Intensive storage digital twinning related system and method

Abstract

The invention belongs to the technical field of intelligent manufacturing and industrial internet, and particularly relates to a dense storage digital twinning related system and method. The system adopts a five-layer architecture design and comprises a physical entity layer, a sensing layer, a data layer, a twin model and virtual debugging layer and an application layer, the system integrates the functions of three-dimensional modeling, virtual simulation, real-time data acquisition, OPC UA communication, Web visual interface, state monitoring and intelligent diagnosis, and aims to realize virtual debugging, efficient monitoring and predictive maintenance of dense storage equipment and remarkably improve the reliability and intelligent level of a storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent logistics and industrial Internet, and specifically relates to a system and method related to dense warehousing digital twins. Background Art

[0002] Traditional flat warehouses, due to issues such as high manual intervention, low space utilization, and severe information silos, are no longer able to meet the demands of large-scale, high-frequency logistics operations. Dense warehousing systems, with their high space utilization efficiency and strong dynamic storage capabilities, are becoming a key technical support for smart logistics hubs.

[0003] However, dense warehousing systems are typically composed of a variety of heterogeneous equipment, including four-way shuttles, cargo elevators, conveyor lines, and control cabinets. These systems are complex in structure and require high precision in coordinated control between devices, making debugging and maintenance difficult and time-consuming. Traditional debugging methods rely heavily on physical entities, resulting in long debugging cycles, high deployment risks, and high subsequent maintenance costs. Furthermore, warehousing equipment constantly operates under frequent start-stop cycles, complex operating conditions, and multiple load variations, making it prone to failure. Existing monitoring methods are unable to fully perceive and predict the health status of these equipment.

[0004] In recent years, the development of digital twin technology has provided a new path to addressing these issues. By constructing a digital representation of a physical system in virtual space, digital twins enable integrated management of the entire lifecycle, from control and commissioning, operational monitoring, fault diagnosis, and system optimization. Existing digital twin-based warehouse systems are primarily limited to data visualization and equipment status display, lacking in-depth support for actual control logic, system collaboration, and fault identification and prediction. Therefore, there is an urgent need to develop a high-precision digital twin system construction and application method for dense warehouse systems that integrates modeling, communication, control, data, and diagnostics. Summary of the Invention

[0005] The present invention provides a system and method related to dense warehousing digital twins to solve the technical problems in the existing technology that traditional warehousing systems are difficult to meet the development trends of intelligence, automation and intensiveness, and have many manual interventions, low space utilization, and information islands.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A dense warehouse digital twin system, including a physical entity layer, a perception layer, a data layer, a twin model and virtual commissioning layer, and an application layer; The physical entity layer includes physical equipment in the storage system; The perception layer is used to obtain status data of devices in the physical layer; The data layer is used for data processing of state data and database storage management. The twin model and virtual debugging layer are used for constructing a warehouse virtual model based on a C / S architecture, integrating PLC programs in a deployment stage, and virtually simulating and testing off-line the operation logic of the equipment; in a running stage, the data layer storage data are dynamically bound to realize real-time state synchronization and action mapping of the virtual model and the physical system, and complete three-dimensional visual presentation of the equipment running process. The application layer is based on B / S architecture and develops a human-computer interaction interface according to the data stored in the data layer, integrates the front-end interface into the Visual Components virtual environment platform, and displays the real-time state of the system; according to the data layer data, a deep learning algorithm is used to diagnose faults of key components of the equipment, and according to the diagnosis result, a decision instruction is outputted, and the corresponding warehouse equipment management is performed according to the decision instruction.

[0007] The perception layer collects state data sources including equipment PLCs and sensors deployed at various positions; the perception layer collects state data of the equipment PLCs by using an OPC UA protocol; and the data of the sensors at various positions are collected by using a TCP / IP protocol, the perception layer transmits the collected state data of the equipment PLCs and the sensor data to the data layer; the data layer transmits the obtained state data of the equipment PLCs to the twin model and virtual debugging layer for virtual-real mapping, and stores the state data in a database; the sensor data are also stored in the database to provide data support for real-time data state display of the application layer.

[0008] The warehouse virtual model is constructed by using SolidWorks to construct geometric models according to the size, shape and height of physical entities, and using SolidWorks to establish geometric models of core equipment of dense warehouses one by one; the geometric models are exported in a STEP format and imported into 3ds Max to perform visual optimization operations such as material rendering, UV mapping and texture setting, and at the same time, lightweight processing such as mesh simplification and polygon compression is performed, and the optimized model is imported into Visual Components, the model is arranged in a scene according to the physical entity, physical properties are added, and a control script is written to construct the warehouse virtual model.

[0009] Based on the warehouse virtual model, the motion behaviors of the key components are modeled in a mathematical way, the space motion modeling method based on a homogeneous transformation matrix is used to systematically express the motion of the components, the homogeneous transformation matrix is a method for uniformly describing the translation and rotation transformation of a rigid body in a three-dimensional space, and its general form is a 4*4 matrix:

[0010] wherein, is the rotation matrix in three-dimensional space, is the translation vector, which represents the displacement of the object in the X, Y, and Z directions.

[0011] In actual operation, some components may have both translation and rotation characteristics. In this case, a composite motion modeling method should be used to combine multiple single actions into one, which can be represented by chain multiplication of multiple sets of homogeneous transformation matrices:

[0012] in, represents the component translation matrix, Represents the component rotation matrix.

[0013] Based on the motion logic of key components in the warehouse virtual model, a PLC control logic program was developed in the TIA Portal, and the S7 protocol bridge between the PLC virtual controller and Visual Components was implemented through the NetToPLCSim software. The motion trajectory, control points, and logic events of each device were configured to simulate the device response, action synchronization, fault simulation, and linkage verification of the warehouse virtual model. A data mapping structure was established between the Visual Components. The Visual Components read the real-time data collected by the OPC UA server through variable pairing to drive the device status in the warehouse virtual model in real time.

[0014] Based on the data in the data layer, a deep learning algorithm is used to diagnose faults in key components of the equipment. Specifically, the equipment status data after data processing is obtained from the data layer, and a deep learning algorithm is used to build a fault diagnosis model based on historical fault data and normal operation data. The real-time collected and processed data is input into the trained model. The model identifies whether there is a fault in the key components of the equipment, the type of fault, and the severity of the fault based on the learned feature patterns, and outputs the fault diagnosis results.

[0015] A method for constructing a dense warehouse digital twin system includes the following steps: Obtain status data of devices in the physical layer; Process status data and perform database storage management; Based on the C / S architecture, a virtual warehouse model is constructed. During the deployment phase, PLC programs are integrated to conduct virtual simulation and offline testing of the equipment's operating logic. During the operation phase, data stored in the data layer is dynamically bound to achieve real-time state synchronization and action mapping between the virtual model and the physical system, completing a three-dimensional visualization of the equipment operation process. According to the data stored in the data layer; based on B / S architecture, develop visual interactive interface, integrate front-end interface into Visual Components virtual environment platform, show real-time state of system; according to data in data layer, adopt deep learning algorithm to carry out fault diagnosis to key components of equipment, according to diagnostic result, output decision instruction, carry out corresponding warehouse equipment management according to decision instruction.

[0016] An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a dense warehouse digital twin system construction method when executing the computer program.

[0017] A storage medium having a computer program stored thereon, the computer program implementing the steps of a dense warehouse digital twin system construction method when executed by a processor.

[0018] A computer product based on a dense warehouse digital twin system construction method, comprising a computer usable medium having computer program logic, the computer program logic comprising: Obtaining state data of the physical entity in the physical entity layer; Data processing and database storage management are performed on the state data; Based on C / S architecture, build a warehouse virtual model, integrate PLC program in the deployment stage, and virtually simulate off-line test of the working logic of the equipment; in the running stage, dynamically bind the data stored in the data layer, realize real-time state synchronization and action mapping of the virtual model and the physical system, and complete three-dimensional visualization presentation of the equipment running process; According to the data stored in the data layer; based on B / S architecture, develop visual interactive interface, integrate front-end interface into Visual Components virtual environment platform, show real-time state of system; according to data in data layer, adopt deep learning algorithm to carry out fault diagnosis to key components of equipment, according to diagnostic result, output decision instruction, carry out corresponding warehouse equipment management according to decision instruction.

[0019] Compared with the prior art, the present application has the following beneficial effects: The dense storage digital twin system provided by the application is based on a C / S and B / S hybrid architecture, combines real-time data acquisition, virtual model construction and visual management, realizes dynamic mapping and intelligent linkage between physical storage equipment and a digital twin platform, and is a digital twin system construction method for a smart storage scene. The application uses a three-dimensional modeling tool and a digital twin simulation platform to construct a high-precision virtual model of the dense storage system on the C / S side, replicates the physical space structure and equipment layout, realizes dynamic simulation and simulation debugging of the storage operation, and greatly improves the debugging efficiency of the storage system in the deployment stage and the transparency and intelligent level in the running stage. At the same time, a visual human-computer interaction interface is designed on the B / S side, and functions such as storage management, information overview, equipment management, data management, data monitoring and fault diagnosis, and system management are integrated. Users can view the running state of the storage system in real time through the web page, and the operation is simple and intuitive, which improves the system management efficiency.

[0020] Further, the application adopts a C / S and B / S hybrid architecture design, which fully utilizes the technical advantages of the two architectures. The C / S side has good graphics rendering capability and computing performance, and is suitable for high-precision three-dimensional modeling, dynamic simulation and local debugging of the dense storage system. The B / S side has the characteristics of cross-platform and light deployment, which is convenient for remote access, centralized management and multi-user collaborative operation. Through the cooperative operation of the two ends, the system realizes the function cooperation of remote control and visual display while ensuring local high-precision real-time monitoring, significantly improves the running flexibility, deployment efficiency and adaptability of the system, and is especially suitable for complex storage management scenarios involving multiple terminals and multiple roles.

[0021] Further, a data interface service is constructed to realize real-time acquisition and fusion of multi-source data such as PLC, sensors and cameras, and synchronous updating of device running state and virtual model through data driving. The system supports dynamic adjustment of virtual model state based on data changes, and meets the needs of device management, state monitoring and simulation training in complex storage environments.

[0022] Further, the system supports recording and playback of device running data, which can be used for state tracking, abnormal analysis and fault positioning; the system also supports device fault diagnosis, power prediction, energy consumption prediction and other functions, and the system reserves a modular interface, which can conveniently access subsequent development of diagnostic algorithms, scheduling strategies and other functional modules, and has good expansibility and flexibility.

[0023] Further, the application realizes real-time mapping and interactive control of the system front-end interface in the virtual environment by integrating the "Experience Website Viewer" component in the virtual scene and configuring the system web page address in the WebBrowserURL attribute. Users can directly control the system interface in the three-dimensional virtual environment constructed on the C / S side, combined with the real-time collected device running data, to realize dynamic monitoring and simulation restoration of the warehouse device running state. At the same time, the system embeds a visual large screen in the virtual scene, enhancing the overall display and interaction ability.

[0024] By integrating data acquisition, model construction, system development and intelligent management into one, the proposed dense warehouse digital twin system effectively improves the visualization level and intelligent management ability of warehouse operation, overcomes the problems of traditional warehouse system such as insufficient visualization, low data utilization, information island, etc., has the advantages of high modeling accuracy, low deployment cost, strong interaction performance and good real-time performance, and has good popularization value and engineering practicability. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is the overall architecture diagram of the dense warehouse digital twin system of the application; Figure 2 is a B / S and C / S hybrid architecture diagram in the dense warehouse digital twin system of the application; Figure 3 is a warehouse virtual model construction flowchart of the application; Figure 4 is a three-dimensional model diagram of the dense warehouse system device established by SolidWorks; Figure 5 is a view of the warehouse rack and four-way shuttle vehicle after rendering and lightweight processing by 3ds Max in the embodiment of the application; Figure 6 is a warehouse virtual model construction flowchart in the embodiment of the application; Figure 7 is a behavior model construction diagram in the embodiment of the application; Figure 8 is a dense warehouse system virtual debugging flowchart in the embodiment of the application; Figure 9 is a system data acquisition and transmission architecture diagram in the embodiment of the application; Figure 10 is an OPC UA server building flowchart in the embodiment of the application; Figure 11 is a C / S side visual interface diagram in the embodiment of the application; Figure 12 is a B / S side large screen monitoring interface diagram in the embodiment of the application; Figure 13 This is a diagram of the B / S terminal device operation status monitoring interface in an embodiment of the present invention; Figure 14 This is a diagram of the B / S terminal fault diagnosis interface in an embodiment of the present invention; Figure 15 This is a test chart of the CPU and GPU occupancy rates when the C / S system is running in an embodiment of the present invention; Figure 16 This is a test chart of the CPU and GPU occupancy rates when the B / S system is running in an embodiment of the present invention; Figure 17 This is a schematic diagram of a dense warehousing digital twin system module of the present invention; Figure 18 Schematic diagram of the system equipment related to the dense storage digital twin in an embodiment of the present invention; Figure 19 The monitoring status of the virtual debugging program in the embodiment of the present invention; Figure 20 A schematic diagram of human-computer interaction visualization of the B / S front-end interface integrated with the Visual Components virtual environment platform in an embodiment of the present invention; Figure 21 This is a functional schematic diagram of a dense warehousing digital twin system in an embodiment of the present invention.

[0026] Explanation of reference numerals: 100, electronic device; 101, memory; 102, processor; 103, computer program; 104, communication bus. DETAILED DESCRIPTION

[0027] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Example 1 like Figure 17 As shown, this embodiment proposes a dense warehouse digital twin system, which mainly includes a physical entity layer, a perception layer, a data layer, a twin model and virtual debugging layer, and an application layer; The physical entity layer includes physical equipment in the storage system; The perception layer is used to obtain status data of devices in the physical layer; The data layer is used to process the status data and perform database storage management; The twin model is combined with a virtual debugging layer, which is used to build a warehouse virtual model based on a C / S architecture. In the deployment stage, the PLC program is integrated to test the operation logic of key equipment such as four-way shuttle vehicles and elevators offline, thereby improving the debugging efficiency before the system goes online. In the running stage, the virtual model and the physical system are synchronized in real time and the action mapping is realized through dynamic binding of the position, speed, task and other data collected by the perception layer in real time, thereby completing the three-dimensional visualization of the device running process.

[0030] The application layer develops a visual interactive interface based on a B / S architecture according to the data stored in the data layer, displays the real-time state of the system, and integrates the front-end interface into the Visual Components virtual environment platform, as shown in Figure 20 The Visual Components virtual environment platform has the functions of the front-end interface and can realize virtual simulation debugging, virtual-real mapping and visualization of the equipment. The front-end interface is integrated into the Visual Components virtual environment platform, which has the functions of the front-end interface and the most core function is that it can realize virtual simulation debugging, virtual-real mapping and three-dimensional visualization of the equipment. Users can not only operate the front-end interface in the virtual environment, but also can drive the equipment data to realize virtual debugging, simulation optimization and real-time monitoring of the equipment running state. At the same time, according to the data layer data, a deep learning algorithm is used to diagnose the faults of the key components of the equipment, according to the diagnosis result, a decision instruction is output, and the corresponding warehouse equipment management is carried out according to the decision instruction. The fault diagnosis algorithm includes but is not limited to CNN, LSTM, AlexNet, RNN, Transformer and ResNet and their improved algorithms. The input of the algorithm is the vibration, temperature and other signals collected by the sensor, and the output is the fault type. According to the data layer data, a deep learning algorithm is used to diagnose the faults of the key components of the equipment, specifically: the device state data after data processing is obtained from the data layer, a deep learning algorithm is used to build a fault diagnosis model based on historical fault data and normal operation data, the real-time collected and processed data is input into the trained model, the model identifies whether there is a fault in the key components of the equipment, the fault type and the fault severity according to the learned feature pattern, and outputs the fault diagnosis result.

[0031] Based on the above dense warehouse digital twin system, the construction method is as follows: Obtain the state data of the physical entity in the physical entity layer; The state data is processed and stored in the database for management; Based on the C / S architecture, a warehouse virtual model is constructed, in the deployment stage, the PLC program is integrated, and the operation logic of the equipment is tested offline; in the running stage, the data stored in the data layer is dynamically bound, the real-time state synchronization and action mapping of the virtual model and the physical system are realized, and the three-dimensional visualization of the equipment running process is completed; According to the data stored in the data layer; based on the B / S architecture, a visual interactive interface is developed, the front-end interface is integrated into the Visual Components virtual environment platform, and the real-time state of the system is displayed; according to the data in the data layer, a deep learning algorithm is used to diagnose the faults of the key components of the equipment, according to the diagnosis result, a decision instruction is output, and the corresponding warehouse equipment management is carried out according to the decision instruction.

[0032] Based on the above system construction method, the construction of the dense warehouse digital twin system is developed and designed, and the specific implementation method is as follows: The overall framework of the system is designed based on the C / S and B / S hybrid architecture, which is divided into a physical entity layer, a perception layer, a data layer, a twin model and a virtual debugging layer, and an application layer. The C / S end is developed based on the Visual Components platform, and the main functions include building a warehouse virtual model, virtual debugging and virtual-real mapping. Specifically, according to Figure 3 The warehouse virtual model construction flowchart shown in the figure, based on the C / S end architecture, the warehouse virtual model is constructed.

[0033] First, the physical components of the dense warehouse system are planned, the basic framework of the system is constructed from the spatial dimension, and the mutual adaptation of the components in the physical layer is ensured, the layout is reasonable, and the structure basis for subsequent geometric modeling is provided. The functions that need to be realized by the dense warehouse system are defined from the functional operation level, the behavior mode of the system is defined, so that the subsequent modeling not only has "shape", but also has "spirit", and the model can truly reflect the actual operation requirements of the system.

[0034] Geometric model construction is performed using SolidWorks to ensure that the model and the physical entity are highly consistent in size and shape. The geometric model of the physical entity established as shown in Figure 4 includes a four-way shuttle car model, a cargo elevator model, a shuttle car elevator model, and a dense warehouse assembly model; The established geometric model is exported in.step format, and the geometric model in.step format is imported into 3dsMax for material rendering and lightweight processing. After processing, the model is exported as a.fbx file. The steps of material rendering and lightweight processing using 3ds Max are as follows: Step1: Export the geometry model established by SolidWorks into.step format and import the.step format geometry model into 3ds Max; Step2: Check the integrity and accuracy of the geometry model imported into 3ds Max, ensure that the proportions and positions of each part of the geometry model are correct; Step3: Open the material editor in 3ds Max, create a new material sphere, adjust the material sphere parameters and basic properties, add corresponding materials to each part of the model, and adjust the material color, reflection and concave-convex properties according to the actual situation; Step4: Use the "unfold UVW" modifier to expand and adjust the UV coordinates of the model, so that the map can be correctly mapped to the model surface. Import the required map file and apply it to the corresponding material channel. Adjust the scaling, rotation and translation parameters of the map to ensure seamless connection.

[0035] Step5: Load the rendered model; Step6: Open the modifier in 3ds Max and set the modifier to "object space modifier" and select "professional optimization"; Step7: Calculate the initial polygon face number and vertex number of the imported model; Step8: Set the optimization mode to "protect the boundary", the material and UV to "keep the material boundary" and "keep the texture", and set the optimization level and threshold angle; Step9: Recalculate the polygon face number and vertex number; Step10: Export the rendered geometry model, the rendered model is shown in Figure 5 .

[0036] Refer to Figure 6During the warehouse virtual modeling process, the .fbx file, modeled and lightweighted in SolidWorks and 3ds Max, was imported into Visual Components. The scene was reconstructed based on the actual warehouse layout, ensuring that the geometric model in the virtual environment conformed to the spatial relationships and layout requirements of the real warehouse. Coordinate parameters were adjusted to ensure accurate positioning of each model. The system divided the geometric model into moving parts, fixed parts, and functional parts. By extracting feature links, each type of component was processed separately: constraints, physical properties, and parent-child nodes were added to moving parts. Fixed parts also required coordinate parameter adjustment to ensure accurate positioning. By building hierarchical relationships and behavioral interfaces, parent-child node attributes and physical characteristics were assigned to each part. In addition to the above operations, functional parts also required defining feature links related to their functions. Component behavior properties were then added to further refine the model's behavior. By defining node behaviors, the specific action logic of each component in the scene was clarified. Behavioral interfaces were added to enable interaction with other components, and paths were added to plan the movement routes of moving parts. At the same time, component containers are created to integrate related parts, sensors are added to detect scene changes, signals are added to enable information transmission between components, and controllers are selected to uniformly manage and coordinate the actions of each component. Finally, control logic is written, and complex custom control strategies are implemented by editing Python scripts. Signal variables are paired to ensure accurate correspondence between signals, allowing the models in the entire virtual scene to operate collaboratively according to the actual warehouse operation logic. Ultimately, a highly realistic and fully functional warehouse virtual model is constructed, achieving component-based management and intelligent simulation of the warehouse virtual model.

[0037] In another preferred embodiment of the present invention, in order to ensure that each device in the dense storage system can accurately simulate and perform predetermined tasks in the virtual environment, a behavior model must be established. Figure 7As shown, the dense storage system includes a four-way shuttle vehicle and a rack part, wherein the four-way shuttle vehicle includes a walking mechanism and a fork mechanism; the rack part includes a shuttle vehicle elevator part, a cargo elevator part, and a conveying line part. When constructing the behavior model, for the four-way shuttle vehicle, the moving speed and path rules are defined for the walking mechanism (X-direction and Y-direction walking parts), and the insertion and extension logic and precision control are set for the fork mechanism (fork jaw extension and rotation, etc.), to ensure that the operations such as walking and fork insertion meet the actual working conditions. For the shuttle vehicle elevator and the cargo elevator, the speed and start-stop conditions are defined for the lifting movement of the lifting platform and the rotation of the conveying drum, so that the elevators can accurately perform the lifting and transfer tasks according to the instructions. In terms of cooperation, the signal interaction rules are set through the behavior model. According to the motion rules and cooperation relationship in the behavior model, each device responds to real-time data to accurately simulate actual operation in the virtual environment, ensuring efficient and orderly operation of the system, and realizing the realistic simulation of the specific actions of the four-way shuttle vehicle, the elevator and other devices. The construction of the behavior model can define the motion rules and cooperation relationship of each device and component under different working conditions, to ensure that the device can respond to real-time data. Through the establishment of the behavior model, the four-way shuttle vehicle, the shuttle vehicle elevator, the cargo elevator and other devices can work cooperatively, and their specific actions such as four-way shuttle vehicle walking and fork insertion can be simulated in the virtual environment.

[0038] In a further preferred embodiment of the present application, to realize the realistic reproduction of device actions in the warehouse virtual model, the motion behavior of each key component must be modeled in a mathematical way. The key devices in the dense storage system have clear motion characteristics, including translation, lifting and rotation, etc. The present application uses a spatial motion modeling method based on homogeneous transformation matrix to systematically express the motion of these components. Homogeneous transformation matrix is a method for uniformly describing the translation and rotation transformation of rigid bodies in three-dimensional space, and its general form is a 4x4 matrix:

[0039] wherein, is a rotation matrix in three-dimensional space, is a translation vector representing the displacement of the object in the X, Y and Z directions.

[0040] For the forward and backward and left and right translation of the four-way shuttle vehicle in the XY plane, the fork extension action of the fork in the Y direction, and the lifting action of the shuttle vehicle elevator and the cargo elevator in the Z direction, they can all be classified as single translation motion. The homogeneous transformation matrix can be expressed as:

[0041] wherein, , and respectively represent the displacement amount of the model in the X, Y, Z directions.

[0042] For the rotation of the paw around the Y axis and the rotation of the roller conveying line around the X axis, it can be classified as single rotation motion around fixed axis. The homogeneous rotation transformation matrix of the rotation of the paw around the Y axis and the homogeneous rotation transformation matrix of the rotation of the roller conveying line around the X axis are as follows.

[0043]

[0044]

[0045] In actual operation, some components may have both translation and rotation characteristics at the same time, for example, the paw needs to complete position movement and posture adjustment before performing the grabbing action. At this time, the compound motion modeling method should be used to combine multiple single actions into one, and the multiple homogeneous transformation matrix chain multiplication is used to represent:

[0046] wherein, represents the component translation matrix, represents the component rotation matrix.

[0047] In a further preferred embodiment of the present application, in order to realize the logical behavior control of the warehouse virtual model, the system uses Python language to write control scripts based on the script control interface of Visual Components. By using the built-in functions such as getComponent(), getApplication(), findBehaviour(), OnSignal(), OnRun() provided by the platform, the behavior triggering, state conversion and task coordination logic between various components are established. The control logic supports information transmission, priority management, mutual exclusion conditions and synchronous promotion, can accurately simulate complex operation scenes such as four-way shuttle car scheduling and elevator fork linkage, and ensure that the virtual and real mapping behaviors and physical processes are highly consistent. In addition, the behaviors of various devices in the warehouse virtual model are set with standard interfaces and feedback mechanisms according to functional requirements, for example, the conveying line is provided with a signal detection sensor, when the material triggers a signal, the subsequent process can be activated in real time; the lifting mechanism is provided with a signal control lifting action response; the four-way car is provided with path points and steering control identifiers to realize free motion in the XY plane.

[0048] Based on the motion logic of each key component in the warehouse virtual model, the virtual debugging and model driving of the dense warehouse digital twin system are carried out. The control logic is verified by the linkage simulation of the PLC program and the warehouse virtual model to reduce the debugging cost. Then the program is deployed on the spot to ensure the stable operation of the system. The virtual-real mapping between the physical entity and the virtual model is realized based on OPC UA. Among them, the virtual debugging of the dense warehouse digital twin system includes three levels: verification simulation of the PLC program, visual monitoring of the logical process, and data-driven virtual-real synchronization. Figure 8 As shown. The TIA Portal platform is used to write PLC programs for controlling four-way shuttles, cargo elevators and conveyor lines. Key state variables and logical jump conditions are defined in the program to realize the automated control of equipment start, stop, lift, translation, avoidance and other operating instructions. In order to avoid the cost and risk brought by physical debugging, start S7-PLCSIM, use the S7 virtual PLC interface, and combine the NetToPLCSim bridge tool to map the PLC logic in the TIA Portal to the Visual Components simulation platform through the S7 protocol, so that the control program can directly drive the motion behavior of the virtual model. Secondly, in the logic process verification stage, in the Visual Components platform, the execution effect of the PLC program is monitored throughout the process through the interactive simulation interface to observe the logic condition triggering, motion behavior response and intermediate state transfer. The platform supports breakpoint debugging, logging and variable tracking, which can gradually verify the stability and correctness of the PLC program, as shown below. Figure 19 As shown in the figure. After logic verification, the control program is officially downloaded to the physical PLC and connected to the real device for functional testing to confirm the consistency and robustness of the program under field conditions. By configuring NetToPLCSim, communication is established between the TIA Portal and Visual Components. If the communication connection between the TIA Portal and Visual Components fails, it is necessary to retrospectively check the correct configuration of the NetToPLCSim platform. If the connection is successful, proceed to the following step.

[0049] In order to achieve the virtual-real mapping between the warehouse virtual model and the physical equipment, the system builds a perception layer to collect the status data of the equipment in the physical entity layer; Figure 9As shown, the state data sources include the device PLC and sensors deployed at each location. The perception layer collects the state data of the device PLC using the OPC UA protocol, and the data layer transmits the obtained device PLC state data to the virtual debugging layer for virtual-real mapping on one hand, and stores it in the database on the other hand; Specifically: an OPC UA server is built using KEPServerEX, and the reading and distribution of PLC variables are realized by configuring the connection channel, adding devices, and defining static variable tags. The variable binding interface on the Visual Components side receives these variable values to drive the synchronous movement of the virtual model, such as the displacement of the shuttle, the insertion and extension of the fork, and the lifting of the elevator. This process ensures that the control logic can be both simulated and verified, and consistent with the actual control hardware. For example Figure 10 As shown, the OPC UA server building process includes the following steps: Step 1: Start KEPSeverEX6 Configuration software; Step 2: New connection channel; Step 3: Add channel devices and configure parameters; Step 4: Add static tags; Step 5: Start Quick Client to check the connection status; The data layer transmits the obtained device PLC state data to the virtual debugging layer for virtual-real mapping on one hand, and the virtual-real mapping interface is as shown in Figure 11

[0050] The perception layer collects data from sensors at each location through TCP / IP protocol, including vibration sensors, temperature sensors, RFID, etc. These sensors are connected to the middleware through TCP / IP protocol, and then uploaded to the MySQL database by the backend program for the front-end system to display the state, monitor the health, and analyze the fault.

[0051] ​The application layer of the dense warehousing digital twin system is based on a B / S architecture, and a human-computer interaction interface has been developed. Web technology is used to develop the front-end UI interface, enabling visual management and remote control of the dense warehousing system. This includes functions such as real-time status display, remote access control, data query and analysis, fault diagnosis, data monitoring, warehouse task scheduling, and alarm information processing. The system adopts a front-end and back-end separated architecture design. The front-end interface is developed based on HTML5, CSS3, JavaScript, and the Vue.js framework. Incorporating the Element UI component library and the ECharts visualization charting library, a multifunctional interactive platform with intuitive operation, rich data, and rapid response has been constructed. Users can access the system through a browser terminal without installing plug-ins, achieving cross-platform compatibility. The front-end page mainly includes: an information overview module, an equipment management module, a fault diagnosis module, a data monitoring module, an alarm notification module, and a remote control module. Furthermore, the B / S-based human-computer interaction interface also integrates a WMS (Warehouse Management System), a WCS (Warehouse Control System), and a warehouse management system with location monitoring capabilities. This allows for coordinated inventory planning, issuing operational instructions for warehouse equipment, and ensuring accurate inventory information. Building on the B / S architecture, the C / S architecture further implements virtual-reality mapping and equipment debugging, responding to physical equipment actions in real time, driving models, and visually presenting 3D scenes for visual monitoring of warehouse equipment. Furthermore, equipment simulation is performed in a virtual environment, and based on the simulation, equipment debugging is performed to verify equipment control strategies.

[0052] Information overview module: used to display the operating status (temperature, vibration, current, etc.) of key equipment in the current dense storage digital twin system, task execution status, energy consumption statistics and alarm event list, such as Figure 12 shown.

[0053] Device management module: Query the historical operation records, fault records and maintenance logs of each device according to the device number, and support one-click remote restart, status refresh and health status assessment.

[0054] Fault diagnosis module: displays the fault type and component location identified by the back-end deep learning algorithm model, and provides diagnostic result confidence, trigger timing data and waveform visualization, such as Figure 14 shown.

[0055] Data monitoring module: Displays data of different devices and time periods in a graphical manner, such as Figure 13 shown.

[0056] Alarm notification module: Receives real-time abnormal signals generated by the data layer based on the status data of PLC or sensors, pops up windows to remind users and records event logs, and supports setting multi-level alarm thresholds and automatic shielding mechanisms; Remote control module: Users with administrator privileges can issue remote commands for operations such as starting and stopping the shuttle, issuing tasks, and adjusting cargo locations.

[0057] The backend is developed in Python, primarily integrating FastAPI and Flask as an interface service framework. This provides a unified RESTful API for handling data access, device control command forwarding, model invocation, and other functions. The system also supports scheduled data synchronization and cache updates with the MySQL database to ensure real-time and consistent front-end display. All interfaces are functionally and performance-tested using Postman.

[0058] In addition, the system supports a multi-role login mechanism, and user permissions are divided into administrators, engineers, operation and maintenance personnel, and read-only users. Accessible modules and operable functions are assigned according to roles to improve system security and data management standardization.

[0059] In addition, in order to realize the visualization and interactive operation of system functions in the virtual environment, the front-end interface is integrated into the Visual Components 4.8 platform, such as Figure 11 As shown. By adding the "ExperienceWebsiteViewer" component in the "Misc" category and configuring the web page address in its "WebBrowserURL" property, virtual mapping of the system interface can be achieved. After system integration, users can not only operate the system interface in the virtual environment, but also combine it with device data to achieve real-time monitoring and simulation restoration of the device's operating status. The large visual screen arranged in the virtual scene further enhances the system's display and interactive capabilities: the left screen supports interaction with the system front end in the virtual environment, and the right screen can display key operating data in real time, such as the power and speed curves of the four-way shuttle, thereby improving the intuitiveness of device operation, the real-time nature of system monitoring, and the overall intelligent level of operation.

[0060] In another preferred embodiment of the present invention, in order to measure the comprehensive performance of the system, the CPU and GPU occupancy rates of the system from start to end are tested. Figure 15 and Figure 16 In the figure, area 1 represents the system startup phase, area 2 represents the system normal operation phase, and area 3 represents the system shutdown phase. Figure 15 This is a test of the system's web-side occupancy rate. During normal system operation, the average CPU and GPU occupancy rates are 34% and 16% respectively. Figure 16To implement the Visual Components client occupancy test for virtual-reality mapping and simulation debugging, the average CPU and GPU occupancy rates were 30% and 47% respectively during the normal system operation phase. The test results show that during the normal operation of the system, both the CPU and GPU can maintain a low system resource occupancy rate, and the overall system performance is good.

[0061] Example 2 To verify the dense warehouse digital twin system proposed in Example 1, a digital twin system platform based on typical equipment such as four-way shuttles, cargo elevators, conveyor lines, and shelves was constructed. The specific implementation steps are as follows: The overall system framework is designed based on the C / S and B / S hybrid architecture to build a dense storage digital twin system platform. The system architecture includes the physical entity layer, perception layer, data layer, twin model and virtual debugging layer, and application layer. The C / S side is based on the Visual Components platform to complete virtual modeling and simulation debugging; the B / S side uses the front-end and back-end separation mode to build a Web management platform to achieve system visualization and remote control. The overall structure of the platform is shown in Figure 1 .

[0062] Based on the parameters of dense storage equipment, SolidWorks was used to construct the geometric models of equipment such as shuttles, shelves, conveyor lines, and elevators. Material processing, UV mapping, and lightweighting were performed in 3ds Max. After outputting the .fbx file, it was imported into the Visual Components platform to complete the scene layout, component binding, and behavior model construction. The motion behavior of the equipment model is expressed using homogeneous transformation matrices to achieve translation, rotation, and compound motion modeling. The behavior logic is controlled through Python scripts to support equipment path planning, action response, and event triggering. For related modeling processes, see Figures 3 to 7 .

[0063] Develop the control program in TIA Portal, configure PLC variables and action logic, use NetToPLCSim and Visual Components to implement S7 protocol data bridging, and complete the simulation verification of the PLC control logic. After the control logic is verified, the program is deployed to the physical PLC controller. The system uses the OPC UA protocol to build a virtual-to-real mapping channel, uses KEPServerEX to configure channel devices and variable tags, and binds related variables in Visual Components to achieve dynamic synchronization between simulation and actual device status. See the OPC UA server configuration process for details. Figure 10 , see the running status mapping interface Figure 11At the same time, data is collected through temperature, vibration, and RFID sensors deployed at key nodes and uploaded to the MySQL database via the TCP / IP protocol, providing perception layer information support for the system.

[0064] The front-end interactive platform of the system is built using Web technology. The front-end is developed using HTML, CSS, JavaScript and Vue.js, and ElementUI and ECharts are introduced to realize the visualization of equipment operation status, energy consumption data and fault diagnosis information. The Web terminal supports information overview ( Figure 12 )、Fault diagnosis( Figure 14 )、Data Monitoring( Figure 13 ), device control, alarm management, and multi-role permission access. The backend is developed in Python, combining FastAPI and Flask to implement interface service functions. Interface testing and performance verification are completed using the Postman tool to ensure the response timeliness of data synchronization and command calls.

[0065] Conduct system performance testing to verify the platform's operational stability and resource usage. During the test, record the changes in CPU and GPU usage on the Web and Visual Components clients. The results show that during normal system operation, the average CPU and GPU usage on the Web side is 34% and 16% respectively; the average CPU and GPU usage on the simulation side is 30% and 47% respectively. Figure 15 and Figure 16 The test shows that the overall system operation is stable, resource usage is controllable, and it meets the continuous online operation and real-time control requirements in dense warehousing scenarios.

[0066] like Figure 21 As shown in the figure, based on the module division and hierarchical relationship in the above dense warehouse digital twin system, its system functional architecture is specifically as follows: The warehouse management system integrates the WMS system and the WCS system to coordinate inventory planning and issue operating instructions for warehouse equipment. The warehouse management system can also monitor storage locations to obtain storage location status data in real time to ensure the accuracy of inventory information.

[0067] The information overview visualizes the global warehouse information of storage equipment information, equipment status, environmental information and task information; the equipment management realizes the equipment life cycle management function around data such as equipment information and equipment maintenance information. The data management receives real-time collected data such as sensor data, shuttle data and alarm information, as well as fault processing data. The data monitoring and fault diagnosis perform fault diagnosis based on real-time monitoring data, and perform fault analysis based on the fault diagnosis results. The system management is used to perform permission management and user information management functions for the dense warehouse digital twin system. The virtual-reality mapping is used to respond to physical equipment actions in real time, perform model driving, and intuitively present visual monitoring of storage equipment through three-dimensional scenes. The simulation debugging is used to perform equipment simulation in a virtual environment, and to debug the equipment based on the simulation to verify the equipment control strategy.

[0068] Example 3 See also Figure 18 As shown, the present invention also provides an electronic device 100 for a method of constructing a dense warehouse digital twin system; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0069] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for constructing a dense warehouse digital twin system described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0070] The at least one processor 102 can be a central processing unit (CPU), and can 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 gates or transistor logic, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc., which is the control center of the electronic device 100 and is connected to various parts of the entire electronic device 100 through various interfaces and lines.

[0071] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a dense warehouse digital twin system construction method, and the processor 102 can execute the plurality of instructions to implement: Obtain state data of an entity device; Perform data processing on the state data and perform database storage management; Based on a C / S architecture, a virtual warehouse environment is constructed, real-time data interaction is performed with a physical device according to data layer data, virtual-real mapping and dynamic simulation are implemented, and a decision instruction is output; According to the decision instruction, corresponding warehouse management is performed, and based on a B / S architecture, a visual interactive interface is developed to display real-time data states according to data layer data.

[0072] Embodiment 4 The modules / units integrated in the electronic device 100, if realized in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the processor executes the computer program, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM).

[0073] Example 5 The present invention also proposes a computer product based on a method for constructing a dense warehouse digital twin system, including a computer-usable medium having computer program logic, wherein the computer program logic is used to implement the steps of the method for constructing a dense warehouse digital twin system in Example 1, and the computer program logic includes: Get the status data of the physical device; Process status data and perform database storage management; Based on the C / S architecture, a virtual warehousing environment is built. According to the data layer data, real-time data interaction is carried out with physical equipment to achieve virtual-reality mapping and dynamic simulation, and output decision instructions. Carry out corresponding warehouse management according to decision-making instructions, and develop a visual interactive interface based on the data layer data and B / S architecture to display real-time data status.

[0074] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 Figure 1

[0078] Furthermore, it should be understood that although the description is made on the basis of the embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and the skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be properly combined to form other embodiments which can be understood by the skilled in the art. The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, and any modification made on the basis of the technical solutions shall fall within the protection scope of the claims of the present application.​​

Claims

1. A dense warehouse digital twin system, characterized by: Includes physical entity layer, perception layer, data layer, twin model and virtual debugging layer, and application layer; The physical entity layer includes physical equipment in the storage system; The perception layer is used to obtain status data of devices in the physical layer; The data layer is used to process the status data and perform database storage management; The twin model and virtual debugging layer are used to build a virtual warehouse model based on the C / S architecture. During the deployment phase, the PLC program is integrated to perform virtual simulation offline testing of the equipment's operating logic. During the operation phase, the data layer stores data dynamically, achieving real-time state synchronization and action mapping between the virtual model and the physical system, and completing a three-dimensional visualization of the equipment operation process. The application layer develops a human-computer interaction interface based on the data stored in the data layer and the B / S architecture, integrates the front-end interface into the Visual Components virtual environment platform to display the real-time status of the system; based on the data layer data, a deep learning algorithm is used to diagnose faults of key components of the equipment, and based on the diagnosis results, decision instructions are output, and corresponding warehouse equipment management is performed according to the decision instructions.

2. A dense storage digital twin system according to claim 1, characterized in that: The perception layer collects status data from sources including device PLCs and sensors deployed at various locations; the perception layer uses the OPC UA protocol to collect status data of device PLCs; and collects data from sensors at various locations through the TCP / IP protocol. The perception layer transmits the collected device PLC status data and sensor data to the data layer; the data layer transmits the obtained device PLC status data to the twin model and virtual debugging layer for virtual-to-real mapping, and stores it in the database; sensor data is also stored in the database to provide data support for the real-time data status display of the application layer.

3. The intensive storage digital twin system according to claim 1, characterized in that: The construction of the warehouse virtual model is specifically as follows: according to the size, shape and height of the physical entity, a geometric model is constructed using SolidWorks, and the geometric models of the dense warehouse core equipment are established one by one using SolidWorks; the geometric model is exported into STEP format and then imported into 3ds Max, and visual optimization operations such as material rendering, UV mapping, and texture setting are performed. At the same time, lightweight processing such as mesh simplification and polygon compression is performed and the optimized model is imported into Visual Components. The scene of the model is arranged according to the physical entity, physical properties are added, and a control script is written to construct the warehouse virtual model.

4. A dense storage digital twin system according to claim 3, characterized in that: Based on the warehouse virtual model, the motion behavior of each key component is mathematically modeled into motion logic. The motion of these components is systematically expressed based on the spatial motion modeling method of the homogeneous transformation matrix. The homogeneous transformation matrix is ​​a method for uniformly describing the translation and rotation transformation of rigid bodies in three-dimensional space. Its general form is a 4×4 matrix: in, is the rotation matrix in three-dimensional space, is a translation vector, representing the displacement of the object in the X, Y, and Z directions. In actual operation, some components may have both translation and rotation characteristics. In this case, a composite motion modeling method should be used to combine multiple single actions into one, represented by chain multiplication of multiple sets of homogeneous transformation matrices: in, represents the component translation matrix, Represents the component rotation matrix.

5. The intensive storage digital twin system according to claim 4, characterized in that: Based on the motion logic of key components in the warehouse virtual model, a PLC control logic program was developed in the TIA Portal, and the S7 protocol bridge between the PLC virtual controller and Visual Components was implemented using NetToPLCSim software. The motion trajectory, control points, and logic events of each device were configured to simulate the warehouse virtual model for device response, action synchronization, fault simulation, and linkage verification. A data mapping structure was established between the Visual Components. Real-time data collected by the OPC UA server was read through variable pairing within Visual Components to drive the device status in the warehouse virtual model in real time.

6. The intensive storage digital twin system according to claim 1, characterized in that: Based on the data in the data layer, a deep learning algorithm is used to diagnose faults in key components of the equipment. Specifically, the equipment status data after data processing is obtained from the data layer, and a deep learning algorithm is used to build a fault diagnosis model based on historical fault data and normal operation data. The real-time collected and processed data is input into the trained model. The model identifies whether there is a fault in the key components of the equipment, the type of fault, and the severity of the fault based on the learned feature patterns, and outputs the fault diagnosis results.

7. A method for constructing a dense storage digital twin system, based on a dense storage digital twin system according to any one of claims 1 to 6, characterized in that: The following steps are involved: Obtain status data of devices in the physical layer; Process status data and perform database storage management; Based on the C / S architecture, a virtual warehouse model is constructed. During the deployment phase, PLC programs are integrated to conduct virtual simulation and offline testing of the equipment's operating logic. During the operation phase, data stored in the data layer is dynamically bound to achieve real-time state synchronization and action mapping between the virtual model and the physical system, completing a three-dimensional visualization of the equipment operation process. Based on the data stored in the data layer; based on the B / S architecture, a visual interactive interface is developed, and the front-end interface is integrated into the Visual Components virtual environment platform to display the real-time status of the system; based on the data layer data, deep learning algorithms are used to diagnose faults of key components of the equipment. According to the diagnosis results, decision instructions are output, and corresponding warehouse equipment management is carried out according to the decision instructions.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for constructing a dense warehouse digital twin system as described in claim 7 are implemented.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing a dense warehouse digital twin system described in claim 7 are implemented.

10. A computer product based on a method for constructing a dense warehouse digital twin system, comprising a computer-usable medium having computer program logic, wherein the computer program logic is configured to implement the steps of the method for constructing a dense warehouse digital twin system as recited in claim 7, the computer program logic comprising: Obtain status data of devices in the physical layer; Process status data and perform database storage management; Based on the C / S architecture, a virtual warehouse model is constructed. During the deployment phase, PLC programs are integrated to conduct virtual simulation and offline testing of the equipment's operating logic. During the operation phase, data stored in the data layer is dynamically bound to achieve real-time state synchronization and action mapping between the virtual model and the physical system, completing a three-dimensional visualization of the equipment operation process. According to the data stored in the data layer; based on the B / S architecture, a human-computer interaction interface is developed, and the front-end interface is integrated into the VisualComponents virtual environment platform to display the real-time status of the system; according to the data layer data, a deep learning algorithm is used to diagnose faults of key components of the equipment, and according to the diagnosis results, decision instructions are output, and corresponding warehouse equipment management is carried out according to the decision instructions.

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