Task management method based on automatic transfer system

Abstract

The invention relates to the technical field of automation control, and discloses a task management method based on an automatic transfer system, comprising the following steps: defining a task scheduling problem, and determining a task set, tasks in the task set comprising execution time, priority and dependency; selecting paths for tasks in the task set, and selecting an optimal transmission path based on the shortest path theorem; selecting paths for tasks in the task set, and selecting an optimal transmission path based on the shortest path theorem; energy consumption optimization is carried out through an optimization control theory, and the minimum energy consumption is ensured; and monitoring the state of the power transmission system in real time, and adjusting path selection and task scheduling according to real-time monitoring feedback. According to the method, the technology of combining task scheduling with a path planning module is adopted, dynamic scheduling of task priorities and dependency relationships is achieved, it is ensured that high-priority tasks can be executed in time, the problem of scheduling delay caused by the dependency relationships among the tasks is solved, and the task execution efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control, and specifically to a task management method based on an automatic transfer system. Background Art

[0002] An Automated Guided Vehicle (AGV) is a system that performs tasks such as material handling and goods transfer through automation technology. It usually relies on technologies such as an AGV fleet, path planning, and task scheduling, and achieves efficient logistics transportation through precise scheduling and path planning. In recent years, with the rapid development of intelligent manufacturing and logistics automation, the automatic transfer system has been widely used, especially in fields such as warehousing, production lines, and distribution centers, which can effectively improve operation efficiency and reduce labor costs; However, there are still obvious deficiencies in the task scheduling, path planning, and energy efficiency optimization of existing automatic transfer systems. Traditional task scheduling methods are mostly based on static priorities and fixed orders, lacking adaptability to real-time changes, and unable to effectively handle problems such as task delays or path blockages, resulting in low execution efficiency. In terms of path planning, existing technologies rely on static graph theory algorithms and fail to consider real-time environmental changes, such as road conditions or obstacles, resulting in the inability of path selection to flexibly handle complex situations and further affecting system efficiency. These deficiencies significantly reduce the efficiency and quality of credit approval, and an innovative method is needed to improve and optimize existing processes. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides a task management method based on an automatic transfer system, which solves the problem that the existing technology usually based on static rules in task scheduling and path selection, unable to flexibly handle changes in the actual environment, lacking the ability to quickly respond to real-time environmental changes, and resulting in inefficient path selection.

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: A task management method based on an automatic transfer system, comprising the following steps: Define the task scheduling problem and determine the task set. The tasks in the task set include execution time, priority, and dependency relationship; Select a path for the tasks in the task set and select the optimal transmission path based on the shortest path theorem; Perform task scheduling and path matching according to the user's power demand and the load capacity of the power transmission network; Conduct energy consumption optimization through optimal control theory to ensure minimum energy consumption; Real-time monitor the state of the power transmission system and adjust path selection and task scheduling according to the real-time monitoring feedback; Evaluate the system performance and optimize task scheduling and path selection based on the evaluation results; According to the actual operation situation and the task execution feedback within the task set, perform dynamic optimization adjustment of task scheduling and path selection, so that the system can respond to environmental changes and user requirements in real time.

[0005] Preferably, the definition of the task scheduling problem includes the following steps: Define the task set T = {T1, T2, T n}, where each task T i includes the execution time t i of the task, the task priority p i and the dependency relationship between tasks, and i represents the task number; According to the task priority p i and the task execution time t i , sort the tasks through a priority queue, and the tasks with higher priorities enter the scheduling queue first; Dynamically adjust the scheduling order of tasks according to the execution progress and feedback of tasks.

[0006] Preferably, the shortest path theorem selects the optimal path by calculating the length l i of each path P i and the speed v j of the trolley, and the execution time of the shortest path is: where t path (C j ) represents the execution time of the trolley C j on the path P i , l i is the path length, and v j is the speed of the trolley.

[0007] Preferably, according to the user's power demand and the load capacity of the power transmission network, perform task scheduling and path matching. The task scheduling and path matching are carried out through a priority queue, select the task with the highest current priority, and allocate the shortest path P i to it, so that the task execution time is the shortest.

[0008] Preferably, the energy consumption optimization introduces the optimal control theory, dynamically adjusts the speed and path selection of the trolley, and minimizes the energy consumption. The energy consumption optimization process includes the following steps: The energy consumption of the trolley: As the speed of the trolley increases, the energy consumption increases in a quadratic relationship. Dynamically adjust the speed of the trolley to reduce energy consumption losses; Optimization of Speed and Path Selection: By applying the optimal control theory, optimize the speed and path selection of the trolley to reduce the impact of long and short paths, obstacles, and other influencing factors on energy consumption; Path Selection and Speed Dependence: By adjusting the path selection and the speed of the trolley, avoid driving on complex and long paths for a long time, thereby reducing energy loss.

[0009] Preferably, the energy consumption optimization formula of the trolley is: where E(C j ) is the energy consumption of trolley C j , α is the energy consumption constant, m j is the mass of the trolley, and v j is the speed of the trolley; The energy loss and path dependence are expressed by the following formula: E path (C j ) = β·l i ·v j ; where E path (C j ) is the energy consumption of trolley C j on path P i , β is a constant related to the path, representing the loss coefficient of the path, usually depending on the type of the path and environmental factors, l i is the length of path P i , and v j is the speed of trolley C j .

[0010] Preferably, the path selection and task scheduling are adjusted through real-time monitoring feedback, and the real-time monitoring feedback monitors the trolley state, task execution progress, path state, environmental changes, and energy consumption information in real time to adjust the path selection and task scheduling.

[0011] Preferably, the system performance is evaluated by calculating the task execution time T total and energy consumption E total , where the calculation formulas of T total and E total are: where t i represents the execution time of task T i , σ j represents the task executed by trolley C j , t path (C j ) is the execution time of the trolley on the path, and E(Cj ) represents the trolley C j 's energy consumption, α is the energy consumption constant, m j is the mass of the trolley, v j is the speed of the trolley, n represents the total number of tasks in the task set T, m represents the total number of paths in the path set C, i is the index in the task set, representing the task number, and j is the index in the path set, representing the path number.

[0012] Preferably, for the dynamic optimization adjustment of task scheduling and path selection, the dynamic optimization adjustment includes the following steps: When a path is blocked or an obstacle appears, reselect the optimal path in real time to avoid task delays; According to the task progress, adjust the task scheduling order in real time, rearrange the dependent tasks, and ensure that the tasks are completed on time; Adjust the speed of the trolley and the task execution strategy according to the energy consumption; According to the feedback of environmental changes, dynamically adjust the operation strategy or path of the trolley.

[0013] The present invention also provides a task management system based on an automatic transfer system, including: A task scheduling module, used to define the task set, task priorities, and sort tasks through a priority queue; A path planning module, used to calculate the optimal transmission path for each task based on the shortest path theorem; An energy consumption optimization module, used to adjust the speed and path selection of the trolley according to the optimal control theory to minimize energy consumption; A real-time feedback module, used to collect the system state in real time and dynamically adjust task scheduling and path planning; A performance evaluation module, used to evaluate the overall performance of the system and optimize task scheduling and path planning according to the evaluation results.

[0014] The present invention provides a task management method based on an automatic transfer system. It has the following beneficial effects: 1. The present invention adopts the combined technology of a task scheduling and path planning module to achieve dynamic scheduling of task priorities and dependencies, ensuring that high-priority tasks can be executed in a timely manner; compared with the commonly used static scheduling methods in the prior art, the present invention can adjust the task order in real time, solve the scheduling delay problem caused by task dependencies, and significantly improve the task execution efficiency.

[0015] 2. The present invention optimizes energy consumption by introducing the optimal control theory, dynamically adjusts the speed and path of the trolley, and minimizes unnecessary energy consumption to the greatest extent; compared with traditional fixed path selection and speed settings, the present invention can adjust according to real-time feedback, avoid excessive energy consumption caused by too long paths or inappropriate trolley speeds, and greatly improve energy efficiency.

[0016] 3. The present invention integrates a real-time feedback mechanism and a reinforcement learning algorithm, enabling the system to dynamically optimize task scheduling and path selection based on data during actual operation. Compared with the scheduling systems in the prior art that only rely on static rules, the present invention has an adaptive ability, can adjust strategies according to real-time situations, cope with problems such as task delays or path blockages, and improves the flexibility and adaptability of the system.

[0017] 4. The present invention continuously evaluates task execution time and energy consumption, and timely discovers and optimizes inefficient links. Different from traditional single evaluation methods, the present invention provides a mechanism for continuous optimization. The system can continuously adjust and improve the task execution plan based on historical data and real-time feedback, thereby improving the long-term efficiency and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flowchart of the method steps of the present invention; Figure 2 is a system architecture diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] Please refer to the attached Figure 1 , the embodiments of the present invention provide a task management method based on an automatic transfer system. By combining task scheduling, path planning, energy consumption optimization and a real-time feedback mechanism, including dynamic adjustment of task priorities, optimization of path selection, energy efficiency control and adjustment of real-time data feedback, it realizes efficient and flexible task completion in a complex working environment while minimizing energy consumption, thereby improving the overall efficiency and stability of the automatic transfer system, including the following steps: S1. Define the task scheduling problem and determine the task set. The tasks in the task set include execution time, priority and dependency relationship; S2. Select paths for the tasks in the task set and select the optimal transmission path based on the shortest path theorem; S3. Match the tasks and paths in the task set according to the user's power demand and the load capacity of the power transmission network; S4. Perform energy consumption optimization through optimal control theory to ensure minimum energy consumption; S5. Monitor the state of the power transmission system in real time and adjust path selection and task scheduling according to the real-time monitoring feedback; S6. Evaluate the system performance and optimize task scheduling and path selection based on the evaluation results; S7. According to the actual operation situation and the task execution feedback within the task set, perform dynamic optimization and adjustment of task scheduling and path selection, so that the system can respond to environmental changes and user needs in real time.

[0021] For step S1, in the process of defining the task scheduling problem, it is first necessary to clarify the task set T = {T1, T2, T n}, where each task T i includes three main characteristics: execution time, priority, and dependency relationship, and these characteristics will determine the scheduling order of tasks in the entire transfer system.

[0022] The task execution time t i represents the time required for task T i to be executed from the start to the completion. During the task scheduling process, the system must consider the execution duration of each task to make enough time for subsequent tasks.

[0023] The task priority p i is another crucial factor. The priority of a task determines the order in which tasks are scheduled. Generally, tasks with higher priorities will be executed first. In a possible implementation, the priority of a task is set by the user or dynamically evaluated according to factors such as the importance and urgency of the task. For tasks with lower priorities, if system resources permit, they can be postponed for execution.

[0024] The task dependency relationship is a key point that cannot be ignored in task scheduling. In some embodiments, the execution of task T i depends on the execution results of other tasks. For example, task T1 must start after task T2 is completed. This dependency relationship needs to be managed by the task scheduling system to ensure a reasonable execution order between tasks and avoid task conflicts or resource competition.

[0025] In this embodiment, the system first forms a complete task scheduling framework by defining the task set T and setting the execution time, priority, and dependency relationship for each task. The key steps of task scheduling are sorting and resolving dependency relationships.

[0026] In task scheduling, tasks are first sorted by priority to ensure that tasks with higher priorities can enter the scheduling queue first; typically, this sorting process is implemented through a priority queue. The priority queue allows tasks to be dynamically adjusted according to their priorities, which can ensure that tasks with higher priorities can be processed first at any time in the system. For example, if the priority p1 of task T1 is higher than the priority p2 of task T2, then T1 will be executed before T2.

[0027] In some embodiments, task priorities are dynamic and may be adjusted according to the current state of the system, resource utilization, and the urgency of the tasks. The system may dynamically re-evaluate the priorities of tasks based on the progress of the current task execution or environmental changes and adjust the scheduling order; for example, task T2 may become more urgent for some reason at a certain moment, and the system will re-adjust the priority queue, increase its priority, and ensure that the task can be executed in a shorter time.

[0028] Task dependencies are an important challenge in task scheduling, especially when there are complex dependencies between tasks; for example, task T2 depends on the execution result of task T1. To avoid conflicts caused by dependencies, the system must ensure that the execution order of tasks meets the requirements of the dependencies. Task T1 must be executed and completed first before task T2 can be started.

[0029] In this embodiment, task dependencies are solved through techniques such as topological sorting. Specifically, the system will first identify tasks with no preceding dependencies and schedule them, and for tasks with dependencies, they will only be scheduled for execution after their preceding tasks are completed. In this way, the entire task scheduling process can ensure that tasks in the system are executed in the correct order, thus avoiding task conflicts or execution errors.

[0030] During the task scheduling process, the system will formulate a task execution plan and allocate resources based on the execution time, priority, and dependencies of the tasks. In this process, the main goal of the task scheduling formula is to ensure the rationality and efficiency of the task order. The total time T of task scheduling total can be expressed as the sum of the task execution time and the path time: where: t i is the execution time of task T i , σ j is the task executed by trolley C j , t path (C j ) is the execution time of trolley C j on the path, and T total is the total time of task scheduling.

[0031] In this embodiment, task scheduling is not only a static process, but a dynamically adjustable process. As the tasks are being executed, the system will collect the status information of task execution in real time, including execution time, resource occupancy, etc. When the system detects a change in the task execution time or a change in the task priority, the system will re-optimize the task scheduling.

[0032] For example, if the execution time of a certain task T2 exceeds the expectation, the system can adjust the scheduling order of other tasks or allocate more resources to accelerate the completion of T2. This kind of dynamic scheduling ensures that the system can flexibly respond to various changes during operation, improving the adaptability and response ability of the system.

[0033] For step S2, in this embodiment, path selection is optimized through the shortest path theorem. Specifically, after each task is scheduled, it is necessary to select a shortest and most suitable path for execution according to factors such as the current environment, obstacles, path capacity, etc. This process is not static but dynamically adjustable. Since in the actual environment, the path selection is not only affected by the path length but also restricted by various factors, this embodiment adopts a graph theory-based algorithm to ensure the optimal selection of the path.

[0034] Generally, path selection is based on the result of task scheduling. Each task will have multiple candidate paths during the scheduling process, and the selection of the optimal path mainly depends on the path length l i and the traveling speed v of the vehicle j . The system calculates the execution time of each path according to the shortest path theorem and selects the path with the minimum execution time for task execution.

[0035] Specifically, the path execution time t path (C j ) can be expressed by the following formula: where t path (C j ) represents the execution time of vehicle C j on path P i , l i is the length of the path, and v j is the speed of the vehicle.

[0036] In a possible implementation, path selection not only focuses on the shortest path but also needs to consider the current driving speed of the vehicle and the complexity of the path. For example, if there are obstacles on a certain path, the system may choose to detour. Even if the path is slightly longer, avoiding obstacles may reduce time or energy consumption. Therefore, the path selection is not just the "shortest path" but the "shortest and most suitable path".

[0037] As an option, environmental factors such as obstacles and terrain complexity also need to be considered during the calculation of path selection. Certain paths may have reduced driving speeds due to the presence of obstacles or uneven paths, thus affecting the execution time. To address these situations, the system will dynamically adjust path selection by combining real-time environmental data.

[0038] For example, in an actual automatic transfer system, the path may be blocked by obstacles or there may be delays due to factors such as traffic congestion and path loss. The system will dynamically adjust the path and select paths with lower risks or fewer obstacles to minimize task execution delays.

[0039] In another embodiment, the system dynamically adjusts the driving route of the vehicle by combining real-time sensor data. The sensor can detect environmental variables such as obstacles ahead and the ground friction coefficient in real time and feed this information back to the path planning module. Based on the data provided by the sensor, the system will calculate the current optimal path and ensure the smooth completion of the task through dynamic path adjustment.

[0040] In some embodiments, the system will consider the current load situation of the vehicle, reasonably allocate tasks, and select a route suitable for the vehicle's load and path complexity. If a certain path is too complex or the vehicle load is too heavy, the system may choose other paths to balance the vehicle's resource utilization and avoid overloading or delaying a single vehicle.

[0041] In the path optimization module of the system, a load balancing algorithm will be introduced to ensure a reasonable load distribution for each vehicle and that path selection can balance the workload of each vehicle as much as possible. When the path selections of multiple tasks are intertwined, the system will dynamically allocate tasks based on the current position, speed of the vehicle, and the priority of the tasks to avoid path conflicts and ensure efficient task execution for each vehicle without resource waste.

[0042] In this embodiment, the path selection algorithm not only considers the path length but also needs to optimize the path in real time. Considering factors such as the vehicle's speed, path complexity, and environmental changes, the energy consumption calculation formula for the path is as follows: Where E path (C j ) is vehicle Cj On path P i energy consumption on the path, β is the path loss coefficient, which represents the characteristics of the path (such as path type, environmental factors, etc.), l i is the path length, v j is the speed of the car.

[0043] As an option, the choice of path is not static. In actual applications, the execution of the path will be affected by many factors such as the environment, obstacles and system status. In order to ensure smooth task execution, the system will monitor the status of the path in real time and make dynamic adjustments based on sensor data and task progress.

[0044] For example, if a path is blocked by an obstacle and is impassable, the system will recalculate the path based on real-time feedback information and select other feasible paths to ensure that the task can be completed on time. This real-time adjustment mechanism improves the flexibility and adaptability of the system, enabling task scheduling to cope with complex and changing environments.

[0045] For step S3, in this embodiment, the core goal of task scheduling and path matching is to combine the execution requirements of each task with the carrying capacity of the transmission network to ensure that each task can be completed within the appropriate time window while avoiding overload in the network. To this end, the system will first sort the tasks based on task priority, execution time and dependencies, and then the system will assign the tasks to the most appropriate path based on power demand and network load.

[0046] Specifically, the task scheduling module will dynamically select the optimal path for each task based on the power demand and time requirements of the task, combined with the remaining capacity of the transmission network. The path selected by the task will calculate the path length and execution time based on the shortest path theorem to ensure optimal power transmission. The goal of this process is not only to optimize the time, but also to ensure that the power network load is taken into account during the path selection process to avoid excessive network load.

[0047] It should be noted that during the power demand dispatching process, the system will monitor the power demand of each task in real time and compare it with the load capacity of the current transmission network; specifically, the power demand d i It is the basic parameter of each task, indicating the amount of power required for the task; the load capacity of the transmission network L j is the maximum load of each transmission line or each car. The task scheduling module needs to ensure that the power demand of the task is within the network load capacity to avoid overload or overload.

[0048] Assume that the power demand for task T1 is d1, and if the load capacity of the current power network is L j , the system will compare d1 and Lj As a result, the path selection is dynamically adjusted; if d1 ≤ L j , then task T1 will be assigned to path P j ; otherwise, the system will select a path with a higher load or perform load balancing to ensure the smooth completion of the task.

[0049] In actual operation, the system will match tasks with paths according to the following steps: Task evaluation: The system first evaluates the power requirements of each task and calculates the power d i and execution time t i required for the task. The priorities and dependencies between tasks are also taken into account to ensure that tasks with higher priorities can be completed in a timely manner.

[0050] Path calculation: Based on the shortest path algorithm, the system calculates the execution time t i of each path P path (C j ), and selects the shortest path according to the length l i of the path and the speed v j of the trolley. The formula for calculating the execution time of the path is as follows: where t path (C j ) is the execution time of trolley C j on path P i , l i is the path length, and v j is the speed of the trolley.

[0051] Power demand matching: For each task T i , the system combines the power demand d i and the load capacity L j of the power transmission network for matching. The system selects a suitable path based on this information and ensures the stability and efficiency of power transmission.

[0052] Once a task is assigned to a path, the system will monitor the task execution status in real time. If the system detects abnormal situations such as task delays, path blockages, or changes in power demand, the system will recalculate the path selection and make dynamic adjustments to ensure the smooth completion of the task.

[0053] To further optimize task scheduling and path selection, the present invention adopts a dynamic scheduling algorithm. Specifically, the system dynamically adjusts the task execution order and path according to the real-time task progress, path selection, and network load conditions. This process uses a reinforcement learning algorithm. Through real-time feedback and self-learning functions, the system can intelligently optimize path selection and load scheduling during task scheduling.

[0054] As an option, the system further optimizes task scheduling through a load balancing algorithm. The goal of load balancing is to ensure that the load on each trolley and each power transmission path is not excessive. Through intelligent scheduling, the system can dynamically allocate tasks to avoid delays caused by network overload for certain tasks.

[0055] For example, assume that in a warehousing system, there are two tasks T1 and T2, which require transmitting 100 and 200 units of electricity respectively. The system evaluates the electricity demands d1 = 100 and d2 = 200, and combines the electricity transmission capabilities L1 = 300 and L2 = 150 of each path to dynamically select paths for task allocation. According to the task priority and path selection algorithm, task T1 will be allocated to path P1, and task T2 will be allocated to path P2 to ensure that each task can be smoothly executed within the electricity transmission capacity range.

[0056] Through the task scheduling and path matching method of this embodiment, it can ensure that the electricity demand of each task is met, and at the same time effectively avoid excessive load on the power transmission network. Real-time path selection and scheduling optimization further improve the efficiency of task execution and ensure the efficient operation of the automatic transfer system in a complex task scheduling environment.

[0057] For step S4, the energy consumption optimization in this embodiment is based on the optimal control theory. This theory optimizes the output of the system (i.e., energy consumption) by controlling the input of the system (which is the speed of the trolley in the present invention). For an electric trolley, as the driving speed increases, the energy consumption increases in a quadratic relationship; therefore, reasonably controlling the speed of the trolley, especially dynamically adjusting the speed of the trolley under different path and task conditions, can effectively reduce unnecessary energy losses.

[0058] In this embodiment, the introduction of the optimal control theory enables the trolley to adjust its speed according to the path length, task requirements, and environmental factors (such as road conditions, obstacles, etc.). The core of this control strategy is to dynamically adjust the speed of the trolley to reduce energy consumption and improve the efficiency of task execution. Generally, as the speed of the trolley increases, the energy consumption increases in a quadratic relationship. Therefore, reasonable variation of the trolley speed is crucial for reducing the overall energy consumption.

[0059] Specifically, the formula is: Where E(C j ) is the energy consumption of trolley C j , α is a constant related to energy consumption, m j is the mass of the trolley, v jLet \(v\) be the speed of the trolley. The energy consumption of the trolley is proportional to the square of its speed. When the speed is too high, the energy loss increases sharply, while when it is too low, the task completion time may be too long. Therefore, reasonably controlling the speed of the trolley, especially in complex paths or environments with obstacles, is the key to achieving optimal energy efficiency.

[0060] As an option, the length and complexity of the path are the main factors affecting energy consumption. When the trolley passes through a long or complex path, the system will dynamically adjust the speed of the trolley to ensure that the task is completed on time and energy waste is avoided as much as possible. It can be understood that when the trolley is traveling on a short or straight path, the system can appropriately increase the speed to speed up task execution, while on a complex or obstacle-rich path, the speed will be appropriately reduced to reduce energy consumption.

[0061] To better understand the principle of energy consumption optimization, a formula for calculating the energy consumption of the trolley is introduced in this embodiment. The specific formula is as follows: Where \(E(C\) j ) represents the energy consumption of trolley \(C\) j , \(\alpha\) is a constant related to energy consumption, \(m\) j is the mass of the trolley, \(v\) j is the speed of the trolley. This formula shows that as the speed \(v\) j of the trolley increases, the energy consumption will increase in a square relationship. Therefore, the system controls the speed of the trolley through optimal control to minimize energy loss during task execution.

[0062] Furthermore, the system also considers the influence of the length and complexity of the path on energy consumption. Depending on the path selection, the energy consumption will vary. On a long or complex path, the energy loss will increase significantly. To avoid this, the system will calculate the length \(l\) i of the path and the speed \(v\) j of the trolley, and combine the complexity of the path to adjust the speed and driving strategy of the trolley to ensure minimum energy consumption. Specifically, the energy consumption of the path depends on the following factors: Where \(E\) path (C\) j ) represents the energy consumption of the trolley on path \(P\) i , \(\beta\) is the path loss coefficient, which usually depends on the type of path, environmental factors, etc., \(l\) i is the path length, and \(v\) j is the speed of the trolley. In this way, the system can intelligently select paths and dynamically adjust the speed of the trolley during path selection to ensure minimum total energy consumption.

[0063] In this embodiment, path selection and energy consumption optimization are not two independent steps, but are closely integrated. During the path selection process, the system not only considers the length and execution time of the path, but also takes energy consumption as an important constraint condition. Through the optimal control theory, the system can dynamically adjust the path selection and the driving speed of the trolley according to the characteristics of different paths and the current operating conditions of the trolley, so as to ensure the optimization of energy efficiency.

[0064] For example, in some cases, although a certain path p i has a shorter length, due to its containing a large number of turns, slopes or obstacles, it may cause the trolley to consume more energy on this path; at this time, the system will select a more suitable path for the current environment, or appropriately adjust the speed of the trolley, so as to reduce the total energy consumption.

[0065] In practical applications, the system performs energy consumption optimization according to real-time environmental feedback. Before the task starts, the system will pre-calculate the ideal speed and energy consumption range of each path according to the length and complexity of the path. During the task execution process, the system continuously adjusts the speed of the trolley according to the real-time state of the trolley (such as speed, battery power, obstacle situation, etc.). Through this real-time feedback mechanism, the system can ensure that the task is completed within the specified time while minimizing energy consumption.

[0066] For example, when executing a short straight path, the system will increase the speed of the trolley to shorten the execution time; while when passing through a more complex path, the system will reduce the speed to avoid a sharp increase in energy consumption. The system continuously adjusts the driving speed of the trolley to ensure that the task can be completed on time and the energy efficiency is optimized.

[0067] In some special scenarios, such as large-scale sample transfer or large warehouse tasks, the execution time of the task and the complexity of the path may change greatly. At this time, the energy efficiency optimization strategy will be adaptively adjusted according to the specific situation. For example, when the trolley travels for a long time, the system will calculate the energy consumption according to the battery power and the current speed, and automatically adjust the driving strategy of the trolley to avoid premature depletion of the battery.

[0068] At the same time, when there are multiple tasks to be executed in parallel in the environment, the system can intelligently allocate tasks to different trolleys, and reasonably adjust the speed and path according to the task load, current energy consumption and running speed of each trolley to ensure that all tasks can be executed efficiently.

[0069] For step S5, in this embodiment, real-time monitoring of the power transmission system's status and adjustment of path selection and task scheduling through a feedback mechanism are crucial for ensuring the efficient and accurate execution of tasks. As the system operates, path selection and task scheduling are not only influenced by the pre-designed plan but also need to be adjusted based on real-time data feedback. This process enables the system to flexibly respond to emergencies, system load changes, task delays, etc., thus ensuring the efficiency and stability during the power transmission process.

[0070] Specifically, the system's real-time monitoring includes collecting key information such as the position, speed, task execution progress, path status, and energy consumption of the trolleys. Through real-time monitoring, the system can grasp the task execution situation in real time, analyze possible abnormal situations in the system, and when problems such as path blockage and task delay occur, timely adjust task scheduling and path planning to ensure the system can continue to operate efficiently.

[0071] It should be noted that the real-time feedback mechanism and the adjustment of path selection and task scheduling complement each other. In this feedback mechanism, the system evaluates information such as the execution progress of the current task, path status, and energy consumption based on the real-time acquired data, and adjusts the path and task scheduling strategies based on this information. For example, when the system detects a delay in the execution of a certain task or a blockage in the current path, the system can immediately make adjustments, select a new path, or reschedule the task to avoid affecting the overall task execution.

[0072] In a possible implementation, the system collects the status information of each trolley in the power transmission process in real time, including but not limited to the current position, speed, path status, task execution progress, and energy consumption of the trolley. This information is fed back to the system in real time through sensor devices to form a data stream, and the system evaluates the real-time data through data analysis methods to analyze whether the task is completed on time, whether the path is smooth, and whether the energy consumption meets expectations.

[0073] For example, assume that a certain trolley is currently executing task T1, and path P1 is blocked due to some reasons, resulting in a delay in task execution. After the system identifies this situation through real-time monitoring, it immediately triggers the feedback mechanism and recalculates the path selection and scheduling order of task T1. The system may select another idle path P2 and schedule the task to another idle trolley C2 for execution, thus avoiding an overall delay of the system due to path blockage.

[0074] In this implementation, the role of the feedback mechanism is very crucial. Whenever there is a change in path selection or task scheduling, the system automatically calculates a new execution plan and verifies its effect based on real-time data; for example, the system will calculate the execution time t of the new path path (C j) To ensure that the selected path is optimal and that the path can complete the task in the shortest time.

[0075] In some embodiments, a reinforcement learning algorithm is used to process real-time feedback data and optimize path selection and task scheduling. The reinforcement learning algorithm optimizes task scheduling and path planning strategies through continuous "trial and error". In this way, the system can identify potential problems in task execution through the status information obtained from real-time monitoring, and dynamically adjust the task order, path selection, and the speed of the vehicle, so as to achieve efficient task execution.

[0076] For example, assume that task T2 can only be executed after task T1 is completed. The system monitors and discovers that task T1 is delayed in completion. Based on the reinforcement learning algorithm, the system adjusts the priority and execution path of task T2 in real time according to the information of the delay of task T1 to ensure the timely execution of the task.

[0077] It should be noted that the system feedback mechanism is not limited to path adjustment, but also includes monitoring of task execution progress and energy consumption. In practical applications, path selection may be affected by environmental factors (such as vehicle load, battery power, environmental temperature, etc.); therefore, during task execution, the system monitors the energy consumption of the vehicle in real time and adjusts the speed of the vehicle to reduce unnecessary energy loss and ensure the timely completion of the task.

[0078] For example, vehicle C j When executing path P1, the system monitors that the energy consumption of the vehicle is too high, and may reduce the energy consumption by reducing the speed v of the vehicle j or select other paths with higher energy efficiency. This process is achieved by applying the optimal control theory to dynamically adjust the speed and path selection of the vehicle.

[0079] It can be understood that the adjustment of path selection and task scheduling is a dynamic process, involving multiple aspects of optimization. When selecting a path, the system will dynamically adjust the path based on the current state of the path, such as factors like obstacles and traffic conditions. At the same time, task scheduling will also be adjusted according to the real-time state of the vehicle and the real-time changes of the path; for example, when the path is blocked, the system can re-schedule other vehicles to execute the task, or transfer the task to an unoccupied path.

[0080] For step S6, in this embodiment, the core of the performance evaluation process is to calculate the total time of task execution and the overall energy consumption of the system. This process is not only a detection of the current system state, but also a timely feedback on potential problems of the system. Through these evaluations, the system can obtain the effect of the current scheduling and path planning, discover efficiency bottlenecks and make adjustments.

[0081] When evaluating the system performance, first, the task execution time T needs to be evaluated totaland energy consumption E total Calculations are carried out. Specifically, the task execution time refers to the total time required from the start to the end of the task. This metric measures the response speed and efficiency of the system, while the energy consumption reflects the electrical resources consumed by the entire system during task execution, which is directly related to the cost and sustainability of system operation.

[0082] Specifically, the task execution time T total can be expressed by the following formula: where t i represents the execution time of task T i , σ j is the task executed by trolley C j , t path (C j ) represents the execution time of trolley C j on path P i . n represents the total number of tasks in task set T, m is the number of all trolleys participating in the execution, i is the index in the task set representing the task number, and j is the index in the path set representing the path number.

[0083] During this process, the task execution time is affected by multiple factors, including task complexity, priority, path selection, etc. Path selection has a direct impact on the task execution time. If the path is inappropriate, the task may take longer to complete, and even due to factors such as complex paths or obstacles, the execution time may be extended.

[0084] The calculation formula for energy consumption is as follows: where E(C j ) is the energy consumed by trolley C j during execution, α is the energy consumption constant, m j is the mass of trolley C j , v j is the speed of the trolley. Energy consumption is directly related to the speed and running time of the trolley. n represents the total number of tasks in task set T, m represents the total number of paths in path set C, and j is the index in the path set representing the path number.

[0085] After evaluating the system performance, the system will be adjusted and optimized according to the calculated total task execution time and energy consumption metrics. The system will optimize task scheduling and path planning based on the evaluation results to ensure that the system can complete tasks with the minimum execution time and energy consumption.

[0086] For example, if the system evaluation results indicate that the execution time of certain tasks is too long, the system will prioritize adjusting the scheduling strategies of these tasks. Possible optimization strategies include changing the execution order of tasks, adjusting the execution time of tasks, or changing the path selection. For example, if certain paths have long execution times due to poor road conditions or obstacles, the system will consider reselecting the path to prevent the vehicle from consuming excessive time and energy on these paths.

[0087] As an option, the system may also adjust the speed of the vehicle based on the evaluation results to reduce its energy consumption and improve efficiency. For paths or tasks with high energy consumption, the system may adjust the speed v of the vehicle j to reduce energy loss.

[0088] It should be noted that after each task execution, the system will re-evaluate the execution time and energy consumption of the task, and continuously adjust the scheduling strategy and path planning according to the evaluation results. This optimization mechanism enables the system to self-adjust during operation and gradually improve the efficiency of task scheduling.

[0089] Specifically, the system will adjust the priority of tasks and the path selection according to real-time feedback. For example, when a delay occurs during task execution, the system will re-arrange the task order based on the evaluation results to ensure that the delayed task does not affect the execution of other tasks. For path selection, the system will adjust the path according to the evaluation results and select a more efficient route to avoid unnecessary energy waste.

[0090] Generally, the evaluation results of task execution not only depend on the speed and path selection of the vehicle, but task complexity is also an important factor. For example, complex tasks may require more time and resources to complete. To further optimize, the system can combine task complexity and dynamically adjust task priorities to ensure that the system can respond more flexibly to different task requirements.

[0091] For step S7, the system identifies potential efficiency bottlenecks based on the evaluation data and dynamically adjusts task scheduling and path selection to ensure that the entire system achieves optimal performance when executing tasks. Based on the evaluation results, the system will identify possible bottlenecks and make adjustments to the path and task scheduling. For example, if the execution time of certain paths is too long or the energy consumption is too high, the system will select a more efficient path. The scheduling order of tasks may also be adjusted due to changes in path selection to ensure that tasks can be completed on time and executed with minimum energy consumption.

[0092] For example, if the system evaluation results show that a certain path has high energy consumption, the system may consider adjusting the speed v of the vehicle jAlternatively, to select other more energy-efficient paths, the system can adopt optimal control theory to optimize speed and path selection, reducing unnecessary energy consumption. For example, if the paths of certain tasks are affected by environmental conditions or obstacles and result in longer execution times, the system can reselect the paths and adjust the speed of the trolley to avoid unnecessary delays and energy losses.

[0093] It should be noted that the task priorities can also be adjusted according to the evaluation results. When the evaluation results show that the execution times of certain tasks are too long or the energy consumption is too high, the system will give priority to high-priority tasks and reduce the impact of low-priority tasks on the overall execution efficiency. This adjustment is not only based on path changes but also includes the optimization of task sequences. For example, the system may appropriately delay the execution of some low-priority tasks to make time for more urgent tasks.

[0094] In practical applications, the system will continuously provide feedback and make adjustments based on real-time data. This means that the evaluation results after each task execution will be fed back into the system's scheduling and path selection algorithms, driving the continuous optimization of the system. By continuously adjusting task scheduling, path selection, and trolley speed, the system can effectively respond to changes under different environmental and task requirements, ensuring the high efficiency and stability of task execution.

[0095] As an option, the system may also use reinforcement learning algorithms. After each task execution, by learning from historical data, it continuously optimizes the task scheduling and path selection strategies. By learning the performance of different paths and tasks, the system further improves the execution efficiency of future tasks.

[0096] A task management system based on an automatic transfer system described below can be correspondingly referred to the task management method based on an automatic transfer system described above.

[0097] Please refer to the appendix Figure 2 The present invention also provides a task management system based on an automatic transfer system. This system combines a task scheduling module, a path planning module, and an energy consumption optimization module. Through a real-time monitoring and feedback mechanism and dynamic adjustment in combination with optimal control theory, this system can optimize energy consumption and improve the overall operating efficiency of the system while ensuring the efficient execution of tasks.

[0098] The task scheduling module performs dynamic scheduling according to the priorities, execution times, and dependencies between tasks. Through a priority queue mechanism, the task scheduling module ensures that high-priority tasks are processed in a timely manner, avoiding delays caused by dependencies between tasks. In implementation, the task scheduling module closely cooperates with the path planning module to ensure that tasks can be executed immediately when the paths are available.

[0099] The path planning module is responsible for selecting the optimal path for each task. By combining the shortest path algorithm and real-time environmental data, this module can dynamically calculate and adjust the path according to factors such as the current position of the vehicle, task objectives, road conditions, etc. Path planning not only considers the length of the path, but also evaluates the energy consumption, complexity of the path and its impact on the task execution time.

[0100] The energy consumption optimization module uses optimal control theory to adjust the speed and path selection of the vehicle to ensure maximum energy efficiency. Specifically, by adjusting the speed v of the vehicle j , the system can dynamically change the energy consumption level to adapt to the execution requirements of different tasks. Energy consumption optimization reduces energy loss by controlling the speed of the vehicle, and adjusts the path and speed according to real-time feedback to avoid unnecessary energy consumption.

[0101] The real-time feedback module monitors the vehicle status, task execution progress and path conditions based on real-time data. By collecting information such as position, speed, and task execution progress in real time, the feedback module can promptly identify abnormal situations such as task delays or path blockages, and perform adaptive adjustments through reinforcement learning algorithms. This enables the task management system to flexibly respond to complex environments and changing task requirements, ensuring efficient execution.

[0102] The performance evaluation module is responsible for monitoring the overall performance of the system, evaluating the task execution time and energy consumption, and optimizing and adjusting the task scheduling and path planning strategies according to the evaluation results. By continuously monitoring and evaluating the task execution effect, the system can discover and optimize any potential inefficient links, thereby further improving the efficiency of task execution.

[0103] The system of this embodiment can be used to execute the above method embodiment, and its principle and technical effect are similar, so they will not be elaborated here.

[0104] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A task management method based on an automatic transport system, characterized in that: The following steps are involved: Defining a task scheduling problem and determining a task set, wherein the tasks in the task set include execution time, priority, and dependency; Select paths for tasks in the task set and select the optimal transmission path based on the shortest path theorem; Perform task scheduling and path matching based on user power demand and transmission network load capacity; Energy consumption is optimized through optimal control theory to ensure minimum energy consumption; Monitor the status of the power transmission system in real time and adjust the path selection and task scheduling based on real-time monitoring feedback; Evaluate system performance and optimize task scheduling and path selection based on the evaluation results; Based on the actual operating conditions and task execution feedback within the task set, dynamic optimization and adjustment of task scheduling and path selection are performed to enable the system to respond to environmental changes and user needs in real time.

2. A task management method based on an automatic transport system according to claim 1, characterized in that: Defining the task scheduling problem includes the following steps: Define the task set T = {T1, T2, T n }, where each task T i Contains the execution time t of the task i , task priority p i The dependency relationship between tasks, i represents the task number; According to the priority of the task i and task execution time t i , tasks are sorted by priority queue, and tasks with high priority enter the scheduling queue first; Dynamically adjust the scheduling order of tasks based on the execution progress and feedback of the tasks.

3. A task management method based on an automatic transport system according to claim 1, characterized in that: The shortest path theorem is obtained by calculating the i The length l i and the speed of the car v j To select the optimal path, the execution time of the shortest path is: Among them, t path (C j ) represents car C j On path P i The execution time on i is the path length, v j is the speed of the car.

4. The task management method based on the automatic transport system according to claim 1, characterized in that: According to the power demand of users and the load capacity of the transmission network, task scheduling and path matching are performed. The task scheduling and path matching are performed through a priority queue, and the task with the highest current priority is selected and the shortest path P is assigned to it. i , making the task execution time as short as possible.

5. The task management method based on the automatic transport system according to claim 1, characterized in that: The energy consumption optimization introduces the optimal control theory to dynamically adjust the speed and path selection of the car to minimize energy consumption. The energy consumption optimization process includes the following steps: Energy consumption of the car: As the speed of the car increases, the energy consumption increases in a quadratic relationship. The speed of the car is dynamically adjusted to reduce energy loss. Optimization of speed and path selection: Through the application of optimal control theory, the speed and path selection of the car are optimized to reduce the impact of long and short paths, obstacles and other influencing factors on energy consumption; Path selection and speed dependence: By adjusting the path selection and the speed of the car, it is possible to avoid driving on complex and long paths for a long time, thereby reducing energy loss.

6. A task management method based on an automatic transport system according to claim 5, characterized in that: The energy consumption optimization formula of the car is: Among them, E(C j ) is the car C j energy consumption, α is the energy consumption constant, m j is the mass of the car, v j is the speed of the car; The energy loss and path dependence are expressed by the following formula: AND path (C j )=β·l i ·v j 4 Among them, E path (C j ) is car C j On path P i energy consumption on the path, β is a constant related to the path, which represents the loss coefficient of the path and usually depends on the type of path and environmental factors. i For path P i The length, v j For car C j speed.

7. The task management method based on the automatic transport system according to claim 1, characterized in that: The path selection and task scheduling are adjusted through real-time monitoring feedback. The real-time monitoring feedback monitors the vehicle status, task execution progress, path status, environmental changes and energy consumption information in real time to adjust the path selection and task scheduling.

8. The task management method based on the automatic transport system according to claim 1 is characterized in that: The system performance is calculated by calculating the task execution time T in the task set. total and energy consumption E total To evaluate, T total and E total The calculation formula is: Among them, t i Represents task T i The execution time of j Indicates car C j The tasks performed, t path (C j ) is the execution time of the car on the path, E(C j ) represents car C j energy consumption, α is the energy consumption constant, m j is the mass of the car, v j is the speed of the car, n represents the total number of tasks in the task set T, m represents the total number of paths in the path set C, i is the index in the task set, indicating the number of the task, and j is the index in the path set, indicating the number of the path.

9. The task management method based on the automatic transport system according to claim 1, characterized in that: The dynamic optimization adjustment of task scheduling and path selection comprises the following steps: When the path is blocked or an obstacle appears, the optimal path is reselected in real time to avoid task delays; According to the task progress, adjust the task scheduling order in real time and rearrange dependent tasks to ensure that tasks are completed on time; Adjust the speed and task execution strategy of the car according to energy consumption; Dynamically adjust the car's operating strategy or path based on feedback from environmental changes.

10. A task management system based on an automatic transport system, applied to a task management method based on an automatic transport system according to any one of claims 1 to 9, characterized in that: include: Task scheduling module, which is used to define task sets, task priorities, and sort tasks through priority queues; Path planning module, used to calculate the optimal transmission path for each task based on the shortest path theorem; Energy consumption optimization module, which is used to adjust the speed and path selection of the car according to the optimal control theory to minimize energy consumption; Real-time feedback module, used to collect system status in real time and dynamically adjust task scheduling and path planning; The performance evaluation module is used to evaluate the overall performance of the system and optimize task scheduling and path planning based on the evaluation results.

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