Logistics information prediction method and device and logistics distribution method and device

Abstract

The invention provides a logistics information prediction method and device. A specific embodiment of the method comprises the steps of determining associated data based on order and waybill data of commodity distribution at different sites in a historical period; based on the associated data, determining the distribution station and a distribution commodity corresponding to the distribution station; on the basis of the associated data, predicting a delivery period of the delivery commodity; and taking the distribution commodity and the distribution period as distribution commodity information. According to the embodiment, the accuracy of logistics information prediction is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a logistics information prediction method and device, a logistics distribution method and device, an electronic device, and a computer-readable medium. Background Art

[0002] Currently, consumers browse products and place orders on shopping websites or apps. After the logistics system receives the order message, it picks, reviews, packs and ships the products out of the warehouse, sorts and transports them to the terminal station, and finally delivers them by delivery staff or delivers them to the pick-up cabinet.

[0003] With the current rising logistics and warehousing costs and the increasing requirements for user experience, the existing logistics system is sometimes unable to meet users' logistics and delivery requirements in a timely manner. Although there are currently some solutions that predict users' personal shopping behaviors and provide independent shopping and advance delivery, personal predictions have certain limitations, low accuracy, and insufficient logistics and delivery scale. Summary of the invention

[0004] The embodiments of the present disclosure provide a logistics information prediction method and device, a logistics distribution method and device, an electronic device, and a computer-readable medium.

[0005] In a first aspect, an embodiment of the present disclosure provides a logistics information prediction method, the method comprising: determining related data based on order data of commodity delivery at different stations in a historical period; determining the delivery station and the delivery commodity corresponding to the delivery station based on the related data; predicting the delivery cycle of the delivery commodity based on the related data; and using the delivery commodity and the delivery cycle as delivery commodity information.

[0006] In some embodiments, the above-mentioned order and waybill data based on the delivery of goods at different sites in the historical period, determining the associated data includes: obtaining the order data and waybill data in the historical period, both the order data and the waybill data include the waybill number; based on the waybill number, associating the order data and the waybill data to obtain first data, and using the first data as the associated data.

[0007] In some embodiments, the determining of associated data based on the order data of the merchandise delivery at different sites in the historical period further includes: obtaining site delivery information; and associating the site delivery information with the first data to obtain associated data.

[0008] In some embodiments, the above-mentioned determination of the delivery site and the delivery goods corresponding to the delivery site based on the associated data includes: based on the associated data, screening the sites whose current available area meets the preset area conditions to obtain the pre-allocated sites; based on the associated data, screening the goods in the pre-allocated sites that meet the preset delivery conditions to obtain the delivery goods, and using the site where the delivery goods are located as the delivery site.

[0009] In some embodiments, the above-mentioned prediction of the delivery cycle of the delivered goods based on the associated data includes: based on the associated data, counting the number of delivery times of the delivered goods in the historical period; in response to the number of delivery times being greater than a first predetermined number, and the interval between any two adjacent delivery times in the delivery time being less than an interval threshold, calculating the turnover rate of high-frequency delivery goods in a set time period; based on the product turnover rate, determining the delivery cycle of the delivered goods.

[0010] In some embodiments, the above-mentioned prediction of the delivery cycle of the delivered goods based on the associated data includes: based on the associated data, counting the number of delivery times of the delivered goods in the historical period; in response to the number of delivery times being greater than a second predetermined number, and each delivery time in the delivery time corresponds to each quarter, determining the delivery cycle of the delivered goods through a seasonal algorithm.

[0011] In a second aspect, an embodiment of the present disclosure provides a logistics distribution method, the method comprising: obtaining distribution product information and distribution sites, the distribution product information and distribution sites being predicted by the logistics information prediction method of any embodiment of the first aspect; dispatching the distribution products to the distribution sites based on the distribution product information; in response to receiving the user's order information, generating the user's distribution results based on the order information and the site products of the distribution site.

[0012] In some embodiments, the above-mentioned response to receiving the user's order information, based on the order information and the site commodities of the delivery site, generating the user's delivery result includes: in response to receiving the user's order information, determining the delivery location of the ordered goods based on the order information; detecting whether the delivery location is covered by the delivery site; in response to the delivery location being covered by the delivery site, detecting whether the number of site commodities of the delivery site corresponding to the delivery location is greater than the number of ordered goods in the order information; in response to the number of site commodities being greater than the number of ordered goods, using the delivery site to deliver the ordered goods to the user.

[0013] In a third aspect, an embodiment of the present disclosure provides a logistics information prediction device, which includes: a data determination unit, configured to determine related data based on order data of commodity delivery at different sites in a historical period; a logistics determination unit, configured to determine the delivery site and the delivery commodity corresponding to the delivery site based on the related data; a prediction unit, configured to predict the delivery cycle of the delivery commodity based on the related data, and use the delivery commodity and the delivery cycle as delivery commodity information.

[0014] In some embodiments, the data determination unit is further configured to: obtain order data and waybill data in a historical period, both the order data and waybill data including waybill numbers; based on the waybill number, associate the order data and waybill data to obtain first data, and use the first data as associated data.

[0015] In some embodiments, the data determination unit is further configured to: obtain site delivery information; and associate the site delivery information with the first data to obtain associated data.

[0016] In some embodiments, the above-mentioned logistics determination unit is further configured to: based on the associated data, filter the sites whose current available area meets the preset area conditions to obtain the pre-allocated sites; based on the associated data, filter the goods in the pre-allocated sites that meet the preset delivery conditions to obtain the delivered goods, and use the site where the delivered goods are located as the delivery site.

[0017] In some embodiments, the above-mentioned prediction unit is further configured to: based on the associated data, count the number of delivery times of delivered goods in the historical period; in response to the number of delivery times being greater than a first predetermined number, and the interval between any two adjacent delivery times in the delivery time being less than an interval threshold, calculate the product turnover rate of high-frequency delivery goods in a set time period; based on the product turnover rate, determine the delivery cycle of the delivered goods.

[0018] In some embodiments, the above-mentioned prediction unit is further configured to: based on the associated data, count the number of delivery times of the delivered goods in the historical period; in response to the number of delivery times being greater than a second predetermined number, and each delivery time in the delivery time corresponds to a quarter, determine the delivery cycle of the delivered goods through a seasonal algorithm.

[0019] In a fourth aspect, an embodiment of the present disclosure provides a logistics distribution device, which includes: an acquisition unit, configured to acquire distribution product information and distribution sites, wherein the distribution product information and the distribution sites are predicted by a logistics information prediction device of any embodiment of the third aspect; a distribution unit, configured to dispatch distribution products to distribution sites based on the distribution product information; and an order placement unit, configured to generate a distribution result for the user based on the order information and the site products of the distribution site in response to receiving the user's order information.

[0020] In some embodiments, the order unit is further configured to: in response to receiving the user's order information, determine the delivery location of the ordered goods based on the order information; detect whether the delivery location is covered by the delivery site; in response to the delivery location being covered by the delivery site, detect whether the number of site goods at the delivery site corresponding to the delivery location is greater than the number of ordered goods in the order information; in response to the number of site goods being greater than the number of ordered goods, use the delivery site to deliver the ordered goods to the user.

[0021] In a fifth aspect, an embodiment of the present disclosure provides an electronic device, comprising: one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any embodiment of the first aspect.

[0022] In a sixth aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the first aspect.

[0023] The logistics information prediction method and device provided by the embodiments of the present disclosure first determine the associated data based on the order data of the goods delivered at different stations in the historical period; secondly, determine the distribution station and the distribution goods corresponding to the distribution station based on the associated data; finally, predict the distribution cycle of the distribution goods based on the associated data, and use the distribution goods and the distribution cycle as the distribution goods information. Thus, the distribution goods information and distribution stations are predicted through the order data of the goods delivery, which improves the accuracy of the logistics information prediction; by predicting the distribution goods information and distribution stations through the present disclosure, the goods at the distribution stations can be delivered to the distribution stations, and the goods can be transported to the terminal stations in advance, which provides reliable support for reducing the storage costs of logistics companies, improving the full load rate of logistics distribution, and improving the logistics delivery timeliness and customer experience.

[0024] The logistics distribution method and device provided by the embodiments of the present disclosure first obtain the pre-predicted distribution commodity information and distribution site; secondly, based on the distribution commodity information, the distribution commodity is dispatched to the distribution site; finally, in response to receiving the user's order information, the user's distribution result is generated based on the order information and the site commodity of the distribution site. Therefore, the present disclosure dispatches site commodities to the distribution site based on the pre-predicted distribution commodity information and distribution site, and can transport the commodities to the terminal site in advance, reduce the warehousing cost of the logistics enterprise, improve the full load rate of logistics distribution, improve the logistics delivery timeliness and customer experience, and enable some of the commodities ordered by the user to be delivered within hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Other features, objects and advantages of the present disclosure will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0026] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0027] Figure 2 is a flow chart of an embodiment of a logistics information prediction method according to the present disclosure;

[0028] Figure 3 is a flow chart of an embodiment of a logistics distribution method according to the present disclosure;

[0029] Figure 4 It is a structural schematic diagram of a distribution system corresponding to the logistics distribution method disclosed in the present invention;

[0030] Figure 5 is a schematic structural diagram of an embodiment of a logistics information prediction device according to the present disclosure;

[0031] Figure 6 is a structural schematic diagram of an embodiment of a logistics distribution device according to the present disclosure;

[0032] Figure 7 It is a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. DETAILED DESCRIPTION

[0033] The present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0034] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0035] Figure 1 An exemplary system architecture 100 is shown to which the logistics distribution method of the present disclosure can be applied.

[0036] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, and may generally include wireless communication links and the like.

[0037] The terminal devices 101, 102, 103 interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications, such as instant messaging tools, email clients, etc., may be installed on the terminal devices 101, 102, 103.

[0038] The terminal devices 101, 102, 103 may be hardware or software; when the terminal devices 101, 102, 103 are hardware, they may be user devices with communication and control functions, and the user devices may communicate with the server 105. When the terminal devices 101, 102, 103 are software, they may be installed in the user devices; the terminal devices 101, 102, 103 may be implemented as multiple software or software modules (for example, software or software modules for providing distributed services), or may be implemented as a single software or software module. No specific limitation is made here.

[0039] The terminal devices 101 , 102 , and 103 generate delivery commodity information and delivery sites based on the order data of commodity delivery at different sites in the historical period, and report the delivery commodity information and delivery sites to the server 105 .

[0040] The server 105 may be a server that provides various services, such as dispatching delivery goods to the delivery site based on the delivery goods information uploaded by the terminal devices 101, 102, and 103. The backend server may receive the user's order information sent by the terminal devices 101, 102, and 103, generate the user's delivery result based on the order information and the site goods of the delivery site, and feed back the delivery result to the terminal devices 101, 102, and 103.

[0041] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0042] It should be noted that the logistics information prediction method provided in the embodiments of the present disclosure is generally executed by the server 105, and the logistics distribution method provided in the embodiments of the present disclosure is generally executed by the server 105; the logistics information prediction device provided in the embodiments of the present disclosure is generally set in the server 105, and the logistics distribution device provided in the embodiments of the present disclosure is generally set in the server 105.

[0043] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0044] like Figure 2 , shows a process 200 of an embodiment of a logistics information prediction method according to the present disclosure, the logistics information prediction method comprises the following steps:

[0045] Step 201, determining related data based on the order data of the goods delivered at different sites in the historical period.

[0046] In this embodiment, the order data for the delivery of goods at different sites in the historical period are the order data and waybill data obtained by processing the offline business data and warehouse data in the historical period after various users place orders. The order data includes: order time, product code, product name, order number, waybill number; the waybill data includes: order number, delivery time, delivery site, product code.

[0047] Specifically, the steps for processing order data include: extracting business data related to orders in the historical period from the order business system to obtain preliminary data, cleaning, converting, and loading the preliminary data, and obtaining and storing the order data in the data warehouse of the big data platform. The steps for processing waybill data include: extracting business data related to waybill in the historical period from the order business system to obtain preliminary transportation data, cleaning, converting, and loading the preliminary transportation data, and obtaining and storing the waybill data in the data warehouse of the big data platform.

[0048] In this embodiment, the associated data is the data obtained after associating the order data and the waybill data. Specifically, the product number primary key of the order data and the waybill data is obtained in the form of an SQL (Structured Query Language) task in the big data platform, and the key order data and waybill data are associated with the product number primary key to obtain the associated data of the order data and the waybill data.

[0049] Step 202: Determine the delivery site and the delivery goods corresponding to the delivery site based on the associated data.

[0050] In this embodiment, the above-mentioned determination of the delivery site and the delivery goods corresponding to the delivery site based on the associated data includes: determining the product name in the associated data whose number of delivery times at the same site is greater than the delivery threshold (for example, 50 times), and using the site as the delivery site and the product corresponding to the product name as the delivery product.

[0051] Step 203: predict the delivery cycle of the delivered goods based on the associated data, and use the delivered goods and the delivery cycle as the delivered goods information.

[0052] In this optional implementation, the above-mentioned prediction of the delivery cycle of the delivered goods based on the associated data includes: collecting the number of deliveries at each station related to the delivered goods in the associated data, and taking the delivery interval of the station corresponding to the minimum number of delivery times as the delivery cycle of the delivered goods.

[0053] The logistics information prediction method provided in the present embodiment determines associated data based on the order data of the goods delivered to different stations in the historical period; determines the delivery station and the delivery goods corresponding to the delivery station based on the associated data; predicts the delivery cycle of the delivery goods based on the associated data; uses the delivery goods and the delivery cycle as the delivery goods information, thereby, through the analysis of the goods in the historical period, determining the goods that need to be delivered to the station for multiple times, the commodity cycle and the delivery station where the goods are located, thereby improving the accuracy of logistics information prediction; by obtaining the delivery commodity information and the delivery station through the prediction disclosed by the present invention, the station goods are delivered to the distribution station, and the goods can be transported to the terminal station in advance, which provides reliable support for reducing the warehousing cost of logistics enterprises, improving the full load rate of logistics distribution, and improving the logistics delivery timeliness and customer experience.

[0054] Optionally, the delivery product information and delivery site are predicted through the following steps: determine the associated data based on the order data of the delivery of goods at different sites in the historical period; determine the delivery site and the delivery products corresponding to the delivery site based on the associated data; predict the quantity and delivery cycle of the delivery products based on the associated data, and use the delivery products, delivery cycle and the quantity of the delivery products as the delivery product information.

[0055] In some optional implementations of the present disclosure, the above-mentioned order and waybill data for commodity delivery at different sites in the historical period, determining the associated data includes: obtaining the order data and waybill data in the historical period, both the order data and the waybill data including the waybill number; based on the waybill number, associating the order data and the waybill data to obtain first data, and using the first data as the associated data.

[0056] The method for obtaining related data provided by this optional implementation method associates order data and waybill data based on the waybill number shared by the order data and waybill data to obtain related data, thereby providing a reliable implementation method for obtaining related data.

[0057] Optionally, based on the order and waybill data of commodity delivery at different sites in a historical period, determining the associated data includes: obtaining the order data and waybill data in the historical period, both the order data and the waybill data including commodity codes; associating the order data and the waybill data through the same commodity codes at the same time in the order data and the waybill data to obtain the associated data.

[0058] In the above optional implementation, in order to better describe the storage capacity of each distribution site, the above order data based on the commodity distribution of different sites in the historical period, determining the associated data also includes: obtaining site distribution information; associating the site distribution information with the first data to obtain associated data.

[0059] In this optional implementation, the site distribution information includes: site name, site number, initial available area of ​​each site, and current available area of ​​each site. The steps of obtaining the site distribution information include: obtaining the site information (including site name, site number, initial available area of ​​each site) from the basic data of the warehouse system, loading the site information into the data platform, performing daily data updates (including current available area of ​​each site) through the scheduling system of the big data platform, and obtaining the site distribution information.

[0060] In this optional implementation, after obtaining the first data, the first data has multiple site information (such as site number, site name), and the first data and the site distribution information are associated through the same data content in the first data and the site distribution information to obtain associated data.

[0061] The method for obtaining associated data provided by this optional implementation method associates the site distribution information in the associated data, so that the associated data can include more site information, providing a reliable data basis for enriching the associated data, and through the site distribution information in the associated data, reliable site capacity information can be provided for the distribution site to deliver the delivery goods.

[0062] In some optional implementations of the present embodiment, the above-mentioned determination of the distribution site and the delivery goods corresponding to the distribution site based on the associated data includes: based on the associated data, screening the sites whose current available area meets the preset area conditions to obtain the pre-allocated sites; based on the associated data, screening the goods that meet the preset delivery conditions in the pre-allocated sites to obtain the delivery goods, and using the site where the delivery goods are located as the distribution site.

[0063] In this optional implementation, the preset area condition is an area condition that the available area of ​​the site needs to meet, for example, the preset area condition is that the available area of ​​the site is greater than zero. The pre-allocated site is a site that meets the requirements for early delivery of goods for the first time, and the delivery site can be obtained by further screening the pre-allocated sites.

[0064] In this optional implementation, the associated data includes the site delivery information of the site, and the site delivery information includes the available area of ​​the site. The degree to which the site can accommodate goods can be determined by judging whether the available area of ​​the site meets the preset area condition.

[0065] In this optional implementation, the above-mentioned preset delivery conditions are conditions for whether the goods need to be pre-delivered at the delivery site. For example, for some goods frequently used by some users, the average daily delivery quantity of the goods by the site reaches a set number (for example, 10 pieces) or more, and it is determined that the daily necessities meet the preset delivery conditions; for another example, the preset delivery conditions include: the goods are hot-selling goods, the goods are high-value goods, the goods category is a preset category of goods, and the goods are used for preset purposes, any one or more of the following.

[0066] The method for determining delivery sites and delivery goods provided in this embodiment screens pre-allocated sites that meet preset area conditions based on associated data; then screens goods that meet preset delivery conditions in the pre-allocated sites based on the associated data to obtain delivery goods, thereby improving the accuracy of determining delivery sites and delivery goods.

[0067] Optionally, the above-mentioned determining the distribution site and the delivery goods corresponding to the distribution site based on the associated data includes: based on the associated data, screening the goods that meet the preset delivery conditions in each site to obtain the delivery goods; based on the associated data, screening the sites corresponding to the delivery goods that meet the preset area conditions to obtain the distribution site.

[0068] Optionally, the above-mentioned determination of the distribution site and the distribution goods corresponding to the distribution site based on the associated data includes: based on the associated data, screening the sites whose pickup volume is greater than the pickup threshold (for example, 10) in each site to obtain the pre-allocated site; based on the associated data, screening the goods in the distribution site whose delivery cycle is greater than the preset cycle value to obtain the distribution goods and the distribution site corresponding to the distribution goods. For example, a site with a large volume of goods is selected as the pre-allocated site, and goods with a large periodic delivery in the pre-allocated site are selected as the delivery goods. For the analysis of a certain site, a site with a delivery volume greater than 10 SKUs (Stock Keeping Units) is selected as the pre-allocated site. The delivery cycle of goods in the pre-allocated site is 1 to 2 days, the average daily delivery number is 22 pieces, and the number of delivery pieces within the cycle is 53 pieces.

[0069] Optionally, in the case where the heights of the various sites in the associated data are different, the associated data include: site name, site number, site initial volume, and site current available volume. The above-mentioned determining the distribution site and the distribution commodity corresponding to the distribution site based on the associated data includes: based on the associated data, screening sites whose current available volumes meet preset volume conditions to obtain pre-allocated sites; based on the associated data, screening commodities that meet preset distribution conditions in the pre-allocated sites to obtain distribution commodities, and using the site where the distribution commodity is located as the distribution site. Among them, the preset volume condition includes: the available volume of the site is greater than zero.

[0070] In some optional implementations of the present disclosure, the above-mentioned prediction of the delivery cycle of the delivered goods based on the associated data includes: based on the associated data, counting the number of delivery times of the delivered goods in the historical period; in response to the number of delivery times being greater than a first predetermined number, and the interval between any two adjacent delivery times in the delivery time being less than an interval threshold, calculating the turnover rate of high-frequency delivery goods in a set time period; based on the product turnover rate, determining the delivery cycle of the delivered goods.

[0071] In this embodiment, the first predetermined number can be configured according to business needs, for example, the first predetermined number is 100; the set time period can be configured according to business needs, for example, the preset time period is one month, and the turnover rate of the goods is equal to the total number of goods shipped out (goods sales) divided by the average inventory. The above-mentioned determination of the delivery cycle of the delivered goods based on the turnover rate of the goods includes: multiplying the turnover rate of the goods by the fixed delivery coefficient to obtain the delivery cycle. Among them, the fixed delivery coefficient can be a coefficient set based on business needs, and the delivery coefficients of different types of delivered goods are different.

[0072] The method for predicting the delivery cycle provided by this optional implementation method determines the delivery goods with high frequency delivery in the associated data based on the number of delivery times, and determines the delivery cycle of the delivery goods based on the turnover rate of the delivery goods, thereby providing a reliable implementation method for obtaining the delivery cycle.

[0073] In some optional implementations of the present disclosure, the above-mentioned prediction of the delivery cycle of the delivered goods based on the associated data includes: based on the associated data, counting the number of delivery times of the delivered goods in the historical period; in response to the number of delivery times being greater than a second predetermined number, and each delivery time in the delivery time corresponds to each quarter, determining the delivery cycle of the delivered goods by using a seasonal algorithm.

[0074] In this embodiment, the second predetermined number is data that is smaller than the first preset number, and the second predetermined number can be configured according to business requirements, for example, the second predetermined number is 10; the seasonal algorithm is an algorithm related to the attributes of the product. Optionally, the seasonal algorithm can use an ARIMA (Autoregressive Integrated Moving Average Model) algorithm.

[0075] The method for predicting the delivery cycle provided by this optional implementation method determines the delivery products with seasonal characteristics in the associated data based on the number of delivery times, and determines the delivery cycle of the delivery products based on the seasonal algorithm, providing another reliable implementation method for obtaining the delivery cycle.

[0076] Optionally, the above-mentioned prediction of the delivery cycle of the delivered goods based on the associated data includes: selecting the periodicity of goods for other goods in combination with the time series prediction algorithm, and selecting the product cycle with high accuracy through algorithm evaluation.

[0077] In this embodiment, there may be multiple delivered goods, and the delivery cycles of the multiple deliveries are different. The above-mentioned logistics delivery method may also include: generating an automated order generation task according to the calculation result of the commodity cycle, and delivering the delivered goods to each delivery site based on the order generation task. By analyzing that the delivery of goods at some sites is periodic, continuous, and stable, the step of obtaining the periodic order generation task for the site delivery of goods includes: sorting the delivery cycles of all the delivered goods in the delivery goods on the time axis, generating a time series axis including the delivery time of each delivered commodity, and delivering the delivered goods to each delivery site based on the time series axis.

[0078] In view of the fact that terminal sites such as logistics sales departments in traditional technologies generally have certain storage functions, the present disclosure provides a logistics distribution method that can upgrade the category warehouse to a forward warehouse in advance, thereby continuously improving the user's shopping experience and quickly delivering the goods to the user's hands. Figure 3 , shows a process 300 of an embodiment of a logistics distribution method according to the present disclosure, the logistics distribution method includes the following steps:

[0079] Step 301, obtaining the pre-predicted delivery commodity information and delivery site.

[0080] In this embodiment, the delivery commodity information and the delivery site are information pre-predicted based on the order data of the commodity delivery at different sites in the historical period. The delivery commodity information may include: delivery commodity, delivery commodity name, delivery commodity origin, delivery cycle, delivery commodity quantity, delivery commodity category, and delivery commodity purpose. The delivery site is a site among the above-mentioned different sites, and is also the site where the logistics system is actually running. Generally, the delivery site has a certain storage function. Regardless of whether the user has placed an order, based on the delivery commodity information, the delivery commodity indicated by the delivery commodity information can be delivered to the delivery site in advance, so that the delivered delivery commodity is pre-stored at the delivery site.

[0081] In this embodiment, the execution subject on which the logistics distribution method runs can obtain commodity information and distribution sites according to a certain acquisition cycle, wherein the acquisition cycle can be based on the prediction unit (such as Figure 3 For example, if the prediction unit predicts the order data of the historical period once every 10 days, the acquisition cycle is 10 days. Optionally, the execution subject on which the logistics distribution method runs can also obtain the distribution commodity information and distribution site obtained by the prediction unit in advance based on the trigger signal of the prediction unit.

[0082] Step 302: dispatch the delivered goods to the delivery site based on the delivered goods information.

[0083] In this embodiment, the delivery product information may include: the name of the delivery product and the quantity of the delivery product. The execution entity on which the logistics delivery method runs determines the name of the delivery product and the quantity of the delivery product based on the delivery product information; and determines the site to which the delivery product currently needs to be delivered based on the acquired delivery site.

[0084] Specifically, the above step 302 includes: based on the name of the delivered commodity and the quantity of the delivered commodity in the delivered commodity information, controlling the warehouse to ship out the delivered commodity in the quantity, and dispatching the shipped commodity in the quantity to the delivery site.

[0085] Optionally, the distribution commodity information may further include: a distribution cycle, and the execution entity on which the logistics distribution method runs may further: periodically control the warehouse to ship out a certain number of distribution commodities according to the distribution cycle, and deliver the shipped number of distribution commodities to the distribution site.

[0086] In this embodiment, the execution entity obtains the delivery commodity information and delivery site from the prediction system, and automatically generates a warehouse outbound order. It only needs to update the inventory information of the site and warehouse to prepare the delivery commodities in advance at the delivery site.

[0087] Step 303, in response to receiving the user's order information, generating the user's delivery result based on the order information and the site commodities of the delivery site.

[0088] In this embodiment, the user's order information is the information of the goods purchased by the user in the order system. The order information may include: the order product number, the quantity of the order products, the origin of the order products, the shipping location and the receiving location of the order products; among which, the order products are the products in the user's order information and are also the products that the user needs to purchase; the receiving location is the specific location where the user receives the order products.

[0089] In this embodiment, the above step 303 includes: after receiving the user's order information, extracting the quantity of the ordered goods in the order information, detecting whether the quantity of the site goods at the distribution site is greater than the quantity of the order goods in the order information; if it is greater than the quantity of the order goods in the order information, extracting the delivery location in the order information, and detecting whether the delivery location is covered by the distribution site; if the delivery location is covered by the distribution site, directly delivering the ordered goods to the user through the distribution site, and obtaining the delivery result of the distribution site delivering the goods to the user.

[0090] Optionally, when the receiving location is not covered by the distribution site, the warehouse is controlled to distribute the ordered goods to the user, and a distribution result of the warehouse distributing the goods to the user is obtained.

[0091] The logistics distribution method provided in this embodiment first obtains the pre-predicted distribution commodity information and distribution site; secondly, based on the distribution commodity information, the distribution commodity is delivered to the distribution site; finally, in response to receiving the user's order information, the user's distribution result is generated based on the order information and the site commodity of the distribution site. Therefore, the present disclosure delivers site commodities to the distribution site based on the pre-predicted distribution commodity information and distribution site, and can transport the commodities to the terminal site in advance, reduce the warehousing cost of the logistics enterprise, improve the full load rate of logistics distribution, improve the logistics delivery timeliness and customer experience, and enable some of the commodities ordered by the user to be delivered within hours.

[0092] In some optional implementations disclosed above, in response to receiving the user's order information, generating the user's delivery result based on the order information and the site commodities of the delivery site includes: in response to receiving the user's order information, determining the delivery location of the ordered commodities based on the order information; detecting whether the delivery location is covered by the delivery site; in response to the delivery location being covered by the delivery site, detecting whether the number of site commodities of the delivery site corresponding to the delivery location is greater than the number of ordered commodities in the order information; in response to the number of site commodities being greater than the number of ordered commodities, using the delivery site to deliver the ordered commodities to the user.

[0093] In this optional implementation, whether there is an overlapping relationship between the distribution site and the receiving location can be determined by a pre-set site location table, wherein the site location table is a table of corresponding relationships between sites and actual physical locations in the logistics system. After obtaining the receiving location, the execution subject compares the receiving location with the actual physical location in the site location table to obtain the actual physical location closest to the receiving location, and queries the site location table through the obtained actual physical location to obtain the site of the logistics system, and detects whether the site is a distribution site. If it is a distribution site, it is determined that the receiving location is covered by the distribution site.

[0094] In this optional implementation method, when the quantity of site commodities in the distribution site corresponding to the delivery location is greater than the quantity of order commodities in the order information, the site commodities in the distribution site corresponding to the delivery location can fully meet the delivery of the order commodities. For this reason, the order commodities can be provided to the user directly from the distribution site.

[0095] The method for generating delivery results for users provided in this implementation directly delivers the ordered goods to users through the delivery site when the delivery location of the ordered goods is covered by the delivery site and the number of goods at the delivery site is greater than the number of ordered goods. There is no need to directly distribute the goods to users from the warehouse, thereby improving the flexibility of logistics delivery.

[0096] Optionally, in response to receiving the user's order information, generating the user's delivery result based on the order information and the site commodities of the distribution site may also include: in response to receiving the user's order information, determining the delivery location of the ordered commodities based on the order information; detecting whether the delivery location is covered by the distribution site; in response to the delivery location being covered by the distribution site, detecting whether the number of site commodities of the distribution site corresponding to the delivery location is greater than the number of ordered commodities in the order information; in response to the number of the site commodities being less than the number of ordered commodities, using the distribution site and the warehouse to deliver the ordered commodities to the user. Among them, using the distribution site and the warehouse to deliver the ordered commodities to the user includes: determining the difference between the number of the ordered commodities and the number of the site commodities, directly delivering the site commodities of the distribution site corresponding to the delivery location to the user, and retrieving the order commodities with the difference from the warehouse and delivering them to the user.

[0097] like Figure 4 , showing a structural schematic diagram of a distribution system corresponding to the logistics distribution method disclosed in the present invention.

[0098] exist Figure 4 In the distribution system, the distribution system includes: an order processing unit, a warehouse management unit and a transport delivery unit. Compared with the traditional distribution system, the distribution system corresponding to the logistics distribution method of the present disclosure has an order processing unit that adds a virtual order module. The virtual order module is added, and the distribution goods can be pre-delivered to the distribution site through the virtual order module. The delivery method of the goods can be one-time or periodic. The virtual order module can obtain the distribution goods information and the distribution site from the prediction unit when delivering the distribution goods.

[0099] like Figure 4 As shown, the order processing unit includes: a virtual order module and a user order module. The virtual order module can generate an automatic order task based on the prediction result of the prediction unit, and dispatch the delivery goods to the delivery site in advance through virtual ordering and other methods.

[0100] exist Figure 4 In the order processing unit, the virtual order module and the user order module are not contradictory. The virtual order module is driven by a periodic forecasting algorithm to generate a virtual order plan every day, ship the delivery goods from the warehouse, and deliver the delivery goods to the delivery site. The virtual order module can automatically generate warehouse outbound orders based on the forecast unit delivery product information and delivery sites. At this time, you only need to update the site of the logistics system and the inventory information of the warehouse.

[0101] The user order module is for users to place orders in the order center. After the user pays the order, the order center will calculate the location of the goods based on the user's order information. If the user's delivery address is covered by the outlet and the site has goods in stock, it will be delivered directly through the outlet. If the user's delivery address is not covered by the site or the site does not have goods in stock, the original business logic of the central warehouse, forward warehouse, etc. will be used for ordering, shipping, and delivery. After the user places an order, the site will update the inventory information when delivering the goods or the warehouse ships the goods.

[0102] In this optional implementation, the prediction unit implements periodic prediction algorithm and seasonal prediction algorithm on the associated data to screen the goods delivered by the site, and screens out the delivery goods with periodic delivery and the delivery sites with the average daily inventory level within the period. For example, if site X delivers an average of 10 pieces of A goods per day, then the present disclosure will generate a virtual order module to achieve 70 pieces of A goods delivered from the warehouse to site X every week. The order frequency and the number of goods in the present disclosure are set by parameters. If the weekly delivery frequency is set to 70 pieces of goods, it is necessary to meet the periodic average daily delivery of 10 pieces. The prediction unit includes: a data processing module, a site product selection module, and a cycle prediction module, wherein the data processing module determines the associated data based on the order data of the goods delivered at different sites in the historical period; the site product selection module determines the delivery site and the delivery goods corresponding to the delivery site based on the associated data; the cycle prediction module predicts the delivery cycle of the delivery goods based on the associated data.

[0103] exist Figure 4 In the warehouse management unit, the warehouse management unit is used to receive order messages, produce, pick, review, pack and ship out of the warehouse. The transport and delivery unit: realizes the order package through sorting and transportation, transports it to the site, and stores the goods transported to the site in advance. When the user places an order, it first checks whether the goods transported to the site in advance meet the user's needs. If they meet the needs, it will be pushed to the delivery person to reprint the waybill for delivery. If it does not meet the user's needs, the message will be transmitted to the warehouse for packaging and production.

[0104] Further references Figure 5 As an implementation of the logistics distribution method shown in the above figures, the present disclosure provides an embodiment of a logistics information prediction device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0105] like Figure 5As shown, an embodiment of the present disclosure provides a logistics information prediction device 500, and the device 500 includes: a data determination unit 501, a logistics determination unit 502, and a prediction unit 503. Among them, the above-mentioned data determination unit 501 can be configured to determine the associated data based on the order data of the goods delivered at different sites in the historical period. The above-mentioned logistics determination unit 502 can be configured to determine the distribution site and the distribution goods corresponding to the distribution site based on the associated data. The above-mentioned prediction unit 503 can be configured to predict the delivery cycle of the distribution goods based on the associated data, and use the distribution goods and the delivery cycle as the distribution goods information.

[0106] In this embodiment, the specific processing of the data determination unit 501, the logistics determination unit 502, and the prediction unit 503 and the technical effects thereof can be referred to in Figure 2 Corresponding to step 201, step 202, and step 203 in the embodiment.

[0107] In some embodiments, the data determination unit 501 is further configured to: obtain order data and waybill data in a historical period, both of which include waybill numbers; based on the waybill number, associate the order data and waybill data to obtain first data, and use the first data as associated data.

[0108] In some embodiments, the data determination unit 501 is further configured to: obtain site delivery information; and associate the site delivery information with the first data to obtain associated data.

[0109] In some embodiments, the above-mentioned logistics determination unit 502 is further configured to: based on the associated data, filter the sites whose current available area meets the preset area conditions to obtain the pre-allocated sites; based on the associated data, filter the goods in the pre-allocated sites that meet the preset delivery conditions to obtain the delivered goods, and use the site where the delivered goods are located as the delivery site.

[0110] In some embodiments, the prediction unit 503 is further configured to: based on the associated data, count the number of delivery times for delivered goods in the historical period; in response to the number of delivery times being greater than a first predetermined number, and the interval between any two adjacent delivery times in the delivery time being less than an interval threshold, calculate the turnover rate of high-frequency delivery goods in a set time period; based on the product turnover rate, determine the delivery cycle of the delivered goods.

[0111] In some embodiments, the prediction unit 503 is further configured to: based on the associated data, count the number of delivery times for delivered goods in the historical period; in response to the number of delivery times being greater than a second predetermined number, and each delivery time in the delivery time corresponds to a quarter, determine the delivery cycle of the delivered goods through a seasonal algorithm.

[0112] In the logistics information prediction device provided by the present embodiment, the data determination unit 501 determines the associated data based on the order data of the goods delivered at different stations in the historical period; the logistics determination unit 502 determines the distribution station and the distribution goods corresponding to the distribution station based on the associated data; the prediction unit 503 predicts the delivery cycle of the distribution goods based on the associated data, and uses the distribution goods and the delivery cycle as the distribution goods information, thereby, through the analysis of the goods in the historical period, the goods that need to be delivered to the station for multiple times, the commodity cycle and the distribution station where the goods are located are determined, thereby improving the accuracy of the logistics information prediction; by obtaining the distribution goods information and the distribution station through the prediction disclosed by the present invention, the station goods are dispatched to the distribution station, and the goods can be transported to the terminal station in advance, which provides reliable support for reducing the warehousing cost of logistics enterprises, improving the full load rate of logistics distribution, and improving the logistics delivery timeliness and customer experience.

[0113] Further references Figure 6 As an implementation of the logistics distribution method shown in the above figures, the present disclosure provides an embodiment of a logistics distribution device. Figure 3 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0114] like Figure 6 As shown, an embodiment of the present disclosure provides a logistics distribution device 600, which includes: an acquisition unit 601, a distribution unit 602, and an order unit 603. Among them, the acquisition unit 601 can be configured to obtain pre-predicted distribution product information and distribution sites. The distribution unit 602 can be configured to dispatch distribution products to distribution sites based on the distribution product information. The order unit 603 can be configured to generate a distribution result for the user based on the order information and the site products of the distribution site in response to receiving the user's order information.

[0115] In this embodiment, the specific processing of the acquisition unit 601, the distribution unit 602, and the order unit 603 and the technical effects thereof can be referred to in the respective Figure 3 Corresponding to step 301, step 302, and step 303 in the embodiment.

[0116] In some embodiments, the order unit 603 is further configured to: in response to receiving the order information of the user, determine the delivery location of the ordered goods based on the order information; detect whether the delivery location is covered by the delivery site; in response to the delivery location being covered by the delivery site, detect whether the number of site goods of the delivery site corresponding to the delivery location is greater than the number of ordered goods in the order information; in response to the number of site goods being greater than the number of ordered goods, use the delivery site to deliver the ordered goods to the user.

[0117] The logistics distribution method and device provided by the embodiment of the present disclosure, first, the acquisition unit 601 acquires the pre-predicted distribution commodity information and distribution site; secondly, the distribution unit 602 dispatches the distribution commodity to the distribution site based on the distribution commodity information; finally, the order unit 603 generates the user's distribution result based on the order information and the site commodities of the distribution site in response to receiving the user's order information. Therefore, the present disclosure dispatches site commodities to the distribution site based on the pre-predicted distribution commodity information and distribution site, and can transport the commodities to the terminal site in advance, reduce the warehousing cost of the logistics enterprise, improve the full load rate of logistics distribution, improve the logistics delivery timeliness and user experience, and enable some of the commodities ordered by the user to be delivered within hours.

[0118] Reference below Figure 7 , which shows a schematic structural diagram of an electronic device 700 suitable for implementing an embodiment of the present disclosure.

[0119] like Figure 7 As shown, the electronic device 700 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0120] Typically, the following devices may be connected to the I / O interface 705: an input device 706 including, for example, a touch screen, a touch pad, a keyboard, a mouse, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 708 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Figure 7The electronic device 700 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 7 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0121] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 709, or installed from a storage device 708, or installed from a ROM 702. When the computer program is executed by the processing device 701, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0122] It should be noted that the computer-readable medium of the embodiment of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the embodiment of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0123] The computer-readable medium may be included in the server; or it may exist independently without being installed in the server. The computer-readable medium carries one or more programs. When the one or more programs are executed by the server, the server: determines the associated data based on the order data of the goods delivered at different sites in the historical period; determines the delivery site and the delivery goods corresponding to the delivery site based on the associated data; predicts the delivery cycle of the delivery goods based on the associated data; and uses the delivery goods and the delivery cycle as the delivery goods information. Or the server: obtains the delivery goods information and delivery site predicted in advance; dispatches the delivery goods to the delivery site based on the delivery goods information; generates the delivery result for the user based on the order information and the site goods of the delivery site in response to receiving the user's order information.

[0124] Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The described units may also be set in a processor, for example, may be described as: a processor including an acquisition unit, a distribution unit, and an order placement unit. The names of these units do not constitute limitations on the units themselves in certain circumstances, for example, the acquisition unit may also be described as a unit "configured to obtain pre-forecasted distribution commodity information and distribution sites".

[0127] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) to form a technical solution.

Claims

1. A logistics information prediction method, the method comprising: Determine the associated data based on the order data of the goods delivered at different stations in the historical period; Determine the delivery site and the delivery commodity corresponding to the delivery site based on the associated data; Based on the associated data, predicting the delivery cycle of the delivered goods; The delivery product and the delivery period are used as delivery product information.

2. The method according to claim 1, wherein: The determination of the associated data based on the shipping order data of the goods delivered at different sites in the historical period includes: Obtain order data and waybill data in a historical period, wherein the order data and waybill data both include waybill numbers; Based on the waybill number, the order data and the waybill data are associated to obtain first data, and the first data is used as associated data.

3. The method according to claim 2, wherein: The determining of the associated data based on the order data of the goods delivered at different stations in the historical period also includes: Get site delivery information; The site delivery information is associated with the first data to obtain associated data.

4. The method according to claim 1, wherein: The determining, based on the associated data, the delivery site and the delivery commodity corresponding to the delivery site comprises: Based on the associated data, sites whose currently available areas meet preset area conditions are selected to obtain pre-allocated sites; Based on the associated data, the commodities in the pre-allocated sites that meet the preset delivery conditions are screened to obtain delivery commodities, and the sites where the delivery commodities are located are used as delivery sites.

5. The method according to claim 1, wherein: The predicting the delivery cycle of the delivered goods based on the associated data includes: Based on the associated data, counting the number of delivery times of the delivered goods in the historical period; In response to the number of the delivery times being greater than a first predetermined number, and the interval between any two adjacent delivery times among the delivery times being less than an interval threshold, calculating the commodity turnover rate of the high-frequency delivery commodity in a set time period; Based on the commodity turnover rate, a delivery cycle of the delivered commodity is determined.

6. The method according to claim 1, wherein: The predicting the delivery cycle of the delivered goods based on the associated data includes: Based on the associated data, counting the number of delivery times of the delivered goods in the historical period; In response to the number of the delivery times being greater than a second predetermined number, and each of the delivery times corresponding to a quarter, a delivery cycle of the delivered commodity is determined by a seasonal algorithm.

7. A logistics distribution method, the method comprising: Obtaining delivery product information and delivery sites, wherein the delivery product information and the delivery sites are predicted using the logistics information prediction method according to any one of claims 1 to 6; dispatching the delivery goods to the delivery site based on the delivery goods information; In response to receiving the user's order information, a delivery result for the user is generated based on the order information and the site merchandise of the delivery site.

8. The method according to claim 7, wherein: In response to receiving the order information of the user, generating the delivery result of the user based on the order information and the site commodities of the delivery site includes: In response to receiving the order information from the user, determining the delivery location of the ordered goods based on the order information; Detecting whether the delivery location is covered by the delivery site; In response to the receiving location being covered by the delivery site, detecting whether the quantity of site commodities of the delivery site corresponding to the receiving location is greater than the quantity of the ordered commodities in the order information; In response to the quantity of the goods at the site being greater than the quantity of the ordered goods, the ordered goods are delivered to the user using the delivery site.

9. A logistics information prediction device, comprising: A data determination unit is configured to determine associated data based on shipping order data of goods delivered at different sites in a historical period; A logistics determination unit, configured to determine the delivery site and the delivery commodity corresponding to the delivery site based on the associated data; The prediction unit is configured to predict a delivery period of the delivered product based on the associated data, and use the delivered product and the delivery period as delivered product information.

10. A logistics distribution device, comprising: an acquisition unit configured to acquire delivery commodity information and a delivery site, wherein the delivery commodity information and the delivery site are predicted by the logistics information prediction device according to claim 9; a delivery unit configured to dispatch the delivery commodity to the delivery site based on the delivery commodity information; The order placing unit is configured to generate a delivery result for the user based on the order placing information and the site commodities of the delivery site in response to receiving the order placing information of the user.

11. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

12. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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