Intelligent manufacturing production practical training method and device, electronic equipment and storage medium

Abstract

The invention provides an intelligent manufacturing production practical training method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent manufacturing, in particular to the technical field of production processes, intelligent management and the like. According to the specific implementation scheme, ordering data are received and responded, and production order data and transportation control data capable of controlling transportation equipment are generated according to the ordering data and a scheduling optimization algorithm; obtaining finished product completion data and order completion data according to the production order data and the production equipment state data; according to the finished product completion data and the transportation equipment control data, the transportation equipment can be controlled to transport the finished product to a designated position; the production equipment and the transportation equipment can be controlled to return to the initial positions according to the order completion data and the transportation equipment control data, and intelligent manufacturing and production practical training is completed.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent manufacturing training technology, particularly to technical fields such as production processes and intelligent management. Specifically, it relates to intelligent manufacturing production training methods, devices, electronic equipment, and storage media. Background Technology

[0002] With industrial development, lean manufacturing has placed comprehensive and detailed demands on the professional capabilities of personnel in enterprise production systems to effectively serve the goals of high quality, high efficiency, and low cost. These demands include employees' ability to perform standardized work at their workstations, maintain production site operations, solve practical problems, improve total productivity, and enhance their awareness of quality, cost, efficiency, teamwork, and rapid response. The market's demand for intelligent manufacturing engineering talent is increasingly urgent, thus creating a need for retraining employees' basic skills and professional capabilities—the prototype of practical training factories. After years of development, practical training factories are very mature and widely used, but they still focus on training basic skills, cultivating management techniques, and developing professional ethics, lacking comprehensive applications of advanced manufacturing automation, digitalization, and intelligence. Domestic exploration in this area is more profound. With industrial development and the promotion of lean management models, practical training factories are also being continuously expanded and applied in China. Therefore, there is an urgent need for an intelligent manufacturing production training method. Summary of the Invention

[0003] This disclosure provides a smart manufacturing production training method, apparatus, electronic device, and storage medium.

[0004] According to one aspect of this disclosure, a smart manufacturing production training method is provided, comprising:

[0005] Receive and respond to order data, and generate production order data and transportation control data that can control transportation equipment based on the order data and scheduling optimization algorithm;

[0006] Based on the production order data and production equipment status data, finished product completion data and order completion data are obtained;

[0007] Based on the finished product completion data and the transportation equipment control data, the transportation equipment can be controlled to transport the finished product to a designated location.

[0008] Based on the order completion data and the transportation equipment control data, the production equipment and the transportation equipment can be controlled to return to their initial positions, thus completing the intelligent manufacturing production training.

[0009] According to embodiments of this disclosure, the step of receiving and responding to order data, and generating production order data and transportation control data capable of controlling transportation equipment based on the order data and a scheduling optimization algorithm, includes:

[0010] The production order data includes material requisition forms, and the transportation control data includes trolley loading forms;

[0011] The material requisition form is optimized using the aforementioned scheduling optimization algorithm to obtain an outbound application form and a picking form;

[0012] The scheduling optimization algorithm is used to optimize the transportation control data to obtain the trolley loading list.

[0013] According to embodiments of this disclosure, the scheduling optimization algorithm is a multi-agent system, which includes:

[0014] The system structure of the intelligent manufacturing production training production line is described by one master agent, n sub-agents, and multiple agents; the multi-agent system can autonomously make decisions and optimize the production process of the intelligent manufacturing production training production line.

[0015] According to embodiments of this disclosure, the multi-agent system optimization process includes:

[0016] (1) An Agent is a collection of all agents with specific goals and complex behaviors:

[0017] Agent={Agent,SubAgenti|i=1,2,3…m}

[0018] Where StillObject is the object, and m is the number of objects;

[0019] (2) Inference is the reasoning engine and a core component of the Agent; it can specify the name of the invoked behavior, the rules, the triggering conditions of the behavior, and the time constraints of the behavior.

[0020] (3)StillObject is the collection of all static and fluctuating entities that do not affect the excitation:

[0021] StillObject={StillObjectj|j=1,2,3…n}

[0022] (4) Communicate is a collection of all messages through which communication and collaboration are carried out:

[0023] Communicate={(i,j,k)|i,j∈Agent∪StillObject,k∈Communicate(L)}

[0024] Where: i is the sender of the message, j is the receiver, k is the message content, and k∈Communicate(L);

[0025] (5) Knowledge includes the constraints of dynamic objectives;

[0026] (6) The Calculator is capable of performing the necessary corrective and theoretical calculations;

[0027] get:

[0028] IMPLA=(Agent,inference,StillObject,communicate,knowledge,calculator)

[0029] In this context, IMPLA is used to describe the production line of the intelligent manufacturing production training, Agent is a virtual intelligent entity, inference is a reasoning engine, communication is communication, knowledge is a knowledge base, and calculator is a calculator.

[0030] According to embodiments of this disclosure, obtaining finished product completion data and order completion data based on the production order data and production equipment status data includes:

[0031] Based on the material requisition form and the production equipment status data, the box-carrying robot is controlled to load and unload materials to obtain loading data and unloading data.

[0032] The finished product data is obtained based on the loading data and the unloading data.

[0033] The order completion data is obtained based on the finished product completion data reaching the order placement data.

[0034] According to embodiments of this disclosure, the step of controlling the transport equipment to transport the finished product to a designated location based on the finished product completion data and the transport equipment control data includes:

[0035] The location to which the finished product needs to be transported is determined based on the finished product completion data;

[0036] Based on the control data of the transport equipment, a trolley can be called to the location of the finished product;

[0037] The finished product is transported to the designated location based on the required location and the control data of the transport equipment.

[0038] According to embodiments of this disclosure, the step of controlling the production equipment and the transportation equipment to return to their initial positions based on the order completion data and the transportation equipment control data includes:

[0039] Initialize the production equipment status data based on the order completion data;

[0040] Based on the initialized production equipment status data, control the equipment used to produce the finished product to return to the initial position;

[0041] Based on the order completion data and the transportation equipment control data, the transportation equipment is controlled to return to the initial position.

[0042] According to another aspect of this disclosure, a smart manufacturing production training device is provided, comprising:

[0043] The receiving module is used to receive and respond to order data, and generate production order data and transportation control data that can control transportation equipment based on the order data and the scheduling optimization algorithm.

[0044] The acquisition module is used to obtain finished product completion data and order completion data based on the production order data and production equipment status data;

[0045] The control module is used to control the transportation equipment to transport the finished product to a designated location based on the finished product completion data and the transportation equipment control data.

[0046] The recovery module is used to control the production equipment and the transportation equipment to return to their initial positions based on the order completion data and the transportation equipment control data, thereby completing the intelligent manufacturing production training.

[0047] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0048] At least one processor; and

[0049] A memory communicatively connected to the at least one processor; wherein,

[0050] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform any of the methods described above.

[0051] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform any of the methods described above. Attached Figure Description

[0052] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0053] Figure 1 This illustration schematically shows an exemplary system architecture for applying intelligent manufacturing production training methods and apparatus according to embodiments of the present disclosure;

[0054] Figure 2A flowchart illustrating an order data processing method according to an embodiment of the present disclosure is shown schematically.

[0055] Figure 3 A flowchart illustrating a production order data processing method according to an embodiment of the present disclosure is shown schematically.

[0056] Figure 4 A flowchart illustrating the determination of finished product completion data and order completion data according to an embodiment of the present disclosure is shown schematically.

[0057] Figure 5 A flowchart illustrating the application of transport equipment control data according to an embodiment of the present disclosure is shown schematically.

[0058] Figure 6 A flowchart illustrating a method for returning a device to its initial position according to an embodiment of the present disclosure is shown schematically.

[0059] Figure 7 A block diagram of an intelligent manufacturing production training device according to an embodiment of the present disclosure is schematically shown; and

[0060] Figure 8 A block diagram of an electronic device suitable for implementing a smart manufacturing production training method according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0061] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0062] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0063] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0064] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0065] This disclosure provides intelligent manufacturing production training methods, devices, electronic equipment, and storage media.

[0066] According to embodiments of this disclosure, the intelligent manufacturing production training method may include: receiving and responding to order data; generating production order data and transportation control data capable of controlling transportation equipment based on the order data and a scheduling optimization algorithm; obtaining finished product completion data and order completion data based on the production order data and production equipment status data; controlling the transportation equipment to transport the finished product to a designated location based on the finished product completion data and transportation equipment control data; and controlling the production equipment and transportation equipment to return to their initial positions based on the order completion data and transportation equipment control data, thereby completing the intelligent manufacturing production training.

[0067] The intelligent manufacturing production training method provided in this disclosure can determine the production process information corresponding to instructing the production equipment to carry out production based on user order information and information on production equipment available for production. This enables precise determination of product production process information and improves the intelligence and accuracy of production process planning in the manufacturing chain. It provides practical training guidance for factory production lines, helping to clearly understand the relationship between core specialties such as sensor detection, industrial internet, industrial data, artificial intelligence, marketing, and media art design and production line operation. It also helps participants grasp the core responsibilities and operational points of each specialty in production line operation, thus providing more efficient training guidance and achieving a deep integration of professional knowledge and practical production.

[0068] The collection, storage, use, processing, transmission, provision, and disclosure of user and device information in the technical solution disclosed herein comply with relevant laws and regulations and do not violate public order and good morals.

[0069] Figure 1 The illustration schematically shows an exemplary system architecture for applying intelligent manufacturing production training methods and apparatus according to embodiments of the present disclosure.

[0070] It is important to note that Figure 1 The examples shown are merely illustrative of system architectures applicable to embodiments of this disclosure, intended to help those skilled in the art understand the technical content of this disclosure. They do not imply that embodiments of this disclosure cannot be used in other devices, systems, environments, or scenarios. For instance, in another embodiment, an exemplary system architecture for intelligent manufacturing production training methods and apparatus can be applied.

[0071] like Figure 1 As shown, the system architecture 100 according to this embodiment may include order interaction devices 101, 102, and 103 on the user side, a network 104, and a training management server 105 on the production equipment side. The network 104 serves as a medium for providing communication links between the order interaction devices 101, 102, and 103 and the training management server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0072] Users can send order data information through order interaction devices 101, 102, and 103, and interact with the training management server 105 via network 104, sending user order information to the training management server 105 so that the training management server 105 can customize the process based on the user order information. Order interaction devices 101, 102, and 103 can also receive information, such as "production process information obtained by the training management server 105 in process customization," etc. After confirming the production process information obtained from the user order information, order interaction devices 101, 102, and 103 interact with the training management server 105 via network 104. Users can confirm the production process information through order interaction devices 101, 102, and 103, and interact with the training management server 105 via network 104 so that the training management server 105 can instruct the production equipment to carry out production based on the production process information.

[0073] Various communication client applications can be installed on order interaction devices 101, 102, and 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software (for example only).

[0074] Order interaction devices 101, 102, and 103 may have data input functions, such as connecting to mobile storage input functions and on-site input functions, to input user order information. Order interaction devices 101, 102, and 103 may also have user order information preview functions, such as a display screen, to display the user order information entered by the order interaction device.

[0075] The order interaction devices 101, 102, and 103 can be any electronic device capable of interacting via data signals. These devices may include, but are not limited to, smartphones, tablets, laptops, smart speakers, car speakers, smart tutoring devices, and smart robots.

[0076] The training management server 105 can be a server that provides various services, such as identifying the order data information sent by the order interaction devices 101, 102, and 103, and performing subsequent processes such as searching and analyzing based on the order data information, as well as instructing the production equipment to carry out production based on the production process information (for example only).

[0077] The training management server 105 can be a cloud server, also known as a cloud computing server or cloud host. It is a host product in the cloud computing service system, which solves the shortcomings of traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short) in terms of high management difficulty and weak business scalability. The server can also be a server for a distributed system or a server combined with blockchain.

[0078] It should be noted that the intelligent manufacturing production training method provided in this embodiment can generally be executed by the training management server 105. Correspondingly, the intelligent manufacturing production training device provided in this embodiment can also be set in the training management server 105.

[0079] It should be understood that Figure 1 The number of order interaction devices, networks, and training management servers shown in the diagram is merely illustrative. Depending on implementation needs, any number of order interaction devices, networks, and training management servers can be included.

[0080] Figure 2 A flowchart illustrating a smart manufacturing production training method according to an embodiment of the present disclosure is shown schematically.

[0081] like Figure 2 As shown, the method includes operations S201 to S204.

[0082] In operation S201, the system receives and responds to order data, and generates production order data and transportation control data that can control transportation equipment based on the order data and scheduling optimization algorithm.

[0083] In operation S202, finished product completion data and order completion data are obtained based on production order data and production equipment status data.

[0084] In operation S203, based on the finished product completion data and the transportation equipment control data, the transportation equipment can be controlled to transport the finished product to the designated location.

[0085] For example, after receiving order data, the MES (Production Executive System) creates a production plan and marks its status as "issued." The collaborative platform system then notifies the MES to call for a material handling trolley. The MES creates a material requisition form. Upon receiving the material requisition signal from the MES, the WMS (Warehouse Management System) creates an outbound request and a picking list. The WCS (Automatic Warehouse Control System), upon receiving the picking list signal, controls the AGV (Automated Guided Vehicle) to retrieve the material. Once the material box arrives at the roller conveyor's outbound docking point, the WMS notifies the AGV to retrieve the box and deliver it to the loading station. The WMS then notifies the MES that the trolley has arrived at the designated station, and the MES notifies the collaborative platform system of the trolley arrival signal. The collaborative platform system then controls and informs the loading and unloading equipment that the material can be retrieved. After the loading and unloading equipment completes its material handling, it notifies the collaborative platform. The collaborative platform then informs the MES system, which in turn notifies the WMS system to call a trolley to retrieve the loading box. The WMS system then notifies the WCS system to retrieve the box and deliver it to the rolling line's inbound docking point. The WCS system notifies the WMS system that the box can be stored and the inventory updated. The collaborative system then notifies the MES system to call a trolley to bring the finished product box. The MES system creates a finished product inbound order. Upon receiving the finished product inbound order signal from the MES system, the WMS system creates an expected arrival notification and a putaway task order. Upon receiving the putaway task order signal, the WCS system controls the box-carrying robot to retrieve the material. After the material bin arrives at the roller conveyor's outbound docking point, the AGV is notified to pick up the bin and deliver it to the unloading station. The MES notifies the collaborative system that the trolley has arrived. The collaborative platform system notifies the MES to call the trolley to pick up the loaded finished products (while reporting the NG quantity and OK quantity). When the MES receives the production receipt corresponding to the production order and the status changes to "inbound", the production order is set to end. The MES notifies the collaborative platform system of the quantity of qualified and unqualified products. The MES creates a finished product outbound order (the process is similar to raw material outbound). The MES notifies the collaborative platform system that the finished products have been pulled to the designated location.

[0086] The collaborative control system is primarily used to manage production line operating modes, automatically coordinating with production line hardware and production-related systems such as WMS, MES, WCS, and AGV scheduling systems. WCS (Automatic Warehouse Control System) is an intelligent warehousing system that efficiently utilizes vertical space and automates storage and retrieval. It integrates mechanical, control, and software technologies to improve warehousing efficiency and accuracy, serving as a core tool for modern logistics management. WMS (Warehouse Management System) is a comprehensive management platform that optimizes inventory, tracks materials, and automates operations, improving warehousing efficiency and ensuring cargo safety.

[0087] The following is for reference. Figures 3-7 In conjunction with specific embodiments, for example Figure 2 The intelligent manufacturing production training method shown will be further explained.

[0088] Figure 3 A flowchart illustrating a production order data processing method according to an embodiment of the present disclosure is shown schematically.

[0089] According to an embodiment of this disclosure, in operation S201, the production order data includes a material requisition form, and the transportation control data includes a trolley loading form. Receiving and responding to the order data, and generating production order data and transportation control data capable of controlling the transportation equipment based on the order data and the scheduling optimization algorithm, includes operations S301 to S302.

[0090] In operation S301, the material requisition form is optimized through a scheduling optimization algorithm to obtain the outbound application form and the picking form.

[0091] In operation S302, the transportation control data is optimized through a scheduling optimization algorithm to obtain the trolley loading list.

[0092] According to embodiments of this disclosure, the scheduling optimization algorithm is a multi-Agent system, which includes: one master agent and n sub-agents, and the system structure of the intelligent manufacturing production training production line can be described by the multi-Agent system; the multi-Agent system can autonomously make decisions and optimize the production process of the intelligent manufacturing production training production line.

[0093] According to embodiments of this disclosure, the multi-agent system optimization process includes:

[0094] (1) An Agent is a collection of all agents with specific goals and complex behaviors:

[0095] Agent={Agent,SubAgenti|i=1,2,3…m}

[0096] Where StillObject is the object, and m is the number of objects;

[0097] (2) Inference is the reasoning engine and a core component of the Agent; it can specify the name of the invoked behavior, the rules, the triggering conditions of the behavior, and the time constraints of the behavior.

[0098] (3)StillObject is the collection of all static and fluctuating entities that do not affect the excitation:

[0099] StillObject={StillObjectj|j=1,2,3…n}

[0100] (4) Communicate is a collection of all messages, through which communication and collaboration are carried out:

[0101] Communicate={(i,j,k)|i,j∈Agent∪StillObject,k∈Communicate(L)}

[0102] Where: i is the sender of the message, j is the receiver, k is the message content, and k∈Communicate(L);

[0103] (5) Knowledge includes the constraints of dynamic objectives;

[0104] (6) The Calculator is capable of performing the necessary corrective and theoretical calculations;

[0105] get:

[0106] IMPLA=(Agent,inference,StillObject,communicate,knowledge,calculator)

[0107] In this context, IMPLA is used to describe the production line of intelligent manufacturing production training, where Agent is a virtual intelligent entity, inference is a reasoning engine, communication is communication, knowledge is a knowledge base, and calculator is a calculator.

[0108] For example, optimizing the production line scheduling in intelligent manufacturing training can optimize the processes and tasks in the actual production process. The virtual environment, database, and component models of the production line contain a vast amount of information, and the behavioral functions are complex, making it difficult to obtain all model information and control and manage it during simulation. Using a multi-agent system enables the agent to make autonomous decisions, generate plans, and take actions, facilitating the description, operation, management, and distribution of deep, multi-dimensional information models. A multi-agent system is an integration of multiple intelligent entities (Agents). These agents interact with each other through communication. These agents can act within the environment, and different agents have different "scopes of influence," meaning they can control, or at least influence, different parts of the environment. In some cases, the scope of influence may overlap, and this overlap creates dependencies between agents.

[0109] According to embodiments of this disclosure, the multi-Agent system structure of the intelligent manufacturing production training production line has one master control agent and n sub-agents (n = 1, 2, ..., m). The system structure of the intelligent manufacturing production training production line can be described using the multi-Agent approach. The intelligent manufacturing production training production line can be divided into one master control agent and multiple sub-agents, as shown below:

[0110] (1) Main Control Agent of LECPL;

[0111] (2) Global Database;

[0112] (3) Processing equipment SubAgent1;

[0113] (4) SubAgent2 for workpiece;

[0114] (5) Workstation fixture SubAgent3;

[0115] (6) Detection equipment SubAgent4;

[0116] (7) Transportation equipment SubAgent5;

[0117] (8) Warehouse Management System SubAgent6;

[0118] (9) SubAgent7 automated warehouse control system;

[0119] (10) AGV control system SubAgent8;

[0120] (11) Production Management System SubAgent9;

[0121] (12) Other SubAgent10;

[0122] Within the Agent, a network structure is formed with the master data at its core. Each SubAgent is associated with the master Agent, thus forming a spatial multidimensional network structure.

[0123] Using the production line of IMPLA (Intelligent Manufacturing Production Line Agent) intelligent manufacturing training to describe the main control agent structure in the production line, it is a six-tuple consisting of virtual intelligent entity Agent, inference engine, StillObject, communication, knowledge base, and calculator:

[0124] IMPLA=(Agent,inference,StillObject,communicate,knowledge,calculator)where:

[0125] (1) An Agent is a collection of all agents with specific goals and complex behaviors:

[0126] Agent={Agent,SubAgenti|i=1,2,3…m}

[0127] The production line for intelligent manufacturing production training: m = 10, plus 1 main control agent, the total number is: m + 1 = 11.

[0128] The behavioral model in a multi-agent virtual environment comprises several parts, including attributes, autonomous behavior models, and interoperability behavior models. The autonomous behavior model consists of messages and rules, and is related to local agent behavioral constraints. The interoperability behavior model consists of messages, rules, and constraints, and is related to global behavioral constraints.

[0129] (2) Inference is the reasoning engine and a core component of the Agent. When an entity meets the triggering conditions, the corresponding behavior function is called, that is, the corresponding behavior procedure is activated. Inference requires specifying the name of the behavior to be called, the rule, the behavior triggering condition, and the behavior time constraint.

[0130] (3)StillObject is a collection of all static and fluctuating entities that do not affect the excitation, such as the ground, factory buildings, etc.

[0131] StillObject={StillObjectj|j=1,2,3…n}

[0132] (4) Communicate is the collection of all messages. A message is the medium through which entities communicate and collaborate in a virtual environment.

[0133] Communicate = {(i,j,k)|i,j∈Agent∪StillObject,k∈Communicate(L)} where: i is the sender of this message, j is the receiver, k is the message content, k∈Communicate(L), and the message is represented by Msg_name(i,j,k).

[0134] (5) Knowledge defines the constraints, rules, knowledge and new concepts required for decision-making for dynamic objectives.

[0135] (6) The Calculator is a set of corrective and theoretical calculations required by the inference engine.

[0136] The interaction between agent behavior and message passing plays a crucial role in virtual simulation environments. For example, the occurrence of equipment behavior on a production line, changes in busy / idle (action / free) status, and operations of the host (SubAgent2) on the workpiece (SubAgent3) are all driven by agent behavior and the transmission of messages through communication.

[0137] Figure 4A flowchart illustrating the determination of finished product completion data and order completion data according to an embodiment of the present disclosure is shown.

[0138] According to embodiments of this disclosure, operation S202, obtaining finished product completion data and order completion data based on production order data and production equipment status data, includes operations S401 to S403:

[0139] When operating S401, the loading and unloading robot is controlled according to the material requisition form and production equipment status data to obtain loading and unloading data.

[0140] During operation S402, finished product completion data is obtained based on the feeding and unloading data.

[0141] When operating S403, the order completion data is obtained based on the finished product completion data and the order placement data.

[0142] Figure 5 A flowchart illustrating the application of transport equipment control data according to an embodiment of the present disclosure is shown.

[0143] According to embodiments of this disclosure, operation S203, which controls the transport equipment to transport the finished product to a designated location based on the finished product completion data and the transport equipment control data, includes operations S501 to S503:

[0144] In operation S501, the location where the finished product needs to be transported is determined based on the finished product completion data.

[0145] When operating S502, the trolley can be called to the location of the finished product based on the control data of the transport equipment.

[0146] In operation S503, the finished product is transported to the designated location based on the required location and the control data of the transport equipment.

[0147] Figure 6 A flowchart illustrating a method for returning a device to its initial position according to an embodiment of the present disclosure is shown schematically.

[0148] According to embodiments of this disclosure, operation S204, which controls the transport equipment to transport the finished product to a designated location based on the finished product completion data and the transport equipment control data, includes operations S601 to S603:

[0149] In operation S601, the production equipment status data is initialized based on the order completion data.

[0150] In operation S602, the equipment used to produce finished products is controlled to return to its initial position based on the initialized production equipment status data.

[0151] In operation S603, the transport equipment is controlled to return to its initial position based on the order completion data and transport equipment control data.

[0152] like Figure 7 As shown, the intelligent manufacturing production training device 700 may include a receiving module 701, an acquisition module 702, a control module 703, and a recovery module 704.

[0153] The receiving module 701 is used to receive and respond to the order data, and generate production order data and transportation control data that can control the transportation equipment based on the order data and the scheduling optimization algorithm.

[0154] The acquisition module 702 is used to obtain finished product completion data and order completion data based on production order data and production equipment status data.

[0155] The control module 703 is used to control the transportation equipment to transport the finished product to a designated location based on the finished product completion data and the transportation equipment control data.

[0156] The recovery module 704 is used to control the production equipment and transportation equipment to return to their initial positions based on order completion data and transportation equipment control data, thereby completing the intelligent manufacturing production training.

[0157] According to embodiments of this disclosure, the receiving module 701 is used to receive and respond to order data, and generate production order data and transportation control data capable of controlling transportation equipment based on the order data and a scheduling optimization algorithm. In embodiments of this disclosure, the receiving module 701 can be used to perform the operation S201 described above, which will not be repeated here.

[0158] According to embodiments of this disclosure, production order data includes material requisition forms, and transportation control data includes trolley loading forms. The receiving module 701 may include a first receiving module and a second receiving module.

[0159] The first receiving module is used to optimize the material requisition form through a scheduling optimization algorithm to obtain the outbound application form and the picking form.

[0160] The second receiving module is used to optimize the transportation control data through a scheduling optimization algorithm to obtain the trolley loading list.

[0161] According to embodiments of this disclosure, the acquisition module 702 is used to obtain finished product completion data and order completion data based on production order data and production equipment status data. In embodiments of this disclosure, the acquisition module 702 can be used to perform the operation S202 described above, which will not be repeated here.

[0162] According to embodiments of this disclosure, the acquisition module 702 may include a first acquisition module, a second acquisition module, and a third acquisition module.

[0163] The first acquisition module is used to control the loading and unloading of the box-carrying robot based on the material requisition form and the production equipment status data, thereby obtaining loading and unloading data.

[0164] The second acquisition module is used to obtain finished product completion data based on the loading and unloading data.

[0165] The third acquisition module is used to obtain order completion data based on the finished product completion data and the order placement data.

[0166] According to embodiments of this disclosure, the control module 703 is used to control the transport equipment to transport the finished product to a designated location based on the finished product completion data and the transport equipment control data. In embodiments of this disclosure, the control module 703 can be used to execute the operation S203 described above, which will not be repeated here.

[0167] According to embodiments of this disclosure, the control module 703 may include a first control module, a second control module, and a third control module.

[0168] The first control module is used to determine the location where the finished product needs to be transported based on the finished product completion data.

[0169] The second control module is used to call the trolley to the location of the finished product based on the control data of the transportation equipment.

[0170] The third control module is used to transport the finished product to the designated location based on the required location and the control data of the transportation equipment.

[0171] According to embodiments of this disclosure, the recovery module 704 is used to control the production equipment and transportation equipment to return to their initial positions based on order completion data and transportation equipment control data, thereby completing intelligent manufacturing production training. In embodiments of this disclosure, the control module 704 can be used to execute the operation S204 described above, which will not be repeated here.

[0172] According to embodiments of this disclosure, recovery module 704 may include a first recovery module, a second recovery module, and a third recovery module.

[0173] The first recovery module is used to initialize the production equipment status data based on the order completion data.

[0174] The second recovery module is used to control the equipment used to produce finished products to return to its initial position based on the initialized production equipment status data.

[0175] The third recovery module is used to control the transportation equipment to return to its initial position based on the order completion data and transportation equipment control data.

[0176] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0177] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause a computer to perform the method described above.

[0178] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0179] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0180] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 808, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0181] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as speech processing methods. For example, in some embodiments, the speech processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the speech processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform speech processing methods by any other suitable means (e.g., by means of firmware).

[0182] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0183] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0184] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0185] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0186] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0187] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, distributed system servers, or servers incorporating blockchain technology.

[0188] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0189] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A smart manufacturing production training method, comprising: Receive and respond to order data, and generate production order data and transportation control data that can control transportation equipment based on the order data and scheduling optimization algorithm; Based on the production order data and production equipment status data, finished product completion data and order completion data are obtained; Based on the finished product completion data and the transportation equipment control data, the transportation equipment can be controlled to transport the finished product to a designated location. Based on the order completion data and the transportation equipment control data, the production equipment and the transportation equipment can be controlled to return to their initial positions, thus completing the intelligent manufacturing production training.

2. The method according to claim 1, wherein, The process of receiving and responding to order data, and generating production order data and transportation control data capable of controlling transportation equipment based on the order data and the scheduling optimization algorithm, includes: The production order data includes material requisition forms, and the transportation control data includes trolley loading forms; The material requisition form is optimized using the aforementioned scheduling optimization algorithm to obtain an outbound application form and a picking form; The scheduling optimization algorithm is used to optimize the transportation control data to obtain the trolley loading list.

3. The method according to claim 1, wherein, The scheduling optimization algorithm is a multi-agent system, which includes: The system structure of the intelligent manufacturing production training production line is described by one master agent, n sub-agents, and multiple agents; the multi-agent system can autonomously make decisions and optimize the production process of the intelligent manufacturing production training production line.

4. The method according to claim 3, wherein, The multi-agent system optimization process includes: (1) An Agent is a collection of all agents with specific goals and complex behaviors: Agent={Agent,SubAgenti|i=1,2,3…m} Where StillObject is the object, and m is the number of objects; (2) Inference is the reasoning engine and a core component of the Agent; it can specify the name of the invoked behavior, the rules, the triggering conditions of the behavior, and the time constraints of the behavior. (3) StillObject is the collection of all static and fluctuating entities that do not affect the excitation: StillObject={StillObjectj|j=1,2,3…n} (4) Communicate is a collection of all messages through which communication and collaboration are carried out: Communicate={(i,j,k)|i,j∈Agent∪StillObject,k∈Communicate(L)} Where: i is the sender of the message, j is the receiver, k is the message content, and k∈Communicate(L); (5) Knowledge includes the constraints of dynamic objectives; (6) The Calculator is capable of performing the necessary corrective and theoretical calculations; get: IMPLA=(Agent,inference,StillObject,communicate,knowledge,calculator) In this context, IMPLA is used to describe the production line of the intelligent manufacturing production training, Agent is a virtual intelligent entity, inference is a reasoning engine, communication is communication, knowledge is a knowledge base, and calculator is a calculator.

5. The method according to claim 2, wherein, The process of obtaining finished product completion data and order completion data based on the production order data and production equipment status data includes: Based on the material requisition form and the production equipment status data, the box-carrying robot is controlled to load and unload materials to obtain loading data and unloading data. The finished product data is obtained based on the loading data and the unloading data. The order completion data is obtained based on the finished product completion data reaching the order placement data.

6. The method according to claim 5, wherein, The step of controlling the transportation equipment to transport the finished product to a designated location based on the finished product completion data and the transportation equipment control data includes: The location to which the finished product needs to be transported is determined based on the finished product completion data; Based on the control data of the transport equipment, a trolley can be called to the location of the finished product; The finished product is transported to the designated location based on the required location and the control data of the transport equipment.

7. The method according to claim 6, wherein, The ability to control the production equipment and the transportation equipment to return to their initial positions based on the order completion data and the transportation equipment control data includes: Initialize the production equipment status data based on the order completion data; Based on the initialized production equipment status data, control the equipment used to produce the finished product to return to the initial position; Based on the order completion data and the transportation equipment control data, the transportation equipment is controlled to return to the initial position.

8. A smart manufacturing production training device, comprising: The receiving module is used to receive and respond to order data, and generate production order data and transportation control data that can control transportation equipment based on the order data and the scheduling optimization algorithm. The acquisition module is used to obtain finished product completion data and order completion data based on the production order data and production equipment status data; The control module is used to control the transportation equipment to transport the finished product to a designated location based on the finished product completion data and the transportation equipment control data. The recovery module is used to control the production equipment and the transportation equipment to return to their initial positions based on the order completion data and the transportation equipment control data, thereby completing the intelligent manufacturing production training.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

Images