A control method and system for automated handling equipment for a dedicated transport line

Abstract

The present invention proposes a control method and system for automated handling equipment on a dedicated transport line. The method includes: constructing a microgrid-based local environment model to collect and transmit equipment status information; performing task assignment and path planning for automated equipment based on the microgrid environment model; dynamically optimizing global task assignment and path planning based on task assignment and path planning within each microgrid; and monitoring the execution of the optimized task assignment and path planning in real time, dynamically adjusting the execution order of tasks and path planning through an intelligent scheduling algorithm, and making flexible adaptive adjustments based on resource status and environmental changes. By combining a microgrid network with an ant colony algorithm, the present invention solves the problems of single-point failure risk, poor path planning adaptability, and inflexible task scheduling in traditional automated transport systems, providing a new solution for the efficient, reliable, and flexible operation of automated transport lines.

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, dedicated automated handling equipment (such as automated guided vehicles (AGVs) and handling robots) is widely used in a variety of fields, including warehousing, logistics distribution, and intelligent manufacturing. The core function of these systems is to utilize automated equipment to efficiently perform tasks such as item handling, distribution, and sorting. To improve production efficiency and reduce human intervention, transportation equipment often utilizes centralized control systems for task scheduling and path planning. These control systems typically rely on a central computing unit to coordinate the actions of individual devices, determine device paths and task allocation, and execute tasks using traditional path planning algorithms (such as A* and Dijkstra's algorithm).

[0003] Although current automated transportation systems have achieved certain results in many application scenarios, with the increasing complexity of dedicated transportation lines, existing technologies face many difficult-to-overcome bottlenecks. First, the architecture of centralized control systems typically relies on a central server for route planning and task scheduling, which often carries 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 severely affected, resulting in task delays or idle equipment. This system that relies on a single control node is particularly prominent in high-density transportation environments. When the volume 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 have difficulty efficiently handling the interactions and resource scheduling between a large number of devices, often resulting in inefficiency. Especially in large-scale, complex systems, path selection and task scheduling are often not optimal, resulting in excessive energy consumption of equipment and wasted operating time.

[0005] Furthermore, existing task scheduling mechanisms often employ static, priority-based scheduling algorithms, which typically rely on predefined task priorities and static resource allocation. However, in real-world applications, task priorities and resource availability frequently change, such as due to low device power, device failures, or adjustments to task order. These factors 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 wasted resources and task delays.

[0006] Therefore, existing automated transportation systems have problems such as the risk of single point failure, poor path planning adaptability, and inflexible task scheduling. A distributed, dynamic, and adaptable solution is urgently needed to overcome these shortcomings and improve the efficiency and reliability of the overall system. Summary of the Invention

[0007] The purpose of this invention is to propose a control method and system for automated handling equipment for dedicated transport lines, based on a distributed collaborative control system of a microgrid network and a dynamic path optimization and task scheduling method of 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-mentioned object, a first aspect of the present invention provides a method for controlling automated handling equipment for a dedicated transport line, the method comprising:

[0009] S1. Construct a microgrid-based local environment model 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 automated equipment based on the microgrid 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 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.

[0013] Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted using 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 N represents the number of devices in microgrid i; 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 for adjusting 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 outdated. 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 approach to prevent outdated information from negatively affecting 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, which includes device location information, task allocation status, and device load information. The local environment model is expressed as:

[0020] M local (i) = w4P i +w5L i +w6E i +w7T 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; L i represents the load of device i; E i Indicates the power of device i; T irepresents 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;

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

[0023] Furthermore, the S2 specifically includes:

[0024] Calculate the priority of each task 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 assessment model to evaluate the real-time status of the device and select appropriate tasks based on the device status; the real-time status includes location, load, power, and task requirements;

[0026] Once 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 deal with emergencies. When a problem occurs during the execution of a device, the task allocation and path planning are readjusted based on the device adaptability evaluation model to optimize resource utilization efficiency, while also achieving information sharing and dynamic scheduling 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 location of device i and the task location; 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,∈ represents the weight coefficient 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 ijrepresents the distance from device i to task j; C load (i) represents the current load of device i; E i represents the battery level 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.

[0034] Furthermore, 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 on a global scale; 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 Indicates 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 term;

[0037] The collaborative adjustment model optimizes task redistribution 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 through 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 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;

[0041] At the same time, a dynamic resource scheduling algorithm is designed to ensure task completion 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; 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, which balances the trade-off between risk and resource sharing.

[0044] Furthermore, the S4 specifically includes:

[0045] Collect feedback from devices and adjust task execution plans in real time based on this feedback. When the device's task execution deviation exceeds a preset threshold, it automatically adjusts task allocation and path planning.

[0046] During task execution, 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 overall global operating status; wherein the global feedback adjustment function is expressed as:

[0048]

[0049] Among them, F global Represents the global scheduling feedback adjustment value, indicating the intensity of the global scheduling adjustment; ΔP i Indicates the path adjustment amplitude; ΔT task (i) represents the task adjustment time; C delay (i) represents the delay cost of task execution, reflecting the additional cost of the task due to delay; γ1, γ2, and γ3 represent the weight coefficients of global path adjustment, task adjustment, and delay cost.

[0050] Furthermore, during the task execution process, if an abnormal feedback occurs on the device, the path is adjusted in real time and the task is reallocated according to the task priority. The real-time adjustment of the path is performed 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 according to the task execution progress, device resources, and device load of each microgrid, and adjust the execution order of tasks according to the resource status, 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, which indicates 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 demand of microgrid i; κ1, κ2 represent the weight coefficients of 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 build a microgrid-based local environment model to collect and transmit device status information; wherein the device status information includes location, load, power and task status;

[0058] Micro-grid task allocation and path planning unit, used to allocate tasks and plan paths for automation equipment based on the micro-grid environment model;

[0059] A global task allocation and path planning unit, which is used to dynamically optimize the global task allocation and path planning based on the task allocation and path planning within 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 using 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 N represents the number of devices in microgrid i; 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 for adjusting 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 transport line into multiple autonomous small units (microgrids). Each microgrid performs path planning and task scheduling within a local area 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 system's high robustness and rapid recovery capabilities when facing 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 system's reliability and fault tolerance.

[0066] Secondly, in order to address 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 searching for food by gradually optimizing path selection through pheromone accumulation. 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, enabling the device to 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, to address the problem of inflexible task scheduling, the present invention proposes a dynamic task scheduling mechanism based on device status. The system can obtain information such as the location, power, and load of each device in real time, and dynamically adjust the allocation of tasks based on this information. Tasks are intelligently scheduled based on priority, device status, and operating environment, so that each device can perform the appropriate task at the appropriate 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 path planning adaptability, and inflexible task scheduling in traditional automated transportation systems through the combination of micro-grid networks and ant colony algorithms, providing 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 with reference to the accompanying drawings. However, 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 effort.

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

[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] The following describes embodiments of the present invention in detail. Examples of the embodiments 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 intended only to explain the present invention and are not to be construed as limiting the present invention.

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

[0074] S1. Construct a microgrid-based local environment model 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 automated equipment based on the microgrid 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. Real-time monitoring of the execution of optimized task allocation and path planning. 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 includes:

[0079] The core goal of this step is to establish a microgrid environment model to provide fundamental real-time data support for the entire automated handling system. This allows each device to obtain localized information about its surroundings and make intelligent decisions based on this information. Specifically, this step focuses on two aspects: microgrid division and device allocation, and the real-time collection and synchronization of device status information. This process provides 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") based on factors such as the size of the entire transport line, the number of devices, and the complexity of the tasks. Each microgrid is composed of several automated handling devices (AGVs or robots). The devices have a high degree of autonomy in task scheduling and path planning within the grid, but are still monitored by the global control system. The division of microgrids is based on factors such as the number of devices, task requirements, the topology of the physical space, and the communication capabilities between devices. Within a large area, there can be multiple microgrids, each of which performs tasks 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 using the following formula:

[0083]

[0084] Among them, Grid i A represents the partition weight of microgrid i. i Denotes 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 requirements of microgrid i. w1, w2, and w3 are weight adjustment coefficients, which are dynamically adjusted based on the priorities of different tasks. This formula weights the optimization of each microgrid partition to ensure that the microgrid partitioning meets multiple factors such as the number of devices, task requirements, and communication bandwidth.

[0085] Furthermore, each device is equipped with multiple sensors (such as lidar, vision sensors, load sensors, temperature sensors, etc.), which continuously obtain device status information such as location, speed, load, power, etc. The device status information is updated in real time and transmitted to the local control system or management node within 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 device, reflecting the working intensity of the device.

[0089] Electricity (E i ) indicates the battery level 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, to ensure that each device's status information is not outdated, a periodic update mechanism is employed. Each device updates its status information at a set interval (Δt) and synchronizes with surrounding devices or a central control system via wireless communication. Device status updates are performed using a smooth weighted approach to prevent outdated information from negatively impacting subsequent decisions. The update mechanism is implemented using 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 state information collected by device i via its sensors. α is a smoothing factor that controls the balance between new and old information (0 < α < 1). This formula ensures that device state information does not fluctuate significantly due to a single update in a dynamic environment, while maintaining real-time performance.

[0095] Furthermore, the devices within each microgrid build a local environment model based on the collected status information. This model includes the real-time status of the devices within the microgrid (location, load, power, task status, etc.), 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, movement direction, speed, etc. of each device.

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

[0099] Device load information: The workload and current power of each device are used to determine whether the device is suitable for continuing to perform tasks.

[0100] The local environment model is derived from the state information of each device and can be integrated through weighted average method, so that each microgrid can evaluate the overall state of the device locally. The construction formula of the local environment model is as follows:

[0101] M local (i) = w4Pi +w5L i +w6E i +w7T 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. L i Represents the load of device i. E i Indicates the power level of device i. i Represents the task status of device i. w1, w2, w3, and w4 represent weight coefficients, which adjust the relative importance of different information items. This local environment model provides data support for subsequent task allocation and path planning, helping the system determine whether the device can continue to perform its task or whether it needs to be recharged, scheduled, or route adjusted.

[0103] Furthermore, devices within the microgrid share status information in real time via 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 determine whether device B should continue to perform its task, or whether device B needs to be charged or dispatched.

[0104] To ensure information synchronization, devices use a timer mechanism to update information at regular intervals (Δt). This mechanism ensures that the information used by devices for task scheduling or route planning is up to date.

[0105] After the equipment status information is updated, it will be synchronized with the global control system. This ensures 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 local This model will provide accurate data support for subsequent task allocation, path planning, and collaborative control. Throughout the solution, device status information is continuously updated and synchronized. Through mechanisms such as weighted averaging, accurate reflection and dynamic adaptation of device status are achieved, providing real-time and accurate basis for subsequent global control and task execution.

[0107] Specifically, the S2 includes:

[0108] Among them, in this step, the core goal is to build a local environment model (M local) to efficiently assign tasks and plan routes for automated equipment. Because each device's status (such as location, load, and power) changes dynamically, task assignment and route planning must not only consider each device's real-time status but also maximize the utilization of device resources, avoiding idle or overloaded operation while ensuring the coordinated 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) = w8·R task (t)+w9·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 (t) represents the resource requirements of task t (such as equipment load, required power, etc.). task (t) represents the time limit or execution deadline of task t. 10 represents the weight coefficient, which is adjusted based on the nature of the task (for example, urgent tasks might be given a higher weight for w1). This formula calculates the priority of each task based on its urgency, required resources, and time constraints. The weight coefficient is then adjusted based on the task's priority requirements, enabling dynamic prioritization of 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 ) in the equipment status information, and build an equipment adaptability evaluation model (M adapt ) to determine whether each device is suitable for a task. This model considers the device's load, location, power level, 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, Madapt (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. E i Indicates the power level of device i. task (t) represents the execution duration or time requirement of task t. δ, ε, and ∈ represent weight coefficients adjusted based on device status. The device adaptability assessment model comprehensively considers factors such as the device's location, load, and power level to calculate a suitability score for each device for each task. Devices with higher scores are given priority for executing that task.

[0116] Once the matching relationship between devices and tasks is determined, the next step is to calculate the optimal path for the devices. The goal of path planning is to select the shortest path that best suits the current device state, 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. This algorithm considers device status (such as load and power) as well as environmental factors (such as obstacles and path blockages) to provide devices with real-time optimized paths.

[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 C represents the distance from device i to task j. load (i) represents the current load of device i. E i represents the battery level of device i. Obstacles(i) represents the number of obstacles encountered by device i along its path. θ1 and θ2 represent the weight coefficients used in path planning, which are dynamically adjusted based on the urgency of the task and the device's status. Path planning calculates the optimal path for each device through dynamic optimization. The optimization goal is not only to achieve the shortest path, but also to consider factors such as the device's load, battery level, and obstacles encountered, thereby providing a more intelligent path planning solution.

[0120] Furthermore, after task assignment and route planning are complete, all devices will begin executing their tasks under the system's instructions. During this process, the devices' task execution and route planning must be dynamically adjusted to address unexpected events (such as device failures and route obstructions). The system ensures smooth mission execution by monitoring and adjusting device status 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 achieved 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, device adaptability assessment, 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, thereby achieving the optimal configuration of resources. In addition, the calculation of the dynamic optimization path avoids device conflicts and improves the efficiency and robustness of the overall system. The goal of this entire step is to ensure that the device can intelligently select tasks and plan paths based on its current status, so as to achieve efficient task execution and collaborative work in a dynamic environment.

[0124] Specifically, the S3 includes:

[0125] Among them, in the above steps, the present invention completes the task allocation and local path planning (step 2), and the tasks within 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 and even the inability to complete the target task. To overcome these problems, this step proposes a solution for inter-microgrid collaboration and global path optimization, aiming to improve the operating efficiency of the entire 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 that dynamically adjusts tasks and balances loads globally by monitoring the task progress, remaining power, and load status of each microgrid in real time. The coordination adjustment model is shown below:

[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. Ej (i) represents the current power 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 Indicates the total execution time of all tasks. Indicates the weight coefficients of task load, distance, time, etc. overload (i) represents the load overload value of microgrid i, reflecting whether microgrid i is currently overloaded. If so, the task is adjusted. τ represents the adjustment coefficient for the load overload term, further controlling the flexibility of load adjustment. This model optimizes task redistribution by comprehensively evaluating each microgrid's load, power, distance, and task time, and dynamically calculating the load overload term. Specifically, when the load of a microgrid exceeds a predetermined threshold, the system automatically dispatches other microgrids to share the task, ensuring global load balancing.

[0130] Furthermore, as tasks are redistributed, path planning between devices may overlap and conflict. Therefore, the present invention requires global path planning to adjust the paths of devices, avoid path intersections, reduce conflicts, and minimize the total length of paths.

[0131] Traditional path optimization algorithms often ignore the interactions between devices, which can lead to local optimal solutions for the path. This paper proposes a global collaborative path optimization algorithm that considers the interactions between devices and path conflicts, and replans 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 C 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 non-smooth paths, promotes path smoothness, and avoids abrupt corners. σ1 and σ2 represent the weight coefficients of path conflict cost and smoothness.

[0135] Among them, the path conflict item 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 operating in the same area.

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

[0137] Furthermore, between microgrids, different devices may be unable to complete tasks due to insufficient resources (such as power and battery). Therefore, a dynamic resource scheduling algorithm must be designed to enable microgrids with more abundant resources to globally support microgrids with less resources, ensuring task completion while avoiding global resource waste.

[0138] The resource sharing scheme of the present invention is based on a comprehensive evaluation of dynamic priorities and microgrid loads for scheduling. 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 E represents the load requirement of microgrid i. 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, representing the resource risk (e.g., impending power depletion) in microgrid i. It is used to avoid excessive resource sharing, thereby reducing overall system risk. σ4 is the adjustment coefficient for the risk cost term, balancing the trade-off between risk and resource sharing. This optimization model comprehensively considers the load, power consumption, and resource risk of each microgrid. By dynamically adjusting the resource sharing strategy, it prevents individual microgrids from over-relying on the resources of other microgrids, ensuring optimal resource utilization for the global system.

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

[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 includes:

[0144] Among them, in the aforementioned step (step 3), the present invention ensures the balance of task allocation and path planning between each microgrid through 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, 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. This system monitors each microgrid's task execution status, path execution progress, and device status (such as battery level, faults, and environmental changes) in real time. The system continuously collects feedback and adjusts task execution plans in real time based on this feedback. If a device's task execution deviation exceeds a preset threshold, the system automatically adjusts 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 for microgrid i to adjust 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. ΔToverrun (i) represents the time deviation caused by task timeout or execution delay. γ is the feedback mechanism adjustment coefficient, which is used to control the sensitivity of feedback adjustment. This formula describes how to dynamically adjust task execution time based on real-time feedback and task execution timeout to ensure smooth task execution and avoid time deviation.

[0149] Furthermore, during task execution, the system dynamically schedules tasks based on real-time status feedback (e.g., device power level, battery life, task progress, etc.). If a microgrid runs low on power or encounters an obstacle, the task order and path planning for that microgrid must be readjusted. By introducing an adaptive path adjustment model, the model can adjust paths in real time based on current device status and task priority, ensuring efficient task execution and preventing devices from failing to complete tasks 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 β 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. 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 of adjusting the path. This model dynamically optimizes the path by integrating the path adjustment amplitude, task execution priority, and adaptive adjustment weights. The core of path adjustment is to ensure that the device can adjust the path according to real-time conditions to avoid task interruptions or delays caused by obstacles, equipment failures, and other factors.

[0153] Furthermore, in addition to path adjustments, task reallocation and resource optimization become key when the device's task execution status changes. At this point, task reallocation and resource optimization algorithms dynamically adjust based on each microgrid's task execution progress, device resources (such as power and battery capacity), and device load.

[0154] The algorithm proposed in this 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 and preventing 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 redistribution value of microgrid i. load (j) represents the load value of task j, which indicates the computation or execution requirements of the task. j (i) represents the remaining power available in microgrid i. L j (i) represents the task load demand of microgrid i. κ1 and κ2 represent the weight coefficients of the load and task-to-power ratio. This task reallocation model optimizes task allocation by comprehensively considering device load, task-to-power ratio, and task priority. The goal is to enable microgrids with insufficient resources to reallocate tasks to microgrids with sufficient resources while avoiding unnecessary task overlap and conflict.

[0158] Furthermore, due to 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 that can make global scheduling adjustments based on the overall operating status of the system (such as task completion rate, device status, path completion status, etc.) to ensure optimal completion of tasks globally. The global feedback adjustment model is as follows:

[0159]

[0160] Among them, F global Indicates the global scheduling feedback adjustment value, indicating the intensity of the global scheduling adjustment. ΔP i Indicates the path adjustment amplitude. ΔT task (i) represents the task adjustment time. C delay (i) represents the delay cost of task execution, reflecting the additional cost incurred by task delays. γ1, γ2, and γ3 represent the weighting coefficients for global path adjustment, task adjustment, and delay cost. This global scheduling feedback system evaluates the task status, path status, and delay cost of each microgrid in real time, enabling global adjustments. This global optimization ensures that the entire system maintains a high task completion rate and path efficiency even in the face of sudden changes.

[0161] By combining dynamic task scheduling with adaptive path adjustment, this invention achieves system flexibility and efficiency in dynamic environments. The system not only adjusts task execution order based on real-time feedback, but also dynamically adjusts paths based on the real-time status of devices and automatically reallocates tasks when resources are limited. By comprehensively considering multiple factors, including device status, task priority, and path adjustment, this solution ensures efficient task execution under uncertain conditions.

[0162] like Figure 2 As 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 microgrid-based local environment model 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 based on the micro-grid environment model;

[0165] A global task allocation and path planning unit 103 is configured to dynamically optimize the global task allocation and path planning based on the task allocation and path planning within each microgrid;

[0166] The global task allocation and path planning dynamic optimization unit 104 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;

[0167] Each microgrid is composed of several automated handling devices, and the microgrid division is weighted and adjusted using 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 N represents the number of devices in microgrid i; 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 for adjusting weights, which are dynamically adjusted according to the priorities of different tasks.

[0170] Those skilled in the art will 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 this 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 merely schematic. For example, the division of the units is merely a division of logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, 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 devices or units 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, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0173] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the 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 microgrid-based local environment model 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 automated equipment based on the microgrid 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 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 using the following formula: Among them, Grid i represents the partition weight of microgrid i; A i represents the area of ​​microgrid i; D i N represents the number of devices in microgrid i; 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 weight adjustment, which are dynamically adjusted according to the priority of different tasks; Said S2 specifically includes: Calculate the priority of each task 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 assessment model to evaluate the real-time status of the device and select appropriate tasks based on the device status; the real-time status includes location, load, power, and task requirements; Once 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 problem occurs during the execution of a device, the task allocation and path planning are readjusted based on the device adaptability evaluation model to optimize resource utilization efficiency. At the same time, information sharing and dynamic scheduling are carried out between local microgrids. 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 location of device i and the task location; 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,∈ represents the weight coefficient 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 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 battery level 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.

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 outdated. 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 approach to prevent outdated information from negatively affecting 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: Each device in the microgrid builds 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, which includes device location information, task allocation status, and device 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: 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 on a global scale; 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 Indicates 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 term; The collaborative adjustment model optimizes task redistribution by comprehensively evaluating the load, power, distance and task time of each microgrid and dynamically calculating the load overload term.

5. The control method of the transportation line automated handling equipment according to claim 4, 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 and σ2 represent the weight coefficients of path conflict cost and smoothness; At the same time, a dynamic resource scheduling algorithm is designed to ensure task completion 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, which balances the trade-off between risk and resource sharing.

6. The control method of the transportation line automated handling equipment according to claim 5, characterized in that: Said S4 specifically includes: Collect feedback from devices and adjust task execution plans in real time based on this feedback. When the device's task execution deviation exceeds a preset threshold, it automatically adjusts task allocation and path planning. During task execution, 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 overall global operating 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 the global scheduling adjustment; ΔP i Indicates the path adjustment amplitude; ΔT task (i) represents the task adjustment time; C delay (i) represents the delay cost of task execution, reflecting the additional cost of the task due to delay; γ1, γ2, γ3 represent the weight coefficients of global path adjustment, task adjustment and delay cost.

7. The control method of the automated handling equipment for a dedicated transport line according to claim 6, characterized in that: During the task execution process, if the device has abnormal feedback, the path is adjusted and the task is reallocated in real time according to the task priority. The real-time adjustment of the path is achieved 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 according to the task execution progress, device resources, and device load of each microgrid, and adjust the execution order of tasks according to the resource status, 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, which indicates 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.

8. A system for executing the control method of a dedicated transport line automated handling equipment according to claim 1, characterized in that: The system comprises: A microgrid unit is used to build a microgrid-based local environment model to collect and transmit device status information; wherein the device status information includes location, load, power and task status; Micro-grid task allocation and path planning unit, used to allocate tasks and plan paths for automation equipment based on the micro-grid environment model; A global task allocation and path planning unit, which is used to dynamically optimize the global task allocation and path planning based on the task allocation and path planning within 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 using the following formula: Among them, Grid i represents the partition weight of microgrid i; A i represents the area of ​​microgrid i; D i N represents the number of devices in microgrid i; 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 for adjusting weights, which are dynamically adjusted according to the priorities of different tasks.

Images