Control method and system for automatic carrying equipment of special transportation line

Abstract

The invention provides a control method and system for automatic carrying equipment of a special transportation line. The method comprises the following steps: constructing a local environment model based on micro-gridding to collect and transmit equipment state information; performing task allocation and path planning on the automation equipment according to the micro-grid environment model; dynamically optimizing task allocation and path planning in a global range according to the task allocation and path planning in each micro-grid; the execution conditions of optimized task allocation and path planning are monitored in real time, the execution sequence of tasks and path planning are dynamically adjusted through an intelligent scheduling algorithm, and flexible adaptive adjustment is made according to resource states and environment changes. Through combination of the micro-grid network and the ant colony algorithm, the problems of single-point fault risk, poor path planning adaptability, inflexible task scheduling and the like in a traditional automatic transportation system are solved, and a brand new solution is provided for efficient, reliable and flexible operation of an automatic transportation special line.

Description

Technical Field

[0001] The present invention belongs to the field of automated control of dedicated transport lines, and in particular relates to a control method and system for automated handling equipment of dedicated transport lines. Background Art

[0002] In modern automated transportation systems, automated handling equipment (such as automated guided vehicles (AGVs), handling robots, etc.) for transportation lines are widely used in warehousing, logistics distribution, intelligent manufacturing and other fields. The core function of these systems is to use automated equipment to efficiently carry out tasks such as object handling, distribution and sorting. In order to improve production efficiency and reduce manual intervention, transportation equipment often uses centralized control systems to complete task scheduling and path planning. These control systems usually rely on central computing units to coordinate the actions of various devices, determine the path planning and task allocation of the devices, and execute tasks through traditional path planning algorithms (such as A*, Dijkstra algorithm, etc.).

[0003] Although the current automated transportation system has achieved certain results in many application scenarios, with the increasing complexity of transportation lines, existing technologies are facing many bottlenecks that are difficult to overcome. First, the architecture of centralized control systems usually relies on a central server for path planning and task scheduling, and this architecture often has the risk of single point failure. Once the central control unit fails or the network connection is interrupted, the operation of the entire system will be seriously affected, resulting in task delays or equipment idleness. This system that relies on a single control node is particularly prominent in high-density transportation environments. When the amount of transportation tasks increases, the processing power of the central computing unit may be difficult to keep up, resulting in scheduling delays, which in turn affects the overall efficiency and response speed of the system.

[0004] Secondly, existing path planning algorithms have poor adaptability in dynamic environments. The environment of modern transportation systems is often dynamically changing, such as the appearance of obstacles, equipment failures, and adjustments to task priorities. These changes may cause path planning to no longer be effective. Traditional path planning methods rely on static rules and models. Once the environment changes, it is difficult for traditional algorithms to quickly and flexibly adjust or optimize the path. Even some real-time path adjustment algorithms find it difficult to efficiently handle the interactions and resource scheduling between a large number of devices, which often leads to inefficiency. Especially in large-scale and complex systems, path selection and task scheduling often cannot be optimized, resulting in excessive energy consumption of equipment and waste of operating time.

[0005] In addition, existing task scheduling mechanisms often use priority-based static scheduling algorithms, which usually rely on predefined task priorities and static resource allocation methods. However, in practical applications, task priorities and resource conditions often change, such as low device power, device failure, and adjustment of task order, which require the system to dynamically adapt to environmental changes and adjust task allocation and path planning. Existing task scheduling systems lack sufficient flexibility and adaptability, making it difficult to respond quickly to emergencies, resulting in waste of resources and task delays.

[0006] Therefore, the existing automated transportation system has the risk of single point failure, poor path planning adaptability, inflexible task scheduling and other problems. There is an urgent need for a distributed, dynamic and adaptable solution to overcome these shortcomings and improve the efficiency and reliability of the overall system. Summary of the invention

[0007] The purpose of the present invention is to propose a control method and system for automated handling equipment for a dedicated transport line, a distributed collaborative control system based on a microgrid network and a dynamic path optimization and task scheduling method based on a bio-inspired algorithm. By combining these two technologies, the shortcomings of traditional systems can be effectively overcome.

[0008] In order to achieve the above object, a first aspect of the present invention provides a control method for automated handling equipment for a dedicated transport line, the method comprising:

[0009] S1. Construct a local environment model based on microgrid to collect and transmit device status information; wherein the device status information includes location, load, power and task status;

[0010] S2, assign tasks and plan paths for automation equipment based on the micro-grid environment model;

[0011] S3, dynamically optimizing the global task allocation and path planning based on the task allocation and path planning within each microgrid;

[0012] S4. Real-time monitoring of the execution of optimized task allocation and path planning, dynamically adjusting the execution order of tasks and path planning through intelligent scheduling algorithms, and making flexible adaptive adjustments based on resource status and environmental changes;

[0013] Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted by the following formula:

[0014]

[0015] Among them, Grid i represents the partition weight of microgrid i; Ai represents the area of ​​microgrid i; D i represents the number of devices in microgrid i; N i represents the task complexity within microgrid i; M i represents the maximum carrying capacity of the equipment in microgrid i; S i represents the communication bandwidth or frequency bandwidth of microgrid i; R i represents the real-time requirement of microgrid i; w 1 ,w 2 ,w 3 They represent the coefficients for adjusting the weights, which are dynamically adjusted according to the priorities of different tasks.

[0016] Furthermore, in S1, a periodic update mechanism is used to ensure that the device status information of each device is not delayed, wherein each device updates its status information according to a set time interval and synchronizes with surrounding devices or a central control system through wireless communication. The device status update adopts a smooth weighted method to prevent outdated information from having a negative impact on subsequent decisions, which is expressed as:

[0017] S new (i) = α·S old (i)+(1-α)·S sensor (i)

[0018] Among them, S new (i) represents the updated status information of device i, S old (i) indicates the status information last recorded by device i, S sensor (i) represents the latest status information collected by device i through sensors, α represents the smoothing factor, which controls the balance between new and old information, and 0<α<1.

[0019] Furthermore, the devices in each microgrid construct a local environment model based on the collected status information. The local environment model includes the real-time status of the devices in the microgrid. The real-time status includes device location information, task allocation status and device load information. The local environment model is expressed as:

[0020] M local (i) = w 4 P i +w 5 L i +w 6 E i +w 7 T i

[0021] Among them, M local (i) represents the comprehensive state of device i in the local microgrid; P i Indicates the location of device i; Li represents the load of device i; E i Indicates the power of device i; T i represents the task status of device i; w 4 ,w 5 ,w 6 ,w 7 They represent weight coefficients, respectively, which are used to adjust the relative importance of different information items;

[0022] The devices in the microgrid share status information in real time through a local communication protocol.

[0023] Furthermore, the S2 specifically includes:

[0024] The priority of each task is calculated based on the local environment model within each microgrid according to the urgency of each task, the resources required for the task, and the current status of the equipment;

[0025] Constructing a device adaptability evaluation model to evaluate the real-time status of the device and select appropriate tasks according to the status of the device; wherein the real-time status includes location, load, power and task requirements;

[0026] When the matching relationship between the device and the task is determined, the optimal path is calculated for the device;

[0027] After task allocation and path planning are completed, all devices begin to execute tasks. During this process, the task execution and path planning of the devices are dynamically adjusted to cope with emergencies. When a sudden problem occurs during the execution of a device, task allocation and path planning are readjusted based on the device adaptability evaluation model to optimize resource utilization efficiency, while at the same time, information sharing and dynamic scheduling are carried out between local microgrids.

[0028] Furthermore, the equipment adaptability evaluation model is expressed as follows:

[0029]

[0030] Among them, M adapt (i, t) represents the suitability score of device i for task t; P i Indicates the distance between the current position of device i and the task position; L i Indicates the current load of device i; E i Indicates the power of device i; T task (t) represents the execution time or time requirement of task t; δ, ε, ∈ represent the weight coefficients adjusted according to the different device states;

[0031] When the matching relationship between the device and the task is determined, the optimal path is calculated for the device, wherein the objective function of the path planning is expressed as:

[0032]

[0033] Among them, f path (i) represents the optimal path of device i; d ij represents the distance from device i to task j; C load (i) represents the current load of device i; E i represents the power of device i; Obstacles(i) represents the number of obstacles encountered by device i on the path; θ 1 ,θ 2 Represents the weight coefficient in path planning, which is dynamically adjusted according to the urgency of the task and the status of the equipment.

[0034] Furthermore, in S3, a collaborative adjustment model is constructed based on real-time monitoring of the status information of each microgrid, and dynamic task adjustment and load balancing are performed globally; wherein the collaborative adjustment model is expressed as:

[0035]

[0036] Among them, T sync (i) represents the collaborative task adjustment value of microgrid i; L j (i) represents the load requirement of task j in microgrid i; E j (i) represents the current power of microgrid i; d ij represents the distance from task j to microgrid i; T task (j) represents the scheduled execution time of task j; T total Represents the total execution time of all tasks; Represents the weight coefficients of task load, distance, time, etc.; C overload (i) represents the load overload value of microgrid i, reflecting whether the current load of microgrid i is too heavy. If it is too heavy, the task is adjusted; τ represents the adjustment coefficient of the load overload item;

[0037] The collaborative adjustment model optimizes the reallocation of tasks by comprehensively evaluating the load, power, distance and task time of each microgrid and dynamically calculating the load overload term.

[0038] Furthermore, in S3, a global collaborative path optimization algorithm is designed to avoid interactions and path conflicts between devices, and the global path is replanned by collaborative optimization. The global collaborative path optimization algorithm is expressed as:

[0039]

[0040] Among them, f global represents the objective function of global path optimization; d ijrepresents the shortest path from task j to microgrid i; C conflict (i) represents the path conflict cost, which indicates the degree of overlap between path i and other device paths, and the maximum conflict cost is the highest; λ smooth (i) represents the path smoothness term, which penalizes non-smooth paths, promotes path smoothness, and avoids abrupt corners; σ 1 ,σ 2 The weight coefficients representing the path conflict cost and smoothness;

[0041] At the same time, a dynamic resource scheduling algorithm is designed to ensure that tasks are completed while avoiding global resource waste, which is expressed as:

[0042]

[0043] Among them, R share (i) represents the resource sharing value of microgrid i; R i represents the current remaining resources of microgrid i; R max represents the maximum amount of resources that microgrid i can support; L i represents the load requirement of microgrid i; E i represents the current power of microgrid i; σ 3 Indicates the weight coefficient of the impact of load and power on resource sharing; C risk (i) represents the risk cost term, which indicates the resource risk in microgrid i; σ 4 Represents the adjustment coefficient of the risk cost item, balancing the trade-off between risk and resource sharing.

[0044] Furthermore, the S4 specifically includes:

[0045] Collect feedback information from devices and adjust task execution plans in real time based on these feedbacks. When the task execution deviation of the device exceeds the preset threshold, automatically adjust task allocation and path planning;

[0046] During the task execution process, if the device has abnormal feedback, the path will be adjusted and the task will be reallocated in real time according to the task priority;

[0047] Finally, the optimal completion of the task in the global scope is ensured according to the global overall operation status; wherein the global feedback adjustment function of the global scope is expressed as:

[0048]

[0049] Among them, F global Represents the global scheduling feedback adjustment value, indicating the intensity of global scheduling adjustment; ΔP i Indicates the path adjustment amplitude; ΔT task (i) indicates the task adjustment time; C delay(i) represents the delay cost of task execution, reflecting the additional cost caused by the delay of the task; γ 1 ,γ 2 ,γ 3 Represents the weight coefficients of global path adjustment, task adjustment, and delay cost.

[0050] Furthermore, during the task execution process, if the device has abnormal feedback, the path is adjusted in real time and the task is reallocated according to the task priority. The path is adjusted in real time by introducing an adaptive path adjustment model. The adaptive path adjustment model adjusts the path in real time based on the current device status and task priority, thereby ensuring efficient task execution and avoiding the device from failing to complete the task due to insufficient resources. It is expressed as:

[0051]

[0052] Among them, α i represents the path adjustment coefficient of microgrid i, which indicates the path adjustment flexibility of the microgrid; ΔP i represents the path adjustment amplitude of microgrid i, that is, the distance difference from the current path to the adjusted path; β i represents the priority coefficient of microgrid i, which is used to judge the urgency of the task; ΔD i Indicates the change of task execution distance, reflecting the impact of path change on task execution distance; γ i represents the adaptive adjustment weight coefficient of microgrid i, which controls the degree of adaptive adjustment; C adjust (i) represents the cost of the path adjustment process, reflecting the time, energy and other costs required to adjust the path;

[0053] The task redistribution introduces a task redistribution model to adjust the priority of tasks in real time and adjust the execution order of tasks according to the resource status according to the task execution progress, equipment resources and equipment load of each microgrid, so as to ensure that high-priority tasks can be completed first and avoid overloaded microgrids from failing to complete tasks due to insufficient resources. It is expressed as:

[0054]

[0055] Among them, T reassign (i) represents: the task redistribution value of microgrid i; C load (j) represents the load value of task j, indicating the computation or execution requirement of the task; E j 9i) represents the remaining power available in microgrid i; L j 9i) represents the task load requirement of microgrid i; κ 1 ,κ 2 The weight coefficient representing the load and task power ratio.

[0056] In a second aspect of the present invention, a control system for automated handling equipment for a dedicated transport line is provided, the system comprising:

[0057] A microgrid unit is used to construct a local environment model based on microgrid to collect and transmit device status information; wherein the device status information includes location, load, power and task status;

[0058] A microgrid task allocation and path planning unit is used to allocate tasks and plan paths for automation equipment based on a microgrid environment model;

[0059] A global task allocation and path planning unit, used to dynamically optimize the global task allocation and path planning according to the task allocation and path planning in each microgrid;

[0060] The global task allocation and path planning dynamic optimization unit is used to monitor the execution of optimized task allocation and path planning in real time, dynamically adjust the execution order of tasks and path planning through intelligent scheduling algorithms, and make flexible adaptive adjustments based on resource status and environmental changes;

[0061] Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted by the following formula:

[0062]

[0063] Among them, Grid i represents the partition weight of microgrid i; A i represents the area of ​​microgrid i; D i represents the number of devices in microgrid i; N i represents the task complexity within microgrid i; M i represents the maximum carrying capacity of the equipment in microgrid i; S i represents the communication bandwidth or frequency bandwidth of microgrid i; R i represents the real-time requirement of microgrid i; w 1 ,w 2 ,w 3 They represent the coefficients for adjusting the weights, which are dynamically adjusted according to the priorities of different tasks.

[0064] The beneficial technical effects of the present invention are at least as follows:

[0065] First, to address the problem of single-point failure, the present invention adopts a microgrid network architecture, dividing the equipment in the dedicated transport line into multiple autonomous small units (microgrids), each microgrid performs path planning and task scheduling in a local range, and can share information with adjacent microgrids through communication. Under this architecture, even if some microgrids fail or the network is interrupted, other microgrids can still work independently and will not have a fatal impact on the operation of the entire system. Through local decision-making and information sharing, the microgrid network can ensure the high robustness and rapid recovery capabilities of the system in the face of emergencies such as equipment failures and communication interruptions. This distributed control structure makes the system no longer dependent on a single central control unit, thereby avoiding the impact of single-point failures on the entire system and improving the reliability and fault tolerance of the system.

[0066] Secondly, in order to solve the problem of poor adaptability of path planning, the present invention introduces a dynamic path optimization mechanism based on the Ant Colony Optimization (ACO) algorithm. The ant colony algorithm simulates the process of ants looking for food by accumulating pheromones to gradually optimize path selection. During operation, each device can dynamically adjust the driving path according to real-time changes in the surrounding environment (such as obstacles, traffic flow, equipment failure, etc.). Through the pheromone feedback mechanism, the system can optimize the global path, so that the device can efficiently select the shortest and optimal path in a complex and dynamic working environment, thereby significantly reducing transportation time and energy consumption. In addition, the ant colony algorithm can also form a collaborative network between devices, and through information sharing and feedback between devices, it ensures the collaborative work between devices and improves the overall task execution efficiency.

[0067] Finally, in response to the problem of inflexible task scheduling, the present invention proposes a dynamic task scheduling mechanism based on device status. The system can obtain the location, power, load and other information of each device in real time, and dynamically adjust the task allocation based on this information. Tasks are intelligently scheduled according to priority, device status and operating environment, so that each device can perform the appropriate task at the right time. Through real-time information sharing and local collaborative scheduling between devices, the system can continuously adjust task allocation during task execution, ensuring that the system can quickly adapt to sudden environmental changes (such as equipment failures, changes in task urgency, etc.), avoiding resource waste and task delays.

[0068] In summary, the present invention solves the problems of single point failure risk, poor adaptability of path planning, inflexible task scheduling, etc. in traditional automated transportation systems through the combination of micro-grid networks and ant colony algorithms, and provides a new solution for the efficient, reliable and flexible operation of automated transportation lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0070] Figure 1 The present invention is a flow chart of a control method of automated handling equipment for a dedicated transport line.

[0071] Figure 2 This is a control system framework diagram of an automated handling equipment for a dedicated transport line according to the present invention. DETAILED DESCRIPTION

[0072] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0073] like Figure 1 As shown, an embodiment of the present invention provides a control method for automated handling equipment for a dedicated transport line, the method comprising the following steps:

[0074] S1. Construct a local environment model based on microgrid to collect and transmit device status information; wherein the device status information includes location, load, power and task status;

[0075] S2, assign tasks and plan paths for automation equipment based on the micro-grid environment model;

[0076] S3, dynamically optimizing the global task allocation and path planning based on the task allocation and path planning within each microgrid;

[0077] S4. Monitor the execution of optimized task allocation and path planning in real time, dynamically adjust the execution order of tasks and path planning through intelligent scheduling algorithms, and make flexible adaptive adjustments based on resource status and environmental changes.

[0078] Specifically, the S1 specifically includes:

[0079] The core goal of this step is to provide basic real-time data support for the entire automated handling system by establishing a micro-grid environment model, so that each device can obtain information about the surrounding environment in a local area and make intelligent decisions based on this information. Specifically, this step focuses on two aspects: micro-grid division and equipment allocation, and real-time collection and synchronization of equipment status information. This process will provide reliable information support for subsequent task allocation, path planning, and collaborative control.

[0080] Furthermore, the spatial area is divided into multiple autonomous microgrids (each grid is called a "sub-area") according to factors such as the size of the entire transportation line, the number of equipment, and the complexity of the task. Each microgrid is composed of several automated handling equipment (AGV or robots). The task scheduling and path planning of the equipment within the grid have a high degree of autonomy, but are still monitored by the global control system. The division of microgrids is based on factors such as the number of equipment, task requirements, the topological structure of the physical space, and the communication capabilities between equipment. In a large area, there can be multiple microgrids, and the tasks of each microgrid are performed independently, but the microgrids need to work together.

[0081] For example, when the number of devices increases, dividing into more microgrids can ensure that there is no interference between devices. At the same time, the load of each microgrid can be dynamically adjusted according to actual conditions to achieve higher efficiency.

[0082] Furthermore, the microgrid division process can be weighted and adjusted by the following formula:

[0083]

[0084] Among them, Grid i A represents the partition weight of microgrid i. i represents the area of ​​microgrid i. i N represents the number of devices in microgrid i. i M represents the task complexity (number of tasks or load) within microgrid i. i S represents the maximum carrying capacity of the equipment in microgrid i. i R represents the communication bandwidth or frequency bandwidth of microgrid i. i represents the real-time requirement of microgrid i. 1 ,w 2 ,w 3 The coefficient representing the weight adjustment is dynamically adjusted according to the priority of different tasks. The function of this formula is to weight the optimization of each microgrid division to ensure that the division of microgrids meets the balance of multiple factors such as the number of devices, task requirements and communication bandwidth.

[0085] Furthermore, each device is equipped with a variety of sensors (such as lidar, visual sensors, load sensors, temperature sensors, etc.), through which the device status information is continuously obtained, such as position, speed, load, power, etc. The status information of the device will be updated in real time and transmitted to the local control system or the management node in the microgrid.

[0086] The status information for each device includes:

[0087] Position (P i) indicates the current location of the device, which can be obtained through positioning technologies such as GPS and UWB.

[0088] Load(L i ) indicates the current load information of the equipment, reflecting the working intensity of the equipment.

[0089] Electricity (E i ) indicates the battery power of the device and is used to predict whether the device can continue to work.

[0090] Task status (T i ) indicates the execution status of the device's current task, such as whether the task is completed, task priority, etc.

[0091] Each device transmits this information to the management node of the microgrid, which aggregates the status information of all devices and generates a local environment model.

[0092] Furthermore, in order to ensure that the status information of each device is not outdated, a periodic update mechanism is adopted. Each device updates its status information according to the set time interval (Δt) and synchronizes with surrounding devices or central control systems through wireless communication. The device status update adopts a smooth weighted method to prevent outdated information from having a negative impact on subsequent decisions. The update mechanism is implemented through the following formula:

[0093] S new 9i)=α·S old (i)+91-α)·S sensor (i)

[0094] Among them, S new (i) indicates the updated status information of device i. old (i) indicates the status information last recorded by device i. sensor (i) represents the latest status information collected by device i through sensors. α represents the smoothing factor, which controls the balance between new and old information (0<α<1). This formula can ensure that in a dynamic environment, the status information of the device will not fluctuate too much due to a single update, while maintaining real-time performance.

[0095] Furthermore, the devices in each microgrid build a local environment model based on the collected status information. The model includes the real-time status (location, load, power, task status, etc.) of the devices in the microgrid and provides input for subsequent task allocation and path planning.

[0096] Among them, the local environment model (M local ) will include the following information:

[0097] Device location information: includes the current location, moving direction, speed, etc. of each device.

[0098] Task allocation status: the tasks currently executed by each device, the task priority, the estimated completion time, etc.

[0099] Device load information: The workload and current power of each device, which is used to determine whether the device is suitable to continue to perform the task.

[0100] The local environment model is summarized based on the status information of each device, and can be integrated through the weighted average method, so that each microgrid can evaluate the device status locally as a whole. The construction formula of the local environment model is as follows:

[0101] M local (i) = w 4 P i +w 5 L i +w 6 E i +w 7 T i

[0102] Among them, M local (i) represents the comprehensive state of device i in the local microgrid. i Indicates the location of device i. i represents the load of device i. i Indicates the power of device i. i Indicates the task status of device i. 1 ,w 2 ,w 3 ,w 4 Represents the weight coefficient, which is used to adjust the relative importance of different information items. The local environment model provides data support for subsequent task allocation and path planning, helping the system determine whether the device can continue to perform the task, or whether it needs to be charged, scheduled or route adjusted.

[0103] Furthermore, the devices in the microgrid share status information in real time through local communication protocols (such as Wi-Fi, Bluetooth or dedicated wireless networks), ensuring that each device in the microgrid has an accurate understanding of the status of other devices. For example, device A can obtain the power and load status of device B to decide whether to let device B continue to perform the task, or whether device B needs to be charged or scheduled.

[0104] To ensure the synchronization of information, the device uses a timer mechanism when synchronizing data, and updates the information every certain period of time (Δt). This mechanism ensures that the information used by the device is the latest when performing task scheduling or path planning.

[0105] After the equipment status information is updated, it will be synchronized with the global control system, which can ensure that the entire automated handling system has a global understanding of the status of all equipment, thereby achieving more efficient collaborative control.

[0106] Step 1: Through micro-grid division and real-time acquisition and synchronization of equipment status information, a dynamic local environment model (M) that reflects the current state is established. local ), the model will provide accurate data support for subsequent task allocation, path planning and collaborative control. In the whole scheme, the status information of the equipment is constantly updated and synchronized, and through mechanisms such as weighted average method, the accurate reflection and dynamic adaptation of the equipment status are achieved, thus providing real-time and accurate basis for subsequent global control and task execution.

[0107] Specifically, the S2 specifically includes:

[0108] Among them, in this step, the core goal is to generate a local environment model (M local ) to efficiently assign tasks and plan routes for automated equipment. Since the status of each device (such as location, load, power, etc.) changes dynamically, task assignment and route planning not only need to take into account the real-time status of each device, but also need to maximize the utilization efficiency of device resources, avoid equipment running at no load or overload, and ensure the collaborative efficiency of the entire system.

[0109] Furthermore, based on the local environment model (M local ), first calculate the priority of each task. Task priority takes into account factors such as the urgency of the task, the resources required for the task, the current status of the equipment, etc. The calculation formula for task priority is:

[0110] P task (t) = w 8 ·R task (t)+w 9 ·D task (t)+w 10 ·L task (t)

[0111] Among them, P task (t) represents the priority of task t. task (t) represents the urgency of task t. task L (t) represents the resource requirement of task t (such as equipment load, required power, etc.). task w (t) represents the time limit or execution deadline of task t. 8 ,w 9 ,w 10 represents the weight coefficient, which is adjusted according to the nature of the task (for example, urgent tasks may be given w1 The formula calculates the priority of each task based on the urgency of the task, the resources required, and the time limit. The weight coefficient is adjusted according to the priority requirements of the task, thereby achieving dynamic priority adjustment for different tasks.

[0112] Furthermore, after the task priority is calculated, the real-time status of the device needs to be evaluated and the appropriate task needs to be selected based on the device status. The device status evaluation includes information such as location, load, and power, which will play an important role in task allocation. local ) to build a device adaptability evaluation model (M adapt ) to determine whether each device is suitable for a task. The model considers the device's load, location, power, and task requirements to help determine the most suitable device for a task.

[0113] The equipment adaptability evaluation formula is as follows:

[0114]

[0115] Among them, M adapt (i,t) represents the fitness score of device i to perform task t. i Indicates the distance between the current location of device i and the task location. i Indicates the current load of device i. i Indicates the power of device i. task (t) represents the execution time or time requirement of task t. δ,ε,∈ represent the weight coefficients adjusted according to the different device states. The device adaptability evaluation model comprehensively considers factors such as the location, load, and power of the device to calculate the adaptability score of each device to perform each task. The device with the higher score will be given priority to perform the task.

[0116] Furthermore, once the matching relationship between the device and the task is determined, the optimal path needs to be calculated for the device. The goal of path planning is to select the shortest path that best suits the current device status, while also taking into account the tasks and paths of other devices to avoid path conflicts and resource waste. To this end, a path optimization algorithm based on dynamic programming is used to consider device status (such as load, power, etc.) and environmental factors (such as obstacles, path blockage, etc.) to provide real-time optimized paths for devices.

[0117] The objective function of path planning can be expressed as:

[0118]

[0119] Among them, f path(i) represents the optimal path of device i. ij represents the distance from device i to task j. load (i) represents the current load of device i. i Represents the power of device i. Obstacles(i) represents the number of obstacles encountered by device i on the path. θ 1 ,θ 2 Represents the weight coefficient in path planning, which is dynamically adjusted according to the urgency of the task and the status of the device. Path planning calculates the optimal path for the device through dynamic optimization. The optimization goal is not only the shortest path, but also factors such as the device load, power, and obstacles encountered need to be considered to provide a more intelligent path planning solution.

[0120] Furthermore, after task allocation and path planning are completed, all devices will start to execute tasks under the instructions of the system. During this process, the task execution and path planning of the equipment need to be dynamically adjusted to deal with emergencies (such as equipment failure, path blockage, etc.). The system ensures the smooth progress of the task by monitoring and adjusting the status of the equipment in real time.

[0121] When the device encounters problems such as overload and insufficient power during execution, the system can evaluate the device adaptability based on the device adaptability evaluation model (M adapt )Re-adjust task allocation and path planning to optimize resource utilization efficiency.

[0122] The collaborative control mechanism is through information sharing and dynamic scheduling between local microgrids. The entire automation system can adjust task allocation and path planning in real time to avoid equipment overload or conflict and achieve collaborative work.

[0123] In this step, the efficiency of task allocation and the intelligence of path planning are ensured through task priority calculation based on the local environment model, equipment adaptability evaluation and dynamic path planning. The status information of each device (such as location, power, load, etc.) is incorporated into the decision-making of task allocation and path planning in real time, so as to achieve the optimal allocation of resources. In addition, the calculation of dynamic optimization path avoids device conflicts and improves the efficiency and robustness of the overall system. The goal of the entire step is to ensure that the equipment can intelligently select tasks and plan paths according to the current status, so as to achieve efficient task execution and collaborative work in a dynamic environment.

[0124] Specifically, the S3 specifically includes:

[0125] Among them, in the above steps, the present invention completes the allocation of tasks and local path planning (step 2), and the tasks in each microgrid have been processed based on the local environment model and path optimization. However, as the number of devices increases and the complexity of tasks increases, problems such as task conflicts, path overlaps, and resource competition may occur between microgrids, resulting in a decrease in the efficiency of the entire system, or even failure to complete the target task. In order to overcome these problems, this step proposes a solution for collaboration and global path optimization between microgrids, aiming to improve the operating efficiency of the overall system through coordination between microgrids, avoidance of path conflicts, and global path optimization.

[0126] Furthermore, in step 2, tasks have been assigned to each microgrid and path planning has been performed based on the local environment model. However, with the dynamic operation of the equipment, some microgrids may be overloaded or idle, so tasks need to be redistributed to avoid unbalanced loads.

[0127] This paper proposes a microgrid adaptive task coordination algorithm, which can dynamically adjust tasks and balance loads in a global scope by real-time monitoring of the task progress, remaining power, load status and other information of each microgrid. The coordination adjustment model is as follows:

[0128]

[0129] Among them, T sync (i) represents the collaborative task adjustment value of microgrid i. j (i) represents the load requirement of task j in microgrid i. j (i) represents the current power consumption of microgrid i. ij T represents the distance from task j to microgrid i. task (j) represents the scheduled execution time of task j. total Represents the total execution time of all tasks. Represents the weight coefficients of task load, distance, time, etc. overload (i) represents the load overload value of microgrid i, reflecting whether the current load of microgrid i is too heavy. If it is too heavy, the task will be adjusted. τ represents the adjustment coefficient of the load overload term, which further controls the flexibility of load adjustment. The model optimizes the redistribution of tasks by comprehensively evaluating the load, power, distance and task time of each microgrid, and dynamically calculating the load overload term. Specifically, when the load of a microgrid exceeds the predetermined threshold, the system will automatically schedule other microgrids to share the task to ensure global load balancing of tasks.

[0130] Furthermore, with the redistribution of tasks, the path planning between devices may also overlap and conflict. Therefore, the present invention needs to adjust the paths of the devices through global path planning to avoid path intersection, reduce conflicts, and minimize the total length of the paths.

[0131] In traditional path optimization algorithms, the mutual influence between devices is usually ignored, which may lead to local optimal solutions of the path. The present invention proposes a global collaborative path optimization algorithm, which can consider the interaction between devices and path conflicts and re-plan the global path through collaborative optimization.

[0132] The global path optimization objective function is as follows:

[0133]

[0134] Among them, f global represents the objective function of global path optimization. ij represents the shortest path from task j to microgrid i. conflict (i represents the path conflict cost, which indicates the degree of overlap between path i and other device paths. The greater the conflict, the higher the cost. smooth (i represents the path smoothing term, which penalizes the non-smooth path, promotes the smoothness of the path, and avoids abrupt corners. σ 1 ,σ 2 A weight factor representing the path conflict cost and smoothness.

[0135] Among them, the path conflict term C conflict (i) Measures the intersection or overlap area between different device paths. This item is used to penalize path conflicts, optimize path planning, and avoid multiple devices from being active in the same area.

[0136] Path smoothing term λ smooth (i) By optimizing the curvature of the path, the path is made smooth and fluent, the turning frequency of the device is reduced, and the movement efficiency is improved. This item is crucial for patent scenarios with dynamically changing environments, especially in areas with dense path planning.

[0137] Furthermore, between microgrids, different devices may fail to complete tasks due to insufficient resources (such as power, battery, etc.). Therefore, a dynamic resource scheduling algorithm must be designed so that microgrids with more abundant resources can support microgrids with scarce resources globally, ensuring that tasks are completed while avoiding global resource waste.

[0138] The resource sharing scheme of the present invention is scheduled based on the comprehensive evaluation of dynamic priority and microgrid load. To this end, the present invention designs a dynamic resource sharing and scheduling optimization model. The resource sharing optimization formula is as follows:

[0139]

[0140] Among them, R share (i) represents the resource sharing value of microgrid i. i Represents the current remaining resources of microgrid i. max L represents the maximum amount of resources that microgrid i can support. i represents the load requirement of microgrid i. i Represents the current power of microgrid i. 3 Indicates the weight coefficient of the impact of load and power on resource sharing. C risk (i) represents the risk cost term, which indicates the resource risk in microgrid i (such as power is about to run out, etc.), and is used to avoid excessive resource sharing, thereby reducing the risk of the entire system. 4 The adjustment coefficient of the risk cost item is used to balance the trade-off between risk and resource sharing. The optimization model comprehensively considers the load, power and resource risk of the microgrid, and avoids excessive dependence of a single microgrid on the resources of other microgrids by dynamically adjusting the resource sharing strategy, thus ensuring the optimal resource utilization of the global system.

[0141] Furthermore, due to environmental changes, failures or unexpected events that the equipment may encounter during task execution, the original task and path planning may need to be adjusted. Therefore, the system will dynamically update the path and task scheduling based on real-time data feedback to ensure the optimal state of the global path and task. The present invention designs a real-time dynamic scheduling and feedback mechanism to dynamically adjust the task and path planning through real-time monitoring of the task execution status.

[0142] The inter-microgrid collaboration and global path optimization scheme proposed in this step fully considers key issues such as task redistribution, path conflict, and resource scheduling, and significantly improves the efficiency and stability of the entire system through innovative optimization models and dynamic update mechanisms. Through this step, the present invention not only ensures the rationality of local tasks and path planning, but also achieves the optimal operation of the entire system in a dynamic environment through global optimization and resource sharing mechanisms. The innovation of this scheme lies in the integration of multiple optimization models, especially in task allocation, path planning, and resource scheduling, providing a highly adaptable scheme that adapts to complex scenarios of multi-device collaboration and provides strong support for the patented system.

[0143] Specifically, the S4 specifically includes:

[0144] Among them, in the aforementioned step (step 3), the present invention ensures the balance of task allocation and path planning between microgrids through the collaboration between microgrids and global path optimization, and avoids path conflicts, uneven task loads and other problems to the greatest extent. However, with the movement of equipment, the execution of tasks, and changes in the external environment (such as equipment failures, obstacle changes, etc.), the task scheduling and path planning of the entire system may need to be dynamically adjusted according to real-time conditions. At this time, dynamic task scheduling and adaptive path adjustment will become a crucial step. Its core goal is to adjust task allocation, path planning and resource scheduling in real time according to changes in system status to ensure that the system can cope with uncertainty and continuously optimize the efficiency of task completion.

[0145] Furthermore, in order to ensure the real-time and accuracy of task scheduling and path planning, this step first builds a real-time task execution status monitoring system. The system can monitor the task execution status, path execution progress, and equipment status (such as power, faults, environmental changes, etc.) of each microgrid in real time. The system continuously collects feedback information and adjusts the task execution plan in real time based on these feedbacks. When the task execution deviation of the device exceeds the preset threshold, the system will automatically adjust the task allocation and path planning.

[0146] The model of the monitoring and feedback mechanism is as follows:

[0147] ΔT task (i) = γ·(ΔT feedback (i)+ΔT overrun (i))

[0148] Where, ΔT task (i) represents the task adjustment time of microgrid i, that is, the time to reschedule according to task deviation. ΔT feedback (i) represents the time that microgrid i adjusts according to real-time feedback data. This time is affected by the feedback information of task execution, such as the delay or advance of task completion progress. ΔT overrun (i) represents the time deviation of task timeout or execution delay. γ represents the feedback mechanism adjustment coefficient, which is used to control the sensitivity of feedback adjustment. This formula describes how to dynamically adjust the task execution time according to real-time feedback and task execution timeout, so as to ensure the smooth progress of the task and avoid time deviation.

[0149] Furthermore, during the task execution process, the system dynamically schedules based on real-time status feedback (such as device power, battery life, task execution progress, etc.). If a microgrid is low on power or encounters an obstacle, the task order and path planning of the microgrid need to be readjusted. By introducing an adaptive path adjustment model, the model can adjust the path in real time based on the current device status and task priority, thereby ensuring efficient task execution and avoiding the failure of the device to complete the task due to insufficient resources.

[0150] The path adjustment model is as follows:

[0151]

[0152] Among them, α i ΔP represents the path adjustment coefficient of microgrid i, which indicates the path adjustment flexibility of the microgrid. i represents the path adjustment amplitude of microgrid i, that is, the distance difference from the current path to the adjusted path. i Represents the priority coefficient of microgrid i, which is used to judge the urgency of the task. ΔD i Indicates the change in task execution distance, reflecting the impact of path changes on task execution distance. i Represents the adaptive adjustment weight coefficient of microgrid i, which controls the degree of adaptive adjustment. adjust (i) represents the cost of the path adjustment process, reflecting the time, energy and other costs required to adjust the path. This model dynamically optimizes the path by integrating the path adjustment amplitude, task execution priority and adaptive adjustment weight. The core of path adjustment is to ensure that the device can adjust the path according to the real-time situation to avoid task interruption or delay caused by obstacles, equipment failures, etc.

[0153] Furthermore, in addition to path adjustment, when the task execution status of the device changes, task reallocation and resource optimization will become the key. At this time, the task reallocation and resource optimization algorithm will be dynamically adjusted according to the task execution progress of each microgrid, device resources (such as power, battery capacity) and device load.

[0154] The algorithm proposed in the present invention can adjust the priority of tasks in real time and adjust the execution order of tasks according to resource status, ensuring that high-priority tasks can be completed first, while avoiding overloaded microgrids from failing to complete tasks due to insufficient resources.

[0155] The task redistribution model is as follows:

[0156]

[0157] Among them, T reassign (i) represents the task reallocation value of microgrid i.load (j) represents the load value of task j, indicating the computation or execution requirements of the task. j (i) represents the remaining power available in microgrid i. j (i) represents the task load requirement of microgrid i. 1 ,κ 2 The weight coefficient represents the load and task power ratio. The task reallocation model optimizes the task allocation by comprehensively considering the equipment load, task power ratio and task priority. The goal is to allow microgrids with insufficient resources to reallocate tasks to microgrids with sufficient resources while avoiding unnecessary task overlap and conflict.

[0158] Furthermore, due to the dynamic environmental changes during task execution, task scheduling and path planning will continuously provide feedback and self-adjust. To this end, the present invention constructs a global adaptive adjustment and scheduling feedback system, which can make global scheduling adjustments based on the overall operating status of the system (such as task completion rate, equipment status, path completion status, etc.) to ensure the optimal completion of tasks in a global scope. The global feedback adjustment model is as follows:

[0159]

[0160] Among them, F global Represents the global scheduling feedback adjustment value, indicating the strength of the global scheduling adjustment. ΔP i Indicates the path adjustment amplitude. ΔT task (i) indicates the task adjustment time. delay (i) represents the delay cost of task execution, reflecting the additional cost caused by the delay of the task. 1 ,γ 2 ,γ 3 Represents the weight coefficient of global path adjustment, task adjustment and delay cost. The global scheduling feedback system can evaluate the task status, path status and delay cost of each microgrid in real time, so as to make global adjustments. Through global optimization, it can ensure that the entire system can maintain a high task completion rate and path efficiency in the face of sudden changes.

[0161] Through the scheme of dynamic task scheduling and adaptive path adjustment, the present invention realizes the flexibility and efficiency of the entire system in a dynamic environment. The system can not only adjust the order of task execution according to real-time feedback, but also dynamically adjust the path according to the real-time status of the device, and automatically reallocate tasks when resources are limited. This scheme ensures efficient execution of tasks under uncertain conditions by comprehensively considering multiple factors such as device status, task priority, path adjustment, etc.

[0162] like Figure 2As shown, an embodiment of the present invention further provides a control system for automated handling equipment for a dedicated transport line, the system comprising:

[0163] The microgrid unit 101 is used to construct a local environment model based on microgrid to collect and transmit device status information; wherein the device status information includes location, load, power and task status;

[0164] A micro-grid task allocation and path planning unit 102 is used to perform task allocation and path planning for automation equipment according to a micro-grid environment model;

[0165] A global task allocation and path planning unit 103, used to dynamically optimize the global task allocation and path planning according to the task allocation and path planning in each microgrid;

[0166] The global task allocation and path planning dynamic optimization unit 104 is used to monitor the execution of the optimized task allocation and path planning in real time, dynamically adjust the execution order of tasks and path planning through intelligent scheduling algorithms, and make flexible adaptive adjustments according to resource status and environmental changes;

[0167] Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted by the following formula:

[0168]

[0169] Among them, Grid i represents the partition weight of microgrid i; A i represents the area of ​​microgrid i; D i represents the number of devices in microgrid i; N i represents the task complexity within microgrid i; M i represents the maximum carrying capacity of the equipment in microgrid i; S i represents the communication bandwidth or frequency bandwidth of microgrid i; R i represents the real-time requirement of microgrid i; w 1 ,w 2 ,w 3 They represent the coefficients for adjusting the weights, which are dynamically adjusted according to the priorities of different tasks.

[0170] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0171] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a division of logical functions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0172] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0173] Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A control method for automated handling equipment for a dedicated transport line, characterized in that: The method comprises: S1. Construct a local environment model based on microgrid to collect and transmit device status information; wherein the device status information includes location, load, power and task status; S2, assign tasks and plan paths for automation equipment based on the micro-grid environment model; S3, dynamically optimizing the global task allocation and path planning based on the task allocation and path planning within each microgrid; S4. Real-time monitoring of the execution of optimized task allocation and path planning, dynamically adjusting the execution order of tasks and path planning through intelligent scheduling algorithms, and making flexible adaptive adjustments based on resource status and environmental changes; Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted by the following formula: Among them, Grid i represents the partition weight of microgrid i; A i represents the area of ​​microgrid i; D i represents the number of devices in microgrid i; N i represents the task complexity within microgrid i; M i represents the maximum carrying capacity of the equipment in microgrid i; S i represents the communication bandwidth or frequency bandwidth of microgrid i; R i represents the real-time requirement of microgrid i; w1, w2, and w3 represent the coefficients of adjusting weights, which are dynamically adjusted according to the priorities of different tasks.

2. The control method of a dedicated transport line automated handling equipment according to claim 1, characterized in that: In S1, a periodic update mechanism is used to ensure that the device status information of each device is not delayed, wherein each device updates its status information according to a set time interval and synchronizes with surrounding devices or a central control system through wireless communication. The device status update adopts a smooth weighted method to prevent outdated information from having a negative impact on subsequent decisions, which is expressed as: S new (i)=α·S old (i)+(1-α)·S sensor (i) Among them, S new (i) represents the updated status information of device i, S old (i) indicates the status information last recorded by device i, S sensor (i) represents the latest status information collected by device i through sensors, α represents the smoothing factor, which controls the balance between new and old information, and 0<α<1.

3. The control method of a dedicated transport line automated handling equipment according to claim 2, characterized in that: The equipment in each microgrid builds a local environment model based on the collected status information. The local environment model includes the real-time status of the equipment in the microgrid. The real-time status includes equipment location information, task allocation status and equipment load information. The local environment model is expressed as: M local (i)=w4P i +w5L i +w6E i +w7T i Among them, M local (i) represents the comprehensive state of device i in the local microgrid; P i Indicates the location of device i; L i represents the load of device i; E i Indicates the power of device i; T i represents the task status of device i; w4, w5, w6, and w7 represent weight coefficients, which are used to adjust the relative importance of different information items; The devices in the microgrid share status information in real time through a local communication protocol.

4. The control method of a dedicated transport line automated handling equipment according to claim 1, characterized in that: The S2 specifically includes: The priority of each task is calculated based on the local environment model within each microgrid according to the urgency of each task, the resources required for the task, and the current status of the equipment; Constructing a device adaptability evaluation model to evaluate the real-time status of the device and select appropriate tasks according to the status of the device; wherein the real-time status includes location, load, power and task requirements; When the matching relationship between the device and the task is determined, the optimal path is calculated for the device; After task allocation and path planning are completed, all devices begin to execute tasks. During this process, the task execution and path planning of the devices are dynamically adjusted to cope with emergencies. When a sudden problem occurs during the execution of a device, task allocation and path planning are readjusted based on the device adaptability evaluation model to optimize resource utilization efficiency, while at the same time, information sharing and dynamic scheduling are carried out between local microgrids.

5. The control method of a dedicated transport line automated handling equipment according to claim 4, characterized in that: The equipment adaptability evaluation model is expressed as follows: Among them, M adapt (i, t) represents the suitability score of device i for task t; P i Indicates the distance between the current position of device i and the task position; L i Indicates the current load of device i; E i Indicates the power of device i; T task (t) represents the execution time or time requirement of task t; δ, ε, ∈ represent the weight coefficients adjusted according to the different device states; When the matching relationship between the device and the task is determined, the optimal path is calculated for the device, wherein the objective function of the path planning is expressed as: Among them, f path (i(represents the optimal path for device i; d ij represents the distance from device i to task j; C load (i) represents the current load of device i; E i represents the power of device i; Obstacles(i) represents the number of obstacles encountered by device i on the path; θ1, θ2 represent the weight coefficients in path planning, which are dynamically adjusted according to the urgency of the task and the status of the device.

6. The control method of a dedicated transport line automated handling equipment according to claim 1, characterized in that: In S3, based on the real-time monitoring of the status information of each microgrid, a collaborative adjustment model is constructed to perform dynamic task adjustment and load balancing in a global scope; wherein the collaborative adjustment model is expressed as: Among them, T sync (i) represents the collaborative task adjustment value of microgrid i; L j (i) represents the load requirement of task j in microgrid i; E j (i) represents the current power of microgrid i; d ij represents the distance from task j to microgrid i; T task (j) represents the scheduled execution time of task j; T total Represents the total execution time of all tasks; Represents the weight coefficients of task load, distance, time, etc.; C overload (i) represents the load overload value of microgrid i, reflecting whether the current load of microgrid i is too heavy. If it is too heavy, the task is adjusted; τ represents the adjustment coefficient of the load overload item; The collaborative adjustment model optimizes the reallocation of tasks by comprehensively evaluating the load, power, distance and task time of each microgrid and dynamically calculating the load overload term.

7. The control method of the transportation dedicated line automated handling equipment according to claim 6, characterized in that: In S3, a global collaborative path optimization algorithm is designed to avoid interactions and path conflicts between devices, and the global path is replanned through collaborative optimization. The global collaborative path optimization algorithm is expressed as: Among them, f global represents the objective function of global path optimization; d ij represents the shortest path from task j to microgrid i; C conflict (i) represents the path conflict cost, which indicates the degree of overlap between path i and other device paths, and the maximum conflict cost is the highest; λ smooth (i) represents the path smoothing term, which penalizes non-smooth paths, promotes path smoothness, and avoids abrupt corners; σ1, σ2 represent the weight coefficients of path conflict cost and smoothness; At the same time, a dynamic resource scheduling algorithm is designed to ensure that tasks are completed while avoiding global resource waste, which is expressed as: Among them, R share (i) represents the resource sharing value of microgrid i; R i represents the current remaining resources of microgrid i; R max represents the maximum amount of resources that microgrid i can support; L i represents the load requirement of microgrid i; E i represents the current power of microgrid i; σ3 represents the weight coefficient of the impact of load and power on resource sharing; C risk (i) represents the risk cost term, which indicates the resource risk in microgrid i; σ4 represents the adjustment coefficient of the risk cost term, balancing the trade-off between risk and resource sharing.

8. The control method of a dedicated transport line automated handling equipment according to claim 1, characterized in that: The S4 specifically includes: Collect feedback information from devices and adjust task execution plans in real time based on these feedbacks. When the task execution deviation of the device exceeds the preset threshold, automatically adjust task allocation and path planning; During the task execution process, if the device has abnormal feedback, the path will be adjusted and the task will be reallocated in real time according to the task priority; Finally, the optimal completion of the task in the global scope is ensured according to the global overall operation status; wherein the global feedback adjustment function is expressed as: Among them, F global Represents the global scheduling feedback adjustment value, indicating the intensity of global scheduling adjustment; ΔP i Indicates the path adjustment amplitude; ΔT task (i) indicates the task adjustment time; C delay (i) represents the delay cost of task execution, reflecting the additional cost of the task caused by delay; γ1, γ2, γ3 represent the weight coefficients of global path adjustment, task adjustment and delay cost.

9. The control method of the transportation dedicated line automated handling equipment according to claim 8, characterized in that: During the task execution process, if the device has abnormal feedback, the path is adjusted in real time and the task is reallocated according to the task priority. The path is adjusted in real time by introducing an adaptive path adjustment model. The adaptive path adjustment model adjusts the path in real time based on the current device status and task priority, thereby ensuring efficient task execution and avoiding the device from failing to complete the task due to insufficient resources. It is expressed as: Among them, α i represents the path adjustment coefficient of microgrid i, which indicates the path adjustment flexibility of the microgrid; ΔP i represents the path adjustment amplitude of microgrid i, that is, the distance difference from the current path to the adjusted path; β i represents the priority coefficient of microgrid i, which is used to judge the urgency of the task; ΔD i Indicates the change of task execution distance, reflecting the impact of path change on task execution distance; γ i represents the adaptive adjustment weight coefficient of microgrid i, which controls the degree of adaptive adjustment; C adjust (i) represents the cost of the path adjustment process, reflecting the time, energy and other costs required to adjust the path; The task redistribution introduces a task redistribution model to adjust the priority of tasks in real time and adjust the execution order of tasks according to the resource status according to the task execution progress, equipment resources and equipment load of each microgrid, so as to ensure that high-priority tasks can be completed first and avoid overloaded microgrids from failing to complete tasks due to insufficient resources. It is expressed as: Among them, T reassign (i) represents: the task redistribution value of microgrid i; C load (j) represents the load value of task j, indicating the computation or execution requirement of the task; E j (i) represents the remaining power available in microgrid i; L j (i) represents the task load demand of microgrid i; κ1, κ2 represent the weight coefficients of load and task power ratio.

10. A control system for automated handling equipment for a dedicated transport line, characterized in that: The system comprises: A microgrid unit is used to construct a local environment model based on microgrid to collect and transmit device status information; wherein the device status information includes location, load, power and task status; A microgrid task allocation and path planning unit is used to allocate tasks and plan paths for automation equipment based on a microgrid environment model; A global task allocation and path planning unit, used to dynamically optimize the global task allocation and path planning according to the task allocation and path planning in each microgrid; The global task allocation and path planning dynamic optimization unit is used to monitor the execution of optimized task allocation and path planning in real time, dynamically adjust the execution order of tasks and path planning through intelligent scheduling algorithms, and make flexible adaptive adjustments based on resource status and environmental changes; Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted by the following formula: Among them, Grid i represents the partition weight of microgrid i; A i represents the area of ​​microgrid i; D i represents the number of devices in microgrid i; N i represents the task complexity within microgrid i; M i represents the maximum carrying capacity of the equipment in microgrid i; S i represents the communication bandwidth or frequency bandwidth of microgrid i; R i represents the real-time requirement of microgrid i; w1, w2, and w3 represent the coefficients of adjusting weights, which are dynamically adjusted according to the priorities of different tasks.

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