Flexible scheduling group and scheduling method

Abstract

The application provides a flexible scheduling group and a scheduling method. The scheduling group comprises a manufacturing execution system, a logistics execution system, an intelligent group control system, a logistics intelligent trolley scheduling system and a process intelligent trolley scheduling system. The intelligent group control system receives a production plan issued by the manufacturing execution system, receives inventory material information of the logistics execution system, receives vehicle position information of the logistics intelligent trolley scheduling system and the process intelligent trolley scheduling system respectively, and formulates scheduling tasks of the process intelligent trolley and the logistics intelligent trolley in real time. Through deep cooperation of the intelligent group control system with the manufacturing execution system, the logistics execution system and other systems, overall management of the whole production process is realized. Through real-time sensing of data of environmental changes and / or data of demand fluctuations by the intelligent group control system, and through on-demand pulling, the coordination and stability of line operation are improved through cooperation with multiple systems.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and in particular to a flexible scheduling group and scheduling method. Background Technology

[0002] As the automotive manufacturing industry transforms towards intelligent manufacturing, production lines typically employ a linear scheduling model. Vehicles flow in a fixed sequence via a mechanical transport chain, exhibiting a strict sequential characteristic throughout the entire process from painting to final production. While this traditional model ensures basic production order, its limitations in rigid scheduling become increasingly apparent when facing the demands of multi-variety, small-batch production.

[0003] The current linear production line scheduling method suffers from the following main problems: First, although each subsystem can operate independently, it lacks an effective coordination mechanism. For example, the Manufacturing Execution System (MES) and the Logistics Execution System (LES) operate independently, with poor information exchange, making it difficult to coordinate production rhythms. Second, the disconnect between material delivery and mainline production is widespread. Common scenarios include mainline vehicles arriving at their workstations but the required parts not yet in place, or parts arriving prematurely and causing inventory buildup. Both situations lead to wasted time and reduced space utilization.

[0004] Furthermore, the existing system has a weak ability to respond to changes in production. When production plans are adjusted or abnormal operating conditions occur, the system often needs a long time to reschedule, which can easily lead to problems such as material mismatch and process delays. Especially in multi-model mixed-line production scenarios, the parts requirements of different models vary greatly, and the traditional fixed-cycle delivery mode is difficult to meet the requirements of accurate delivery. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a flexible scheduling group and scheduling method. Through data collaboration between the intelligent group control system and the manufacturing execution system and logistics execution system, flexible scheduling is achieved for the entire vehicle assembly process, realizing unified management of the entire production process. This significantly improves the coordination and stability of production line operation, and can dynamically adjust the process flow based on real-time production status. Through intelligent scheduling of process intelligent vehicles and logistics intelligent vehicles, it ensures that parts are completely synchronized with the mainline vehicles, which not only saves cycle time but also significantly reduces material backlog and space occupation. By tracking vehicle information throughout the process and establishing a one-to-one correspondence between parts and main components, a complete production traceability system is established, improving quality control capabilities.

[0006] According to a specific embodiment of this application, in a first aspect, this application provides a flexible scheduling group, including: The manufacturing execution system is configured to create and publish production plans, track execution information, and quality inspection information based on raw data from vehicle production. The logistics execution system is configured to generate inventory material information based on the raw material data and logistics information. The intelligent group control system is connected to the manufacturing execution system and the logistics execution system respectively, and is configured to: receive the production plan issued by the manufacturing execution system, receive the inventory material information of the logistics execution system, receive the vehicle location information of the logistics intelligent vehicle scheduling system and the process intelligent vehicle scheduling system respectively, and formulate the scheduling tasks of the process intelligent vehicle and the logistics intelligent vehicle in real time. The logistics intelligent vehicle scheduling system is communicatively connected to the intelligent group control system and configured to schedule logistics intelligent vehicles based on scheduling tasks, and to determine the vehicle location information in real time. The process intelligent vehicle scheduling system is communicatively connected to the intelligent group control system and is configured to schedule process intelligent vehicles based on scheduling tasks, and to determine the vehicle location information in real time.

[0007] Optionally, the group also includes an enterprise-level master data management system, which is communicatively connected to the manufacturing execution system and the logistics execution system, respectively, and configured to maintain the raw data of vehicle production and materials.

[0008] Optionally, the group also includes an assembly process management system, which is communicatively connected to the intelligent group control system and configured to define process parameter values ​​for each workstation in the process island; the intelligent group control system is further configured to send the process parameter values ​​for each workstation in the process island to the corresponding process island controller according to the process islands involved in the production plan.

[0009] Optionally, the group also includes a dark light management system, which communicates with the intelligent process vehicle scheduling system based on the Hypertext Transfer Protocol, and is configured to receive the operation data of the intelligent process vehicle sent by the intelligent process vehicle scheduling system, and control the start, stop and alarm of the intelligent process vehicle through the intelligent process vehicle scheduling system according to the operation data.

[0010] Optionally, the group also includes a bastion host, which is located between the user terminal and the intelligent group control system, and communicates with the user terminal via an intranet or VPN, and is configured to audit the communication data between the user terminal and the intelligent group control system.

[0011] According to a specific embodiment of this application, in a second aspect, this application provides a flexible scheduling method applied to an intelligent group control system in the aforementioned group, comprising: It receives production plans and real-time production progress of vehicles from the Manufacturing Execution System, receives inventory material information from the Logistics Execution System, and receives vehicle location information from the Logistics Intelligent Vehicle Scheduling System and the Process Intelligent Vehicle Scheduling System, respectively. Based on the production plan and the real-time production progress of the vehicles, the real-time process requirements of the vehicles are determined. Based on these real-time process requirements and the location information of the intelligent process vehicle, the scheduling tasks for the intelligent process vehicle are formulated in real time. Based on the production plan and the inventory material information, the real-time material requirements of the process island are determined, and the scheduling tasks of the intelligent logistics vehicles are formulated in real time in conjunction with the vehicle location information of the intelligent logistics vehicles.

[0012] Optionally, based on the real-time process requirements and the location information of the intelligent process vehicle, a scheduling task for the intelligent process vehicle is formulated in real time, including: When the vehicle completes the assembly of the current process island, multiple downstream processes of the current process island are determined according to the real-time process requirements of the vehicle. Based on the multiple downstream processes, a multi-flow process queue for the current process island is determined, wherein the multi-flow process queue for the current process island includes multiple candidate process islands that execute the multiple downstream processes; Obtain the assembly queue for each candidate process island, wherein the assembly queue includes the number of intelligent process vehicles waiting to enter each candidate process island; The priority order of multiple candidate process islands is determined based on the assembly queue; The process intelligent vehicle used to transport the vehicle is determined based on the vehicle's location information. The target process island is determined based on the priority order of multiple candidate process islands in the multi-flow process queue of the current process island; The driving path of the intelligent process vehicle is determined based on the current location of the process island and the location of the target process island. The scheduling task of the intelligent process vehicle is determined, wherein the scheduling task includes scheduling the intelligent process vehicle used for transporting vehicles to travel along the driving path.

[0013] Optionally, based on the production plan and the inventory material information, the real-time material requirements of the process island are determined, and the scheduling tasks of the intelligent logistics vehicles are formulated in real time in conjunction with the vehicle location information, including: When the vehicle completes the assembly of the current process island, the current shortage of materials on the current process island is calculated based on the remaining amount of materials in the inventory material information, and it is determined whether there are any target materials whose current shortage of materials exceeds the preset shortage warning threshold. When the target material is determined to exist, the production cycle of the current process island is determined based on the production plan; The real-time material requirements of the process island are determined based on the current shortage of materials and the production cycle time. The real-time material requirements include target material requirements, wherein the target material requirements include the supply time window of each target material and the final shortage amount when supplying materials within the supply time window. The number of intelligent logistics vehicles to be collected and the material loading capacity of each intelligent logistics vehicle are determined based on the final shortage amount. The travel path and travel time of the intelligent logistics vehicle are determined based on the location of the loaded materials and the current location of the process island. Based on the supply time window, the travel time, and the preset redundancy time, the loading time window for the target material demand is determined in conjunction with the inventory material information. The scheduling task of the intelligent logistics vehicle is determined, wherein the scheduling task includes: scheduling the intelligent logistics vehicle to load the target material according to the material loading amount within the loading time window, traveling according to the driving path, and transporting the target material to the current process island within the supply time window.

[0014] Optionally, determining the supply time window and the final shortage amount within the target material demand based on the current shortage quantity and the production cycle time includes: The target duration is determined as t = ((1 / P) / M). (N0-N1); Where P represents the production cycle time, M represents the preset material consumption of the target material in each vehicle, N0 represents the preset shortage danger threshold, and N1 represents the current shortage of the target material. Based on the current time point and the target duration, obtain the supply time window in the target material demand; Predict future material consumption based on the remaining production requirement of the target material in the current process island in the production plan and the current shortage of materials; Calculate the sum of the current shortage of materials and the future consumption of materials to obtain the final shortage in the target material demand.

[0015] Optionally, after determining the scheduling task of the intelligent logistics vehicle, the method further includes: The process parameter values ​​of each station in each process island related to the production plan are acquired in real time and sent to the process island controller. The system receives real-time operation parameter values ​​from the process island controller after production based on process parameter values. When the first difference between the real-time operating parameter value of the target material and the process parameter value of the target material is greater than the preset material loss threshold, the final shortage of the target material demand is increased, the supply time window of the process island is adjusted forward, and the driving speed of the logistics intelligent vehicle executing the scheduling task is adjusted. Based on the adjusted final shortage amount, supply time window, and driving speed value, the scheduling tasks of the intelligent logistics vehicles are redefined.

[0016] Optionally, after determining the scheduling task of the intelligent logistics vehicle, the method further includes: The process parameter values ​​of each station in each process island related to the production plan are acquired in real time and sent to the process island controller. The system receives real-time operation parameter values ​​from the process island controller after production based on process parameter values. Calculate the actual number of target materials installed and the actual cumulative installation time, and calculate the product of the standard installation time of the target materials and the actual number of installations to obtain the total standard installation time; When the second difference between the actual cumulative installation time and the total standard installation time is greater than the preset assembly delay threshold, the final shortage amount for the process island is reduced, and the scheduling task of the logistics intelligent vehicle is re-determined based on the reduced final shortage amount.

[0017] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects: This application provides a flexible scheduling group and scheduling method. The scheduling group includes: a Manufacturing Execution System (MAS), a Logistics Execution System (LOAS), an Intelligent Group Control System (ICS), a Logistics Intelligent Vehicle Scheduling System, and a Process Intelligent Vehicle Scheduling System. The ICS receives production plans from the MAS, inventory material information from the LOAS, and vehicle location information from the LOAS and Process Intelligent Vehicle Scheduling Systems respectively, and formulates scheduling tasks for the Process Intelligent Vehicles and Logistics Intelligent Vehicles in real time. Through deep collaboration between the ICS and MAS, LOAS, and other systems, the "information silos" caused by the independent operation of each system are broken down, achieving unified management of the entire production process. The ICS senses real-time environmental changes and / or demand fluctuations, and through on-demand pulling and collaborative work with multiple systems, improves the coordination and stability of production line operation. Based on production plans and material inventory information, resource allocation and task execution plans are dynamically adjusted in real time, reducing material backlog and space occupation, improving production response efficiency. Through adaptive optimization scheduling methods, the ability to cope with uncertainties in the production process is improved, achieving optimal utilization of system performance and realizing flexible control.

[0018] The scheduling method includes: receiving production plans from the Manufacturing Execution System (MAS), receiving inventory material information from the Logistics Execution System (LOAS), and receiving vehicle location information from the Logistics Intelligent Vehicle Scheduling System and the Process Intelligent Vehicle Scheduling System, respectively; determining the real-time process requirements of vehicles based on the production plan and the actual production progress of the vehicles; formulating real-time scheduling tasks for the process intelligent vehicles based on the actual process requirements of the vehicles and the vehicle location information of the process intelligent vehicles; and formulating real-time scheduling tasks for the logistics intelligent vehicles based on the real-time material requirements of the process island, combined with the production plan, inventory material information, and the vehicle location information of the logistics intelligent vehicles. Through deep collaboration between the intelligent group control system and systems such as the MAS and LOAS, the "information silos" caused by the independent operation of each system in the past are broken down, achieving unified management of the entire production process. By using the intelligent group control system to perceive real-time data on environmental changes and / or demand fluctuations, and through on-demand pull mechanisms and collaborative work with multiple systems, the coordination and stability of production line operation are improved. Based on production plans and material inventory information, resource allocation and task execution plans are dynamically adjusted in real time to reduce material backlog and site occupation, improve production response efficiency, and enhance the ability to cope with uncertainties in the production process through adaptive optimization scheduling methods, thereby achieving optimal utilization of system performance and realizing flexible control. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the architecture of a flexible scheduling group disclosed in one embodiment of the present invention; Figure 2 A flowchart of a flexible scheduling method according to an embodiment of this application is shown. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 like Figure 1 As shown in the figure, this application provides a flexible scheduling group, including: a manufacturing execution system, a logistics execution system, an intelligent group control system, a logistics intelligent vehicle scheduling system, and a process intelligent vehicle scheduling system.

[0022] Flexible scheduling refers to a scheduling method that dynamically adjusts resource allocation and task execution plans based on environmental changes and / or demand fluctuations. Its core lies in addressing uncertainty through real-time perception and adaptive optimization to achieve optimal or near-optimal system performance. The manufacturing execution system is configured to create and publish production plans, track execution information, and quality inspection information based on raw data from vehicle model production.

[0023] The raw data for vehicle model production forms the foundation for establishing a Manufacturing Execution System (MES). Only with the support of this raw data can the MES establish a production management system that meets actual production needs. For example, the raw data for vehicle model production includes: the production process for each model, the production efficiency of each process island, a list of tracking items, and quality inspection indicators. The MES develops a production plan based on the production process and production efficiency of each process island, determines tracking execution information based on the tracking item list of each process island, and generates quality inspection information through the quality inspection indicators of each process island. The methods for the MES to develop and publish production plans, tracking execution information, and quality inspection information can refer to existing technologies.

[0024] The Manufacturing Execution System (MES) integrates the production processes of various vehicle models through factory modeling, real-time data acquisition and monitoring, and integrates core functions such as production plan creation and release, execution information tracking, and quality inspection information. Based on an object-oriented architecture, it supports modular combination and expansion, covering multiple business modules, including production performance feedback, process management, and a cross-departmental cost control network. It employs RAID 5 storage and off-site disaster recovery mechanisms to ensure data security and collaborates with the Logistics Execution System, Intelligent Group Control System, Logistics Intelligent Vehicle Scheduling System, and Process Intelligent Vehicle Scheduling System to form a comprehensive information management and control platform.

[0025] The logistics execution system is configured to generate inventory material information based on the raw material data and logistics information.

[0026] Raw material data forms the foundation for establishing a logistics execution system. Only with the support of raw data can a logistics execution system build an inventory management system that meets actual production needs. For example, raw material data includes: the maximum purchase quantity per transaction, maximum inventory quantity, storage area, material type for each vehicle model on the process island, and the maximum storage quantity of each material type on the process island.

[0027] The logistics execution system obtains logistics information data from procurement information, and automatically classifies and aggregates the logistics information and raw material data to generate inventory material information.

[0028] The Logistics Execution System (LOAS) is a real-time management information system that connects inventory logistics management plans with the logistics operations of process islands. It primarily focuses on the execution process of logistics links such as warehousing, transportation, and distribution. Through real-time data collection, task scheduling, process monitoring, and collaborative management, it formulates and publishes inventory material information to ensure the efficient implementation of logistics plans and improve the efficiency, accuracy, and transparency of logistics operations. The LOAS automatically categorizes and aggregates inventory material information based on raw logistics data and materials. Methods for generating inventory material information include determining the remaining material quantity for each process island by the difference between the preset maximum inventory level of materials and the statistical material consumption.

[0029] The intelligent group control system communicates with the manufacturing execution system and the logistics execution system respectively, and is configured to: receive production plans issued by the manufacturing execution system, receive inventory material information from the logistics execution system, receive vehicle location information from the logistics intelligent vehicle scheduling system and the process intelligent vehicle scheduling system respectively, and formulate scheduling tasks for the process intelligent vehicle and the logistics intelligent vehicle in real time.

[0030] Intelligent vehicles can be either Automated Guided Vehicles (AGVs) or Intelligent Guided Vehicles (IGVs).

[0031] Both process intelligent vehicles and logistics intelligent vehicles perform different tasks. Process intelligent vehicles are responsible for transporting assembly vehicles on the main line; logistics intelligent vehicles are responsible for transporting the materials needed for assembly to the process islands.

[0032] The intelligent group control system is the core of the flexible scheduling group. It is responsible for data collection, coordination and management between equipment, task sequencing, flexible scheduling, and digital twin simulation of the manufacturing execution system, logistics execution system, logistics intelligent vehicle scheduling system, and process intelligent vehicle scheduling system.

[0033] The real-time scheduling tasks for process intelligent vehicles and logistics intelligent vehicles can be understood as follows: the scheduling tasks for process intelligent vehicles are formulated in real time based on the real-time process requirements of the vehicles, combined with the production plan and vehicle location information; and the scheduling tasks for logistics intelligent vehicles are formulated in real time based on the real-time material requirements, combined with the production plan, inventory material information and vehicle location information.

[0034] The logistics intelligent vehicle scheduling system is connected to the intelligent group control system and is configured to schedule logistics intelligent vehicles based on scheduling tasks, and to determine the vehicle location information of logistics intelligent vehicles in real time.

[0035] The process intelligent vehicle scheduling system is communicatively connected to the intelligent group control system and is configured to schedule process intelligent vehicles based on scheduling tasks, and to determine the vehicle location information of process intelligent vehicles in real time.

[0036] This application embodiment controls logistics intelligent vehicles through a logistics intelligent vehicle scheduling system and process intelligent vehicles through a process intelligent vehicle scheduling system. This allows the logistics intelligent vehicle scheduling system and the process intelligent vehicle scheduling system to focus solely on control work, thereby reducing the management pressure on the intelligent group control system, decreasing the resource occupancy rate of the intelligent group control system, and improving the management efficiency of the intelligent group control system.

[0037] The group described in this application, through deep collaboration between the intelligent group control system and systems such as the manufacturing execution system and the logistics execution system, breaks down the "information silos" caused by the independent operation of traditional systems, and achieves unified management of the entire production process. By sensing real-time environmental changes and / or demand fluctuations through the intelligent group control system, and through on-demand pulling and collaborative work with multiple systems, the coordination and stability of production line operations are improved. Based on production plans and material inventory information, resource allocation and task execution plans are dynamically adjusted in real time, reducing material backlog and space occupation, improving production response efficiency, and enhancing the ability to cope with uncertainties in the production process through adaptive optimization scheduling methods, achieving optimal utilization of system performance and realizing flexible control.

[0038] In some specific embodiments, the group also includes an enterprise master data management system, which is communicatively connected to the manufacturing execution system and the logistics execution system, respectively, and configured to maintain the raw data of vehicle production and materials.

[0039] An enterprise-level master data management system is a fundamental tool for establishing coordination among systems within a group. It provides the basic data for collaboration between systems in the group.

[0040] The enterprise-level master data management system communicates with the manufacturing execution system and the logistics execution system through the Hypertext Transfer Security Protocol. It maintains the original data of vehicle production and materials through manual input, and then transmits the original data of vehicle production and materials to the manufacturing execution system and the logistics execution system respectively.

[0041] In some specific embodiments, the group further includes an assembly process management system, which is communicatively connected to the intelligent group control system and configured to determine the process parameter values ​​for each workstation in the process island; the intelligent group control system is also configured to send the process parameter values ​​for each workstation in the process island involved in the production plan to the process island controller.

[0042] The assembly process management system obtains the vehicle model's production plan from the manufacturing execution system and, based on the vehicle model in the production plan, determines the process islands that require process parameter values. The process parameter values ​​for each workstation within the process island are then manually determined (by staff based on actual conditions, work manuals, etc.). These process parameter values ​​include: the parking position of the intelligent process cart, the parking position of tooling within the process island, the positioning position of fixtures within the process island, and the installation position of parts to be processed within the process island. After determining the process parameter values ​​for each workstation, the assembly process management system sends the process island and its corresponding process parameter values ​​to the intelligent group control system via an industrial standard protocol. The intelligent group control system then sends the process parameter values ​​for each workstation within that process island to the corresponding process island controller via the same industrial standard protocol. The process island controller then controls the robots and other machinery within the process island to perform production and assembly work based on these process parameter values. For the specific management method of the above assembly process management system, please refer to existing technologies.

[0043] For example, when the process island is a roof-mounted process island, the process parameter values ​​for the roof-mounted process island include: the standard parking position of the intelligent process vehicle, the standard tooling parking position of the support fixture after the body-in-white is removed from the intelligent vehicle bracket, the standard positioning position of the built-in fixture on the body-in-white, and the standard installation position of the roof mounting point. The assembly process management system formulates these process parameter values ​​through manual input and auxiliary management tools and sends them to the intelligent group control system, which then sends them to the roof-mounted process island controller. The process island controller controls the support fixture to remove the body-in-white from the intelligent vehicle bracket according to the standard parking position of the intelligent process vehicle. After that, the robot automatically installs the roof of the body-in-white according to these process parameter values.

[0044] This specific embodiment group sets the process parameter values ​​for each station in the process island through the assembly process management system. The process island controller realizes the standardization and automation of on-demand installation through the process parameter values ​​of each station, thereby improving production efficiency.

[0045] In some specific embodiments, the group also includes a dark lighting management system, which communicates with the process intelligent vehicle scheduling system based on the Hypertext Transfer Security Protocol and is configured to control the start, stop, and alarm of the process intelligent vehicle through the process intelligent vehicle scheduling system.

[0046] The dim lighting management system provides operational data of the intelligent process vehicle through the intelligent process vehicle scheduling system, monitors the working process of the intelligent process vehicle, and analyzes the working data. When the intelligent process vehicle malfunctions, the dim lighting management system will issue an alarm in real time and send the alarm information to the intelligent process vehicle scheduling system. The intelligent process vehicle scheduling system will then control the alarm of the intelligent process vehicle to ensure the smooth operation of the production process.

[0047] The operational data includes: driving speed, driving path, vehicle position, and task status. For example, when the task status is "execution" and the vehicle position remains unchanged for an extended period, the dimming management system determines that the intelligent process vehicle has experienced a prolonged pause on its navigation path. The dimming management system analyzes the environmental conditions surrounding the intelligent process vehicle's location using real-time traffic information on an electronic map. If the environmental conditions around the location are normal (e.g., no congestion or obstacles), it determines that the intelligent process vehicle has malfunctioned, and the dimming management system issues an alarm. Simultaneously, it sends the alarm information to the intelligent process vehicle dispatch system, notifying the intelligent process vehicle to issue an alarm. After the alarm is triggered, the dimming management system plans a route to an area that does not affect traffic, and controls the intelligent process vehicle's start, drive, and stop via the intelligent process vehicle dispatch system to transfer the intelligent process vehicle to an area that does not affect traffic.

[0048] This specific embodiment uses a dark light management system to ensure that the intelligent process cart can be dealt with in a timely manner when an anomaly occurs, thus ensuring the smooth operation of the production process.

[0049] In some specific embodiments, the group further includes a bastion host, which is located between the user terminal and the intelligent group control system, and communicates with the user terminal via an intranet or VPN, and is configured to audit the communication data between the user terminal and the intelligent group control system.

[0050] The bastion host communicates with the intelligent group control system via Hypertext Transfer Protocol Security (HTTP). When a user terminal needs to communicate remotely with the intelligent group control system, the bastion host performs security audits on the communication information between the user terminal and the intelligent group control system. This includes auditing whether the user terminal is a legitimate user through identity authentication and auditing the security of the communication information through security analysis, preventing unauthorized intrusion and damage to the core intelligent group control system. The bastion host improves the flexibility and security of group applications.

[0051] The method of auditing user terminals through identity authentication includes verifying whether the user identity of the target information transmitted by the user terminal is consistent with the pre-stored user identity in the system.

[0052] A method for auditing the security of communication information through security analysis includes: receiving encrypted information transmitted by a user terminal; obtaining target information and a verification code after decrypting the encrypted target information; performing a verification operation on the target information to obtain a target verification code; verifying whether the target verification code is consistent with the decrypted verification code; and determining that the target information is secure when they are consistent.

[0053] In some specific embodiments, the intelligent group control system communicates with the manufacturing execution system and the logistics execution system based on industry standard protocols. The intelligent group control system also communicates with the logistics intelligent vehicle scheduling system and the process intelligent vehicle scheduling system based on the Hypertext Transfer Security Protocol (HTTP).

[0054] Industrial standard protocols are communication protocols used in industrial automation. They define device behavior and data access methods through object modeling, support physical media such as fieldbus standards, open real-time fieldbus networks, and Ethernet, and provide both explicit messaging and implicit I / O data exchange mechanisms. This ensures the specificity and reliability of communication between the intelligent group control system and the manufacturing execution system and the logistics execution system.

[0055] Hypertext Transfer Protocol Secure (HTTPS) is a secure communication protocol used over computer networks. HTTPS communicates via HTTP and uses SSL / TLS to encrypt data packets. It provides authentication and protects the privacy and integrity of exchanged data. Using HTTPS for communication between the intelligent group control system, the logistics intelligent vehicle scheduling system, and the process intelligent vehicle scheduling system improves communication versatility and reduces communication costs.

[0056] Example 2 This application also provides method embodiments that follow the above embodiments. The interpretation of the same names is the same as that of the above embodiments, and they have the same technical effects as those of the above embodiments. They will not be repeated here.

[0057] like Figure 2 As shown, this application provides a flexible scheduling method applied to an intelligent group control system in the aforementioned group, comprising: Step S201: Receive the production plan and real-time production progress of vehicles published by the Manufacturing Execution System, receive the inventory material information from the Logistics Execution System, and receive the vehicle location information from the Logistics Intelligent Vehicle Scheduling System and the Process Intelligent Vehicle Scheduling System, respectively.

[0058] Step S202a: Determine the real-time process requirements of the vehicle based on the production plan and the real-time production progress of the vehicle, and formulate the scheduling task of the intelligent process vehicle in real time based on the production plan of the real-time process requirements and the vehicle location information of the intelligent process vehicle.

[0059] The real-time process requirements include the types of processes required for the vehicle to complete all assembly processes but which have not yet been carried out. The production plan determines all the assembly processes that the vehicle needs to perform, and the types of processes that have been carried out are determined based on the real-time production progress of the vehicle, thereby obtaining the types of processes that have not yet been carried out.

[0060] Step S202b: Determine the real-time material requirements of the process island based on the production plan and the inventory material information, and formulate the scheduling task of the intelligent logistics vehicle in real time by combining the vehicle location information of the intelligent logistics vehicle.

[0061] In some specific embodiments, the scheduling task of the intelligent process vehicle is formulated in real time based on the real-time process requirements and the vehicle location information, including: Step S202a-1: When the vehicle completes the assembly of the current process island, determine multiple downstream processes of the current process island according to the production plan and the real-time process requirements of the vehicle.

[0062] The downstream processes mentioned here refer to processes that are not yet installed on the vehicle but are suitable for installation. Based on the process types included in the real-time process requirements, multiple downstream processes for the current process island can be determined.

[0063] Step S202a-2: Determine the multi-flow process queue of the current process island based on the multiple downstream processes.

[0064] The current process island's multi-flow process queue includes multiple candidate process islands for executing the multiple downstream processes. Each candidate process island is used to execute at least one downstream process.

[0065] Step S202a-3: Obtain the assembly queue for each candidate process island.

[0066] The assembly queue includes the number of intelligent process vehicles waiting to enter each candidate process island.

[0067] Step S202a-4: Determine the priority order of multiple candidate process islands based on the assembly queue.

[0068] After a vehicle completes its first process assembly on the current process island, according to traditional production requirements, it must proceed to the corresponding process island for the downstream second process for assembly. However, this specific embodiment provides a flexible production method where the vehicle can select a suitable process island from multiple candidate process islands across multiple downstream processes (including the second process). To this end, a multi-flow process queue is provided to store multiple candidate process islands, which are arranged according to priority. For example, after the vehicle completes its assembly on process island A, the downstream processes that still need to be executed are determined based on the real-time process requirements following assembly on process island A. This leads to the selection of process islands B, C, D, and E as candidate process islands for executing the corresponding downstream processes.

[0069] The priority order of multiple candidate process islands is determined based on the number of intelligent process vehicles in the assembly queue of each candidate process island. The fewer the number, the higher the priority. When the number of intelligent process vehicles in the assembly queue of two process islands is the same, the priority order is determined based on the distance value to process island A. The closer the distance value, the higher the priority.

[0070] For example: the assembly queue length of process island B is 2, the assembly queue length of process island C is 3, the assembly queue length of process island D is 3, and the assembly queue length of process island E is 5; the distance between process island C and process island A is 5m, and the distance between process island D and process island A is 8m. Then the priority order of the multi-flow process queues of process island A is: process island B, process island C, process island D, and process island E.

[0071] Since the assembly processes of multiple candidate process islands can be the same or different, after sorting the multiple candidate process islands according to the priority order, for process islands that perform the same process, only the process island with the highest priority is retained, thus finally determining the priority order of the multiple candidate process islands.

[0072] For example, the priority order of the multi-flow process queue of process island A is: process island B, process island C, process island D, and process island E. Since process island B and process island C have the same assembly process, and process islands B, D, and E have different assembly processes, process island C, which has a lower priority than process island B, is excluded. Therefore, the final multi-flow process queue of process island A is determined to be: process island B, process island D, and process island E.

[0073] Step S202a-5: Determine the process intelligent vehicle used for transporting the vehicle based on the vehicle location information of the process intelligent vehicle.

[0074] For example, identify multiple candidate process intelligent vehicles that are idle around the current process island, obtain the distance value of the corresponding candidate process intelligent vehicle based on the position of each candidate process intelligent vehicle and the position of the current process island, and determine the candidate process intelligent vehicle with the closest distance value as the process intelligent vehicle of the transport vehicle.

[0075] Step S202a-6: Determine the target process island based on the priority order of multiple candidate process islands in the multi-flow process queue of the current process island.

[0076] Specifically, the target process island is determined from among multiple candidate process islands with the highest priority.

[0077] Step S202a-7: Determine the driving path of the intelligent process vehicle based on the position of the current process island and the position of the target process island.

[0078] For example, using graph search algorithms (such as...) (e.g., Dijkstra's algorithm or its variants) excludes impassable road segments or turns affected by traffic control and the environment in the road network graph, generates multiple candidate driving paths, and selects a driving path from the multiple candidate driving paths, for example, by using the K-shortest path algorithm or path enumeration method to obtain the shortest driving path.

[0079] Step S202a-8: Determine the scheduling task of the intelligent process vehicle, wherein the scheduling task includes scheduling the intelligent process vehicle used for transporting vehicles to travel along the driving path.

[0080] After scheduling the intelligent process vehicle used for transporting the vehicle to travel along the driving path, the process further includes adjusting the assembly queue of the target process island so that subsequent vehicles can determine their own target process island. For example, continuing the above example, process island B is determined as the target process island, and the information of the intelligent process vehicle corresponding to the current vehicle is inserted at the end of the assembly queue of process island B. At this time, the assembly queue length of process island B is 3, which is the same as the assembly queue length of process islands C and D. The distance between process island B and process island A is 6m. Then, for subsequent vehicles, the priority order for adjusting the multi-flow process queue of process island A is: process island C, process island D, and process island E.

[0081] This specific embodiment breaks through the limitations of traditional linear scheduling modes in terms of scheduling flexibility. Through multi-process island, multi-direction process queues and assembly queues, it supports multi-direction nonlinear scheduling strategies, enabling dynamic adjustment of process flows based on real-time production status. This ensures the continuity and adaptability of the production process, achieving flexible management of the assembly process. It can select suitable target process islands from multiple candidate process islands, thereby reducing the number of queues and pairing waiting times, enabling rapid vehicle assembly and improving production efficiency.

[0082] In other specific embodiments, the real-time material requirements of the process island are determined based on the production plan and the inventory material information, and the scheduling tasks of the intelligent logistics vehicles are formulated in real time in conjunction with the vehicle location information, including: Step S202b-1: When the vehicle completes the assembly of the current process island, based on the remaining material quantity of the current process island in the inventory material information, calculate the current shortage material quantity of the current process island, and determine whether there is a target material whose current shortage material quantity exceeds the preset shortage warning threshold.

[0083] The shortage quantity of materials refers to the difference between the preset maximum inventory quantity of materials in the process island side warehouse and the remaining quantity of materials.

[0084] For example, if the preset maximum inventory of the current process island is 100 units, when the first vehicle finishes assembling the current process island, the remaining material quantity of the current process island is 90 units, then the current shortage of material quantity of the current process island is 10 units; when the second vehicle finishes assembling the current process island, the remaining material quantity of the current process island is 80 units, then the current shortage of material quantity of the current process island = 100 - 80 = 20 units; and so on.

[0085] The preset shortage warning threshold is an empirical value obtained through extensive experimentation and is set in advance by staff. When the current shortage of materials on the process island exceeds the preset shortage warning threshold, a delivery task needs to be arranged, ensuring sufficient time to replenish the shortage of materials on the current process island within the supply time window.

[0086] Step S202b-2: When it is determined that the target material exists, the production cycle of the current process island is determined based on the production plan.

[0087] The production cycle time of a process island refers to the number of vehicles assembled on that process island within a cycle time. For example, if the cycle time is 1 hour and one vehicle is assembled every 6 minutes, the production cycle time is 10 vehicles / hour.

[0088] The production cycle time of the target process island can be preset in the production plan by the staff. In some specific embodiments, determining the production cycle time of the current process island based on the production plan includes: Step S202b-21: Obtain the process parameter values ​​of each station in the current process island related to the production plan in real time, and send them to the current process island controller.

[0089] The process parameter value refers to the parameter value used by the current process island controller to control the robot arm during the current process island assembly. The current process island controller uses the process parameter value to control the robot arm to automatically complete the assembly task of the current process island.

[0090] Step S202b-22: Obtain the historical assembly time consumed by the current process island controller to complete vehicle assembly based on process parameter values.

[0091] Step S202b-23: Determine the production cycle of the current process island based on the average historical assembly time.

[0092] For example, when installing a process island on the roof, the process parameters include: the standard parking position of the intelligent process trolley, the standard tooling parking position of the support fixture after the body-in-white is removed from the intelligent trolley bracket, the standard positioning position of the built-in fixture on the body-in-white, and the standard installation position of the roof mounting point. The intelligent group control system obtains these process parameter values ​​in real time from the assembly process management system and then sends them to the roof-mounted process island controller. The process island controller retrieves the first intelligent process trolley in the assembly queue of the roof-mounted process island, transports the body-in-white to the standard parking position of the roof-mounted process island, controls the support fixture to remove the body-in-white from the intelligent trolley bracket to the standard tooling parking position, and controls the built-in fixture... The fixture picks up the body-in-white at the standard positioning position, then the robot installs the roof at the standard installation position. Finally, a process intelligent cart is retrieved from the standard cart parking position to pick up the body-in-white with the roof installed, and then drives away from the roof installation process island. After installation, the process island controller reports the assembly time from the time the first process intelligent cart in the assembly queue is retrieved to the time it leaves the roof installation process island to the intelligent group control system. The intelligent group control system obtains the average assembly time for each car based on the average of multiple recent historical assembly times. If the average assembly time for each car is 6 minutes and the cycle time is 60 minutes, then the 60-minute transfer vehicle (i.e., production takt time) = 60 / 6 = 10 vehicles / hour. This allows the intelligent group control system to monitor the main production rhythm in real time, improving flexible management and on-demand pull management capabilities.

[0093] Step S202b-3: Determine the real-time material requirements of the process island based on the current shortage of materials and the production cycle time. The real-time material requirements include the target material requirements.

[0094] The target material requirement includes the supply time window of the target material and the final shortage when supplying the material within the supply time window.

[0095] The final shortage amount refers to the predicted shortage amount of materials when supplied within the supply time window. The final shortage amount is different from the current shortage amount of materials in the current process island when the preset shortage warning threshold is exceeded. The final shortage amount is a predicted shortage amount that is greater than the current shortage amount of materials in the current process island when the preset shortage warning threshold is exceeded.

[0096] In some specific embodiments, determining the target material requirement based on the current shortage of materials and the production cycle time includes: Step S202b-31: Divide the reciprocal of the production cycle by the preset material consumption per vehicle, and then multiply by the difference between the preset shortage danger threshold and the current shortage material quantity to obtain the target duration.

[0097] The preset shortage risk threshold is an empirical value obtained through extensive experimentation. The preset shortage risk threshold is greater than the preset shortage warning threshold. The preset shortage risk threshold is the critical value at which production delays occur. When the amount of material in short supply at the current process island exceeds the preset shortage risk threshold, it indicates that the supplied materials are insufficient to meet the production cycle requirements, and production will be delayed.

[0098] The target duration is calculated using the following formula: t = ((1 / P) / M) (N0-N1); Where t represents the target duration, P represents the production cycle time, M represents the preset material consumption of the target material in each vehicle, N0 represents the preset shortage danger threshold, and N1 represents the current shortage of the target material.

[0099] For example, if the current maximum preset inventory on the process island is 100 units, the current shortage quantity of the target material is 60 units, the preset shortage danger threshold is 80 units, and there are 20 units needed to reach the preset shortage danger threshold, and the production cycle is 10 vehicles / hour, then the production time per vehicle = 1 / (10 vehicles / hour) = 6 minutes / vehicle. If the preset material consumption of the target material per vehicle is 2 units / vehicle, then the average allocated time for each unit of the target material used per vehicle = (6 minutes / vehicle) / (2 units / vehicle) = 3 minutes / unit, and the target production time = (3 minutes / unit). 20 pieces = 60 minutes.

[0100] Step S202b-32: Obtain the supply time window in the target material demand based on the current time point and the target duration.

[0101] Specifically, the starting point of the supply time window is the current time plus the target duration, and the ending point is the starting point plus the redundant time.

[0102] For example, if the current time is 8:00:00, the target duration is 60 minutes, and the redundancy time is 10 minutes, then the supply time window is [9:00:00, 9:10:00]. Furthermore, the redundancy time can be the unloading time of the logistics intelligent vehicle. For example, if the preset unloading time is 10 minutes, then the supply time window is [9:00:00, 9:10:00].

[0103] Step S202b-33: Based on the remaining production required amount of the target material in the current process island in the production plan and the current shortage amount of material, predict the future material consumption.

[0104] Since the intelligent logistics vehicles that transport materials must deliver them before reaching the preset shortage risk threshold, that is, within the supply time window, it is necessary to predict the amount of materials to be consumed in the future based on the preset shortage risk threshold. This ensures that the materials delivered by the intelligent material vehicles within the supply time window include the amount of materials that are currently in short supply and the amount of materials that may be consumed in the future, so as to ensure that the amount of inventory materials on the process island when delivered will not be lower than the preset shortage risk threshold.

[0105] Specifically, the total quantity of each vehicle model planned to be produced in each process island of the production plan, the material type corresponding to each vehicle model, and the material quantity of each material type are obtained. By subtracting the quantity of vehicles that have been produced from the total quantity, the quantity of the current vehicle model to be produced can be obtained. Based on the quantity of vehicles to be produced, combined with the corresponding material type and the material quantity of each material type, the remaining material quantity required for the production of each material type in the vehicles to be produced can be obtained.

[0106] Specifically, if the remaining production requirement of the target material in the process island in the production plan is greater than or equal to the preset maximum inventory of the process island, the future material consumption will be calculated according to the following formula: N2 = N0 - N1; Where N2 represents the future material consumption of the process island, N0 represents the preset shortage danger threshold, and N1 represents the current shortage of the target material of the process island.

[0107] In this specific embodiment, a preset shortage risk threshold is used as the minimum inventory level of the target material on the process island. If the inventory level of the target material on the process island falls below this threshold, it may cause work delays on the process island and affect the overall production schedule. Therefore, the future material consumption cannot exceed the difference between the preset shortage risk threshold and the current shortage material level.

[0108] For example, continuing the above example, if the preset maximum inventory of the process island is 100 units, the current shortage of the target material of the process island is 60 units, and the preset shortage danger threshold of the process island is 80 units, then there are still 20 units short of reaching the preset shortage danger threshold. When the production plan of the process island remains unchanged, if the remaining production requirement of the target material of the process island in the production plan is 200 units, which is greater than the preset maximum inventory of the process island of 100 units, then the future material consumption will be 80 - 60 = 20 units.

[0109] If the remaining production requirement of the target material in the process island in the production plan is less than the preset maximum inventory of the process island, the future material consumption will be calculated according to the following formula: N2 = Nn - (Nmax - N1); Where N2 represents the future material consumption of the process island, Nn represents the remaining material required for the production of the target material of the process island in the production plan, Nmax represents the preset maximum inventory of the process island, and N1 represents the current shortage of the target material of the process island.

[0110] The difference between the preset maximum inventory and the current shortage of materials is equal to the remaining quantity of the target material in the process island. Since the production model is changed and the material is no longer used after the remaining production required quantity is consumed, the remaining production required quantity of the target material in the process island minus the remaining quantity is the future consumption quantity.

[0111] For example, if the current shortage of the target material in the process island is 60 units, and the production plan shows that the current production quantity of the model in the process island is 10 vehicles, and the consumption of the target material for each vehicle is 7 units, then the remaining production material required for the target material is 70 units, which is less than the preset maximum inventory of 100 units in the process island. After consuming 70 units of the target material, the production model will be changed and the target material will no longer be needed. Therefore, the future material consumption = 70 - (100 - 60) = 30 units.

[0112] Step S202b-34: Calculate the sum of the current shortage of materials and the future consumption of materials to obtain the final shortage of the target material demand.

[0113] For example, if the current shortage is 60 units and the future material consumption is 10 units, then the final shortage will be 60 units + 10 units = 70 units.

[0114] This specific embodiment determines the final shortage amount in the target material demand based on the current shortage amount and the future material consumption amount, ensuring that the process island edge warehouse can be replenished with sufficient materials at one time, reducing the frequency of use of intelligent logistics vehicles in the production workshop, reducing the congestion rate of intelligent logistics vehicles in the production area, and improving material distribution efficiency.

[0115] Step S202b-4: Determine the number of logistics smart vehicles to be collected and the material loading capacity of each logistics smart vehicle based on the final shortage amount.

[0116] Each intelligent logistics vehicle has a preset maximum loading capacity (rated loading capacity). The number of intelligent logistics vehicles collected is the number obtained by rounding up the quotient of the final shortage and the preset maximum loading capacity. The material loading capacity of each intelligent logistics vehicle is the quotient of the final shortage and the collected quantity. Alternatively, among n intelligent logistics vehicles, the material loading capacity of n-1 intelligent logistics vehicles is the preset maximum loading capacity, and the material loading capacity of the other intelligent logistics vehicle is the difference between the final shortage and the total material loading capacity of N-1 intelligent logistics vehicles.

[0117] For example, continuing the above example, the current final shortage of the process island is 70 pieces, and the preset maximum loading capacity of the logistics intelligent vehicle is 35 pieces / vehicle. Therefore, the number of logistics intelligent vehicles to be collected = 70 pieces / (35 pieces / vehicle) = 2 vehicles, and the material loading capacity of each logistics intelligent vehicle is 35 pieces.

[0118] Step S202b-5: Determine the driving path and driving time of the logistics intelligent vehicle based on the location of the loaded materials and the current process island.

[0119] The location for loading materials is typically a fixed material storage location within the factory, using a graph search algorithm (e.g., ...). The algorithm (or its variants) excludes impassable road segments or turns affected by traffic control and the environment in the road network graph, generates multiple candidate driving paths from the current process island to the material storage location, selects the driving path of the logistics intelligent vehicle from the multiple candidate driving paths, for example, by using the K-shortest path algorithm or path enumeration method to obtain the shortest driving path, calculates the quotient of the driving path length value and the preset normal driving speed value, and obtains the driving time.

[0120] Step S202b-6: Based on the supply time window, the travel time, and the preset redundancy time, and in conjunction with the inventory material information, determine the loading time window for the target material demand.

[0121] The preset redundancy time is used to avoid delays during the execution process, which could prevent the timely delivery of supplies and cause production delays.

[0122] The loading time window refers to the time period during which a logistics intelligent vehicle loads the target material.

[0123] The loading time window is usually determined according to the following rules: the minimum time point is equal to the difference between the minimum time point of the supply time window and the driving time, minus the preset redundancy time; the maximum time point is equal to the difference between the maximum time point of the supply time window and the driving time, minus the preset redundancy time.

[0124] Furthermore, since the current inventory level cannot meet the final shortage of the target material in the process island, it is necessary to determine the loading time window based on the relationship between the current inventory level and the final shortage level of the target material in the process island.

[0125] When the current inventory (i.e., remaining quantity) of the target material in the process island is greater than or equal to the final shortage quantity of the target material, the minimum time point of the loading time window is equal to the difference between the minimum time point of the supply time window and the travel time, minus the preset redundancy time; the maximum time point of the loading time window is equal to the difference between the maximum time point of the supply time window and the travel time, minus the preset redundancy time.

[0126] When the current inventory of the target material in the process island is less than the final shortage of the process island, the inventory replenishment waiting time is determined, and then the first loading time window and the second loading time window are determined. The minimum time point of the first loading time window is equal to the difference between the minimum time point of the supply time window and the travel time, minus the preset redundancy time. The maximum time point of the first loading time window is equal to the difference between the maximum time point of the supply time window and the travel time, minus the preset redundancy time. The minimum time point of the second loading time window is equal to the difference between the inventory replenishment time point and the preset time window duration. The maximum time point of the second loading time window is equal to the inventory replenishment time point.

[0127] The inventory replenishment time point is determined by the planned arrival time of the target materials in the material procurement plan within the logistics execution system. Once the planned arrival time is determined, the inventory replenishment time point is also determined.

[0128] For example, continuing the above example, the supply time window is [9:00:00, 9:10:00], the travel time is 30 minutes, the preset redundancy time is 5 minutes, when the final shortage is 70 units and the target material inventory is sufficient, the minimum loading time window = 9:00:00 - 30 minutes - 5 minutes = 8:25:00, and the maximum loading time window = 9:10:00 - 30 minutes - 5 minutes = 8:35:00, that is, the loading time window is [8:2...]. [5:00, 8:35:00]; When the inventory of the target material is insufficient, the final shortage is 70 units, the current inventory of the target material is 50 units, and the planned arrival time of the target material in the material procurement plan in the logistics execution system is 8:45:00. The inventory can be increased by 100 units. The first loading time window is [8:25:00, 8:35:00], loading 50 units. The preset time window duration is 10 minutes. The second loading time window is [8:35:00, 8:45:00], loading 20 units.

[0129] This allows the real-time material requirements of the process island to be met without affecting the production schedule.

[0130] Step S202b-7: Determine the scheduling task of the intelligent logistics vehicle.

[0131] The scheduling task includes: scheduling a logistics intelligent vehicle to load the target material according to the material loading amount within the loading time window, driving according to the driving path, and transporting the target material to the current process island within the supply time window.

[0132] In some specific embodiments, the method further includes: Step S202b-11: Determine the collection time window of the logistics intelligent vehicle based on the loading time window of the target material demand of the current process island and the preset advance collection time.

[0133] The preset advance collection period is used to avoid time delays during the collection process, which could prevent normal loading from being completed within the loading time window and ultimately cause production delays.

[0134] The collection time window is used to limit the time period during which the intelligent logistics vehicle needs to arrive at the loading location (e.g., the material warehouse location). In other words, the intelligent logistics vehicle must arrive at the loading location within the collection time window. This specific embodiment uses the collection time window to ensure that the intelligent logistics vehicle arrives at the loading location on time, while avoiding congestion caused by the vehicle arriving too early. This improves loading efficiency. The minimum and maximum time points of the collection time window are obtained by subtracting the preset advance collection time from the minimum and maximum time points of the loading time window, respectively.

[0135] For example, continuing the above example, if the loading time window is [8:25:00, 8:35:00] and the preset advance collection time is 5 minutes, then the collection time window is [8:20:00, 8:30:00].

[0136] Step S202b-12: Based on the vehicle location information, the collection time window, and the preset normal driving speed value of the logistics intelligent vehicle, determine the logistics intelligent vehicles that arrive at the loading position within the collection time window, and select the logistics intelligent vehicles to perform the assembly task based on the vehicle location information and the collection quantity.

[0137] Based on the vehicle's location information, the path length from the current location of the intelligent logistics vehicle to the loading location is determined. Combined with the preset normal driving speed value of the intelligent logistics vehicle, the travel time to the loading location is calculated. It is then determined whether the current time point plus the travel time is within the collection time window. This allows us to identify the intelligent logistics vehicle that arrives at the loading location within the collection time window.

[0138] When the number of intelligent logistics vehicles that arrive at the loading location within the collection time window is greater than or equal to the collection quantity, based on the vehicle location information, the intelligent logistics vehicles of the collection quantity that are closest to the loading location are selected as the intelligent logistics vehicles to perform the assembly task.

[0139] For example, if two intelligent logistics vehicles are collected, and the preset normal driving speed of the intelligent logistics vehicles is 2 m / s, the path length from the vehicle position to the loading position of the first intelligent logistics vehicle is 12 m, and the travel time is 12 m / (2 m / s) = 6 s; the path length from the vehicle position to the loading position of the second intelligent logistics vehicle is 17 m, and the travel time is 17 m / (2 m / s) = 8.5 s; the path length from the vehicle position to the loading position of the third intelligent logistics vehicle is 28 m, and the travel time is 28 m / (2 m / s) = 14 s. s; The path length from the fourth logistics intelligent vehicle to the loading position is 56m, and the travel time is 56m / (2m / s) = 48s; If the current time point is 20s away from the collection time window [8:20:00, 8:30:00], then the first, second, and third logistics intelligent vehicles meet the requirement of arriving at the loading position within the collection time window, and the positions of the first and second logistics intelligent vehicles are closest to the loading position. Therefore, it is determined that the first and second logistics intelligent vehicles will perform the assembly task.

[0140] When the number of intelligent logistics vehicles that reach the loading position within the collection time window is less than the collection quantity, the intelligent logistics vehicles that can reach the loading position within the collection time window are used as intelligent logistics vehicles to perform assembly tasks; after the materials are supplied to the current process island, step S202b-1 is repeated.

[0141] Step S202b-13: Determine the scheduling task of the intelligent logistics vehicle.

[0142] The scheduling task of the intelligent logistics vehicle includes: scheduling the intelligent logistics vehicle to load the target material according to the material loading amount within the loading time window, driving according to the driving path, and transporting the target material to the current process island within the supply time window.

[0143] Furthermore, the speed of the intelligent logistics vehicle on the travel route is determined based on the length of the travel path and the travel time.

[0144] This specific embodiment implements rapid, real-time dynamic management of the delivery task queue, thereby improving the response speed of material supply. Precise collaborative material delivery is achieved. Through intelligent scheduling of logistics intelligent vehicles, it ensures complete synchronization between parts and mainline vehicles; the required parts are accurately delivered as the mainline vehicles arrive at the workstation, completely eliminating production delays caused by untimely materials. This precise delivery not only saves cycle time but also significantly reduces material backlog and space occupation. Based on the current shortage of materials and future material consumption, the final shortage quantity in the target material demand is determined, ensuring that the process island-side warehouse can be replenished sufficiently in one go, reducing the frequency of use of logistics intelligent vehicles in the production workshop, reducing congestion rates of logistics intelligent vehicles in the production area, and improving material delivery efficiency. By pre-setting redundancy time and pre-setting advance collection time, delays during delivery execution and logistics intelligent vehicle collection are avoided. The resulting scheduling tasks ensure timely delivery of supplied materials, guaranteeing production efficiency. A demand-driven pull mechanism achieves refined material management. Based on real-time vehicle location information, the timing of material demand can be accurately predicted, achieving timely material delivery.

[0145] In some specific embodiments, after determining the scheduling task of the intelligent logistics vehicle, the method further includes: Step S211: Obtain the process parameter values ​​of each station in each process island related to the production plan in real time, and send them to the process island controller.

[0146] Step S212: Receive real-time operation parameter values ​​fed back by the process island controller after production based on process parameter values.

[0147] Real-time operation parameter values ​​are assembly result parameter values ​​fed back after production based on process parameter values. For example, when installing a process island on the roof, the process parameter values ​​are the standard installation position of the roof mounting point, the standard quantity of installation materials used, and the standard installation time. For instance, the standard installation position of the roof mounting point is the upper left corner of the roof, and the standard quantity of installation materials used is 2 screws. After installation is completed at the upper left corner of the roof based on the process parameter values, the real-time operation parameter value of the screws (i.e., the actual quantity used) is 4.

[0148] Step S213: When the first difference between the actual usage quantity of the target material and the standard usage quantity of the target material is greater than the preset material loss threshold, the final shortage quantity of the target material demand is increased, the supply time window of the process island is adjusted forward, and the driving speed of the logistics intelligent vehicle performing the scheduling task is adjusted.

[0149] Step S214: Based on the adjusted final shortage amount, supply time window, and driving speed value, redetermine the scheduling task of the logistics intelligent vehicle.

[0150] Specifically, the quantity of materials used refers to the total amount of materials consumed when assembling each vehicle in the process island. Process parameter values ​​include the standard usage quantity of materials, and real-time operation parameter values ​​include the actual usage quantity of materials. When the first difference between the actual usage quantity and the standard usage quantity of materials exceeds a preset material loss threshold, the final shortage quantity is calculated by summing the final shortage quantity with the first difference, thus obtaining the adjusted final shortage quantity. The material loss threshold is preset by staff based on experience.

[0151] Based on the adjusted final shortage, the number of logistics smart vehicles to be collected and the material loading capacity of each logistics smart vehicle are re-determined, and the determination method is the same as step S202b-4.

[0152] Calculate the product of the actual installation interval of each material in the process island and the first difference to obtain the total lead time. Based on the total lead time, adjust the supply time window forward (that is, subtract the total lead time from both the minimum and maximum time points of the supply time window) to obtain the adjusted supply time window.

[0153] Adjust the speed of the intelligent logistics vehicle according to the adjusted supply time window: The remaining travel time is obtained by calculating the quotient of the remaining path length and the current speed of the intelligent logistics vehicle. The adjusted travel time is then obtained by summing the remaining travel time with the total lead time. The adjusted speed is obtained by calculating the quotient of the remaining path length and the adjusted travel time. The remaining path length is the distance from the intelligent logistics vehicle's current position to the end point of the travel path.

[0154] Based on the adjusted final shortage amount, supply time window, and travel speed, the scheduling tasks for the intelligent logistics vehicles are redefined, including: For unexecuted scheduling tasks, the scheduling task of the logistics intelligent vehicle is adjusted to: collect logistics intelligent vehicles according to the adjusted collection quantity, schedule the logistics intelligent vehicles to load the target material according to the re-determined material loading amount, travel on the driving path determined by the original scheduling task, and transport the target material to the current process island within the adjusted supply time window.

[0155] For the executed scheduling task, the intelligent logistics vehicle travels on the route determined by the original scheduling task according to the adjusted driving speed value, and delivers the target material to the current process island within the adjusted supply time window.

[0156] If the first difference between the real-time operating parameters and the process parameters of a material exceeds a preset material loss threshold, it can be understood that after a scheduling task is determined, excessive material loss means that if materials are replenished according to the original scheduling task, the material shortage at the process island warehouse will exceed a preset shortage danger threshold before the supply time window, potentially causing production delays. Therefore, when a scheduling task is not executed, the material loading capacity, the number of intelligent logistics vehicles, the material loading capacity of each intelligent logistics vehicle, the supply time window, and the speed of the intelligent logistics vehicles are adjusted to ensure that the intelligent logistics vehicles executing the scheduling task replenish materials before the material shortage at the process island exceeds the preset shortage danger threshold, ensuring normal production. When a scheduling task has already been executed, the supply time window and the speed of the intelligent logistics vehicles are adjusted to ensure that materials are delivered before the intelligent logistics vehicles executing the scheduling task replenish materials at the process island before the material shortage exceeds the preset shortage danger threshold. Although materials cannot be replenished, normal production can be ensured, and materials at the process island will be replenished in the next replenishment cycle.

[0157] For example, when installing a process island on the roof, the process parameters are the standard installation location of the roof mounting point, the standard quantity of installation materials, and the standard installation time. For instance, the standard installation location of the roof mounting point is the upper left corner of the roof, and the standard quantity of installation materials is 2 screws. After determining the scheduling task, when the installation is completed at the upper left corner of the roof based on the process parameters, if the real-time operation parameter value of the screws (i.e., the actual quantity used) is 4, the first difference = 4 - 2 = 2. If the preset material loss threshold is 1, then the final shortage amount in the scheduling task is increased for the first difference (i.e., 2) of the process island. If the supply time window is [9:00:00, 9:10:00], and the average allocation time for each screw on the process island is 3 minutes / screw, then the total lead time = (3 minutes / screw). 2 = 6 min, the final supply time window is advanced to [8:54:00, 9:04:00]; if the remaining path length is 20m and the current driving speed of the logistics intelligent vehicle is 2m / min, then the original remaining driving time = 20m / (2m / min) = 10min, the adjusted driving time = 10min - 6min = 4min, and the adjusted driving speed = 20m / 4min = 5 m / min.

[0158] In some specific embodiments, the method further includes: Step S221: Obtain the process parameter values ​​of each station in each process island related to the production plan in real time, and send them to the process island controller.

[0159] Step S222: Receive real-time operation parameter values ​​fed back by the process island controller after production based on process parameter values.

[0160] Specifically, the installation time of a material refers to the time spent installing the material during vehicle assembly in the process island. The process parameter values ​​include the standard installation time of the material, which is the time preset by the staff, and the real-time operation parameter values ​​include the actual installation time of the material, which is the time fed back after production based on the process parameter values.

[0161] Step S223: Calculate the actual number of target materials installed and the actual cumulative installation time, and calculate the product of the standard installation time of the target materials and the actual number of installations to obtain the total standard installation time.

[0162] Since the delay between the standard installation time and the actual installation quantity of a material is very small, but it can accumulate and cause discrepancies between planned and actual material usage, this specific embodiment calculates the actual installation quantity and actual cumulative installation time of the target material in order to eliminate the impact of this situation on production.

[0163] Step S224a: When the second difference between the actual installation time of the target material and the standard installation time of the target material is greater than the preset assembly delay threshold, the final shortage amount for the process island is reduced, and the scheduling task of the logistics intelligent vehicle is re-determined based on the reduced final shortage amount. The preset assembly delay threshold is pre-set by staff based on experience.

[0164] The reduction of the final shortage in the scheduling task of the process island can be understood as follows: after the scheduling task is determined, if the second difference is greater than the preset assembly delay threshold, it indicates that the production progress is delayed. If the logistics intelligent vehicle replenishes the process island with materials according to the final shortage in the original scheduling task within the original supply time window, there will still be excess materials that cannot be replenished after the process island is full of materials. Therefore, to reduce the final shortage in the process island, return to step S202b-1 and re-determine the scheduling task of the logistics intelligent vehicle.

[0165] Calculate the difference between the actual cumulative installation time of the target material and the total standard installation time of the target material to obtain a second difference. When the second difference is greater than a preset assembly delay threshold, calculate the quotient of the second difference and the standard installation time of the target material to obtain the reduction amount of the target material. Calculate the difference between the final shortage amount of the installed materials and the reduction amount of the installed materials to obtain the adjusted final shortage amount.

[0166] For example, a process island is installed on the roof of a vehicle. The process parameter value is the standard installation time of the screws. For example, the total standard installation time of the roof screws is 6 minutes. After the installation is completed, if the actual cumulative installation time of the screws is 9 minutes, then the second difference = 9 - 6 = 3 minutes. If the preset assembly delay threshold is 2 minutes, then the final shortage of screws in the scheduling task is reduced for the process island. If the standard installation time of the screws on the process island is 3 minutes / screw, then the reduction in screws = 3 minutes / (3 minutes / screw) = 1 screw. If the original final shortage is 10 screws, then the adjusted final shortage = 10 - 1 = 9 screws.

[0167] Step S224b: When the second difference is greater than the preset assembly anomaly threshold, stop executing the scheduling task of the process island and return the materials in transit.

[0168] Wherein, the preset assembly anomaly threshold is greater than the preset assembly delay threshold.

[0169] If the second difference exceeds the preset assembly anomaly threshold, it can be interpreted as an assembly anomaly on the process island, resulting in significant material loss. In this case, the scheduling task for the process island will be stopped, and the materials in transit will be returned. Material delivery will resume only after the process island returns to normal.

[0170] This specific embodiment monitors the production process in real time, dynamically adjusting the final shortage amount in the scheduling tasks for the process islands based on production changes. This ensures that materials in the process islands are replenished in a timely and sufficient manner, guaranteeing normal production. A demand-driven pull mechanism enables refined material management. Based on real-time vehicle location information, the timing of material demand can be accurately predicted, achieving on-time material delivery. When changes in the production plan are detected, the downstream system can promptly adjust the material allocation scheme, avoiding material mismatch and waste, and improving material turnover efficiency.

[0171] This application embodiment receives production plans published by the Manufacturing Execution System (MAS), inventory material information from the Logistics Execution System (LOAS), and vehicle location information from the Logistics Intelligent Vehicle Scheduling System and the Process Intelligent Vehicle Scheduling System, respectively. Based on the production plan and the vehicle's actual production progress, it determines the real-time process requirements of the vehicles. Based on the actual process requirements of the vehicles and the vehicle location information of the process intelligent vehicles, it formulates real-time scheduling tasks for the process intelligent vehicles. It also formulates real-time scheduling tasks for the logistics intelligent vehicles based on the real-time material requirements of the process island, combined with the production plan, inventory material information, and the vehicle location information of the logistics intelligent vehicles. Through deep collaboration between the intelligent group control system and systems such as the MAS and LOAS, it breaks down the "information silos" caused by the independent operation of traditional systems, achieving unified management of the entire production process. By using the intelligent group control system to perceive real-time environmental changes and / or demand fluctuations, and through on-demand pull mechanisms and collaborative work with multiple systems, it improves the coordination and stability of production line operations. Based on production plans and material inventory information, resource allocation and task execution plans are dynamically adjusted in real time to reduce material backlog and site occupation, improve production response efficiency, and enhance the ability to cope with uncertainties in the production process through adaptive optimization scheduling methods, thereby achieving optimal utilization of system performance and realizing flexible control.

[0172] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A flexible dispatch group, characterized in that, include: The manufacturing execution system is configured to create and publish production plans, track execution information, and quality inspection information based on raw data from vehicle production. The logistics execution system is configured to generate inventory material information based on the raw material data and logistics information. The intelligent group control system is connected to the manufacturing execution system and the logistics execution system respectively, and is configured to: receive the production plan issued by the manufacturing execution system, receive the inventory material information of the logistics execution system, receive the vehicle location information of the logistics intelligent vehicle scheduling system and the process intelligent vehicle scheduling system respectively, and formulate the scheduling tasks of the process intelligent vehicle and the logistics intelligent vehicle in real time. The logistics intelligent vehicle scheduling system is communicatively connected to the intelligent group control system and configured to schedule logistics intelligent vehicles based on scheduling tasks, and to determine the vehicle location information in real time. The process intelligent vehicle scheduling system is communicatively connected to the intelligent group control system and is configured to schedule process intelligent vehicles based on scheduling tasks, and to determine the vehicle location information in real time.

2. The group according to claim 1, characterized in that, The group also includes an enterprise-level master data management system, which is connected to the manufacturing execution system and the logistics execution system respectively, and is configured to maintain the raw data of vehicle production and materials.

3. The group according to claim 1, characterized in that, The group also includes an assembly process management system, which is connected to the intelligent group control system and configured to set process parameter values ​​for each workstation in the process island; the intelligent group control system is further configured to send the process parameter values ​​for each workstation in the process island to the corresponding process island controller according to the process island involved in the production plan.

4. The group according to claim 1, characterized in that, The group also includes a dark light management system, which communicates with the intelligent process vehicle scheduling system based on the Hypertext Transfer Protocol. The dark light management system is configured to receive the operation data of the intelligent process vehicle sent by the intelligent process vehicle scheduling system, and control the start, stop and alarm of the intelligent process vehicle through the intelligent process vehicle scheduling system based on the operation data.

5. The group according to claim 1, characterized in that, The group also includes a bastion host, which is set between the user terminal and the intelligent group control system. The bastion host communicates with the user terminal through an intranet or VPN and is configured to audit the communication data between the user terminal and the intelligent group control system.

6. A flexible scheduling method, applied to an intelligent group control system in a flexible scheduling group as described in any one of claims 1-5, characterized in that, include: It receives production plans and real-time production progress of vehicles from the Manufacturing Execution System, receives inventory material information from the Logistics Execution System, and receives vehicle location information from the Logistics Intelligent Vehicle Scheduling System and the Process Intelligent Vehicle Scheduling System, respectively. Based on the production plan and the real-time production progress of the vehicles, the real-time process requirements of the vehicles are determined. Based on these real-time process requirements and the location information of the intelligent process vehicle, the scheduling tasks for the intelligent process vehicle are formulated in real time. Based on the production plan and the inventory material information, the real-time material requirements of the process island are determined, and the scheduling tasks of the intelligent logistics vehicles are formulated in real time in conjunction with the vehicle location information of the intelligent logistics vehicles.

7. The method according to claim 6, characterized in that, Based on the real-time process requirements and the location information of the intelligent process vehicle, a real-time scheduling task for the intelligent process vehicle is formulated, including: When the vehicle completes the assembly of the current process island, multiple downstream processes of the current process island are determined according to the real-time process requirements of the vehicle. Based on the multiple downstream processes, a multi-flow process queue for the current process island is determined, wherein the multi-flow process queue for the current process island includes multiple candidate process islands that execute the multiple downstream processes; Obtain the assembly queue for each candidate process island, wherein the assembly queue includes the number of intelligent process vehicles waiting to enter each candidate process island; The priority order of multiple candidate process islands is determined based on the assembly queue; The intelligent process vehicle used to transport the vehicle is determined based on the vehicle's location information. The target process island is determined based on the priority order of multiple candidate process islands in the multi-flow process queue of the current process island; The driving path of the intelligent process vehicle is determined based on the current location of the process island and the location of the target process island. The scheduling task of the intelligent process vehicle is determined, wherein the scheduling task includes scheduling the intelligent process vehicle used for transporting vehicles to travel according to the driving path.

8. The method according to claim 6, characterized in that, Based on the production plan and inventory information, the real-time material requirements of the process island are determined. Combined with the location information of the intelligent logistics vehicles, real-time scheduling tasks for the intelligent logistics vehicles are formulated, including: When the vehicle completes the assembly of the current process island, the current shortage of materials on the current process island is calculated based on the remaining amount of materials in the inventory material information, and it is determined whether there are any target materials whose current shortage of materials exceeds the preset shortage warning threshold. When the target material is determined to exist, the production cycle of the current process island is determined based on the production plan; The real-time material requirements of the process island are determined based on the current shortage of materials and the production cycle time. The real-time material requirements include target material requirements, wherein the target material requirements include the supply time window of each target material and the final shortage amount when supplying materials within the supply time window. The number of intelligent logistics vehicles to be collected and the material loading capacity of each intelligent logistics vehicle are determined based on the final shortage amount. The travel path and travel time of the intelligent logistics vehicle are determined based on the location of the loaded materials and the current location of the process island. Based on the supply time window, the travel time, and the preset redundancy time, the loading time window for the target material demand is determined in conjunction with the inventory material information. The scheduling task of the intelligent logistics vehicle is determined, wherein the scheduling task includes: scheduling the intelligent logistics vehicle to load the target material according to the material loading amount within the loading time window, traveling according to the driving path, and transporting the target material to the current process island within the supply time window.

9. The method according to claim 8, characterized in that, The determination of the supply time window and the final shortage amount within the target material demand based on the current shortage quantity and the production cycle time includes: The target duration is determined as t = ((1 / P) / M). (N0-N1); Where P represents the production cycle time, M represents the preset material consumption of the target material in each vehicle, N0 represents the preset shortage danger threshold, and N1 represents the current shortage of the target material. Based on the current time point and the target duration, obtain the supply time window in the target material demand; Predict future material consumption based on the remaining production requirement of the target material in the current process island in the production plan and the current shortage of materials; Calculate the sum of the current shortage of materials and the future consumption of materials to obtain the final shortage in the target material demand.

10. The method according to claim 8, characterized in that, After determining the scheduling task of the intelligent logistics vehicle, the method further includes: The process parameter values ​​of each station in each process island related to the production plan are acquired in real time and sent to the process island controller. The system receives real-time operation parameter values ​​from the process island controller after production based on process parameter values. When the first difference between the real-time operating parameter value of the target material and the process parameter value of the target material is greater than the preset material loss threshold, the final shortage of the target material demand is increased, the supply time window of the process island is adjusted forward, and the driving speed of the logistics intelligent vehicle executing the scheduling task is adjusted. Based on the adjusted final shortage amount, supply time window, and driving speed value, the scheduling tasks of the intelligent logistics vehicles are redefined.

11. The method according to claim 8, characterized in that, After determining the scheduling task of the intelligent logistics vehicle, the method further includes: The process parameter values ​​of each station in each process island related to the production plan are acquired in real time and sent to the process island controller. The system receives real-time operation parameter values ​​from the process island controller after production based on process parameter values. Calculate the actual number of target materials installed and the actual cumulative installation time, and calculate the product of the standard installation time of the target materials and the actual number of installations to obtain the total standard installation time; When the second difference between the actual cumulative installation time and the total standard installation time is greater than the preset assembly delay threshold, the final shortage amount for the process island is reduced, and the scheduling task of the logistics intelligent vehicle is re-determined based on the reduced final shortage amount.

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