at time 10:00. In a specific event The status of each piece of equipment and the completion of tasks in the mixed-flow production workshop at the time of the incident;
[0039] Step 4.4: Based on the probability of an event occurring, prioritize the deduction of the branch nodes on the corresponding branches until the time limit for spatiotemporal deduction is reached or the phased production task is completed, and finally generate leaf nodes. The leaf nodes represent the final status of each piece of equipment and the completion status of the task in the mixed-flow production workshop when a specific combination of events occurs.
[0040] Furthermore, step 5 includes:
[0041] Step 5.1: Based on a draggable time window parallel to the process tree trunk, browse the equipment status of the mixed-flow production workshop within a specified time period to realize the spatiotemporal simulation of the digital twin workshop;
[0042] Step 5.2: Construct the leaf nodes of the production process tree based on the equipment status of the mixed-flow production workshop, and directly read the production prediction results under different combinations of events and production schemes to realize the verification of the production scheme of the mixed-flow production digital twin workshop.
[0043] The contents not described in detail in this specification are existing technologies known to those skilled in the art.
[0044] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for spatiotemporal simulation and verification of a digital twin shop floor for mixed-flow production, characterized in that, include: Step 1: Digitally and formally describe the appearance, function, behavior, and operating mechanism of the production equipment, and construct a digital twin model of the production equipment; Step 2: Digitally formalize the production process flow, the equipment that can realize each process, and the implementation process of each process on different equipment, and build a digital twin model of the production process; Step 3: Based on the digital twin model of the production process and the digital twin model of the assembly production equipment according to production requirements, construct a digital twin model of the mixed-flow production workshop; Step 4: Construct a production process tree based on the digital twin model of the production process and the digital twin model of the mixed-flow production workshop; Step 5: Realize the spatiotemporal simulation and verification of the mixed-flow production digital twin workshop based on time windows.
2. The method for spatiotemporal simulation and verification of a digital twin shop floor for mixed-flow production as described in claim 1, characterized in that, Step 1 includes: Step 1.1: Break down the entire mixed-flow production workshop into the smallest functional units that cannot be further subdivided, based on the production equipment. Step 1.2: Use SolidWorks and Unity3D software to construct a three-dimensional geometric model to recreate the spatial location and shape of equipment, warehouses, and production lines; Step 1.3: Based on the three-dimensional model of the physical entity, incrementally digitize and formalize its operating mechanism, behavioral response, production function, and rules to form a digital twin model of the production equipment.
3. The method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production as described in claim 1, characterized in that, Step 2 includes: Step 2.1: Construct processing procedure models based on tree diagrams for different product models, and connect or parallel all processing procedure models to form the final digital twin model of the production process. Step 2.2: Based on semantic matching and string matching methods, select equipment with corresponding processing capabilities for each process from the constructed digital twin model of production equipment; Step 2.3: With the goal of completing the process, construct production capacity evaluation indicators for different processes, conduct targeted evaluation of the production capacity of the production equipment that can realize the process, and add the quantified process completion capacity value as the core attribute of the equipment in the process to the processing process model; Step 2.4: Based on historical operation data analysis, add production process details and constraints for different equipment to each processing step model as a supplement to the process completion capability; Step 2.5: For processes or steps that require the collaborative operation of multiple functional units to complete, incrementally describe the equipment interaction logic and interaction constraints in the production process model, and add different combinations of functional units as a whole into the processing process model.
4. The method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production as described in claim 1, characterized in that, Step 3 includes: Step 3.1: Configure the digital twin model parameters of the production equipment according to the current equipment composition and layout of the mixed-flow production workshop, including the equipment's location, orientation, and the production tasks it is currently performing; Step 3.2: Initialize the production process digital twin model based on production needs and the current production process, and update the production process digital twin model for each work-in-process product. Step 3.3: Integrate the digital twin models of each production equipment and the digital twin models of each product's production process to form a digital twin model of the mixed-flow production workshop.
5. The method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production as described in claim 1, characterized in that, Step 4 includes: Step 4.1: In the simulation environment, based on the current... Constructing the root node of the production process tree based on the real-time equipment status and task completion status of the mixed-flow production workshop. ; Step 4.2: Based on the digital twin model of the production process and the digital twin model of the mixed-flow production workshop, conduct a spatiotemporal simulation of the digital twin workshop. Accelerate the simulation at appropriate time intervals. Generate several new backbone nodes to Main node For a moment Status of each piece of equipment and task completion in the mixed-flow production workshop; Step 4.3: During the expansion of the main node, introduce possible events such as changes in production tasks, changes in priorities, equipment performance degradation, and changes in production plans to create new branch root nodes. Branch root nodes For at any time In a specific event The status of each piece of equipment and the completion status of tasks in the mixed-flow production workshop at the time of the incident; Step 4.4: Based on the probability of an event occurring, prioritize the deduction of the root nodes of the corresponding branches until the time limit for spatiotemporal deduction is reached or the phased production task is completed, and finally generate leaf nodes. The leaf nodes represent the final status of each piece of equipment and the completion status of the task in the mixed-flow production workshop when a specific combination of events occurs.
6. The method for spatiotemporal simulation and verification of a digital twin shop floor for mixed-flow production as described in claim 1, characterized in that, Step 5 includes: Step 5.1: Based on a draggable time window parallel to the process tree trunk, browse the equipment status of the mixed-flow production workshop within a specified time period to realize the spatiotemporal simulation of the digital twin workshop; Step 5.2: Construct the leaf nodes of the production process tree based on the equipment status of the mixed-flow production workshop, and directly read the production prediction results under different combinations of events and production schemes to realize the verification of the production scheme of the mixed-flow production digital twin workshop.
7. The method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production as described in claim 2, characterized in that, In step 1.1, the functions of each functional unit are divided into two categories: active functions and passive functions. For example, machine tools have active functions such as cutting, drilling, and punching, while shelves have passive functions such as carrying goods.
8. The method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production as described in claim 2, characterized in that, In step 1.2, a geometric model is constructed using SolidWorks and Unity3D software to describe the geometric features, spatial position, and operating posture of each functional unit. The geometric features include the shape, geometric structure, and size of the functional unit. The spatial position refers to the coordinates of the functional unit in the Cartesian coordinate system at a certain moment. The operating posture refers to the angle of rotation of the functional unit around the x, y, and z axes in the spatial coordinate system at a certain moment relative to the reference posture.
9. The method for spatiotemporal simulation and verification of a digital twin workshop for mixed-flow production as described in claim 2, characterized in that, In step 1.3, based on the geometric model of the functional unit, incremental digital and formal descriptions are performed on it from the aspects of operation mechanism, behavior response, production function, and regular rules to form a digital twin model of the functional unit. The operation mechanism includes physical laws and the principle of functional unit function realization. The behavior response includes the rules for the change of the functional unit's operating state under different external inputs. The production function refers to the processing tasks that the functional unit can achieve and the degree of achievement. The regular rules refer to the correlation, constraint and derivation relationships between parameters based on production data mining and expert experience input.
10. The method for spatiotemporal simulation and verification of a digital twin shop floor for mixed-flow production as described in claim 9, characterized in that, In step 1.3, the digital and formal descriptions of the operating mechanism, behavioral response, production function, and rules are implemented based on different software and programming languages. The different software is compatible and interactive. By binding these digital description files with the geometric model of the functional unit, the construction of a complete digital twin model of the functional unit is realized.