Mobile robot equipment intelligent scheduling auxiliary decision-making system and method

Abstract

The invention belongs to the technical field of intelligent scheduling systems, and discloses an intelligent scheduling auxiliary decision making system and method for mobile robot equipment. The communication interface and data preprocessing module serves as a data interaction channel between the system and an external information source and is used for receiving information such as production plans, task requirements and equipment states. The alternate iteration intelligent scheduling optimization module is composed of a transportation task robot assignment sub-module and a robot operation time optimization sub-module, the passing sequence of the robots is dynamically adjusted by optimizing the arrival and passing time of each mobile device at each loading / unloading point, an intersection and the like, a reasonable time interval is kept, and the real-time performance of the robot is improved. And an overall collision-free operation scheme of the system is realized. And the simulation verification and result output module verifies and visually displays the obtained scheduling optimization scheme to ensure the feasibility and reliability of a scheduling solution result. According to the method, efficient and safe scheduling of the mobile robot equipment in the production workshop can be realized, and the intelligent level and the operation efficiency of scheduling decision are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling system technology, and in particular to an intelligent scheduling auxiliary decision-making system and method for mobile robot equipment. Background Technology

[0002] With the continuous development of manufacturing and smart factories, the arrangement of equipment, materials, and personnel is becoming increasingly compact. Especially for material transportation tasks that rely on mobile robots within the workshop, developing safe and efficient material transportation and mobile robot utilization plans, ensuring reasonable safety distances between mobile devices during operation, and preventing collisions, particularly at loading / unloading points and intersections, has become a crucial technical means to improve equipment utilization, shorten production cycles, and enhance economic efficiency. Intelligent scheduling of mobile robots is a core component of the smart manufacturing system. Compared to traditional scheduling methods relying on human experience, manual scheduling is not only arduous but also inefficient, easily leading to uneven resource utilization, transportation delays, and safety hazards. Furthermore, since a large amount of material transportation relies on mobile robots, limited channels and workspaces are frequently occupied, further exacerbating scheduling complexity. Therefore, it is necessary to utilize computer technology to construct an intelligent scheduling auxiliary decision support system for the safe and efficient use of mobile robots. This system optimizes the management of transportation tasks and mobile robot utilization in smart manufacturing, significantly improving equipment utilization efficiency and production management levels while ensuring equipment and personnel safety.

[0003] Establishing a complete intelligent scheduling and decision-making support system, and designing applicable intelligent scheduling methods, can provide strong support for the material transportation management level of production workshops. In particular, for complex working conditions such as large area spans and multiple overlapping operations in production workshops, a unified and efficient scheduling optimization system and real-time monitoring system can be built to achieve centralized scheduling and dynamic management of mobile robot equipment, thereby effectively improving the safety and efficiency of material transportation.

[0004] Currently, several related patents have proposed different solutions in the field of material transportation and production scheduling. For example, patent CN202510223483.X proposes an intelligent scheduling method and system for autonomous mobile robots, mainly addressing the task allocation problem of autonomous mobile robots in general scenarios; patent CN202510019970.4 designs a flexible intelligent manufacturing system for multi-variety, small-batch production to improve production flexibility and responsiveness; patent CN202410277443.9 proposes a joint scheduling method for order processing and robot task orchestration, emphasizing overall scheduling optimization driven by orders; and patent CN201010271149.5 involves a method for processing intelligent scheduling tasks to improve the processing efficiency of tasks.

[0005] However, existing mobile robot scheduling methods mainly focus on task allocation and path planning, and can be divided into two levels: The first level is task allocation-centric scheduling methods. Most research concentrates on rationally allocating transportation tasks to various mobile robots, but lacks systematic consideration of potential path conflicts during task execution. These methods typically do not generate detailed operating schedules for mobile devices. During actual operation, mobile devices rely primarily on the obstacle avoidance function of the underlying controller to avoid conflicts, leading to frequent "braking-starting" actions by mobile robots in critical areas such as loading / unloading points and intersections. This not only prolongs transportation time and causes delays, but also increases the uncertainty of fulfilling transportation plans and significantly increases energy consumption. The second level of methods, based on task allocation, avoids driving conflicts through path planning, mainly for grid-like road networks. The path planning stage avoids intersecting paths to reduce conflicts. However, this method is too conservative, relying on a large number of redundant paths, resulting in low utilization efficiency of critical resources such as intersections. In actual manufacturing workshops, pre-defined routes are the main focus, typically using grid-like road networks with a large number of redundant paths.

[0006] The existing improved methods employ a time window approach, explicitly allocating time slices for key resources such as intersections and loading / unloading stations. While this method achieves conflict-free scheduling, the need to allocate specific time windows for each mobile robot at each key node dramatically increases the number of variables, leading to high optimization model complexity and difficulty in efficiently solving large-scale problems. Therefore, there is still a lack of an intelligent scheduling auxiliary decision-making system for mobile robots that can address the complex production needs of workshops while balancing real-time performance, safety, and optimization efficiency, thus achieving efficient, safe, and intelligent scheduling of AGV equipment.

[0007] In summary, this invention is an intelligent scheduling auxiliary decision-making system and method for mobile robot equipment. It focuses on considering the level of each production material transportation task and the material transportation scheduling under special or emergency situations. By designing an alternating iterative intelligent scheduling method for solving the problem, it can not only detect and eliminate driving conflicts, but also optimize the entire intelligent scheduling of material transportation. Summary of the Invention

[0008] This invention relates to an intelligent scheduling and decision-making support system and method for mobile robot equipment. To achieve this goal, the focus of this invention is on considering the priority of various material transportation tasks and intelligent vehicle operation under special or emergency circumstances. It solves this problem by designing an alternating iterative intelligent scheduling method to eliminate conflicts arising from the occupation of key resources such as intersections and loading / unloading stations by mobile equipment during operation. Simulation verification demonstrates the implementation of intelligent scheduling and decision-making support for mobile robot equipment, thereby improving the automation level and intelligent scheduling of material transportation processes in intelligent manufacturing.

[0009] The technical solution of the present invention is as follows: a mobile robot equipment intelligent scheduling auxiliary decision-making system, including a communication interface and data preprocessing module, an alternating iterative intelligent scheduling optimization module, and a simulation verification and result output module;

[0010] The communication interface and data preprocessing module construct a material transport task from the Manufacturing Execution System (MES) production plan, resulting in a set of material transport tasks. Afterwards, The data is sent to the alternating iterative intelligent scheduling and optimization module, which then processes the set... Schedule the transportation tasks for each material in the process;

[0011] The simulation verification and result output module is used to simulate and verify the intelligent scheduling scheme for the production workshop generated by the alternating iterative intelligent scheduling optimization module, and to visualize and output the scheme to the user.

[0012] The communication interface and data preprocessing module include a communication submodule and a data preprocessing submodule;

[0013] The communication submodule is used for data communication between the server and the vehicle terminal device, enabling the vehicle terminal to report robot status information to the server, and the server to send control commands and query commands to the vehicle terminal.

[0014] The data preprocessing submodule is used for preprocessing mobile robot equipment scheduling data. The mobile robot equipment scheduling data is a standardized initial material transfer task obtained by each production line in the production workshop through the reporting of daily material transportation plans and after review by the superior department. It includes task name, task number, loading station, unloading station, expected completion time, earliest pickup time, latest delivery time, and task energy consumption data.

[0015] The data preprocessing submodule obtains the initial "Material Transportation Task Requirement Table" after reporting and review from the Manufacturing Execution System (MES). This table is stored in a CSV-type task requirement description file.

[0016] The data preprocessing submodule, based on the initial material transportation task requirement information obtained from the Manufacturing Execution System (MES), including the loading station (LoadStation), unloading station (UnloadStation), earliest start time (T10i_Zuizao), latest end time (T20i_Zuiwan), estimated task duration (T30i_Shichang), and energy consumption (Ei_Nenghao) required to complete the task, uses a set... Construct a material transport task.

[0017] The specific operation process for the material transportation task is as follows:

[0018] Each material transport task is described by an 8-tuple;

[0019]

[0020] in, subscript Representing the One material transportation task Indicates material transportation task The earliest loading time; Indicates the earliest start time of the task; Indicates material transportation task Expected completion time;

[0021] All material transportation tasks are collected To indicate;

[0022]

[0023] The data preprocessing submodule extracts the set of material transport tasks from the Manufacturing Execution System (MES) according to a specified date range. Afterwards, The data is sent to the alternating iterative intelligent scheduling and optimization module, which then processes the set... Schedule the transportation of each material in the process.

[0024] The alternating iterative intelligent scheduling optimization module is used to optimize material transportation scheduling time, including a transportation task robot assignment submodule and a robot running time optimization submodule;

[0025] Furthermore, the transportation task robot assignment submodule constructs a directed acyclic graph based on constraints such as task level, succession relationship, operation time, and robot equipment, generating an initial robot assignment and succession scheme. The robot running time optimization submodule constructs a spatiotemporal model of mobile robot equipment operation based on the assignment scheme. Through conditional constraints, conflict detection, and conflict resolution, it obtains the practically feasible shortest operation time and shortest succession time, and feeds the results back to the task assignment submodule for iterative optimization of the transportation task assignment scheme. By optimizing the arrival and passage times of each mobile device at loading / unloading points, intersections, etc., the robot's passage order is dynamically adjusted to maintain a reasonable headway, thereby ensuring that all mobile robots pass through key points in the running path at a safe distance, effectively avoiding conflicts and collisions between mobile devices, and achieving a collision-free operation scheme for the entire system.

[0026] The optimization objective function for intelligent scheduling of material transportation in the transportation task robot assignment submodule is as follows:

[0027]

[0028] In the above formula, This indicates the total completion time for the material transportation task. Indicates material transportation task The objective function aims to balance two points: first, to shorten the total project completion time by completing the last transportation task as early as possible; and second, to complete each transportation task as early as possible.

[0029] The constraints This represents the completion time constraint for the total project completion time, specifically the completion time of the last transportation task, expressed as:

[0030] .

[0031] The robot runtime optimization submodule includes runtime constraints and charging constraints;

[0032] The operational constraints include the material transportation task start time constraint, the material transportation task end time constraint, the same material transportation task start time and end time constraint, the material transportation task connection constraint, and the material transportation task and subsequent task time constraint.

[0033] The charging constraints include charging constraints for material transport tasks and charging completion time constraints for mobile robots.

[0034] Constraints The specific expression for the start time constraint of the material transportation task is as follows:

[0035]

[0036] in, Indicates material transportation task Start time;

[0037] Constraints The specific expression for the material transportation task completion time constraint is as follows:

[0038]

[0039] Constraints The specific expression for the start and end time constraints of the same material transportation task is as follows:

[0040]

[0041] Constraints The specific expression for the material transportation task coordination constraint is as follows:

[0042]

[0043] in, Indicates task The same mobile robot is performing the task. The next task following;

[0044] Constraints The specific expression for the time constraints of material transportation tasks and subsequent tasks is as follows:

[0045]

[0046] in, Indicates that the mobile robot has completed Then execute The return trip time, This indicates whether the mobile robot stops at charging station c to recharge on its return journey from completing task i to performing subsequent task j. This indicates the total number of charging stations. This represents the charging time of the mobile robot during its return journey from completing task i to performing the subsequent task j;

[0047] Constraints The charging constraint for the material transportation task is specifically expressed as follows:

[0048]

[0049] In the above formula, the subscript 'c' represents the serial number of the charging station. This indicates that the mobile robot uses charging station c to charge. This indicates that the mobile robot does not use charging station c for charging; if all , , indicates that the mobile robot does not need to be charged;

[0050] Constraints The charging end time constraint for the mobile robot is specifically expressed as follows:

[0051]

[0052]

[0053] In the above formula, Indicates material transportation task Completion time, Indicates the charging start time. Indicates the end time of charging. Indicates charging time.

[0054] The decision-making method of the intelligent scheduling auxiliary decision-making system for mobile robot equipment constructs a material transportation task from the manufacturing execution system (MES) production plan, thereby obtaining a set of material transportation tasks. Afterwards, Alternating iterative intelligent scheduling optimization is performed, which includes optimization of transportation task robot assignment and robot running time, based on various constraints. Schedule the various material transportation tasks; visualize the scheduling results.

[0055] The beneficial effects of this invention are:

[0056] (1) Intelligent scheduling and decision support system for mobile robot equipment. It enables comprehensive and three-dimensional intelligent operation and scheduling of the production workshop, conducts safe transportation management, and improves the efficiency of intelligent scheduling while ensuring the safety of vehicle operation in the production workshop.

[0057] (2) Alternating Iterative Intelligent Scheduling Method. Based on the intelligent identification of conflicts in the daily plans proposed by each production team, the intelligent scheduling scheme is optimized and solved in a reasonable and rapid manner, effectively alleviating the labor intensity of manual scheduling.

[0058] (3) Simulation verification can test and verify the possible conflicts in the scheduling scheme generated by intelligent scheduling, and further optimize and eliminate the conflict.

[0059] Therefore, the present invention provides a method and system for intelligent scheduling of mobile robot equipment, which is beneficial to strengthening the safety management of mobile robot equipment operation in production workshops and improving the efficiency of intelligent scheduling of mobile robot equipment in production workshops. Attached Figure Description

[0060] Figure 1 Example of a material transport operation diagram for mobile robot equipment;

[0061] Figure 2 A schematic diagram of the structure of an intelligent scheduling and auxiliary decision-making system for mobile robot equipment;

[0062] Figure 3 Example of a transportation task requirement form;

[0063] Figure 4 Example of Gantt chart for task assignment and equipment use for mobile robot devices;

[0064] Figure 5 This is an example of a mobile robot device operating after system optimization. Detailed Implementation

[0065] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0066] A mobile robot equipment intelligent scheduling auxiliary decision-making system consists of three main functional modules: a communication interface and data preprocessing module, an alternating iterative intelligent scheduling optimization module, and a simulation verification and result output module.

[0067] The communication interface and the communication submodule in the data preprocessing module are used for data communication between the server and the vehicle-mounted terminal device, enabling the vehicle-mounted terminal to report robot status information to the server, and the server to send control commands and query commands to the vehicle-mounted terminal.

[0068] The communication interface and the data preprocessing submodule in the data preprocessing module are used for the preprocessing of mobile robot equipment scheduling data. The data is a standardized initial material transfer task obtained by each production line in the production workshop through the reporting of daily material transportation plans and after review by the superior department. It includes task name, task number, loading station, unloading station, expected completion time, earliest pickup time, latest delivery time, and task energy consumption data.

[0069] The data preprocessing module obtains the initial "Material Transportation Task Requirement Table" after reporting and review from the Manufacturing Execution System (MES). This table is stored in a CSV-type task requirement description file, and the format and definition of each field in the file are as follows:

[0070] field name type Range of values unit illustrate TaskID Integer 1-99999999 none Material transport task number TaskDescription character 1000 bytes none Material transportation operation content LoadStation Integer 1-10 none Material loading point UnloadStation Integer 1-10 none Material unloading point T10i_Zuizao time YYYY.MM:DD hh:mm:ss Second Earliest loading time T20i_Zuiwan time YYYY.MM:DD hh:mm:ss Second Latest uninstall time T30i_Shichang time 1-1000 Second Expected mission duration Ei_Nenghao floating-point numbers 1.00-999.00 kWh Energy consumption required to complete the task

[0071] The data preprocessing module constructs a material transportation task based on the initial material transportation task requirements information obtained from the Manufacturing Execution System (MES), including the loading station (LoadStation), unloading station (UnloadStation), earliest start time (T10i_Zuizao), latest end time (T20i_Zuiwan), estimated task duration (T30i_Shichang), and energy consumption required to complete the task (Ei_Nenghao). This information is then used to create a set of data. The specific operation process is as follows:

[0072] Each material transport task can be described by an octet.

[0073]

[0074] in, subscript Representing the There are individual material transport tasks, therefore all material transport tasks can be represented by a set. to indicate

[0075]

[0076] The data preprocessing module extracts the set of material transport tasks from the Manufacturing Execution System (MES) according to a specified date range. Afterwards, The data is sent to the alternating iterative intelligent scheduling and optimization module, which then processes the set... Schedule the transportation of each material in the process.

[0077] The alternating iterative intelligent scheduling optimization module is used to optimize the material transportation scheduling time, and consists of two parts: a transportation task robot assignment submodule and a driving time optimization submodule.

[0078] The optimization objective function for intelligent scheduling of material transportation in the transportation task robot assignment submodule is as follows:

[0079]

[0080] In the above formula, This indicates the total completion time for the material transportation task. Indicates material transportation task The objective function aims to balance two points: first, to shorten the total project completion time by completing the last transportation task as early as possible; and second, to complete each transportation task as early as possible.

[0081] Constraints The total project completion time constraint represents the completion time of the last transportation task, and its specific expression is:

[0082]

[0083] Constraints The specific expression for the start time constraint of the material transportation task is as follows:

[0084]

[0085] In the above formula, Indicates material transportation task The start time, Indicates material transportation task The earliest loading time.

[0086] Constraints The specific expression for the material transportation task completion time constraint is as follows:

[0087]

[0088] Constraints The specific expression for the start and end time constraints of the same material transportation task is as follows:

[0089]

[0090] In the above formula, Indicates material transportation task The expected completion time, and the final travel time, is the sum of the minimum running times of each segment; these minimum running times of each segment are based on the minimum travel time between two points as constraints, and are calculated by the optimization solver.

[0091] Constraints The specific expression for the material transportation task coordination constraint is as follows:

[0092]

[0093] In the above formula, Indicates task The same mobile robot is performing the task. The next task following this.

[0094] Constraints The specific expression for the time constraints of material transportation tasks and subsequent tasks is as follows:

[0095]

[0096] In the above formula, Indicates that the mobile robot has completed Then execute The return trip time, This indicates whether the mobile robot stops at charging station c to recharge on its return journey from completing task i to performing subsequent task j. This indicates the total number of charging stations. This represents the charging time of the mobile robot during its return journey from completing task i to performing the subsequent task j.

[0097] Constraints The charging constraint for the material transport task is specifically expressed as follows:

[0098]

[0099] In the above formula, the subscript 'c' represents the serial number of the charging station. This indicates that the mobile robot uses charging station c to charge. This indicates that the mobile robot does not use charging station c for charging; if all , This indicates that the mobile robot does not need to be charged.

[0100] Constraints The specific expression for the charging end time constraint for the mobile robot is as follows:

[0101]

[0102]

[0103] In the above formula, Indicates material transportation task Completion time, Indicates the charging start time. Indicates the end time of charging. Indicates charging time.

[0104] Constraints The constraints for collision avoidance in mobile robots are specifically expressed as follows:

[0105]

[0106] In the above formula, Indicates task The time of entering node o, Indicates task The time of entering node o, Indicates task The dwell time at node o includes the operation time required to perform loading and unloading operations at the loading / unloading node. This constraint ensures that when any two mobile robots performing the same task pass through the same node, a certain time interval (headyway) must be maintained to avoid collisions. Number of nodes It is determined by the complexity of the factory layout and path grid.

[0107] The simulation verification and result output module is used to simulate and verify the intelligent scheduling scheme for the production workshop generated by the alternating iterative intelligent scheduling optimization module, and to visualize and output the scheme to the user. Through simulation, users can be provided with a more intuitive intelligent scheduling auxiliary decision-making scheme for mobile robot equipment in the production workshop, allowing them to understand the possible conflicts during the intelligent scheduling process, and to further optimize the scheduling scheme by adjusting the expected start time, expected end time parameters and material transportation task requirements through human-computer interaction.

[0108] Robot assignment and operation plan message: The results generated by intelligent scheduling are sent to the communication interface program. The results are saved in a CSV-type mobile robot transportation plan file. The format and definition of each field in this file are as follows:

[0109]

[0110] After receiving the mobile robot transportation plan file, the communication interface program decomposes the transportation plan, constructs a command queue, and issues scheduling instructions to each mobile robot device in a time-driven manner. The mobile robot devices then execute the transportation tasks according to the instructions.

[0111] This invention includes an intelligent scheduling and decision-making support system and method for mobile robot equipment, which achieves intelligent scheduling of mobile robot equipment transportation operations through alternating iterative optimization.

[0112] Consider the following table, which shows a material transport task request form submitted by a mobile robot in a production workshop from the "Manufacturing Execution System (MES)" on a certain day. It contains 15 material transport tasks, covering the time range of 2025 / 5 / 10 09:00:00-2025 / 5 / 10 09:10:00. The user-specified time range for scheduling material transport tasks is:

[0113] arrive

[0114]

[0115]

[0116] Data preprocessing module: The data preprocessing module schedules the material transportation tasks according to the user's specified requirements. and From the above 15 transportation tasks, after removing the 5 transportation tasks that do not meet the time range (corresponding to taskIDs Task11, Task12, Task13, Task14, and Task15), the set of transportation tasks to be scheduled is constructed. , This can be represented in a table as follows:

[0117]

[0118] The data preprocessing module generates a set of material transportation tasks to be scheduled. ,Will The data is then sent to the transportation task robot assignment module and the driving time optimization module for optimized scheduling.

[0119] The transportation task robot assignment submodule assigns tasks based on the location and time of the material transportation task. The material transportation tasks in the process are carried out, and the generated unscheduled material transportation tasks are assigned:

[0120] AMR1: Task1, Task7, Task8, Task9;

[0121] AMR2: Task2, Task4, Task5;

[0122] AMR3: Task3, Task6, Task10;

[0123] The driving time optimization submodule constructs a spatial model where loading stations are S1 and S2, unloading stations are X1, X2, X3, X4, and X5, and there are two charging stations. Through conditional constraints, conflict detection, and conflict resolution, the shortest completion time and shortest connection time are calculated, and the results are fed back to the task assignment scheme for iterative optimization, thereby obtaining the globally optimal scheduling result.

[0124]

[0125] Simulation verification and result output module: This module simulates the auxiliary decision-making scheme for the operation of mobile robots in the production workshop, output by the alternating iterative intelligent scheduling optimization module, to verify the execution effect and visualization of the intelligent scheduling scheme. It also determines whether further adjustments and optimizations are needed by the scheduler. After confirmation by the scheduler, the final intelligent scheduling scheme for the production workshop is output.

[0126] The simulation results of the auxiliary decision-making scheme for the operation of mobile robot equipment in the production workshop are obtained according to the method of the present invention, such as... Figure 5 As shown. Figure 5 The graph shows the optimized material transportation task arrangement, with the horizontal axis representing travel time and the vertical axis representing the critical path points corresponding to the material transportation trajectory.

[0127] Depend on Figure 5 It can be seen that the present invention realizes a mobile robot equipment intelligent scheduling auxiliary decision-making system and method, which can obtain intelligent scheduling auxiliary decision-making schemes for production workshops, fully resolve potential driving conflicts of material transportation tasks reported by various production teams, and optimize the arrangement of various material transportation tasks.

Claims

1. A mobile robot equipment intelligent scheduling and auxiliary decision-making system, characterized in that, The intelligent scheduling and auxiliary decision-making system for mobile robot equipment includes a communication interface and data preprocessing module, an alternating iterative intelligent scheduling optimization module, and a simulation verification and result output module. The communication interface and data preprocessing module construct a material transport task from the Manufacturing Execution System (MES) production plan, resulting in a set of material transport tasks. Afterwards, The data is sent to the alternating iterative intelligent scheduling and optimization module, which then processes the set... Schedule the transportation tasks for each material in the process; The simulation verification and result output module is used to simulate and verify the intelligent scheduling scheme for the production workshop generated by the alternating iterative intelligent scheduling optimization module, and to visualize and output the scheme to the user.

2. The intelligent scheduling and decision-making auxiliary system for mobile robot equipment according to claim 1, characterized in that, The communication interface and data preprocessing module include a communication submodule and a data preprocessing submodule; The communication submodule is used for data communication between the server and the vehicle terminal device, enabling the vehicle terminal to report robot status information to the server, and the server to send control commands and query commands to the vehicle terminal. The data preprocessing submodule is used for preprocessing mobile robot equipment scheduling data. The mobile robot equipment scheduling data is a standardized initial material transfer task obtained by each production line in the production workshop through the reporting of daily material transportation plans and after review by the superior department. It includes task name, task number, loading station, unloading station, expected completion time, earliest pickup time, latest delivery time, and task energy consumption data. The data preprocessing submodule obtains the initial "Material Transportation Task Requirement Table" after reporting and review from the Manufacturing Execution System (MES). This table is stored in a CSV-type task requirement description file. The data preprocessing submodule, based on the initial material transportation task requirement information obtained from the Manufacturing Execution System (MES), including the loading station (LoadStation), unloading station (UnloadStation), earliest start time (T10i_Zuizao), latest end time (T20i_Zuiwan), estimated task duration (T30i_Shichang), and energy consumption (Ei_Nenghao) required to complete the task, uses a set... Construct a material transport task.

3. The intelligent scheduling and auxiliary decision-making system for mobile robot equipment according to claim 2, characterized in that, The specific operation process for the material transportation task is as follows: Each material transport task is described by an 8-tuple; ； in, subscript Representing the One material transportation task Indicates material transportation task The earliest loading time; Indicates the earliest start time of the task; Indicates material transportation task Expected completion time; All material transportation tasks are collected To indicate; ； The data preprocessing submodule extracts the set of material transport tasks from the Manufacturing Execution System (MES) according to a specified date range. Afterwards, The data is sent to the alternating iterative intelligent scheduling and optimization module, which then processes the set... Schedule the transportation of each material in the process.

4. The intelligent scheduling and auxiliary decision-making system for mobile robot equipment according to claim 1, characterized in that, The alternating iterative intelligent scheduling optimization module is used to optimize material transportation scheduling time, including a transportation task robot assignment submodule and a robot running time optimization submodule; The optimization objective function for intelligent scheduling of material transportation in the transportation task robot assignment submodule is as follows: ； In the above formula, This indicates the total completion time for the material transportation task. Indicates material transportation task The objective function aims to balance two points: first, to shorten the total project completion time by completing the last transportation task as early as possible; and second, to complete each transportation task as early as possible.

5. The intelligent scheduling and auxiliary decision-making system for mobile robot equipment according to claim 4, characterized in that, The constraints This represents the completion time constraint for the total project completion time, specifically the completion time of the last transportation task, expressed as: 。 6. The intelligent scheduling and auxiliary decision-making system for mobile robot equipment according to claim 4, characterized in that, The robot runtime optimization submodule includes runtime constraints and charging constraints; The operational constraints include the material transportation task start time constraint, the material transportation task end time constraint, the same material transportation task start time and end time constraint, the material transportation task connection constraint, and the material transportation task and subsequent task time constraint. The charging constraints include charging constraints for material transport tasks and charging completion time constraints for mobile robots.

7. The intelligent scheduling and auxiliary decision-making system for mobile robot equipment according to claim 6, characterized in that, Constraints The specific expression for the start time constraint of the material transportation task is as follows: ； in, Indicates material transportation task Start time; Constraints The specific expression for the material transportation task completion time constraint is as follows: ； Constraints The specific expression for the start and end time constraints of the same material transportation task is as follows: ； Constraints The specific expression for the material transportation task coordination constraint is as follows: ； in, Indicates task The same mobile robot is performing the task. The next task following; Constraints The specific expression for the time constraints of material transportation tasks and subsequent tasks is as follows: ； in, Indicates that the mobile robot has completed Then execute The return trip time, This indicates whether the mobile robot stops at charging station c to recharge on its return journey from completing task i to performing subsequent task j. This indicates the total number of charging stations. This represents the charging time of the mobile robot during its return journey from completing task i to performing the subsequent task j; Constraints The charging constraint for the material transportation task is specifically expressed as follows: ； In the above formula, the subscript 'c' represents the serial number of the charging station. This indicates that the mobile robot uses charging station c to charge. This indicates that the mobile robot does not use charging station c for charging; if all , This indicates that the mobile robot does not need to be charged. Constraints The charging end time constraint for the mobile robot is specifically expressed as follows: ； ； In the above formula, Indicates material transportation task Completion time, Indicates the charging start time. Indicates the end time of charging. Indicates charging time.

8. The decision-making method of the intelligent scheduling auxiliary decision-making system for mobile robot equipment according to any one of claims 1-7, characterized in that, From the manufacturing execution system (MES) production plan, a material handling task is constructed to obtain a set of material handling tasks. Afterwards, Alternating iterative intelligent scheduling optimization is performed, which includes optimization of transportation task robot assignment and robot running time, based on various constraints. Schedule the various material transportation tasks; visualize the scheduling results.

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