A material task processing method and device

Abstract

The application discloses a kind of material task processing method and device, it is related to computer technical field.The specific embodiment of the method includes: determining to be handled bin and task processing type based on material task information;When task processing type is warehouse-in task, determine warehouse-in task execution scheme and generate first bin carrying task, first bin carrying task is issued to warehouse-in and warehouse-out carrying equipment, to make warehouse-in and warehouse-out carrying equipment move to target warehousing area To-be-processed bin;When task processing type is inventory task or picking task, determine target task execution scheme and generate second bin carrying task, second bin carrying task is issued to warehouse-in and warehouse-out carrying equipment, to make warehouse-in and warehouse-out carrying equipment move to mechanical arm moving inventory picking station To-be-processed bin, using mechanical arm moving inventory picking station To-be-processed bin is carried out inventory task or picking task processing;The embodiment reduces labor cost, improves warehouse automation degree and task processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a material task processing method and apparatus. Background Technology

[0002] In traditional storage warehouses, material handling is primarily done manually. For example, in an inventory count, personnel need to use a PDA or computer to locate the materials to be counted, then move the materials to the counting station using trolleys, AGVs, conveyors, or other manual or automated methods. Finally, they upload the quantity and other inventory information to the system via PDA or computer to complete the count. Similarly, in a picking process, picking personnel need to use a PDA or computer to find picking orders and material locations, then push trolleys or picking boxes to the corresponding picking locations to pick the items. After picking, they place the trolleys or picking boxes on the packing station for packaging. These methods are labor-intensive, have low automation, and result in poor task processing efficiency. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a material task processing method and apparatus, which determines the task execution plan by analyzing the material bins to be processed and the task processing type, and automatically completes the material receiving, inventory, picking and other tasks in the storage warehouse according to the task execution plan, thereby reducing labor costs and improving the degree of warehouse automation and task processing efficiency.

[0004] To achieve the above objectives, according to one aspect of the present invention, a material task processing method is provided, comprising: Based on the received material task information, determine the material bin to be processed and the task processing type; In response to the task processing type being an inbound task, an inbound task execution plan is determined, and a first material box handling task is generated based on the inbound task execution plan. The first material box handling task is then sent to the inbound and outbound handling equipment so that the inbound and outbound handling equipment can move the material boxes to be processed to the target storage area. In response to the task processing type being inventory or picking, the target task execution plan corresponding to the inventory or picking task is determined. Based on the target task execution plan, a second bin handling task is generated and sent to the inbound and outbound handling equipment so that the inbound and outbound handling equipment can move the bins to be processed to the robotic arm mobile inventory and picking station. The robotic arm mobile inventory and picking station is then used to process the inventory or picking tasks of the bins to be processed.

[0005] Optionally, in response to the task processing type being an inbound task, an execution plan for the inbound task is determined, including: Obtain the total weight information, empty weight information, and quantity information of the material to be processed corresponding to the material bin to be processed; where the total weight information is the total weight of the material bin to be processed after the material to be processed is loaded into the empty material bin. Based on the total weight information, the weight information of the empty bin, and the quantity information of the materials to be processed, the weight information of a single material to be processed is determined. Based on the total weight information, empty bin weight information, quantity information of materials to be processed, and weight information of individual materials to be processed, an inbound execution plan is generated and an inbound record is established.

[0006] Optionally, a robotic arm is used to move the inventory picking station to process the inventory tasks of the bins to be processed, including: Obtain the current weight information of the bin to be processed, compare the current weight information with the preset grasping conditions, and obtain the condition comparison result; Based on the condition comparison results, determine the inventory strategy for the bins to be processed; The inventory task is processed according to the inventory strategy for the boxes to be processed.

[0007] Optionally, inventory count tasks are performed on the bins to be processed according to the inventory count strategy, including: Release the material to be processed from the material bin, and then remove the stacked material after release. Obtain image information of the material to be processed after de-stacking, and determine the quantity information of the material to be processed based on the image information using a visual recognition algorithm. The inventory task processing results are generated based on the quantity information of the materials to be processed.

[0008] Optionally, a robotic arm is used to move the inventory picking station to process the picking tasks of the boxes to be processed, including: Acquire image information of the bin to be processed, and determine the pose information of the material to be processed in the bin based on the image information of the bin to be processed; Picking tasks are performed on the bins to be processed based on the pose information of the materials to be processed.

[0009] Optionally, the picking task is processed based on the pose information of the material to be processed in the bin, including: The gripping position and gripping path information corresponding to the material to be processed are determined based on the pose information of the material to be processed. Based on the gripping location information and gripping path information, the robotic arm moves to the target robotic arm in the inventory picking station to grip and process the materials to be processed, and then moves the gripped materials to the target picking bin.

[0010] According to a second aspect of the present invention, a material task processing apparatus is provided, comprising: The task determination module is used to determine the material bin to be processed and the task processing type based on the received material task information; The first processing module is used to respond to the task processing type being an inbound task, determine the inbound task execution plan, generate a first material box handling task based on the inbound task execution plan, and send the first material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment can move the material box to be processed to the target storage area. The second processing module is used to respond to the task processing type as inventory task or picking task, determine the target task execution plan corresponding to the inventory task or picking task, generate a second material box handling task based on the target task execution plan, and send the second material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment moves the material box to be processed to the robotic arm mobile inventory and picking station, and uses the robotic arm mobile inventory and picking station to process the material box to be processed for inventory task or picking task.

[0011] According to a third aspect of the present invention, an electronic device is provided, comprising: One or more processors; Memory, used to store one or more programs. When one or more programs are executed by one or more processors, the one or more processors implement the methods of any of the above embodiments.

[0012] According to a fourth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method of any of the above embodiments.

[0013] According to a fifth aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method of any of the above embodiments.

[0014] One embodiment of the above invention has the following advantages or beneficial effects: Based on received material task information, the system determines the bin to be processed and the task processing type; in response to the task processing type being an inbound task, it determines an inbound task execution plan, generates a first bin handling task based on the inbound task execution plan, and sends the first bin handling task to the inbound / outbound handling equipment, so that the inbound / outbound handling equipment moves the bin to be processed to the target storage area; in response to the task processing type being an inventory task or a picking task, it determines the target task execution plan corresponding to the inventory task or picking task, generates a second bin handling task based on the target task execution plan, and sends the second bin handling task to the inbound / outbound handling equipment, so that the inbound / outbound handling equipment moves the bin to be processed to the robotic arm mobile inventory and picking station, and uses the robotic arm mobile inventory and picking station to process the bin for inventory or picking tasks. This embodiment determines the task execution plan by analyzing the bin to be processed and the task processing type, and automatically completes the material inbound, inventory, and picking work in the storage warehouse according to the task execution plan, reducing labor costs and improving the warehouse automation level and task processing efficiency.

[0015] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0016] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein: Figure 1 This is a schematic diagram of the main flow of the material task processing method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the main flow of a material task processing method according to an optional embodiment of the present invention; Figure 3 This is a schematic diagram of the main modules of the material task processing device according to an embodiment of the present invention; Figure 4 This is an exemplary system architecture diagram in which embodiments of the present invention can be applied; Figure 5 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0017] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0018] It should be noted that the acquisition, storage, and application of personal information involved in the embodiments of the present invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0019] In traditional storage warehouses, material handling is primarily done manually. For example, in an inventory count, personnel need to use a PDA or computer to locate the materials to be counted, then move the materials to the counting station using trolleys, AGVs, conveyors, or other manual or automated methods. Finally, they upload the quantity and other inventory information to the system via PDA or computer to complete the count. Similarly, in a picking process, picking personnel need to use a PDA or computer to find picking orders and material locations, then push trolleys or picking boxes to the corresponding picking locations to pick the items. After picking, they place the trolleys or picking boxes on the packing station for packaging. These methods are labor-intensive, have low automation, and result in poor task processing efficiency.

[0020] In view of this, according to one aspect of the present invention, a material task processing method is provided.

[0021] Figure 1 This is a schematic diagram of the main flow of a material task processing method according to an embodiment of the present invention. Figure 1 As shown, the material task processing method according to an embodiment of the present invention includes the following steps S101 to S103.

[0022] Step S101: Based on the received material task information, determine the material bin to be processed and the task processing type.

[0023] In this embodiment, material task information describes the execution requirements of a warehousing operation, including at least task identification information, material identification information, bin identification information, target operation type information, and time constraint information. A bin to be processed is a material carrying unit identified in the material task information as needing to participate in this operation. This bin stores materials to be received, inventoried, or picked, and can be uniquely located in the warehousing system through its bin identification information. The task processing type is the specific operation category determined based on the material task information, used to guide the selection of subsequent task execution plans, such as receiving tasks, outbound tasks, inventory tasks, or picking tasks, etc.

[0024] Specifically, one approach is to determine the material bin and task type based on explicit field parsing. This involves directly extracting bin identification and target operation type information from the material task information and using field mapping relationships to determine the corresponding bin and task type. This method is suitable for scenarios with a high degree of standardization in task information structure. Another approach is based on task context reasoning. When the material task information does not directly contain a complete identifier, the corresponding bin is determined by parsing the material identification information, storage location information, and historical inventory data, combined with preset rules or scheduling strategies. The task type is then determined by comprehensively considering the task source, operation instruction semantics, or current inventory status. These methods allow for accurate determination of bins and task types under varying levels of data completeness.

[0025] Step S102: In response to the task processing type being an inbound task, an inbound task execution plan is determined, a first material box handling task is generated based on the inbound task execution plan, and the first material box handling task is sent to the inbound and outbound handling equipment so that the inbound and outbound handling equipment can move the material boxes to be processed to the target storage area.

[0026] This embodiment first parses the inbound task execution plan to obtain the first execution parameters related to the material box handling, including but not limited to the identification information of the material box to be processed, the target storage area information, and the handling priority information. Based on this, a first material box handling task is generated according to the first execution parameters. The first material box handling task describes the complete handling path and operation requirements for moving the material box to be processed from its current location to the target storage area. The first material box handling task is sent to the inbound and outbound handling equipment through the task scheduling interface. The inbound and outbound handling equipment includes automated guided vehicles or autonomous mobile robots. After receiving the first material box handling task, the inbound and outbound handling equipment first determines its current location based on its own navigation and positioning system, generates a driving path according to the path planning information in the first material box handling task, and then moves to the location of the material box to be processed to complete the box retrieval operation. After the box is retrieved, the equipment performs path navigation according to the target storage area information, transports the material box to be processed to the corresponding target storage area, and completes the placement operation at the target location, thereby completing a complete inbound handling process.

[0027] In addition, the generation process of the first bin handling task can be dynamically optimized by incorporating real-time status information of the warehousing environment. Specifically, when generating the first handling task, not only is the target warehousing area information considered, but also the current warehousing aisle congestion, the availability of inbound and outbound handling equipment, and historical handling efficiency data are taken into account to dynamically plan the handling path and select the optimal path from multiple candidate paths as the execution path. Simultaneously, after the task is issued, the inbound and outbound handling equipment continuously acquires environmental awareness data during execution. When path obstruction or changes in equipment status are detected, the original path is adjusted in real time to ensure the smooth completion of the handling task. Furthermore, after the handling is completed, the actual execution path is compared with the preset path, and the execution result is fed back to the system for subsequent task optimization.

[0028] Through the above embodiments, the automatic generation and execution of bin handling tasks can be achieved based on the inbound task execution plan, improving the automation level of inbound operations. By combining real-time environmental information for dynamic path optimization and execution feedback, handling efficiency can be effectively improved and the risk of path conflicts can be reduced, thereby enhancing the intelligence level of task processing.

[0029] Step S103: In response to the task processing type being inventory task or picking task, determine the target task execution plan corresponding to the inventory task or picking task, generate a second material box handling task based on the target task execution plan, and send the second material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment moves the material box to be processed to the robotic arm mobile inventory and picking station, and uses the robotic arm mobile inventory and picking station to process the material box to be processed for inventory or picking tasks.

[0030] When the task processing type is an inventory task or a picking task, a target task execution plan is determined. This target task execution plan includes inventory task execution plans and picking task execution plans, etc. First, based on the inventory task execution plan or picking task execution plan, second execution parameters related to the transfer of material boxes are obtained, including but not limited to the identification information of the material boxes to be processed, the target location of the robotic arm mobile inventory and picking station, and the operation priority information. Based on these, a second material box handling task is generated based on the second execution parameters. This second material box handling task describes the transfer path and operational requirements for moving the material boxes to be processed from their current storage location to the robotic arm mobile inventory and picking station. The second material box handling task is then issued to the inbound / outbound handling equipment, which may include automated guided vehicles (AGVs) or autonomous mobile robots. Upon receiving the second material box handling task, the inbound / outbound handling equipment determines its current location using its own navigation and positioning system and moves to the location of the material box to be processed according to the path planning information in the second material box handling task. After completing the box retrieval operation, the material box to be processed is transported to the robotic arm mobile inventory and picking station and placed at the designated workstation. Once the bins to be processed arrive, the robotic arm moves the inventory and picking station to process the materials in the bins according to the inventory task execution plan or the picking task execution plan. In the inventory task, the quantity of materials is determined by acquiring weight information and visual recognition analysis. In the picking task, the materials are processed one by one and loaded into the target container according to the material position information, thereby achieving the corresponding operation goal.

[0031] In another embodiment, after the bins to be processed arrive, the robotic arm moving inventory and picking station can further refine the processing flow. For example, it can first obtain the current weight information of the bins to be processed and perform preliminary verification, then release and disperse the materials, then acquire image data and perform visual recognition to obtain the quantity and pose information of the materials, and complete the inventory statistics or picking process based on this. During the processing, it can also make corrections based on the difference between the recognition results and the actual processing results, thereby improving the processing accuracy.

[0032] Through the above embodiments, it is possible to realize the automatic transfer of the boxes to be processed between the storage area and the robotic arm mobile inventory and picking station, and to complete the collaborative processing of inventory or picking tasks, thereby improving the degree of automation of operations and effectively improving handling efficiency and processing accuracy.

[0033] This embodiment determines the material bins to be processed and the task processing type based on received material task information. In response to a warehousing task, it determines an warehousing task execution plan, generates a first material bin handling task based on the plan, and sends it to the inbound / outbound handling equipment to move the material bins to the target storage area. In response to an inventory or picking task, it determines a target task execution plan, generates a second material bin handling task based on the plan, and sends it to the inbound / outbound handling equipment to move the material bins to the robotic arm mobile inventory and picking station. The robotic arm mobile inventory and picking station then performs the inventory or picking tasks on the material bins. This embodiment determines the task execution plan by analyzing the material bins to be processed and the task processing type, and automatically completes material warehousing, inventory, and picking in the storage warehouse according to the plan, reducing labor costs and improving warehouse automation and task processing efficiency.

[0034] Figure 2 This is a schematic diagram of the main flow of a material task processing method according to an optional embodiment of the present invention. Figure 2 As shown, in response to the task processing type being an inbound task, an inbound task execution plan is determined, including the following steps S201 to S203.

[0035] Step S201: Obtain the total weight information, empty weight information, and quantity information of the material to be processed corresponding to the material bin to be processed; wherein, the total weight information is the total weight of the material bin to be processed after the material to be processed is loaded into the empty material bin.

[0036] Step S202: Based on the total weight information, the weight information of the empty bin, and the quantity information of the materials to be processed, determine the weight information of a single material to be processed.

[0037] Step S203: Based on the total weight information, empty bin weight information, quantity information of materials to be processed, and weight information of individual materials to be processed, generate an inbound execution plan and establish an inbound record.

[0038] When the task processing type is an inbound task, the weight data of the bins to be processed is first acquired. Specifically, the total weight of the bins after loading the materials to be processed is obtained through weighing equipment, and the weight of the corresponding empty bins is also obtained. The difference between the total weight and the empty bin weight is used to represent the overall weight of the materials to be processed. The quantity of materials to be processed can be obtained from the bill of materials data provided by the upstream system or determined through pre-entered order information, thus forming a basic data set containing weight and quantity. Based on this, the total weight of the materials to be processed is obtained by calculating the difference between the total weight and the empty bin weight, and then averaged out according to the quantity of materials to be processed to determine the weight of each individual material to be processed. Further, the total weight, empty bin weight, quantity, and individual material weight are comprehensively analyzed to generate an inbound execution plan. The inbound execution plan includes inbound verification rules, target storage strategies, and inbound record generation logic, and corresponding inbound records are established in the warehouse management system accordingly to achieve standardized management of inbound data.

[0039] In another embodiment, the quantity information of the materials to be processed does not rely directly on the upstream system. Instead, it is obtained by capturing images of the materials in the bins and using a visual recognition algorithm to identify them. Based on this, the total weight of the materials is calculated by combining the total weight information with the weight information of the empty bins. The visual recognition result is then verified. When the deviation between the identified quantity information and the weight derivation result exceeds a preset threshold, the visual recognition result is corrected or a re-recognition is triggered to obtain more accurate quantity information of the materials to be processed. Furthermore, the weight information of individual materials to be processed is calculated, ultimately generating a more reliable warehousing execution plan and establishing warehousing records.

[0040] The above methods achieve two main goals: firstly, by combining weight and quantity information, accurate determination of the weight of individual materials to be processed can be achieved, thereby improving the accuracy of inbound data. Secondly, by introducing a visual recognition and verification mechanism, abnormal data can be corrected or filtered, improving the reliability and robustness of the inbound process, and ultimately enhancing the data consistency and automation capabilities of the overall warehouse management system.

[0041] Optionally, the robotic arm is used to move the inventory picking station to process the inventory task of the boxes to be processed, including: obtaining the current weight information of the boxes to be processed, comparing the current weight information with preset grasping conditions to obtain the condition comparison result; determining the inventory strategy of the boxes to be processed based on the condition comparison result; and processing the inventory task of the boxes to be processed according to the inventory strategy.

[0042] Specifically, when using a robotic arm to move the inventory picking station to process the inventory of the boxes to be processed, the current weight information of the boxes is first obtained through a weighing device, and then compared with preset gripping conditions. The preset gripping conditions characterize the processability of the materials in the boxes in terms of quantity or distribution, such as corresponding to a weight range threshold or weight stability interval. By matching the current weight information with the preset gripping conditions, a condition comparison result is obtained. After obtaining the condition comparison result, an appropriate inventory strategy is determined based on different comparison results. For example, when the weight information is within the preset range, a standard inventory strategy is adopted. Under this strategy, the materials in the boxes are released and dispersed, and material image data is acquired. The quantity information of the materials is determined through a visual recognition algorithm, thus completing the inventory. When the weight information deviates from the preset range, an enhanced inventory strategy is adopted, which adds repeated recognition or data verification processing on top of visual recognition to improve the accuracy of the inventory results. Based on the determined inventory strategy, the corresponding inventory task processing flow is executed for the boxes to be processed, thus obtaining the final inventory result.

[0043] In other embodiments, the preset capture conditions are not solely determined based on weight thresholds, but are comprehensively constructed by combining historical inventory data, material standard weight information, and an identification error model. In specific implementation, a difference analysis is first performed based on the current weight information and historical inventory records to obtain an initial judgment result. Based on this, the theoretical quantity range is calculated by combining the standard weight distribution of the materials, and this range is matched with the acceptable error range for visual recognition, thus forming a more refined conditional comparison result. Based on this result, an inventory strategy can be dynamically generated. For example, when the weight is highly consistent with the theoretical range, only one visual recognition operation is needed to complete the inventory; when the consistency is low, multiple rounds of material dispersion and visual recognition are performed, and the results of multiple recognitions are fused to obtain more accurate material quantity information, or manual inventory can be conducted. Furthermore, during the inventory process, the inventory results can be corrected or anomalies marked based on the deviation between the actual recognition results and the weight derivation results, thereby further improving inventory accuracy.

[0044] The above embodiments enable preliminary judgment of the inventory process based on weight information, thereby selecting an appropriate inventory strategy and improving processing efficiency. By introducing multi-source data fusion and dynamic strategy adjustment mechanisms, the accuracy and reliability of inventory results can be effectively improved.

[0045] Optionally, the inventory task processing for the bins to be processed is carried out according to the inventory strategy, including: releasing the materials to be processed in the bins to be processed, and destacking the materials after release; obtaining image information of the materials to be processed after destacking, and determining the quantity information of the materials to be processed based on the image information using a visual recognition algorithm; and generating the inventory task processing result based on the quantity information of the materials to be processed.

[0046] This embodiment first performs a release process on the materials to be processed in the material bins, transferring the materials originally stored in the bins to the inventory work area. The release process can be achieved through dumping or diversion, ensuring the materials are evenly distributed into the subsequent processing area. After release, the materials are de-stacking. Vibration, guided separation, or mechanical disturbance are used to disperse the previously stacked or obstructed materials, creating a relatively flat distribution in the work area. Subsequently, image information of the de-stacking materials is acquired using image acquisition devices positioned above or to the side of the work area. This image information is then input into a pre-trained visual recognition algorithm model for analysis, identifying the quantity of materials to be processed. Based on the quantity information, an inventory task processing result is generated, which serves as the basis for updating inventory data or subsequent processing.

[0047] Furthermore, to further improve recognition accuracy, multi-stage image acquisition and recognition processing can be performed on the materials to be processed after the de-stacking process. Specifically, after the first image acquisition and visual recognition, the recognition results are used to determine whether there are any incomplete or occluded recognition issues. If an anomaly is found, the materials to be processed are subjected to local perturbation or redispersed, and a second image acquisition and visual recognition process is performed. The results of multiple recognitions are then fused and calculated to obtain more accurate quantity information. Alternatively, the visual recognition results can be verified by combining the weight information of the bins to be processed. When there is a deviation between the theoretical weight derived from the quantity information and the actual weight, the recognition results are corrected or marked as abnormal, thereby further improving the reliability of the inventory results.

[0048] This embodiment effectively reduces occlusion between materials through release and de-stacking processes, improving the accuracy of visual recognition; through a fusion processing mechanism of multiple recognitions and weight verification, the accuracy and stability of inventory results can be improved.

[0049] Optionally, the robotic arm moves the inventory picking station to process the picking task of the boxes to be processed, including: acquiring image information of the boxes to be processed, determining the pose information of the materials to be processed in the boxes based on the image information of the boxes to be processed, and processing the picking task of the boxes to be processed based on the pose information of the materials to be processed.

[0050] Specifically, when using a robotic arm to move and sort the bins, the first step is to acquire image information of the bins. Specifically, an image acquisition device positioned in the work area captures images of the inside of the bins, obtaining image data showing the distribution of the materials to be processed. The image information is then preprocessed to eliminate lighting variations and noise interference. Based on this, a visual recognition algorithm is used to identify the materials in the bins, extracting the spatial position and posture information of each material to obtain their pose information. Then, based on the pose information, a corresponding picking path and processing sequence are generated, and the robotic arm is driven to process the materials one by one, removing them from the bins and placing them at the target location, thus completing the picking task.

[0051] To improve picking success rate and processing efficiency, the processing flow can be further refined based on the acquired pose information. Specifically, in the visual recognition stage, not only is the pose information of the materials extracted, but also their size and morphological features are combined to classify different materials and determine the corresponding processing strategy based on the classification results. During the picking process, an appropriate processing method is selected according to the material type, and the picking order is optimized, for example, prioritizing unobstructed and easily manipulated materials to reduce operational difficulty. Simultaneously, processing results are recorded in real time during picking. When processing failures or deviations occur, subsequent pose information is dynamically corrected based on feedback information, thereby improving the overall stability of picking. Furthermore, after completing one round of picking, the remaining materials can be re-image-acquired and recognized to update pose information until all materials have been processed.

[0052] The above embodiments enable accurate acquisition of the pose information of the materials to be processed based on image information, thereby achieving precise positioning and processing of the materials. By introducing classification processing and dynamic adjustment mechanisms, the picking success rate and operational efficiency can be effectively improved.

[0053] Optionally, the picking task is processed based on the pose information of the material to be processed, including: determining the gripping position information and gripping path information corresponding to the material to be processed based on the pose information of the material to be processed; controlling the robotic arm to move and count the target robotic arm in the picking station based on the gripping position information and gripping path information to grip and process the material to be processed, and moving the gripped material to be processed to the target picking bin.

[0054] In this embodiment, the graspable area of ​​the material to be processed is first analyzed based on the pose information, and the contact position between the target robotic arm and the material to be processed is determined to obtain the grasping position information. Simultaneously, based on the current position of the robotic arm moving inventory picking station, the position of the material to be processed, and the target motion constraints, the motion trajectory of each joint of the robotic arm is planned, generating grasping path information from the current position to the grasping position. The grasping position information and grasping path information are sent to the target robotic arm in the robotic arm moving inventory picking station, causing the target robotic arm to move to the designated position according to the planned path and grasp the material to be processed. After the grasping is completed, according to preset stacking rules or picking task requirements, the grasped material to be processed is moved to the corresponding position in the target picking bin, thus completing one material picking process.

[0055] In another implementation, to improve the accuracy and stability of the picking process, a corresponding gripping strategy can be determined by combining the attribute information of the material to be processed before performing the gripping operation. Specifically, after obtaining the pose information of the material to be processed, the size, shape, and material information of the material to be processed are further obtained, and an appropriate robotic arm end effector is selected or the gripping parameters are adjusted based on the above information, such as adjusting the gripping force, gripping angle, and robotic arm movement speed. After determining the gripping strategy, corresponding gripping position information and gripping path information are generated according to the gripping strategy, and the target robotic arm is controlled to perform the gripping operation. During the gripping process, gripping feedback information can also be obtained through sensors installed on the robotic arm moving inventory picking station. When gripping failure, material slippage, or gripping position deviation is detected, the pose information of the material to be processed is re-obtained based on the feedback information, and the gripping path is replanned to complete the gripping again.

[0056] By planning the gripping position and path based on the pose information of the materials to be processed, the robotic arm achieves precise positioning and automatic picking of materials in the bin, reducing the degree of human intervention. In addition, by dynamically adjusting the gripping strategy by combining material attribute information and gripping feedback information, the success rate of gripping materials of different sizes and shapes is improved, enhancing the adaptability and operational reliability of the mobile robotic arm workstation in complex warehousing environments.

[0057] According to a preferred embodiment of the present invention, an automatic inventory and picking method for materials in a warehousing scenario is proposed, which mainly includes an inbound workstation, an automated guided vehicle (AGV) or autonomous mobile robot (AMR), and a robotic arm mobile inventory and picking station. The devices coordinate tasks and interact with each other through a warehouse control system or a warehouse execution system.

[0058] The receiving workstation is used to collect basic information and check the weight of incoming materials. This workstation is equipped with an electronic scale. During the material receiving process, the electronic scale obtains the weight of empty boxes and the weight of full boxes after loading materials, and uploads the relevant weight information to the warehouse control system or warehouse execution system for subsequent inventory management, inventory calculation, and picking task scheduling.

[0059] Automated Guided Vehicles (AGVs) or Autonomous Mobile Robots (AMOs) are primarily used to perform the retrieval and placement of boxes within a warehousing system. Depending on the specific automation implementation plan, AGVs or AMOs can employ two methods for picking up and placing boxes. One method integrates a dedicated mechanical structure onto the mobile platform, using a customized box-grabbing mechanism to automatically grasp and place boxes. The other method integrates a robotic arm or humanoid robot onto the mobile platform, equipped with multi-purpose grippers or dexterous hands, utilizing the general-purpose robotic arm's grasping capabilities to perform box picking and placing operations, thereby enhancing the system's adaptability to boxes of different sizes.

[0060] The robotic arm mobile inventory and picking station is used to complete the automatic inventory and picking tasks of stored materials. Since this equipment involves many functional units and occupies a relatively large area, this embodiment separates the robotic arm mobile inventory and picking station from the automated guided vehicles or autonomous mobile robots for inbound and outbound operations. This reduces the complexity of the automatic inventory and picking process, while also reducing the occupation of warehouse storage space and increasing the overall storage density.

[0061] The robotic arm mobile inventory and picking station mainly includes a bin handling unit, a weighing and counting unit, a bin tilting unit, a material destacking mechanism, a vision counting unit, a robotic arm picking and counting unit, a quick-change end effector unit, and an automated guided vehicle or autonomous mobile robot chassis, etc.

[0062] The bin handling unit is used to transfer bins between different functional units. Specifically, this unit is responsible for performing bin handling actions between the bin-carrying mechanism carried by the automated guided vehicle or autonomous mobile robot and the weighing and counting unit, so as to ensure that the bins can smoothly enter the subsequent inventory process.

[0063] The weighing and counting unit is used to acquire the weight information of the bin and its contents, and combines it with visual recognition technology to identify the material's shape. This unit integrates weighing sensors and visual recognition equipment to acquire both weight and shape characteristics of the materials, thus providing foundational data for subsequent material quantity estimation and inventory analysis.

[0064] The bin tipping unit is used to transfer materials from the bin to the worktable of the vision counting unit. This unit uses a low-cost robotic arm or a dedicated mechanical structure to tip the bin, ensuring that the materials are evenly released into the vision detection area for subsequent visual recognition and quantity counting.

[0065] The material destabilization mechanism is used to disperse the dumped materials. By setting up a non-standard mechanical structure to vibrate or separate the materials, the originally stacked materials are laid out flat on the vision counting worktable, thereby improving the recognition accuracy and inventory efficiency of the vision recognition system.

[0066] The vision counting unit is used to visually identify materials successfully picked up by the robotic arm or materials awaiting stacking. By deploying vision acquisition devices, images of the materials on the worktable are acquired and recognized, thereby obtaining the quantity and spatial pose information of the materials, providing a positioning basis for subsequent robotic arm grasping and stacking operations.

[0067] The robotic arm picking and counting unit is used to perform automated material picking and stacking operations. The robotic arm performs grasping operations based on material pose information provided by visual recognition and assists in inventory counting based on the success rate of grasping. Simultaneously, this unit supports automatically selecting appropriate end effectors based on the material's size, material, and other characteristics, improving the success rate of robotic arm grasping and stacking through a quick end effector change mechanism.

[0068] The quick-change end effector unit is used to quickly change the end effector of the robotic arm according to the size, material, and gripping method requirements of different materials. Through the automatic end effector switching mechanism, the robotic arm can select the most suitable gripping tool in different material gripping scenarios, thereby improving the overall gripping stability and adaptability of the system.

[0069] Automated guided vehicles (AGVs) or autonomous mobile robot chassis are used to enable robotic arms to move inventory and picking stations within warehouse environments. These chassis integrate navigation and positioning systems, using automated navigation technology to plan routes, locate, and move autonomously within the warehouse. This allows the equipment to move to different shelving areas to perform inventory or picking tasks according to warehousing requirements.

[0070] During system operation, the receiving workstation first collects and uploads the weight information of the material boxes to the warehouse control system or warehouse execution system. Then, the automated guided vehicle (AGV) or autonomous mobile robot executes the material box picking and placing tasks according to system scheduling instructions, transporting the target material boxes to the robotic arm mobile inventory picking station. The material box handling unit transfers the material boxes to the weighing and counting unit for weight detection and preliminary identification, and then releases the materials to the vision counting worktable via the material tilting unit. After being processed by the material destacking mechanism, the materials are laid flat, and the vision counting unit acquires the quantity and pose information of the materials. The robotic arm picking and counting unit completes the material picking and stacking operations based on the identification results, and combines the grasping success rate and weight information to achieve inventory counting.

[0071] In practice, the process begins with the warehouse management system receiving new inbound or inventory counting tasks. Based on current warehouse inventory, task priority, and equipment operating status, it generates corresponding work instructions. These instructions are then sent to the inbound workstation and automated guided vehicles (AGVs) or autonomous mobile robots to initiate the automated workflow. When an inbound task begins, operators or automated conveyors place the materials to be inbound into the bins at the inbound workstation. The electronic weighing equipment at the workstation performs weight checks, first acquiring the weight of an empty bin, and then the weight of a full bin after materials are loaded. The weighing equipment uploads the weight data to the warehouse control system or warehouse execution system. The system calculates the total weight of the materials based on the weight difference and, combined with the material's basic information, establishes an inbound record, thus completing the inbound data initialization.

[0072] After the weight is collected, the warehouse control system generates a bin handling task based on the task scheduling strategy and issues the task to the automated guided vehicle (AGV) or autonomous mobile robot. The AGV or autonomous mobile robot moves to the inbound workstation according to the navigation and positioning system, picks up the bin using its integrated bin-retrieving mechanism or robotic arm gripping mechanism, and transports it to the target storage area or robotic arm mobile inventory picking station according to the system's planned path.

[0073] When the system needs to inventory or pick materials, the warehouse control system sends the target bin task to the automated guided vehicle (AGV) or autonomous mobile robot. The mobile robot then proceeds to the designated shelf location, retrieves the corresponding bin, and transports it to the working area of ​​the robotic arm's mobile inventory and picking station.

[0074] After the bins arrive at the robotic arm's mobile inventory and picking station, they are first transferred from the automated guided vehicle (AGV) or autonomous mobile robot's platform to the weighing and counting unit via the bin handling unit. The weighing and counting unit re-detects the overall weight of the bin and simultaneously uses visual recognition equipment to perform preliminary identification of the material's shape inside the bin, thereby obtaining the material's weight and appearance characteristics. This data is then uploaded to the warehouse control system for data verification.

[0075] Subsequently, the material handling unit or a dedicated tilting mechanism pours the material from the bin into the visual counting unit's worktable area. Since some material may be stacked or obstructed within the bin, it needs to be dispersed by a material de-stacking mechanism after entering the visual counting area. This de-stacking mechanism separates the material through vibration, guide plates, or mechanical agitation, ensuring the material forms a relatively uniform, flat surface on the visual worktable, thereby reducing obstruction and improving the accuracy of subsequent visual recognition.

[0076] After the materials are dispersed and spread, the visual counting unit acquires images of the materials using a two-dimensional or three-dimensional vision camera, and analyzes and processes the images using artificial intelligence visual recognition algorithms to identify the quantity and spatial orientation of the materials. The recognition results will serve as an important basis for subsequent robotic arm grasping and stacking operations.

[0077] Based on the recognition results of the visual counting unit, the robotic arm performs picking operations. The robotic arm performs a grasping operation one by one according to the recognized material position coordinates, and re-places the materials into the target bins or containers according to the system's set stacking rules. During the grasping process, the system records the robotic arm's grasping success rate and the actual number of materials picked in real time, and feeds this information back to the warehouse control system for inventory verification.

[0078] Before the robotic arm performs a picking task, the system can automatically select a suitable end effector based on the identified material size, shape, and material information. The quick-change end effector unit can quickly replace the robotic arm's end effector, such as a suction cup gripper, a clamping gripper, or a flexible dexterous hand, to adapt to the grasping needs of different types of materials, thereby improving the overall grasping success rate and operational stability.

[0079] After the robotic arm completes a round of picking or inventory operations, the system will determine whether all the materials in the current bin have been processed. If there are still unprocessed materials, the vision recognition and robotic arm grasping process will continue; if the materials have been processed, the result data will be uploaded to the warehouse control system, and new bin status information will be generated.

[0080] After completing the picking or inventory task, the robotic arm mobile inventory and picking station places the processed boxes back onto the platform of the automated guided vehicle (AGV) or autonomous mobile robot. Subsequently, according to the task instructions issued by the warehouse control system, the AGV or autonomous mobile robot transports the boxes to the corresponding storage rack location for re-entry into the warehouse, or transports them to the outbound area to complete the outbound operation.

[0081] Once all tasks are completed, the system records and updates the weight information, visual recognition results, picking quantity, and inventory changes during the operation, thus completing a full automated inventory and picking process.

[0082] This embodiment introduces equipment such as 2D or 3D vision cameras, weighing sensors, robotic arms, and multi-functional chassis for automated guided vehicles (AGVs) or autonomous mobile robots. It combines artificial intelligence visual recognition technology, weight calculation technology, mobile robot navigation and positioning technology, and robotic arm picking and stacking technology to achieve automated inventory and picking of stored materials. This improves the automation level and efficiency of warehousing operations and demonstrates strong innovation in system structure design and technology integration. The automated guided vehicles or autonomous mobile robots for inbound and outbound operations are functionally separated from the robotic arm mobile inventory and picking station, allowing different devices to perform the tasks of transportation and inventory picking. This separate design not only reduces the overall complexity of the equipment but also minimizes the space occupied by the equipment. Simultaneously, it increases warehouse storage density while ensuring automated inventory and picking efficiency, thereby improving the overall operational efficiency of the warehousing system.

[0083] According to a second aspect of the present invention, a material task processing apparatus is provided.

[0084] Figure 3 This is a schematic diagram of the main modules of a material task processing device according to an embodiment of the present invention. Figure 3 As shown, the material task processing device 300 includes: The task determination module 301 is used to determine the material bin to be processed and the task processing type based on the received material task information; The first processing module 302 is used to respond to the task processing type being an inbound task, determine the inbound task execution plan, generate a first material box handling task based on the inbound task execution plan, and send the first material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment can move the material box to be processed to the target storage area. The second processing module 303 is used to respond to the task processing type being an inventory task or a picking task, determine the target task execution plan corresponding to the inventory task or picking task, generate a second material box handling task based on the target task execution plan, and send the second material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment moves the material box to be processed to the robotic arm mobile inventory and picking station, and uses the robotic arm mobile inventory and picking station to process the material box to be processed for inventory or picking tasks.

[0085] Optionally, the first processing module 302 is further configured to: Obtain the total weight information, empty weight information, and quantity information of the material to be processed corresponding to the material bin to be processed; where the total weight information is the total weight of the material bin to be processed after the material to be processed is loaded into the empty material bin. Based on the total weight information, the weight information of the empty bin, and the quantity information of the materials to be processed, the weight information of a single material to be processed is determined. Based on the total weight information, empty bin weight information, quantity information of materials to be processed, and weight information of individual materials to be processed, an inbound execution plan is generated and an inbound record is established.

[0086] Optionally, the second processing module 303 is also used for: Obtain the current weight information of the bin to be processed, compare the current weight information with the preset grasping conditions, and obtain the condition comparison result; Based on the condition comparison results, determine the inventory strategy for the bins to be processed; The inventory task is processed according to the inventory strategy for the boxes to be processed.

[0087] Optionally, the second processing module 303 is also used for: Release the material to be processed from the material bin, and then remove the stacked material after release. Obtain image information of the material to be processed after de-stacking, and determine the quantity information of the material to be processed based on the image information using a visual recognition algorithm. The inventory task processing results are generated based on the quantity information of the materials to be processed.

[0088] Optionally, the second processing module 303 is also used for: Acquire image information of the bin to be processed, and determine the pose information of the material to be processed in the bin based on the image information of the bin to be processed; Picking tasks are performed on the bins to be processed based on the pose information of the materials to be processed.

[0089] Optionally, the second processing module 303 is also used for: The gripping position and gripping path information corresponding to the material to be processed are determined based on the pose information of the material to be processed. Based on the gripping location information and gripping path information, the robotic arm moves to the target robotic arm in the inventory picking station to grip and process the materials to be processed, and then moves the gripped materials to the target picking bin.

[0090] It should be noted that the specific implementation details of the material task processing device of the present invention have been described in detail in the above material task processing method, so the details will not be repeated here.

[0091] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the present invention.

[0092] According to a fourth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method provided in the first aspect of the present invention.

[0093] According to a fifth aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided in the first aspect of the present invention.

[0094] Figure 4 An exemplary system architecture 400 is shown for which the material task processing method or material task processing apparatus of the present invention can be applied.

[0095] like Figure 4 As shown, system architecture 400 may include terminal devices 401, 402, and 403, a network 404, and a server 405. Network 404 serves as the medium for providing communication links between terminal devices 401, 402, and 403 and server 405. Network 404 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0096] Users can use terminal devices 401, 402, and 403 to interact with server 405 via network 404 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 401, 402, and 403, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0097] Terminal devices 401, 402, and 403 can be various electronic devices with displays that support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0098] Server 405 can be a server that provides various services, such as a backend management server that supports shopping websites browsed by users using terminal devices 401, 402, and 403 (for example only). The backend management server can analyze and process data such as received material task processing requests, and feed back the processing results (such as material task processing results - for example only) to the terminal devices.

[0099] It should be noted that the material task processing method provided in the embodiments of the present invention is generally run by server 405, and correspondingly, the material task processing device is generally set in server 405.

[0100] It should be understood that Figure 4 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0101] The following is for reference. Figure 5 It shows a schematic diagram of the structure of a computer system 500 suitable for implementing a terminal device of the present invention. Figure 5 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0102] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 502 or programs loaded from storage section 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the system 500. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0103] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. Drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0104] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention 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 communication section 509, and / or installed from removable medium 511. When the computer program is run by the central processing unit (CPU) 501, it performs the functions defined above in the system of this invention.

[0105] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more operable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually operate substantially in parallel, and they may sometimes operate in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0107] The modules described in the embodiments of the present invention can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor can be described as including a task determination module, a first processing module, and a second processing module. The names of these modules do not necessarily limit the module itself; for example, the task determination module can also be described as "a module for determining the material bin to be processed and the task processing type based on received material task information."

[0108] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs that, when executed by the device, cause the device to include: determining a bin to be processed and a task processing type based on received material task information; determining an inbound task execution plan in response to the task processing type being an inbound task; generating a first bin handling task based on the inbound task execution plan; and issuing the first bin handling task to an inbound / outbound handling device to move the bin to be processed to a target storage area; and determining a target task execution plan corresponding to the inventory task or picking task in response to the task processing type being an inventory task or picking task; generating a second bin handling task based on the target task execution plan; and issuing the second bin handling task to the inbound / outbound handling device to move the bin to be processed to a robotic arm mobile inventory and picking station, whereby the robotic arm mobile inventory and picking station performs inventory or picking tasks on the bin to be processed.

[0109] The computer program product provided in this embodiment of the invention includes a computer program that, when executed by a processor, implements the material task processing method in this embodiment of the invention.

[0110] According to the technical solution of the present invention, the following advantages or beneficial effects are achieved: Based on the received material task information, the material box to be processed and the task processing type are determined; in response to the task processing type being an inbound task, an inbound task execution plan is determined, a first material box handling task is generated based on the inbound task execution plan, and the first material box handling task is sent to the inbound / outbound handling equipment so that the inbound / outbound handling equipment moves the material box to be processed to the target storage area; in response to the task processing type being an inventory task or a picking task, a target task execution plan corresponding to the inventory task or picking task is determined, a second material box handling task is generated based on the target task execution plan, and the second material box handling task is sent to the inbound / outbound handling equipment so that the inbound / outbound handling equipment moves the material box to be processed to the robotic arm mobile inventory and picking station, and the robotic arm mobile inventory and picking station is used to process the material box to be processed for inventory or picking tasks. This embodiment determines the task execution plan by analyzing the bins to be processed and the task processing type, and automatically completes the material receiving, inventory, picking and other tasks in the storage warehouse according to the task execution plan, thereby reducing labor costs and improving the degree of warehouse automation and task processing efficiency.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0112] It should be noted that the acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

Claims

1. A material task processing method, characterized in that, include: Based on the received material task information, determine the material bin to be processed and the task processing type; In response to the task processing type being an inbound task, an inbound task execution plan is determined, and a first material box handling task is generated based on the inbound task execution plan. The first material box handling task is then sent to the inbound and outbound handling equipment so that the inbound and outbound handling equipment can move the material box to be processed to the target storage area. In response to the task processing type being an inventory task or a picking task, a target task execution plan corresponding to the inventory task or the picking task is determined. A second material box handling task is generated based on the target task execution plan, and the second material box handling task is sent to the inbound and outbound handling equipment so that the inbound and outbound handling equipment moves the material box to be processed to the robotic arm mobile inventory and picking station. The robotic arm mobile inventory and picking station is used to process the material box to be processed for inventory or picking tasks.

2. The method according to claim 1, characterized in that, In response to the task processing type being an inbound task, an inbound task execution plan is determined, including: Obtain the total weight information, empty weight information, and quantity information of the material to be processed corresponding to the material bin to be processed; wherein, the total weight information is the total weight of the material bin to be processed after the material to be processed is loaded into the empty material bin; Based on the total weight information, empty bin weight information, and quantity of materials to be processed, the weight information of a single material to be processed is determined. Based on the total weight information, empty bin weight information, quantity information of materials to be processed, and weight information of individual materials to be processed, an inbound execution plan is generated and an inbound record is established.

3. The method according to claim 1, characterized in that, The robotic arm is used to move the inventory and picking station to perform inventory tasks on the bins to be processed, including: Obtain the current weight information of the bin to be processed, compare the current weight information with the preset grasping conditions, and obtain the condition comparison result; Based on the condition comparison results, the inventory strategy for the bins to be processed is determined; The inventory task of the bins to be processed is carried out according to the inventory strategy.

4. The method according to claim 3, characterized in that, The inventory task for the unprocessed bins is processed according to the inventory strategy, including: The material to be processed in the material bin is released, and the released material is de-stacking. Obtain image information of the material to be processed after de-stacking, and determine the quantity information of the material to be processed based on the image information using a visual recognition algorithm; The inventory task processing result is generated based on the quantity information of the materials to be processed.

5. The method according to claim 1, characterized in that, The robotic arm moves the inventory picking station to process the picking tasks of the bins to be processed, including: Acquire image information of the bin to be processed, and determine the pose information of the material to be processed in the bin based on the image information of the bin to be processed; The picking task is performed on the bin based on the pose information of the material to be processed.

6. The method according to claim 5, characterized in that, Based on the pose information of the material to be processed, the picking task of the material bin to be processed is performed, including: The grasping position information and grasping path information corresponding to the material to be processed are determined based on the pose information of the material to be processed. Based on the grasping position information and grasping path information, the robotic arm is controlled to move to the target robotic arm in the inventory and picking station to grasp the material to be processed, and then move the grasped material to the target picking bin.

7. A material handling device, characterized in that, include: The task determination module is used to determine the material bin to be processed and the task processing type based on the received material task information; The first processing module is used to respond to the task processing type being an inbound task, determine an inbound task execution plan, generate a first material box handling task based on the inbound task execution plan, and send the first material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment moves the material box to be processed to the target storage area. The second processing module is used to respond to the task processing type being an inventory task or a picking task, determine the target task execution plan corresponding to the inventory task or the picking task, generate a second material box handling task based on the target task execution plan, and issue the second material box handling task to the inbound and outbound handling equipment so that the inbound and outbound handling equipment moves the material box to be processed to the robotic arm mobile inventory and picking station, and uses the robotic arm mobile inventory and picking station to process the material box to be processed for inventory or picking tasks.

8. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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