Article sorting method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention provides an article sorting method and device, electronic equipment and a computer readable medium. The method comprises the following steps: receiving first imaging data associated with a target article at a first sorting position and second imaging data associated with the target article at a second sorting position in the article sorting system; a target label of the target object is generated at least based on the first imaging data, and the target label indicates whether the target object can be grabbed by a mechanical arm in the object sorting system or not; based on the second imaging data and the target label, a sorting scheme for the target article is determined, and the sorting scheme indicates a sorting grid where the target article is placed; and enabling the object sorting system to transport the target objects to the sorting grids based on the sorting scheme. In this way, the stacking efficiency can be improved.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computer technology, and more particularly, to methods, apparatuses, electronic devices, and computer-readable media for item sorting. Background Art

[0002] When sorting items, the system typically sorts them into a sorting slot, where a robotic arm grabs and palletizes them. Because different items vary in material, shape, size, and weight, it's impossible for the robotic arm to fully accommodate all types of items. This can cause items that can't be grabbed by the robotic arm to become stuck in the sorting slot or fall during palletizing, requiring manual handling and reducing palletizing efficiency. Summary of the Invention

[0003] In a first aspect of the present disclosure, a method for item sorting is provided. The method comprises: receiving first imaging data associated with a target item at a first sorting position and second imaging data associated with the target item at a second sorting position in an item sorting system; generating a target tag for the target item based at least on the first imaging data, the target tag indicating whether the target item can be grasped by a robotic arm in the item sorting system; determining a sorting scheme for the target item based on the second imaging data and the target tag, the sorting scheme indicating a sorting slot to place the target item; and causing the item sorting system to transport the target item to the sorting slot based on the sorting scheme.

[0004] In a second aspect of the present disclosure, a device for item sorting is provided. The device includes: a receiving module configured to receive first imaging data associated with a target item at a first sorting position and second imaging data associated with a target item at a second sorting position in an item sorting system; a generating module configured to generate a first label for the target item based on at least the first imaging data, the first label indicating whether the target item can be grasped by a robotic arm in the item sorting system; a determining module configured to determine a sorting scheme for the target item based on the second imaging data and the first label, the sorting scheme indicating a sorting slot to place the target item; and a controlling module configured to cause the item sorting system to transport the target item to the sorting slot based on the sorting scheme.

[0005] In a third aspect of the present disclosure, an electronic device is provided. The electronic device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the electronic device to perform the method of the first aspect of the present disclosure.

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium and can be executed by a processor to perform the method according to the first aspect of the present disclosure.

[0007] In a fifth aspect of the present disclosure, a computer program product is provided, which includes computer-executable instructions, wherein the computer-executable instructions execute the method of the first aspect of the present disclosure when executed by a processor.

[0008] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent hereinafter with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram illustrating an example system in which embodiments of the present disclosure can be implemented;

[0011] Figure 2A A schematic diagram showing the architecture of a logistics sorting center according to some embodiments of the present disclosure;

[0012] Figure 2B A schematic diagram showing a partial architecture of a logistics sorting center according to some embodiments of the present disclosure is shown;

[0013] Figure 3 A schematic diagram illustrating an example of a package that cannot be grasped according to some embodiments of the present disclosure;

[0014] Figure 4 A schematic diagram illustrating a process for item sorting according to some embodiments of the present disclosure is shown;

[0015] Figure 5 A flowchart showing a process of item sorting in a logistics sorting scenario according to some embodiments of the present disclosure is shown;

[0016] Figure 6 A schematic diagram showing an apparatus for item sorting according to some embodiments of the present disclosure; and

[0017] Figure 7 A block diagram of an electronic device is shown in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0018] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0019] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to." The term "based on" should be understood as "based at least in part on." The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment." The term "some embodiments" should be understood as "at least some embodiments." Other explicit and implicit definitions may be included below.

[0020] As used herein, the term "model" can learn the association between corresponding inputs and outputs from training data, so that after training is completed, corresponding outputs can be generated for given inputs. The generation of the model can be based on machine learning technology. Deep learning is a machine learning algorithm that processes inputs and provides corresponding outputs by using multiple layers of processing units. In this article, "model" may also be referred to as "machine learning model", "machine learning network", "neural network" or "network", and these terms are used interchangeably in this article.

[0021] A "neural network" is a machine learning network based on deep learning. A neural network is capable of processing inputs and providing corresponding outputs. It typically includes an input layer, an output layer, and one or more hidden layers between the input and output layers. Neural networks used in deep learning applications typically include many hidden layers, thereby increasing the depth of the network. The layers of a neural network are connected in sequence so that the output of the previous layer is provided as input to the next layer, where the input layer receives the input of the neural network and the output of the output layer serves as the final output of the neural network. Each layer of a neural network includes one or more nodes (also called processing nodes or neurons), each of which processes the input from the previous layer.

[0022] Generally speaking, machine learning can be roughly divided into three stages, namely the training stage, the testing stage, and the application stage (also known as the inference stage). In the training stage, a given model can be trained using a large amount of training data, and the parameter values ​​are continuously updated iteratively until the model can obtain consistent inferences that meet the expected goals from the training data. Through training, the model can be considered to be able to learn the association between input and output (also known as input-to-output mapping) from the training data. The parameter values ​​of the trained model are determined. In the testing stage, the test input is applied to the trained model to test whether the model can provide the correct output, thereby determining the performance of the model. In the application stage, the model can be used to process the actual input based on the parameter values ​​obtained through training to determine the corresponding output.

[0023] It should be noted that the acquisition, storage and application of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0024] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0025] For example, in response to receiving a user's active request, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information, so that the user can independently choose whether to provide personal information to the electronic device, application, server or storage medium and other software or hardware that performs the operation of the technical solution of the present disclosure based on the prompt message.

[0026] As an optional but non-limiting implementation, in response to receiving a user's active request, a prompt message may be sent to the user, for example, in the form of a pop-up window, in which the prompt message may be presented in text form. Furthermore, the pop-up window may also include a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0027] In order to facilitate understanding of the embodiments of the present disclosure, some terms involved in the embodiments of the present disclosure are first explained.

[0028] A robotic arm is an automated device that can move objects by "suction cup absorption" or "grasping".

[0029] Sorting conveyor belts, a common automated sorting equipment in logistics scenarios, separate items into corresponding sorting grids according to their destination.

[0030] At the sorting grid, the sorting conveyor belt rolls through the swing wheel, sliding the packages to a temporary storage area for subsequent palletizing or stacking.

[0031] The six-sided scanning device can obtain the information of the item (for example, the parcel number, three-dimensional volume, weight, etc.). Generally, the six-sided scanning device can include a multi-sided high-speed camera and a weighing device.

[0032] Next, some example embodiments of the present disclosure will be described with reference to the accompanying drawings.

[0033] Figure 1 1 shows a schematic diagram of an example system 100 in which embodiments of the present disclosure can be implemented. Figure 1 As shown, the system 100 includes an electronic device 110, a first imaging device 121, a second imaging device 122, a sorting conveyor belt 123, a sorting opening 124, and a robotic arm (not shown). In some embodiments, a balance wheel (not shown) can be provided on the sorting conveyor belt 123 at the sorting opening, and the electronic device 110 can control the balance wheel to perform corresponding actions.

[0034] In the system 100, during the process of the conveyor belt 123 transporting the target item 125 to the sorting slot 124 (as shown by the dotted line in the figure), when the target item 125 passes the first imaging device 121, the first imaging device 121 can capture first imaging data of the target item 125 and send the first imaging data to the electronic device 110; when the target item 125 passes the second imaging device 122, the second imaging device 122 can capture second imaging data of the target item 125 and send the second imaging data to the electronic device 110. The electronic device 110 can use the first imaging data and the second imaging data to determine the sorting slot 124 to which the target item 125 is transported.

[0035] In some embodiments, the sorting slots 124 may include a first sorting slot and a second sorting slot. The first sorting slot indicates a slot for sorting using a robotic arm, while the second sorting slot indicates a slot for manual sorting. The electronic device 110 may determine, based on the first and second imaging data, whether the target item 125 is to be transported to the first or second sorting slot. For example, the electronic device 110 may control the balance wheel based on the results of processing the first and second imaging data to ensure that the target item 125 is transported to the first or second sorting slot.

[0036] In some embodiments, the system 100 may further include a weighing device, which may be provided at the first imaging device 121. When the target item 125 passes by the first imaging device 121, the weighing device may weigh the target item 125 and transmit the weight of the target item 125 to the electronic device 110. The electronic device 110 may also determine whether the target item 125 is transported to the first sorting slot or the second sorting slot based on the first imaging data, the weight of the target item 125, and the second imaging data.

[0037] In some embodiments, the first imaging device 121 and the second imaging device 122 may each include a multi-sided high-speed camera, for example, a six-sided high-speed camera.

[0038] exist Figure 1 In the present invention, electronic device 110 may include any computing system with computing capabilities, such as various computing devices / systems, terminal devices, servers, etc. Terminal devices may refer to any type of mobile, fixed, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. Servers include but are not limited to mainframes, edge computing nodes, computing devices in cloud environments, etc.

[0039] The communication connection between the electronic device 110, the first imaging device 121, the second imaging device 122, the weighing device, and the balance wheel can be established via a wired or wireless method. The communication connection may include, but is not limited to, a Bluetooth connection, a mobile network connection, a Universal Serial Bus (USB) connection, a Wireless Fidelity (WiFi) connection, etc., and the embodiments of the present disclosure are not limited in this respect.

[0040] It should be understood that Figure 1 The components and arrangements of the system 100 shown are merely examples, and a computing system suitable for implementing the exemplary implementations described herein may include one or more different components, other components, and / or different arrangements. Implementations of the present disclosure are not limited in this respect.

[0041] As described above, when the item sorting system is sorting items, the items are usually sorted into a sorting grid, and the robotic arm grabs and stacks the items in the sorting grid.

[0042] Taking the logistics scenario as an example, see Figure 2A and Figure 2B . Figure 2AThe structure 200A of a logistics sorting center according to some embodiments of the present disclosure is shown. Figure 2B A partial architecture 200B of a logistics sorting center according to some embodiments of the present disclosure is shown.

[0043] like Figure 2A and Figure 2B As shown, the parcel sorting process can include unloading from the carriage, small piece delivery, handling of NC parts, handling of bulk bags, and palletizing. During palletizing, the parcel is first scanned by the six-sided scanning device 210 along the direction of transport by the sorting conveyor 123 to determine the final sorting slot 124 to which it will be transported. When the parcel reaches the corresponding sorting slot 124, the swing wheel swings to cause the parcel to fall into the corresponding sorting slot 124. The robotic arm 220 then grabs the parcel at the sorting slot 124 and palletizes it. In some embodiments, palletizing can be performed by stacking the parcel into a cage car or by stacking the parcel into a bare pallet.

[0044] In the above scenario, all the parcels of a courier station are sorted into a sorting grid. However, it is difficult for the robotic arm used for palletizing to adapt to all the parcels. For example, the suction cup type end picker uses an air pump to suck the parcel through the suction cup and move it to achieve palletizing. Due to the characteristics of the end picker itself, some parcels cannot be sucked up, and it is impossible for the robotic arm to perform operations such as palletizing or stacking. These parcels may include, but are not limited to: soft packages, packages with irregular surfaces, special-shaped packages, etc. For example, see Figure 3 , Figure 3 An example of a package 300 that cannot be grasped is shown, according to some embodiments of the present disclosure.

[0045] This hardware defect causes a large number of packages that cannot be sucked or grasped by the robotic arm to be stranded at the sorting grid (or fall during the stacking process) and cannot be correctly stacked (or stacked into cage trucks). At this time, manual intervention (manual handling of the stranded packages) is required to correctly stack these packages (or stack them into cage trucks).

[0046] In addition, since the robotic arm will repeatedly try to pick up packages that cannot be picked up normally during the palletizing process, it may cause a backlog of packages, affect the efficiency of palletizing, and may even cause the palletizing work to be stuck and unable to continue.

[0047] Embodiments of the present disclosure provide a solution for item sorting. In the embodiment of the present disclosure, first imaging data associated with a target item at a first sorting position and second imaging data associated with a target item at a second sorting position in an item sorting system are first received. Furthermore, based at least on the first imaging data, a target tag is generated for the target item, the target tag indicating whether the target item can be grasped by a robotic arm in the item sorting system. Furthermore, based on the second imaging data and the target tag, a sorting scheme for the target item is determined, the sorting scheme indicating a sorting slot to place the target item. Finally, the item sorting system is instructed to transport the target item to the sorting slot based on the sorting scheme. In this way, the target item can be placed at the corresponding sorting slot based on whether the target item can be grasped by the robotic arm, preventing target items that cannot be grasped by the robotic arm from being placed at the sorting slot corresponding to the robotic arm, thereby improving palletizing efficiency. Furthermore, the time difference between the target item's movement from the first position to the second position is utilized to generate a target tag for the target item, further improving palletizing efficiency.

[0048] Some example embodiments of the present disclosure will be described below with continued reference to the accompanying drawings.

[0049] Figure 4 A schematic diagram of a process 400 for item sorting according to some embodiments of the present disclosure is shown. Figure 1 For example, Figure 1 The electronic device 110 is shown implementing a process 400 for item sorting.

[0050] At block 410 , the electronic device 110 receives first imaging data associated with a target item 125 at a first sorting location and second imaging data associated with a target item 125 at a second sorting location in an item sorting system.

[0051] In some embodiments, in the item sorting system, the second sorting position is located downstream of the first sorting position. For example, the first sorting position may be the position where the first imaging device 121 is located, and the second sorting position may be the position where the second imaging device 122 is located.

[0052] In some embodiments, the first imaging device 121 may include multiple high-speed cameras. For example, by placing six high-speed cameras at different locations on the sorting conveyor 123, images of different sides of the target item 125 can be captured. Of course, the first imaging device 121 may also utilize other imaging devices to capture images of different sides of the target item 125. For example, with reference to the target item 125, a high-speed camera may be placed in front of, behind, to the left, to the right, top, and bottom of the target item 125. Accordingly, the first imaging data includes images or videos of the target item 125 in the six directions: front, back, left, right, top, and bottom. It should be understood that the second imaging device 122 and the first imaging device 121 may be identical or similar (e.g., including the same components and having the same configuration of the components), and this description will not be repeated here. Of course, the components and configuration of the second imaging device 122 may also differ from those of the first imaging device 121.

[0053] The first imaging data may be a picture or video including the target object 125. The second imaging data may also be a picture or video including the target object 125. Furthermore, the first imaging data and the second imaging data may also be pre-processed pictures or videos. For example, the pre-processing may include adjusting the resolution, dividing the video into segments of predetermined length, etc.

[0054] At block 420 , the electronic device 110 generates a target tag for the target item based on at least the first imaging data, wherein the target tag indicates whether the target item can be grasped by a robot arm in the item sorting system.

[0055] In some embodiments, the electronic device 110 may determine the material characteristics and geometric characteristics of the target object 125 based on the first imaging data, and then generate a target tag for the target object based on the material characteristics, geometric characteristics, and predetermined conditions.

[0056] In some embodiments, the material characteristics of the target item 125 may include, but are not limited to, the hardness, toughness, and brittleness of the item. In some embodiments, the electronic device 110 may utilize machine vision or deep learning to determine the material characteristics of the target item 125 based on the first imaging data. For example, methods for determining the material characteristics of the target item 125 may include, but are not limited to, convolutional neural networks (CNN), feature pyramid networks (FPN), and pyramid scene parsing networks (PSPNet).

[0057] In some embodiments, the geometric features of the target item 125 may include, but are not limited to, the item's shape, size, and volume. In some embodiments, the electronic device 110 may utilize computer vision, image processing techniques, or the like to determine the geometric features of the target item 125 based on the first imaging data. For example, the electronic device 110 may utilize an edge detection algorithm, a contour extraction algorithm, a convolutional neural network, or the like to determine the shape of the target item 125. For another example, the electronic device 110 may determine the size of the target item 125 based on the extracted contour of the target item 125, or determine the size of the target item 125 using a scaling method using a reference object of known size in the first imaging data. For another example, the electronic device 110 may utilize a trained machine learning model to determine the volume of the target item 125. It should be understood that the machine learning model can be trained using imaging data and corresponding volumes of multiple items.

[0058] In the disclosed embodiments, predetermined conditions indicate the conditions that must be met for the robotic arm to be able to grasp an object. In some embodiments, the predetermined conditions can be set based on the material characteristics and geometric characteristics of the target object 125. For example, the material of the object that the robotic arm can grasp (sometimes also referred to as "suction") is a rigid material, and the volume, shape, and size of the object that the robotic arm can grasp are predetermined. The predetermined conditions can then include a rigid material, a predetermined volume, a predetermined shape, and a predetermined size.

[0059] After the electronic device 110 determines the material characteristics and geometric characteristics of the target object 125 based on the first imaging data, it can make a judgment based on the predetermined conditions, that is, determine whether the material characteristics and geometric characteristics of the target object 125 fully meet the predetermined conditions. If the predetermined conditions are fully met, a target label indicating that it can be grasped by the robotic arm can be generated; if the predetermined conditions are not fully met, a target label indicating that it cannot be grasped by the robotic arm can be generated.

[0060] For example, the target label can be “robotic arm slot” or “manual slot.” Here, the “robotic arm slot” label indicates that the robotic arm can grab the target object 125 , and the “manual slot” label indicates that the robotic arm cannot grab the target object 125 .

[0061] In some embodiments, the target tag is also determined based on the weight of the target item. That is, when generating the target tag, the electronic device 110 needs to consider not only the first imaging data, but also the weight of the target item 125. Therefore, in addition to the above-mentioned indicators, the predetermined conditions also need to include a weight indicator. The weight indicator can be set based on the maximum weight of the item that the robotic arm can grasp. For example, the maximum weight of the item that the robotic arm can grasp can be used as the weight indicator, or the maximum weight of the item that the robotic arm can grasp can be multiplied by a predetermined percentage to determine the weight indicator. The embodiments of the present disclosure are not limited to this.

[0062] In some embodiments, the weight of the target item 125 can be obtained by weighing the target item 125 using a weighing device provided at the first imaging device 121. In other embodiments, a dimension weight scanning (DWS) device can also be used to obtain the first imaging data and weight of the target item 125.

[0063] In some embodiments, the electronic device 110 may determine a unique identifier for the target item based on the first imaging data, and then associate the target tag with the unique identifier. In some embodiments, a logistics document may be attached to the target item 125, and the logistics document may include at least the logistics document number of the target item 125. The electronic device 110 may identify the logistics document number on the logistics document of the target item 125 based on the first imaging data as the unique identifier of the target item 125. For example, the electronic device 110 may utilize a deep neural network (DNN), a convolutional neural network, or the like to identify the logistics document number.

[0064] In some embodiments, the electronic device 110 may further associate the unique identifier with the material characteristics and geometric characteristics of the target object 125 to facilitate associating the target identifier with the unique identifier.

[0065] At block 430, the electronic device 110 determines a sorting scheme for the target item based on the second imaging data and the target tag. The sorting scheme indicates a sorting slot into which the target item is to be placed. In some embodiments, the sorting slot may include a first sorting slot or a second sorting slot, where the first sorting slot indicates a slot for sorting using a robotic arm, and the second sorting slot indicates a slot for manual sorting.

[0066] In some embodiments, the electronic device 110 can determine the unique identifier of the target item 125 based on the second imaging data. Further, based on the association between the target tag and the unique identifier, the target tag corresponding to the unique identifier is determined. Finally, based on the target tag, a sorting scheme is determined. For example, if the target tag is "robotic arm grid", the corresponding sorting scheme is to place the target item in a grid for sorting using a robotic arm (e.g., a first sorting grid). For example, if the target tag is "manual grid", the corresponding sorting scheme is to place the target item in a grid based on manual sorting (e.g., a second sorting grid).

[0067] In block 440 , the electronic device 110 causes the item sorting system to transport the target item to a sorting slot based on the sorting scheme.

[0068] In some embodiments, the sorting grid can be configured before the items are sorted. Figure 1 , Figure 1 The dotted-line area includes two sorting slots, which can correspond to a destination or a sales department (logistics station). One of the sorting slots is used for robotic sorting, while the other is used for manual sorting. For example, "Slot 01" can be used to represent a slot for robotic sorting, while "Slot 02" can be used to represent a slot for manual sorting.

[0069] The electronic device 110 can control the swing of the balance wheel at the corresponding position based on the sorting scheme ("Slot 01" or "Slot 02"), so that the target item 125 is transported to the corresponding sorting slot.

[0070] The solution for item sorting provided by the embodiments of the present disclosure can be applied to any suitable scenario for sorting items, such as logistics sorting, food processing, and manufacturing production.

[0071] Hereinafter, some example embodiments of the present disclosure will be described with reference to the accompanying drawings in combination with the scenario of logistics sorting.

[0072] Figure 5 FIG. 5 shows a flow chart of a process 500 for sorting items in a logistics sorting scenario according to some embodiments of the present disclosure. Figure 1 The item sorting system shown implements the item sorting process 500. Figure 5 In the embodiment of the present invention, the process 500 is described by taking packages as an example of items to be sorted.

[0073] In block 510 , the electronic device 110 performs configuration work to configure the robotic arm grid and the manual grid.

[0074] For example, the robotic arm slot can be configured as the A1 slot, which is used to receive packages from a certain logistics site, and these packages can be grabbed by the robotic arm; the manual slot can be configured as the A2 slot, which is used to receive packages from the logistics site, and these packages cannot be grabbed by the robotic arm.

[0075] At block 520 , when the package passes by the first imaging device 121 , the electronic device 110 controls the first imaging device 121 to scan the package (scanning the package for the first time).

[0076] The electronic device 110 can determine the package's characteristics such as the order number, three-dimensional dimensions, weight, volume, and material based on the imaging data sent by the first imaging device 121, and mark the package based on these characteristics and predetermined conditions.

[0077] For example, the tracking number of a package is 0001, and the corresponding label is A1; the tracking number of another package is 0002, and the corresponding label is A2.

[0078] After labeling is completed, the electronic device 110 can associate the order number and the label, and store the association between the two locally or in the cloud.

[0079] At block 530 , when the package passes by the second imaging device 122 , the electronic device 110 controls the second imaging device 122 to scan the package again (scan the package again).

[0080] The electronic device 110 may determine the tracking number of the package based on the imaging data sent by the second imaging device 122 and call the association between the tracking number and the label to determine the label corresponding to the tracking number of the package.

[0081] In block 540 , the electronic device 110 controls the balance wheel to swing, so that the item sorting system transports the package to a corresponding sorting slot (narrow-belt drop slot) based on the sorting plan.

[0082] The electronic device 110 can control the swing of the balance wheel based on the package label, so that the item sorting system can transport the package to the corresponding sorting slot based on the sorting plan. For example, the package labeled A1 is transported to the robotic slot, and the package labeled A2 is transported to the manual slot.

[0083] In the disclosed embodiment, during the sorting process of target items, when the target items move to the first sorting position, imaging data of the target items is collected, and the target items are labeled based on the imaging data. When the target items move to the second sorting position, imaging data of the target items is collected again, and the labels corresponding to the target items are determined based on the imaging data. Finally, based on the determined labels, the item sorting system transports the target items to the sorting slots according to the sorting plan.

[0084] Since the process of marking target items takes a long time (for example, the time required to transmit imaging data, the time required to perform algorithm analysis, the time required to return the target label, etc.), if the target items are marked during the process of performing grid coding, the marking process may not be completed after the target items have been transported to the sorting grid. Therefore, the embodiment of the present disclosure uses imaging data (component link) at the first sorting position to mark the target items, and uses imaging data (subdivision link) at the second sorting position to identify the target item's label, thereby utilizing the time difference between the target items moving from the first sorting position to the second sorting position to mark the target items, thereby improving sorting efficiency.

[0085] Figure 6 FIG2 is a schematic diagram of an apparatus 600 for item sorting according to some embodiments of the present disclosure. The apparatus 600 may be implemented as or include the electronic device 110.

[0086] like Figure 6 As shown, the device 600 includes a receiving module 610, which is configured to receive first imaging data associated with a target item at a first sorting position in an item sorting system and second imaging data associated with a target item at a second sorting position. The device 600 also includes a generating module 620, which is configured to generate a first label of the target item based on at least the first imaging data, the first label indicating whether the target item can be grasped by a robotic arm in the item sorting system. The device 600 also includes a determining module 630, which is configured to determine a sorting scheme for the target item based on the second imaging data and the first label, the sorting scheme indicating a sorting grid into which the target item is to be placed. The device 600 also includes a control module 640, which is configured to cause the item sorting system to transport the target item to the sorting grid based on the sorting scheme.

[0087] In some embodiments, in the item sorting system, the second sorting location is located downstream of the first sorting location.

[0088] In some embodiments, the generation module 620 is further configured to determine the material characteristics and geometric characteristics of the target object based on the first imaging data; and generate a target label for the target object based on the material characteristics, geometric characteristics and predetermined conditions, where the predetermined conditions indicate the conditions that must be met for the robotic arm to be able to grasp the object.

[0089] In some embodiments, the target tag is also determined based on the weight of the target item.

[0090] In some embodiments, the apparatus 600 further includes an association module configured to determine a unique identifier of the target object based on the first imaging data; and associate the target tag with the unique identifier.

[0091] In some embodiments, the determination module 630 is further configured to determine a unique identifier of the target item based on the second imaging data; determine a target tag corresponding to the unique identifier based on an association between the target tag and the unique identifier; and determine a sorting scheme based on the target tag.

[0092] In some embodiments, the sorting plan indicates that the target item is placed in a first sorting slot or a second sorting slot, the first sorting slot indicates a slot for sorting using a robotic arm, and the second sorting slot indicates a slot based on manual sorting.

[0093] The modules included in the device 600 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units in the device 700 can be implemented at least in part by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0094] Figure 7 1 shows a block diagram of an electronic device 700 in which one or more embodiments of the present disclosure may be implemented. Figure 7 The illustrated electronic device 700 is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein.

[0095] like Figure 7 As shown, electronic device 700 is in the form of a general-purpose computing device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 710, memory 720, storage device 730, one or more communication units 740, one or more input devices 750, and one or more output devices 760. Processing unit 710 may be a real or virtual processor and is capable of performing various processes according to programs stored in memory 720. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to increase the parallel processing capabilities of electronic device 700.

[0096] The electronic device 700 typically includes a plurality of computer storage media. Such media can be any available media accessible to the electronic device 700, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 720 can be a volatile memory (e.g., registers, cache, random access memory (RAM)), a non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory) or some combination thereof. The storage device 730 can be a removable or non-removable medium and can include a machine-readable medium, such as a flash drive, a disk or any other medium, which can be used to store information and / or data (e.g., training data for training) and can be accessed within the electronic device 700.

[0097] The electronic device 700 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 7 As shown in FIG, a magnetic disk drive for reading from or writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. The memory 720 may include a computer program product 725 having one or more program modules configured to perform the various methods or actions of various embodiments of the present disclosure.

[0098] The communication unit 740 enables communication with other electronic devices via a communication medium. Additionally, the functions of the components of the electronic device 700 can be implemented as a single computing cluster or multiple computing machines that can communicate via a communication connection. Thus, the electronic device 700 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0099] Input device 750 may be one or more input devices, such as a mouse, keyboard, or trackball. Output device 760 may be one or more output devices, such as a display, a speaker, or a printer. Electronic device 700 may also communicate with one or more external devices (not shown) via communication unit 740 as needed, such as storage devices, display devices, or the like, with one or more devices that allow a user to interact with electronic device 700, or with any device that allows electronic device 700 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0100] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0101] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products implemented according to the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0102] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, such that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0103] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0104] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple implementations of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part for a module, program segment or instruction, and a part for a module, program segment or instruction comprises one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0105] While various implementations of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is selected to best explain the principles of the implementations, their practical applications, or improvements to existing technologies, or to enable others skilled in the art to understand the implementations disclosed herein.

Claims

1. A method for sorting items, comprising: receiving first imaging data associated with a target item at a first sorting position and second imaging data associated with the target item at a second sorting position in an item sorting system; generating a target tag for the target object based at least on the first imaging data, the target tag indicating whether the target object can be grasped by a robot arm in the object sorting system; Determine a sorting scheme for the target object based on the second imaging data and the target tag, the sorting scheme indicating a sorting slot into which the target object is to be placed; as well as The object sorting system is enabled to transport the target object to the sorting slot based on the sorting scheme.

2. The method according to claim 1, wherein in the item sorting system, the second sorting position is located downstream of the first sorting position.

3. The method according to claim 1, wherein generating a target label of the target object based at least on the first imaging data comprises: Determining material characteristics and geometric characteristics of the target object based on the first imaging data; as well as The target tag of the target object is generated based on the material feature, the geometric feature and a predetermined condition, wherein the predetermined condition indicates a condition that needs to be satisfied for the object to be grasped by the robotic arm. The method of claim 1 , wherein the target tag is further determined based on a weight of the target item.

5. The method according to claim 1, further comprising: Determining a unique identifier of the target object based on the first imaging data; as well as The target tag is associated with the unique identifier.

6. The method of claim 5, wherein determining the sorting scheme based on the second imaging data and the target tag comprises: Determining the unique identifier of the target object based on the second imaging data; Determine the target tag corresponding to the unique identifier based on the association relationship between the target tag and the unique identifier; as well as Based on the target tag, the sorting solution is determined.

7. The method according to claim 1, wherein the sorting scheme indicates that the target item is placed in a first sorting grid or a second sorting grid, the first sorting grid indicates a grid for sorting using a robotic arm, and the second sorting grid indicates a grid based on manual sorting.

8. A device for sorting items, comprising: A receiving module configured to receive first imaging data associated with a target item at a first sorting position and second imaging data associated with the target item at a second sorting position in an item sorting system; A generating module, configured to generate a first label of the target object based at least on the first imaging data, wherein the first label indicates whether the target object can be grasped by a robot arm in the object sorting system; a determination module configured to determine a sorting scheme for the target object based on the second imaging data and the first label, the sorting scheme indicating a sorting slot into which the target object is to be placed; as well as The control module is configured to enable the object sorting system to transport the target object to the sorting slot based on the sorting scheme.

9. An electronic device, comprising: at least one processing unit; as well as At least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the electronic device to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 7.

11. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 7.

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