Method and device for generating sales data list

Abstract

The invention discloses a method and a device for generating a sales data list, and relates to the technical field of warehouse management. One specific embodiment of the method comprises the following steps: acquiring at least one piece of moving and selling data at the current moment; inputting the at least one piece of dynamic sales data into a preset inventory task prediction model to obtain a target probability of occurrence of inventory loss or inventory interference at the next moment corresponding to each piece of dynamic sales data; on the basis of the target probability corresponding to each piece of animation and sales data, determining a first animation and sales data list in the at least one piece of animation and sales data; and based on the first mobilization and sales data list, determining a target mobilization and sales data list. According to the embodiment, the validity and pertinence of the generated animation and sales data list are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, specifically to the field of warehouse management technology, and in particular to a method and device for generating a dynamic sales data list. Background Art

[0002] Dynamic sales inventory is a method of dynamic inventory counting. It is mainly used to count the goods that have inventory flow records such as entry, in-stock, and exit from the storage location within a certain period of time, so as to check the corresponding differences in each link, promote the improvement of the entire chain, and enhance the stability and security of inventory.

[0003] At present, the inventory counting range of dynamic sales is manually selected by a single warehouse, and the creation of dynamic sales tasks is very arbitrary. A single warehouse often fails to conduct inventory counting in the designated area as required, resulting in the inability to obtain timely inventory verification in areas with large dynamic sales volumes. In addition, the average daily dynamic sales data volume of a single warehouse is large, so the amount of dynamic sales tasks created, that is, the dynamic sales data list, will also be extremely large, which reduces the input-output ratio of the inventory counting, makes it difficult to focus on those places where inventory errors are more likely to occur, and the dynamic sales data list has poor pertinence and effectiveness. Summary of the invention

[0004] Embodiments of the present application provide a method, device, equipment, and storage medium for generating a dynamic sales data list.

[0005] According to the first aspect, an embodiment of the present application provides a method for generating a list of dynamic sales data, the method comprising: obtaining at least one dynamic sales data at the current moment; inputting at least one dynamic sales data into a preset inventory task prediction model to obtain a target probability of inventory shortage or inventory surplus at the next moment corresponding to each dynamic sales data; based on the target probability corresponding to each dynamic sales data, determining a first dynamic sales data list in at least one dynamic sales data; based on the first dynamic sales data list, determining a target dynamic sales data list.

[0006] According to the second aspect, an embodiment of the present application provides a device for generating a list of dynamic sales data, the device comprising: a data acquisition module, configured to acquire at least one dynamic sales data at the current moment; a prediction probability module, configured to input at least one dynamic sales data into a preset inventory task prediction model to obtain a target probability of shortage or surplus at the next moment corresponding to each dynamic sales data; a first determination module, configured to determine a first dynamic sales data list in at least one dynamic sales data based on the target probability corresponding to each dynamic sales data; and a second determination module, configured to determine a target dynamic sales data list based on the first dynamic sales data list.

[0007] According to the third aspect, an embodiment of the present application provides an electronic device, which includes one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement a method for generating a dynamic sales data list as in any embodiment of the first aspect.

[0008] According to a fourth aspect, an embodiment of the present application provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements a method for generating a dynamic sales data list as in any embodiment of the first aspect.

[0009] The present application obtains at least one moving sales data at the current moment; inputs at least one moving sales data into a preset inventory task prediction model to obtain the target probability of shortage or surplus at the next moment corresponding to each moving sales data; based on the target probability corresponding to each moving sales data, a first moving sales data list is determined in at least one moving sales data; based on the first moving sales data list, a target moving sales data list is determined, that is, by predicting whether the moving sales data at the current moment will have shortage or surplus at the next moment, and screening at least one moving sales data according to the prediction result, the filtered moving sales data with a higher probability of shortage or surplus is determined as the first moving sales data list, and based on the first moving sales data list, the target moving sales data list is determined, thereby improving the effectiveness and pertinence of the generated moving sales data list, helping front-line personnel to find storage locations or items that need to be counted more, and achieving more efficient moving sales within limited manpower efficiency, that is, dynamic inventory. At the same time, the system automatically generates task orders to save operation links, improve efficiency, and the system records the completion results according to the task orders, and comprehensively completes the recording of the inventory process, providing an effective grip for the operation side to achieve monitoring and management.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0012] Figure 2a is a flow chart of an embodiment of a method for generating a dynamic sales data list according to the present application;

[0013] Figure 2b is a schematic diagram of another embodiment of the method for generating a dynamic sales data list according to the present application;

[0014] Figure 3 It is a schematic diagram of an application scenario of the method for generating a dynamic sales data list according to the present application;

[0015] Figure 4 is a flow chart of another embodiment of the method for generating a dynamic sales data list according to the present application;

[0016] Figure 5 is a flow chart of another embodiment of the device for generating a dynamic sales data list according to the present application;

[0017] Figure 6 It is a structural diagram of a computer system suitable for implementing a server of an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0019] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of the method for generating a dynamic sales data list of the present application can be applied.

[0021] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0022] The terminal devices 101, 102, 103 interact with the server 105 via the network 104 to receive or send messages, etc. Various application platforms may be installed on the terminal devices 101, 102, 103.

[0023] Terminal devices 101, 102, 103 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens, including but not limited to mobile phones, laptops, and scanning devices. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example, to provide dynamic sales data list generation services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0024] Server 105 can be a server that provides various services, for example, obtaining at least one moving sales data at the current moment; inputting at least one moving sales data into a preset inventory task prediction model to obtain a target probability of shortage or surplus at the next moment corresponding to each moving sales data; based on the target probability corresponding to each moving sales data, determining a first moving sales data list in at least one moving sales data; and determining a target moving sales data list based on the first moving sales data list.

[0025] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, used to provide a dynamic sales data list generation service), or it can be implemented as a single software or software module. No specific limitation is made here.

[0026] It should be noted that the method for generating a dynamic sales data list provided in the embodiments of the present disclosure can be executed by the server 105, or by the terminal devices 101, 102, 103, or by the server 105 and the terminal devices 101, 102, 103 cooperating with each other. Accordingly, the various parts (such as various units, sub-units, modules, and sub-modules) included in the dynamic sales data list generating device can be all set in the server 105, or all set in the terminal devices 101, 102, 103, or respectively set in the server 105 and the terminal devices 101, 102, 103.

[0027] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0028] Figure 2a The flowchart 200 of an embodiment of the method for generating a dynamic sales data list that can be applied to the present application is shown. In this embodiment, the method for generating a dynamic sales data list includes the following steps:

[0029] Step 201, obtaining at least one piece of dynamic sales data at the current moment.

[0030] In this embodiment, the execution subject (such as Figure 1 The server 105 or terminal devices 101, 102, 103) shown in the figure can use a wired connection method or a wireless connection method to obtain one or more dynamic sales data at the current moment, such as 10 dynamic sales data or 20 dynamic sales data of a warehouse or a storage area. The dynamic sales data may include a storage location and the SKU type corresponding to the storage location and the quantity of each type of SKU.

[0031] Here, the dynamic sales data may also include information such as storage area, storage location type, dynamic sales type, SKU code, SKU price, etc.

[0032] The wireless connection methods mentioned above may include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0033] Step 202, input at least one piece of dynamic sales data into a preset inventory task prediction model to obtain a target probability of inventory shortage or inventory surplus occurring at the next moment corresponding to each piece of dynamic sales data.

[0034] In this implementation, after obtaining at least one piece of dynamic sales data, the execution entity may input at least one piece of dynamic sales data into a preset inventory task prediction model to obtain the target probability of inventory shortage or surplus occurring at the next moment corresponding to each piece of dynamic sales data.

[0035] Among them, the preset inventory task prediction model can be obtained based on the recurrent neural network in the existing technology or future development technology, for example, RNN (Rerrent Neural Network), LSTM (Long Short Term Memory) network, GRU (Gated Recurrent Unit) network, etc., and this application does not limit this.

[0036] Here, the preset inventory task prediction model can be trained based on historical dynamic sales data samples marked with the target probability of inventory shortage or inventory surplus occurring at the next moment.

[0037] In some optional methods, the preset inventory task prediction model is built based on the LSTM network.

[0038] In this implementation, the preset inventory task prediction model is built based on the LSTM network. The long short-term memory network (LSTM) is derived from the recurrent neural network structure. It is good at mining the change relationship over a longer time interval and can effectively solve the problems such as gradient disappearance or gradient explosion caused by the increase of time series.

[0039] Among them, the LSTM network structure is as follows Figure 2b As shown in the figure, the retention and discard of historical information are controlled through the input gate, forget gate and output gate.

[0040] Among them, c t-1 and h t-1 Respectively represent the cell state and hidden layer state at the previous moment, and x t Represents the input sequence at the current time t. Forget gate represents the forget gate, which determines the retention degree of the cell state at time t-1 by inputting the hidden layer state at time t-1 and the input at time t. t Input gate represents the input gate, which determines the degree to which the input at time t updates the cell state at the current time. t In addition, by using the tanh activation function, the candidate value of the cell state at the current moment can be obtained Output gate represents the output gate, which determines the cell state at time t-1 on the output h t The degree of influence t The internal calculation process of LSTM is shown in equations (1) to (6):

[0041] i t =σ(W xi x t +W hi h t-1 +b i ) (1)

[0042] f t =σ(W xf x t +W hf h t-1 +b f ) (2)

[0043] o t =σ(W xo x t +W ho h t-1 +b o ) (3)

[0044]

[0045]

[0046] h t =o t ⊙tanh(c t ) (6)

[0047] Where W represents the weight matrix of the corresponding gate; b represents the corresponding offset; σ(·) represents the sigmoid activation function; tanh represents the tangent function; ⊙ represents the multiplication between vectors.

[0048] Here, the preset inventory task prediction model can be specifically trained in the following manner: obtain historical dynamic sales data samples, sort the historical dynamic sales data samples in ascending order according to the corresponding moments, and obtain a data set, wherein the time intervals of any adjacent samples in the data set are the same, for example, 5 hours, 12 hours, etc.; further, perform an iterative operation to train the initial inventory task prediction model, and the iterative operation includes: taking the dynamic sales data samples corresponding to the first moment in the data set and the probability of inventory shortage or surplus corresponding to the previous moment of the first moment as input data, inputting into the initial inventory task prediction model, and obtaining the probability of inventory shortage or surplus corresponding to the first moment; in response to the trained initial inventory task prediction model meeting the preset conditions, determining the trained initial inventory task prediction model as the preset inventory task prediction model; in response to the trained initial inventory task prediction model not meeting the preset conditions, determining the next moment of the first moment as the new first moment, and continuing to perform the iterative operation.

[0049] Here, the preset conditions can be set according to actual needs, for example, the training time exceeds the preset duration; the number of training times exceeds the preset number of times; the loss value calculated using the preset loss function is less than the preset loss value threshold; the network parameters meet the specified conditions, etc.

[0050] Specifically, the execution entity can periodically obtain relevant configuration information of the movable pin disk, such as the movable pin type, the online switch status of the movable pin disk, etc., and determine whether the online push switch of the movable pin disk is turned on. If the switch is turned on, the single warehouse starts the system to push the movable pin disk task.

[0051] Furthermore, the storage area, storage location type, dynamic sales type, SKU code, SKU price, etc. in the dynamic sales data are combined into x t Array, the input parameter combination of T-1 is h t-1 The array is input into the inventory task prediction model built based on the LSTM network to obtain the target probability of inventory shortage or surplus at the next moment corresponding to each dynamic sales data.

[0052] The detailed training process based on historical sales data can be as follows: set the initial weight matrix W0 to [1,1,1,1,1,1], b0 = +10, σ(·) = 1 / (1+e -Π). Input x0 = [storage area, storage location type, dynamic sales type, sku code, sku price] T (Sales data at the time of inventory counting at T0), generate h0 = whether there is inventory shortage or inventory surplus, 0 for no, 1 for yes. Based on h1 and x2 data, continue to substitute into formula (1) to generate h2 data. After this cycle, stable network parameters are trained, and the trained inventory task prediction model is obtained.

[0053] This implementation method builds a preset inventory task prediction model based on the LSTM network, which can effectively solve the problems such as gradient disappearance or gradient explosion caused by the increase of time series, thereby improving the accuracy of the determined target inventory task list.

[0054] Step 203: Based on the target probability corresponding to each piece of dynamic sales data, a first dynamic sales data list is determined from at least one piece of dynamic sales data.

[0055] In this embodiment, after determining the target probability corresponding to each piece of dynamic sales data, the executing entity can directly judge whether the target probability corresponding to each piece of dynamic sales data is greater than or equal to the preset probability threshold, and determine the first dynamic sales data list based on the dynamic sales data whose corresponding target probability is greater than or equal to the preset probability threshold.

[0056] Among them, the preset probability threshold can be determined based on experience, actual needs and specific application scenarios, for example, 30%, 40%, etc., and this application does not limit this.

[0057] Step 204: Determine a target dynamic sales data list based on the first dynamic sales data list.

[0058] In this embodiment, the executing entity may directly determine the first dynamic sales data list as the target dynamic sales data list; or further screen the first dynamic sales data list, such as eliminating the dynamic sales data that is identical to the dynamic sales data in the inventory task that was not completed at the previous moment, eliminating the dynamic sales data that is identical to the dynamic sales data in the cyclic inventory task, etc., and determine the screened first dynamic sales data list as the target dynamic sales data list, which is not limited in this application.

[0059] Continue to see Figure 3 , Figure 3 It is a schematic diagram of an application scenario of the method for generating a dynamic sales data list according to this embodiment.

[0060] exist Figure 3In the application scenario, the execution entity 301 can obtain at least one dynamic sales data 302 at the current moment, for example, the day, wherein the dynamic sales data may include the storage location, the SKU type corresponding to the storage location, the quantity of each type of SKU, the storage area, the storage location type, the dynamic sales type, etc.; input at least one dynamic sales data 302 into the preset inventory task prediction model 303 to obtain the target probability of inventory shortage or inventory surplus at the next moment corresponding to each dynamic sales data, such as tomorrow; based on the target probability corresponding to each dynamic sales data, determine the first dynamic sales data list 304 in the at least one dynamic sales data; based on the first dynamic sales data list 304, determine the target dynamic sales data list 305.

[0061] The method for generating a moving sales data list disclosed in the present invention obtains at least one moving sales data at the current moment; inputs at least one moving sales data into a preset inventory task prediction model to obtain a target probability of shortage or surplus at the next moment corresponding to each moving sales data; based on the target probability corresponding to each moving sales data, a first moving sales data list is determined from at least one moving sales data; based on the first moving sales data list, a target moving sales data list is determined, which effectively improves the effectiveness and pertinence of the generated moving sales data list.

[0062] Figure 4 The flowchart 400 of an embodiment of the method for generating a dynamic sales data list that can be applied to the present application is shown. In this embodiment, the method for generating a dynamic sales data list includes the following steps:

[0063] Step 401, obtaining at least one piece of dynamic sales data at the current moment.

[0064] In this embodiment, the implementation details and technical effects of step 401 can refer to the description of step 201 and will not be repeated here.

[0065] Step 402, input at least one piece of dynamic sales data into a preset inventory task prediction model to obtain a target probability of inventory shortage or inventory surplus occurring at the next moment corresponding to each piece of dynamic sales data.

[0066] In this embodiment, the implementation details and technical effects of step 402 can refer to the description of step 202 and will not be repeated here.

[0067] Step 403: Based on the target probability corresponding to each piece of dynamic sales data, a first dynamic sales data list is determined from at least one piece of dynamic sales data.

[0068] In this embodiment, the implementation details and technical effects of step 403 may refer to the description of step 203 and will not be repeated here.

[0069] Step 404, determining a second dynamic sales data list based on the first dynamic sales data list.

[0070] In this embodiment, the first dynamic sales data list includes multiple dynamic sales data. The executing entity can directly determine the first dynamic sales data list as the second dynamic sales data list, or further filter the first dynamic sales data list, such as eliminating the dynamic sales data that is the same as the dynamic sales data in the inventory task that was not completed at the previous moment, eliminating the dynamic sales data that is the same as the dynamic sales data in the cyclic inventory task, etc., and determine the filtered first dynamic sales data list as the second dynamic sales data list. This application does not limit this.

[0071] In some optional methods, a second list of moving sales data is determined based on the first list of moving sales data, including: in the first list of moving sales data, the moving sales data that are identical to the moving sales data in the inventory task that was not completed at the previous moment are eliminated to obtain a fifth list of moving sales data; and the second list of moving sales data is determined based on the fifth list of moving sales data.

[0072] In this implementation, in response to the existence of active sales data in the inventory task that was not completed at the previous moment, the execution entity can eliminate the active sales data that is the same as the active sales data in the inventory task that was not completed at the previous moment in the first active sales data list to obtain a second active sales data list.

[0073] This implementation method obtains a fifth list of dynamic sales data by eliminating the dynamic sales data that are identical to the dynamic sales data in the inventory task that was not completed at the previous moment in the first list of dynamic sales data; based on the fifth list of dynamic sales data, the second list of dynamic sales data is determined, thereby avoiding the influence of the dynamic sales data in the inventory task that was not completed at the previous moment on the target list of dynamic sales data, and improving the pertinence and effectiveness of generating the target list of dynamic sales data.

[0074] In some optional methods, based on the fifth moving sales data list, a second moving sales data list is determined, including: in the fifth moving sales data list, eliminating the moving sales data that are the same as the moving sales data in the cycle inventory task to obtain the second moving sales data list.

[0075] In this implementation, the execution entity may eliminate the same moving sales data as the moving sales data in the cycle inventory task, that is, the data for routine inspection, in the fifth moving sales data list to obtain the second moving sales data list.

[0076] This implementation method eliminates the same moving sales data as the moving sales data in the cycle inventory task in the fifth moving sales data list to obtain the second moving sales data list, thereby further improving the pertinence and effectiveness of generating the target moving sales data list.

[0077] Step 405, sorting the dynamic sales data in the second dynamic sales data list from large to small according to the dynamic sales quantity corresponding to each dynamic sales data in the second dynamic sales data list to obtain a third dynamic sales data list.

[0078] In this embodiment, the execution entity may sort the dynamic sales data in the second dynamic sales data list from large to small according to the dynamic sales quantity corresponding to each dynamic sales data in the second dynamic sales data list to obtain a third dynamic sales data list.

[0079] Step 406: Determine a preset number of moving sales data items that are ranked top in the third moving sales data list as a fourth moving sales data list.

[0080] In this embodiment, the execution entity may determine a preset number of moving sales data items that are ranked at the top in the third moving sales data list as the fourth moving sales data list.

[0081] Among them, the preset number can be set according to experience and actual needs. Specifically, the preset number can be determined according to the product of the total amount of sales data in the third sales data list and the preset task volume ratio. Among them, the preset task volume ratio can be set according to experience and actual needs, for example, 10%, 30%, etc., and this application does not limit this.

[0082] Step 407: Determine a target dynamic sales data list based on the fourth dynamic sales data list.

[0083] In this embodiment, the execution entity may directly determine the fifth dynamic sales data list as the target dynamic sales data list, or may determine the fifth dynamic sales data list and the dynamic sales data list in the unfinished inventory task at the previous moment as the target dynamic sales data list.

[0084] from Figure 4 It can be seen that, compared with the embodiment corresponding to FIG2 , the process 400 of the method for generating a list of dynamic sales data in this embodiment embodies determining a second list of dynamic sales data based on the first list of dynamic sales data; sorting the dynamic sales data in the second list of dynamic sales data from large to small according to the number of dynamic sales corresponding to each dynamic sales data in the second list of dynamic sales data to obtain a third list of dynamic sales data; determining a preset number of dynamic sales data items that are ranked at the top in the third list of dynamic sales data as a fourth list of dynamic sales data; and determining a target list of dynamic sales data based on the fourth list of dynamic sales data, thereby improving the pertinence and effectiveness of generating the target list of dynamic sales data.

[0085] Further references Figure 5 As an implementation of the methods shown in the above figures, the present application provides an embodiment of a device for generating a dynamic sales data list. The device embodiment corresponds to the method embodiment shown in Figure 2, and the device can be specifically applied to various electronic devices.

[0086] like Figure 5 As shown, the device 500 for generating a dynamic sales data list of this embodiment includes: a data acquisition module 501, a prediction probability module 502, a first determination module 503 and a second determination module 504.

[0087] The data acquisition module 501 may be configured to acquire at least one piece of dynamic sales data at the current moment.

[0088] The prediction probability module 502 may be configured to input at least one piece of dynamic sales data into a preset inventory task prediction model to obtain a target probability of inventory shortage or inventory surplus occurring at the next moment corresponding to each piece of dynamic sales data.

[0089] The first determining module 503 may be configured to determine a first dynamic sales data list from at least one dynamic sales data based on the target probability corresponding to each dynamic sales data.

[0090] The second determining module 504 may be configured to determine a target dynamic sales data list based on the first dynamic sales data list.

[0091] In some optional modes of the present embodiment, the second determination module includes: a first determination unit, configured to determine a second dynamic sales data list based on the first dynamic sales data list; a data sorting unit, configured to sort the dynamic sales data in the second dynamic sales data list from large to small according to the dynamic sales quantity corresponding to each dynamic sales data in the second dynamic sales data list, to obtain a third dynamic sales data list; the second determination unit, configured to determine a preset number of dynamic sales data with a top ranking in the third dynamic sales data list as a fourth dynamic sales data list; the third determination unit, configured to determine a target dynamic sales data list based on the fourth dynamic sales data list.

[0092] In some optional modes of the present embodiment, the first determination unit is further configured to: in the first dynamic sales data list, eliminate the dynamic sales data that are the same as the dynamic sales data in the inventory task that was not completed at the previous moment, to obtain the fifth dynamic sales data list; based on the fifth dynamic sales data list, determine the second dynamic sales data list.

[0093] In some optional aspects of this embodiment, the first determination unit is further configured to eliminate the moving sales data that are identical to the moving sales data in the cycle inventory task in the fifth moving sales data list to obtain a second moving sales data list.

[0094] In some optional embodiments of this embodiment, the preset inventory task prediction model is constructed based on an LSTM network.

[0095] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0096] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0097] like Figure 6 , is a block diagram of an electronic device according to the method for generating a dynamic sales data list according to an embodiment of the present application.

[0098] 600 is a block diagram of an electronic device according to a method for generating a dynamic sales data list according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0099] like Figure 6 As shown, the electronic device includes: one or more processors 601, a memory 602, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 601 is taken as an example.

[0100] The memory 602 is a non-transitory computer-readable storage medium provided in the present application. The memory stores instructions executable by at least one processor to enable the at least one processor to execute the method for generating a dynamic sales data list provided in the present application. The non-transitory computer-readable storage medium of the present application stores computer instructions, which are used to enable a computer to execute the method for generating a dynamic sales data list provided in the present application.

[0101] The memory 602 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as the program instructions / modules corresponding to the method for generating a dynamic sales data list in the embodiment of the present application (for example, the attached Figure 5The processor 601 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 602, that is, the method for generating a dynamic sales data list in the above method embodiment is implemented.

[0102] The memory 602 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created by the use of the electronic device for generating the dynamic sales data list, etc. In addition, the memory 602 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 602 may optionally include a memory remotely arranged relative to the processor 601, and these remote memories may be connected to the electronic device for generating the dynamic sales data list via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] The electronic device of the method for generating a dynamic sales data list may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0104] The input device 603 can receive input digital or character information, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a track ball, a joystick, and other input devices. The output device 604 may include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0105] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, dedicated ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] These computer programs (also referred to as programs, software, software applications, or code) include machine instructions for programmable processors and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or means (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0109] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0110] According to the technical solution of the embodiment of the present application, the effectiveness and pertinence of the generated dynamic sales data list are improved.

[0111] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution disclosed in this application can be achieved, and this document is not limited here.

[0112] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for generating a dynamic sales data list, the method comprising: Obtain at least one piece of dynamic sales data at the current moment, wherein the dynamic sales data includes: a storage location and the type of SKU corresponding to the storage location and the quantity of each type of SKU; Inputting the at least one piece of dynamic sales data into a preset inventory task prediction model to obtain a target probability of inventory shortage or inventory surplus occurring at the next moment corresponding to each piece of dynamic sales data; Based on the target probability corresponding to each piece of dynamic sales data, a first dynamic sales data list is determined from the at least one piece of dynamic sales data; Based on the first dynamic sales data list, a target dynamic sales data list is determined.

2. The method according to claim 1, wherein: The step of determining a target dynamic sales data list based on the first dynamic sales data list includes: Determine a second dynamic sales data list based on the first dynamic sales data list; According to the sales quantity corresponding to each sales data in the second sales data list, the sales data in the second sales data list are sorted from large to small to obtain a third sales data list; Determine a preset number of moving sales data items ranked first in the third moving sales data list as a fourth moving sales data list; Based on the fourth dynamic sales data list, a target dynamic sales data list is determined.

3. The method according to claim 2, wherein: The determining of the second dynamic sales data list based on the first dynamic sales data list includes: In the first moving sales data list, remove the moving sales data that are the same as the moving sales data in the unfinished inventory task at the last moment, to obtain a fifth moving sales data list; Based on the fifth dynamic sales data list, a second dynamic sales data list is determined.

4. The method according to claim 3, wherein: The step of determining a second dynamic sales data list based on the fifth dynamic sales data list includes: In the fifth dynamic sales data list, dynamic sales data that are the same as the dynamic sales data in the cycle inventory task are eliminated to obtain a second dynamic sales data list.

5. The method according to claim 1, wherein: The preset inventory task prediction model is constructed based on the LSTM network, and the preset inventory task prediction model is trained in the following way: Acquire historical dynamic sales data samples, and sort the historical dynamic sales data samples in ascending order according to corresponding time points to obtain a data set, wherein the time intervals of any adjacent samples in the data set are the same; An iterative operation is performed to train the initial inventory task prediction model, the iterative operation comprising: taking the dynamic sales data sample corresponding to the first moment in the data set and the probability of inventory shortage or inventory surplus corresponding to the moment before the first moment as input data, inputting them into the initial inventory task prediction model, and obtaining the probability of inventory shortage or inventory surplus corresponding to the first moment; In response to the trained initial inventory task prediction model meeting a preset condition, determining the trained initial inventory task prediction model as a preset inventory task prediction model; In response to the trained initial inventory task prediction model not meeting a preset condition, a next moment of the first moment is determined as a new first moment, and the iterative operation is continued.

6. A device for generating a dynamic sales data list, the device comprising: The data acquisition module is configured to acquire at least one piece of dynamic sales data at the current moment, wherein the dynamic sales data includes: a storage location and a type of SKU corresponding to the storage location and a quantity of each type of SKU; A prediction probability module is configured to input the at least one piece of dynamic sales data into a preset inventory task prediction model to obtain a target probability of inventory shortage or inventory surplus occurring at the next moment corresponding to each piece of dynamic sales data; A first determining module is configured to determine a first dynamic sales data list from the at least one dynamic sales data based on a target probability corresponding to each dynamic sales data; The second determination module is configured to determine a target dynamic sales data list based on the first dynamic sales data list.

7. The device according to claim 6, wherein: The second determining module comprises: A first determining unit is configured to determine a second dynamic sales data list based on the first dynamic sales data list; a data sorting unit configured to sort the moving sales data in the second moving sales data list from large to small according to the moving sales quantity corresponding to each moving sales data in the second moving sales data list, so as to obtain a third moving sales data list; A second determining unit is configured to determine a preset number of moving sales data items ranked top in the third moving sales data list as a fourth moving sales data list; The third determining unit is configured to determine a target dynamic sales data list based on the fourth dynamic sales data list.

8. The device according to claim 7, wherein: The first determining unit is further configured to: In the first moving sales data list, remove the moving sales data that are the same as the moving sales data in the unfinished inventory task at the last moment, to obtain a fifth moving sales data list; Based on the fifth dynamic sales data list, a second dynamic sales data list is determined.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 5.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

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