Warehouse task dynamic allocation method and device, equipment and storage medium

Abstract

This invention discloses a method, apparatus, device, and storage medium for dynamic allocation of warehousing tasks. The method includes: receiving a warehousing task request; determining a target task to be processed based on the warehousing task request; determining multiple candidate execution devices currently available for executing the target task based on preset resource status data; determining a task adaptation value corresponding to each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices; selecting a candidate execution device that meets preset conditions as the target execution device based on the task adaptation value, and issuing the target task to the target execution device. Because this invention determines the task adaptation value corresponding to each candidate execution device based on task attributes and device attributes, and then selects the target execution device based on the task adaptation value, compared with the prior art, this invention improves the scientific nature of warehousing task allocation, thereby improving the overall warehousing operation efficiency and system resource utilization.

Description

Technical Field

[0001] This invention relates to the field of logistics and warehousing automation technology, and in particular to a method, apparatus, equipment and storage medium for dynamic allocation of warehousing tasks. Background Technology

[0002] With the increasing demands for automation and intelligence in discrete manufacturing sectors such as aerospace standard parts, modern warehousing systems have widely integrated various automated equipment, such as container robots (CTUs), roller conveyors, automated picker walls, and counting packaging machines, along with warehouse management systems (WMS) and warehouse control systems (WCS), to achieve automated storage, retrieval, sorting, and distribution of materials. In actual operation, warehousing systems need to respond in real time to various warehousing task requests from Enterprise Resource Planning (ERP) or Manufacturing Execution System (MES), including related plans, picking tasks, and replenishment tasks, and to schedule different equipment to work collaboratively.

[0003] Currently, common warehouse task allocation methods mostly employ scheduling strategies based on fixed priorities or simple rules (such as first-come, first-served, or nearest-neighbor allocation). For example, the system allocates tasks to idle equipment in the order they arrive, or binds tasks to specific equipment based on preset fixed aisle areas. However, these methods ignore the urgency of tasks, differences in material attributes (such as size, weight, and storage location), and dynamic factors such as the real-time load status, current location, and task queue length of each executing device. In actual warehouse operations, different tasks have different requirements for equipment capacity, and the availability of equipment changes in real time as the operation progresses. If task allocation is unreasonable, it can easily lead to some equipment being overloaded and congested while others are idle, or assigning tasks to equipment that is too far away or mismatched, resulting in detours in material handling paths and extended task waiting times, thus seriously affecting the overall efficiency of warehouse operations and the comprehensive utilization rate of equipment resources in the system.

[0004] Therefore, there is an urgent need for a dynamic allocation method for warehousing tasks that can improve the scientific nature of warehousing task allocation, thereby improving the overall efficiency of warehousing operations and the utilization rate of system resources. Summary of the Invention

[0005] The main objective of this invention is to provide a method, apparatus, equipment, and storage medium for dynamic allocation of warehousing tasks, aiming to solve the technical problem in the prior art where unreasonable allocation of warehousing tasks affects the overall efficiency of warehousing operations and the utilization rate of system resources.

[0006] To achieve the above objectives, the present invention provides a method for dynamic allocation of warehousing tasks, the method comprising the following steps: Receive a warehousing task request and determine the target operation task to be processed based on the warehousing task request; Based on preset resource status data, multiple candidate execution devices that can be used to execute the target job task are identified. Based on the task attributes of the target task and the device attributes of the candidate execution devices, determine the task adaptation value corresponding to each candidate execution device; Based on the task adaptation value, candidate execution devices that meet preset conditions are selected as target execution devices, and the target job task is issued to the target execution devices.

[0007] Optionally, the step of determining the task adaptation value corresponding to each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices includes: Obtain the task urgency level, material attribute information, and target location information of the target task; Obtain the current load status, location coordinates, and task queue length of each candidate execution device; Based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length, the task adaptation value corresponding to each candidate execution device is determined.

[0008] Optionally, the step of determining the task adaptation value corresponding to each of the candidate execution devices based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length includes: Based on the current load status and the task queue length, determine the current busyness parameter of each of the candidate execution devices; Based on the positioning coordinates and the target location information, the movement cost parameters between each candidate execution device and the task execution location are determined; Based on the task urgency level and the material attribute information, determine the execution constraint parameters of the target operation task; Based on the current busyness parameter, the movement cost parameter, and the execution constraint parameter, the task adaptation value corresponding to each of the candidate execution devices is determined.

[0009] Optionally, the step of receiving a warehousing task request and determining the target operation task to be processed based on the warehousing task request includes: Receive warehousing task requests and divide the warehousing task requests into multiple wave task groups according to wave aggregation rules; Based on the priority parameters corresponding to each wave task group and the inventory matching status of the materials associated with each wave task group, determine the wave task groups to be processed from the plurality of wave task groups. At least one warehousing task request in the pending wave task group is identified as the target operation task to be processed, and the task attributes of the target operation task are updated according to the task scheduling results of the pending wave task group.

[0010] Optionally, the step of selecting candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices and issuing the target job task to the target execution devices includes: The task adaptation values ​​corresponding to each candidate execution device are compared, and the candidate execution device with the highest task adaptation value is selected as the target execution device based on the comparison results. Based on the task attributes of the target task and the current position of the target execution device, generate control instructions containing path guidance information or action sequences; The control commands are sent to the target execution device via a communication module deployed at a fixed location.

[0011] Optionally, after the step of selecting candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices and issuing the target job task to the target execution devices, the method further includes: Receive task execution status information fed back by the target execution device; When the task execution status information indicates that the material container has arrived at the sorting station, the identification information of the material container is read. Based on the read identification information, a picking guidance signal is generated to indicate the location of the target compartment in the material container to the operator.

[0012] Optionally, the step of determining multiple candidate execution devices currently available for executing the target job task based on preset resource status data includes: Obtain real-time status information for each storage device, including device operating status, device fault flag, device location coordinates, and device current task queue length. Storage equipment whose working status is idle and whose fault flag is fault-free is identified as usable equipment. Based on the starting position of the target task, available devices whose distance from the device's location coordinates to the starting position is less than a preset distance threshold are selected as multiple candidate execution devices that can be used to execute the target task.

[0013] Furthermore, to achieve the above objectives, the present invention also proposes a dynamic allocation device for warehousing tasks, the device comprising: The task determination module is used to receive warehousing task requests and determine the target operation tasks to be processed based on the warehousing task requests. The device candidate module is used to determine multiple candidate execution devices that can be used to execute the target job task based on preset resource status data; The task adaptation module is used to determine the task adaptation value corresponding to each of the candidate execution devices based on the task attributes of the target job task and the device attributes of the candidate execution devices. The task allocation module is used to select candidate execution devices that meet preset conditions as target execution devices based on the task adaptation value, and to issue the target job task to the target execution device.

[0014] Furthermore, to achieve the above objectives, the present invention also proposes a dynamic allocation device for warehouse tasks, the device comprising: a memory, a processor, and a dynamic allocation program for warehouse tasks stored in the memory and executable on the processor, the dynamic allocation program for warehouse tasks being configured to implement the steps of the dynamic allocation method for warehouse tasks as described above.

[0015] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a dynamic allocation program for warehouse tasks, wherein when the dynamic allocation program for warehouse tasks is executed by a processor, it implements the steps of the dynamic allocation method for warehouse tasks as described above.

[0016] This invention discloses a method for receiving warehousing task requests, determining target tasks to be processed based on the requests, identifying multiple candidate execution devices that can currently be used to execute the target tasks based on preset resource status data, determining a task adaptation value for each candidate execution device based on the task attributes of the target tasks and the device attributes of the candidate execution devices, selecting candidate execution devices that meet preset conditions as target execution devices based on the task adaptation values, and issuing the target tasks to the target execution devices. Because this invention determines the task adaptation values ​​for each candidate execution device based on the task attributes of the target tasks and the device attributes of the candidate execution devices, then selects target execution devices based on the task adaptation values, and then issues target tasks to the target execution devices, compared to existing technologies, this invention improves the scientific nature of warehousing task allocation, thereby increasing overall warehousing operation efficiency and system resource utilization. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the dynamic allocation method for warehousing tasks according to the present invention. Figure 2 This is a flowchart illustrating the second embodiment of the dynamic allocation method for warehousing tasks of the present invention; Figure 3This is a flowchart illustrating the third embodiment of the dynamic allocation method for warehousing tasks of the present invention; Figure 4 This is a structural block diagram of the first embodiment of the dynamic allocation device for warehouse tasks of the present invention; Figure 5 This is a schematic diagram of the structure of the warehouse task dynamic allocation device in the hardware operating environment involved in the embodiments of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0020] This invention provides a method for dynamically allocating warehousing tasks, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the dynamic allocation method for warehousing tasks according to the present invention.

[0021] In this embodiment, the dynamic allocation method for warehousing tasks includes steps S10 to S40: Step S10: Receive a warehousing task request and determine the target operation task to be processed based on the warehousing task request.

[0022] It should be noted that the executing entity in this embodiment can be a computer server device with data processing, network communication, and program execution functions applied in intelligent warehouse scheduling and control scenarios, such as a server, tablet computer, or personal computer, or an electronic device capable of realizing the above functions (such as a warehouse task dynamic allocation device). The following uses a system including a warehouse task dynamic allocation device (hereinafter referred to as the system) as an example to illustrate this embodiment and the following embodiments.

[0023] It is understandable that the aforementioned warehousing task requests can refer to work instructions issued by upstream systems (such as enterprise resource planning systems or manufacturing execution systems) to the system that require the system to execute, such as inbound instructions, outbound instructions, replenishment instructions, or supporting instructions.

[0024] It should be explained that the aforementioned target task can refer to the smallest operational unit that the system, based on the received warehousing task request, needs to be executed at the warehousing equipment level. For example, it could be moving a bin from a specific storage location to a designated inbound / outbound workbench, or transporting a batch of materials from a sorting station to a sorting wall. For instance, a warehousing task request might contain multiple material outbound requests, which the system can break down into multiple independent bin handling tasks, each of which constitutes a target task.

[0025] In its implementation, the system receives warehousing task requests from the upper-level system or a manual input interface. The system parses the received warehousing task requests, extracting key information such as task type, material identifier, quantity requirements, and target address. Then, based on the parsed information, the system transforms the warehousing task request into one or more target operation tasks that can be directly processed by warehousing execution equipment. The system generates a unique task identifier for each target operation task and stores it in a task queue awaiting allocation and execution.

[0026] To facilitate understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment. For instance, suppose the upper-level manufacturing execution system issues a warehousing task request to the system, requesting the outbound delivery of 10 aerospace standard parts with model number "AB123" to the assembly workshop. After receiving the warehousing task request, the system parses the task type as "outbound," the material code as "AB123," and the quantity as 10. After checking the inventory, the system determines that these materials are distributed and stored in three different bins, each containing 4, 4, and 2 parts respectively. Therefore, the system breaks down the warehousing task request into three independent target tasks: Task 1 is to move a box containing four AB123 containers from storage location A-01-02 to outbound workbench 1; Task 2 is to move a box containing four AB123 containers from storage location B-03-05 to outbound workbench 1; and Task 3 is to move a box containing two AB123 containers from storage location C-02-11 to outbound workbench 1. The system then stores these three target tasks sequentially in an allocation queue.

[0027] Step S20: Based on preset resource status data, determine multiple candidate execution devices that can be used to execute the target job task.

[0028] Understandably, the aforementioned preset resource status data can refer to a set of information pre-stored in the system to describe the real-time operating status of warehousing equipment, including the equipment's working status (e.g., idle, busy, offline), fault flags (e.g., faulty, fault-free), location coordinates, and current task queue length. This resource status data is typically updated through real-time communication between the system and the equipment; for example, the equipment reports its status to the system at regular intervals, or the system actively queries the equipment status.

[0029] It should be understood that the aforementioned candidate execution equipment may refer to equipment selected from all warehousing equipment that has the potential to perform the target operation task, such as bin robots (CTU), stacker cranes, conveyor drive units, etc.

[0030] In its implementation, after identifying the target task to be processed, the system first acquires preset resource status data to select equipment capable of executing it. The system reads real-time status information of each storage device from this data, including whether the device is idle, whether there are any fault alarms, the current location coordinates, and the number of unfinished tasks. Next, based on the target task's basic requirements for equipment (e.g., the device type must match the task type, and a bin handling task requires a bin robot), the system filters out storage devices with matching device types from all available equipment. Subsequently, the system identifies devices that are idle, have no fault indicators, and possess sufficient power or energy as available devices. These available devices are then designated as candidate execution devices for executing the target task, and the system records the device identifier and current location information of each candidate device for subsequent task allocation calculations.

[0031] To facilitate understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment. For instance, suppose a smart warehousing system is configured with 5 box-carrying robots (CTUs). The system obtains the following status information for the 5 devices through real-time communication: Device A is idle, fault-free, located at coordinates (10,20), and its current task queue length is 0; Device B is busy, fault-free, located at coordinates (15,25), and its current task queue length is 2; Device C is idle, faulty (LiDAR malfunction), located at coordinates (5,30), and its current task queue length is 0; Device D is idle, fault-free, located at coordinates (30,10), and its current task queue length is 0; Device E is idle, fault-free, located at coordinates (12,22), and its current task queue length is 1. The system currently needs to execute a target operation task of retrieving a box from the receiving workbench and placing it on the shelf. The system filters all 5 devices as matching the task type "box handling". Next, the system excludes device B, which is busy, and device C, which is faulty, and identifies devices A, D, and E as candidate execution devices.

[0032] To improve the accuracy and efficiency of warehouse task allocation, the step of determining multiple candidate execution devices that can be used to execute the target task based on preset resource status data may include: obtaining real-time status information of each warehouse device, including device working status, device fault flag, device location coordinates, and current task queue length; identifying warehouse devices whose working status is idle and whose fault flag is fault-free as available devices; and selecting available devices whose distance between the device location coordinates and the starting position is less than a preset distance threshold as multiple candidate execution devices that can be used to execute the target task.

[0033] It should be explained that the aforementioned real-time status information can refer to dynamic data continuously reported by the warehousing equipment during operation or obtained through periodic system queries, reflecting the equipment's current true condition. The aforementioned equipment operating status can refer to whether the warehousing equipment is currently in a state where it can perform tasks, typically including idle, busy, offline, and dormant states. The aforementioned equipment fault flag can refer to binary information used to identify whether the equipment has hardware or software abnormalities; for example, "0" indicates no fault, and "1" indicates a fault. The aforementioned equipment location coordinates can refer to the warehousing equipment's location coordinates on a two-dimensional or three-dimensional map of the warehouse, such as precise coordinate values ​​obtained based on QR code navigation or laser positioning. The aforementioned current task queue length can refer to the number of tasks currently assigned to the equipment but not yet completed.

[0034] It is understood that the aforementioned available equipment may refer to equipment selected from all warehousing equipment that meets the basic admission criteria (idle and fault-free). The aforementioned starting position may refer to the position that the equipment needs to reach first when performing the target operation task. For example, if the target operation task requires retrieving a material box from a certain storage location, then the location of that storage location is the starting position. The aforementioned preset distance threshold may refer to a pre-set distance value used to determine whether the distance between the equipment and the starting position is within an acceptable range, such as 10 meters or 20 meters. Equipment exceeding this threshold will not be included in the candidate range due to excessively high movement costs.

[0035] Step S30: Determine the task adaptation value corresponding to each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices.

[0036] It should be explained that the above-mentioned task attributes can refer to a set of information describing the characteristics of the target task itself, such as task type (inbound, outbound, replenishment, inventory), task urgency level, attributes of the materials to be handled (weight, volume, whether fragile), and target location coordinates, etc.

[0037] The aforementioned equipment attributes can refer to a set of information describing the characteristics of the candidate execution equipment itself, such as equipment type (bin robot, stacker crane, conveyor line), current load status (currently carried material weight or occupancy rate), positioning coordinates, task queue length, equipment speed, and battery life.

[0038] It should be noted that the above task suitability value can be a numerical indicator used to quantify the degree of matching between a candidate execution device and the target task. The higher the task suitability value, the more suitable the device is for executing the target task. For example, it can be calculated based on factors such as the distance of the device from the task start point, the current busy level of the device, and whether the device has the ability to process the material.

[0039] In its implementation, after identifying multiple candidate execution devices, the system needs to evaluate each candidate device to select the most suitable one for performing the target task. The system first obtains the task attributes of the target task, including the task type, urgency, characteristics of the materials to be processed, and the target or starting location. Next, the system obtains the device attributes of each candidate device, including its device type, current location coordinates, length of the currently assigned task queue, current load, and device operating status parameters. Based on preset matching rules or calculation models, the system compares and weights the parameters in the task attributes with the corresponding parameters in the device attributes of each candidate device. The system calculates a value for each candidate device, namely the task suitability value. The task suitability value reflects the overall suitability of the candidate device for performing the target task; for example, the closer the device is, the fewer the current tasks, and the better the device's capabilities match, the higher the task suitability value. The system associates and stores each candidate device and its corresponding task suitability value for use in subsequent selection steps.

[0040] Step S40: Select candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices, and issue the target job task to the target execution devices.

[0041] It should be understood that the aforementioned preset conditions may refer to pre-set judgment rules for selecting the final execution device from multiple candidate execution devices, such as selecting the candidate execution device with the highest task adaptation value, or selecting the candidate execution device whose task adaptation value exceeds a certain set threshold.

[0042] Furthermore, the aforementioned target execution device can refer to warehousing equipment selected by the system to actually undertake and execute the target task, such as a specific bin robot or stacker crane. The aforementioned "issuance" can refer to the process by which the system transmits the relevant instruction data of the target task to the control unit of the target execution device via a wired or wireless communication network.

[0043] In its implementation, after calculating the task adaptation value for each candidate execution device, the system obtains the task adaptation value for each candidate execution device and its associated value. The system then filters each candidate execution device based on pre-stored preset conditions. These preset conditions include, but are not limited to, selecting the candidate execution device with the highest task adaptation value, or selecting the candidate execution device with a task adaptation value greater than a preset threshold. When multiple candidate execution devices simultaneously meet the same preset conditions (e.g., two devices have the same and highest task adaptation values), the system can further employ auxiliary rules for selection, such as randomly selecting or selecting the device with the smaller device number. After filtering, the system determines a unique target execution device. Next, the system obtains the complete instruction data for the target task, including task type, target location, material identifier, path information, etc. The system encapsulates the target task into an instruction format recognizable by the target execution device via an internal warehouse communication network (e.g., infrared communication, wireless LAN) and issues the target task to the target execution device. The system simultaneously records the timestamp of task issuance and the task status, updating the status of the target task to "issued" or "in execution."

[0044] To improve the coordination efficiency of task scheduling and device response, the step of selecting candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices and issuing the target task to the target execution devices may include: comparing the task adaptation values ​​corresponding to each candidate execution device, selecting the candidate execution device with the highest task adaptation value as the target execution device based on the comparison result; generating control instructions containing path guidance information or action sequences based on the task attributes of the target task and the current location of the target execution device; and sending the control instructions to the target execution device through a communication module deployed at a fixed location.

[0045] It should be explained that the aforementioned path guidance information can refer to data describing the path that the target execution device needs to take from its current location to the task execution location, such as a sequence of coordinates of a series of waypoints, driving direction instructions (forward, backward, left turn, right turn), or a set of navigation segment identifiers. The aforementioned action sequence can refer to a set of instructions describing multiple specific actions that the target execution device needs to complete sequentially during the execution of the task, such as "extend the fork arm", "lift the forks", "retract the fork arm", and "move to the target location".

[0046] It should be understood that the aforementioned communication modules deployed at fixed locations may refer to communication devices pre-installed in specific locations within the warehouse (e.g., at the entrance of the alley, in front of the charging pile, or in front of the roller line connection point) to establish short-distance, low-latency data exchange between the system and the execution equipment.

[0047] In its implementation, after identifying the target execution device, the system needs to issue the target task to that device. First, the system obtains the task adaptation value for each candidate execution device, compares these values, and selects the candidate device with the highest adaptation value as the target execution device. Next, the system obtains the task attributes of the target task, including target location information, material type, and operation type (picking or placing). Simultaneously, the system obtains the current coordinates of the target execution device. Based on the spatial relationship between the starting or target location of the target task and the current location of the target execution device, the system calls pre-stored warehouse map data to plan the optimal driving path. The system converts the planned path into path guidance information that the target execution device can parse, or into a sequence of consecutively executed actions, such as "move 3 meters along the X direction from the current location, turn right, move 5 meters along the Y direction, and stop." The system encapsulates the path guidance information or action sequence, along with task identifiers, target locations, and other information, into a control command. Subsequently, the system sends the control commands to the target execution device via communication modules deployed at fixed locations. These communication modules are pre-installed at fixed locations within the warehouse (e.g., at alleyway entrances, connection points, etc.), and each module has a unique address identifier. The system selects the nearest or designated communication module based on the current location of the target execution device to issue commands, ensuring the reliability and real-time nature of command transmission.

[0048] To guide operators to accurately locate material picking positions, effectively reduce manual search time, significantly improve sorting efficiency, and reduce picking error rates, after the steps of selecting candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices and issuing the target task to the target execution devices, the method further includes: receiving task execution status information fed back by the target execution device; when the task execution status information indicates that the material container has arrived at the sorting station, triggering the reading of the identification information of the material container; and generating a picking guidance signal to instruct operators to pick at the target compartment position within the material container based on the read identification information.

[0049] It is understood that the aforementioned task execution status information can refer to the status data reported to the system by the target execution device during task execution, such as the device's current location, task completion progress, whether it has reached the designated location, and whether any abnormalities have occurred. The aforementioned material container can refer to a box or carrier used to hold materials, with each container having a barcode or RFID tag for unique identification affixed to its surface. The aforementioned sorting station can refer to the work location in a warehouse used for manual or automated material picking.

[0050] It should be understood that the aforementioned identification information can refer to coded data used to uniquely identify the material container, such as a barcode string or an ID number stored in an RFID tag. The aforementioned picking guidance signal can refer to an indication signal used to guide operators to accurately retrieve the target material from the material container. For example, the system pointing a laser light to the compartment where the corresponding material is stored is one implementation of the picking guidance signal. The aforementioned target compartment location can refer to the specific location information of the compartment containing the target material among multiple compartments within the material container. The material container can be divided into compartments in various ways, such as 1, 2, 4, 8, etc.

[0051] In its implementation, after issuing a target task to the target execution device, the system continuously monitors and receives task execution status information from the target execution device. This status information is periodically or event-triggered by the target execution device via a communication module or wireless network deployed at fixed locations. The system parses the received task execution status information, extracting the device identifier, task identifier, and current execution stage identifier. When the system recognizes an indication from the task execution status information that a material container has arrived at the sorting station (e.g., the status information contains a true "arrived at sorting station" flag), the system triggers an operation to read the identification information of the material container. The system uses a fixed barcode scanner or RFID reader deployed at the sorting station to scan the barcode or RFID tag attached to the material container to obtain its unique identification information. Then, based on the read identification information, the system queries its internal database for details of the task associated with the material container, including the types and quantities of materials stored in each compartment of the container, and the target material information to be picked. The system determines the specific compartment location corresponding to the target material, i.e., the target compartment location. Subsequently, the system generates a picking guidance signal and sends the picking guidance signal to the light guidance device or display screen at the sorting station to instruct the operator to find the target compartment location in the material container and perform the picking operation.

[0052] To enhance the system's adaptability and robustness to business changes, the implementation includes receiving a task cancellation request, which includes the task identifier of the task to be cancelled; querying the current execution status of the task to be cancelled based on the task identifier; when the current execution status is "not started," issuing a task termination instruction to the allocated target execution device and releasing the inventory resources and device resources occupied by the task to be cancelled; when the current execution status is "in execution," determining whether the task to be cancelled meets the interruption conditions, and if so, generating an interruption instruction and performing a task rollback operation.

[0053] Furthermore, in a specific implementation, an emergency task insertion request is received, and an emergency target task is determined based on the emergency task insertion request; the priority parameters of all currently executing tasks are obtained, and the priority parameter of the emergency target task is set to be higher than the priority parameters of all currently executing tasks; the emergency target task is inserted at the head of the current task queue, and the step of determining candidate execution devices based on preset resource status data is triggered to be re-executed.

[0054] It is understood that the aforementioned task cancellation request refers to an instruction used to terminate a warehousing operation task that has been issued but not yet completed (including tasks that have not started execution or are in progress). This request can be initiated proactively by the upper-level system (such as Enterprise Resource Planning (ERP) or Manufacturing Execution System (MES)) or manually triggered by the operator through the system's human-machine interface.

[0055] The aforementioned emergency order insertion request refers to an instruction used to insert a new task or task group into the task scheduling queue with a higher priority than the currently executing regular tasks. This request is typically used to handle sudden and time-sensitive warehousing operation needs, such as customers urgently placing additional orders, production lines experiencing material shortages requiring immediate replenishment, or special batches of materials requiring priority outbound delivery.

[0056] This embodiment discloses receiving a warehousing task request, determining a target task to be processed based on the request, identifying multiple candidate execution devices that can currently be used to execute the target task based on preset resource status data, determining a task adaptation value for each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices, selecting a candidate execution device that meets preset conditions as the target execution device based on the task adaptation value, and issuing the target task to the target execution device. Because this embodiment determines the task adaptation value for each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices, then selects the target execution device based on the task adaptation value, and then issues the target task to the target execution device, compared to existing technologies, this embodiment improves the scientific nature of warehousing task allocation, thereby improving overall warehousing operation efficiency and system resource utilization.

[0057] refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the dynamic allocation method for warehousing tasks according to the present invention.

[0058] Based on the first embodiment described above, in this embodiment, step S30 includes steps S301 to S303: Step S301: Obtain the task urgency level, material attribute information, and target location information of the target task.

[0059] Step S302: Obtain the current load status, location coordinates and task queue length of each candidate execution device.

[0060] Step S303: Determine the task adaptation value corresponding to each of the candidate execution devices based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length.

[0061] It should be explained that the above-mentioned task urgency level can refer to a quantitative indicator used to measure the urgency of handling the target task, such as four levels: highest urgency, higher urgency, normal urgency, and lower urgency. Tasks with higher urgency levels should receive higher priority when allocating equipment.

[0062] The aforementioned material attribute information can refer to a set of data describing the physical characteristics and storage requirements of the materials involved in the target task, such as material weight, material volume, material type (standard parts, fragile parts, valuable parts), and whether a special temperature and humidity environment is required.

[0063] The aforementioned current load status can refer to the task burden or material weight that the candidate execution device has undertaken at the current moment. For example, the number of bins that the bin robot has currently moved or the percentage of the load weight it has carried relative to its maximum load capacity. For instance, if the bin robot has a maximum load capacity of 50 kg, its current load status can be "empty", "half-load (25 kg)" or "full-load (50 kg)".

[0064] In its implementation, when determining the task adaptation value for each candidate execution device, the system first obtains three key parameters from the task attributes of the target task: task urgency level, material attribute information, and target location information. The task urgency level can be directly parsed from the task request issued by the upper-level system. If not explicitly specified, the system can automatically assign a default level based on the task type (e.g., automatically assigning a higher urgency level to an insert task) or the task generation time. The material attribute information includes material weight, material volume, and material type, used to determine whether the candidate execution device has the capacity to carry or process the material. The target location information represents the coordinates of the location to be reached when executing the task, such as the target storage location coordinates for an inbound task or the starting storage location coordinates for an outbound task.

[0065] Next, the system obtains three key parameters from the device attributes of each candidate execution device: current load status, location coordinates, and task queue length. The current load status can be read from the real-time status data reported by the device, reflecting the current weight or occupancy rate of the device. The location coordinates represent the current location of the device on the warehouse map. The task queue length represents the number of tasks currently assigned to the device but not yet completed; the larger this value, the busier the device.

[0066] Subsequently, the system calculates the task suitability value for each candidate execution device based on the task urgency level, material attribute information, target location information, current load status, positioning coordinates, and task queue length. Specifically, the calculation can be a weighted sum of the above parameters. For example, a higher task urgency level is assigned a higher weight coefficient; a closer distance between the device's positioning coordinates and the target location information is assigned a higher score; a shorter task queue length is assigned a higher score; a more idle current load status is assigned a higher score; and a higher match between the material attribute information and the device's capabilities is assigned a higher score. The system then accumulates these scores according to a preset weight ratio to obtain the task suitability value for each candidate execution device. Candidate execution devices with higher task suitability values ​​are considered more suitable for performing the target task.

[0067] It should be noted that the step of determining the task adaptation value corresponding to each of the candidate execution devices based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length may include: determining the current busyness parameter of each of the candidate execution devices based on the current load status and the task queue length; determining the movement cost parameter between each of the candidate execution devices and the task execution location based on the positioning coordinates and the target location information; determining the execution constraint parameter of the target operation task based on the task urgency level and the material attribute information; and determining the task adaptation value corresponding to each of the candidate execution devices based on the current busyness parameter, the movement cost parameter, and the execution constraint parameter.

[0068] It should be understood that by quantifying the current busyness parameters of the equipment, the movement cost parameters, and the execution constraint parameters of the task, multi-dimensional influencing factors are transformed into calculable task adaptation values, thereby achieving objectivity and refinement in task allocation decisions. This effectively balances equipment utilization and task response time, reduces the probability of scheduling conflicts, and improves the overall stability and resource utilization efficiency of the system.

[0069] It should be explained that the aforementioned current busyness parameter can refer to a numerical indicator that quantifies the current busyness of the candidate execution device. The value of this parameter is positively correlated with the current load status of the device and the length of the task queue. That is, the higher the current load status and the longer the task queue length, the larger the current busyness parameter value.

[0070] The aforementioned movement cost parameter can be a numerical indicator that quantifies the cost required for a candidate execution device to move from its current location coordinates to the task execution location indicated by the target location information. Examples include movement distance, estimated movement time, or energy consumption. The larger the movement cost parameter value, the higher the cost for the device to reach the task location.

[0071] The aforementioned execution constraint parameters can refer to special weights or threshold values ​​determined based on the task urgency level and material attribute information, used to constrain the calculation of task adaptation values. For example, the higher the task urgency level, the greater the weight of timeliness assigned in the execution constraint parameters; and the greater the material weight in the material attribute information, the stronger the constraint on the equipment load capacity.

[0072] It is understandable that the aforementioned current busyness parameter, movement cost parameter, and execution constraint parameter are all intermediate calculation variables used to finally synthesize the task adaptation value corresponding to each candidate execution device.

[0073] In its implementation, after obtaining the current load status and task queue length of each candidate execution device, the system calculates the current busyness parameter for each device based on these parameters. The calculation involves weighted summation of the current load status (e.g., 0 for idle, 0.5 for half-load, and 1 for full load) and the task queue length (e.g., the queue length value) to obtain a comprehensive value reflecting the device's busyness. A higher current load status and a longer task queue length result in a larger current busyness parameter value, indicating a busier device that is less suitable for accepting new tasks.

[0074] Next, the system calculates the movement cost parameter between each candidate execution device and the target location information of the target task, based on the device's location coordinates. The movement cost parameter can be calculated by calculating the Euclidean or Manhattan distance between the location coordinates and the target location, or by combining the actual walkable path length on the warehouse map. A larger movement distance results in a higher movement cost parameter value, indicating a higher time or energy cost required for the device to move to the task location.

[0075] Subsequently, the system determines the execution constraint parameters of the target task based on its task urgency level and material attribute information. A higher task urgency level allows for a higher urgency coefficient in the execution constraint parameters, tilting the adaptation value calculation towards equipment with low workload and low movement costs. If the material attribute information includes heavy materials (e.g., weighing over 30kg), the execution constraint parameters can set a load capacity threshold, requiring the remaining load capacity of candidate execution equipment to be greater than the material weight; otherwise, its adaptation value will be reduced or it will be directly excluded.

[0076] Finally, the system comprehensively determines the task suitability value for each candidate execution device based on the calculated current busyness parameter, movement cost parameter, and execution constraint parameter. The comprehensive determination method includes weighting and summing the current busyness parameter, movement cost parameter, and execution constraint parameter according to a preset weight ratio, or multiplying the execution constraint parameter as a multiplier factor by the base suitability value. A higher task suitability value indicates a higher overall suitability of the candidate execution device when performing the target task.

[0077] This embodiment discloses the acquisition of the task urgency level, material attribute information, and target location information of the target operation task; the acquisition of the current load status, positioning coordinates, and task queue length of each candidate execution device; and the determination of the task adaptation value corresponding to each candidate execution device based on the task urgency level, material attribute information, target location information, current load status, positioning coordinates, and task queue length. Because this embodiment comprehensively considers the task urgency level, material attributes, and target location, combined with the real-time load of the devices, positioning coordinates, and queue length, it achieves multi-dimensional and precise matching between tasks and execution devices. Compared with existing technologies, this embodiment improves the scientific nature, balance, and overall execution efficiency of warehouse operation scheduling.

[0078] refer to Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the dynamic allocation method for warehousing tasks according to the present invention.

[0079] Based on the above embodiments, in this embodiment, step S10 includes steps S101 to S103: Step S101: Receive warehousing task requests and divide the warehousing task requests into multiple wave task groups according to the wave aggregation rules.

[0080] Step S102: Based on the priority parameters corresponding to each wave task group and the inventory matching status of the materials associated with each wave task group, determine the wave task group to be processed from the multiple wave task groups.

[0081] Step S103: Determine at least one warehousing task request in the pending wave task group as the target operation task to be processed, and update the task attribute of the target operation task according to the task scheduling result of the pending wave task group.

[0082] It should be explained that the aforementioned wave aggregation rules can refer to pre-defined logical rules used to group multiple warehousing task requests into a task group according to specific dimensions, such as by task type (outbound, inbound, replenishment), by the recipient's route, by the order quantity limit, by the order line limit, or by a fixed time period. For example, wave aggregation rules include timing rules, route rules, order quantity rules, order line rules, and product quantity rules.

[0083] It should be noted that the aforementioned wave task group can refer to a set of tasks formed by merging multiple warehousing task requests according to wave aggregation rules. Tasks within the same wave task group can be merged for execution to improve operational efficiency.

[0084] It should be understood that the aforementioned priority parameters can refer to numerical indicators used to measure the processing order of each wave of task groups. For example, the wave of task groups corresponding to urgent orders are assigned higher priority parameters, while the wave of task groups corresponding to regular replenishment tasks are assigned lower priority parameters. The aforementioned inventory matching status can refer to the presence of materials associated with the wave of task groups in the current warehouse inventory, such as all materials being in sufficient inventory, some materials being in insufficient inventory, or all materials being out of stock.

[0085] It should be explained that the above task scheduling results may refer to the output information obtained after the system optimizes the execution order and resource allocation of each task in the wave of tasks to be processed, such as determining the execution time window of each task and the allocated execution equipment or workstation.

[0086] In its implementation, the system first receives one or more warehousing task requests from the upper-level system or manually created requests. The system then reads pre-stored wave aggregation rules, such as grouping outbound tasks by task type, grouping tasks on the same delivery route by recipient route, or grouping all tasks arriving within the current time window by timing rules. The system then iterates through and categorizes the received warehousing task requests according to these wave aggregation rules, assigning requests that match the same set of rules to the same wave task group, ultimately resulting in multiple non-overlapping wave task groups. Each wave task group has a unique group identifier.

[0087] Next, the system obtains the priority parameters corresponding to each wave of tasks. These priority parameters can be directly parsed from task requests issued by the upper-level system, or they can be automatically calculated based on the urgency of the warehousing task requests contained within the wave of tasks. For example, wave of tasks containing urgent order insertion tasks are automatically assigned higher priority parameters. The system also queries the inventory matching status of the materials associated with each wave of tasks in the current inventory, for example, checking whether the required material inventory quantity for each task meets the requirements. The system sorts the priority parameters of each wave of tasks from highest to lowest value, while excluding wave of tasks with a total stockout status. The system then identifies one or more wave of tasks with the highest priority parameters from the remaining wave of tasks as the wave of tasks to be processed.

[0088] Subsequently, the system selects at least one warehousing task request from the pending wave of tasks and designates it as the target operation task. The system schedules the pending wave of tasks, including determining the execution order of tasks within the group, assigning workstations or equipment to each task, and calculating the estimated execution time for each task. Based on the scheduling results, the system updates the task attributes of the target operation task. These updated attributes include, but are not limited to, task execution sequence number, assigned workstation identifier, estimated start time, estimated completion time, and task priority. The updated task attributes are used for subsequent candidate equipment selection and task suitability value calculation.

[0089] This embodiment discloses receiving warehousing task requests and dividing them into multiple wave task groups according to wave aggregation rules. Based on the priority parameters corresponding to each wave task group and the inventory matching status of the materials associated with each wave task group, a wave task group to be processed is determined from the multiple wave task groups. At least one warehousing task request in the wave task group to be processed is identified as a target operation task to be processed, and the task attributes of the target operation task are updated according to the task scheduling results of the wave task group to be processed. This embodiment achieves orderly grouping of scattered tasks through wave aggregation rules and filters the wave to be processed by combining priority parameters and inventory matching status, realizing the orderly organization and dynamic scheduling update of tasks, avoiding task backlog and disorderly competition, improving the planning and execution continuity of warehousing operations, and ensuring that high-priority tasks receive timely responses.

[0090] Furthermore, this embodiment of the invention also proposes a storage medium storing a dynamic allocation program for warehouse tasks. When the dynamic allocation program for warehouse tasks is executed by a processor, it implements the steps of the dynamic allocation method for warehouse tasks as described above.

[0091] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the dynamic allocation device for warehousing tasks of the present invention.

[0092] like Figure 4 As shown, the warehouse task dynamic allocation device proposed in this embodiment of the invention includes: a task determination module 501, an equipment candidate module 502, a task adaptation module 503, and a task allocation module 504.

[0093] The task determination module 501 is used to receive warehousing task requests and determine the target operation tasks to be processed based on the warehousing task requests.

[0094] The device candidate module 502 is used to determine multiple candidate execution devices that can be used to execute the target job task based on preset resource status data.

[0095] The task adaptation module 503 is used to determine the task adaptation value corresponding to each candidate execution device based on the task attributes of the target job task and the device attributes of the candidate execution devices.

[0096] The task allocation module 504 is used to select candidate execution devices that meet preset conditions as target execution devices based on the task adaptation value, and to issue the target job task to the target execution device.

[0097] The device candidate module 502 is further configured to acquire real-time status information of each storage device, including device working status, device fault flag, device location coordinates, and current task queue length; determine storage devices whose working status is idle and whose fault flag is fault-free as available devices; and filter available devices whose distance between the device location coordinates and the starting position is less than a preset distance threshold based on the starting position of the target task, as multiple candidate execution devices that can be used to execute the target task.

[0098] The task allocation module 504 is further configured to compare the task adaptation values ​​corresponding to each of the candidate execution devices, select the candidate execution device with the highest task adaptation value as the target execution device based on the comparison result; generate control instructions containing path guidance information or action sequences based on the task attributes of the target task and the current position of the target execution device; and send the control instructions to the target execution device through a communication module deployed at a fixed point.

[0099] The task allocation module 504 is also used to receive task execution status information fed back by the target execution device; when the task execution status information indicates that the material container has arrived at the sorting station, it triggers the reading of the identification information of the material container; based on the read identification information, it generates a picking guidance signal to instruct the operator to pick the target compartment position in the material container.

[0100] This device embodiment discloses receiving a warehousing task request, determining a target task to be processed based on the request, identifying multiple candidate execution devices currently available to execute the target task based on preset resource status data, determining a task adaptation value for each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices, selecting a candidate execution device that meets preset conditions as the target execution device based on the task adaptation value, and issuing the target task to the target execution device. Because this device embodiment determines the task adaptation value for each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices, then selects the target execution device based on the task adaptation value, and then issues the target task to the target execution device, compared to existing technologies, this device embodiment improves the scientific nature of warehousing task allocation, thereby improving overall warehousing operation efficiency and system resource utilization.

[0101] Based on the first embodiment of the dynamic allocation device for warehousing tasks of the present invention, a second embodiment of the dynamic allocation device for warehousing tasks of the present invention is proposed.

[0102] In this embodiment, the task adaptation module 503 is further configured to obtain the task urgency level, material attribute information, and target location information of the target task; obtain the current load status, positioning coordinates, and task queue length of each candidate execution device; and determine the task adaptation value corresponding to each candidate execution device based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length.

[0103] The task adaptation module 503 is further configured to: determine the current busyness parameter of each candidate execution device based on the current load status and the task queue length; determine the movement cost parameter between each candidate execution device and the task execution location based on the positioning coordinates and the target location information; determine the execution constraint parameter of the target operation task based on the task urgency level and the material attribute information; and determine the task adaptation value corresponding to each candidate execution device based on the current busyness parameter, the movement cost parameter, and the execution constraint parameter.

[0104] Other embodiments or specific implementations of the dynamic allocation device for warehousing tasks of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0105] This application provides a dynamic allocation device for warehouse tasks, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the dynamic allocation method for warehouse tasks in the first embodiment described above.

[0106] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing a dynamic warehouse task allocation device according to embodiments of this application. The dynamic warehouse task allocation device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The illustrated warehouse task dynamic allocation device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0107] like Figure 5As shown, the warehouse task dynamic allocation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the warehouse task dynamic allocation device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the warehouse task dynamic allocation device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows warehouse task dynamic allocation devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0108] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0109] The warehousing task dynamic allocation device provided in this application, employing the warehousing task dynamic allocation method in the above embodiments, can solve the technical problem in the prior art where unreasonable warehousing task allocation affects the overall warehousing operation efficiency and system resource utilization. Compared with the prior art, the beneficial effects of the warehousing task dynamic allocation device provided in this application are the same as those of the warehousing task dynamic allocation method provided in the above embodiments, and other technical features in this warehousing task dynamic allocation device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0110] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0112] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0113] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0115] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for dynamically allocating warehousing tasks, characterized in that, The method includes: Receive a warehousing task request and determine the target operation task to be processed based on the warehousing task request; Based on preset resource status data, multiple candidate execution devices that can be used to execute the target job task are determined; Based on the task attributes of the target task and the device attributes of the candidate execution devices, determine the task adaptation value corresponding to each candidate execution device; Based on the task adaptation value, candidate execution devices that meet preset conditions are selected as target execution devices, and the target job task is issued to the target execution devices.

2. The dynamic allocation method for warehousing tasks as described in claim 1, characterized in that, The step of determining the task adaptation value corresponding to each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices includes: Obtain the task urgency level, material attribute information, and target location information of the target task; Obtain the current load status, location coordinates, and task queue length of each candidate execution device; Based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length, the task adaptation value corresponding to each candidate execution device is determined.

3. The dynamic allocation method for warehousing tasks as described in claim 2, characterized in that, The step of determining the task adaptation value corresponding to each of the candidate execution devices based on the task urgency level, the material attribute information, the target location information, the current load status, the positioning coordinates, and the task queue length includes: Based on the current load status and the task queue length, determine the current busyness parameter of each of the candidate execution devices; Based on the positioning coordinates and the target location information, the movement cost parameters between each candidate execution device and the task execution location are determined; Based on the task urgency level and the material attribute information, determine the execution constraint parameters of the target operation task; Based on the current busyness parameter, the movement cost parameter, and the execution constraint parameter, the task adaptation value corresponding to each of the candidate execution devices is determined.

4. The dynamic allocation method for warehousing tasks as described in claim 1, characterized in that, The steps of receiving a warehousing task request and determining the target operation task to be processed based on the warehousing task request include: Receive warehousing task requests and divide the warehousing task requests into multiple wave task groups according to wave aggregation rules; Based on the priority parameters corresponding to each wave task group and the inventory matching status of the materials associated with each wave task group, determine the wave task groups to be processed from the plurality of wave task groups. At least one warehousing task request in the pending wave task group is identified as the target operation task to be processed, and the task attributes of the target operation task are updated according to the task scheduling results of the pending wave task group.

5. The dynamic allocation method for warehousing tasks as described in claim 1, characterized in that, The step of selecting candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices and issuing the target job task to the target execution devices includes: The task adaptation values ​​corresponding to each candidate execution device are compared, and the candidate execution device with the highest task adaptation value is selected as the target execution device based on the comparison results. Based on the task attributes of the target task and the current position of the target execution device, generate control instructions containing path guidance information or action sequences; The control commands are sent to the target execution device via a communication module deployed at a fixed location.

6. The dynamic allocation method for warehousing tasks as described in claim 5, characterized in that, After the step of selecting candidate execution devices that meet preset conditions based on the task adaptation value as target execution devices and issuing the target job task to the target execution devices, the method further includes: Receive task execution status information fed back by the target execution device; When the task execution status information indicates that the material container has arrived at the sorting station, the identification information of the material container is read. Based on the read identification information, a picking guidance signal is generated to indicate the location of the target compartment in the material container to the operator.

7. The dynamic allocation method for warehousing tasks as described in claim 1, characterized in that, The step of determining multiple candidate execution devices that can be used to execute the target job task based on preset resource status data includes: Obtain real-time status information for each storage device, including device operating status, device fault flag, device location coordinates, and device current task queue length. Storage equipment whose working status is idle and whose fault flag is fault-free is identified as usable equipment. Based on the starting position of the target task, available devices whose distance from the device's location coordinates to the starting position is less than a preset distance threshold are selected as multiple candidate execution devices that can be used to execute the target task.

8. A dynamic allocation device for warehousing tasks, characterized in that, The device includes: The task determination module is used to receive warehousing task requests and determine the target operation tasks to be processed based on the warehousing task requests. The device candidate module is used to determine multiple candidate execution devices that can be used to execute the target job task based on preset resource status data; The task adaptation module is used to determine the task adaptation value corresponding to each of the candidate execution devices based on the task attributes of the target job task and the device attributes of the candidate execution devices. The task allocation module is used to select candidate execution devices that meet preset conditions as target execution devices based on the task adaptation value, and to issue the target job task to the target execution device.

9. A dynamic allocation device for warehousing tasks, characterized in that, The device includes: a memory, a processor, and a dynamic allocation program for warehouse tasks stored in the memory and executable on the processor, the dynamic allocation program for warehouse tasks being configured to implement the steps of the dynamic allocation method for warehouse tasks as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a dynamic allocation program for warehouse tasks, which, when executed by a processor, implements the steps of the dynamic allocation method for warehouse tasks as described in any one of claims 1 to 7.

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