Article distribution method, device and equipment and computer readable medium

Abstract

The invention discloses an article distribution method, device and equipment and a computer readable medium, and relates to the technical field of logistics transportation. A specific embodiment of the method comprises the steps of aggregating historical orders into a plurality of order clustering regions according to order regions and order processing time; the order clustering area, the order processing time and the order area are input into a path model, the path model outputs a historical delivery path of a delivery end and a historical connection point of a delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order; and inputting an order area and order processing time of a current order into the path model, outputting a current delivery path of a delivery end and a current connection point of the delivery area by the path model, and delivering articles of the current order according to a current connection path constructed by the current delivery path and the current connection point. According to the embodiment, the article delivery speed can be increased, and the article delivery time limit is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of logistics and transportation technology, and in particular to a method, device, equipment and computer-readable medium for distributing items. Background Art

[0002] Driven by e-commerce, logistics companies have developed rapidly, and each city has built a corresponding express delivery system. Generally speaking, logistics companies divide cities into districts according to administrative boundaries and road networks. The picking and distribution of packages in each area are the responsibility of a specific sales department. Each sales department further subdivides the region into multiple distribution areas based on the characteristics of the buildings within its service area, and arranges the distribution end to handle the collection and delivery tasks of each distribution area.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art: some delivery areas are far away from the business department, and the delivery end consumes a lot of time on the way, so it is necessary to set up a shuttle bus to deliver packages between the business department and each delivery area. The delivery end waits for the shuttle bus to deliver the package, resulting in a long delivery time. Summary of the invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus, device and computer-readable medium for delivering items, which can speed up the delivery of items and ensure the delivery time limit of items.

[0005] To achieve the above object, according to one aspect of an embodiment of the present invention, a method for distributing items is provided, comprising:

[0006] Aggregate historical orders into multiple order clustering areas based on order area and order processing time;

[0007] The order clustering area, the order processing time and the order area are input into a path model, and the path model outputs a historical delivery path of the delivery end and a historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order;

[0008] The order area and order processing time of the current order are input into the path model, and the path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, and the items of the current order are delivered using the current delivery path and the current docking path constructed by the current docking point.

[0009] According to the order area and order processing time, the historical orders are aggregated into multiple order clustering areas, including:

[0010] Dividing the historical orders into orders of multiple time periods according to order processing time;

[0011] Based on the order area, the orders in the same time period are aggregated to obtain the multiple order clustering areas.

[0012] The step of inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs a historical delivery path of a delivery terminal and a historical connection point of a delivery area, and the historical delivery path and the historical connection point satisfy the order processing time of the historical order, comprises:

[0013] Inputting the order clustering area, the order processing time and the order area into a path model, the path model outputting the historical delivery path of the delivery end and the historical connection points of the delivery area;

[0014] The historical docking points in the delivery area meet the docking time of the historical orders;

[0015] The actual processing time of the historical order is determined in the historical delivery path to meet the order processing time of the historical order.

[0016] The historical connection points of the delivery area meet the connection time of the historical orders, including:

[0017] The historical connection points of the delivery area meet the connection times of the historical orders in the multiple historical delivery routes.

[0018] The step of inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area, comprises:

[0019] inputting the order clustering area, the order processing time and the order area into a path model;

[0020] The path model outputs the historical delivery path of a delivery end and the historical docking point of the delivery area corresponding to the delivery end. After shielding the order area in the delivery area, the path model outputs the historical delivery path of the next delivery end and the historical docking point of the delivery area corresponding to the next delivery end.

[0021] The step of inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area, comprises:

[0022] The order clustering area, the order processing time and the order area are input into the path model. After the path model is trained with a measured gradient algorithm, the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area.

[0023] The current delivery path and the current docking path constructed by the current docking point deliver the items of the current order, including:

[0024] The delivery end obtains the items of the current order at the current docking point according to the docking time;

[0025] The delivery end delivers the items of the current order according to the current delivery route to meet the order processing time.

[0026] According to a second aspect of an embodiment of the present invention, there is provided a device for distributing items, comprising:

[0027] Aggregation module, used to aggregate historical orders into multiple order clustering areas based on order area and order processing time;

[0028] A training module, used for inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs a historical delivery path of the delivery end and a historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order;

[0029] The output module is used to input the order area and order processing time of the current order into the path model, and the path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, and delivers the items of the current order along the current delivery path and the current docking path constructed by the current docking point.

[0030] According to a third aspect of an embodiment of the present invention, there is provided an electronic device for delivering items, comprising:

[0031] one or more processors;

[0032] a storage device for storing one or more programs,

[0033] When the one or more programs are executed by the one or more processors, the one or more processors implement the above method.

[0034] According to a fourth aspect of an embodiment of the present invention, there is provided a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, the method as described above is implemented.

[0035] One embodiment of the above invention has the following advantages or beneficial effects: historical orders are aggregated into multiple order clustering areas based on order areas and order processing time; the order clustering areas, the order processing time and the order areas are input into the path model, the path model outputs the historical delivery paths of the delivery end and the historical connection points of the delivery area, and the historical delivery paths and the historical connection points meet the order processing time of the historical orders; the order area and order processing time of the current order are input into the path model, the path model outputs the current delivery path of the delivery end and the current connection points of the delivery area, and the items of the current order are delivered using the current connection path constructed by the current delivery path and the current connection points. The use of the path model can directly obtain the delivery path and the connection path, so it can speed up the delivery of items and ensure the delivery time limit.

[0036] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0038] Figure 1 is a schematic diagram of the main flow of a method for delivering items according to an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of a process of aggregating historical orders into multiple order clustering areas according to an embodiment of the present invention;

[0040] Figure 3 is a schematic diagram of a flow chart of a training path model according to an embodiment of the present invention;

[0041] Figure 4 is a flow chart of another training path model according to an embodiment of the present invention;

[0042] Figure 5 is a schematic diagram of the structure of a training path model according to an embodiment of the present invention;

[0043] Figure 6 is a schematic diagram of a process of delivering items of a current order according to an embodiment of the present invention;

[0044] Figure 7 is a schematic diagram of the main structure of an apparatus for distributing items according to an embodiment of the present invention;

[0045] Figure 8 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0046] Fig. 9It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0048] The distribution of goods in the distribution area has an important impact on the overall efficiency of logistics transportation. In a distribution area, a large number of logistics orders need to be processed in a coordinated manner, including order allocation, processing sequence determination, and docking point selection with the docking end. However, due to the large number of orders and time pressure, the work efficiency of the distribution end is low.

[0049] With the development of database technology and data-driven methods, logistics companies are increasingly looking to manage delivery and dispatch orders through intelligent means. For example, dispatching and dispatching can be modeled as a vehicle routing problem (VRP) and solved using exact methods, heuristic methods, or deep learning methods. However, considering the complexity of the logistics system, the large volume of orders, and the non-deterministic polynomial (NP-hard) nature of the vehicle routing problem itself, there are significant limitations:

[0050] In the collaborative operation mode, it is necessary to quickly generate an operation plan based on the current task. However, precise methods, such as the branch and bound method, are slow in solving and cannot meet the real-time requirements.

[0051] When using heuristic methods to solve vehicle routing problems, the algorithm generates solutions based on heuristic operators iteratively. However, due to the large scale of the problem, heuristic methods are prone to fall into local optimal solutions and cannot guarantee the overall optimization effect. Moreover, heuristic methods often only generate scheduling plans based on the tasks of the day, and cannot learn and predict task rules from historical data, making it difficult to effectively use data-driven optimization.

[0052] Therefore, the delivery end has to wait for the docking end to deliver the items in the order, which results in a long time spent on delivering the items.

[0053] In order to solve the problem of long time consumption in delivering items, the following technical solutions in the embodiments of the present invention may be adopted.

[0054] See also Figure 1 , Figure 11 is a schematic diagram of the main flow of the method for delivering items according to an embodiment of the present invention, wherein the delivery path and the connection points are input through the path model to deliver items according to the order processing time. Figure 1 As shown in 100, the following steps are specifically included:

[0055] S101. Aggregate historical orders into multiple order clustering areas based on order areas and order processing time.

[0056] In an embodiment of the present invention, the items in the order are delivered by a delivery end. As an example, the delivery end includes a robot, and the robot carries the items in the order and delivers them according to the delivery address in the order.

[0057] A delivery terminal is set in a delivery area, and the delivery terminal is responsible for the orders in the delivery area. As an example, one delivery area corresponds to one delivery terminal.

[0058] The delivery area includes multiple addresses, and each address can be divided into corresponding order areas according to longitude and latitude. The order area is a collection of delivery addresses within a preset range. As an example, the delivery addresses within 1 km of the preset origin are collected as order area 1. The delivery area includes multiple order areas. Each order area includes multiple delivery addresses.

[0059] Considering that the delivery end is far away from the business department, the docking end is required to transport the items in the order at the docking point. As an example, the docking end includes a docking vehicle. The delivery end obtains the items in the order at the docking point and then transports the items in the order to the delivery address. For the delivery end, it is necessary to wait for the docking end to obtain the items in the order.

[0060] In actual application scenarios, orders need to be shipped according to the order processing time. As an example, order 1 needs to be delivered to the delivery address before 15:00 the next day.

[0061] Considering that the delivery items need to meet the order processing time, the historical orders can be aggregated into multiple order clustering areas based on the order area and the order processing time. The order clustering area is a collection of order areas established by the order processing time.

[0062] See also Figure 2 That is 200, Figure 2 The flowchart of aggregating historical orders into multiple order clustering areas according to an embodiment of the present invention is shown in FIG. The flowchart specifically includes the following steps:

[0063] S201. Divide historical orders into orders of multiple time periods according to order processing time.

[0064] Each order has a corresponding order processing time. According to the order processing time, historical orders can be divided into orders in multiple time periods. As an example, the order processing time includes: before 9:00; 9:00 to 15:00; 15:00 to 20:00, which divides historical orders into three time periods.

[0065] S202: Based on the order area, aggregate the orders in the same time period to obtain multiple order clustering areas.

[0066] For each order, the order area is determined according to the delivery address. Based on the order area, orders in multiple time periods are aggregated to obtain multiple order clustering areas. Among them, the time window of the order clustering area is the time period corresponding to the order.

[0067] exist Figure 2 In the embodiment, historical orders with the same order area and order processing time are aggregated into order clustering areas.

[0068] In one embodiment of the present invention, the docking point is set in the order area of ​​the delivery area. As an example, a docking point is set in the delivery area. Considering that the order clustering area involves the order processing time, from the time dimension, each time period has a corresponding docking point. The order areas of the docking points in multiple time periods can be the same or different.

[0069] S102. The order clustering area, order processing time and order area are input into the path model. The path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area, and the historical delivery path and the historical connection points meet the order processing time of the historical orders.

[0070] In an embodiment of the present invention, the delivery path and the connecting path are predicted by the path model. Specifically, the path model can be trained by using historical orders. It should be noted that an order has an order processing time, that is, the delivery of the order needs to meet the order processing time. As an example, the order processing time includes delivery before 15 o'clock. Then, meeting the order processing time can be used as a convergence condition for training the path model. For example, the convergence condition includes meeting the order processing time of 90% of the orders.

[0071] The order clustering area, order processing time and order area are used as input parameters of the path model. The output parameters of the path model include: the historical delivery path of the delivery end and the historical connection points of the delivery area. The model training is completed when the historical delivery path and historical connection points meet the order processing time of the historical orders to obtain the path model.

[0072] See also Figure 3 That is 300, Figure 3 1 is a flow chart of a training path model according to an embodiment of the present invention. Specifically, it includes the following steps:

[0073] S301. Input the order clustering area, order processing time and order area into the path model, and the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area.

[0074] The order clustering area represents the area of ​​orders with the same order processing time. The order clustering area, order processing time and order area are used as inputs of the path model. The path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area.

[0075] In one embodiment of the present invention, the path model includes an encoder and a decoder. The encoder is established based on the attention mechanism. The decoder is established based on the multi-head attention mechanism and the single-head attention mechanism.

[0076] As an example, after the path model outputs the historical delivery path, the order processing time of the order area is used to determine whether the historical delivery path meets the order processing time. The historical delivery path includes the order processing time: before 9:00, order area 1, order area 3, order area 4, and order area 5. That is, the delivery end starts to deliver items from order area 1, and then delivers items to each order area after passing through order area 3, order area 4, and order area 5 in turn. To ensure that the delivery of items is completed before 9:00. Among them, order area 1 is used as the historical connection point of the delivery area.

[0077] S302: The historical connection points in the delivery area meet the connection time of the historical orders.

[0078] In an embodiment of the present invention, the historical delivery path needs to meet not only the order processing time, but also the docking time of the historical order. The docking time is the time when the delivery end obtains the items in the order at the docking point. The docking end needs to transport the items in the order to the docking point according to the docking time.

[0079] From the perspective of the docking end, the docking end moves from one docking point to another according to the docking time of each docking point, and transports the items in the order at multiple docking points. The docking point is the order area in the delivery path. In this way, after the delivery end obtains the items in the order at the docking point, it can directly deliver the items to improve the efficiency of delivering the items.

[0080] In one embodiment of the present invention, the historical docking points of the delivery area meet the docking times of historical orders in multiple historical delivery routes.

[0081] For the distribution area, a docking point is set. As an example, a distribution area usually has one docking point. Each distribution area has a corresponding docking point. The docking end transports the items in the order at the docking point and needs to meet the docking time. Then, the distribution end of each distribution area corresponds to the distribution path of the distribution area, and there are multiple distribution paths for multiple distribution areas. The historical docking points of the distribution area meet the docking time of historical orders in multiple historical distribution paths. That is, the docking time needs to match multiple historical distribution paths to achieve the docking of items in multiple distribution areas.

[0082] S303: Determine the actual processing time of the historical orders in the historical delivery paths to meet the order processing time of the historical orders.

[0083] In an embodiment of the present invention, the path model can be used to output multiple historical delivery paths and historical connection points of the delivery area. The order processing time of the historical orders is used as the convergence condition for training the path model.

[0084] If the historical delivery path of the distribution end and the historical connection points of the distribution area output by the path model meet the order processing time of the historical orders, the path model completes training; if the historical delivery path of the distribution end and the historical connection points of the distribution area output by the path model do not meet the order processing time of the historical orders, the path model continues to train.

[0085] exist Figure 3 In the embodiment, the path model is trained by taking the order processing time as the convergence condition.

[0086] See also Figure 4 That is 400, Figure 4 FIG. 1 is a flow chart of another training path model according to an embodiment of the present invention. Specifically, the following steps are included:

[0087] S401, inputting order clustering area, order processing time and order area into a path model.

[0088] In order to train the path model, the order clustering area, order processing time and order area can be used as training data and output to the path model.

[0089] In one embodiment of the present invention, in order to improve the accuracy of the path model, the training data may be amplified by adding noise based on the above training data.

[0090] S402. The path model outputs a historical delivery path of a delivery terminal and a historical connection point of the delivery area corresponding to the delivery terminal. After shielding the order area in the delivery area, the path model outputs a historical delivery path of the next delivery terminal and a historical connection point of the delivery area corresponding to the next delivery terminal.

[0091] In an embodiment of the present invention, the path model needs to output the historical delivery paths of multiple delivery terminals and the historical docking points of the delivery areas corresponding to the delivery terminals, and construct the docking paths with the historical docking points of multiple delivery areas. The historical docking points belong to the historical delivery paths, that is, the historical docking points are the order areas in the historical delivery paths.

[0092] In the training process of the path model, the order area in the distribution area needs to be involved. After the historical distribution path and historical connection points in a distribution area are determined, there is no need to consider the order area in the distribution area to ensure the effectiveness of generating the historical distribution path. Specifically, the order area in the distribution area where the historical distribution path has been generated can be shielded. Then, the path model outputs the historical distribution path of the next distribution end and the historical connection points of the distribution area corresponding to the next distribution end.

[0093] exist Figure 4 In the embodiment of the invention, the order area in the delivery area for which the historical delivery path has been generated is shielded to ensure the validity of the historical delivery path output by the path model.

[0094] See also Figure 5 That is 500, Figure 5 is a structural diagram of a training path model according to an embodiment of the present invention. Figure 5 The training path model involves three time points, namely T=1; T=2; T=3. At T=1, the docking end docks the items in the order at the historical docking point 1; at T=2, the docking end docks the items in the order at the historical docking point 2; at T=3, the docking end docks the items in the order at the historical docking point 3.

[0095] in, Figure 5 The Mask is used as a mask to shield the order area in the distribution area of ​​the generated historical distribution path to ensure the validity of the historical distribution path output by the path model. For example, during the training of the path model at T = 2, the Mask is used to shield the order area in the historical distribution path output by the path model at T = 1.

[0096] In one embodiment of the present invention, the order clustering area, order processing time and order area are input into the path model. After the path model is trained by the measured gradient algorithm, the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area.

[0097] The measured gradient algorithm, namely the REINFORCE algorithm, can optimize the path model to further improve delivery efficiency. REINFORCE is a policy gradient algorithm whose goal is to update parameters by gradient ascent to reduce the expected average delivery time of docking orders.

[0098] Specifically, the REINFORCE algorithm trains and optimizes the path model by iteratively sampling and evaluating the delivery paths. In each iteration, a batch of delivery paths is generated and the delivery time of the orders in each delivery path is calculated. Then, the gradient of each delivery path is calculated based on the performance of the delivery path, and the parameters of the path model are updated using the gradient ascent method. In this way, the path model is gradually optimized so that it can better select the delivery path.

[0099] S103. The order area and order processing time of the current order are input into the path model. The path model outputs the current delivery path of the delivery end and the current docking point of the delivery area. The items of the current order are delivered using the current docking path constructed by the current delivery path and the current docking point.

[0100] After the path model is trained, the order area and order processing time of the current order can be input into the path model. The path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, thereby enabling the delivery end to deliver the items of the current order.

[0101] See also Figure 6 That is 600, Figure 6 This is a schematic diagram of the process of delivering items of the current order according to an embodiment of the present invention. Specifically, it includes the following steps:

[0102] S601. The delivery end obtains the items of the current order at the current docking point according to the docking time.

[0103] The docking end transports the items of the current order to the current docking point according to the docking time. The delivery end can obtain the items of the current order at the current docking point according to the docking time.

[0104] The docking terminal moves from the current docking point to the next docking point according to the docking time of the next docking point.

[0105] S602: The delivery end delivers the items of the current order according to the current delivery route to meet the order processing time.

[0106] After the delivery end obtains the items of the current order, it can deliver the items of the current order according to the current delivery route to meet the order processing time.

[0107] exist Figure 6 In the embodiment, the docking end and the delivery end exchange items in the order at the docking point, and then the delivery end delivers the items in the order according to the current delivery path.

[0108] In the above-mentioned embodiment of the present invention, historical orders are aggregated into multiple order clustering areas according to the order area and the order processing time; the order clustering area, the order processing time and the order area are input into the path model, and the path model outputs the historical delivery path of the delivery end and the historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order; the order area and the order processing time of the current order are input into the path model, and the path model outputs the current delivery path of the delivery end and the current connection point of the delivery area, and the items of the current order are delivered by the current connection path constructed by the current delivery path and the current connection point. The use of the path model can directly know the delivery path and the connection path, so it can speed up the delivery of items and ensure the delivery time limit.

[0109] See also Figure 7 , Figure 7 is a schematic diagram of the main structure of an apparatus for distributing items according to an embodiment of the present invention. The apparatus for distributing items can implement a method for distributing items, such as Figure 7 As shown in 700, the device for distributing items specifically includes:

[0110] Aggregation module 701, used to aggregate historical orders into multiple order clustering areas according to order areas and order processing time;

[0111] A training module 702 is used to input the order clustering area, the order processing time and the order area into a path model, and the path model outputs a historical delivery path of the delivery end and a historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order;

[0112] The output module 703 is used to input the order area and order processing time of the current order into the path model, and the path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, and delivers the items of the current order along the current docking path constructed by the current delivery path and the current docking point.

[0113] Aggregation module 701 is specifically used to divide the historical orders into orders in multiple time periods according to order processing time; and aggregate orders in the same time period based on order regions to obtain the multiple order clustering regions.

[0114] In one embodiment of the present invention, the training module 702 is specifically used to input the order clustering area, the order processing time and the order area into a path model, and the path model outputs the historical delivery path of the delivery end and the historical connection point of the delivery area;

[0115] The historical docking points in the delivery area meet the docking time of the historical orders;

[0116] The actual processing time of the historical order is determined in the historical delivery path to meet the order processing time of the historical order.

[0117] The training module 702 is specifically used for the historical docking points in the delivery area to meet the docking time of the historical orders in multiple historical delivery routes.

[0118] In one embodiment of the present invention, the training module 702 is specifically configured to input the order clustering area, the order processing time and the order area into a path model;

[0119] The path model outputs the historical delivery path of a delivery end and the historical docking point of the delivery area corresponding to the delivery end. After shielding the order area in the delivery area, the path model outputs the historical delivery path of the next delivery end and the historical docking point of the delivery area corresponding to the next delivery end.

[0120] In one embodiment of the present invention, the training module 702 is specifically used to input the order clustering area, the order processing time and the order area into the path model. After the path model is trained with a measured gradient algorithm, the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area.

[0121] In one embodiment of the present invention, the output module 703 is specifically used for the delivery end to obtain the items of the current order at the current docking point according to the docking time;

[0122] The delivery end delivers the items of the current order according to the current delivery route to meet the order processing time.

[0123] Figure 8 An exemplary system architecture 800 is shown to which the method for delivering items or the apparatus for delivering items according to the embodiments of the present invention may be applied.

[0124] like Figure 8 As shown, system architecture 800 may include terminal devices 801, 802, 803, a network 804, and a server 805. Network 804 is used to provide a medium for communication links between terminal devices 801, 802, 803 and server 805. Network 804 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0125] Users can use terminal devices 801, 802, and 803 to interact with server 805 through network 804 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 801, 802, and 803, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

[0126] The terminal devices 801 , 802 , and 803 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0127] The server 805 may be a server that provides various services, such as a backend management server (only an example) that provides support for shopping websites browsed by users using the terminal devices 801, 802, and 803. The backend management server may analyze and process the received data such as product information query requests, and feed back the processing results (such as target push information, product information - only an example) to the terminal device.

[0128] It should be noted that the method for delivering items provided in the embodiment of the present invention is generally executed by the server 805 , and accordingly, the device for delivering items is generally disposed in the server 805 .

[0129] It should be understood that Figure 8 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.

[0130] Reference below Fig. 9 , which shows a schematic diagram of the structure of a computer system 900 of a terminal device suitable for implementing an embodiment of the present invention. Fig. 9 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0131] like Fig. 9 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the system 900 are also stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0132] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage section 908 as needed.

[0133] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include 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 the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, the above-mentioned functions defined in the system of the present invention are executed.

[0134] It should be noted that the computer-readable medium shown in the present invention 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 present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. 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. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, 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 appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0135] 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 invention. 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 above-mentioned module, program segment, or a part of a 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 or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0136] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes an aggregation module, a training module and an output module. The names of these modules do not constitute a limitation on the modules themselves in some cases. For example, the aggregation module may also be described as "used to aggregate historical orders into multiple order clustering areas based on order areas and order processing times".

[0137] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes:

[0138] Aggregate historical orders into multiple order clustering areas based on order area and order processing time;

[0139] The order clustering area, the order processing time and the order area are input into a path model, and the path model outputs a historical delivery path of the delivery end and a historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order;

[0140] The order area and order processing time of the current order are input into the path model, and the path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, and the items of the current order are delivered using the current delivery path and the current docking path constructed by the current docking point.

[0141] According to the technical solution of the embodiment of the present invention, historical orders are aggregated into multiple order clustering areas based on the order area and the order processing time; the order clustering area, the order processing time and the order area are input into the path model, and the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area, and the historical delivery path and the historical connection points meet the order processing time of the historical order; the order area and order processing time of the current order are input into the path model, and the path model outputs the current delivery path of the delivery end and the current connection points of the delivery area, and the items of the current order are delivered using the current connection path constructed by the current delivery path and the current connection points. The use of the path model can directly obtain the delivery path and the connection path, so it can speed up the delivery of items and ensure the delivery time limit.

[0142] The above specific implementation methods do not constitute limitations on the scope of protection of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention. It should be noted that in the technical solution disclosed in this disclosure, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

Claims

1. A method for delivering items, characterized in that: include: Aggregate historical orders into multiple order clustering areas based on order area and order processing time; The order clustering area, the order processing time and the order area are input into a path model, and the path model outputs a historical delivery path of the delivery end and a historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order; The order area and order processing time of the current order are input into the path model, and the path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, and the items of the current order are delivered using the current delivery path and the current docking path constructed by the current docking point.

2. The method for distributing items according to claim 1, characterized in that: According to the order area and order processing time, the historical orders are aggregated into multiple order clustering areas, including: Dividing the historical orders into orders of multiple time periods according to order processing time; Based on the order area, the orders in the same time period are aggregated to obtain the multiple order clustering areas.

3. The method for distributing items according to claim 1, characterized in that: The step of inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs a historical delivery path of a delivery terminal and a historical connection point of a delivery area, and the historical delivery path and the historical connection point satisfy the order processing time of the historical order, comprises: Inputting the order clustering area, the order processing time and the order area into a path model, the path model outputting the historical delivery path of the delivery end and the historical connection points of the delivery area; The historical docking points in the delivery area meet the docking time of the historical orders; The actual processing time of the historical order is determined in the historical delivery path to meet the order processing time of the historical order.

4. The method for distributing items according to claim 3, characterized in that: The historical connection points of the delivery area meet the connection time of the historical orders, including: The historical connection points of the delivery area meet the connection times of the historical orders in the multiple historical delivery routes.

5. The method for distributing items according to claim 1, characterized in that: The step of inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area, comprises: inputting the order clustering area, the order processing time and the order area into a path model; The path model outputs the historical delivery path of a delivery end and the historical docking point of the delivery area corresponding to the delivery end. After shielding the order area in the delivery area, the path model outputs the historical delivery path of the next delivery end and the historical docking point of the delivery area corresponding to the next delivery end.

6. The method for distributing items according to claim 1, characterized in that: The step of inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area, comprises: The order clustering area, the order processing time and the order area are input into the path model. After the path model is trained with a measured gradient algorithm, the path model outputs the historical delivery path of the delivery end and the historical connection points of the delivery area.

7. The method for distributing items according to claim 1, characterized in that: The current delivery path and the current docking path constructed by the current docking point deliver the items of the current order, including: The delivery end obtains the items of the current order at the current docking point according to the docking time; The delivery end delivers the items of the current order according to the current delivery route to meet the order processing time.

8. A device for distributing items, characterized in that: include: Aggregation module, used to aggregate historical orders into multiple order clustering areas based on order area and order processing time; A training module, used for inputting the order clustering area, the order processing time and the order area into a path model, wherein the path model outputs a historical delivery path of the delivery end and a historical connection point of the delivery area, and the historical delivery path and the historical connection point meet the order processing time of the historical order; The output module is used to input the order area and order processing time of the current order into the path model, and the path model outputs the current delivery path of the delivery end and the current docking point of the delivery area, and delivers the items of the current order along the current delivery path and the current docking path constructed by the current docking point.

9. An electronic device for delivering items, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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