Book commodity resource management method, system, equipment and medium

Abstract

The embodiment of the invention provides a book commodity resource management method, system and device and a medium, relates to the technical field of computers, aims to relieve the technical problem that errors are prone to occurring during interaction of online data and offline data in the prior art, and provides a book commodity resource management method and system based on initial inventory data of offline commodities and operation feature data of the online commodities. Predicting target inventory data of each online commodity corresponding to each online sales platform by adopting a dynamic inventory distribution prediction model, and sending the target inventory data to each online sales platform; based on the initial inventory data of each offline commodity and the operation data of each online commodity corresponding to each online sales platform, updating the inventory data of each offline commodity, and obtaining the sales data of each offline commodity; and sending the updated inventory data of each offline commodity and the updated sales data of each offline commodity to an offline ERP system, thereby improving the inventory management efficiency of the offline ERP system.

Description

Technical Field

[0001] The present invention relates to the technical field of computers, and in particular, to a method, system, device and medium for managing book commodity resources. Background Art

[0002] With the development of the express delivery business, its online shopping business has also been expanded, and online platform stores such as Taobao, Tmall, JD.com, Dangdang and other e-commerce platforms have seized the online sales market.

[0003] In the prior art, an offline inventory management system is generally used to be responsible for the most core and fundamental data comprehensive calculations such as the establishment of commodity files, the purchase records of commodities, the sales records of commodities and inventory management. However, the data of online platform stores (i.e., online data) and the data of the offline inventory management system (i.e., offline data) usually use manual labor or a third-party e-commerce management system for data interaction, but it has the following defects: Interacting online data and offline data through manual labor increases the operation cost and is prone to errors; while interacting online data and offline data through a third-party e-commerce management system, due to system replacement, historical data will be lost, and the current third-party e-commerce management system lacks the dedicated attributes of commodities, resulting in insufficient query and statistics dimension effects and poor adaptability. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, system, device and medium for managing book commodity resources to realize the data interaction between the offline ERP system and the online sales platform, thereby improving the inventory management efficiency of the offline ERP system.

[0005] In a first aspect, the present invention provides a method for managing book commodity resources, which is applied to a book data middle platform system. The book data middle platform system is respectively communicatively connected to an offline ERP system and each online sales platform; the method includes:

[0006] Obtaining the initial inventory data of each offline commodity from the offline ERP system, and respectively obtaining the operation characteristic data of each online commodity from each online sales platform;

[0007] Based on the initial inventory data of each offline commodity and the operation characteristic data of each online commodity, using a dynamic inventory allocation prediction model to predict the target inventory data of each online commodity corresponding to each online sales platform;

[0008] Sending the target inventory data of each online commodity corresponding to each online sales platform to each online sales platform;

[0009] Update the inventory data of each offline product and obtain the sales data of each offline product based on the initial inventory data of each offline product and the operation data of each online product corresponding to each online sales platform;

[0010] Send the updated inventory data of each offline product and the sales data of each offline product to the offline ERP system.

[0011] Optionally, the operation feature data includes historical sales data, historical browsing data, historical favorite data, and real-time order data.

[0012] Optionally, based on the initial inventory data of each offline product and the operation feature data of each online product, use a dynamic inventory allocation prediction model to predict the target inventory data of each online product corresponding to each online sales platform, including:

[0013] Process the historical sales data, historical browsing data, and historical favorite data based on the dynamic inventory allocation prediction model to obtain the predicted sales data of each online product corresponding to each online sales platform;

[0014] Process the predicted sales data of each online product corresponding to each online sales platform and the initial inventory data of each offline product based on the dynamic inventory allocation prediction model to obtain the target inventory data of each online product corresponding to each online sales platform.

[0015] Optionally, update the inventory data of each offline product and obtain the sales data of each offline product based on the initial inventory data of each offline product and the operation data of each online product corresponding to each online sales platform, including:

[0016] Obtain the remaining inventory data of each offline product based on the initial inventory data of each offline product and the real-time order data of each online product corresponding to each online sales platform;

[0017] Obtain the sales data of each offline product based on the real-time order data of each online product corresponding to each online sales platform.

[0018] Optionally, the method further includes: obtaining a training data set; wherein, the training data set includes multiple training sample data; each training sample data includes the historical sales data, historical browsing data, historical favorite data, initial inventory data, target sales data, and actual inventory data of each online product in each online sales platform;

[0019] Based on the training data set, perform iterative training operations on the initial dynamic inventory allocation prediction model until it is determined that the iterative training termination condition is met. Then, based on the weights and thresholds of the initial dynamic inventory allocation prediction model updated during the last execution of the iterative training operation, obtain the dynamic inventory allocation prediction model. Among them, the iterative training operation includes:

[0020] Select target training sample data from the training data set;

[0021] Input the historical sales data, historical browsing data, and historical collection data in the target training sample data into the dynamic inventory allocation prediction model, so that the initial dynamic inventory allocation prediction model can obtain the predicted sales data of each online product corresponding to each online sales platform based on the historical sales data, historical browsing data, and historical collection data; Based on the predicted sales data of each online product corresponding to each online sales platform and the initial inventory data of each offline product, obtain the target inventory data of each online product corresponding to each online sales platform.

[0022] Based on the prediction error between the predicted sales data and the target sales data in the target training sample data, and the prediction error between the target inventory data and the actual inventory data in the target training sample data, update the weights and thresholds of the initial dynamic inventory allocation prediction model.

[0023] Optionally, the method further includes: updating the historical sales data, historical browsing data, and historical collection data respectively based on the attention mechanism.

[0024] Optionally, the method further includes: obtaining the image data of each offline product from the offline ERP system; determining the display feature data of each offline product based on the image data of each offline product; and sending the display feature data of each offline product to each online sales platform.

[0025] In a second aspect, the present invention provides a book product resource management system, including a book data middle platform system, an offline ERP system, and an online sales platform; the book data middle platform system is communicatively connected to the offline ERP system and the online sales platform respectively;

[0026] The offline ERP system is used to count the inventory data and sales data of products;

[0027] A book data middle platform system is used to predict the target inventory data of each online product corresponding to each online sales platform by using a dynamic inventory allocation prediction model based on the initial inventory data of each offline product and the operation characteristic data of each online product; send the target inventory data of each online product corresponding to each online sales platform to each online sales platform; and update the inventory data of each offline product and obtain the sales data of each offline product based on the initial inventory data of each offline product and the operation data of each online product corresponding to each online sales platform.

[0028] An online sales platform is used to sell each product through the Internet.

[0029] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned book product resource management method is implemented.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the above-mentioned book product resource management method is implemented.

[0031] A book product resource management method, system, device and medium provided by an embodiment of the present invention obtain the initial inventory data of each offline product from an offline ERP system and the operation characteristic data of each online product from each online sales platform respectively; predict the target inventory data of each online product corresponding to each online sales platform by using a dynamic inventory allocation prediction model based on the initial inventory data of each offline product and the operation characteristic data of each online product; send the target inventory data of each online product corresponding to each online sales platform to each online sales platform; update the inventory data of each offline product and obtain the sales data of each offline product based on the initial inventory data of each offline product and the operation data of each online product corresponding to each online sales platform; and send the updated inventory data and sales data of each offline product to the offline ERP system, realizing data interaction between the offline ERP system and the online sales platform, thereby improving the inventory management efficiency of the offline ERP system.

[0032] To make the above objects, features and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 Shows a flowchart of a method for managing book commodity resources provided by an embodiment of the present invention;

[0035] Figure 2 Shows a flowchart of a method for training a dynamic inventory allocation prediction model provided by an embodiment of the present invention;

[0036] Figure 3 Shows a schematic structural diagram of a book commodity resource management system provided by an embodiment of the present invention;

[0037] Figure 4 Shows a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0039] Figure 1 Is a schematic flowchart of a method for managing book commodity resources provided by an embodiment of the present invention. This method is applied to a book data middle platform system, and the book data middle platform system is communicatively connected to an offline ERP system and each online sales platform respectively, as Figure 1 shown. This method at least includes the following steps:

[0040] Step 110: Obtain the initial inventory data of each offline commodity from the offline ERP system, and obtain the operation characteristic data of each online commodity from each online sales platform respectively.

[0041] In the embodiments of the present application, the operation feature data includes historical sales data, historical browsing data, historical collection data, and real-time order data.

[0042] Furthermore, the operation feature data of each online product can be obtained from each online sales platform according to a preset time period, and the time period can be 1 day, 12 hours, 8 hours, etc.

[0043] Step 120: Based on the initial inventory data of each offline product and the operation feature data of each online product, use a dynamic inventory allocation prediction model to predict the target inventory data of each online product corresponding to each online sales platform.

[0044] In the embodiments of the present application, the dynamic inventory allocation prediction model includes a sales prediction sub-model and an inventory allocation sub-model. The historical sales data, historical browsing data, historical collection data, and real-time order data are processed by the sales prediction sub-model to output the predicted sales data of each online product on each online sales platform; the predicted sales data of each online product on each online sales platform and the initial inventory data of each offline product are processed by the inventory allocation sub-model to output the target inventory data corresponding to each online product on each online sales platform.

[0045] Furthermore, the operation feature data of each online product is obtained from each online sales platform through a preset cycle, and the initial inventory data of each offline product is obtained from the offline ERP system. The operation feature data of each online product obtained from each online sales platform and the initial inventory data of each offline product are input into the dynamic inventory allocation prediction model, so as to obtain the target inventory data corresponding to each online product on each online sales platform, so as to dynamically adjust the inventory data corresponding to each online product on each online sales platform, thereby avoiding the problem of no product inventory on the online sales platform.

[0046] Furthermore, in the embodiments of the present application, after the target inventory data corresponding to each online product on each online sales platform is predicted by the dynamic inventory allocation prediction model, it further includes:

[0047] Obtain the current inventory data of each online product from each online sales platform;

[0048] Based on the difference between the target inventory data and the current inventory data corresponding to each online product on each online sales platform, determine whether to update the current inventory data of each online product. If not, update the quantity of each online sales platform, and based on the updated quantity of each online sales platform, update the target inventory data corresponding to each online product on each online sales platform.

[0049] Specifically, the current number of online sales platforms is 5, and the initial inventory data of offline products is 30 pieces. If the current inventory data of the online products on one of the online sales platforms is 10 pieces, and the target inventory data corresponding to the online products on this online sales platform predicted by the dynamic inventory allocation prediction model is 11 pieces, and the difference between the target inventory data and the current inventory data is less than the preset threshold, then the current inventory data of the online products on this online sales platform is not updated. At this time, the target inventory data corresponding to the online products on the online sales platform predicted by the dynamic inventory allocation prediction model is predicted based on the initial inventory data of 30 pieces of offline products and the number of online sales platforms being 4.

[0050] Step 130: Send the target inventory data of each online product corresponding to each online sales platform to each online sales platform.

[0051] Step 140: Update the inventory data of each offline product and obtain the sales data of each offline product based on the initial inventory data of each offline product and the operation data of each online product corresponding to each online sales platform.

[0052] In the embodiment of the present application, updating the inventory data of each offline product and obtaining the sales data of each offline product includes, but is not limited to, the following methods:

[0053] Based on the initial inventory data of each of the offline products and the real-time order data of each of the online products corresponding to each of the online sales platforms, obtain the remaining inventory data of each of the offline products;

[0054] Based on the real-time order data of each of the online products corresponding to each of the online sales platforms, obtain the sales data of each of the offline products.

[0055] Specifically, obtain the real-time order data of each online product from each online sales platform. Based on the real-time order data of each online product on each online sales platform, obtain the total real-time order data of each online product on the online sales platform. Based on the total real-time order data of each online product on the sales platform and the initial inventory data of each offline product, obtain the remaining inventory data of each offline product. Among them, the total real-time order data of each online product on the online sales platform is both the sales data of the online product and the sales data of the offline product.

[0056] Step 150: Send the updated inventory data of each offline product and the sales data of each offline product to the offline ERP system.

[0057] In the embodiment of the present application, the book product resource management method further includes:

[0058] Obtain the image data of each offline commodity from the offline ERP system;

[0059] Based on the image data of each offline commodity, determine the display feature data of each offline commodity;

[0060] Send the display feature data of each offline commodity to each online sales platform.

[0061] Among them, the display feature data includes the main picture, sub-pictures, content introduction, author introduction, etc.

[0062] Specifically, the book data middle platform system can extract the display feature data from the image data through a deep learning model and send the display feature data to each online sales platform, so as to realize the data maintenance of commodities and provide sales information (display feature data) for the online sales platform at the same time.

[0063] In the embodiments of the present application, the data interaction between each online sales platform and the offline ERP system is realized through the book data middle platform system, so as to improve the inventory management efficiency of the offline ERP system; by using the dynamic inventory allocation model to allocate the inventory data of each online commodity on each online sales platform, the inventory data of each commodity on each online sales platform can be dynamically and automatically adjusted based on the actual sales data and predicted demand, so as to realize efficient and reasonable inventory management, and at the same time make the inventory meet the sales demand and avoid excessive inventory backlog, so as to ensure the flexibility and accuracy of the inventory.

[0064] Figure 2 It is a schematic flow chart of a training method for a dynamic inventory allocation prediction model provided by an embodiment of the present invention, as Figure 2 shown, the training method includes the following steps:

[0065] 210. Obtain a training data set; among them, the training data set includes multiple training sample data; each training sample data includes the historical sales data, historical browsing data, historical collection data, initial inventory data, target sales data, and actual inventory data of each online commodity on each online sales platform.

[0066] 220. Select target training sample data from the training data set.

[0067] 230. Input the historical sales data, historical browsing data, and historical collection data in the target training sample data into the dynamic inventory allocation prediction model, so that the initial dynamic inventory allocation prediction model can obtain the predicted sales data of each online product corresponding to each online sales platform based on the historical sales data, historical browsing data, and historical collection data; based on the predicted sales data of each online product corresponding to each online sales platform and the initial inventory data of each offline product, obtain the target inventory data of each online product corresponding to each online sales platform.

[0068] 240. Update the weights and thresholds of the initial dynamic inventory allocation prediction model based on the prediction error between the predicted sales data and the target sales data in the target training sample data, and the prediction error between the target inventory data and the actual inventory data in the target training sample data.

[0069] 250. Determine whether the iterative optimization termination condition is satisfied; if so, execute step 260; if not, return to step 220; where the iterative optimization termination condition is that the number of iterations is not less than the number threshold, or the prediction error is not higher than the error threshold.

[0070] 260. Obtain the dynamic inventory allocation prediction model based on the weights and thresholds of the initial dynamic inventory allocation prediction model updated when the iterative training operation is last executed.

[0071] In order to improve the information interaction between different feature data of the dynamic inventory allocation prediction model, in the embodiments of the present application, during the iterative training process of the initial dynamic inventory allocation prediction model, weights can also be assigned to the feature data of the initial dynamic inventory allocation prediction model, and the historical sales data, historical browsing data, and historical collection data can be updated respectively through the attention mechanism.

[0072] Specifically, taking the historical sales data and historical collection data as an example, calculate the attention weight A of the historical sales data relative to the historical collection data:

[0073]

[0074] In the formula, S x and S c are feature data, d is the feature dimension, and Softmax is the activation function;

[0075] Weightedly update the historical sales data according to the attention weight A:

[0076] S' x =AS x

[0077] Similarly, the attention weights between other feature data can be calculated and the features can be updated to enhance the information interaction between different modality features.

[0078] Figure 3 FIG. is a schematic structural diagram of a book commodity resource management system provided by an embodiment of the present invention. As Figure 3 shown, the system includes a book data center system 310, an offline ERP system 320, and an online sales platform 330; the book data center system 310 is respectively communicatively connected to the offline ERP system 320 and the online sales platform 330;

[0079] The offline ERP system 320 is used to count the inventory data and sales data of commodities;

[0080] The book data center system 310 is used to predict the target inventory data of each online commodity corresponding to each online sales platform by using a dynamic inventory allocation prediction model based on the initial inventory data of each offline commodity and the operation feature data of each online commodity; send the target inventory data of each online commodity corresponding to each online sales platform to each online sales platform; and update the inventory data of each offline commodity based on the initial inventory data of each offline commodity and the operation data of each online commodity corresponding to each online sales platform, and obtain the sales data of each offline commodity;

[0081] The online sales platform 330 is used to sell each commodity through the Internet.

[0082] In an alternative embodiment, the operation feature data includes historical sales data, historical browsing data, historical collection data, and real-time order data.

[0083] In an alternative embodiment, the book data center system 310 is further used for:

[0084] Processing the historical sales data, historical browsing data, and historical collection data based on the dynamic inventory allocation prediction model to obtain the predicted sales data of each online commodity corresponding to each online sales platform;

[0085] Processing the predicted sales data of each online commodity corresponding to each online sales platform and the initial inventory data of each offline commodity based on the dynamic inventory allocation prediction model to obtain the target inventory data of each online commodity corresponding to each online sales platform.

[0086] In an alternative embodiment, the book data center system 310 is further used for:

[0087] Obtaining the remaining inventory data of each offline commodity based on the initial inventory data of each offline commodity and the real-time order data of each online commodity corresponding to each online sales platform;

[0088] Based on the real-time order data of each online product corresponding to each online sales platform, the sales data of each offline product is obtained.

[0089] In an alternative embodiment, the book data middle platform system 310 is further configured to:

[0090] Obtain a training data set; wherein, the training data set includes a plurality of training sample data; each training sample data includes the historical sales data, historical browsing data, historical collection data, initial inventory data, target sales data, and actual inventory data of each online product in each online sales platform;

[0091] Based on the training data set, perform iterative training operations on the initial dynamic inventory allocation prediction model until, when it is determined that the iterative training termination condition is met, based on the weights and thresholds of the initial dynamic inventory allocation prediction model updated during the last execution of the iterative training operation, obtain a dynamic inventory allocation prediction model; wherein, the iterative training operations include:

[0092] Select target training sample data from the training data set;

[0093] Input the historical sales data, historical browsing data, and historical collection data in the target training sample data into the dynamic inventory allocation prediction model, so that the initial dynamic inventory allocation prediction model obtains the predicted sales data of each online product corresponding to each online sales platform based on the historical sales data, historical browsing data, and historical collection data; based on the predicted sales data of each online product corresponding to each online sales platform and the initial inventory data of each offline product, obtain the target inventory data of each online product corresponding to each online sales platform;

[0094] Based on the prediction error between the predicted sales data and the target sales data in the target training sample data, and the prediction error between the target inventory data and the actual inventory data in the target training sample data, update the weights and thresholds of the initial dynamic inventory allocation prediction model.

[0095] In an alternative embodiment, the book data middle platform system 310 is further configured to:

[0096] Based on the attention mechanism, update the historical sales data, historical browsing data, and historical collection data respectively.

[0097] In an alternative embodiment, the book data middle platform system 310 is further configured to:

[0098] Obtain the image data of each offline commodity from the offline ERP system; determine the display feature data of each offline commodity based on the image data of each offline commodity; and send the display feature data of each offline commodity to each online sales platform.

[0099] For the system provided by the embodiments of the present application, its implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the system embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0100] As Figure 4 As shown, an electronic device 400 provided by an embodiment of the present application includes: a processor 410, a memory 420, and a bus. The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device runs, communication between the processor 410 and the memory 420 is carried out through the bus, and the processor 410 executes the machine-readable instructions to perform the steps of the book commodity resource management method as described above.

[0101] Specifically, the above-mentioned memory 420 and processor 410 can be general memories and processors, which are not specifically limited here. When the processor 410 runs the computer program stored in the memory 420, it can execute the above-mentioned book commodity resource management method.

[0102] The processor 410 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 410 or the instructions in the form of software. The above-mentioned processor 410 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 420, and the processor 410 reads the information in the memory 420 and combines its hardware to complete the steps of the above method.

[0103] Corresponding to the above book commodity resource management method, an embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores machine-executable instructions. When the machine-executable instructions are called and run by a processor, the machine-executable instructions cause the processor to run the steps of the above book commodity resource management method.

[0104] The book commodity resource management device provided by the embodiments of the present application may be specific hardware on a device or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, the implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the above method embodiments, and will not be repeated here.

[0105] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0106] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code. A module, a program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0107] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0108] In addition, the various functional units in the embodiments provided in the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0109] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of each embodiment of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks, or optical discs.

[0110] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0111] Finally, it should be noted that: the above embodiments are only specific implementation manners of this application, used to illustrate the technical solution of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in this application can still modify the technical solution recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for managing book commodity resources, characterized in that, Applied to the book data middle platform system, the book data middle platform system is communicatively connected to the offline ERP system and each online sales platform respectively; the method includes: Obtain the initial inventory data of each offline commodity from the offline ERP system, and obtain the operation characteristic data of each online commodity from each of the online sales platforms respectively; Based on the initial inventory data of each offline commodity and the operation characteristic data of each online commodity, use a dynamic inventory allocation prediction model to predict the target inventory data of each online commodity corresponding to each of the online sales platforms; Send the target inventory data of each online commodity corresponding to each of the online sales platforms to each of the online sales platforms; Based on the initial inventory data of each offline commodity and the operation data of each online commodity corresponding to each of the online sales platforms, update the inventory data of each offline commodity, and obtain the sales data of each offline commodity; Send the updated inventory data of each offline commodity and the sales data of each offline commodity to the offline ERP system.

2. The book product resource management method according to claim 1, wherein The operation characteristic data includes historical sales data, historical browsing data, historical collection data, and real-time order data.

3. The book product resource management method according to claim 2, characterized in that Based on the initial inventory data of each offline commodity and the operation characteristic data of each online commodity, using a dynamic inventory allocation prediction model to predict the target inventory data of each online commodity corresponding to each of the online sales platforms, includes: Based on the dynamic inventory allocation prediction model, process the historical sales data, the historical browsing data, and the historical collection data to obtain the predicted sales data of each online commodity corresponding to each of the online sales platforms; Based on the dynamic inventory allocation prediction model, process the predicted sales data of each online commodity corresponding to each of the online sales platforms and the initial inventory data of each offline commodity to obtain the target inventory data of each online commodity corresponding to each of the online sales platforms.

4. The book commodity resource management method according to claim 2, wherein Based on the initial inventory data of each offline commodity and the operation data of each online commodity corresponding to each of the online sales platforms, update the inventory data of each offline commodity, and obtain the sales data of each offline commodity, includes: Based on the initial inventory data of each offline commodity and the real-time order data of each online commodity corresponding to each of the online sales platforms, obtain the remaining inventory data of each offline commodity; Based on the real-time order data of each online commodity corresponding to each of the online sales platforms, obtain the sales data of each offline commodity.

5. The method for managing book commodity resources according to any one of claims 1 to 4, characterized in that, Further includes: Obtain a training data set; wherein, the training data set includes a plurality of training sample data; each training sample data includes the historical sales data, historical browsing data, historical collection data, initial inventory data, target sales data, and actual inventory data of each online commodity in each online sales platform; Based on the training data set, perform iterative training operations on the initial dynamic inventory allocation prediction model until, when it is determined that the iterative training termination condition is met, obtain the dynamic inventory allocation prediction model based on the weights and thresholds of the initial dynamic inventory allocation prediction model updated during the last execution of the iterative training operation; wherein, the iterative training operation includes: Select target training sample data from the training data set; Input the historical sales data, the historical browsing data, and the historical collection data in the target training sample data into the dynamic inventory allocation prediction model, so that the initial dynamic inventory allocation prediction model obtains the predicted sales data of each online product corresponding to each online sales platform based on the historical sales data, the historical browsing data, and the historical collection data; based on the predicted sales data of each online product corresponding to each online sales platform and the initial inventory data of each offline product, obtain the target inventory data of each online product corresponding to each online sales platform; Update the weights and thresholds of the initial dynamic inventory allocation prediction model based on the prediction error between the predicted sales data and the target sales data in the target training sample data, and the prediction error between the target inventory data and the actual inventory data in the target training sample data.

6. The book product resource management method according to claim 5, wherein, Further includes: Update the historical sales data, the historical browsing data, and the historical collection data respectively based on the attention mechanism.

7. The book commodity resource management method according to claim 1, characterized in that Further includes: Obtain the image data of each offline product from the offline ERP system; Determine the display feature data of each offline product based on the image data of each offline product; Send the display feature data of each offline product to each online sales platform.

8. A book commodity resource management system, characterized in that, Includes a book data middle platform system, an offline ERP system, and an online sales platform; the book data middle platform system is communicatively connected to the offline ERP system and the online sales platform respectively; The offline ERP system is used to count the inventory data and sales data of products; The book data middle platform system is used to predict the target inventory data of each online product corresponding to each online sales platform by using a dynamic inventory allocation prediction model based on the initial inventory data of each offline product and the operation feature data of each online product; send the target inventory data of each online product corresponding to each online sales platform to each online sales platform; And update the inventory data of each offline product based on the initial inventory data of each offline product and the operation data of each online product corresponding to each online sales platform, and obtain the sales data of each offline product; The online sales platform is used to sell each product through the Internet.

9. An electronic device, characterized in that, Includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the book product resource management method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method for managing book commodity resources as described in any one of claims 1-7 is implemented.

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