Information generation method, device and electronic equipment for live scene

Abstract

Embodiments of the present disclosure disclose a method and apparatus for generating information of a live broadcast scene and an electronic device. A specific embodiment of the method includes: obtaining live broadcast plan information, wherein the live broadcast plan information includes anchor information; predicting live broadcast sales based on the live broadcast plan information to obtain a sales prediction result; determining a predicted shipment quantity corresponding to each warehouse based on the sales prediction result and a warehouse distribution ratio corresponding to an anchor, wherein the warehouse distribution ratio represents a ratio between a quantity of goods circulated by the anchor to each region; and generating a replenishment quantity for the warehouse based on the predicted shipment quantity corresponding to each warehouse. Thus, a new live broadcast inventory preparation method is provided.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus and electronic device for generating information in a live streaming scenario. Background Technology

[0002] In traditional online retail environments or e-commerce live streaming scenarios, replenishment work largely relies on manual operations. Staff need to constantly monitor product inventory, and when inventory falls below a certain threshold, they manually create replenishment orders in the inventory management system. Manual monitoring is prone to delays, leading to stockouts and disrupting product flow. Summary of the Invention

[0003] This disclosure is provided to briefly introduce the concepts, which will be described in detail in the subsequent Detailed Description section. This disclosure is not intended to identify key or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0004] In a first aspect, embodiments of this disclosure provide a method for generating information in a live streaming scenario. The method includes: obtaining live streaming plan information for a target live stream, wherein the live streaming plan information includes streamer information; predicting live streaming sales based on the live streaming plan information to obtain a sales prediction result; determining the predicted shipment volume corresponding to each warehouse based on the sales prediction result and the warehouse allocation ratio corresponding to the streamer, wherein the warehouse allocation ratio is used to characterize the proportion between the quantities of goods distributed to different regions based on the streamer; and generating a replenishment volume for each warehouse based on the predicted shipment volume corresponding to each warehouse.

[0005] Secondly, this disclosure provides an information generation device for a live streaming scenario, comprising: an acquisition unit for acquiring live streaming plan information for a target live stream, wherein the live streaming plan information includes anchor information; a prediction unit for predicting live streaming sales based on the live streaming plan information to obtain a sales prediction result; a determination unit for determining the predicted shipment volume corresponding to each warehouse based on the sales prediction result and the warehouse allocation ratio corresponding to the anchor, wherein the warehouse allocation ratio is used to characterize the proportion between the quantities of goods distributed to different regions based on the anchor; and a generation unit for generating replenishment quantities for each warehouse based on the predicted shipment volume corresponding to each warehouse.

[0006] Thirdly, embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the information generation method for a live streaming scene as described in the first aspect.

[0007] Fourthly, embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the information generation method for a live streaming scene as described in the first aspect. Attached Figure Description

[0008] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0009] Figure 1 This is a flowchart of an embodiment of the information generation method for live streaming scenarios according to this disclosure;

[0010] Figure 2 This is a schematic diagram of an application scenario of the information generation method for live streaming scenarios disclosed herein;

[0011] Figure 3 This is a schematic diagram of the structure of an embodiment of the information generation device for a live streaming scenario according to the present disclosure;

[0012] Figure 4 This is an example of a live streaming scenario information generation method that can be applied to an exemplary system architecture, as described in one embodiment of this disclosure.

[0013] Figure 5 This is a schematic diagram of the basic structure of an electronic device provided according to an embodiment of the present disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0016] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0017] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0018] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0019] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0020] In one or more embodiments of this disclosure, a method for preparing inventory for live streaming scenarios on e-commerce platforms can be provided. Unlike time-based shelf forecasting, a sales forecasting model based on live streaming has emerged to report, predict, and manage live streaming activities, improving the accuracy of inventory preparation for live streaming scenarios, making inventory preparation more efficient, and live streaming review more accurate.

[0021] In one or more embodiments of this disclosure, a stock preparation solution for live streaming scenarios can be provided. The overall process may include: obtaining live streaming event information, predicting live streaming event sales, predicting warehouse allocation ratio, formulating a stock preparation plan, and reviewing and optimizing the process.

[0022] In one or more embodiments of this disclosure, after obtaining the above-mentioned live broadcast plan information, the sales volume of goods in the target live broadcast can be predicted and the sales prediction result can be output.

[0023] In one or more embodiments of this disclosure, after obtaining the sales forecast results, the sales distribution ratio of the goods can be predicted based on the historical arrival data of the corresponding livestreamer, and the sales volume of each warehouse can be preset. Based on the actual inventory in the warehouse, the inventory gap of the goods is calculated, and the inventory quantity is generated.

[0024] In one or more embodiments of this disclosure, after the target live broadcast is completed, data such as timing sales and live broadcast traffic can be obtained to review the sales forecast and inventory preparation for this live broadcast.

[0025] In one or more embodiments of this disclosure, reviewing the accuracy of product sales forecasting may include comparing actual sales volume with forecasted sales volume. In one or more embodiments of this disclosure, reviewing the accuracy of warehouse allocation ratio determination may include comparing actual warehouse shipments.

[0026] Please refer to Figure 1 This illustrates the flow of one embodiment of a method for generating information in a live streaming scenario according to this disclosure. For example... Figure 1 The information generation method for this live streaming scenario shown includes the following steps:

[0027] Step 101: Obtain the live streaming schedule information for the target session.

[0028] In this embodiment, the execution entity of the information generation method for the live streaming scene (e.g., a server and / or a terminal device) can obtain the live streaming plan information for the target live streaming session.

[0029] Here, the live streaming plan information can include the streamer's information.

[0030] Information about live streaming events can be obtained by proactively submitting a live streaming plan.

[0031] Step 102: Based on the live streaming plan information, predict the live streaming sales to obtain the sales prediction results.

[0032] Step 103: Based on the sales forecast results and the corresponding warehouse allocation ratio of the live streamer, determine the predicted shipment volume for each warehouse.

[0033] Here, the distribution ratio is used to characterize the proportion between the quantities of goods distributed to different regions based on the streamer.

[0034] Step 104: Based on the predicted shipment volume for each warehouse, generate replenishment volume for each warehouse.

[0035] Here, you can obtain the warehouse inventory, which includes both in-warehouse stock and in-transit stock. Based on the predicted shipment volume and in-warehouse inventory, you can determine the replenishment quantity.

[0036] Here, replenishment can be achieved through inter-warehouse transfers and / or ordering from suppliers.

[0037] It should be noted that the live-streaming inventory preparation method provided in this embodiment obtains the live-streaming plan information for the target live-streaming session, including the streamer's information; then, it predicts the live-streaming sales based on the live-streaming plan information to obtain a sales prediction result; based on the sales prediction result and the warehouse allocation ratio corresponding to the streamer, it determines the predicted shipment volume corresponding to each warehouse, wherein the warehouse allocation ratio is used to characterize the proportion between the quantities of goods distributed to different regions based on the streamer; based on the predicted shipment volume corresponding to each warehouse, it generates replenishment quantities for each warehouse; thus, it can cope with sales fluctuations. Live-streaming activities may experience sales peaks due to the streamer's influence, promotional activities, etc., at which time the demand for goods will increase significantly; by predicting the sales peak, it can prepare for replenishment in advance, and determine the replenishment quantity for each warehouse based on the streamer's warehouse allocation ratio, ensuring sufficient supply of goods during the sales peak and avoiding stockouts.

[0038] In some embodiments, the overall process may include: live-stream sales forecasting, warehouse allocation ratio forecasting, inventory preparation process, and post-mortem optimization.

[0039] For live-streaming sales forecasts, the first step is to collect key information about the live stream to predict sales.

[0040] In some embodiments, the live streaming plan information includes one or more of the following: live streaming method information and product information.

[0041] The live streaming format information may include one or more of the following: host information, sales method. Product information may include product attributes, such as price, category, etc.

[0042] Here, the host information indicates the host for this live stream, and the number of hosts can be one or more.

[0043] Here, "sales method" indicates how the product will appear in this live-streaming event.

[0044] Here, price information indicates the actual selling price of the goods.

[0045] In some embodiments, the step of predicting live sales based on live broadcast plan information to obtain a sales prediction result includes: recalling historical live broadcast sessions based on the live broadcast plan information; and generating a first sales prediction value based on the historical live broadcast sessions.

[0046] The static attributes reported in the live broadcast (such as merchant information, host information, user information, product information, and sales mechanism) are used to recall historical sessions. The recall includes similarity retrieval.

[0047] Similarity retrieval can utilize a similarity retrieval model (recalling N days of samples based on broadcaster and mechanism information), where mechanism information features include whether it was explained, secondary category, broadcaster identifier, mechanism identifier, etc. These patterns are input into the prediction model to produce prediction results.

[0048] This allows us to predict peak sales periods based on historical sales data and prepare for replenishment in advance to avoid stockouts.

[0049] In some embodiments, the method further includes: recalling historical transactions related to the streamer based on live streaming plan information; determining the order volume corresponding to the warehouse based on the geographical distribution of the goods corresponding to the historical transactions; and determining the warehouse allocation ratio related to the streamer based on the order volume corresponding to the warehouse.

[0050] Here, based on the submitted live streaming plan information, historical transactions are recalled. The regions to which the goods corresponding to the historical transactions were distributed can be identified as shipping warehouses. By calculating the order volume corresponding to each shipping warehouse and the proportion of order volume corresponding to each warehouse, the warehouse allocation ratio for the relevant streamer can be determined.

[0051] Optionally, for new streamers (without a history of transactions), a backup strategy can be adopted, using the live stream room or a preset allocation ratio.

[0052] In some embodiments, determining the order volume corresponding to the warehouse based on the region to which the goods corresponding to historical transactions flow includes: determining the regional order volume based on the region to which the goods corresponding to historical transactions flow; and converting the regional order volume into the order volume corresponding to the warehouse based on the correspondence between the region and the warehouse.

[0053] Here, the correspondence between regions and warehouses is determined based on one or more of the following: warehouse deployment information, warehouse fulfillment priority information, and warehouse network coverage information.

[0054] As an example, data can be aggregated from one or more dimensions such as the time of the live streamer's transaction, the city of delivery, the secondary category, the brand, and the average order value.

[0055] As an example, based on the product warehouse deployment, city warehouse fulfillment priority, warehouse network coverage, and product sales restrictions, the region-order volume information can be converted into warehouse-order volume information, and the order volume ratio of each warehouse can be calculated to form the warehouse allocation ratio.

[0056] Therefore, the warehouse allocation ratio can be quickly determined by statistically analyzing the number of orders in a region and the correspondence between regions and warehouses.

[0057] In some embodiments, recalling the historical transactions related to the streamer based on the live broadcast plan information includes: obtaining recall conditions corresponding to the priority, recalling historical transactions; in response to the number of recalled historical transactions being less than a preset quantity threshold, using a lower-level recall condition to continue recalling historical transactions until the number of recalled historical transactions is not less than the preset quantity threshold.

[0058] Here, historical transactions can be recalled according to different priorities.

[0059] As an example, the preset quantity threshold can be set according to the actual scenario.

[0060] Optionally, a recall period can be set, such as 30 days.

[0061] As an example, a recall is conducted for price fluctuations of 15%, and if the sample size is insufficient, a priority strategy is used to gradually downgrade and continue the recall.

[0062] Priority strategies: Product identification matching; Brand matching and category matching; Category matching.

[0063] Fallback strategy: If the priority strategy does not have enough available samples, ignore the price factor and continue to downgrade the recall.

[0064] Therefore, we can try to recall historical orders with high relevance. When the sample size of orders is insufficient, we can use a method of gradually reducing the relevance to recall as many orders as possible, thereby improving the accuracy of the distribution ratio in terms of both relevance and quantity.

[0065] In some embodiments, generating replenishment quantities for warehouses based on the predicted shipment quantities for each warehouse includes: generating replenishment quantities for warehouses based on the predicted shipment quantities and inventory quantities.

[0066] Here, inventory levels can include both warehouse inventory and in-transit inventory. In-transit inventory can be the quantity of goods in transit bound for a target warehouse.

[0067] Here, the warehouse replenishment quantity can be the difference between the predicted shipment quantity and the inventory quantity.

[0068] This allows for accurate stocking of warehouses, preventing stockpiling or shortages.

[0069] In some embodiments, after the live stream ends, the system automatically retrieves data such as actual sales and live stream traffic based on the submitted information to conduct a review of the live stream.

[0070] The metrics used in the post-mortem analysis can include one or more of the following: sales forecast accuracy, warehouse distribution ratio forecast accuracy, sell-through rate, and product shelf availability.

[0071] Here, sales forecast accuracy can refer to the accuracy of the sales forecast in this algorithm, which is used to determine the potential for improvement in forecasting.

[0072] Here, we can analyze the prediction accuracy of the allocation ratio by drilling down to the allocation dimension and review the prediction performance of the allocation ratio.

[0073] Here, sell-through rate is used to analyze the sell-through status of goods.

[0074] Here, "product availability rate" refers to whether the products are available on the shelves the day after the live stream, and is used to review the inventory preparation situation.

[0075] The method may further include: obtaining the actual sales volume of the target live broadcast after the live broadcast ends; and generating a sales prediction accuracy rate based on the actual sales volume and the sales prediction result.

[0076] In some embodiments, the method may further include: determining the actual warehouse allocation ratio for the target live stream based on the geographical distribution of goods corresponding to the orders of the target live stream; and determining the warehouse allocation ratio prediction accuracy based on the actual warehouse allocation ratio and the warehouse allocation ratio on which the predicted shipment volume of each warehouse is determined.

[0077] Therefore, by reviewing the process, the accuracy of live-streaming product preparation can be further improved.

[0078] Further reference Figure 3 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of an information generation device for live streaming scenarios, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0079] like Figure 3 As shown, the information generation device for the live streaming scene in this embodiment includes: an acquisition unit 301, a prediction unit 302, a determination unit 303, and a generation unit 304. The acquisition unit is used to acquire live streaming plan information for the target live stream, including streamer information; the prediction unit is used to predict live streaming sales based on the live streaming plan information to obtain a sales prediction result; the determination unit is used to determine the predicted shipment volume for each warehouse based on the sales prediction result and the warehouse allocation ratio corresponding to the streamer, wherein the warehouse allocation ratio represents the proportion of goods distributed to different regions based on the streamer; and the generation unit is used to generate replenishment quantities for each warehouse based on the predicted shipment volume corresponding to each warehouse.

[0080] In this embodiment, the specific processing of the acquisition unit 301, prediction unit 302, determination unit 303, and generation unit 304 of the information generation device for the live streaming scene, and the resulting technical effects, can be referred to respectively. Figure 1The relevant descriptions of steps 101, 102, 103 and 104 in the corresponding embodiments will not be repeated here.

[0081] In some embodiments, the live streaming plan information includes one or more of the following: live streaming method information and product information.

[0082] In some embodiments, the step of predicting live sales based on live broadcast plan information to obtain a sales prediction result includes: recalling historical live broadcast sessions based on the live broadcast plan information; and generating a first sales prediction value based on the historical live broadcast sessions.

[0083] In some embodiments, the device is further configured to: recall historical transactions related to the streamer based on live streaming plan information; determine the order volume corresponding to the warehouse based on the geographical distribution of the goods corresponding to the historical transactions; and determine the warehouse allocation ratio related to the streamer based on the order volume corresponding to the warehouse.

[0084] In some embodiments, determining the order volume corresponding to a warehouse based on the geographical distribution of goods corresponding to historical transactions includes: determining the regional order volume based on the geographical distribution of goods corresponding to historical transactions; and converting the regional order volume into the order volume corresponding to a warehouse based on the correspondence between cities and warehouses; wherein the correspondence between regions and warehouses is determined based on one or more of the following: warehouse deployment information, warehouse fulfillment priority information, and warehouse network coverage information.

[0085] In some embodiments, recalling the historical transactions related to the streamer based on the live broadcast plan information includes: obtaining recall conditions corresponding to the priority, recalling historical transactions; in response to the number of recalled historical transactions being less than a preset quantity threshold, using a lower-level recall condition to continue recalling historical transactions until the number of recalled historical transactions is not less than the preset quantity threshold.

[0086] In some embodiments, generating replenishment quantities for warehouses based on the predicted shipment quantities for each warehouse includes: generating replenishment quantities for warehouses based on the predicted shipment quantities and inventory quantities.

[0087] In some embodiments, the apparatus is further configured to: obtain the actual sales volume of the target live broadcast after the live broadcast ends; generate a sales prediction accuracy rate based on the actual sales volume and the sales prediction result; determine the actual warehouse allocation ratio of the target live broadcast based on the geographical distribution of the goods corresponding to the orders of the target live broadcast; and determine the warehouse allocation ratio prediction accuracy rate based on the actual warehouse allocation ratio and the warehouse allocation ratio on which the predicted shipment volume of each warehouse is determined.

[0088] Please refer to Figure 4 , Figure 4 An exemplary system architecture in which the information generation method for a live streaming scene, according to an embodiment of this disclosure, can be applied.

[0089] like Figure 4 As shown, the system architecture may include terminal devices 401, 402, and 403, a network 404, and a server 405. Network 404 serves as the medium for providing a communication link between terminal devices 401, 402, and 403 and server 405. Network 404 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0090] Terminal devices 401, 402, and 403 can interact with server 405 via network 404 to receive or send messages, etc. Various client applications, such as web browsers, search engines, and news apps, can be installed on terminal devices 401, 402, and 403. These client applications can receive user commands and perform corresponding functions, such as adding information to a message based on user instructions.

[0091] Terminal devices 401, 402, and 403 can be either hardware or software. When terminal devices 401, 402, and 403 are hardware, they can be various electronic devices with a display screen and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc. When terminal devices 401, 402, and 403 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules (e.g., software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are imposed here.

[0092] Server 405 can be a server that provides various services, such as receiving information retrieval requests sent by terminal devices 401, 402, and 403, retrieving the corresponding display information according to the information retrieval request through various methods, and sending the relevant data for displaying the information back to terminal devices 401, 402, and 403.

[0093] It should be noted that the live streaming scene information generation method provided in this embodiment can be executed by a terminal device, and correspondingly, the live streaming scene information generation device can be set in terminal devices 401, 402, and 403. Furthermore, the live streaming scene information generation method provided in this embodiment can also be executed by a server 405, and correspondingly, the live streaming scene information generation device can be set in server 405.

[0094] It should be understood that Figure 4 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0095] The following is for reference. Figure 5 It illustrates an electronic device suitable for implementing embodiments of the present disclosure (e.g., Figure 4 The diagram shows the structure of the terminal device or server in this disclosure. The terminal device in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0096] like Figure 5 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0097] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0098] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0099] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0100] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0101] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0102] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire live streaming plan information for a target live stream, wherein the live streaming plan information includes streamer information; predict live streaming sales based on the live streaming plan information to obtain a sales prediction result; determine the predicted shipment volume corresponding to each warehouse based on the sales prediction result and the warehouse allocation ratio corresponding to the streamer, wherein the warehouse allocation ratio is used to characterize the proportion between the quantities of goods distributed to different regions based on the streamer; and generate replenishment quantities for each warehouse based on the predicted shipment volume corresponding to each warehouse.

[0103] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, a generating unit can also be described as a "unit for generating replenishment quantities".

[0106] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0107] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, 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 of the foregoing.

[0108] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0109] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0110] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for generating information in a live streaming scenario, characterized in that, include: Obtain the live streaming schedule information for the target session, including the streamer information; Based on the live streaming plan information, predict the live streaming sales to obtain the sales forecast results; Based on the sales forecast results and the warehouse allocation ratio corresponding to the live streamer, the predicted shipment volume corresponding to each warehouse is determined, wherein the warehouse allocation ratio is used to characterize the proportion between the volume of goods distributed to different regions based on the live streamer. Based on the predicted shipment volume for each warehouse, replenishment volume is generated for each warehouse.

2. The method according to claim 1, characterized in that, The live streaming plan information includes one or more of the following: live streaming method information and product information.

3. The method according to claim 1, characterized in that, The process of predicting livestream sales based on the livestream plan information to obtain sales prediction results includes: Based on the live streaming schedule information, recall historical live streaming sessions; The first sales forecast is generated based on the number of historical live broadcasts.

4. The method according to claim 1, characterized in that, The method further includes: Based on the live streaming schedule information, recall the historical transactions related to the aforementioned streamer; Determine the order volume corresponding to the warehouse based on the geographical distribution of goods corresponding to historical transactions; The warehouse allocation ratio for each livestreamer is determined based on the order volume corresponding to the warehouse.

5. The method according to claim 4, characterized in that, The process of determining the order volume corresponding to a warehouse based on the geographical distribution of goods corresponding to historical transactions includes: Determine the regional order volume based on the geographical distribution of goods corresponding to historical transactions; Based on the correspondence between regions and warehouses, the order volume by region is converted into the order volume corresponding to the warehouse; among which, The correspondence between regions and warehouses is determined based on one or more of the following: warehouse deployment information, warehouse fulfillment priority information, and warehouse network coverage information.

6. The method according to claim 4, characterized in that, The step of recalling the streamer's historical transactions based on the live streaming schedule information includes: Obtain the recall conditions corresponding to the priority level and recall historical transactions; If the number of historical orders recalled is less than the preset threshold, a lower-level recall condition is adopted to continue recalling historical transactions until the number of historical orders recalled is not less than the preset threshold.

7. The method according to claim 1, characterized in that, The process of generating replenishment quantities for each warehouse based on the predicted shipment volume includes: Based on the predicted shipment and inventory levels, replenishment quantities are generated for the warehouse.

8. The method according to claim 1, characterized in that, The method further includes: After the live stream ends, obtain the actual sales volume of the target live stream session; based on the actual sales volume and the sales prediction results, generate the sales prediction accuracy rate. Based on the geographical distribution of goods corresponding to the orders of the target live broadcast, determine the actual warehouse allocation ratio for the target live broadcast; based on the actual warehouse allocation ratio and the warehouse allocation ratio on which the predicted shipment volume of each warehouse is determined, determine the accuracy of the warehouse allocation ratio prediction.

9. An information generation device for a live streaming scene, characterized in that, include: The acquisition unit is used to acquire the live broadcast plan information of the target live broadcast, wherein the live broadcast plan information includes the anchor information; The prediction unit is used to predict live streaming sales based on the live streaming plan information and obtain the sales prediction results. The determining unit is used to determine the predicted shipment volume of each warehouse based on the sales forecast results and the warehouse allocation ratio corresponding to the anchor, wherein the warehouse allocation ratio is used to characterize the proportion between the amount of goods distributed to different regions based on the anchor. The generation unit is used to generate replenishment quantities for each warehouse based on the predicted shipment volume.

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

11. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-8.

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