Warehouse Logistics Simulation Optimization Platform and Simulation Optimization Method Based on Digital Twin

Abstract

The present application provides a warehousing and logistics simulation optimization platform and a simulation optimization method based on digital twins. Based on the solution of the present application, before the production line runs, the process simulation objectives, process plans, and process flows can be determined according to the design plan, and then based on the unit-level twin models integrated in the model library, assembly and fusion are carried out to construct system-level and complex system-level digital twin models, complete the construction of the entire warehousing and logistics production line, and then perform simulation by inputting relevant parameters, and adjust and modify the plan according to the simulation optimization results to improve the accuracy and efficiency of the plan. During the operation and maintenance stage of the production line, production data can also be directly mapped into the digital twin model, then simulation is carried out through the platform, and the optimization results of the simulation are promptly applied to the physical entity. Taking this as a cycle, it can effectively promote the interaction and integration of the physical space and the information space of the production system.

Description

Technical Field

[0001] The present application relates to a warehousing and logistics simulation optimization platform and a simulation optimization method based on digital twin, belonging to the technical field of simulation. Background Art

[0002] System simulation software is an important part of core industrial software, occupies a core position in product development, and is the key to the digital transformation of industry. As an open system, the production system is prone to problems such as a long design cycle, difficult prediction of production process efficiency, and inability to verify the manufacturing capacity of the production system. The production system simulation optimization technology can well analyze the dynamic characteristics of the production system, check whether the manufacturing system and decision-making are operating efficiently, and promote the intelligent upgrade and digital transformation of production logistics and warehousing logistics.

[0003] In addition, the use of digital twin technology can meet the needs of enterprise digital transformation, digitally model and manage manufacturing resources, enhance the flexibility of the production system through digital management means, intelligently adjust the production process using production data, and achieve full traceability of key production links.

[0004] However, in the existing solutions, the digital twin technology cannot be well applied to the simulation optimization system. Summary of the Invention

[0005] To solve the above problems, the present application provides a warehousing and logistics simulation optimization platform and a simulation optimization method based on digital twin.

[0006] To achieve the above object, the present application provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides a warehousing and logistics simulation optimization platform based on digital twin, which includes:

[0008] A basic support layer, a common interface layer, a resource configuration layer, and a terminal display layer;

[0009] The basic support layer includes a model library, a database, an algorithm library, a formula library, and a virtual PLC; the model library stores device virtual models corresponding to production logistics and warehousing logistics, the database stores simulation-related data, the algorithm library stores simulation-related algorithms, the formula library stores simulation-related calculation formulas, and the virtual PLC is a SoftPLC that can import, edit, and output ladder diagrams;

[0010] The common interface layer includes a drive algorithm interface, a data interaction interface, and an interface between the virtual PLC and the simulation platform;

[0011] The resource configuration layer is used for scenario layout, operation configuration, and algorithm selection; the scenario layout is used to generate scenario scenes, the operation configuration is used to configure the operation parameters of the scenario scenes, and the algorithm selection is used to configure corresponding scheduling algorithms and optimization algorithms for the scenario scenes;

[0012] The terminal display layer is used to display the process and results of simulation optimization, including the display of equipment operation process, configuration parameter display, calculation process display, calculation result display, and control program logic and function verification.

[0013] Based on the above warehouse logistics simulation optimization platform, optionally, it includes the following functional modules: model configuration module, parameter configuration module, data storage module, simulation optimization module, human-computer interaction module, result output module, and virtual debugging module;

[0014] The model configuration module is used to complete the construction of the scenario module;

[0015] The parameter configuration module is used to input operation parameters, including the input of equipment parameters, simulation parameters, and constraint parameters;

[0016] The data storage module is used to store knowledge data and result data, and to collect and store on-site equipment data in real time;

[0017] The simulation optimization module is used for operation process simulation and process optimization;

[0018] The human-computer interaction module is used for interaction and display with users, including 3D roaming display, simulation result display, design parameter display, and calculation process display;

[0019] The result output module is used to output simulation results and the bottleneck equipment and bottleneck links affecting production efficiency;

[0020] The virtual debugging module is used to complete the virtual operation and debugging of the PLC control program.

[0021] Based on the above warehouse logistics simulation optimization platform, optionally, it is implemented using a browser / server architecture, that is, relevant functions are configured through the server, and users log in through the browser and use relevant functions.

[0022] Based on the above warehouse logistics simulation optimization platform, optionally, the display of each functional module of the platform and the visualization of the 3D scene are implemented in the front-end browser, including account login, scene visualization, analysis and design optimization, parameter configuration, and interface debugging; the back-end server configures user management and data / business service management functions, and integrates a database and a model library.

[0023] Based on the above storage and logistics simulation optimization platform, optionally, the construction process of the platform includes building a model library, developing configurable models, designing configurable parameters, developing an algorithm library, developing a formula library, and virtual-real operation and verification;

[0024] The building of the model library includes: by analyzing the model requirements, using 3DSMAX and Substance Paiter software to build virtual models of production logistics equipment and storage logistics equipment, and making the virtual models have corresponding textures under virtual lighting and environment; among them, for each virtual model, its low-poly model and high-poly model are built, the low-poly model is used for scene layout construction and simulation, and the high-poly model is used for fine equipment display;

[0025] The development of the configurable model includes: using threeJS, CSS, Javascript and SQL Server database to develop a configurable model library;

[0026] The design of the configurable parameters includes: based on threeJS, CSS and Javascript, realizing the digital twin expression and characterization of the platform, virtual-real interaction configuration, and production task and disturbance parameter configuration;

[0027] The development of the algorithm library includes: developing device scheduling and optimization algorithms based on Javascript and Python;

[0028] The development of the formula library includes: using Javascript, Python and SQL Server database to realize the development of the formula library, including formulas for production capacity and bottleneck analysis, formulas for analyzing the operation efficiency, production efficiency, peak output, equipment operation / idle time, equipment utilization rate, equipment idle ratio, and production capacity redundancy of production units under different sequence drives;

[0029] The virtual-real operation and verification includes: after configuring the models, equipment performance parameters and related driving algorithms, performing virtual operation on the constructed application scenario and displaying the operation effect; and, by reading the equipment data collected in real time in the data storage module, realizing the interaction between real data and virtual data, mapping the actual operation state of the production line, connecting the real-time data to the digital twin simulation model in the platform, and applying the simulation optimization results to the physical entity to realize the linkage and integration of the simulation system and the actual production line.

[0030] In a second aspect, the embodiments of the present application further provide a storage and logistics simulation optimization method, which is applied to the digital twin-based storage and logistics simulation optimization platform as described in any item of the first aspect. The method includes:

[0031] Based on the layout operation performed by the user according to the engineering drawing of the workshop production line plan, perform virtual scene layout to reproduce each physical device included in the production line, and obtain a twin warehousing and logistics production line model;

[0032] If the warehousing and logistics production line is not installed and debugged, determine the simulation parameters based on the user's input operation and complete the parameter configuration;

[0033] Perform simulation operation on the twin warehousing and logistics production line model based on the configured simulation parameters to obtain simulation results;

[0034] Based on the simulation results, perform simulation analysis, select a suitable optimization algorithm model, and optimize the layout and process of the virtual scene.

[0035] Based on the above method, optionally, after performing the virtual scene layout based on the layout operation performed by the user according to the engineering drawing of the workshop production line plan to reproduce each physical device included in the production line and obtaining a twin warehousing and logistics production line model, it further includes:

[0036] If the warehousing and logistics production line is already in operation, collect data on the equipment on the warehousing and logistics production line and save the collected data to the backend database;

[0037] Based on the data stored in the backend database, reproduce the real-time operation status of the production line in the front-end browser, calculate various evaluation indicators of the production line based on the data in the database, obtain the actual production line operation indicators and visualize them;

[0038] Based on the user's operation, change the production line layout, and perform simulation operation on the re-layout production line based on the preset scheduling strategy to obtain the optimized results and operation indicators.

[0039] Based on the above method, optionally, after changing the production line layout based on the user's operation and performing simulation operation on the re-layout production line based on the preset scheduling strategy to obtain the optimized results and operation indicators, it further includes:

[0040] Compare the optimized indicators with the actual indicators to determine whether the results after optimization and simulation meet the requirements. If they do not meet the requirements, re-optimize and simulate until the results after optimization and simulation meet the requirements; if the results after optimization and simulation meet the requirements, plan and organize the existing production line according to the optimized simulation layout to make the physical production line layout consistent with the virtual production line layout, and upload the corresponding scheduling strategy to the control system of the actual equipment through data conversion by the backend server.

[0041] The warehousing and logistics simulation optimization platform and simulation optimization method based on digital twin provided by this application can, before the production line runs, determine the process simulation objectives, process plans, and process flows according to the design plan, and then, based on the unit-level twin models integrated in the model library of the basic support layer, assemble and fuse them to construct system-level and complex system-level digital twin models, complete the construction of the entire warehousing and logistics production line, then input relevant parameters through the resource configuration layer, and use the data, algorithms, and formulas in the basic support layer for simulation, so as to use the data statistics and optimization functions of the platform to preview the future operation of the workshop, and adjust and modify the plan according to the simulation optimization results displayed on the terminal display layer, improving the accuracy and efficiency of the plan. Brief Description of the Drawings

[0042] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application. In addition, these drawings and the text description are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments.

[0043] Figure 1 Schematic diagram of the architecture of the warehousing and logistics simulation optimization platform based on digital twin provided by an embodiment of this application;

[0044] Figure 2 Schematic diagram of the process of the warehousing and logistics simulation optimization method provided by an embodiment of this application. Detailed Embodiments

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of this application without creative efforts fall within the scope of protection of this application. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0046] The existing simulation optimization system cannot directly generate 3D models in the field of warehousing and logistics. Instead, it is necessary to build models using built-in basic shapes such as cuboids and cylinders, redraw the 3D models, and more often stay at the stage of solution verification. When performing data-driven operations, more often existing data or self-set data is used, and real-time data interaction cannot be achieved, nor can the linkage and integration between the simulation system and the actual warehousing and logistics system be realized. It can be summarized that the traditional simulation optimization system has high requirements for the efficient construction of simulation models; it cannot combine digital twins to reflect the real-time and accuracy of its data, and at the same time has high requirements for the data representation of multi-dimensional digital twin models; it has high requirements for the adaptability of computer hardware and software.

[0047] Based on this, the present application provides a warehousing and logistics simulation optimization solution based on digital twins. Aiming at the requirements of solution planning and simulation optimization in the fields of production logistics and warehousing and logistics, to promote the gradual transformation of production logistics and warehousing and logistics into digital intelligent logistics characterized by ubiquitous perception, dynamic optimization, and agile response. On the basis of ensuring the generality and generalization of the simulation platform, give full play to the advantages of digital twin technology, complete solution simulation optimization, logic verification, and virtual presentation of production lines before the implementation of engineering projects, improve the accuracy of solution planning and solution efficiency. At the same time, in the production operation and maintenance link of the production line, the actual process of the warehousing and logistics production line can also be dynamically optimized based on the simulation optimization results of the platform, improve production efficiency, and reduce the bottleneck of production line equipment. The following provides a non-limiting description of the specific implementation solutions through several examples or embodiments.

[0048] The warehousing and logistics simulation optimization solution based on digital twins aims to achieve design verification, layout optimization, and linkage and integration with subsequent virtual commissioning systems and 3D monitoring systems through simulation optimization and virtual verification. Therefore, the overall design quality of the entire platform system architecture is crucial for the generality and generalization of the software. Therefore, based on the overall development architecture of the platform, the present application develops the simulation optimization process of warehousing and logistics, and proposes a simulation optimization method for warehousing and logistics to achieve the development and verification of each functional module of warehousing and logistics.

[0049] First, refer to Figure 1 , Figure 1 which is a schematic structural diagram of a warehousing and logistics simulation optimization platform based on digital twins provided by an embodiment of the present application.

[0050] As Figure 1 shown, the architecture of the warehousing and logistics simulation optimization platform based on digital twins includes: a basic support layer, a common interface layer, a resource configuration layer, and a terminal display layer.

[0051] Among them, the basic support layer includes a model library, a database, an algorithm library, a formula library, and a virtual PLC; the model library stores the virtual models of the equipment corresponding to production logistics and warehousing logistics, the database stores simulation-related data, the algorithm library stores simulation-related algorithms, the formula library stores simulation-related calculation formulas, and the virtual PLC is a SoftPLC (Soft Programmable Logic Controller) that can import, edit ladder diagrams, and output results.

[0052] More specifically, the virtual models of the equipment in the model library include, but are not limited to, stackers, wire dropping machines, manipulators, AGVs, four-way vehicles, multi-shuttle vehicles, conveyors, shelves, etc. These virtual models can be called unit-level twin models, that is, twin models constructed for a specific entity (equipment). The simulation-related data in the database includes scenario data, operation data, task data, configuration data, etc. The simulation-related algorithms in the algorithm library include, but are not limited to, wire dropping machine scheduling algorithms, automated storage and retrieval system operation scheduling algorithms, four-way vehicle path planning algorithms and congestion avoidance algorithms, four-way vehicle cross-layer operation scheduling algorithms, packaging operation scheduling algorithms, etc. The simulation-related calculation formulas in the formula library include, but are not limited to, time utilization rate formulas, equipment utilization rate formulas, equipment utilization formulas, production line balance rate formulas, etc. Through the virtual models of the equipment in the model library, before simulation, relevant models can be directly called to quickly construct a simulation scenario, without having to first construct a model based on basic entities, so the efficiency can be effectively improved. Through the simulation-related data in the database and the relevant algorithms in the algorithm library, the simulation process can be quickly realized. And through the virtual PLC, compared with traditional hardware PLCs, it can run on a general-purpose computer, so it has greater flexibility and scalability, can customize different control and monitoring functions through software, has a lower cost, and also has richer diagnostic and maintenance functions, which is convenient for operators to troubleshoot faults and maintain the system.

[0053] The public interface layer includes a drive algorithm interface, a data interaction interface, and an interface between the virtual PLC and the simulation platform. The drive algorithm interface is used for the platform to obtain drive algorithms, the data interaction interface is used for the platform to obtain the actual operation data of the real equipment in the logistics workshop, and the interface between the virtual PLC and the simulation platform is used for data interaction between the virtual PLC and the simulation platform. These interfaces can be completed by writing interface algorithms.

[0054] The resource configuration layer is used for scheme layout, operation configuration, and algorithm selection; the scheme layout is used to generate a scheme scenario, the operation configuration is used to configure the operation parameters of the scheme scenario, and the algorithm selection is used to configure the corresponding scheduling algorithms and optimization algorithms for the scheme scenario.

[0055] More specifically, the solution layout includes scenario management, device configuration, production line configuration, etc., in order to generate solution scenarios. The operation configuration includes device operation parameter configuration, enabling parameter configuration, motion characteristic configuration, rule constraint configuration, production task configuration, disturbance parameter configuration, etc.; algorithm selection is used to configure corresponding scheduling algorithms and optimization algorithms for the solution scenarios, covering various scenarios such as roll dropping, packaging, stereoscopic warehouse, and dense warehouse.

[0056] The terminal display layer is used to display the process and results of simulation optimization, including the display of the equipment operation process, configuration parameter display, calculation process display, calculation result display, and control program logic and function verification. Through the terminal display layer, the process of simulation optimization can be displayed to users in real time, so that users can better understand the relevant situation and make adjustments or improvements according to the actual situation.

[0057] Based on the above-mentioned warehousing and logistics simulation optimization platform, before the production line runs, the process simulation objectives, process plans, and process flows can be determined according to the design plan. Then, based on the unit-level digital twin models integrated in the model library of the basic support layer, they are assembled and fused to build system-level and complex system-level digital twin models, completing the construction of the entire warehousing and logistics production line. Then, relevant parameters are input through the resource configuration layer, and simulation is carried out using the data, algorithms, and formulas in the basic support layer. Thus, using the data statistics and optimization functions of the platform, the future operation of the workshop can be previewed, and the plan can be adjusted and modified according to the simulation optimization results displayed by the terminal display layer, improving the accuracy of plan planning and the efficiency of the plan.

[0058] In addition, during the production line operation and maintenance stage, production data can also be directly mapped into the digital twin model, then simulated through the platform, and the optimized results of the simulation are promptly applied to the physical entity (i.e., the equipment). Taking this as a cycle, the interaction and integration of the physical space and information space of the production system are promoted.

[0059] Furthermore, the warehousing and logistics simulation optimization platform can include the following functional modules: model configuration module, parameter configuration module, data storage module, simulation optimization module, human-machine interaction module, result output module, and virtual commissioning module.

[0060] Among them, the model configuration module is used to complete the construction of the scenario module. In practice, it can have functions such as drag-and-drop (selecting and configuring models by dragging and dropping), adding, deleting, and modifying (adding, deleting, and modifying the configured models), retrieving (retrieving models from the model library through keywords, etc.), cooperating (realizing the cooperation of different models), evaluating (evaluating the configured model scenarios), and adding (customizing and adding models to the model library).

[0061] The parameter configuration module is used to input operation parameters, including the input of equipment parameters, simulation parameters, and constraint parameters. The equipment parameters include motion parameters such as speed, enabling parameters such as PLC interaction points, motion characteristic parameters such as motion rules, and attribute parameters such as failure rates. The simulation parameters include parameters such as production tasks and disturbance conditions. The constraint parameters include parameters such as simulation boundary conditions.

[0062] The data storage module is used to store knowledge data and result data, including knowledge data such as model libraries, databases, algorithm libraries, and formula libraries, as well as result data such as simulation results, and is used to collect and store field device data in real time.

[0063] The simulation optimization module is used for process simulation and process optimization during operation. In the simulation optimization module, based on the established twin model, the production process of the established production line can be simulated by setting relevant parameters in the parameter configuration module. It can also map the production and processing process of the twin production line based on the real-time collected operation data and the production line scheduling strategy corresponding to the project, and can act on the physical entity according to the simulation optimization results.

[0064] The human-machine interaction module is used for interaction and display with users, including 3D roaming display, simulation result display, design parameter display, and calculation process display.

[0065] The result output module is used to output simulation results (such as operating rate, utilization rate, yield rate, balance rate) and bottleneck equipment and bottleneck links that affect production efficiency.

[0066] The virtual commissioning module is used to complete the virtual operation and debugging of the PLC control program, mainly including ladder diagram import and editing, access and simulation operation of the control program, logic and function verification, etc.

[0067] Through the cooperation of the above-mentioned functional modules, various functions of the warehousing and logistics simulation optimization platform can be realized, meeting the needs of users for simulation optimization.

[0068] In addition, in some embodiments, the warehousing and logistics simulation optimization platform is implemented using the Browser / Server (B / S) architecture. That is, relevant functions are configured through the server, and users log in through a browser and use the relevant functions. In the B / S architecture, users access the warehousing and logistics simulation optimization platform through a browser (such as Chrome, Firefox, etc.), and all the logic and data of the warehousing and logistics simulation optimization platform are stored on the server side. The browser sends a request to the server, the server receives the request and processes it, and then returns the corresponding result to the browser for display. In this mode, the browser is mainly responsible for presenting the user interaction interface, while the server is responsible for tasks such as data processing and business logic. The advantages of the B / S architecture include easy maintenance, good cross-platform compatibility, and the ability to achieve centralized management and maintenance, etc. The corresponding one is the C / S architecture (Client / Server architecture), that is, the client / server architecture. In the C / S architecture, the client application performs part of the logical operations, while the server is responsible for the other part. In this mode, the client application needs to be installed on the user's computer, so it has higher requirements for the user's computer.

[0069] Based on the B / S architecture, the display of each functional module of the platform and the visualization of the three-dimensional scene can be realized in the front-end browser, including account login, scene visualization, analysis and design optimization, parameter configuration, and interface debugging; while in the back-end server, user management and data / business service management functions are configured, and a database and a model library are integrated.

[0070] In terms of the development of the platform, vite can be used as the front-end technical architecture, the front-end development languages are Javascript, html, and CSS, the back-end service is built with nodejs, the back-end development language is Javascript, and the database uses SQL Server. Some algorithms are developed using Javascript or Python languages, and the visualization engine uses threejs.

[0071] Furthermore, the construction process of the platform includes the construction of the model library, the development of configurable models, the design of configurable parameters, the development of the algorithm library, the development of the formula library, and the virtual-real operation and verification.

[0072] Among them, the construction of the model library includes: by analyzing the model requirements, using 3DSMAX and Substance Paiter software to build virtual models of production logistics equipment and warehousing logistics equipment, and making the virtual models have corresponding textures under virtual lighting and environment. Among them, for each virtual model, its low-poly and high-poly models are constructed. The low-poly model is applied to the construction and simulation of the scene layout, and the high-poly model is used for the refined display of the equipment. The constructed models include, but are not limited to, virtual models of different types and models such as stackers, shelves, conveyors, shuttle cars, four-way vehicles, multi-shuttle vehicles, filament droppers, filament cars, filament boxes, turntables, elevators, AGVs, four-way vehicles, manipulators, film wrapping machines, bagging machines, strapping machines, etc. When constructing the models, the coupling relationship between the components of the configurable models is fully considered to ensure that the size can be correctly modified in the same proportion and different proportions. At the same time, a device query function is provided in this module. By querying the device type, brand, model, and parameters, the corresponding device model can be directly called inside the platform.

[0073] The development of configurable models includes: using threeJS, CSS, Javascript, and SQL Server database to develop a configurable model library. This model library allows users to retrieve, query, and call the existing equipment models in the model library, supports dragging and adding the models to the constructed scene, supports modifying the size and scale parameters of the models, and setting the spatial position parameters of the equipment models for assembling the production line. The constructed digital twin model of logistics equipment supports the modification and adjustment of size and scale, so as to configure equipment models of different sizes in different application scenarios; allows users to retrieve and call existing models from the digital twin model library, modify the size and scale of the models, and add them to the constructed scene by dragging and dropping, and set the spatial position parameters of the equipment models to assemble the production line; allows users to import their newly created digital twin models of equipment. Users can expand the configurable model library. Users can import their newly created digital twin models of equipment, and the operations of the models all support the above functions such as retrieval, call, and modification.

[0074] The configurable parameter design includes: realizing the digital twin expression and characterization, virtual-real interaction configuration, and production task and disturbance parameter configuration of the platform based on threeJS, CSS, and Javascript. The digital twin expression and characterization means digitally describing all the equipment in production logistics and warehousing logistics, expressing the production capacity parameters of each piece of equipment, such as the production speed range, the time required to complete one operation, operation accuracy, and other equipment capacity-related parameters, and at the same time supporting the characterization of all control signals, control parameters, status parameters, etc. in the PLC control program corresponding to each piece of equipment. The virtual-real interaction configuration means that the platform supports users to parametrically configure the motion characteristics of each device, so as to realize the configuration of the operation actions of the twin devices. Specifically, it includes: ① The action forms and action paths of each piece of equipment and each mechanism it contains can be set and configured; ② The enabling signal configuration and association corresponding to the motion actions of each piece of equipment and each mechanism it contains, that is, the digital-analog association setting; ③ The configuration of interaction setting rules, such as the motion range limit; The production task and disturbance parameter configuration. The operation of the production line is driven by production tasks. Therefore, users are allowed to set production tasks and disturbance parameters on the platform to provide task drive for the virtual operation of the subsequent logistics production line. Specifically, it can include: ① Allowing the generation and setting of random task sequences and positional relationships; ② Allowing the configuration of possible randomly occurring disturbance factors to interfere with and affect the current production state.

[0075] The algorithm library development includes: developing device scheduling and optimization algorithms based on Javascript and Python. For common production operation scenarios in warehousing logistics, such as the coiling production unit, packaging production unit, automated storage and retrieval system (AS / RS) warehousing unit, compact storage unit, etc., configure the operation processes and operation modes of the digital twin production line, and configure different adaptive optimization solution algorithms for the operation scheduling forms of different operation units, such as intelligent optimization algorithms like simulated annealing, genetic algorithm, tabu search, neural network, etc., so as to support the virtual operation of the digital twin production line. Taking the filament coiling as an example, construct a multi-objective optimization solution algorithm for the operation scheduling of the filament coiling production unit with the average filament waiting time of full coils and the shortest filament coiling operation path as the optimization objectives, and with full coil explosion, longest waiting time, filament breakage interruption, buffer full, and cart waiting time as the constraint rules; taking the packaging production operation unit as an example, establish a method model for the operation scheduling of the packaging production unit with the optimal operation efficiency (including the transfer of cone barrels (when to stop / when to move forward), when the robotic arm grabs the filament cake, when and where to wind the filament cake, and the filament cake stacking process) as the optimization objective and the normal operation of the packaging line as the constraint condition (the rhythm of each link is coordinated and unified, and the filament cone stops and moves forward in a timely manner without causing faults, etc.), and construct a multi-objective optimization solution algorithm for the operation scheduling of the packaging production unit.

[0076] The development of the formula library includes: using Javascript, Python, and SQL Server database to develop the formula library, including formulas for production capacity and bottleneck analysis, and formulas for analyzing the operation efficiency, production efficiency, peak output, equipment operation / idle time, equipment utilization rate, equipment idle ratio, and production capacity redundancy of production units under different sequence drives. At the same time, to ensure the accuracy of various indicators obtained from virtual operation calculations, it also supports comparison with the analysis results of traditional simulation tool software.

[0077] Virtual and real operation and verification include virtual operation, actual operation, and corresponding verification: Virtual operation and verification, that is, after configuring the model, equipment performance parameters, and related driving algorithms, perform virtual operation on the constructed application scenario of the production line plan and display the operation effect; among them, by setting the operation time period and operation step size, users can view the operation effect of the designed production line in an intuitive form, restore the equipment, equipment actions, and movement conditions that occur in the actual production line to the greatest extent, and obtain corresponding various production operation performance and indicators through virtual operation according to the input simulation parameters. In addition to supporting virtual operation, the system also supports actual operation and corresponding verification, that is, by reading the equipment data collected in real time in the data storage module, realize the interaction between real data and virtual data, map the actual operation state of the production line, connect the real-time data to the digital twin simulation model in the platform, and apply the simulation optimization results to the physical entity to realize the linkage and integration of the simulation system and the actual production line, and promote the interaction and integration of the physical space and the information space.

[0078] Through the above solution, a digital-twin-based warehousing and logistics simulation optimization platform can be constructed and applied in the simulation optimization of warehousing and logistics to improve the accuracy and efficiency of the plan.

[0079] In addition, based on the above warehousing and logistics simulation optimization platform, the embodiment of the present application also provides a warehousing and logistics simulation optimization method, which is applied to the digital-twin-based warehousing and logistics simulation optimization platform described in any of the above embodiments. Refer to Figure 2 , Figure 2 which is a schematic flowchart of the warehousing and logistics simulation optimization method provided in an embodiment of the present application. As Figure 2 shown, the method includes the following steps:

[0080] Step S101: Based on the layout operation performed by the user according to the engineering drawing of the workshop production line plan, perform virtual scene layout to reproduce each physical device included in the production line, and obtain a twin warehousing and logistics production line model.

[0081] Specifically, in this step, the user can configure the models of each physical device in a drag-and-drop manner according to the engineering drawing of the production line plan, correctly reproduce the physical devices in a graphical form, including the number of devices, the types of devices, the device positions, and the dimensional relationships, etc., to obtain the twin warehousing and logistics production line model.

[0082] Step S102: If the warehousing and logistics production line is not installed and debugged yet, determine the simulation parameters based on the user's input operations and complete the parameter configuration.

[0083] Specifically, this step is applicable to the scenario where the warehousing and logistics production line is not installed and debugged yet. At this time, the user performs parameter configuration to simulate the production line situation, including the configuration of control logic, simulation parameters, scheduling optimization algorithms, and control programs, etc. Among them, the simulation parameters include device operation parameters, device enable parameters, motion characteristics, rule constraints, production tasks, and disturbance parameters. The scheduling optimization algorithms include algorithm optimization models for each device. The control program is to perform operations such as importing and editing the program. When performing virtual simulation, motion characteristic parameters such as speed, path operation, production rhythm, and rule constraints can be randomly and freely set, production tasks can be configured according to requirements, disturbance situations can be randomly generated, and the corresponding scheduling optimization algorithms can be configured.

[0084] Step S103: Perform a simulation run on the twin warehousing and logistics production line model based on the configured simulation parameters to obtain the simulation results.

[0085] Specifically, after the user completes the configuration of the production line and parameters, the platform can perform a simulation to obtain the simulation results of the warehousing and logistics production line, including data such as production line yield, utilization rate, availability, balance rate, and production capacity.

[0086] Step S104: Conduct a simulation analysis based on the simulation results, select a suitable optimization algorithm model, and perform layout optimization and process optimization on the virtual scenario.

[0087] Specifically, a simulation analysis can be conducted according to the simulation results to determine bottleneck devices, bottleneck links, problem devices, and problem links, etc. Then, select the corresponding optimization algorithm model to perform layout optimization and process optimization. Obtain the layout situations such as the optimized man-machine ratio, the number of devices, the device positions, and the types of devices, and the process situations such as processing procedures, production rhythm, and operation rules. Finally, layout the physical warehousing and logistics production line based on the simulation optimization results.

[0088] Thus, before the production line runs, the process simulation objectives, process plans, and process flows can be determined according to the design plan. Then, based on the unit-level twin models integrated in the model library, assembly and fusion are carried out to construct digital twin models at the system level and complex system level, complete the construction of the entire warehousing and logistics production line, and then perform simulations by inputting relevant parameters. Thus, by using the data statistics and optimization functions of the platform, the future operation of the workshop can be pre-enacted, and the plan can be adjusted and modified according to the simulation optimization results to improve the accuracy and efficiency of the plan.

[0089] In addition, as Figure 2 shown, in some embodiments, after step S101, the following steps may further be included:

[0090] Step S105: If the warehousing and logistics production line is already running, collect data on the equipment on the warehousing and logistics production line and save the collected data to the backend database.

[0091] Specifically, in practice, since there are many types of equipment on the production line and the communication interfaces between various equipment are also different, the data collection of the production line equipment can be realized based on the OPC-UA protocol, and the collected data is saved to the backend database.

[0092] Step S106: Based on the data stored in the backend database, reproduce the real-time operation status of the production line in the front-end browser, calculate various evaluation indicators of the production line based on the data in the database, obtain the actual production line operation indicators, and visually display them.

[0093] Specifically, in the front-end browser of the platform, the reproduced production line model will reproduce the real-time operation status of the production line by retrieving the real-time data of the equipment stored in the backend database, realizing the real-time monitoring of the production line, and calculating various evaluation indicators of the production line based on the data in the database, obtaining the actual production line operation indicators and visually displaying them. By analyzing the actual production line operation indicators, the bottleneck problems of the production line can be obtained, and the production line layout and scheduling strategy can be changed for specific bottleneck problems.

[0094] Step S107: Change the production line layout based on the user's operation, and perform simulation operation on the re-layouted production line based on the preset scheduling strategy to obtain the optimized results and operation indicators.

[0095] Specifically, in the process of optimizing the production line layout and changing the scheduling strategy, first, in the platform, according to the user's experience, the production line layout is changed by performing corresponding operations on the model, and the re-layouted production line is simulated based on the preset scheduling strategy in the platform to obtain the optimized results and operation indicators.

[0096] In addition, in some embodiments, after step S107, the following steps may further be included:

[0097] Compare the optimized metrics with the actual metrics to determine whether the results after the optimized simulation meet the requirements (i.e., whether the problem is solved). If they do not meet the requirements (the problem is not solved), then re-optimize and simulate until the results after the optimized simulation meet the requirements (the problem is solved). If the results after the optimized simulation meet the requirements, then plan and organize the existing production line according to the optimized simulation layout to make the physical production line layout consistent with the virtual production line layout, and upload the corresponding scheduling strategy to the control system of the actual device after data conversion through the back-end server.

[0098] In this way, during the production line operation and maintenance stage, by directly mapping the production data into the digital twin model, then simulating through the platform, and promptly applying the optimized results of the simulation to the physical entity (i.e., the device), and taking this as a cycle, the interaction and integration between the physical space and the information space of the production system can be effectively promoted.

[0099] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be referred to the same or similar content in other embodiments.

[0100] It should be noted that in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality of" refers to at least two.

[0101] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0102] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A warehouse logistics simulation optimization platform based on digital twins, characterized in that: include: Basic support layer, public interface layer, resource configuration layer and terminal display layer; The basic support layer includes a model library, a database, an algorithm library, a formula library and a virtual PLC; the model library stores virtual models of equipment corresponding to production logistics and warehousing logistics, the database stores simulation-related data, the algorithm library stores simulation-related algorithms, the formula library stores simulation-related calculation formulas, and the virtual PLC is a SoftPLC that can import, edit and output ladder diagrams; The public interface layer includes a driving algorithm interface, a data interaction interface, and a virtual PLC and simulation platform interface; The resource configuration layer is used for solution layout, operation configuration and algorithm selection; the solution layout is used to generate solution scenarios, the operation configuration is used to configure the operation parameters of the solution scenarios, and the algorithm selection is used to configure the corresponding scheduling algorithm and optimization algorithm for the solution scenarios; The terminal display layer is used to display the process and results of simulation optimization, including equipment operation process display, configuration parameter display, calculation process display, calculation result display, and control program logic and function verification.

2. The platform according to claim 1, characterized in that It includes the following functional modules: model configuration module, parameter configuration module, data storage module, simulation optimization module, human-computer interaction module, result output module and virtual debugging module; The model configuration module is used to complete the construction of the scene module; The parameter configuration module is used to input operating parameters, including input of equipment parameters, simulation parameters and constraint parameters; The data storage module is used for storing knowledge data and result data, and for real-time collection and storage of field equipment data; The simulation optimization module is used for running process simulation and process optimization; The human-computer interaction module is used for interaction and display with the user, including three-dimensional roaming display, simulation result display, design parameter display and calculation process display; The result output module is used to output the simulation results and the bottleneck equipment and bottleneck links that affect the production efficiency; The virtual debugging module is used to complete the virtual operation and debugging of the PLC control program.

3. The platform according to claim 1, characterized in that: It is implemented using a browser / server architecture, that is, the relevant functions are configured through the server, and users log in through the browser and use the relevant functions.

4. The platform according to claim 3, characterized in that: Display each functional module of the platform and visualize the 3D scene in the front-end browser, including account login, scene visualization, analysis and design optimization, parameter configuration and interface debugging; The back-end server is configured with user management and data / business service management functions, and integrates the database and model library.

5. The platform according to claim 1, characterized in that: The construction process of the platform includes model library construction, configurable model development, configurable parameter design, algorithm library development, formula library development, and virtual and real operation and verification; The model library construction includes: by analyzing the model requirements, using 3DSMAX and Substance Painter software to build virtual models of production logistics equipment and warehousing logistics equipment, and making the virtual models have corresponding textures under virtual lighting and environment; wherein, for each virtual model, a low-poly model and a high-poly model are constructed, the low-poly model is used for scene layout construction and simulation, and the high-poly model is used for refined display of equipment; The configurable model development includes: developing a configurable model library using threeJS, CSS, Javascript and SQL Server database; The configurable parameter design includes: digital twin expression and characterization of the platform based on threeJS, CSS and Javascript, virtual-real interaction configuration and production task and disturbance parameter configuration; The algorithm library development includes: developing equipment scheduling and optimization algorithms based on Javascript and Python; The formula library development includes: using Javascript, Python and SQL Server database to realize the development of the formula library, including formulas for production capacity and bottleneck analysis, formulas for analyzing the operating efficiency of production units under different sequence drives, production efficiency, peak output, equipment operation / idle time, equipment utilization, equipment idle ratio, and production capacity redundancy; The virtual-reality operation and verification include: after configuring the model, equipment performance parameters and related driving algorithms, virtually running the production line plan for the constructed application scenario and displaying the operation effect; and, by reading the equipment data collected in real time in the data storage module, realizing the interaction between real data and virtual data, mapping the actual operation status of the production line, connecting the real-time data to the digital twin simulation model in the platform, and applying the simulation optimization results to the physical entity, so as to realize the linkage and integration of the simulation system and the actual production line.

6. A warehouse logistics simulation optimization method, characterized in that: Applied to the warehouse logistics simulation optimization platform based on digital twin as claimed in any one of claims 1 to 5, the method comprises: Based on the layout operation performed by the user according to the workshop production line plan engineering drawing, a virtual scene layout is performed to reproduce the various physical equipment included in the production line and obtain a twin warehouse logistics production line model; If the storage logistics production line has not been installed and debugged, the simulation parameters are determined based on the user's input operation and the parameter configuration is completed; The twin warehouse logistics production line model is simulated and run based on the configured simulation parameters to obtain simulation results; Based on the simulation results, simulation analysis is performed, and a suitable optimization algorithm model is selected to perform layout optimization and process optimization on the virtual scene.

7. The method according to claim 6, characterized in that The method further includes: performing virtual scene layout based on the layout operation performed by the user according to the workshop production line plan engineering drawing to reproduce the various physical equipment included in the production line and obtain the twin warehouse logistics production line model. If the warehousing logistics production line is already running, data collection is performed on the equipment on the warehousing logistics production line, and the collected data is saved in the back-end database; Based on the data stored in the back-end database, the real-time operation status of the production line is reproduced in the front-end browser, and various evaluation indicators of the production line are calculated based on the data in the database to obtain the actual production line operation indicators and visualize them; The production line layout is changed based on user operations, and the re-layout production line is simulated based on the preset scheduling strategy to obtain optimized results and operating indicators.

8. The method according to claim 7, characterized in that The method further includes: changing the production line layout based on the user's operation, and simulating the re-layout production line based on the preset scheduling strategy to obtain the optimized results and operation indicators. Compare the optimized indicators with the actual indicators to determine whether the results of the optimized simulation meet the requirements. If not, re-optimize and simulate until the results of the optimized simulation meet the requirements. If the results of the optimized simulation meet the requirements, plan and organize the existing production lines according to the optimized simulation layout to make the physical production line layout consistent with the virtual production line layout, and upload the corresponding scheduling strategy to the control system of the actual equipment through the back-end server after data conversion.

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