Product management method based on big data and related device

Abstract

The application discloses a product management method and related device based on big data, comprising: obtaining a target product to be produced from a target production line, matching a pre-sale order according to the target product; obtaining a historical sales record, determining a shipment tendency corresponding to the target product according to the historical sales record combined with the pre-sale order; obtaining position information of all warehouses, determining a target shipment warehouse and a receiving weight corresponding to the target shipment warehouse combined with the shipment tendency corresponding to the target product; generating a product distribution strategy according to the receiving weight corresponding to the shipment tendency; matching the pre-sale order for the target product and determining the shipment tendency combined with the historical sales record, determining the target shipment warehouse and the receiving weight according to the warehouse position information, and finally generating the product distribution strategy; and further realizing the technical effect of reasonably distributing product storage locations according to the shipment tendency of the product.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of product management, and in particular to a product management method based on big data and related devices. BACKGROUND

[0002] The existing factory has realized automation, digitization and informatization to a certain extent. In the production process of the factory, the product will be packaged and transported to the warehouse after production, and then the warehouse will be transported through product order. The destination of the product transportation is often allocated and manually input by artificial. In the face of large product shipment, the number of required staff is further increased.

[0003] Therefore, how to reasonably arrange the product shipment has become a technical problem to be solved. SUMMARY

[0004] In order to reasonably arrange the product shipment, the present application provides a product management method based on big data and related devices.

[0005] In the first aspect, the present application provides a product management method based on big data, which adopts the following technical scheme:

[0006] A product management method based on big data, comprising:

[0007] Obtaining a target product to be produced from a target production line, and matching a pre-sale order according to the target product;

[0008] Obtaining historical sales records, and determining a shipment tendency of the target product according to the historical sales records combined with the pre-sale order;

[0009] Obtaining position information of all warehouses, and determining a target shipment warehouse and a corresponding receiving weight of the target shipment warehouse combined with the shipment tendency of the target product;

[0010] Generating a product distribution strategy according to the corresponding receiving weight of the shipment tendency.

[0011] Optionally, the step of obtaining a target product to be produced from a target production line and matching a pre-sale order according to the target product comprises:

[0012] Obtaining a current production task from a target production line, and determining a target product according to the current production task;

[0013] Determining whether the target product is a product for sale;

[0014] If not, determining a target product for sale according to the current production task combined with the target product, and obtaining a pre-sale order corresponding to the target product for sale;

[0015] If yes, a pre-order is matched according to model information of the target product.

[0016] Optionally, the step of obtaining historical sales records and determining the historical shipping tendency of the target product in the historical sales records comprises:

[0017] obtaining historical sales records and determining the historical shipping tendency of the target product in the historical sales records;

[0018] determining the pre-order shipping tendency of the target product according to the pre-order;

[0019] generating the shipping tendency corresponding to the target product according to the historical shipping tendency and the pre-order shipping tendency.

[0020] Optionally, the step of obtaining historical sales records and determining the historical shipping tendency of the target product in the historical sales records comprises:

[0021] obtaining historical sales records and matching target sales data of the target product in the historical sales records;

[0022] obtaining shipping location information in the target sales data;

[0023] obtaining preset map block information and generating a proportion report of the shipping location information in the map block information;

[0024] determining the historical shipping tendency of the target product according to the proportion report.

[0025] Optionally, the step of generating the shipping tendency corresponding to the target product according to the historical shipping tendency and the pre-order shipping tendency comprises:

[0026] obtaining first weight information corresponding to the historical shipping tendency and second weight information corresponding to the pre-order shipping tendency;

[0027] generating a first tendency report according to the first weight information and the historical shipping tendency;

[0028] generating a second tendency report according to the second weight information and the pre-order shipping tendency;

[0029] generating the shipping tendency corresponding to the target product according to the first tendency report and the second tendency report.

[0030] Optionally, the step of obtaining location information of all warehouses and determining a target shipping warehouse and a receiving weight corresponding to the target shipping warehouse according to the shipping tendency corresponding to the target product comprises:

[0031] acquire location information of all warehouses, and determine a warehouse list according to a shipping tendency of the target product;

[0032] acquire location information of each warehouse in the matching warehouse list, and determine a target shipping warehouse and a corresponding receiving weight of the target shipping warehouse according to the location information of each warehouse and the shipping tendency of the target product.

[0033] Optionally, the step of acquiring location information of each warehouse in the matching warehouse list and determining a target shipping warehouse and a corresponding receiving weight of the target shipping warehouse according to the location information of each warehouse and the shipping tendency of the target product comprises:

[0034] acquire location information of each warehouse in the matching warehouse list, and acquire current location information of the target production line at the same time;

[0035] determine a transportation loss according to the location information of each warehouse and the current location information of the target production line;

[0036] generate a corresponding receiving weight of each warehouse according to the transportation loss and the shipping tendency;

[0037] determine a target shipping warehouse according to the receiving weight that meets a preset threshold.

[0038] In a second aspect, the present application provides a product management device based on big data, which comprises:

[0039] a target product acquisition module, configured to acquire a target product to be produced from a target production line, and match a pre-sale order according to the target product;

[0040] a shipping tendency determination module, configured to acquire historical sales records, and determine a shipping tendency corresponding to the target product according to the historical sales records and the pre-sale order;

[0041] a receiving weight determination module, configured to acquire location information of all warehouses, and determine a target shipping warehouse and a corresponding receiving weight of the target shipping warehouse according to the shipping tendency corresponding to the target product;

[0042] a strategy generation module, configured to generate a product distribution strategy according to the receiving weight corresponding to the shipping tendency.

[0043] In a third aspect, the present application provides a computer device, which comprises a memory and a processor, wherein the processor executes a method as described in any one of the above aspects when running computer instructions stored in the memory.

[0044] In a fourth aspect, the present application provides a computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method described above.

[0045] In summary, the present application includes the following beneficial technical effects:

[0046] The present application obtains a target product to be produced from a target production line, matches a pre-sale order according to the target product, obtains a historical sales record, determines a shipping tendency corresponding to the target product according to the historical sales record in combination with the pre-sale order, obtains position information of all warehouses, determines a target shipping warehouse and a receiving weight corresponding to the target shipping warehouse in combination with the shipping tendency corresponding to the target product, generates a product distribution strategy according to the receiving weight corresponding to the shipping tendency, matches the pre-sale order for the target product and determines the shipping tendency in combination with the historical sales record, determines the target shipping warehouse and the receiving weight according to the warehouse position information, and finally generates the product distribution strategy, thereby further achieving the technical effect of reasonably distributing product storage locations according to the shipping tendency of the product. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a computer device structure schematic diagram of a hardware running environment related to an embodiment scheme of the present application.

[0048] Figure 2 is a flow schematic diagram of a first embodiment of the product management method based on big data of the present application.

[0049] Figure 3 is a flow schematic diagram of a second embodiment of the product management method based on big data of the present application.

[0050] Figure 4 is a structure block diagram of a first embodiment of the product management device based on big data of the present application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below by means of the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0052] Referring to Figure 1 , Figure 1 is a computer device structure schematic diagram of a hardware running environment related to an embodiment scheme of the present application.

[0053] As Figure 1As shown, the computer device can include a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection and communication between the components. The user interface 1003 can include a display, an input unit such as a keyboard, and can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a wireless fidelity (Wi-Fi) interface). The memory 1005 can be a high-speed random access memory (RAM), and can also be a stable non-volatile memory (NVM), such as a disk memory. The memory 1005 can also be a storage device independent of the aforementioned processor 1001.

[0054] Those skilled in the art can understand that, Figure 1 The structure shown in the figure does not constitute a limitation on the computer device, and can include more or fewer components than the figure, or combine certain components, or different component arrangements.

[0055] As Figure 1 As shown, the memory 1005 as a storage medium can include an operating system, a network communication module, a user interface module, and a big data-based product management program.

[0056] In Figure 1 In the computer device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the computer device of the present application can be provided in the computer device, and the computer device calls the big data-based product management program stored in the memory 1005 through the processor 1001, and executes the big data-based product management method provided by the embodiment of the present application.

[0057] The embodiment of the present application provides a big data-based product management method, which is described with reference to Figure 2 , Figure 2 The flowchart of the first embodiment of the big data-based product management method of the present application is shown.

[0058] In this embodiment, the big data-based product management method includes the following steps:

[0059] Step S10: obtaining a target product to be produced from a target production line, and matching a pre-sale order according to the target product.

[0060] It should be noted that the product to be produced on the target production line can be obtained from the production task log of the production line or by manually inputting information.

[0061] It can be understood that the pre-sale order refers to the product that can be pre-sold through other channels before being formally sold, and the channels include but are not limited to Internet channels, cooperation channels between companies, etc.

[0062] In a specific implementation, obtaining a target product to be produced from a target production line and matching a pre-sale order according to the target product refers to obtaining the target product to be produced from the production line, and traversing the sales system through the product label or product name in the product information to obtain the pre-sale order corresponding to the target product.

[0063] Further, in order to obtain a target product to be produced from a target production line and match a pre-sale order according to the target product, the step includes: obtaining a current production task from the target production line, determining a target product according to the current production task; judging whether the target product is a product for sale; if not, determining a target product for sale according to the current production task and the target product, and obtaining a pre-sale order corresponding to the target product for sale; if yes, matching a pre-sale order according to the model information of the target product.

[0064] It should be noted that the product produced on the target production line can be a finished product that has been assembled, or a certain part of the product for sale. Therefore, the properties of the product need to be analyzed to determine whether the target product is a product for sale. In this embodiment, the product for sale means that the product processed by the production line will be directly sold.

[0065] Step S20: obtaining a historical sales record, and determining a shipment tendency of the target product according to the historical sales record and the pre-sale order.

[0066] It should be noted that the historical sales record is an electronic record stored in the electronic processing device. By obtaining the historical sales record, the sales record of the target product in the past can be obtained. In order to reduce the amount of calculation, the historical sales record can be filtered, for example, the historical sales record within 1 year is selected.

[0067] It can be understood that the shipment tendency refers to the delivery address tendency of the product after being sold.

[0068] In a specific implementation, the historical sales records are acquired, and the delivery tendency of the target product is determined according to the historical sales records and the pre-sale order by acquiring the historical delivery addresses of the target product in the historical sales records, and combining the pre-sale delivery address in the pre-sale order to count the delivery addresses of the target product. The delivery tendency of the target product is determined by the distribution of the delivery addresses. For example, the company has 5 storage warehouses in China, of which 3 are in Guangdong Province and 2 are in Beijing. After analyzing the delivery tendency, it is found that 80% of the product delivery addresses are near Beijing, so according to the delivery tendency, 80% of the target product can be sent to the storage warehouse in Beijing after the target product is produced.

[0069] Step S30: Acquire the location information of all warehouses, and determine the target delivery warehouse and the corresponding delivery weight of the target delivery warehouse according to the delivery tendency of the target product.

[0070] It should be noted that the location information of all warehouses is set according to the specific circumstances of each use. For example, if the company has many storage warehouses, the warehouse addresses can be classified by prefecture-level cities, and if the storage addresses are less, they can be divided by provinces.

[0071] It can be understood that the delivery weight refers to the funds consumed by the product from the factory to the warehouse. Generally speaking, the farther the factory is from the delivery warehouse, the more funds it will spend, and therefore the lower the delivery weight.

[0072] In a specific implementation, the delivery tendency of the target product is determined according to the delivery tendency of the target product, and the corresponding delivery weight of the target delivery warehouse is determined by the location information of the warehouse to determine the delivery loss of the warehouse from the factory, and the delivery weight of the product in the warehouse is combined to generate the delivery weight.

[0073] It can be understood that in this embodiment, the formula used in this step is:

[0074] Z=(X·U1+Y·U2)

[0075] Wherein, Z is the delivery weight of the warehouse, X is the delivery tendency of the target product to the warehouse, U1 is the delivery coefficient, Y is the transportation loss of the factory and the warehouse, and U2 is the loss coefficient, and U1+U2=1.

[0076] Further, in order to acquire the location information of all warehouses, the step of determining the target delivery warehouse and the receiving weight corresponding to the target delivery warehouse according to the delivery tendency of the target product in combination with the delivery tendency of the target product comprises: acquiring the location information of all warehouses, determining a warehouse list according to the delivery tendency of the target product; acquiring the location information of each warehouse in the matching warehouse list, and determining the target delivery warehouse and the receiving weight corresponding to the target delivery warehouse according to the location information of each warehouse in combination with the delivery tendency of the target product.

[0077] In a specific implementation, the step of acquiring the location information of each warehouse in the matching warehouse list, and determining the target delivery warehouse and the receiving weight corresponding to the target delivery warehouse according to the location information of each warehouse in combination with the delivery tendency of the target product comprises: acquiring the location information of each warehouse in the matching warehouse list, and acquiring the location information of the current target production line at the same time; determining the transportation loss according to the location information of each warehouse in combination with the location information of the current target production line; generating the receiving weight corresponding to each warehouse according to the transportation loss and the delivery tendency in each warehouse; and taking the warehouse whose receiving weight meets a preset threshold as the target delivery warehouse.

[0078] Step S40: generating a product allocation strategy according to the receiving weight corresponding to the delivery tendency.

[0079] The embodiment generates a product allocation strategy according to the receiving weight corresponding to the delivery tendency.

[0080] Reference Figure 3 FIG. 2 is a flowchart of a product management method based on big data according to a second embodiment of the present application.

[0081] Based on the first embodiment, the step S20 of the product management method based on big data in the second embodiment further comprises: step S201: acquiring historical sales records, and determining the historical delivery tendency of the target product in the historical sales records.

[0082] It should be noted that before determining the historical delivery tendency of the target product in the historical sales record, a screening time needs to be set, and in the case of no historical sales record, the historical sales record of the same type of product will be obtained, which can be screened through the product label. It can also be distinguished by the constituent parts of the product. For example, A and two products, 80% of which are the same type of parts, but A product has no historical sales record, at this time, the historical sales record of B product can be obtained instead of the historical sales record of A.

[0083] It can be understood that when a product belonging to a derivative series is encountered, the historical sales record of the whole series of products will be used as a reference.

[0084] Further, in order to accurately evaluate the historical delivery tendency, the step of obtaining the historical sales record and determining the historical delivery tendency of the target product in the historical sales record comprises: obtaining the historical sales record, matching the target sales data in the historical sales record according to the target product; obtaining the delivery location information in the target sales data; obtaining the preset map block information and generating the proportion report of the delivery location information in the map block information; determining the historical delivery tendency of the target product according to the proportion report.

[0085] Step S202: determining the pre-sale delivery tendency of the target product according to the pre-sale order.

[0086] In a specific implementation, the delivery address of the product pre-sale is obtained in the pre-sale order, and the pre-sale delivery tendency of the product is determined according to the delivery address.

[0087] Step S203: generating the delivery tendency corresponding to the target product according to the historical delivery tendency and the pre-sale delivery tendency.

[0088] Further, in order to make the evaluation result more accurate in the delivery tendency analysis, the first weight information corresponding to the historical delivery tendency is obtained, and the second weight information corresponding to the pre-sale delivery tendency is obtained; the first tendency report is generated according to the first weight information combined with the historical delivery tendency; the second tendency report is generated according to the second weight information combined with the pre-sale delivery tendency; the delivery tendency corresponding to the target product is generated according to the first tendency report and the second tendency report.

[0089] The embodiment obtains the historical sales record, determines the historical delivery tendency of the target product in the historical sales record, determines the pre-sale delivery tendency of the target product according to the pre-sale order, and generates the delivery tendency corresponding to the target product according to the historical delivery tendency and the pre-sale delivery tendency. The product delivery tendency is accurately generated through the product pre-sale delivery tendency and the historical delivery tendency, the product delivery trend is accurately grasped, and the accurate management of the product is further improved.

[0090] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing a product management program based on big data, wherein when the product management program based on big data is executed by a processor, it implements the steps of the product management method based on big data as described above.

[0091] Reference Figure 4 , Figure 4 This is a structural block diagram of the first embodiment of the product management device based on big data of the present invention.

[0092] like Figure 4 As shown, this invention is implemented; for example, a product management device based on big data includes:

[0093] The target product acquisition module 10 is used to acquire target products to be produced from the target production line and match pre-sale orders according to the target products.

[0094] The shipment tendency determination module 20 is used to obtain historical sales records and determine the shipment tendency corresponding to the target product based on the historical sales records and the pre-sale orders.

[0095] The receiving weight determination module 30 is used to obtain the location information of all warehouses and determine the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse by combining the shipping tendency corresponding to the target product.

[0096] The strategy generation module 40 is used to generate a product allocation strategy based on the receiving weight corresponding to the shipping tendency.

[0097] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0098] This embodiment obtains target products to be produced from the target production line and matches pre-sale orders with these products; it acquires historical sales records and determines the shipment tendency corresponding to the target products based on these records and the pre-sale orders; it acquires the location information of all warehouses and determines the target shipping warehouse and its corresponding receiving weight based on the shipment tendency of the target products; it generates a product allocation strategy based on the receiving weight corresponding to the shipment tendency; by matching pre-sale orders with target products and combining them with historical sales records to determine the shipment tendency, and by determining the target shipping warehouse and receiving weight based on warehouse location information, a product allocation strategy is finally generated; this further achieves the technical effect of rationally allocating product storage locations based on the product's shipment tendency.

[0099] In one embodiment, the target product acquisition module 10 is further configured to acquire the current production task from the target production line, determine the target product based on the current production task, determine whether the target product is a finished product for sale; if not, determine the target finished product for sale based on the current production task and the target product, and acquire the pre-sale order corresponding to the target finished product for sale; if yes, match the pre-sale order based on the model information of the target product.

[0100] In one embodiment, the shipment tendency determination module 20 is further configured to acquire historical sales records, determine the historical shipment tendency of the target product in the historical sales records; determine the pre-sale shipment tendency of the target product based on the pre-sale order; and generate the shipment tendency corresponding to the target product based on the historical shipment tendency and the pre-sale shipment tendency.

[0101] In one embodiment, the shipment tendency determination module 20 is further configured to acquire historical sales records, match target sales data in the historical sales records according to the target product; acquire shipment location information in the target sales data; acquire preset map segment information and generate a percentage report of the shipment location information in the map segment information; and determine the historical shipment tendency of the target product according to the percentage report.

[0102] In one embodiment, the shipment tendency determination module 20 is further configured to generate the shipment tendency corresponding to the target product based on the historical shipment tendency and the pre-sale shipment tendency, including: obtaining first weight information corresponding to the historical shipment tendency, and simultaneously obtaining second weight information corresponding to the pre-sale shipment tendency; generating a first tendency report based on the first weight information and the historical shipment tendency; generating a second tendency report based on the second weight information and the pre-sale shipment tendency; and generating the shipment tendency corresponding to the target product based on the first tendency report and the second tendency report.

[0103] In one embodiment, the receiving weight determination module 30 is further configured to obtain the location information of all warehouses, determine a warehouse list based on the shipping tendency of the target product, obtain the location information of each warehouse in the matching warehouse list, and determine the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse based on the location information of each warehouse and the shipping tendency corresponding to the target product.

[0104] In one embodiment, the receiving weight determination module 30 is further configured to obtain the location information of each warehouse in the matching warehouse list, and at the same time obtain the location information of the current target production line; determine the transportation loss based on the location information of each warehouse and the location information of the current target production line; generate a receiving weight corresponding to each warehouse based on the transportation loss and the shipping tendency; and designate the warehouse whose receiving weight meets a preset threshold as the target shipping warehouse.

[0105] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0106] In addition, for technical details not described in detail in this embodiment, please refer to the product management method based on big data provided in any embodiment of the present invention, which will not be repeated here.

[0107] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0108] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0110] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A product management method based on big data, characterized in that, include: Obtain target products to be produced from the target production line, and match pre-sale orders based on the target products; Obtain historical sales records, and determine the shipping tendency corresponding to the target product based on the historical sales records and the pre-sale orders. The shipping tendency refers to the preferred shipping address for logistics transportation of the product after it is sold. Obtain the location information of all warehouses, and determine the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse based on the shipping tendency of the target product; A product allocation strategy is generated based on the receiving weight corresponding to the shipping tendency. The step of obtaining the target product to be produced from the target production line and matching pre-sale orders with the target product includes: Obtain the current production task from the target production line, and determine the target product based on the current production task; Determine whether the target product is a finished product currently on sale; If not, then determine the target finished product for sale based on the current production task and the target product, and obtain the pre-sale order corresponding to the target finished product for sale; If so, then match the pre-sale order according to the model information of the target product; The step of obtaining historical sales records and determining the shipment tendency corresponding to the target product based on the historical sales records and the pre-sale orders includes: Obtain historical sales records and determine the historical shipment tendency of the target product from the historical sales records; Determine the pre-sale shipment tendency of the target product based on the pre-sale orders; The shipment tendency corresponding to the target product is generated based on the historical shipment tendency and the pre-sale shipment tendency. The step of generating the shipment tendency corresponding to the target product based on the historical shipment tendency and the pre-sale shipment tendency includes: Obtain the first weight information corresponding to the historical shipment tendency, and at the same time obtain the second weight information corresponding to the pre-sale shipment tendency; A first tendency report is generated based on the first weight information and the historical shipping tendency. A second tendency report is generated based on the second weighting information and the pre-sale shipment tendency. Generate the shipment tendency corresponding to the target product based on the first tendency report and the second tendency report; The step of obtaining the location information of all warehouses and determining the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse based on the shipping tendency of the target product includes: Obtain the location information of all warehouses and determine the warehouse list based on the shipping tendency of the target product; The location information of each warehouse is obtained from the matching warehouse list. Based on the location information of each warehouse and the shipping tendency corresponding to the target product, the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse are determined.

2. The product management method based on big data according to claim 1, characterized in that, The step of obtaining historical sales records and determining the historical shipment tendency of the target product from the historical sales records includes: Obtain historical sales records and match target sales data in the historical sales records based on the target product; Obtain the shipping location information from the target sales data; Obtain preset map segment information and generate a report on the proportion of the shipping location information in the map segment information; The historical shipment tendency of the target product is determined based on the aforementioned percentage report.

3. The product management method based on big data according to claim 1, characterized in that, The step of obtaining the location information of each warehouse in the matching warehouse list, and determining the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse based on the location information of each warehouse and the shipping tendency corresponding to the target product, includes: Obtain the location information of each warehouse in the matching warehouse list, and at the same time obtain the location information of the current target production line; The transportation loss is determined based on the location information of each warehouse and the current location information of the target production line. In each warehouse, a receiving weight is generated based on the transportation loss and shipping tendency. Warehouses whose receiving weight meets the preset threshold will be designated as target shipping warehouses.

4. A product management device based on big data, characterized in that, The big data-based product management device includes: The target product acquisition module is used to acquire target products to be produced from the target production line and match pre-sale orders based on the target products. The shipment tendency determination module is used to obtain historical sales records and determine the shipment tendency of the target product based on the historical sales records and the pre-sale orders. The shipment tendency refers to the shipping address tendency of the product after it is sold. The receiving weight determination module is used to obtain the location information of all warehouses and determine the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse by combining the shipping tendency of the target product. The strategy generation module is used to generate a product allocation strategy based on the receiving weight corresponding to the shipping tendency. The step of obtaining the target product to be produced from the target production line and matching pre-sale orders with the target product includes: Obtain the current production task from the target production line, and determine the target product based on the current production task; Determine whether the target product is a finished product currently on sale; If not, then determine the target finished product for sale based on the current production task and the target product, and obtain the pre-sale order corresponding to the target finished product for sale; If so, then match the pre-sale order according to the model information of the target product; The step of obtaining historical sales records and determining the shipment tendency corresponding to the target product based on the historical sales records and the pre-sale orders includes: Obtain historical sales records and determine the historical shipment tendency of the target product from the historical sales records; Determine the pre-sale shipment tendency of the target product based on the pre-sale orders; The shipment tendency corresponding to the target product is generated based on the historical shipment tendency and the pre-sale shipment tendency. The step of generating the shipment tendency corresponding to the target product based on the historical shipment tendency and the pre-sale shipment tendency includes: Obtain the first weight information corresponding to the historical shipment tendency, and at the same time obtain the second weight information corresponding to the pre-sale shipment tendency; A first tendency report is generated based on the first weight information and the historical shipping tendency. A second tendency report is generated based on the second weighting information and the pre-sale shipment tendency. Generate the shipment tendency corresponding to the target product based on the first tendency report and the second tendency report; The step of obtaining the location information of all warehouses and determining the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse based on the shipping tendency of the target product includes: Obtain the location information of all warehouses and determine the warehouse list based on the shipping tendency of the target product; The location information of each warehouse is obtained from the matching warehouse list. Based on the location information of each warehouse and the shipping tendency corresponding to the target product, the target shipping warehouse and the receiving weight corresponding to the target shipping warehouse are determined.

5. A computer device, characterized in that, The device includes a memory and a processor, wherein the processor, when executing computer instructions stored in the memory, performs the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 3.

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