Method, device and equipment for optimizing dispatching control of automatic guided vehicle and medium

Abstract

The application relates to an automatic guided vehicle scheduling control optimization method, device, equipment and medium, the method comprising: randomly generating a plurality of different task execution scenes by using a preset second scheduling control algorithm; determining vehicle position distances between the automatic guided vehicles based on corresponding task execution speeds of the automatic guided vehicles; when the vehicle position distances are less than a preset distance threshold at a certain moment, triggering a plurality of path conflict resolution strategies corresponding to the task execution scene to determine transportation path trajectories of the automatic guided vehicles under the plurality of path conflict resolution strategies; calculating path conflict probabilities between the transportation path trajectories of the automatic guided vehicles under corresponding path conflict resolution strategies of the transportation path trajectories; and taking the conflict resolution strategy with the minimum path conflict probability as an optimal path conflict resolution strategy under the task execution scene. The application can significantly improve the transportation task scheduling efficiency and flexibility of a discrete manufacturing workshop.

Description

Technical Field

[0001] The present application relates to the field of production and manufacturing, and in particular to a method for optimizing the scheduling and control of an automated guided vehicle, a corresponding device, an electronic device, and a computer-readable storage medium. Background Art

[0002] Automated Guided Vehicles (AGVs) are a type of automated transport equipment. AGV systems are relatively easy to implement digital twin technology due to their simple operating states and control instructions. Many companies have established digital twin platforms for AGV systems at varying levels. Through digital twin technology, actual AGV operational information, such as location, status, mission, and speed, can be synchronized and updated in real time to the digital twin model, and the optimal response plan can be derived based on corresponding algorithms. Furthermore, the optimal decision made based on the digital twin model can be instantly translated into control instructions for the AGV itself. Therefore, AGV digital twin technology provides smarter, more efficient, and more practical decision-making services for enterprises and factory transportation. While AGV digital twin technology offers significant potential and advantages, further exploration and research by both academia and industry is still needed to fully realize its advantages and achieve smarter, more efficient, and more practical decision-making.

[0003] In complex discrete manufacturing workshops, automated guided vehicles are prone to problems such as unreasonable path planning, uneven task scheduling and resource allocation during task execution. At the same time, due to various constraints and changing factors in actual operations, such as conflicts between vehicles and dynamic environmental changes, the existing automated guided vehicle scheduling control algorithms cannot cope well with complex actual production scenarios, and cannot better adapt to and respond to ever-changing needs and environments.

[0004] To sum up, the automated guided vehicles in the existing technology are prone to problems such as unreasonable path planning, uneven task scheduling and resource allocation during task execution, and cannot cope well with complex actual production scenarios. In order to solve this problem, the applicant has made corresponding explorations. Summary of the Invention

[0005] The purpose of this application is to solve the above problems and provide an automatic guided vehicle scheduling control optimization method, corresponding device, electronic equipment and computer-readable storage medium.

[0006] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0007] An automated guided vehicle scheduling control optimization method proposed to meet one of the purposes of this application includes:

[0008] Obtaining pending transport task information corresponding to each pending transport task in a pending transport task pool of a discrete manufacturing workshop, and vehicle status information corresponding to each automated guided vehicle, wherein the pending transport task information includes task priority, and the vehicle status information includes vehicle performance parameters and vehicle load level;

[0009] During the task execution phase, a preset first scheduling control algorithm is used to perform fuzzy reasoning and defuzzification based on the vehicle performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle;

[0010] A preset second scheduling control algorithm is used to randomly generate a plurality of different task execution scenarios, and the vehicle position distance between each automated guided transport vehicle is determined based on the task execution speed corresponding to each automated guided transport vehicle. If it is detected that the vehicle position distance is less than a preset distance threshold at a certain moment, a plurality of path conflict resolution strategies corresponding to the task execution scenario are triggered to determine the transport path trajectory of each automated guided transport vehicle under the plurality of path conflict resolution strategies;

[0011] Calculate and determine the path conflict probability between the transport path trajectories of each automated guided transport vehicle under its corresponding path conflict resolution strategy, and use the conflict resolution strategy with the smallest path conflict probability as the optimal path conflict resolution strategy in the task execution scenario to complete the scheduling control of the automated guided transport vehicle.

[0012] Optionally, the step of using a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle usage performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle includes:

[0013] Obtaining input fuzzy linguistic variables for each input variable, wherein the input variables include task priority, vehicle performance parameters, and vehicle load level;

[0014] calling a preset fuzzy control rule table, and calculating an output fuzzy linguistic variable of an output variable corresponding to the input fuzzy linguistic variable based on the fuzzy control rule table;

[0015] The output fuzzy linguistic variables are defuzzified according to a preset defuzzification algorithm to obtain defuzzified output variables, wherein the output variables represent the task execution speed corresponding to each automated guided vehicle.

[0016] Optionally, the step of using a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle usage performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle includes:

[0017] Determining the number of input fuzzy linguistic variables corresponding to each input variable, the number of output fuzzy linguistic variables corresponding to the output variable, the finite integer discrete domain corresponding to the input fuzzy linguistic variable, and the finite integer discrete domain corresponding to the output fuzzy linguistic variable;

[0018] The proportional factor between the input variable and the output variable is calculated and determined based on the ratio method according to the number of the input fuzzy linguistic variables, the number of the output fuzzy linguistic variables, the finite integer discrete domain corresponding to the input fuzzy linguistic variables, and the finite integer discrete domain corresponding to the output fuzzy linguistic variables.

[0019] Optionally, a preset second scheduling control algorithm is used to randomly generate a plurality of different task execution scenarios, and the vehicle position distance between each automated guided transport vehicle is determined based on the task execution speed corresponding to each automated guided transport vehicle. When it is detected that the vehicle position distance is less than a preset distance threshold at a certain moment, a plurality of path conflict resolution strategies corresponding to the task execution scenario are triggered to determine the transport path trajectory of each automated guided transport vehicle under the plurality of path conflict resolution strategies, including the following steps:

[0020] In response to a path conflict monitoring instruction, obtaining a time range for the automated guided vehicle to be detected during task execution;

[0021] If it is detected that the vehicle position distance between each automated guided transport vehicle within the time range to be detected is less than a preset distance threshold at a certain moment, then multiple preset path conflict resolution strategies are triggered to determine the transport path trajectories of each automated guided transport vehicle under the multiple path conflict resolution strategies, wherein the control parameters corresponding to the path conflict resolution strategies include one or any multiple of path adjustment parameters, speed change parameters and state parameters.

[0022] Optionally, the step of calculating and determining the path conflict probabilities between the transport path trajectories of the automated guided vehicles under their corresponding path conflict resolution strategies, and using the conflict resolution strategy with the minimum path conflict probability as the optimal path conflict resolution strategy in the task execution scenario includes:

[0023] Obtaining a preset number of Monte Carlo simulations, a first transport path trajectory corresponding to the first automated guided transport vehicle under each path conflict resolution strategy, and a second transport path trajectory corresponding to the second automated guided transport vehicle, wherein the second scheduling control algorithm is a Monte Carlo simulation algorithm;

[0024] Using a preset Monte Carlo simulation algorithm and a path conflict judgment function, conflict judgment is performed on the first transport path trajectory and the second transport path trajectory until a preset number of Monte Carlo simulations is reached, so as to determine the number of path conflicts under the path conflict resolution strategy;

[0025] The average value of the number of path conflicts in a preset number of Monte Carlo simulations is calculated to determine the path conflict probability between the first automated guided transport vehicle and the second automated guided transport vehicle under the path conflict resolution strategy, and the conflict resolution strategy with the minimum path conflict probability is used as the optimal path conflict resolution strategy in the task execution scenario.

[0026] Optionally, after the step of obtaining the to-be-transported task information corresponding to each to-be-transported task in the to-be-transported task pool of the discrete manufacturing workshop and the vehicle status information corresponding to each automated guided vehicle, the following steps are included:

[0027] Determining the pending transport task information corresponding to each pending transport task and the vehicle status information corresponding to each automated guided transport vehicle, wherein the pending transport task information includes the transport task arrival time, loading point, and processing point, and the vehicle status information includes whether the automated guided transport vehicle is in a transport state;

[0028] A preset vehicle scheduling strategy is used to assign vehicles to each task to be transported according to the information of the task to be transported and the vehicle status information, so as to determine the automatic guided transport vehicle corresponding to each task to be transported, wherein the vehicle scheduling strategy includes a task-driven assignment strategy and a vehicle-driven assignment strategy, the task-driven assignment strategy includes the nearest vehicle priority rule, and the vehicle-driven assignment strategy includes the nearest task priority rule and the earliest task priority rule.

[0029] Optionally, the first scheduling control algorithm is a fuzzy control algorithm based on the Mamdani method, and the task execution scenario includes one or more of an obstacle event, an activity change of an automated guided vehicle, and a task reset based on task time.

[0030] An automated guided vehicle scheduling control optimization device provided for another purpose of the present application includes:

[0031] The task information determination module is configured to obtain task information corresponding to each task to be transported in a task pool to be transported in a discrete manufacturing workshop and vehicle state information corresponding to each automated guided vehicle, wherein the task information to be transported includes a task priority, and the vehicle state information includes a vehicle performance parameter and a vehicle load level.

[0032] The task speed determination module is configured to determine a task execution speed of each automated guided vehicle in a task execution phase by using a preset first scheduling control algorithm to perform fuzzy reasoning according to the vehicle performance parameter, the vehicle load level or the task priority and to perform defuzzification.

[0033] The transportation path determination module is configured to generate a plurality of different task execution scenarios randomly by using a preset second scheduling control algorithm, to determine a vehicle position distance between each automated guided vehicle based on the task execution speed of each automated guided vehicle, and to trigger a plurality of path conflict resolution strategies corresponding to the task execution scenario to determine a transportation path trajectory of each automated guided vehicle under the plurality of path conflict resolution strategies when the vehicle position distance is detected to be less than a preset distance threshold at a certain moment.

[0034] The optimal strategy determination module is configured to calculate a path conflict probability between the transportation path trajectories of each automated guided vehicle under the path conflict resolution strategy corresponding thereto, to take the conflict resolution strategy with the smallest path conflict probability as an optimal path conflict resolution strategy under the task execution scenario, and to complete scheduling control of the automated guided vehicle.

[0035] Another object of the present application is to provide an electronic device comprising a central processing unit and a memory, wherein the central processing unit is configured to invoke a computer program stored in the memory to execute the steps of the automated guided vehicle scheduling control optimization method.

[0036] Another object of the present application is to provide a computer readable storage medium storing a computer program implemented according to the automated guided vehicle scheduling control optimization method in the form of computer readable instructions, wherein the computer program is invoked and run by a computer to execute the steps included in the corresponding method.

[0037] As can be seen from the above embodiments, compared with the prior art, the present application is aimed at the problems of unreasonable path planning, uneven task scheduling and resource allocation of the automated guided vehicle in the task execution process in the prior art. In addition, due to various constraints and changing factors in actual operation, the existing automated guided vehicle scheduling control algorithm cannot well cope with complex actual production scenarios. The present application includes but is not limited to the following beneficial effects:

[0038] First, the automated guided vehicle (AGV) scheduling control optimization method proposed in this application can significantly improve the efficiency and flexibility of transport task scheduling. Through a fuzzy control algorithm, the task execution speed of each automated guided vehicle (AGV) is flexibly determined based on the vehicle's performance, load level, and task priority, enabling the scheduling system to better adapt to dynamic changes within the workshop. AGV scheduling not only takes priority into account but also enables appropriate adjustments based on the vehicle's real-time status, thereby improving task scheduling efficiency.

[0039] Secondly, the automated guided vehicle scheduling and control optimization method of this application can effectively deal with path conflicts and dynamic environmental changes. It uses a Monte Carlo simulation algorithm to randomly generate multiple different task execution scenarios. This method can not only effectively simulate the possible execution of multiple tasks, but also predict and handle potential path conflicts in advance based on the execution speed and actual position of the AGV. When the vehicle distance is less than a preset safety threshold, multiple path conflict resolution strategies are triggered, thereby reducing the probability of conflict and avoiding vehicle collisions or task delays.

[0040] Third, the automated guided vehicle scheduling and control optimization method of the present application can automatically select the strategy with the lowest conflict probability by calculating the conflict probability under different path conflict resolution strategies, thereby obtaining the optimal path planning, which can minimize interference between vehicles and improve the overall efficiency and safety of the transportation system.

[0041] Fourthly, the automated guided vehicle (AGV) scheduling and control optimization method proposed in this application enables AGVs to dynamically adapt to changing production environments and demands. Traditional AGV scheduling methods typically rely on static parameters and rules, making it difficult to cope with dynamic changes in the workshop environment, such as sudden increases in tasks, equipment failures, or adjustments to workshop layouts. By calculating factors such as task priority, vehicle status, and task scenario simulation in real time, the system can adjust scheduling strategies in real time to adapt to varying production demands and environmental changes.

[0042] Fifth, the automated guided vehicle scheduling and control optimization method proposed in this application significantly improves resource utilization and significantly reduces production costs. By rationally allocating tasks and optimizing vehicle scheduling, it ensures that each AGV performs the appropriate task at the appropriate time, avoiding problems such as task congestion, idle vehicles, or overloads. By combining task priorities, vehicle loads, and performance information, scheduling strategies can be dynamically adjusted to balance workloads and improve the utilization efficiency of workshop resources.

[0043] Furthermore, this application addresses the problems of uneven task scheduling, irrational path planning, and high conflict probability in traditional automated guided vehicle (AGV) scheduling by combining fuzzy control algorithms, Monte Carlo simulation, and path conflict optimization strategies. It dynamically schedules based on actual vehicle performance, load status, and task priority, optimizing path planning and resource allocation to maximize workshop production efficiency and reduce conflict risks. This improves the system's flexibility, robustness, and safety, enabling it to better cope with the complex and ever-changing environment of discrete manufacturing workshops. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0045] Figure 1 Schematic diagram of the flow of the automatic guided vehicle scheduling control optimization method in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a process for determining the automatic guided vehicle corresponding to each transport task in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of a process for determining the task execution speed corresponding to each automated guided vehicle in an embodiment of the present application;

[0048] Figure 4 Schematic diagram of a process for determining a proportional factor between an input variable and the output variable in an embodiment of the present application;

[0049] Figure 5 A schematic diagram of a process for determining the transport path trajectory of each automated guided vehicle under multiple path conflict resolution strategies in an embodiment of the present application;

[0050] Figure 6 A flowchart illustrating the optimal path conflict resolution strategy for each task execution scenario in an embodiment of the present application is provided;

[0051] Figure 7 This is a principle block diagram of the automatic guided vehicle scheduling control optimization device in an embodiment of the present application;

[0052] Figure 8 Schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0053] Embodiments of the present application are described below in the context of example embodiments, which are shown in the drawings, wherein like or similar designations are used to indicate like or similar elements or elements having the same or similar function throughout the several views. The embodiments described below are merely examples, which are used to explain the present application and are not to be construed as limiting the present application.

[0054] Those skilled in the art will understand that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and / or "including," as used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements can be present. In addition, the use of "connection" or "coupling" herein also includes wireless connection or wireless coupling. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0055] Those skilled in the art will appreciate that unless otherwise indicated, as used herein, all terms have their ordinary meanings. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0056] Those skilled in the art will appreciate that the terms "client," "terminal," and "terminal device" as used herein include both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers and tablet computers, which have single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service), which may combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices, which have and / or include a radio frequency receiver. As used herein, "client," "terminal," or "terminal device" may be portable, transportable, installed in a vehicle (air, sea, and / or land), or adapted and / or configured to operate locally and / or in a distributed manner at any other location on Earth and / or in space. The terms "client," "terminal," or "terminal device" may also refer to a communication terminal, an Internet terminal, or a music / video playback terminal, such as a PDA, a mobile internet device (MID), and / or a mobile phone with music / video playback capabilities, as well as a smart TV, a set-top box, or other similar device.

[0057] The hardware referred to by names such as "server", "client", and "service node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. Computer programs are stored in its memory, and the central processing unit loads the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0058] It should be noted that the concept of "server" referred to in this application can also be extended to server clusters. Based on the network deployment principles understood by those skilled in the art, the servers described should be logically divided. In physical space, these servers can be independent of each other but callable through interfaces, or integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method of this application.

[0059] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for implementation.

[0060] Unless expressly specified otherwise, the neural network models referenced or may be referenced in this application may be deployed on a remote server and remotely called on the client, or may be deployed and directly called on a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence may be obtained through transfer learning to reduce the requirements for the client's hardware operating resources and avoid excessive occupation of the client's hardware operating resources.

[0061] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as they are suitable for being called by the technical solution of this application.

[0062] Those skilled in the art should be aware that although the various methods of this application are described based on the same concept and thus exhibit commonality, unless otherwise specified, these methods can be independently executed. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept. Therefore, concepts with the same expression, as well as concepts that are appropriately transformed for convenience despite different expression, should be understood as equivalent.

[0063] Unless expressly stated to be mutually exclusive, the various embodiments disclosed in this application may be cross-combined with the relevant technical features of the various embodiments to flexibly construct new embodiments, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or resolve certain deficiencies in the prior art. Those skilled in the art should be aware of such flexibility.

[0064] See also Figure 1 In one embodiment, the automated guided vehicle scheduling control optimization method of the present application includes:

[0065] Step S10: Acquire the pending transport task information corresponding to each pending transport task in the pending transport task pool of the discrete manufacturing workshop, and the vehicle status information corresponding to each automated guided transport vehicle, wherein the pending transport task information includes the task priority, and the vehicle status information includes the vehicle performance parameters and the vehicle load level;

[0066] The automated guided vehicle scheduling and control system in the terminal device can respond to the automated guided vehicle scheduling and control optimization method of the present application to obtain pending transport task information corresponding to each pending transport task in the pending transport task pool of the discrete manufacturing workshop, as well as vehicle status information corresponding to each automated guided vehicle, wherein the pending transport task information includes task priority, and the vehicle status information includes vehicle performance parameters and vehicle load level;

[0067] Specifically, the pending transport task information reflects the various attributes and characteristics of each pending transport task, including task priority, arrival time, loading point, and processing point. The task priority represents the importance or urgency of each pending transport task within the overall scheduling system. Generally, high-priority tasks are prioritized during scheduling to ensure they are completed as quickly as possible. Priorities can be determined based on task timeliness, task size, production line requirements, or other relevant business rules.

[0068] The vehicle status information reflects the current working status of each automated guided vehicle (AGV) and its vehicle performance parameters, vehicle load level, etc. The vehicle performance parameters include but are not limited to battery power, speed capability, position accuracy, and health status, etc. The vehicle load level includes but is not limited to current load weight and load status, etc.

[0069] In some embodiments, see Figure 2 After the step of obtaining the to-be-transported task information corresponding to each to-be-transported task in the to-be-transported task pool of the discrete manufacturing workshop and the vehicle status information corresponding to each automated guided vehicle, the method includes:

[0070] Step S101: Determine the pending transport task information corresponding to each pending transport task and the vehicle status information corresponding to each automated guided transport vehicle, wherein the pending transport task information includes the arrival time of the transport task, the loading point, and the processing point, and the vehicle status information includes whether the automated guided transport vehicle is in a transport state;

[0071] Step S102: Use a preset vehicle scheduling strategy to assign vehicles to each of the tasks to be transported according to the information of the tasks to be transported and the vehicle status information, so as to determine the automatic guided transport vehicle corresponding to each of the tasks to be transported, wherein the vehicle scheduling strategy includes a task-driven assignment strategy and a vehicle-driven assignment strategy, the task-driven assignment strategy includes a nearest vehicle priority rule, and the vehicle-driven assignment strategy includes a nearest task priority rule and an earliest task priority rule.

[0072] Specifically, a task in the pool of tasks to be transported can be represented as {begin, finish, t}, where begin represents the loading point, finish represents the processing point, and t represents the arrival time of the transport task. In terms of vehicle allocation and scheduling, a preset vehicle scheduling strategy is used to allocate vehicles to each task to be transported based on the task information and vehicle status information to determine the corresponding automated guided vehicle for each task to be transported. The nearest vehicle first (NVF), nearest mission first (NMF), and earliest mission first (EMF) rules can be used. If an automated guided vehicle (AGV) is idle, the nearest vehicle first rule is used.

[0073] When the arrival time of a generated transport task is later than the current system time, the system scheduling module drives the discrete simulation module to run, advancing the system time until the transport task arrives. When the simulation reaches the task arrival time, if there is an idle AGV, the AGV is selected using a task-driven dispatching strategy. If all AGVs are already performing other transport tasks, the task is cached in the task pool, and the discrete simulation module is driven to advance the simulation until an idle AGV appears in the system. Any pending transport tasks that arrive during this period are also cached in the task pool. When an idle AGV is available, the vehicle-driven dispatching strategy (either the most recent task priority rule or the earliest task priority rule) is used to select each pending transport task.

[0074] In some embodiments, the algorithmic process of the task-driven assignment strategy includes: traversing all the transportation tasks to be processed; selecting a nearest idle automated guided vehicle (AGV) from all the automated guided vehicles (AGVs) according to the nearest vehicle first rule and the transportation task to be transported, and setting a time limit, i.e., finding an idle automated guided vehicle (AGV) within 30 seconds; if an idle automated guided vehicle (AGV) is selected, performing task assignment, assigning the task to the selected automated guided vehicle (AGV); and removing the successfully assigned task from the task pool.

[0075] As known from the above embodiments, each task in the task pool is executed by an idle automated guided vehicle (AGV) selected by the nearest vehicle first rule. The selection is based on the path length from the loading point of the task to the automated guided vehicle (AGV), and the nearest automated guided vehicle (AGV) is selected. If the task is successfully assigned, the task is removed from the task pool.

[0076] In some embodiments, the algorithmic process of the vehicle-driven assignment strategy includes: checking whether there is an unprocessed task in the task pool, and if so, entering the task selection stage; selecting a transportation task to be transported from the task pool according to the nearest task first rule (NMF), which represents the earliest time of arrival of the selected task; assigning the selected task to the current idle automated guided vehicle (AGV); after assigning the task, removing the task from the task pool; and if the task pool is empty, i.e., there is no task to be processed, the program continues.

[0077] As known from the above embodiments, if there is a task in the task pool, the task will be cached when there is no idle automated guided vehicle (AGV). If the automated guided vehicle (AGV) is idle, the task is selected by the vehicle-driven strategy and assigned to the idle automated guided vehicle (AGV). The strategy here is the nearest task rule (NMF) or the earliest task rule (EMF) to ensure that the task is processed in time.

[0078] Specifically, during scheduling, the system determines the task status and the availability of automated guided vehicles (AGVs). If an available AGV is available, the system uses the closest vehicle priority rule. Specifically, the system calculates the path length required for each available AGV to reach the task's start point (begin) and selects the AGV closest to the task's start point to perform the task. This process is completed within 30 seconds. If a new transport task arrives but its arrival time is later than the current system time, the system advances the system time through the simulation module until the task arrives. When a task arrives, if an available AGV is available, the system selects the AGV based on a specified task-driven assignment strategy. If no AGV is available, the system caches the task in the task pool and continues the simulation until an available AGV becomes available. The system then examines all tasks in the task pool and selects the most suitable AGV for each task. For each task, the system selects the Automated Guided Vehicle (AGV) closest to the starting point (begin) of the task to execute it, and the selected strategy is closest vehicle first. If a suitable AGV is found, the system assigns the task to the AGV and removes the task from the task pool. If there are still tasks to be processed in the task pool, the system selects a task based on the vehicle-driven strategy of nearest task first or earliest task first. Once a task is selected, the system assigns it to a suitable AGV to execute it and removes it from the task pool. If there are no tasks to be processed, the system will continue to loop and cache the tasks in the task pool, waiting for an idle AGV to be available before assigning tasks.

[0079] Step S20: During the task execution phase, a preset first scheduling control algorithm is used to perform fuzzy reasoning and defuzzification based on the vehicle performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle;

[0080] After obtaining the to-be-transported task information corresponding to each to-be-transported task in the to-be-transported task pool of the discrete manufacturing workshop, and the vehicle status information corresponding to each automatic guided transport vehicle, in the task execution stage, a preset first scheduling control algorithm is used to perform fuzzy reasoning and defuzzification based on the vehicle usage performance parameters, the vehicle load level or the task priority to determine the task execution speed corresponding to each automatic guided transport vehicle; wherein, the first scheduling control algorithm is a fuzzy control algorithm based on the Mamdani method.

[0081] During task execution, the automated guided vehicle (AGV) uses a three-distribution mechanism (TDM) as the core driving force for task execution. The three-distribution mechanism includes a control mechanism, a duplicate and leak prevention mechanism, and a task execution mechanism. The use of this three-distribution mechanism can improve the reliability and efficiency of the automated guided vehicle (AGV) in executing tasks while ensuring task completion.

[0082] However, in the actual production process of the factory, due to the instability and diversity of environmental factors, the traditional linear judgment method cannot meet the needs of the actual production process. In response to the defects of traditional technology, this application incorporates a fuzzy control algorithm based on the Mamdani method into the task execution stage.

[0083] Fuzzy control algorithms (FCA) are control strategies based on fuzzy logic to handle uncertainty and ambiguity. Unlike traditional binary logic, fuzzy logic can handle ambiguity, uncertainty, and fuzzy concepts expressed in human language. These fuzzy concepts can be expressed as control rules, which are then used to control the system. Therefore, fuzzy control algorithms can be applied to a variety of scenarios, especially when the system involves uncertainty or requires flexible decision-making. For example, fuzzy control algorithms can be used in scenarios such as speed control of automated guided vehicles (AGVs) and task scheduling.

[0084] In some embodiments, see Figure 3 The steps of using a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle include:

[0085] Step S201: obtaining input fuzzy linguistic variables of various input variables, wherein the input variables include task priority, vehicle performance parameters, and vehicle load level;

[0086] Step S202: calling a preset fuzzy control rule table, and calculating an output fuzzy linguistic variable of an output variable corresponding to the input fuzzy linguistic variable based on the fuzzy control rule table;

[0087] Step S203: Defuzzify the output fuzzy linguistic variables according to a preset defuzzification algorithm to obtain defuzzified output variables, wherein the output variables represent the task execution speed corresponding to each automated guided vehicle.

[0088] For further examples, please refer to Figure 4The step of using a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle includes:

[0089] Step S2001: Determine the number of input fuzzy linguistic variables corresponding to each input variable, the number of output fuzzy linguistic variables corresponding to the output variable, the finite integer discrete domain corresponding to the input fuzzy linguistic variable, and the finite integer discrete domain corresponding to the output fuzzy linguistic variable;

[0090] Step S2002: Calculate and determine a proportional factor between the input variables and the output variables based on the ratio method according to the number of input fuzzy linguistic variables, the number of output fuzzy linguistic variables, the finite integer discrete domain corresponding to the input fuzzy linguistic variables, and the finite integer discrete domain corresponding to the output fuzzy linguistic variables.

[0091] Specifically, during the task execution phase, the automated guided vehicle scheduling and control system uses a multi-dimensional fuzzy controller to decide the task execution speed corresponding to the automated guided vehicle (AGV). The controller is composed of a fuzzy interface, fuzzy reasoning, defuzzification, a rule base, and other parts. Its input variables include task priority, vehicle performance parameters corresponding to the automated guided vehicle (AGV), vehicle load level corresponding to the automated guided vehicle (AGV), current automated guided vehicle (AGV) speed, and other factors. The output variables represent the task execution speed corresponding to each automated guided vehicle.

[0092] Fuzzification refers to the process of matching the input variables and the output variables into language values, that is, converting the input variables into fuzzy sets in order to perform fuzzy reasoning. The input variables of the automatic guided vehicle scheduling control system have two types, one of which belongs to The input variable X of the function is either The input variable Y of the function, and the output variable belongs to The output variable Z of the function. Usually, the system defines the number of input fuzzy linguistic variables as 3 or 5, and the number of output fuzzy linguistic variables as 5, which are specifically defined as follows:

[0093] ， (1)

[0094] ， (2)

[0095] ， (3)

[0096] Then, according to the preset algorithm control rules, the domains of the input fuzzy linguistic variables and the output fuzzy linguistic variables need to be converted into their corresponding finite integer discrete domains. Based on the ratio method, the proportional factor between the input variable and the output variable is calculated and determined according to the number of the input fuzzy linguistic variables, the number of the output fuzzy linguistic variables, the finite integer discrete domains corresponding to the input fuzzy linguistic variables, and the finite integer discrete domains corresponding to the output fuzzy linguistic variables.

[0097] In a further embodiment, the establishment of a rule base is a necessary prerequisite for the fuzzy control algorithm. The rule base contains empirical knowledge related to process operations. For example, if the task priority is high, the load level is low, and the performance is excellent, the task execution status of the automatic guided vehicle (AGV) controlled by the triple mechanism can be assigned as excellent. The objective function of the automatic guided vehicle (AGV) task execution status is defined here as the task execution speed, based on the five output fuzzy language variables of the output variables, including PS2 (extremely small), PS1 (small), PM (medium), PB1 (large), and PB2 (extremely large).

[0098] By analogy with the above method, a fuzzy control rule table is established based on expert knowledge and operator experience. The fuzzy control rule table is shown in Table 1.

[0099] Table 1 Fuzzy control rules table

[0100]

[0101] Based on the above fuzzy rule control table, according to the Mamdani reasoning method, it can be known that for the input of the vehicle performance parameters of the automatic guided vehicle (AGV) , Vehicle load levels for automated guided vehicles (AGVs) The membership functions of and , the fuzzy relationship is R, when the fuzzy value of the input variable is When , the corresponding output variable takes the value , then there are the following relationships, which include:

[0102] ， (4)

[0103] ， (5)

[0104] The result obtained by fuzzy inference is a fuzzy set, but in practice a certain value must be obtained to control the task execution of the AGV. The process of determining an accurate value that best reflects the fuzzy inference result from the fuzzy set obtained by inference is called defuzzification, i.e. de-fuzzification. The defuzzification algorithm includes the maximum membership function method, the barycenter method, the weighted average method, etc. The output target function can be calculated using the above methods , to obtain the defuzzified accurate output variable .

[0105] From the above steps, through fuzzy inference on different input variables such as vehicle performance parameters, vehicle load levels, task priorities, etc., the system can dynamically adjust the task execution speed of the AGV according to the real-time state, so that the AGV can automatically optimize its task execution speed under different working conditions, avoiding overly fixed or mechanical scheduling methods, thereby improving the overall work efficiency. The task execution environment in reality is often full of uncertainties, such as load changes, performance fluctuations, task priority adjustments, etc. Fuzzy control can effectively handle these uncertainties and fuzziness. Through fuzzy inference, the system can consider multiple factors according to the membership function of different fuzzy sets, thereby making decisions that are more in line with actual needs, rather than simply relying on precise numerical data. Using fuzzy control can make the system have better adaptability to environmental changes. For example, the execution speed of the AGV under different task priorities or different load conditions can be automatically adjusted by the fuzzy control system, so that it can run efficiently under various dynamic conditions. This makes the AGV scheduling and control system have strong flexibility and adaptability, and can cope with complex and variable actual working environments.

[0106] Through automated fuzzy control, the system can self-adjust task execution speed without the need for real-time human intervention. This reduces reliance on human operators, improves the system's automation level, and mitigates the risk of human error. By adjusting task execution speed through fuzzy control algorithms, the AGV's speed can be aligned with factors such as task priority, load, and performance parameters, thereby ensuring more efficient resource allocation and utilization. For example, when a task priority is high, the system can increase the AGV's task execution speed, while when a load is heavy, the system may reduce speed to avoid overloading and ensure efficient resource utilization. Fuzzy control smoothly adjusts the AGV's task execution speed, avoiding excessive speed fluctuations and ensuring smooth system operation. Through effective fuzzy inference and defuzzification, the AGV can maintain stable operation in complex environments, reducing mechanical wear and energy waste caused by excessive acceleration or deceleration. Fuzzy control algorithms are highly robust and can make sound decisions even with incomplete or inaccurate data. In complex or ever-changing environments, the system can reason based on existing fuzzy rules to avoid system failure due to data errors or environmental fluctuations, thereby improving the reliability of decision-making.

[0107] Furthermore, by employing a pre-defined fuzzy control algorithm and combining fuzzy reasoning and defuzzification with factors such as vehicle performance, load level, and task priority, the AGV dispatching system can achieve more efficient, flexible, and precise task execution speed control in complex and changing environments. This approach not only improves the system's automation level and reduces manual intervention, but also optimizes resource allocation and enhances the system's adaptability and stability.

[0108] Step S30: randomly generating a plurality of different task execution scenarios using a preset second scheduling control algorithm, determining the vehicle position distance between each automated guided transport vehicle based on the task execution speed corresponding to each automated guided transport vehicle, and upon detecting that the vehicle position distance is less than a preset distance threshold at a certain moment, triggering a plurality of path conflict resolution strategies corresponding to the task execution scenario to determine the transport path trajectories of each automated guided transport vehicle under the plurality of path conflict resolution strategies;

[0109] During the task execution stage, a preset first scheduling control algorithm is used to perform fuzzy reasoning and defuzzification based on the vehicle usage performance parameters, the vehicle load level or the task priority to determine the task execution speed corresponding to each automated guided vehicle. Then, a preset second scheduling control algorithm is used to randomly generate multiple different task execution scenarios. The vehicle position distance between each automated guided vehicle is determined based on the task execution speed corresponding to each automated guided vehicle. If it is detected that the vehicle position distance is less than a preset distance threshold at a certain moment, multiple path conflict resolution strategies corresponding to the task execution scenario are triggered to determine the transport path trajectory of each automated guided vehicle under the multiple path conflict resolution strategies. The second scheduling control algorithm is a Monte Carlo simulation algorithm, and the task execution scenario includes one or any multiple of obstacle events, changes in automated guided vehicle activity, and task reset based on task time.

[0110] During the task execution phase, a pre-set first scheduling control algorithm performs fuzzy reasoning and defuzzification based on vehicle performance parameters, vehicle load levels, or task priorities to determine the corresponding task execution speeds for each AGV. A conflict-solving strategy (CSS) is then employed to address multi-vehicle coordination. When a path conflict occurs, a corresponding solution is attempted based on the conflict type. If the conflict is determined to be unresolvable, the corresponding AGV is terminated. If the conflict is resolvable, the next path is selected for continued operation. This strategy primarily includes a conflict-avoidance strategy (CAS) and a minimum stoppage strategy (MS).

[0111] During the discrete simulation of the automated guided vehicle scheduling control system, the simulation clock advances sequentially according to the time when a series of discrete events occur. The most important event in this discrete event simulation module is the automated guided vehicle (AGV) activity event, which includes three types: path change, position change, and path termination. According to each different discrete time, it is necessary to substitute priority and make choices to better coordinate and efficiently complete the overall task.

[0112] In addition to AGV activity events, there are other discrete events to consider. One is obstacle events, which include the appearance and disappearance of random obstacles (non-AGVs) at a node. Simplifying obstacle events, when an obstacle randomly appears at a node, if an AGV is already at that node, it can complete its ongoing task and leave normally. After that, all AGVs will no longer be assigned tasks for this processing point. The task will be returned to the cache, and AGVs will not be able to enter the node until the obstacle disappears. In addition, when the conflict response strategy in the system scheduling module is set to the conflict avoidance strategy (CAS) or the minimum pause strategy (MS), since AGVs need to be scheduled in real time (at intervals of 30 to 60 seconds), the effect of the AGV completing the task caused by the scheduling event is also a discrete event.

[0113] The aforementioned discrete events may occur simultaneously. The probability of the three subclasses of AGV activity events occurring simultaneously is zero, so there is no clear priority between them. However, for other discrete events, except for the AGV position change event, the priority order of the remaining events has no effect on the simulation results, so in practice, they can be processed in a random order. For multiple simultaneous AGV position change events, if multiple AGVs have the same next position, the process tasks are processed according to the arrival order of the AGVs, and prioritized by arrival time. The AGV activity time and discrete time priority order are shown in Table 2.

[0114] Table 2 AGV activity time and discrete time priority ranking

[0115]

[0116] Task assignments are handled according to these priorities, ensuring efficient scheduling of multiple automated guided vehicles (AGVs) and preventing collisions. After repeated simulations generate random samples, the conflict resolution strategy (CSS) can be optimized based on these data to better resolve and avoid conflicts.

[0117] In a further embodiment, the present application uses a Monte Carlo simulation algorithm to optimize conflict resolution strategies (CSS). Monte Carlo simulation is a method for evaluating system behavior through random sampling. Within a CSS, Monte Carlo simulation can be used to assess the probability of conflict and help optimize conflict resolution decisions. Specifically, Monte Carlo simulation simulates numerous random scenarios across several given resolution strategies to predict the probability of each strategy leading to conflict, thereby selecting the optimal strategy for execution.

[0118] First, in the conflict resolution strategy (CSS), it is necessary to detect whether there is a conflict between the transport path trajectories of any two automated guided vehicles (AGVs). If a conflict is detected, it is necessary to enter the conflict handling stage.

[0119] For specific embodiments, please refer to Figure 5 The method comprises the following steps: randomly generating a plurality of different task execution scenarios using a preset second scheduling control algorithm, determining a vehicle position distance between each automated guided vehicle based on a task execution speed corresponding to each automated guided vehicle, and detecting that the vehicle position distance is less than a preset distance threshold at a certain moment, triggering a plurality of path conflict resolution strategies corresponding to the task execution scenario, and determining a transport path trajectory of each automated guided vehicle under the plurality of path conflict resolution strategies.

[0120] Step S301: responding to a path conflict monitoring instruction, obtaining a time range for the automated guided vehicle to be detected during task execution;

[0121] Step S302: If the vehicle position distance between each automated guided transport vehicle within the time range to be detected is less than a preset distance threshold at a certain moment, then multiple preset path conflict resolution strategies are triggered to determine the transport path trajectories of each automated guided transport vehicle under the multiple path conflict resolution strategies, wherein the control parameters corresponding to the path conflict resolution strategies include one or any multiple of path adjustment parameters, speed change parameters, and state parameters.

[0122] More specifically, this application sets the paths of the two AGVs as follows: and , then the position at time t is:

[0123] ， (6)

[0124] ， (7)

[0125] Set the conflict condition to be detected within the time range for:

[0126] ， (8)

[0127] wherein, is the minimum distance of conflict occurrence, i.e., a preset distance threshold, is the predicted time range, i.e., a to-be-detected time range.

[0128] In step S40, the path conflict probability between the transport path trajectories of the respective automated guided vehicles under the path conflict resolution strategy corresponding thereto is calculated and determined, and the conflict resolution strategy with the smallest path conflict probability is taken as the optimal path conflict resolution strategy under the task execution scene to complete the scheduling control of the automated guided vehicles.

[0129] A plurality of different task execution scenes are randomly generated by using a preset second scheduling control algorithm, the vehicle position distance between the respective automated guided vehicles is determined based on the task execution speed corresponding to the respective automated guided vehicles, and when the vehicle position distance is detected to be less than a preset distance threshold at a certain moment, a plurality of path conflict resolution strategies corresponding to the task execution scene are triggered to determine the transport path trajectories of the respective automated guided vehicles under the plurality of path conflict resolution strategies, and then the path conflict probability between the transport path trajectories of the respective automated guided vehicles under the path conflict resolution strategy corresponding thereto is calculated and determined, and the conflict resolution strategy with the smallest path conflict probability is taken as the optimal path conflict resolution strategy under the task execution scene to complete the scheduling control of the automated guided vehicles.

[0130] In some embodiments, referring to Figure 6 the step of calculating and determining the path conflict probability between the transport path trajectories of the respective automated guided vehicles under the path conflict resolution strategy corresponding thereto, and taking the conflict resolution strategy with the smallest path conflict probability as the optimal path conflict resolution strategy under the task execution scene, comprises:

[0131] In step S401, a preset number of Monte Carlo simulations, a first transport path trajectory corresponding to a first automated guided vehicle under a respective path conflict resolution strategy, and a second transport path trajectory corresponding to a second automated guided vehicle are obtained, wherein the second scheduling control algorithm is a Monte Carlo simulation algorithm.

[0132] In step S402, the first transport path trajectory and the second transport path trajectory are subjected to conflict judgment by using the preset Monte Carlo simulation algorithm according to a path conflict judgment function until the preset number of Monte Carlo simulations is reached, so as to determine the number of path conflicts under the path conflict resolution strategy.

[0133] Step S403: Calculate and determine the average number of path conflicts in a preset number of Monte Carlo simulations to determine the path conflict probability between the first automated guided transport vehicle and the second automated guided transport vehicle under the path conflict resolution strategy, and use the conflict resolution strategy with the minimum path conflict probability as the optimal path conflict resolution strategy in the task execution scenario.

[0134] Specifically, in order to evaluate the effectiveness of the path conflict resolution strategy, the Monte Carlo simulation algorithm creates multiple different task execution scenarios. Each task execution scenario is based on the following activities, including obstacle events, changes in AGV activity, and task resets based on task time. The set of path conflict resolution strategies corresponding to each task execution scenario is: , each path conflict resolution strategy Corresponding to different control parameters, the control parameters corresponding to the path conflict resolution strategy include one or more of a path adjustment parameter, a speed change parameter, and a state parameter.

[0135] Per-path conflict resolution strategy After being applied to the system, a new set of paths is generated and ,in, , executing different strategies in the new path will result in different random scenarios, such as when the path deviates, and is the path deviation, which obeys the normal distribution law, and the formula for the random task execution scenario can be deduced as:

[0136] ， (9)

[0137] ， (10)

[0138] Path conflict resolution strategy Under this condition, the path conflict probability is estimated by Monte Carlo simulation algorithm, which is expressed as:

[0139] ， (11)

[0140] Where N is the number of simulations, represents the first transport path trajectory of the nth simulation, represents the second transport path trajectory of the nth simulation, represents the path conflict probability.

[0141] Finally, compare the size of the conflict probability and select the one with the lowest conflict probability. Strategy As the optimal path conflict resolution strategy to resolve conflicts.

[0142] In some embodiments, a tolerable conflict probability threshold may be set for comparison with the minimum conflict probability. If the minimum conflict probability is still greater than the tolerable conflict probability threshold, the conflict is not resolved and the corresponding automated guided vehicle (AGV) is terminated.

[0143] As can be seen from the above embodiments, compared with the prior art, the present application addresses the problems in the prior art of automated guided vehicles, such as unreasonable path planning, uneven task scheduling and resource allocation, which are prone to occur during task execution. At the same time, due to various constraints and changing factors in actual operations, the existing automated guided vehicle scheduling control algorithm cannot cope well with complex actual production scenarios. The present application includes but is not limited to the following beneficial effects:

[0144] First, the automated guided vehicle (AGV) scheduling control optimization method proposed in this application can significantly improve the efficiency and flexibility of transport task scheduling. Through a fuzzy control algorithm, the task execution speed of each automated guided vehicle (AGV) is flexibly determined based on the vehicle's performance, load level, and task priority, enabling the scheduling system to better adapt to dynamic changes within the workshop. AGV scheduling not only takes priority into account but also enables appropriate adjustments based on the vehicle's real-time status, thereby improving task scheduling efficiency.

[0145] Secondly, the automated guided vehicle scheduling and control optimization method of this application can effectively deal with path conflicts and dynamic environmental changes. It uses a Monte Carlo simulation algorithm to randomly generate multiple different task execution scenarios. This method can not only effectively simulate the possible execution of multiple tasks, but also predict and handle potential path conflicts in advance based on the execution speed and actual position of the AGV. When the vehicle distance is less than a preset safety threshold, multiple path conflict resolution strategies are triggered, thereby reducing the probability of conflict and avoiding vehicle collisions or task delays.

[0146] Third, the automated guided vehicle scheduling and control optimization method of the present application can automatically select the strategy with the lowest conflict probability by calculating the conflict probability under different path conflict resolution strategies, thereby obtaining the optimal path planning, which can minimize interference between vehicles and improve the overall efficiency and safety of the transportation system.

[0147] Fourthly, the automated guided vehicle (AGV) scheduling and control optimization method proposed in this application enables AGVs to dynamically adapt to changing production environments and demands. Traditional AGV scheduling methods typically rely on static parameters and rules, making it difficult to cope with dynamic changes in the workshop environment, such as sudden increases in tasks, equipment failures, or adjustments to workshop layouts. By calculating factors such as task priority, vehicle status, and task scenario simulation in real time, the system can adjust scheduling strategies in real time to adapt to varying production demands and environmental changes.

[0148] Fifth, the automated guided vehicle scheduling and control optimization method proposed in this application significantly improves resource utilization and significantly reduces production costs. By rationally allocating tasks and optimizing vehicle scheduling, it ensures that each AGV performs the appropriate task at the appropriate time, avoiding problems such as task congestion, idle vehicles, or overloads. By combining task priorities, vehicle loads, and performance information, scheduling strategies can be dynamically adjusted to balance workloads and improve the utilization efficiency of workshop resources.

[0149] Furthermore, this application addresses the problems of uneven task scheduling, irrational path planning, and high conflict probability in traditional automated guided vehicle (AGV) scheduling by combining fuzzy control algorithms, Monte Carlo simulation, and path conflict optimization strategies. It dynamically schedules based on actual vehicle performance, load status, and task priority, optimizing path planning and resource allocation to maximize workshop production efficiency and reduce conflict risks. This improves the system's flexibility, robustness, and safety, enabling it to better cope with the complex and ever-changing environment of discrete manufacturing workshops.

[0150] See also Figure 7, an automatic guided vehicle scheduling control optimization device provided to meet one of the purposes of the present application includes a task information determination module 1100, a task speed determination module 1200, a transportation path determination module 1300 and an optimal strategy determination module 1400. Among them, the task information determination module 1100 is configured to obtain the to-be-transported task information corresponding to each to-be-transported task in the to-be-transported task pool of the discrete manufacturing workshop, as well as the vehicle status information corresponding to each automatic guided vehicle, wherein the to-be-transported task information includes the task priority, and the vehicle status information includes the vehicle usage performance parameters and the vehicle load level; the task speed determination module 1200 is configured to use a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification according to the vehicle usage performance parameters, the vehicle load level or the task priority during the task execution phase to determine the task execution speed corresponding to each automatic guided vehicle; the transportation path determination module 1300 is configured to use a preset second scheduling control algorithm to randomly generate multiple Different task execution scenarios are provided, and the vehicle position distances between the automatic guided transport vehicles are determined based on the task execution speeds corresponding to the automatic guided transport vehicles. When it is detected that the vehicle position distances are less than a preset distance threshold at a certain moment, multiple path conflict resolution strategies corresponding to the task execution scenarios are triggered to determine the transport path trajectories of the automatic guided transport vehicles under the multiple path conflict resolution strategies; an optimal strategy determination module 1400 is configured to calculate and determine the path conflict probabilities between the transport path trajectories of the automatic guided transport vehicles under their corresponding path conflict resolution strategies, and use the conflict resolution strategy with the smallest path conflict probability as the optimal path conflict resolution strategy under the task execution scenario to complete the scheduling control of the automatic guided transport vehicles.

[0151] Based on any embodiment of this application, please refer to Figure 8 Another embodiment of the present application further provides an electronic device, which can be implemented by a computer device, such as Figure 8 As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. Among them, the computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions, and the database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor may implement an automatic guided transport vehicle scheduling control optimization method. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor may execute the automatic guided transport vehicle scheduling control optimization method of the present application. The network interface of the computer device is used to connect and communicate with the terminal. Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0152] In this embodiment, the processor is used to execute Figure 7 The memory stores the program code and various data required to execute the specific functions of each module in the automated guided vehicle scheduling control optimization device. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules in the automated guided vehicle scheduling control optimization device of this application. The server can call the server's program code and data to execute the functions of all sub-modules.

[0153] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the automatic guided vehicle scheduling control optimization method described in any embodiment of the present application.

[0154] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the automatic guided vehicle scheduling control optimization method described in any embodiment of the present application.

[0155] Those skilled in the art will appreciate that all or part of the processes in the above-described embodiments of the present application can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes in the above-described embodiments of the method. The aforementioned storage medium can be a computer-readable storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0156] The above description is only part of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for optimizing the scheduling control of an automated guided vehicle, characterized in that: include: Obtaining pending transport task information corresponding to each pending transport task in a pending transport task pool of a discrete manufacturing workshop, and vehicle status information corresponding to each automated guided vehicle, wherein the pending transport task information includes task priority, and the vehicle status information includes vehicle performance parameters and vehicle load level; During the task execution phase, a preset first scheduling control algorithm is used to perform fuzzy reasoning and defuzzification based on the vehicle performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle; A preset second scheduling control algorithm is used to randomly generate a plurality of different task execution scenarios, and the vehicle position distance between each automated guided transport vehicle is determined based on the task execution speed corresponding to each automated guided transport vehicle. If it is detected that the vehicle position distance is less than a preset distance threshold at a certain moment, a plurality of path conflict resolution strategies corresponding to the task execution scenario are triggered to determine the transport path trajectory of each automated guided transport vehicle under the plurality of path conflict resolution strategies; Calculate and determine the path conflict probability between the transport path trajectories of each automated guided transport vehicle under its corresponding path conflict resolution strategy, and use the conflict resolution strategy with the smallest path conflict probability as the optimal path conflict resolution strategy in the task execution scenario to complete the scheduling control of the automated guided transport vehicle.

2. The automatic guided vehicle scheduling control optimization method according to claim 1, characterized in that: The steps of using a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle usage performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle include: Obtaining input fuzzy linguistic variables for each input variable, wherein the input variables include task priority, vehicle performance parameters, and vehicle load level; calling a preset fuzzy control rule table, and calculating an output fuzzy linguistic variable of an output variable corresponding to the input fuzzy linguistic variable based on the fuzzy control rule table; The output fuzzy linguistic variables are defuzzified according to a preset defuzzification algorithm to obtain defuzzified output variables, wherein the output variables represent the task execution speed corresponding to each automated guided vehicle.

3. The automatic guided vehicle scheduling control optimization method according to claim 2, characterized in that: The step of using a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle usage performance parameters, the vehicle load level, or the task priority to determine the task execution speed corresponding to each automated guided vehicle includes: Determining the number of input fuzzy linguistic variables corresponding to each input variable, the number of output fuzzy linguistic variables corresponding to the output variable, the finite integer discrete domain corresponding to the input fuzzy linguistic variable, and the finite integer discrete domain corresponding to the output fuzzy linguistic variable; The proportional factor between the input variable and the output variable is calculated and determined based on the ratio method according to the number of the input fuzzy linguistic variables, the number of the output fuzzy linguistic variables, the finite integer discrete domain corresponding to the input fuzzy linguistic variables, and the finite integer discrete domain corresponding to the output fuzzy linguistic variables.

4. The automatic guided vehicle scheduling control optimization method according to claim 1, characterized in that: The method comprises the following steps: randomly generating a plurality of different task execution scenarios using a preset second scheduling control algorithm, determining a vehicle position distance between each automated guided vehicle based on a task execution speed corresponding to each automated guided vehicle, and detecting that the vehicle position distance is less than a preset distance threshold at a certain moment, triggering a plurality of path conflict resolution strategies corresponding to the task execution scenario, and determining a transport path trajectory of each automated guided vehicle under the plurality of path conflict resolution strategies. In response to a path conflict monitoring instruction, obtaining a time range for the automated guided vehicle to be detected during task execution; If it is detected that the vehicle position distance between each automated guided transport vehicle within the time range to be detected is less than a preset distance threshold at a certain moment, then multiple preset path conflict resolution strategies are triggered to determine the transport path trajectories of each automated guided transport vehicle under the multiple path conflict resolution strategies, wherein the control parameters corresponding to the path conflict resolution strategies include one or any multiple of path adjustment parameters, speed change parameters and state parameters.

5. The automatic guided vehicle scheduling control optimization method according to claim 1, characterized in that: The steps of calculating and determining the path conflict probabilities between the transport path trajectories of the automated guided vehicles under their corresponding path conflict resolution strategies, and using the conflict resolution strategy with the minimum path conflict probability as the optimal path conflict resolution strategy in the task execution scenario include: Obtaining a preset number of Monte Carlo simulations, a first transport path trajectory corresponding to the first automated guided transport vehicle under each path conflict resolution strategy, and a second transport path trajectory corresponding to the second automated guided transport vehicle, wherein the second scheduling control algorithm is a Monte Carlo simulation algorithm; Using a preset Monte Carlo simulation algorithm and a path conflict judgment function, conflict judgment is performed on the first transport path trajectory and the second transport path trajectory until a preset number of Monte Carlo simulations is reached, so as to determine the number of path conflicts under the path conflict resolution strategy; The average value of the number of path conflicts in a preset number of Monte Carlo simulations is calculated to determine the path conflict probability between the first automated guided transport vehicle and the second automated guided transport vehicle under the path conflict resolution strategy, and the conflict resolution strategy with the minimum path conflict probability is used as the optimal path conflict resolution strategy in the task execution scenario.

6. The automatic guided vehicle scheduling control optimization method according to claim 1, characterized in that: After the step of obtaining the to-be-transported task information corresponding to each to-be-transported task in the to-be-transported task pool of the discrete manufacturing workshop and the vehicle status information corresponding to each automated guided vehicle, the method includes: Determining the pending transport task information corresponding to each pending transport task and the vehicle status information corresponding to each automated guided transport vehicle, wherein the pending transport task information includes the transport task arrival time, loading point, and processing point, and the vehicle status information includes whether the automated guided transport vehicle is in a transport state; A preset vehicle scheduling strategy is used to assign vehicles to each task to be transported according to the information of the task to be transported and the vehicle status information, so as to determine the automatic guided transport vehicle corresponding to each task to be transported, wherein the vehicle scheduling strategy includes a task-driven assignment strategy and a vehicle-driven assignment strategy, the task-driven assignment strategy includes the nearest vehicle priority rule, and the vehicle-driven assignment strategy includes the nearest task priority rule and the earliest task priority rule.

7. The automated guided vehicle scheduling control optimization method according to any one of claims 1 to 6, characterized in that: The first scheduling control algorithm is a fuzzy control algorithm based on the Mamdani method, and the task execution scenario includes one or more of an obstacle event, an activity change of an automated guided vehicle, and a task reset based on task time.

8. An automatic guided vehicle scheduling control optimization device, characterized in that: include: a task information determination module configured to obtain pending transport task information corresponding to each pending transport task in a pending transport task pool of a discrete manufacturing workshop, and vehicle status information corresponding to each automated guided vehicle, wherein the pending transport task information includes a task priority, and the vehicle status information includes vehicle performance parameters and a vehicle load level; a task speed determination module configured to, during a task execution phase, use a preset first scheduling control algorithm to perform fuzzy reasoning and defuzzification based on the vehicle performance parameters, the vehicle load level, or the task priority, so as to determine the task execution speed corresponding to each automated guided vehicle; a transport path determination module configured to randomly generate a plurality of different task execution scenarios using a preset second scheduling control algorithm, determine a vehicle position distance between each automated guided vehicle based on the task execution speed corresponding to each automated guided vehicle, and trigger a plurality of path conflict resolution strategies corresponding to the task execution scenario when it is detected that the vehicle position distance is less than a preset distance threshold at a certain moment, so as to determine the transport path trajectory of each automated guided vehicle under the plurality of path conflict resolution strategies; The optimal strategy determination module is configured to calculate and determine the path conflict probability between the transport path trajectories of each automated guided transport vehicle under its corresponding path conflict resolution strategy, and use the conflict resolution strategy with the smallest path conflict probability as the optimal path conflict resolution strategy in the task execution scenario to complete the scheduling control of the automated guided transport vehicle.

9. An electronic device comprising a central processing unit and a memory, characterized in that: The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that It stores a computer program implemented according to the method described in any one of claims 1 to 7 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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