Digital twinning construction method for double-column stacker

Abstract

The invention discloses a digital twinning construction method for a double-column stacker, belongs to the technical field of warehouse logistics, and aims to solve the problems that the simulation and calculation result of the stacker is inaccurate and the data is not closed-loop in the prior art, a digital twinning platform of the stacker is constructed based on a Unity 3D engine, a theoretical parameterized double-column stacker model is established in the digital platform, and the digital twinning construction of the stacker is realized. And parameterized capability simulation is carried out. And reading actual data in the operation process of the stacker by utilizing a sensor through an industrial Ethernet, and mapping, comparing and correcting sensor data and parameterized simulation data to form a twin model of the double-upright stacker. Through continuous iteration of the twinborn model, the stacker twinborn model can obtain an effect close to actual operation under the condition that the stacker twinborn model is not connected with entity equipment, and further accurate analysis and simulation optimization can be carried out. The effects of reducing the design period and cost and improving the simulation precision are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing and logistics, and in particular to a method for constructing a digital twin of a double-column stacker. Background Art

[0002] The digital twin of the aisle stacker is an important application of digital twin technology in the field of warehousing and logistics. It can realize real-time monitoring, optimized scheduling and intelligent decision-making of the stacker's operating status through digital modeling of the stacker and its working links. It plays an important role in improving the accuracy of the stacker design, reducing the cost of prototype testing and improving the information interaction of the stacker. The double-column stacker consists of two vertical columns, an upper load-bearing beam and a lower load-bearing beam to form a rectangular frame structure, which makes its structure stable, has good bending rigidity, can withstand a large weight, and the double-column structure can effectively reduce the risk of damage to goods due to shaking or tilting. Therefore, the double-column stacker has a wide range of applications. At the same time, the stable operating structure enables the twin model to detect and reflect the equipment status more accurately. Provide a relatively stable reference for the analysis and construction of the model.

[0003] At present, the operation status and capacity analysis of double-column stackers are mainly evaluated through parameter calculation and modeling simulation. However, both methods have corresponding limitations. Parameter calculation cannot reflect the real-time status of the equipment, and modeling simulation simulates the operation under theoretical conditions based on theoretical values, which cannot reflect the actual operation status of the equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing a digital twin of a double-column stacker in response to the current problems of inaccurate simulation and calculation results of stackers and unclosed data loops. The method achieves the purpose of data-driven modeling by modeling the double-column stacker in a digital twin platform to accurately map the stacker entity, and then using the twin model to perform strategy and path optimization analysis, thereby achieving virtual-real interaction between the double-column stacker data.

[0005] The technical solution of the present invention is as follows:

[0006] A method for constructing a digital twin of a double-column stacker, comprising the following steps:

[0007] Obtain theoretical parameter information of stacker and theoretical parameter information of stacker operation shelves;

[0008] Build a model of a double-column stacker and a supporting platform for the shelf in the twin platform according to the parameters;

[0009] Parameterize the double-column stacker model;

[0010] Perform operation simulation according to the parameterized model settings of the double-column stacker;

[0011] Analyze and evaluate the simulation results of the parametric dual-column stacker crane model;

[0012] Deploy sensors on physical stackers to establish data collection channels;

[0013] The twin platform receives real-time data fed back by sensors through the data interface;

[0014] According to the operation path simulated by the parameterized stacker model, the tasks of the physical stacker are arranged and issued so that the operation path of the physical stacker is the same as the operation path of the parameterized stacker model;

[0015] According to the degree of deviation, the stacker parameters and algorithms are optimized to form new model parameters;

[0016] The modified stacker parameters are combined with the stacker model to form a simulation twin model of the double-column stacker.

[0017] Furthermore, the operation simulation according to the parameterized model setting of the double-column stacker specifically includes the following steps:

[0018] Under the parametric model, the double-column stacker crane is simulated for storage and retrieval operations;

[0019] Record the simulated operation process of the parametric double-column stacker;

[0020] Evaluate the stacker crane's operating capability based on simulated operating conditions;

[0021] After the simulation runs for a unit time, the capacity of the double-column stacker crane is evaluated based on the simulated operating status.

[0022] Furthermore, the recording of the simulated operation process of the parameterized double-column stacker specifically includes:

[0023] Record the running speed curves of the double-column stacker in the traveling and lifting directions during operation;

[0024] Record the acceleration curves of the walking and lifting directions of the double-column stacker during operation;

[0025] Record the simulation running path of the parameterized double-column stacker crane model and count the running cycles of each start and stop.

[0026] Furthermore, the process of arranging and issuing tasks to the physical stacker so that the operation path of the physical stacker is the same as the operation path of the parameterized stacker model includes the following steps:

[0027] The running speed curves of the physical stacker in the traveling and lifting directions are recorded by sensors during operation;

[0028] The acceleration curves of the traveling and lifting directions of the physical stacker are recorded by sensors during operation;

[0029] The parameterized running speed curves in the walking and lifting directions are compared and corrected based on the materialized parameter curves;

[0030] The parameterized acceleration curves in the walking and lifting directions are compared and corrected based on the materialized parameter curves.

[0031] Furthermore, the step of arranging and issuing tasks to the physical stacker so that the running path of the physical stacker is the same as the running path of the parameterized stacker model specifically includes the following steps:

[0032] By installing sensors in the PLC, the DB module information of the double-column stacker can be read;

[0033] In the digital twin platform, the read DB block information is mapped to the double-column stacker model to realize data-driven model;

[0034] Compile the program for the recorded parameterized stacker simulation operation path;

[0035] Write the compiled path program into the stacker PLC through the sensor, so that the physical stacker can operate along the same path as the simulation;

[0036] Monitor and record the travel direction, lifting direction and acceleration and deceleration curves of the double-column stacker.

[0037] Furthermore, the theoretical parameter information of the stacker includes the height, cargo platform width, and fork size appearance parameters of the double-column aisle stacker.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. A method for constructing a digital twin of a double-column stacker. Based on the Unity3D engine, a digital twin platform for the stacker is constructed. A theoretical parametric double-column stacker model is established in the digital platform to perform parametric capacity simulation. Then, through industrial Ethernet, sensors are used to read the actual data during the operation of the stacker. The sensor data is mapped, compared and corrected with the parametric simulation data to form a twin model of the double-column stacker. Through the continuous iteration of the twin model, the stacker twin model can obtain an effect close to the actual operation without connecting to the physical equipment, and can perform further precise analysis and simulation optimization. This can reduce the design cycle and cost and improve the simulation accuracy;

[0040] 2. A method for constructing a digital twin of a double-column stacker, which uses parametric modeling to simulate the operation process of the double-column stacker and record the corresponding process data to form a parametric data model and apply it. At the same time, the actual operation data is detected by sensors and mapped into the twin model and recorded to form a twin data model. The two data models are compared, and the parametric model is corrected based on the twin model. The twin model is used to optimize the iterative parameter model multiple times to achieve the optimization of the parameter model. This has an important impact on forming a more accurate parametric simulation model, reducing the cost of model use, and improving design efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The figure is a flow chart of a method for constructing a digital twin of a double-column stacker. DETAILED DESCRIPTION

[0042] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0043] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0044] See also Figure 1 , a method for constructing a digital twin of a double-column stacker, comprising the following steps:

[0045] Obtain the height, cargo platform width, fork size, and other equipment appearance parameters of the double-column aisle stacker;

[0046] The appearance of the parametric double-column stacker model is built according to the appearance parameters, providing a carrier for the stacker simulation;

[0047] Obtain aisle shelf parameters and supporting platform deployment;

[0048] Based on the information obtained, a virtual scene of the solution is built to present a static model of the complete scene;

[0049] Set key parameters for the parametric double-column stacker model, define the travel, lifting speed and acceleration, and edit the fork pick-up and release time;

[0050] Under the parametric model, the double-column stacker crane is simulated for storage and retrieval operations;

[0051] Record the simulated operation process of the parametric double-column stacker;

[0052] Evaluate the stacker crane's operating capability based on simulated operating conditions;

[0053] After the simulation runs for a unit time, the capacity of the double-column stacker is evaluated based on the simulated operating status;

[0054] By installing sensors in the PLC, the DB module information of the double-column stacker can be read;

[0055] In the digital twin platform, the read DB block information is mapped to the double-column stacker model to realize data-driven model;

[0056] Compile the program for the recorded parameterized stacker simulation operation path;

[0057] Write the compiled path program into the stacker PLC through the sensor, so that the physical stacker can operate along the same path as the simulation;

[0058] Monitor and record the travel direction, lifting direction and acceleration and deceleration curves of the double-column stacker;

[0059] Record the operation cycle of the physical stacker;

[0060] Evaluate the stacker crane's operating capacity based on the actual stacker crane's operating status;

[0061] After the physical stacker operation is completed, the parameterized operation process record is compared with the physical stacker operation record;

[0062] Based on the actual operation records, the parameters of the parametric model are modified to restore the actual operation status of the stacker to the greatest extent possible;

[0063] Import the corrected operating parameters into the double-column stacker model to form a digital twin model of the double-column stacker;

[0064] Use the data twin model to perform simulations and evaluate the results;

[0065] Based on the evaluation, some parameters are optimized to form a more accurate double-column digital twin model.

[0066] Digital twin technology can realize real-time interaction between data and models through the collection of real-time data from equipment. It can also realize information interaction between high-fidelity model simulation data and measured data. Then, the simulation data is compared with the real operation data, and the twin model is constantly compared and corrected, so that the twin model can map the physical entity more accurately. Finally, a set of digital twin models of double-column stackers with accurate mapping is formed.

[0067] By building a digital twin of the stacker and combining data collection, transmission, processing, virtual-reality interaction and other technical links, real-time monitoring of the stacker can be achieved and scheduling can be optimized. Using digital twin technology to model the double-column stacker can improve the accuracy of the stacker design and play an important supporting role in the feasibility study stage and digital prototype testing stage. At the same time, it reduces operating costs and promotes the digital transformation of enterprises.

[0068] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for constructing a digital twin of a double-column stacker, characterized in that: The following steps are involved: Obtain theoretical parameter information of stacker and theoretical parameter information of stacker operation shelves; Build a model of a double-column stacker and a supporting platform for the shelf in the twin platform according to the parameters; Parameterize the double-column stacker model; Perform operation simulation according to the parameterized model settings of the double-column stacker; Analyze and evaluate the simulation results of the parametric dual-column stacker crane model; Deploy sensors on physical stackers to establish data collection channels; The twin platform receives real-time data fed back by sensors through the data interface; According to the operation path simulated by the parameterized stacker model, the tasks of the physical stacker are arranged and issued so that the operation path of the physical stacker is the same as the operation path of the parameterized stacker model; According to the degree of deviation, the stacker parameters and algorithms are optimized to form new model parameters; The modified stacker parameters are combined with the stacker model to form a simulation twin model of the double-column stacker.

2. A method for constructing a digital twin of a double-column stacker according to claim 1, characterized in that: The operation simulation according to the parameterized model setting of the double-column stacker specifically comprises the following steps: Under the parametric model, the double-column stacker crane is simulated for storage and retrieval operations; Record the simulated operation process of the parametric double-column stacker; Evaluate the stacker crane's operating capability based on simulated operating conditions; After the simulation runs for a unit time, the capacity of the double-column stacker crane is evaluated based on the simulated operating status.

3. A method for constructing a digital twin of a double-column stacker according to claim 2, characterized in that: The recording of the simulated operation process of the parameterized double-column stacker specifically includes: Record the running speed curves of the double-column stacker in the traveling and lifting directions during operation; Record the acceleration curves of the walking and lifting directions of the double-column stacker during operation; Record the simulation running path of the parameterized double-column stacker crane model and count the running cycles of each start and stop.

4. A method for constructing a digital twin of a double-column stacker according to claim 1, characterized in that: The method of arranging and issuing tasks for the physical stacker so that the running path of the physical stacker is the same as the running path of the parameterized stacker model comprises the following steps: The running speed curves of the physical stacker in the traveling and lifting directions are recorded by sensors during operation; The acceleration curves of the traveling and lifting directions of the physical stacker are recorded by sensors during operation; The parameterized running speed curves in the walking and lifting directions are compared and corrected based on the materialized parameter curves; The parameterized acceleration curves in the walking and lifting directions are compared and corrected based on the materialized parameter curves.

5. A method for constructing a digital twin of a double-column stacker according to claim 4, characterized in that: The process of arranging and issuing tasks to the physical stacker so that the running path of the physical stacker is the same as the running path of the parameterized stacker model specifically includes the following steps: By installing sensors in the PLC, the DB module information of the double-column stacker can be read; In the digital twin platform, the read DB block information is mapped to the double-column stacker model to realize data-driven model; Compile the program for the recorded parameterized stacker simulation operation path; Write the compiled path program into the stacker PLC through the sensor, so that the physical stacker can operate along the same path as the simulation; Monitor and record the travel direction, lifting direction and acceleration and deceleration curves of the double-column stacker.

6. A method for constructing a digital twin of a double-column stacker according to claim 1, characterized in that: The theoretical parameter information of the stacker includes the height, cargo platform width, and fork size appearance parameters of the double-column aisle stacker.

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