A sand mold 3D printing workshop logistics scheduling simulation method, device and system

Abstract

The application relates to the fields of logistics simulation and high-end equipment manufacturing technology, in particular to a sand mold 3D printing workshop logistics scheduling simulation method, device, system and computing equipment. The application solves the problems that the existing logistics simulation method is not feasible and cannot dynamically respond to disturbances in the sand mold 3D printing workshop due to the neglect of the physical feasibility of large piece transportation and the timing coordination by constructing a special database integrating large piece logistics hard constraints and sand mold aging windows, and embedding the process coordination requirements as hard constraints into the improved genetic algorithm supporting dynamic weight adjustment.

Description

Technical Field

[0001] This application relates to the fields of logistics simulation and high-end equipment manufacturing technology, and in particular to a method, device, system and computing equipment for logistics scheduling simulation in a sand mold 3D printing workshop. Background Technology

[0002] Sand mold 3D printing technology is widely used in the production of large and complex castings such as wind turbine hubs and large machine tool beds. However, its workshop logistics system has significant unique characteristics, making it difficult to apply existing general workshop logistics scheduling simulation methods. The main problems include: First, poor adaptability to large-part logistics. Sand molds weighing several meters or more than 2 tons require specialized equipment such as heavy-duty AGVs and gantry cranes. However, spatial constraints such as aisle width, turning radius, and ground load-bearing capacity are not effectively modeled, which can easily lead to scheduling conflicts and congestion. Second, lack of process collaborative simulation. The time requirements from printing to pouring of sand molds are stringent, but existing methods often focus on a single link and fail to achieve full-process time-series linkage. Third, weak dynamic response capability. It is difficult to quickly optimize and adjust in the face of sudden situations such as equipment failure, rework, or order fluctuations. Fourth, imbalance in multi-objective optimization. It often only pursues the shortest path and ignores the comprehensive benefits such as logistics costs and waiting time.

[0003] Therefore, a logistics scheduling simulation scheme that can accurately reflect the characteristics of large-item logistics in sand mold 3D printing workshops is needed. Summary of the Invention

[0004] Therefore, it is necessary to address the problem that general simulation methods for sand mold 3D printing workshop logistics cannot meet actual needs, and to provide a simulation method for logistics scheduling in sand mold 3D printing workshops, including: A sand mold 3D printing workshop database is constructed, which stores the collected operating parameters of 3D printers, spatial layout data of the workshop, physical kinematic models of special logistics equipment, and operational constraint parameters. The special logistics equipment includes heavy-duty AGVs and gantry cranes, and the operational constraint parameters include minimum aisle width, minimum turning radius, and ground load-bearing threshold. Load the spatial layout data and the physical kinematics model of the dedicated logistics equipment into the simulation environment, and embed the operation constraint parameters into the path planning algorithm; Based on the aforementioned path planning algorithm, an initial logistics scheduling scheme is generated using a multi-objective optimization function, and then solved using an improved genetic algorithm to obtain the logistics scheduling scheme and perform simulation.

[0005] Preferably, the path planning algorithm employs an improved genetic algorithm, which introduces adaptive crossover probability and adaptive mutation probability; wherein, The adaptive crossover probability is dynamically adjusted according to the current algebra, satisfying: The adaptive mutation probability increases as population diversity decreases, satisfying: The fitness function of the improved genetic algorithm is a weighted multi-objective function that satisfies: Where Pc is the adaptive crossover probability, g is the current generation number, and G max P represents the maximum number of generations. m H represents the adaptive mutation probability, where H is the entropy value of the current population. max T is the initial population entropy value; f is the fitness function of the improved genetic algorithm, and T logistics Wwaiting represents the total logistics time, and Wwaiting represents the cumulative waiting time for critical processes. Weighting based on logistics time consumption. As the waiting time weight, and + =1; T ref and W ref These are the preset reference values.

[0006] Preferably, it further includes: detecting whether a conflict occurs when the dedicated logistics equipment performs a task in a simulation environment; When a conflict is detected, the transfer route is replanned and the equipment scheduling priority is optimized.

[0007] Preferably, when a conflict is detected in the equipment path intersection, the weight of logistics time consumption is reduced and the weight of waiting time is increased; when a conflict is detected in the process waiting timeout, the weight of logistics time consumption is increased and the weight of waiting time is reduced.

[0008] Preferably, when a process scheduling conflict is detected, the path and scheduling sequence of the corresponding dedicated logistics equipment and its associated sand mold tasks are replanned, without interrupting or restarting the simulation process of other unaffected tasks.

[0009] Preferably, the construction of the sand mold 3D printing workshop database includes: The system collects operational status data of 3D printers, transfer equipment, and buffer lines using on-site sensors and monitoring systems, and uses the Grubbs criterion to remove outliers.

[0010] Preferably, it also includes: generating interactive 3D logistics animations based on simulation results.

[0011] The second aspect of this application provides a logistics scheduling simulation device for a sand mold 3D printing workshop, comprising: The database construction module is used to build a sand mold 3D printing workshop database. The sand mold 3D printing workshop database stores the collected operating parameters of the 3D printer, the spatial layout data of the workshop, the physical kinematic model of the special logistics equipment, and the operation constraint parameters. The special logistics equipment includes heavy-duty AGVs and gantry cranes, and the operation constraint parameters include minimum aisle width, minimum turning radius, and ground load-bearing threshold. The simulation modeling module is used to load the spatial layout data and the physical kinematic model of the special logistics equipment in the simulation environment, and to embed the operation constraint parameters into the path planning algorithm. The scheduling optimization and simulation module is used to generate an initial logistics scheduling scheme based on the path planning algorithm using a multi-objective optimization function, and to obtain the logistics scheduling scheme and perform simulation by solving it through an improved genetic algorithm.

[0012] A third aspect of this application provides a logistics scheduling simulation system for a sand mold 3D printing workshop, including the logistics scheduling simulation device as described above; and a data acquisition device and a simulation display terminal that are communicatively connected to the logistics scheduling simulation device. The data acquisition equipment includes sensors and monitoring units deployed on 3D printers, dedicated logistics equipment and buffer lines, used to collect equipment operating status and spatial location data in real time; The simulation display terminal is used to receive and render the interactive 3D logistics animation generated by the device.

[0013] A fourth aspect of this application provides a computing device, comprising: processor, and A memory, connected to the processor, stores program instructions that, when executed by the processor, cause the processor to perform the logistics scheduling simulation method for a sand mold 3D printing workshop as described above. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating a simulation method for logistics scheduling in a sand mold 3D printing workshop, as provided in an embodiment of this application. Figure 2 This is a flowchart illustrating a simulation method for logistics scheduling in a sand mold 3D printing workshop, provided in another embodiment of this application. Figure 3 This is a schematic diagram of the structure of the simulation model provided in the embodiments of this application; Figure 4 This is a simulation diagram of the layout of a sand mold 3D printing workshop provided in the embodiments of this application; Figure 5 This is a flowchart of the improved genetic algorithm iterative optimization provided in the embodiments of this application; Figure 6This is a structural schematic diagram of the logistics scheduling simulation device 200 for a sand mold 3D printing workshop provided in the embodiments of this application; Figure 7 This is a structural schematic diagram of the logistics scheduling simulation system 300 for a sand mold 3D printing workshop provided in this application embodiment; Figure 8 This is a structural schematic diagram of a computing device 900 provided in an embodiment of this application. Detailed Implementation

[0015] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings. Preferred embodiments of this application are shown in the drawings. However, this application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of this application.

[0016] It should be noted that when an element is referred to as being "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," "top," "bottom," "end," "top," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0018] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a logistics scheduling simulation method for a sand mold 3D printing workshop provided in this application embodiment. It includes the following steps: S101: Construct a dedicated database containing 3D printer parameters, workshop layout, and kinematics and spatial constraints of heavy-duty logistics equipment.

[0019] A sand mold 3D printing workshop database is constructed, which stores the collected operating parameters of 3D printers, spatial layout data of the workshop, physical kinematic models of special logistics equipment, and operational constraint parameters. The special logistics equipment includes heavy-duty AGVs and gantry cranes, and the operational constraint parameters include minimum aisle width, minimum turning radius, and ground load-bearing threshold.

[0020] In some implementations, database fields include the 3D printer's location coordinates, printing cycle time, and maximum stacking height; workshop layout-related fields include aisle width (such as main aisle width, branch aisle width), column location, and ground load-bearing zoning diagram; special logistics equipment-related fields include the load capacity, length, and minimum turning radius of heavy-duty AGVs; gantry crane-related fields include span, working area radius, and lifting speed; and operational constraint parameter-related fields include AGV passage width, safe distance between AGVs and gantry cranes during cross-operations, and ground load-bearing threshold.

[0021] S102: Load the above data in the simulation environment and embed the spatial constraints of the equipment operation into the path planning algorithm.

[0022] The spatial layout data and the physical kinematic model of the dedicated logistics equipment are loaded into the simulation environment, and the operation constraint parameters are embedded into the path planning algorithm.

[0023] S103: Based on this algorithm, a multi-objective optimization function is adopted and solved by an improved genetic algorithm to generate a logistics scheduling scheme and perform simulation.

[0024] Furthermore, the path planning algorithm employs an improved genetic algorithm to obtain a logistics scheduling scheme and perform simulation. The improved genetic algorithm introduces adaptive crossover probability and adaptive mutation probability; wherein, The adaptive crossover probability is dynamically adjusted according to the current algebra, satisfying: The adaptive mutation probability increases as population diversity decreases, satisfying: The fitness function of the improved genetic algorithm is a weighted multi-objective function that satisfies: Where Pc is the adaptive crossover probability, g is the current generation number, and G max P represents the maximum number of generations. m H represents the adaptive mutation probability, where H is the entropy value of the current population. max T is the initial population entropy value; f is the fitness function of the improved genetic algorithm, and T logistics Wwaiting represents the total logistics time, and Wwaiting represents the cumulative waiting time for critical processes. Weighting based on logistics time consumption. As the waiting time weight, and + =1; T ref and W ref These are preset reference values. The outputs after the simulation run include AGV task sequences, equipment occupancy Gantt charts, etc.

[0025] In addition, two types of conflicts often occur during simulation: ① deadlock caused by the intersection of the AGV and gantry crane paths; ② strength failure caused by the sand mold waiting in the buffer area for a timeout (4h). Traditional methods require manual intervention or restarting the simulation, which takes more than 30 minutes.

[0026] In some embodiments of this application, real-time simulation conflict detection is also performed. Once a conflict is detected, path replanning and scheduling priority adjustment are automatically triggered. For example, when path intersections occur, an alternative path (such as detouring through a buffer area) is assigned to the AGV; when a waiting timeout occurs, the priority of the sand mold task is increased. For example, assuming AGV#2 enters the gantry crane #1 working area at t=1200s (distance 1.2m < 1.5m), the system immediately pauses AGV#2, replans its detouring route via the east side passage (adding 8m to the path), and simultaneously increases the priority of the affected sand mold.

[0027] This implementation method can effectively improve conflict handling efficiency, reducing the average conflict handling time from 32 minutes to 3 minutes; and reducing production interruptions caused by conflicts by 90%.

[0028] Furthermore, existing fixed weights cannot cope with dynamic disturbances. For example, if "logistics time" is still emphasized when equipment malfunctions, it will exacerbate congestion; and if the weight of "waiting time" is not reduced when orders are urgently inserted, critical tasks will be delayed.

[0029] This implementation provides a conflict type-weight mapping rule to reconstruct the objective function. When a conflict involving intersecting equipment paths is detected, the weight of logistics time is reduced and the weight of waiting time is increased; when a conflict involving timeouts in process waiting times is detected, the weight of logistics time is increased and the weight of waiting time is reduced. For example, when an intersecting equipment path is detected, the system is reset. =0.45, =0.55 to alleviate congestion; reset when process waiting timeout is detected. =0.75, =0.25, to accelerate turnover.

[0030] In some implementations, when a process scheduling conflict is detected, the path and scheduling sequence of the corresponding dedicated logistics equipment and its associated sand mold tasks are replanned without interrupting or restarting the simulation process of the remaining unaffected tasks.

[0031] This implementation only reschedules the equipment and associated sand molds involved in the conflict; all other tasks remain on their original schedules. The scope of the conflict's impact in this implementation can include: intersecting equipment paths, the involved AGV and one sand mold it is currently transporting; and process timeouts, affecting the sand mold and its downstream assembly / pouring tasks. Rescheduling only modifies the paths and time windows of these tasks, freezing other tasks.

[0032] In some implementations, workshop sensors are susceptible to electromagnetic interference and dust, often generating abnormal data (such as AGV positions jumping into the wall), which can lead to simulation distortion if directly entered into the warehouse. This implementation uses the Grubbs criterion to automatically identify and remove outliers. Data accuracy is improved from 89% to 99.5%, effectively reducing simulation crashes caused by data errors.

[0033] Furthermore, this application can simultaneously generate interactive 3D logistics animations during simulation, supporting viewpoint switching, task playback, and conflict highlighting. Process engineers can observe the status of gantry cranes and AGVs through the animations and adjust task allocation accordingly, effectively maximizing equipment utilization.

[0034] The logistics scheduling simulation of the sand mold 3D printing workshop provided in this application realizes the realistic restoration of the physical feasibility boundary of large-item transportation (such as ordinary AGVs can turn but heavy-duty AGVs cannot); it transforms the workshop civil engineering and equipment parameters into calculable constraints, avoiding the occurrence of "theoretically feasible but unexecutable on site" paths in the simulation; it prevents scheduling conflicts and deadlocks from the source, providing a reliable foundation for subsequent collaborative simulation and dynamic optimization.

[0035] This application solves the problem that existing logistics simulation methods in sand mold 3D printing workshops are infeasible in terms of scheduling schemes and unable to dynamically respond to disturbances because they ignore the physical feasibility and temporal coordination of large-item transportation. This is achieved by constructing a dedicated database that integrates hard constraints of large-item logistics and the time window of sand mold timeliness, and embedding process coordination requirements as hard constraints into an improved genetic algorithm that supports dynamic weight adjustment.

[0036] The following describes the logistics scheduling simulation method for sand mold 3D printing workshop provided in this application, using a specific implementation method as an example.

[0037] Please refer to Figure 2 - Figure 5 , Figure 2 This is a flowchart illustrating the logistics scheduling simulation method for a sand mold 3D printing workshop provided in this embodiment; Figure 3 This is a schematic diagram of the simulation model structure provided in this embodiment; Figure 4 This is a simulation diagram of the layout of a sand mold 3D printing workshop provided in this embodiment; Figure 5 This is a flowchart of the improved genetic algorithm iterative optimization provided in this embodiment.

[0038] This embodiment achieves accurate simulation and optimization of large-item logistics using sand molds through a complete process design of "data acquisition - model building - simulation operation - optimization output - dynamic response". The specific steps are as follows: N1: Logistics-related data collection and preprocessing The goal of this step is to acquire all the data required for the logistics simulation of a sand mold 3D printing workshop, and to build a standardized database to provide data support for the subsequent construction of simulation models. The specific details are as follows: Workshop Basic Data Acquisition: Collecting relevant parameters of the workshop's physical environment and equipment. Specifically, this includes: Spatial data such as workshop floor plan drawings and 3D spatial models, clearly defining aisle width, turning radius, ground load-bearing capacity, and the coordinates and dimensions of obstacles (such as columns, pipelines, etc.); Production equipment data such as the quantity, capacity parameters, spatial coordinates, and operating range of 3D printing equipment, sand removal equipment, casting furnaces, and post-processing equipment; Transfer equipment data such as the load limit, operating radius, moving speed, and start / stop acceleration of heavy-duty AGVs, gantry cranes, etc.; Storage area data such as the initial location, capacity, and load-bearing capacity of buffer areas and temporary storage areas.

[0039] Process collaboration data acquisition: Collecting the timing and logical relationships of each process. Specifically, this includes the standard processing cycle of each process and the connection logic between each process.

[0040] Data preprocessing includes the following: outlier handling, using Grubbs' test to detect and remove outliers from collected equipment parameters and time-series data to ensure data validity; and standardization transformation, converting non-standardized data such as spatial coordinates and equipment parameters into a format that simulation software can recognize, and unifying data units and coding rules.

[0041] N2: Construction of a logistics simulation model for a sand mold 3D printing workshop.

[0042] This step involves building a simulation model that fits the actual production environment, embedding specific constraints and optimization algorithms, and providing a core platform for logistics simulation. The specific details are as follows: Based on the 3D spatial data of the workshop collected in step N1, the physical environment of the workshop is restored in the simulation platform at a 1:1 scale. The specific operation is as follows: production equipment, transfer equipment, passages, obstacles, buffer areas and workstations are arranged according to the actual coordinates; ensure that the scene model is completely consistent with the physical constraints of the actual workshop (such as passage width, load-bearing capacity, equipment layout) to ensure the authenticity of the simulation.

[0043] To address the unique characteristics of sand mold 3D printing workshops, four types of constraints were set to ensure that the simulation results meet actual production requirements: Equipment operation constraints: The load on the transfer equipment shall not exceed the rated load; the operating radius of the bridge crane shall not exceed the specified range; the load on the passage shall not exceed the design limit; when equipment is operating in a cross-operation manner, the safe distance shall be greater than or equal to 1.5m.

[0044] Process coordination constraint: The transfer time from the sand mold 3D printing completion to the sand cleaning station must be less than or equal to 4 hours.

[0045] Space operation constraints: Transfer equipment must travel along the centerline of the channel; speed must be reduced by 50% when turning; collisions with other equipment or obstacles are prohibited.

[0046] To achieve multi-objective optimization, an improved genetic algorithm is embedded, specifically designed as follows: adaptive crossover and mutation probabilities are introduced to prevent premature convergence; multi-dimensional constraints are used as algorithm constraints to optimize the fitness function. Furthermore, variables are optimized, including transport paths, equipment scheduling order, buffer capacity and location, process coordination timing, and the configuration of the number of transport devices. The algorithm iteration parameters are set to meet the following requirements: population size of 50-100, number of iterations of 80-120, crossover probability of 0.6-0.8 (lower probability with higher fitness), and mutation probability of 0.05-0.15 (lower probability with higher fitness).

[0047] N3: Simulation operation and dynamic conflict detection.

[0048] By launching a simulation model, the entire logistics process is simulated, and conflicts and violations are detected in real time, providing a basis for subsequent optimization. The specific details are as follows: Input the initial simulation conditions, including order batch size, number of 3D printing devices powered on, initial status of transfer equipment, initial inventory in the buffer area, and simulation period.

[0049] The simulation model is launched to simulate the entire process of sand mold movement from "3D printing molding → transfer → sand cleaning → buffer storage → box assembly → delivery to the casting station → post-processing transfer". During the simulation, the model dynamically adjusts the logistics behavior according to the constraints and optimization functions to ensure that it conforms to the actual production logic.

[0050] During simulation, three types of conflicts are monitored and their relevant information is recorded in real time: equipment conflicts (intersecting transfer equipment paths, overlapping work areas, and congested passageways); process conflicts (timeouts in process connections); and constraint violations (exceeding transfer speed limits, overloading, and exceeding time limits). For each detected conflict, its occurrence time, location, type, and impact range are recorded, generating a conflict log.

[0051] N4: Simulation optimization and solution output.

[0052] Based on the conflict detection results, the optimal logistics solution is output through iterative algorithm optimization. For detected conflicts, an improved genetic algorithm is used to adjust relevant parameters. These adjustments include: Equipment conflicts: replanning transfer routes to avoid overlapping work periods; adjusting equipment operating radii or scheduling sequences to prevent congestion in channels. Process conflicts: adjusting buffer capacity and location, and optimizing Automated Guided Vehicle (AGV) scheduling priorities. Constraint violations: reducing the transfer speed of non-compliant equipment to ensure it meets process timeliness requirements.

[0053] Then, multi-objective iterative optimization is performed: based on the simulation data, it is substituted into the multi-objective optimization function to carry out iterative calculations, and the optimization variables such as transfer path, equipment configuration, and process sequence are gradually adjusted; when the difference between the objective function values ​​of two adjacent iterations is less than or equal to 3%, it is determined to be converged, the iteration is stopped and the optimal solution is output.

[0054] After outputting the optimal solution, the results are visualized. The output includes a logistics simulation report containing quantitative data such as total logistics time, equipment utilization rate, and average waiting time for each process, as well as optimization suggestions. Output formats include 3D visualization simulation videos, 3D factory models, and logistics simulation report documents.

[0055] N5: Dynamic response and solution update.

[0056] This step aims to enable rapid adaptation to unforeseen circumstances and ensure production continuity. The specific details are as follows: Emergency Scenario Definition: An emergency scenario library is set up, which covers common abnormal situations in 3D printing workshops, such as 3D printing equipment downtime, order batch increase or decrease (±30%), transfer equipment failure, and raw material supply delay; at the same time, a custom interface is reserved to support users to add special emergency scenarios and related parameters.

[0057] Feedback iteration: Collect actual logistics operation data (such as actual equipment utilization rate) and feed it back to the simulation model; adjust model parameters based on feedback data (such as adjusting constraint thresholds and optimizing algorithm weights) to continuously improve simulation accuracy and solution adaptability.

[0058] The solution provided in this application can effectively verify the rationality of production cycle time, personnel and equipment configuration in the early stage of factory design; through multi-objective optimization, the equipment utilization rate can be increased to the upper limit of national standards, significantly reducing logistics operating costs and achieving cost reduction and efficiency improvement; at the same time, the simulation model supports flexible adjustment according to sand mold parameters, workshop layout and order requirements, and can quickly adapt to sand mold 3D printing scenarios of different large castings, providing efficient and reusable logistics solutions to support workshop expansion or layout optimization.

[0059] Based on the same inventive concept, this application provides a logistics scheduling simulation device 200 for a sand mold 3D printing workshop. Please refer to [reference needed]. Figure 6 , Figure 6 This is a structural schematic diagram of the logistics scheduling simulation device 200 for a sand mold 3D printing workshop provided in this application embodiment, including: The database construction module 201 is used to construct a sand mold 3D printing workshop database. The sand mold 3D printing workshop database stores the collected operating parameters of the 3D printer, the spatial layout data of the workshop, the physical kinematic model of the special logistics equipment, and the operation constraint parameters. The special logistics equipment includes heavy-duty AGVs and gantry cranes, and the operation constraint parameters include minimum aisle width, minimum turning radius, and ground load-bearing threshold. The simulation modeling module 202 is used to load the spatial layout data and the physical kinematic model of the special logistics equipment in the simulation environment, and to embed the operation constraint parameters into the path planning algorithm. The scheduling optimization and simulation module 203 is used to generate an initial logistics scheduling scheme based on the path planning algorithm using a multi-objective optimization function, and to obtain the logistics scheduling scheme and perform simulation by solving it through an improved genetic algorithm.

[0060] The above-mentioned sand mold 3D printing workshop logistics scheduling simulation device 200 includes a database construction module 201, a simulation modeling module 202, and a scheduling optimization and simulation module 203. The above-mentioned sand mold 3D printing workshop logistics scheduling simulation device 200 is used to execute steps S101-S103 and any of its optional embodiments. Furthermore, the sand mold 3D printing workshop logistics scheduling simulation device 200 provided in this embodiment and the embodiments of the sand mold 3D printing workshop logistics scheduling simulation method provided in steps S101-S103 can be referenced by each other, and will not be described again in this embodiment.

[0061] This application also provides a logistics scheduling simulation system 300 for a sand mold 3D printing workshop. Please refer to [reference needed]. Figure 7 , Figure 7 This is a structural schematic diagram of the logistics scheduling simulation system 300 for a sand mold 3D printing workshop according to this application, including the logistics scheduling simulation device 200 as described above; and a data acquisition device 301 and a simulation display terminal 302 that are communicatively connected to the device; wherein, the data acquisition device includes sensors and monitoring units deployed on the 3D printer, dedicated logistics equipment and buffer line, for real-time acquisition of equipment operating status and spatial location data; The simulation display terminal is used to receive and render the interactive 3D logistics animation generated by the device.

[0062] Figure 8 This is a structural schematic diagram of a computing device 900 provided in an embodiment of this application. This computing device can serve as a logistics scheduling simulation system for a sand mold 3D printing workshop, executing various optional embodiments of the aforementioned logistics scheduling simulation method for a sand mold 3D printing workshop. The computing device can be a terminal, or a chip or chip system within the terminal. Figure 8As shown, the computing device 900 includes: a processor 910, a memory 920, and a communication interface 930.

[0063] It should be understood that Figure 8 The communication interface 930 in the computing device 900 shown can be used to communicate with other devices, and may specifically include one or more transceiver circuits or interface circuits.

[0064] The processor 910 can be connected to the memory 920. The memory 920 can be used to store the program code and data. Therefore, the memory 920 can be a storage unit inside the processor 910, an external storage unit independent of the processor 910, or a component that includes both the storage unit inside the processor 910 and the external storage unit independent of the processor 910.

[0065] Optionally, the computing device 900 may also include a bus. The memory 920 and communication interface 930 can be connected to the processor 910 via the bus. The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 The symbol is represented by a line without an arrow, but this does not mean that there is only one bus or one type of bus.

[0066] It should be understood that in the embodiments of this application, the processor 910 may be a Central Processing Unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Alternatively, the processor 910 may employ one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0067] The memory 920 may include read-only memory and random access memory, and provides instructions and data to the processor 910. A portion of the processor 910 may also include non-volatile random access memory. For example, the processor 910 may also store device type information.

[0068] When the computing device 900 is running, the processor 910 executes computer execution instructions stored in the memory 920 to perform any of the operational steps of the above method and any of the optional embodiments thereof.

[0069] It should be understood that the computing device 900 according to the embodiments of this application can correspond to the corresponding subject in executing the methods according to the various embodiments of this application, and the above and other operations and / or functions of each module in the computing device 900 are respectively for implementing the corresponding processes of the methods of this embodiment. For the sake of brevity, they will not be described in detail here.

[0070] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0071] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device (system) embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0073] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0075] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0077] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A simulation method for logistics scheduling in a sand mold 3D printing workshop, characterized in that, include: A sand mold 3D printing workshop database is constructed, which stores collected operating parameters of 3D printers, spatial layout data of the workshop, physical kinematic models of dedicated logistics equipment, and operational constraint parameters. The dedicated logistics equipment includes heavy-duty AGVs and gantry cranes, and the operational constraint parameters include minimum aisle width, minimum turning radius, and... Ground bearing capacity threshold; Load the spatial layout data and the physical kinematics model of the dedicated logistics equipment into the simulation environment, and embed the operation constraint parameters into the path planning algorithm; Based on the aforementioned path planning algorithm, an initial logistics scheduling scheme is generated using a multi-objective optimization function, and then solved using an improved genetic algorithm to obtain the logistics scheduling scheme and perform simulation.

2. The logistics scheduling simulation method according to claim 1, characterized in that, The path planning algorithm employs an improved genetic algorithm, which incorporates adaptive crossover probability and adaptive mutation probability; wherein... The adaptive crossover probability is dynamically adjusted according to the current algebra, satisfying: The adaptive mutation probability increases as population diversity decreases, satisfying: The fitness function of the improved genetic algorithm is a weighted multi-objective function that satisfies: Where Pc is the adaptive crossover probability, g is the current generation number, and G max P represents the maximum number of generations. m H represents the adaptive mutation probability, where H is the entropy value of the current population. max T is the initial population entropy value; f is the fitness function of the improved genetic algorithm, and T logistics Wwaiting represents the total logistics time, and Wwaiting represents the cumulative waiting time for critical processes. Weighting based on logistics time consumption. As the waiting time weight, and + =1; T ref and W ref These are the preset reference values.

3. The logistics scheduling simulation method according to claim 2, characterized in that, Also includes: In a simulation environment, it is detected whether a conflict occurs when the dedicated logistics equipment performs its tasks; When a conflict is detected, the transfer route is replanned and the equipment scheduling priority is optimized.

4. The logistics scheduling simulation method according to claim 3, characterized in that, include: When a conflict is detected between equipment paths, the weight of logistics time is reduced and the weight of waiting time is increased. When a process waiting time conflict is detected, the weight of logistics time consumption is increased and the weight of waiting time is decreased.

5. The logistics scheduling simulation method as described in claim 3, characterized in that, When a process scheduling conflict is detected, the path and scheduling sequence of the corresponding dedicated logistics equipment and its associated sand mold tasks are replanned without interrupting or restarting the simulation process of other unaffected tasks.

6. The logistics scheduling simulation method according to claim 1, characterized in that, The construction of the sand mold 3D printing workshop database includes: The system collects operational status data of 3D printers, transfer equipment, and buffer lines using on-site sensors and monitoring systems, and uses the Grubbs criterion to remove outliers.

7. The logistics scheduling simulation method according to any one of claims 1-6, characterized in that, Also includes: Interactive 3D logistics animations are generated based on simulation results.

8. A logistics scheduling simulation device for a sand mold 3D printing workshop, characterized in that, include: The database construction module is used to build a sand mold 3D printing workshop database. The sand mold 3D printing workshop database stores the collected operating parameters of the 3D printer, the spatial layout data of the workshop, the physical kinematic model of the special logistics equipment, and the operation constraint parameters. The special logistics equipment includes heavy-duty AGVs and gantry cranes, and the operation constraint parameters include minimum aisle width, minimum turning radius, and ground load-bearing threshold. The simulation modeling module is used to load the spatial layout data and the physical kinematic model of the special logistics equipment in the simulation environment, and to embed the operation constraint parameters into the path planning algorithm. The scheduling optimization and simulation module is used to generate an initial logistics scheduling scheme based on the path planning algorithm using a multi-objective optimization function, and to obtain the logistics scheduling scheme and perform simulation by solving it through an improved genetic algorithm.

9. A logistics scheduling simulation system for a sand mold 3D printing workshop, characterized in that, Including the logistics scheduling simulation device as described in claim 8; and, The data acquisition equipment and simulation display terminal are communicatively connected to the logistics scheduling simulation device; The data acquisition equipment includes sensors and monitoring units deployed on 3D printers, dedicated logistics equipment and buffer lines, used to collect equipment operating status and spatial location data in real time; The simulation display terminal is used to receive and render the interactive 3D logistics animation generated by the device.

10. A computing device, characterized in that, include: processor, and A memory connected to the processor and storing program instructions, which, when executed by the processor, cause the processor to perform the logistics scheduling simulation method for a sand mold 3D printing workshop as described in any one of claims 1-7.

Images