Logistics distribution method and device, storage medium and electronic equipment

Abstract

This disclosure provides a logistics distribution method, apparatus, storage medium, and electronic device, relating to the field of big data technology. The method includes: merging multiple outbound orders into one or more waves; generating multiple picking tasks based on the merged waves; receiving the picking result of at least one picking task from the multiple picking tasks; and, in response to receiving the picking result, generating distribution tasks based on predetermined distribution logic, so as to distribute goods according to the distribution tasks. This invention achieves the merging and processing of multiple waves, improving distribution efficiency. Furthermore, the predetermined distribution logic enables automation and intelligence of distribution. Further, the distribution method based on distribution logic decouples the distribution from the picking process, thereby improving the flexibility of goods distribution and adaptability to big data logistics, thus improving logistics distribution efficiency and saving manpower and time resources for logistics distribution.

Description

Technical Field

[0001] This disclosure relates to the field of big data technology, and in particular to a logistics distribution method and apparatus, storage medium and electronic equipment. Background Technology

[0002] In recent years, with the development of the logistics industry, the requirements for the processing capacity and efficiency of logistics data have become increasingly higher. This is especially true for the fast-moving consumer goods (FMCG) industry, which is characterized by high consumption frequency, short usage time, a broad consumer base, and high demands for convenience. Fresh produce in FMCG warehouses has an extremely short shelf life and high spoilage rate; every additional hour of picking time or every additional movement significantly increases spoilage. The rapid picking of large quantities of fresh produce has always been a major challenge for warehousing operations.

[0003] In related technologies, picking and sorting are two important stages in warehouse management. Existing logistics sorting methods typically process goods in different waves, and sorting begins only after all goods in the same wave have been picked. This consumes a significant amount of manpower and time resources, and the efficiency, flexibility, and adaptability of logistics sorting are insufficient.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a logistics distribution method and apparatus, storage medium and electronic device, which at least to some extent overcomes the problem of wasted human and time resources caused by the lack of flexibility in the picking and distribution process and the lack of adaptability to big data logistics in related technologies, which affects the efficiency of logistics distribution.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a logistics distribution method is provided, characterized by comprising: combining multiple outbound orders into one or more waves; generating multiple picking tasks based on the combined waves; receiving picking results from at least one picking task among the multiple picking tasks; and, in response to receiving the picking results, generating distribution tasks based on the picking results and a predetermined distribution logic, so as to distribute goods according to the distribution tasks.

[0008] In some embodiments of this disclosure, the step of generating a distribution task based on the picking results and a predetermined distribution logic includes: sorting the orders according to their generation time; generating an order sequence according to the sorting results; and sequentially allocating the picking results to the orders according to the order sequence to generate the distribution task.

[0009] In some embodiments of this disclosure, generating a distribution task based on a predetermined distribution logic according to the picking results includes: allocating the picking results of the first wave of picking tasks to the orders corresponding to the second wave of distribution tasks; and / or allocating the picking results of the first wave of picking tasks to the orders corresponding to the first wave of distribution tasks.

[0010] In some embodiments of this disclosure, multiple picking tasks and distribution tasks are processed asynchronously in parallel.

[0011] In some embodiments of this disclosure, in response to receiving a picking result, generating a splitting task based on a predetermined splitting logic according to the picking result includes: in response to each received picking result, generating a splitting task in real time based on the current picking result and a predetermined splitting logic.

[0012] In some embodiments of this disclosure, generating a distribution task based on a predetermined distribution logic according to the picking results includes: determining whether the quantity of goods in the picking results reaches a first value; if the first value is reached, then according to a preset time period, the picking results are sequentially distributed to orders according to the order sequence to generate a distribution task; if the first value is not reached, then according to a preset quantity period, the picking results are sequentially distributed to orders according to the order sequence to generate a distribution task.

[0013] In some embodiments of this disclosure, picking results are sequentially assigned to orders according to the order sequence to generate a distribution task according to a preset time period, including: timing in each period; and in response to the timing result reaching a second value, picking results are sequentially assigned to orders according to the order sequence to generate a distribution task.

[0014] In some embodiments of this disclosure, picking results are sequentially assigned to orders according to the order sequence in a preset quantity period to generate a distribution task, including: counting the quantity of goods in the picking results in each period; and in response to the counting result reaching a third value, sequentially assigning the picking results to orders according to the order sequence to generate a distribution task.

[0015] In some embodiments of this disclosure, after the goods are distributed according to the distribution task, the process includes: receiving the distribution result generated by distributing the goods according to the distribution task; in response to obtaining the distribution result, determining whether there is a distribution task to be distributed, and if there is a distribution task to be distributed, readjusting the quantity of goods corresponding to the distribution task to be distributed.

[0016] In some embodiments of this disclosure, after the goods are distributed according to the distribution task, the method further includes: obtaining a cumulative picking result from the quantity of goods in the cumulative picking result and obtaining a cumulative distribution result from the quantity of goods in the distribution result, so as to verify the accuracy of the distribution task based on the cumulative picking result and the cumulative distribution result.

[0017] In some embodiments of this disclosure, combining multiple outbound orders into one or more waves includes: combining multiple outbound orders into one or more waves based on the type of goods and / or delivery time and / or delivery location in the outbound orders.

[0018] In some embodiments of this disclosure, generating multiple picking tasks based on a synthesized wave includes generating multiple picking tasks based on the goods in the synthesized wave according to a preset volume or preset weight.

[0019] According to another aspect of this disclosure, a logistics distribution device is provided, comprising: a wave division module configured to combine multiple outbound orders into one or more waves; a picking task generation module configured to generate multiple picking tasks based on the combined waves; a picking result receiving module configured to receive the picking result of at least one picking task among the multiple picking tasks; and a distribution module configured to, in response to receiving the picking result, generate a distribution task based on the picking result and a predetermined distribution logic, so as to distribute the goods according to the distribution task.

[0020] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described logistics distribution method by executing the executable instructions.

[0021] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described logistics distribution method.

[0022] The logistics distribution method provided in the embodiments of this disclosure enables the merging and processing of multiple waves, thereby improving distribution efficiency. Furthermore, it achieves automation and intelligence in distribution through predetermined distribution logic.

[0023] Furthermore, the splitting logic-based splitting method decouples the splitting process from the picking process, thereby improving the flexibility of goods splitting and its adaptability to big data logistics, thus improving logistics splitting efficiency and saving human and time resources for logistics splitting.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0025] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0026] Figure 1 A schematic diagram of an exemplary system architecture for a logistics distribution method or logistics distribution device to which embodiments of the present disclosure can be applied is shown.

[0027] Figure 2 A flowchart of a logistics distribution method according to an embodiment of this disclosure is shown.

[0028] Figure 3 The flowchart illustrates a method for generating distribution tasks based on predetermined distribution logic according to picking results in a logistics distribution method according to an embodiment of this disclosure.

[0029] Figure 4 The diagram illustrates a logistics distribution method according to an embodiment of the present disclosure, in which the picking results are sequentially assigned to the orders based on the order sequence to generate distribution tasks.

[0030] Figure 5 A flowchart of yet another logistics distribution method according to an embodiment of this disclosure is shown.

[0031] Figure 6 A schematic diagram of a logistics distribution method according to an embodiment of this disclosure is shown.

[0032] Figure 7 A schematic diagram of a logistics distribution device according to an embodiment of this disclosure is shown.

[0033] Figure 8 A structural block diagram of a logistics distribution computer device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0034] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0035] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0036] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0037] This application discloses a logistics distribution method based on streaming computing. For ease of understanding, several terms involved in this application will be explained below.

[0038] A Warehouse Management System (WMS) is a standardized, intelligent, process-oriented warehouse management software that can accurately and efficiently manage and track customer orders, purchase orders, and the overall management of the warehouse.

[0039] A Stock Keeping Unit (SKU) is the basic unit for measuring inventory inflows and outflows. It can be a piece, box, pallet, etc. It has now been extended to refer to a unique product number; each product corresponds to a unique SKU. It can also be called a single item: for a product, when its brand, model, configuration, grade, color, packaging capacity, unit, production date, shelf life, use, price, place of origin, etc., differ from other products, it can be called a single item.

[0040] The solutions provided in this application involve technologies such as big data logistics distribution, which are specifically illustrated through the following embodiments:

[0041] Figure 1 A schematic diagram of an exemplary system architecture for which the logistics distribution method or apparatus according to embodiments of the present disclosure can be applied is shown. The system includes: a plurality of terminals 120 and a server 140.

[0042] Terminal 120 can be a mobile terminal such as a mobile phone, game console, tablet computer, e-book reader, smart glasses, MP4 (Moving Picture Experts Group Audio Layer IV) player, smart home device, AR (Augmented Reality) device, VR (Virtual Reality) device, etc. Alternatively, terminal 120 can also be a personal computer (PC), such as a laptop computer and a desktop computer, etc.

[0043] The terminal 120 may be equipped with an application for providing logistics distribution.

[0044] Terminal 120 and server 140 are connected via a communication network. The communication network can be a wired network or a wireless network.

[0045] Server 140 can be a server that provides various services, such as a backend management server that supports users in using the information on items to be distributed provided by terminal 120. The backend management server can perform processing on the received information, such as recommending seeding locations and binding, and feed back the processing results (such as binding results) to the terminal.

[0046] In one embodiment, the application client installed on different terminals 120 is the same, or the application clients installed on two terminals 120 are clients of the same type of application on different control system platforms. Depending on the terminal platform, the specific form of the application client can also differ; for example, the application client can be a mobile client, a PC client, or a World Wide Web (Web) client.

[0047] Those skilled in the art will understand that the number of terminals 120 described above can be more or less. For example, there may be only one terminal, or there may be dozens or hundreds of terminals, or even more. This application does not limit the number of terminals or the type of device.

[0048] In one embodiment, the system may further include a management device ( Figure 1 (Not shown), the management device is connected to the server cluster 140 via a communication network. In some embodiments, the communication network is a wired network or a wireless network.

[0049] In some embodiments, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can be any network, including but not limited to Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile, wired or wireless networks, private networks, or any combination of virtual private networks. In some embodiments, technologies and / or formats, including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPsec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies may be used to replace or supplement the aforementioned data communication technologies.

[0050] The following will describe in more detail each step of the logistics distribution method in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0051] Figure 2 The flowchart illustrates a logistics distribution method according to an embodiment of this disclosure. The method provided in this embodiment can be executed by any electronic device with computing power, such as a warehouse management system (WMS) capable of implementing full-process control and management of all aspects of warehousing, or a sub-module system within the aforementioned warehouse management system used for receiving goods data for warehousing. Figure 1 Terminal 120 and / or server 140 in the middle.

[0052] The following example uses server 140 as the execution subject for illustration.

[0053] like Figure 2 As shown, the method provided in this disclosure embodiment may include the following steps:

[0054] Step 210: Combine multiple outbound orders into one or more waves.

[0055] In some embodiments of this disclosure, the outbound order is a record of goods shipped from the warehouse based on user orders, used to record and manage outbound goods data.

[0056] In some embodiments of this disclosure, in the field of warehousing and logistics, the batch sorting method is to sort goods in units of a batch that summarizes multiple outbound orders. In the industry, this batch is usually referred to as a "wave".

[0057] Step S220: Generate multiple picking tasks based on the synthesized wave sequence.

[0058] In some embodiments of this disclosure, multiple picking tasks can be generated by processing one or more waves of goods based on the goods' attribute information. The attribute information can be information such as the goods' SKU (stock keeping unit), volume, weight, etc.

[0059] Step 230: Receive the picking result of at least one picking task from multiple picking tasks.

[0060] In some embodiments of this disclosure, each picking task generated based on each wave has a corresponding picking result upon completion. Multiple picking results from the same wave can be submitted simultaneously or sequentially.

[0061] Step 240: In response to receiving the picking result, generate a distribution task based on the picking result and a predetermined distribution logic, so as to distribute the goods according to the distribution task.

[0062] In some embodiments of this disclosure, the predefined distribution logic generates corresponding distribution tasks after each picking result is submitted. For example, distribution tasks are automatically generated according to the order sequence based on the quantity of picking results. As another example, when the quantity of goods is very large, distribution tasks can be generated periodically according to the order sequence at preset times to ensure distribution efficiency. In some embodiments of this disclosure, the predefined logic is not limited.

[0063] In some embodiments of this disclosure, when each subcast task is completed, the system generates a corresponding subcast result to indicate that the subcast is complete.

[0064] This invention enables the merging of multiple waves, improving splitting efficiency. Furthermore, it automates and intelligently performs splitting through predetermined splitting logic.

[0065] Furthermore, the splitting logic-based splitting method decouples the splitting process from the picking process, thereby improving the flexibility of goods splitting and its adaptability to big data logistics, thus improving logistics splitting efficiency and saving human and time resources for logistics splitting.

[0066] In some embodiments of this disclosure, generating a distribution task based on a predetermined distribution logic according to the picking results includes: allocating the picking results of the first wave of picking tasks to the orders corresponding to the second wave of distribution tasks; and / or allocating the picking results of the first wave of picking tasks to the orders corresponding to the first wave of distribution tasks.

[0067] For example, the system can match any batch of picking results submitted from different batches with any distribution task containing that item, according to the order sequence.

[0068] By adopting a wave (merging) parallel processing method, multiple waves can be executed simultaneously for sorting operations, resulting in higher efficiency.

[0069] In some embodiments of this disclosure, multiple picking tasks and distribution tasks are processed asynchronously in parallel.

[0070] For example, in this method, the system does not wait for all picking results to be completed before generating the corresponding distribution task. Thus, by processing picking and distribution tasks asynchronously and in parallel, picking and distribution do not interfere with each other, reducing merging actions, saving distribution time, and improving distribution processing efficiency.

[0071] In some embodiments of this disclosure, in response to receiving a picking result, generating a split task based on a predetermined split logic according to the picking result includes: in response to each received picking result, generating a split task in real time based on the current picking result and a predetermined split logic.

[0072] For example, based on the picking results submitted in real time, distribution tasks are generated in real time according to the order sequence. This method can effectively solve the problem of not being able to perform large-scale, real-time, and efficient distribution.

[0073] Figure 3 This illustration shows a flowchart of a logistics distribution method in an embodiment of the present disclosure, which generates distribution tasks based on picking results and predetermined distribution logic. For example... Figure 3 As shown, the method may include the following steps:

[0074] Step S310: Sort the orders according to their creation time.

[0075] Step S320: Generate an order sequence based on the sorting results.

[0076] Step S330: Based on the order sequence, the picking results are sequentially assigned to the orders to generate a distribution task.

[0077] In some embodiments of this disclosure, an order represents a list of goods ordered by a user. The order can be generated at the time the user places the order or at the time the customer schedules delivery. This application does not impose any limitations on this.

[0078] In some embodiments of this disclosure, orders are sorted according to their generation time. The earlier an order is generated, the higher its priority in the order sequence. When the system generates a distribution task each time based on the picking results, it prioritizes the highest-level order and distributes goods in descending order of priority until all the picking results have been distributed.

[0079] The embodiments of the present invention can be used for timeliness control in logistics management, enabling real-time distribution according to the order generation time, thereby improving logistics distribution efficiency.

[0080] Figure 4 This illustration shows a flowchart of a logistics distribution method according to an embodiment of the present disclosure, which involves sequentially allocating picking results to the orders based on the order sequence to generate distribution tasks. Figure 4 As shown, the method may include the following steps:

[0081] Step 410: Determine whether the quantity of goods in the picking results has reached the first value.

[0082] In some embodiments of this disclosure, the quantity of goods (also referred to as the volume of goods data) varies according to changes in the current number of orders and the demand for goods. In particular, during certain events, such as 618 and Double 11, the volume of goods data can also be any value within the load capacity that the current processor can handle. This disclosure does not limit the size of the volume of goods data.

[0083] In this embodiment, the first value is a pre-set total amount of goods data. If the quantity of goods reaches or exceeds the first value, it indicates that the current amount of goods data is large and there are many orders to be processed. In this case, step S420 is executed, and the picking results are sequentially allocated to orders according to the order sequence and a preset time period to generate a distribution task. Processing the picking results according to the time period allows for better control over the distribution operation time, improving the controllability of the distribution operation time and thus helping to improve distribution efficiency.

[0084] If the quantity of goods does not reach the first value, it indicates that the current volume of goods data is small and there are few orders to be processed. In this case, step S430 is executed, and the picking results are sequentially allocated to orders according to the order sequence and a preset quantity period to generate a distribution task. Processing the picking results according to the quantity period facilitates the statistics of picking data, thereby improving distribution efficiency.

[0085] In some embodiments of this disclosure, the first value can be set by the operator according to specific circumstances. For example, the size of the first value may be determined based on the amount of cargo data and the assigned carrier, and then the method for processing the picking results may be selected based on the actual amount of cargo data. In other embodiments, the first value may also be automatically generated by the system, especially the optimal solution obtained from model training. This application does not impose any limitations on this.

[0086] This method can flexibly generate distribution tasks based on the quantity of goods in each picking result. Specifically, when there are many goods, distribution tasks can be created according to time periods, and when there are few goods, distribution tasks can be created according to the quantity of goods. On the one hand, this achieves efficient distribution, and on the other hand, merging multiple goods together for distribution also saves the human resources consumed by distribution tasks.

[0087] In some embodiments of this disclosure, step S420 may further include step S420a, timing in each cycle; and step S420b, in response to the timing result reaching a second value, sequentially allocating the picking results to orders according to the order sequence to generate a distribution task.

[0088] In this embodiment, the second value is a preset time period. Taking a second value of 10 seconds as an example, the system processes the generated picking results every 10 seconds to generate corresponding distribution tasks. Furthermore, the second value can be in seconds or minutes; the time unit can be freely set. The second value is any positive number greater than 0; the exact value is not limited and can be determined based on the computing power of the device, the scale of the goods data, or other factors. By setting the second value to process picking results and generate distribution tasks according to a preset period, the waiting time for executing distribution tasks can be reduced, increasing the speed of goods distribution.

[0089] In some embodiments of this disclosure, step S430 may further include step S430a, in each cycle, counting the quantity of goods in the picking results; and step S430b, in response to the counting result reaching a third value, processing the picking results to generate a distribution task.

[0090] In this embodiment, the third value is a pre-set quantity period. When the number of picking results generated reaches this third value, the system processes these picking results to generate a distribution task. Taking a third value of 1 as an example, the system processes each picking result generated to generate a corresponding distribution task, thus achieving real-time processing of the picking results. Furthermore, the third value can be a positive integer greater than or equal to 1, and its value is not limited. It can be determined based on the computing power of the device with computing capabilities, the scale of the goods data, or other factors. This facilitates the statistical analysis of picking data, and when the number of goods is small, distribution tasks can be established based on quantity, saving labor costs.

[0091] Figure 5 A flowchart of yet another logistics distribution method according to an embodiment of this disclosure is shown. Figure 5 As shown, the method may include the following steps:

[0092] Step S510: Combine multiple outbound orders into one or more waves.

[0093] In some embodiments of this disclosure, step S510 includes step S510a, which combines multiple outbound orders into one or more waves based on the type (SKU) of goods and / or delivery time and / or delivery location in the outbound order.

[0094] This invention categorizes goods based on their attributes, especially their type, delivery time, and delivery location, enabling more organized and efficient goods distribution.

[0095] Step S520: Based on the synthesized wave of goods, generate multiple picking tasks according to preset volume or preset weight.

[0096] In some embodiments of this disclosure, step S520 includes step S520a, where the preset volume and preset weight are set according to the sorting carrier to avoid exceeding the load of the sorting carrier. This invention generates picking tasks based on volume or weight, making the allocation of picking tasks more flexible, adaptable to the picking needs of various sorting carriers, facilitating the sorting of goods, reducing the cost of task sorting, and demonstrating strong adaptability. Step S530 involves receiving the picking result of at least one picking task from a plurality of picking tasks.

[0097] Step S540: Generate a distribution task based on the picking results and a predetermined distribution logic, so as to distribute the goods according to the distribution task.

[0098] In some embodiments of this disclosure, the system creates a distribution task in real time according to a predetermined logic based on the picking results submitted each time the picking is completed. After the distribution task is completed, step S550 is executed to receive the distribution results of the goods distributed according to the distribution task.

[0099] In response to the acquired distribution results, the system automatically executes step S560 to determine whether there are any tasks to be distributed. If there are any tasks to be distributed, the quantity of goods corresponding to those tasks is readjusted. Distributing is completed and then the pending distribution services are updated to save distribution time and improve distribution efficiency.

[0100] If there are no tasks to be distributed, the system assumes the distribution task has been completed. The cumulative picking result is obtained by calculating the total number of goods in the cumulative picking result, and the cumulative distribution result is obtained by calculating the total number of goods in the distribution result. The accuracy of the distribution task is verified based on the cumulative picking result and the cumulative distribution result. This makes the distribution method of the present invention simple, the verification process quick, and the verification results accurate. It also makes the distribution results more intuitive.

[0101] Figure 6 This diagram illustrates a scenario of a logistics distribution method according to an embodiment of the present disclosure. Figure 6 As shown, logistics distribution proceeds in the direction of the arrows, and the specific process is as follows:

[0102] First, assign an order to each slot (1-10). Slots are used to load goods, and the purpose of assigning orders is to ensure that each slot is assigned the goods corresponding to its order. Import the order data (e.g., 100 items) into the system to generate the corresponding internal outbound order (1-10).

[0103] The system will merge several outbound orders with the same characteristics (the same characteristics can be such as a high overlap rate of delivery location / delivery time / product type) into a wave. For example, the three waves in the figure contain outbound orders 1-4, outbound orders 5-8 and outbound orders 9-10 respectively.

[0104] The system simultaneously performs picking operations on 30 items from wave 1, 50 items from wave 2, and 20 items from wave 3. Specifically, to improve picking efficiency, the system can split the goods in each wave into multiple picking tasks based on product type. It can also split them into multiple picking tasks based on the volume or weight of the goods. Taking wave 1 as an example, the system splits the 30 items in wave 1 into four picking tasks containing 11, 4, 6, and 9 items respectively. This allows for the execution of four picking tasks, generating four corresponding picking results. As shown in the figure, the system simultaneously performs picking operations on 11 picking tasks across 3 waves (i.e., 11-4-6-9-11-9-12-13-5-11-9). After each picking task is completed, the goods are submitted to the temporary storage area, and the system receives a picking result. Simultaneously, the system processes the received picking results according to a preset time or quantity to generate distribution tasks based on a predetermined distribution logic, such as the order creation time, starting with the earliest orders. Then, the system retrieves goods from the temporary storage area and matches them to specific distribution tasks to distribute the goods to the corresponding slots. Specifically, retrieving goods from the temporary storage area and matching them to specific distribution tasks can include: the system assigning a tag to each type of goods (each SKU) to identify its identity, or the system determining the identity of goods based on image recognition. By matching the identity of the goods with the distribution tasks, the system ensures that the goods retrieved from the temporary storage area are distributed to the accurate slots.

[0105] Each time a distribution task is completed, the system updates the data of goods to be assigned and regenerates the distribution task to improve distribution efficiency, until all goods are distributed to the corresponding grid.

[0106] The system can also verify the distribution after all goods have been distributed. This involves comparing the picking results of all accumulated goods data with the distribution results; if the results match, the distribution operation is considered accurate. By leveraging the consistent input and output totals characteristic of streaming computing, the system ensures that all goods are successfully distributed. This allows for lower latency in processing goods data, providing a more accurate and timely distribution solution.

[0107] The logistics distribution method provided in this disclosure can select to process the picking results in real time and generate distribution tasks according to time or quantity cycles based on the current cargo data volume. This improves the adaptability to big data logistics and makes logistics distribution more flexible, thereby improving the efficiency of logistics distribution and further saving manpower and time resources.

[0108] Figure 7 A schematic diagram of a logistics distribution device according to an embodiment of this disclosure is shown. Figure 7As shown, the logistics distribution device 700 provided in this embodiment may include: a wave division module 710, a picking task generation module 720, a picking result receiving module 730, and a distribution module 740. The wave division module 710 can be configured to combine multiple outbound orders into one or more waves. The picking task generation module 720 is configured to generate multiple picking tasks based on the combined waves. The picking result receiving module 730 is configured to receive the picking result of at least one picking task among the multiple picking tasks. The task generation module 740 is configured to, in response to receiving the picking result, generate a distribution task based on the picking result and a predetermined distribution logic, so as to distribute the goods according to the distribution task.

[0109] In some embodiments of this disclosure, the task generation module 740 is specifically configured to: sort the orders according to their generation time; generate an order sequence according to the sorting result; and allocate the picking results to the orders in sequence according to the order sequence to generate a distribution task.

[0110] In some embodiments of this disclosure, the task generation module 740 is specifically configured to: assign the picking results of the first wave of picking tasks to the orders corresponding to the second wave of distribution tasks; and / or assign the picking results of the first wave of picking tasks to the orders corresponding to the first wave of distribution tasks.

[0111] In some embodiments of this disclosure, multiple picking tasks and distribution tasks are processed asynchronously in parallel.

[0112] In some embodiments of this disclosure, the task generation module 740 is specifically configured to generate a distribution task in real time based on a predetermined distribution logic in response to each received picking result.

[0113] In some embodiments of this disclosure, the task generation module 740 is further configured to determine whether the quantity of goods in the picking results reaches a first value: if the first value is reached, the picking results are sequentially assigned to orders according to the order sequence according to a preset time period to generate a distribution task; if the first value is not reached, the picking results are sequentially assigned to orders according to the order sequence according to a preset quantity period to generate a distribution task.

[0114] In some embodiments of this disclosure, picking results are sequentially assigned to orders according to the order sequence to generate a distribution task according to a preset time period, including: timing in each period; and in response to the timing result reaching a second value, picking results are sequentially assigned to orders according to the order sequence to generate a distribution task.

[0115] In some embodiments of this disclosure, picking results are sequentially assigned to orders according to the order sequence in a preset quantity period to generate a distribution task, including: counting the quantity of goods in the picking results in each period; and in response to the counting result reaching a third value, sequentially assigning the picking results to orders according to the order sequence to generate a distribution task.

[0116] In some embodiments of this disclosure, the device 700 further includes a determination module configured to receive a distribution result generated by distributing goods according to a distribution task; in response to obtaining the distribution result, determine whether there is a distribution task to be distributed; if there is a distribution task to be distributed, readjust the quantity of goods corresponding to the distribution task to be distributed.

[0117] In some embodiments of this disclosure, the device 700 further includes a verification module configured to obtain a cumulative picking result from the number of goods in the cumulative picking result and a cumulative distribution result from the number of goods in the distribution result, so as to verify the accuracy of the distribution task based on the cumulative picking result and the cumulative distribution result.

[0118] In some embodiments of this disclosure, the wave division module 710 is specifically configured to combine multiple outbound orders into one or more waves based on the type of goods and / or delivery time and / or delivery location in the outbound order.

[0119] In some embodiments of this disclosure, the picking task generation module 720 is specifically configured to generate the plurality of picking tasks based on the synthesized wave of goods according to a preset volume or preset weight.

[0120] The specific implementation of each module in the logistics distribution device for storing object information provided in this embodiment can refer to the content of the above logistics distribution method, and will not be repeated here.

[0121] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0122] The following reference Figure 8 To describe an electronic device 800 according to this embodiment of the present invention. Figure 8 The electronic device 800 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.

[0123] like Figure 8As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0124] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 810 can perform actions such as... Figure 2 S210, as shown, combines multiple outbound orders into one or more waves; S220, generates multiple picking tasks based on the combined waves; S230, receives the picking result of at least one picking task among the multiple picking tasks; S240, in response to receiving the picking result, generates a distribution task based on the picking result and a predetermined distribution logic, so as to distribute the goods according to the distribution task.

[0125] Storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include a read-only memory (ROM) 8203.

[0126] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0127] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0128] Electronic device 800 can also communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 800, and / or any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0129] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0130] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may 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 readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable 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 devices, magnetic storage devices, or any suitable combination thereof.

[0132] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0133] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0134] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0135] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0136] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0137] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure.

[0138] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A logistics distribution method, characterized in that, include: Combine multiple outbound orders into one or more waves; Multiple picking tasks are generated based on the synthesized wave sequence; Receive the picking result of at least one of the multiple picking tasks; In response to receiving the picking result, a distribution task is generated based on the picking result and a predetermined distribution logic, so as to distribute the goods according to the distribution task; The step of generating a distribution task based on the picking results and a predetermined distribution logic includes: The picking results of the first wave of picking tasks are assigned to the orders corresponding to the second wave of distribution tasks; And / or, assign the picking results of the first wave of picking tasks to the orders corresponding to the first wave of distribution tasks; After distributing the goods according to the distribution task, the method further includes: The cumulative picking result is obtained by accumulating the quantity of goods in the picking results, and the cumulative distribution result is obtained by accumulating the quantity of goods in the distribution results. The results are then verified using streaming computation based on the cumulative picking result and the cumulative distribution result. The multiple picking tasks and the distribution tasks are processed asynchronously in parallel.

2. The logistics distribution method according to claim 1, characterized in that, The step of generating a distribution task based on the picking results and a predetermined distribution logic includes: The orders are sorted according to their creation time. Generate an order sequence based on the sorting results; and Based on the order sequence, the picking results are sequentially assigned to the orders to generate a distribution task.

3. The logistics distribution method according to claim 1, characterized in that, The step of responding to receiving the picking result and generating a distribution task based on the picking result and a predetermined distribution logic includes: In response to each received picking result, a splitting task is generated in real time based on the pre-defined splitting logic according to the current picking result.

4. The logistics distribution method according to claim 2, characterized in that, The step of sequentially allocating the picking results to the orders according to the order sequence to generate a distribution task includes: Determine whether the quantity of goods in the picking results reaches the first value: If the first value is reached, the picking results will be sequentially assigned to the orders according to the order sequence and the preset time period to generate a distribution task. If the first value is not reached, the picking results will be sequentially allocated to the orders according to the order sequence and the preset quantity period to generate a distribution task.

5. The logistics distribution method according to claim 4, characterized in that, According to a preset time period, the picking results are sequentially allocated to the orders based on the order sequence to generate a distribution task, including: Timing is performed in each cycle; In response to the timing result reaching the second value, the picking results are sequentially assigned to the orders according to the order sequence to generate a distribution task.

6. The logistics distribution method according to claim 5, characterized in that, According to a preset quantity cycle, the picking results are sequentially allocated to the orders based on the order sequence to generate a distribution task, including: In each cycle, the quantity of the goods in the picking results is counted; In response to the counting result reaching a third value, the picking results are sequentially assigned to the orders according to the order sequence to generate a distribution task.

7. The logistics distribution method according to claim 1, characterized in that, After distributing the goods according to the distribution task, the method further includes: Receive the distribution results generated by distributing goods according to the distribution task; In response to obtaining the distribution result, it is determined whether there is a distribution task to be distributed. If there is a distribution task to be distributed, the quantity of goods corresponding to the distribution task to be distributed is readjusted.

8. The logistics distribution method according to claim 1, characterized in that, Combining multiple outbound orders into one or more waves includes: Based on the type of goods and / or delivery time and / or delivery location in the outbound order, multiple outbound orders are combined into one or more waves.

9. The logistics distribution method according to claim 1, characterized in that, Multiple picking tasks are generated based on the synthesized wave, including: Based on the synthesized wave of goods, the multiple picking tasks are generated according to a preset volume or preset weight.

10. A logistics distribution device, characterized in that, include: The wave division module is configured to combine multiple outbound orders into one or more waves. The picking task generation module is configured to generate multiple picking tasks based on the synthesized wave. The picking result receiving module is configured to receive the picking result of at least one picking task among the plurality of picking tasks; The splitting module is configured to generate a splitting task based on a predetermined splitting logic in response to receiving a picking result, so as to split the goods according to the splitting task; The splitting module is also configured to allocate the picking results of the first wave of picking tasks to the orders corresponding to the second wave of splitting tasks. And / or, assign the picking results of the first wave of picking tasks to the orders corresponding to the first wave of distribution tasks; It also includes: accumulating the quantity of goods in the picking results to obtain a cumulative picking result and the quantity of goods in the distribution results to obtain a cumulative distribution result, and verifying the cumulative picking result and the cumulative distribution result through streaming computation; The multiple picking tasks and the distribution tasks are processed asynchronously in parallel.

11. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to perform the steps of the method according to any one of claims 1-9 by executing the executable instructions.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-9.

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