Intelligent workshop digital twin modeling method and simulation system thereof

Abstract

The invention discloses an intelligent workshop digital twin modeling and simulation system, and belongs to the technical field of intelligent manufacturing. The system comprises a multi-dimensional workshop modeling and management module, an intelligent analysis and decision module and a data management module. The multi-dimensional workshop modeling and management module dynamically displays and generates a scheduling flow through two-dimensional and three-dimensional models which are highly consistent with the actual workshop layout in combination with a production scheduling scheme; the intelligent analysis and decision module processes and analyzes data by applying a scheduling algorithm, optimizes a production process and updates scheduling information in real time; the data management module is based on a server, transmits and integrates data of the multi-dimensional workshop modeling and management module and the intelligent analysis and decision support module, realizes seamless communication between the modules, and ensures consistency and real-time performance of the data. The combination of the three modules effectively solves the problems of insufficient real-time performance, difficulty in comprehensive reaction of data complexity, poor user experience and the like in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent manufacturing, and specifically relates to a digital twin modeling method for an intelligent workshop and a simulation system thereof. Background Art

[0002] In the field of smart workshop technology, workshop production scheduling is a key factor affecting production efficiency and data utilization. However, traditional production scheduling methods often rely on experience and static data, which makes it difficult to cope with complex and changing production environments. With the development of Industry 4.0 and smart manufacturing, digital twin technology has gradually become an effective means to solve this problem.

[0003] Digital Twin technology combines physical entities with virtual models to achieve real-time monitoring and dynamic simulation of physical entities. In the manufacturing industry, digital twin technology can conduct all-round digital modeling of the physical equipment, production processes and environment of the workshop, thereby achieving real-time monitoring, prediction and optimization of the production process, which provides new ideas and tools for production scheduling.

[0004] Existing digital twin workshop production modeling methods mainly focus on the following aspects:

[0005] (1) Data collection and fusion: Various data from workshop equipment and production processes are collected through sensors, IoT devices, etc., and these data are integrated with the two-dimensional and three-dimensional models of the workshop to achieve holographic perception of the workshop;

[0006] (2) 2D and 3D modeling: Use CAD, BIM and other technologies to model the workshop in 2D and 3D to generate virtual models that are consistent with the actual workshop. These models can not only display the layout and equipment of the workshop, but also simulate various dynamic changes in the production process, such as material flow, production process and fault conditions;

[0007] (3) Scheduling simulation system: This system combines the production scheduling algorithm with the digital twin technology to evaluate the feasibility and optimization effects of different scheduling schemes through the simulation operation of the virtual workshop, thereby guiding the actual production scheduling.

[0008] Although the existing digital twin technology has made significant progress in workshop modeling and scheduling optimization, there are still some shortcomings:

[0009] ① Real-time: How to achieve real-time monitoring and dynamic adjustment of the workshop production process is still a technical challenge;

[0010] ② Complexity: The production environment of the workshop is complex and changeable, and traditional modeling and simulation methods are difficult to fully and accurately reflect the actual situation;

[0011] ③User experience: Existing systems are often complex to operate and require high levels of professional knowledge from users, which is not conducive to popularization and application.

[0012] China's invention patent CN117978827A discloses a digital twin workshop data acquisition method and equipment, which builds a workshop data acquisition system independent of the digital twin system, creates basic information of the equipment data source in the workshop data acquisition system, connects the data source and the industrial network server through the industrial network protocol, automatically parses the collected underlying data, and sends it to the database in the workshop industrial network for storage; builds the processing history of the data source, automatically retrieves the data in the specified area of ​​the database, and fills it in the front-end display chart of each device in real time; and automatically sends the data processed by the processing history to the digital twin system. The system focuses on data collection, processing history display and the responsiveness of the physical workshop status. Although it improves the real-time and accuracy of data acquisition, it still has deficiencies in production scheduling flexibility and complex time processing. This technical solution fails to solve how to deal with emergencies in complex production environments, especially the dynamic impact of interference factors on scheduling plans, and its scheduling system is relatively passive and difficult to respond quickly to changing production conditions. Summary of the invention

[0013] In order to overcome at least one of the shortcomings of the prior art, the present invention provides a digital twin workshop modeling method and a scheduling simulation system thereof, which can solve the problems of insufficient real-time performance, difficulty in fully reflecting complexity, and poor user experience in the prior art, so as to cope with the analysis difficulties brought about by the changing production methods and data complexity.

[0014] In order to achieve the purpose of the present invention, the present invention proposes a smart workshop digital twin modeling and simulation system, including:

[0015] A multi-dimensional workshop modeling and management module is used to generate a multi-dimensional workshop model, including a factory modeling submodule, an interference setting submodule, a process management submodule, an order management submodule and a production scheduling submodule. The factory modeling submodule is used to manage the two-dimensional and three-dimensional digital models of the factory and support the dynamic scheduling display of the digital production line. The interference setting submodule is used to set the state of the workshop model and equipment, simulate and manage various factors that affect the physical state of the workshop, allow different types of interference to be set and triggered by inputting interference parameters, and send the response containing the set interference parameters to the scheduling algorithm submodule to trigger a new scheduling calculation. The process management submodule is used to display, query and edit process information, define and optimize the logical flow of the production process, and the order management submodule is used to display, query and edit order information, and handle the entire process from order receipt to preliminary processing. The production scheduling submodule is used to optimize and manage the production process and generate an optimized production plan;

[0016] The data management module is responsible for data storage and transmission;

[0017] The intelligent analysis and decision support module includes a scheduling algorithm submodule, which is used to generate an initial production scheduling plan based on task requirements and the selected scheduling algorithm and store it in the data storage submodule. It can also re-optimize the existing production scheduling based on the received interference parameters and generate a new scheduling plan.

[0018] Among them, the multi-dimensional workshop modeling and management module uses two-dimensional and three-dimensional models that are highly consistent with the actual workshop layout and combines them with the production scheduling plan to dynamically display the production scheduling process; the intelligent analysis and decision support module uses scheduling algorithms to process and analyze data, optimizes the production process and updates the scheduling information in real time; the data management module, in a local area network environment, transmits and integrates the data of the multi-dimensional workshop modeling and management module and the intelligent analysis and decision support module through the server, realizes seamless communication between modules, and ensures data consistency and real-time performance.

[0019] Furthermore, the factory modeling submodule can construct and manage the two-dimensional and three-dimensional layout drawings of the factory, and can also generate the corresponding factory model by uploading existing layout drawing files.

[0020] Furthermore, the scheduling algorithm submodule is used to generate an initial production scheduling plan according to the task requirements and the selected scheduling algorithm and store it in the data storage submodule, and can re-optimize the existing production scheduling according to the received interference parameters to generate a new scheduling plan.

[0021] Furthermore, the interference includes but is not limited to equipment failure, setting up alternative equipment, and setting up material delays.

[0022] Furthermore, the interference setting submodule will also store the interference parameters and related response results in the relevant data table of the data storage submodule for subsequent analysis and tracking.

[0023] Furthermore, in the process management submodule, in terms of process route design, users can define the complete processing flow of the product; in terms of process parameter management, users can set and adjust the specific parameters of each process; in terms of process optimization analysis, they can conduct a systematic analysis of the production process and output a comprehensive evaluation report.

[0024] Furthermore, after receiving the initial scheduling plan transmitted from the scheduling algorithm submodule, the production scheduling submodule performs optimization and adjustment according to the actual production situation and constraints. The constraints include but are not limited to: equipment operating capacity, process priority, material supply, resource allocation, and production order deadline.

[0025] Furthermore, the optimized and adjusted scheduling plan is automatically updated to the multi-dimensional workshop model in the system, and a Gantt chart for monitoring is generated to visually display the time schedule of each process, equipment and resource, realizing comprehensive monitoring and dynamic management of the generation process.

[0026] Furthermore, the data management module includes a data storage submodule and a data transmission submodule. The data storage submodule is used to store all data of the multi-dimensional workshop modeling and management module and the intelligent analysis and decision support module (including factory layout, process management, order information, interference parameters, etc.) and store them in the database for calling by other submodules. The data transmission submodule is used to establish a data interaction channel between the database and other submodules.

[0027] Furthermore, the data transmission sub-module maps the multi-dimensional workshop modeling and management module with the data management module, and the data management module with the intelligent analysis and decision support module through the API interface unit, to ensure seamless data transmission between sub-modules and ensure data consistency and real-time performance.

[0028] Compared with the prior art, the present invention can at least achieve the following beneficial effects:

[0029] (1) The technical solution of the embodiment of the present invention includes a multi-dimensional workshop modeling and management module, an intelligent analysis and decision support module, and a data management module. These three modules are closely integrated to form an overall solution. It is highly integrated and has strong scalability and flexibility, so that it can adapt to different application scenarios and needs, help improve the delivery efficiency of customized products in workshops, and can realize intelligent production in workshops in a simpler and more efficient manner.

[0030] (2) The present invention solves the problem of coping with emergencies and handling complex time dimensions in a complex production environment through close collaboration between the interference setting submodule and the scheduling algorithm submodule.

[0031] The interference setting submodule allows users to flexibly set and simulate various emergencies and interference factors that may affect workshop production, including equipment failure, material supply delay, environmental parameter fluctuation, etc. Users can set the start time, duration and interference parameters such as alternative equipment of the failure on the system interface. The input interference data can be processed by the system in real time and passed to the scheduling algorithm submodule. After receiving the interference parameters, the scheduling algorithm submodule can immediately perform dynamic scheduling optimization according to the current production status and the time parameters set by the user. The scheduling algorithm submodule analyzes the existing production process and combines the input equipment failure time, material delay and other factors to regenerate a new scheduling plan to ensure that production can be quickly adjusted when interference events occur. For example, when an equipment failure occurs, the scheduling algorithm will automatically arrange for alternative equipment to take over the production task to ensure that production is not interrupted. This dynamic scheduling mechanism greatly enhances the system's ability to cope with complex environmental changes and emergencies. Through this flexible setting and scheduling mechanism, the system can not only respond to emergencies in production in real time, but also effectively handle interference in different time dimensions. Whether it is a short-term equipment failure or a long-term material delay, the system can optimize scheduling based on time information to ensure continuity and efficiency in the production process.

[0032] (3) To address the problem of insufficient real-time performance, the present invention uses an efficient data transmission mechanism adopted by the data transmission submodule to ensure that the system's data interaction has strong real-time performance and consistency.

[0033] The data transmission submodule can ensure that data between modules of the system can be transmitted in a universal and easy-to-parse manner by designing a unified API interface and using standardized JSON and XML data formats. This standardized processing provides basic universality for data transmission and improves transmission efficiency.

[0034] The present invention can realize an efficient asynchronous data transmission mechanism by using RabbitMQ message queue. The queue entry and exit operations of the message queue can asynchronously process data requests and instructions between different modules, ensuring that system data can be transmitted in real time during the production process. This mechanism can significantly reduce delays, ensuring that the scheduling algorithm module and the analysis module can obtain production data in real time and make rapid responses. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a block diagram of the components of a digital twin workshop modeling system provided by an embodiment of the present invention.

[0036] Figure 2 It is a flowchart of the scheduling algorithm submodule in an embodiment of the present invention.

[0037] Figure 3 It is a data interaction architecture diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1 The present invention provides a smart workshop digital twin modeling and simulation system, which includes three parts: a multi-dimensional workshop modeling and management module, an intelligent analysis and decision support module, and a data management module.

[0040] The multi-dimensional workshop modeling and management module builds a comprehensive digital model including physical, logical and functional dimensions, providing a foundation for the intelligent analysis and decision support module; the intelligent analysis and decision support module is based on the multi-dimensional workshop digital model, combined with the actual production data uploaded to the data management module by the user through the WEB browser to conduct comprehensive performance analysis and production optimization. Through in-depth analysis of key indicators such as production bottlenecks, resource utilization, and equipment status, the intelligent analysis and decision support module generates a new production scheduling plan and feeds the plan back to the multi-dimensional workshop model, so that the multi-dimensional workshop model executes the production simulation process based on the scheduling plan, thereby verifying the optimization effect and scheduling feasibility; the data management module serves as the hub, storing and managing the production data uploaded by users and all data generated by the other two modules, ensuring seamless transmission, consistency and real-time performance of data through the API interface, realizing the organic combination of the three modules, and jointly promoting the efficient operation and continuous optimization of the smart workshop.

[0041] The present invention is further described in detail below in conjunction with specific embodiments:

[0042] (1) Multi-dimensional workshop modeling and management module

[0043] Through the analysis of many production factors of the physical workshop, this module can be divided into three dimensions: physical, logical, and functional, and the multi-dimensional and multi-level twin model of the workshop can be constructed and managed respectively. The construction of the twin model depends on the five key sub-modules in the multi-dimensional workshop modeling and management module (these five sub-modules are closely related to the model in the factory modeling sub-module, and can directly or indirectly affect the state of the model in different ways. For example, the interference setting sub-module can set the state of the workshop model and equipment, affecting the physical performance of the model; while the process management sub-module and the order management sub-module affect the process execution and order priority of the model through the logical dimension; the production scheduling sub-module optimizes resource allocation and production rhythm through the functional dimension, thereby dynamically adjusting the overall operation state of the model. This multi-dimensional interaction enables the twin model to more accurately reflect the actual situation of the workshop and provide support for decision-making). Among them, the factory modeling sub-module and the interference setting sub-module constitute the physical dimension of this embodiment, the process management sub-module and the order management sub-module constitute the logical dimension of this embodiment, and the production scheduling sub-module constitutes the functional dimension of this embodiment.

[0044] The factory modeling submodule is used to construct and manage two-dimensional and three-dimensional digital models of the factory. The digital models include but are not limited to machines involved in processing and production in the workshop, automatic guided vehicles (AGV, Automated Guided Vehicle), walls, buffer zones, processed workpieces, etc.

[0045] In some embodiments of the present invention, there are two modeling methods in the factory modeling sub-module: the user can build and edit the factory layout diagram in the current page and save the generated model locally; or upload an existing layout diagram file, and the system automatically parses and generates a corresponding factory model.

[0046] In some embodiments of the present invention, the acquisition and construction of geometric model files can be achieved through a variety of channels, including but not limited to: standard models directly provided by equipment manufacturers, purchase of professional 3D resource libraries, independent design using professional 3D modeling software such as CAD, and integration of models obtained by the above methods. Users can upload model files through a browser interface and edit them online.

[0047] The factory modeling submodule supports dynamic scheduling display of digital production lines: this is achieved by receiving the production scheduling plan transmitted by the production scheduling submodule. In some embodiments of the present invention, the scheduling plan includes the movement coordinate information and other key parameters of the machine. The machine, AGV and other equipment models in the model will move and update their positions in real time according to these coordinate information, intuitively presenting the execution process of the production plan.

[0048] The interference setting submodule is used to set the status of the workshop model and equipment, and to simulate and manage various factors that affect the physical status of the workshop, including equipment status, environmental parameters, material supply and personnel factors, etc., allowing users to set and trigger different types of interference through the browser interface. In some embodiments of the present invention, the interference includes but is not limited to equipment failure (users can set whether the equipment fails, the start time and duration of the failure), alternative equipment (users can set alternative machines for failed equipment to take over related production tasks), and material delays (users can set the delay time or interruption of material supply).

[0049] The interference parameters (such as the start time of equipment failure, fault duration, and alternative equipment) entered by the user through the interference setting submodule will be processed in real time. Subsequently, the interference setting submodule will send a response containing these set interference parameters to the scheduling algorithm submodule to trigger a new scheduling calculation. The scheduling algorithm submodule will re-optimize the existing production schedule based on the received interference parameters and generate a new scheduling plan to ensure that the production process can continue smoothly under interference conditions. In addition, the interference setting submodule will also store these interference parameters and related response results in the relevant data tables of the data storage submodule for subsequent analysis and tracking, ensuring that the historical records of all interference events and scheduling plans can be fully preserved.

[0050] The process management submodule is used to display, query and edit process information; it is used to define and optimize the logical flow of the production process, including process route design, process parameter management and process optimization analysis. Specifically, in terms of process route design, users can define the complete processing flow of the product, including the process sequence, workpiece number, required equipment and estimated time of each workpiece; in terms of process parameter management, users can set and adjust the specific parameters of each process, such as process preparation time, waiting time, equipment operation speed, material consumption, material replenishment frequency, workpiece transmission speed, etc.; in terms of process optimization analysis, through a systematic analysis of the production process, a comprehensive evaluation report is output, including multiple key indicators and analysis results, such as: production efficiency indicators (equipment utilization, cycle time, timely delivery rate, etc.), bottleneck analysis report, equipment performance prediction report, etc. For the process information uploaded by the user, the process management submodule will first parse and verify this information, and then store it in the relevant database table of the data storage submodule for subsequent calls. For example, when the scheduling algorithm submodule generates a new scheduling plan, it will perform scheduling calculations based on these process information to ensure that the production plan meets the set process route and parameter requirements.

[0051] The order management submodule is used to display, query and edit order information; it is used to handle the entire process from order receipt to preliminary processing, including order information management and production plan generation. Specifically, in some embodiments of the present invention, users can upload and edit order task information through a browser, including task name, number, order date, delivery date, order quantity, process table, etc. The order management submodule can perform preliminary classification and priority sorting of orders, and provide necessary order data support for subsequent production scheduling.

[0052] The production scheduling submodule is used to optimize and manage the production process and to generate an optimized production plan based on the constraints of process information and order requirements. After receiving the initial scheduling plan transmitted from the scheduling algorithm submodule, the production scheduling submodule performs optimization adjustments according to the actual production situation and constraints. In some embodiments of the present invention, the constraints include but are not limited to: the operating capacity of the equipment, the priority of the process, the material supply situation, the resource allocation, the deadline of the production order, etc.

[0053] During the optimization process, the production scheduling submodule is dynamically adjusted based on the preset constraints in the system (such as equipment capacity, process priority, resource availability, etc.). The optimized scheduling plan is automatically updated to the multi-dimensional workshop model in the system, and a Gantt chart for monitoring is generated to visualize the time schedule of each process, equipment and resource, realizing comprehensive monitoring and dynamic management of the generation process.

[0054] (2) Intelligent analysis and decision support module

[0055] like Figure 2 As shown, the intelligent analysis and decision support module integrates scheduling algorithms and intelligent analysis technologies, receives instructions from the multi-dimensional workshop modeling and management module, conducts in-depth analysis and optimization based on historical and real-time data in the database, and finally generates a scheduling plan and provides decision analysis.

[0056] Specifically, the intelligent analysis and decision support module first analyzes the instructions of the order management submodule in the multi-dimensional workshop modeling and management module, identifies the keywords, parameters and constraints in the instructions, and classifies the instructions into different types of tasks, such as production scheduling, resource allocation, etc., according to the analysis results. At the same time, it assigns priorities to each task type and establishes dependencies and execution orders between tasks. Then, according to the task requirements, the required data type is determined, and the relevant data is extracted from the database through the server and transmitted to the scheduling algorithm submodule. The scheduling algorithm submodule generates the initial production scheduling plan based on the task requirements and the selected scheduling algorithm (such as genetic algorithm, ant colony algorithm, particle swarm optimization, etc.) and stores it in the data storage submodule for subsequent production scheduling submodule calls.

[0057] The keywords of the instruction include product type, order priority and optimization target (maximum completion time, average flow time, total delay time or total machine load).

[0058] The parameters of the instruction include the settings of specific algorithm parameters, for example, the specific parameters of the genetic algorithm include the crossover factor, mutation factor, number of iterations and population size; the specific parameters of the ant colony algorithm include the pheromone importance factor (α), heuristic factor (β), pheromone volatility coefficient (ρ), ant colony size and number of iterations; the specific parameters of the particle swarm optimization algorithm include the inertia weight (ω), individual learning factor (c1), social learning factor (c2), number of particles and number of iterations.

[0059] The constraints of the instructions include workpiece scheduling constraints (each workpiece needs to be processed on a specified machine), machine availability constraints (a machine can only process one workpiece at a time), AGV transportation constraints (AGV can only transport one workpiece during transportation) and time continuity constraints (the start time and end time of each workpiece must satisfy a sequential relationship).

[0060] The relevant data types in the extracted database include multi-source heterogeneous data such as two-dimensional and three-dimensional modeling information, interference data, process data and order data processed by the data management module.

[0061] The production scheduling plan includes workpiece processing sequence, workpiece processing time arrangement, resource allocation (machine allocation plan, AGV scheduling path and operator arrangement), production batch information (batch size, batch time arrangement), performance indicators (equipment utilization, cycle time, timely delivery rate, etc.). The production scheduling plan is first stored in the data storage submodule, and then transmitted to the production scheduling submodule through the data transmission submodule for further optimization.

[0062] (3) Data management module

[0063] like Figure 3 As shown in Figure 1, the data management module is the core component of the entire intelligent workshop digital twin modeling and simulation system, responsible for managing and maintaining all data in the system. This module is divided into a data storage submodule and a data transmission submodule, which are responsible for data storage and transmission respectively.

[0064] Specifically, the data storage submodule is used to store all data of the multi-dimensional workshop modeling and management module and the intelligent analysis and decision support module, including multi-source heterogeneous data such as two-dimensional and three-dimensional layout information, interference parameters, process information and order data uploaded by users. After receiving the above multi-source heterogeneous data, the data storage submodule first pre-processes, standardizes and integrates them. The pre-processed data is stored in the database for call by other sub-modules. In the local area network environment, the data transmission submodule establishes a data interaction channel between the database and other sub-modules based on the server. The data storage submodule extracts the corresponding data from the database for transmission according to the needs of other modules, and ensures the timeliness and consistency of the system data through a real-time synchronization mechanism.

[0065] The preprocessing of multi-source heterogeneous data includes data format conversion and data cleaning. Data format conversion is performed through special functions (specific parsing functions. For JSON data, the system parses the JSON format data into JavaScript objects through JavaScript's built-in function JSON.parse(), and converts the object data into standard JSON format through JSON.stringify(). For XML data, DOMParser is used to parse XML format data into DOM structure objects, and JSON.stringify() is used to convert the parsed objects into JSON strings), and various formats of data (including two-dimensional and three-dimensional layout information as JSON data, and production-related data such as interference parameters, process information, and order data as XML data) are converted into a unified JSON format; data cleaning is performed by making preliminary judgments on the converted data, checking and filling or deleting missing values ​​in the data, identifying and processing outliers in the data, and ensuring the integrity and accuracy of the data.

[0066] The standardization of multi-source heterogeneous data is to store them in a key-value pair format and to express them in a unified manner in JSON format for data transmission.

[0067] The data transmission submodule uses a unified API interface and standardized data formats such as JSON and XML to ensure the universality and ease of parsing of data transmission. The data transmission mechanism uses the RabbitMQ message queue to realize asynchronous real-time data transmission by queuing and dequeuing all data requests and instructions through the message queue.

[0068] In this embodiment, the relational database MySQL is used to achieve efficient data reading and persistence of historical data.

[0069] In summary, a digital twin modeling and simulation system of a smart workshop provided according to an embodiment of the present invention specifically includes a multi-dimensional workshop modeling and management module, an intelligent analysis and decision support module and a data management module integrated. First, by uploading two-dimensional and three-dimensional layout information, setting interference parameters, uploading process information and uploading order data, all production factors of the physical workshop are modeled at multiple levels of physical, logical and functional levels, so that the digital twin workshop can restore the real physical workshop to the greatest extent. Secondly, a data management module is constructed to pre-process, standardize and store the multi-source heterogeneous data transmitted by the multi-dimensional workshop modeling and management module, so as to realize efficient management and seamless transmission of data. Finally, an intelligent analysis and decision support module is constructed, and the production data provided by the data management module is analyzed and processed in real time through an intelligent scheduling algorithm to generate an optimized production scheduling plan to ensure the intuitiveness and efficiency of workshop production.

[0070] It should be noted that the above embodiments are only for explaining the technical principles of the present invention, and are not intended to limit the protection scope of the present invention. Although the present invention has been described in detail through specific embodiments, it should be understood by those skilled in the art that various deformations, modifications or equivalent substitutions may be made to the technical solutions without departing from the core idea of ​​the present invention. These deformations, modifications or equivalent substitutions shall also be regarded as the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the content of the claims.

Claims

1. A digital twin modeling and simulation system for a smart workshop, characterized in that: include: A multi-dimensional workshop modeling and management module is used to generate a multi-dimensional workshop model, including a factory modeling submodule, an interference setting submodule, a process management submodule, an order management submodule and a production scheduling submodule. The factory modeling submodule is used to manage the two-dimensional and three-dimensional digital models of the factory and support the dynamic scheduling display of the digital production line. The interference setting submodule is used to set the state of the workshop model and equipment, simulate and manage various factors that affect the physical state of the workshop, allow different types of interference to be set and triggered by inputting interference parameters, and send the response containing the set interference parameters to the scheduling algorithm submodule to trigger a new scheduling calculation. The process management submodule is used to display, query and edit process information, define and optimize the logical flow of the production process, and the order management submodule is used to display, query and edit order information, and handle the entire process from order receipt to preliminary processing. The production scheduling submodule is used to optimize and manage the production process and generate an optimized production plan; The data management module is responsible for data storage and transmission; The intelligent analysis and decision support module includes a scheduling algorithm sub-module, which is used to receive instructions from the multi-dimensional workshop modeling and management module, conduct in-depth analysis of production data, generate scheduling plans through data-driven decision support, and provide decision-making basis for the production scheduling sub-module.

2. The intelligent workshop digital twin modeling and simulation system according to claim 1 is characterized in that: The modeling methods in the factory modeling submodule include: building and editing in the interface, and saving the generated model locally; or uploading an existing factory layout file, and obtaining the corresponding factory model by parsing the factory layout file.

3. The intelligent workshop digital twin modeling and simulation system according to claim 1 is characterized in that: Such interference includes, but is not limited to, equipment failure, setting up alternative equipment, and setting up material delays.

4. The intelligent workshop digital twin modeling and simulation system according to claim 1 is characterized in that: The interference setting submodule will also store the interference parameters and related response results in the relevant data table of the data storage submodule for subsequent analysis and tracking.

5. The smart workshop digital twin modeling and simulation system according to claim 1, characterized in that: In the process management submodule, in terms of process route design, users can define the complete processing flow of the product; in terms of process parameter management, users can set and adjust the specific parameters of each process; in terms of process optimization analysis, they can output a comprehensive evaluation report by conducting a systematic analysis of the production process.

6. The intelligent workshop digital twin modeling and simulation system according to claim 1, characterized in that: After receiving the initial scheduling plan transmitted by the scheduling algorithm submodule, the production scheduling submodule optimizes and adjusts it according to the actual production situation and constraints. The constraints include but are not limited to: equipment operating capacity, process priority, material supply, resource allocation, and production order deadline.

7. The intelligent workshop digital twin modeling and simulation system according to claim 6 is characterized in that: The optimized and adjusted scheduling plan is automatically updated to the multi-dimensional workshop model in the system, and a Gantt chart for monitoring is generated to visually display the time schedule of each process, equipment and resource, realizing comprehensive monitoring and dynamic management of the generation process.

8. The intelligent workshop digital twin modeling and simulation system according to claim 1, characterized in that: The data management module includes a data storage submodule and a data transmission submodule. The data storage submodule is used to store all data of the multi-dimensional workshop modeling and management module and the intelligent analysis and decision support module and store them in the database for calling by other submodules. The data transmission submodule is used to establish a data interaction channel between the database and other submodules.

9. The intelligent workshop digital twin modeling and simulation system according to claim 1, characterized in that: The data transmission submodule ensures the universality and parseability of data transmission by designing a unified API interface and adopting a standardized data format. The RabbitMQ message queue is used to queue and dequeue all data requests and instructions to achieve asynchronous real-time data transmission.

10. A smart workshop digital twin modeling and simulation system according to any one of claims 1 to 9, characterized in that: The intelligent analysis and decision support module can analyze the instructions of the order management submodule, identify the keywords, parameters and constraints in the instructions, classify the instructions into different types of tasks based on the analysis results, and then determine the required data type based on the task requirements. The relevant data is extracted from the database through the server and transmitted to the scheduling algorithm submodule.

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