Intelligent logistics management system

Abstract

The invention relates to an intelligent logistics management system, which comprises a target mapping device, intelligently analyzing the customer order quantity in the current time segment of the day based on the total browsing quantity of each customer corresponding to each past time segment before the current time segment of the day, the customer order quantity in the same time segment as the current time segment in the day before the day and various associated information of the e-commerce APP; and the personnel conversion device is used for determining the total number of logistics service personnel which are allocated to the e-commerce APP and serve the current time segment of the day based on the analysis result. According to the invention, the number of customer orders of the e-commerce APP in the current time segment of the day can be intelligently analyzed based on various basic information of the e-commerce APP by adopting the deep neural network model, and then the total number of logistics service personnel deployed to serve the current time segment of the day is determined, so that the intelligent level of the e-commerce APP is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of retail order management, and in particular to an intelligent logistics management system. Background Art

[0002] Logistics refers to the use of modern information technology and equipment to deliver goods from the supply point to the receiving point accurately, timely, safely, with guaranteed quality and quantity, and a door-to-door rational service model and advanced service process. Logistics includes the transportation, distribution, warehousing, packaging, handling and loading and unloading, circulation processing, and related logistics information of goods. The specific content of logistics activities includes the following aspects: user service, demand monitoring, order processing, distribution, inventory control, transportation, warehouse management, layout and site selection of factories and warehouses, handling and loading and unloading, procurement, packaging, and intelligence information.

[0003] When offline retail stores conduct online sales, they need to connect to multiple Internet sales platforms, including Meituan, Ele.me, JD.com, Taoxianda, Douyin, etc. Each third-party sales platform has its own operation management platform and corresponding interfaces. If retail companies directly use the backend of the third-party platform to manage online sales, a lot of manpower will be required. If they connect separately through the system, a lot of production and research resources will also be required.

[0004] Therefore, there is a need to meet the needs of merchants to connect to multiple Internet sales platforms in one operation through a unified platform. The applicant's Baichuan platform unifies the content of different third-party platforms into a set of standards. Merchants only need to perform one operation on the Baichuan platform to meet the sales preparation and order processing work of all third-party platforms. For example, a product maintenance synchronizes multiple platforms at a time, and orders from different platforms can be processed uniformly through standardized operating methods. This reduces the problems of repeated maintenance, repeated development of docking, and multiple order processing forms for retail merchants. At the same time, it also meets the needs of real-time synchronization of online sales inventory and sharing of offline inventory by different platforms.

[0005] At present, the conventional operation is to use the Internet sales platform, and the corresponding e-commerce APP of the Internet sales platform can also be used. However, due to the lack of an effective prediction mechanism, it is impossible to accurately predict the number of customer orders of the e-commerce APP in the current time segment of the day, and thus it is impossible to accurately analyze the total number of logistics service personnel deployed by the e-commerce APP to serve the current time segment of the day, so that it is either impossible to fully utilize the limited logistics service personnel, or it is impossible to take into account the logistics distribution needs of the order quantity. Summary of the invention

[0006] In order to solve technical problems in related fields, the present invention provides a smart logistics management system, which adopts a deep neural network model to parse the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing times corresponding to each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment the day before the day, and various related information of the e-commerce APP, thereby completing the targeted prediction processing of the number of customer orders of the e-commerce APP in the future time segment set, and then based on the number of customer orders of the e-commerce APP in the current time segment of the day obtained by analysis, the total number of logistics service personnel deployed by the e-commerce APP to serve the current time segment of the day, thereby achieving full utilization of limited logistics service personnel and dynamic consideration of logistics distribution needs of the order quantity.

[0007] According to the present invention, a smart logistics management system is provided, the system comprising: A browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing of the e-commerce APP corresponding to each previous time segment before the current time segment of the day; An order collection mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to collect the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the previous day; An information capture mechanism, which is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain various related information of the e-commerce APP, wherein the various related information of the e-commerce APP is the number of registered customers of the e-commerce APP, the types of goods on sale, and the geographical area of ​​the service area; The target mapping device is connected to the browsing analysis mechanism, the order collection mechanism and the information capture mechanism respectively, and is used to analyze the number of customer orders of the e-commerce APP in the current time segment of the day by using a deep neural network model based on the total number of customer browsing of each past time segment corresponding to each previous time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP; A personnel conversion device, connected to the target mapping device, is used to allocate the total number of logistics service personnel to serve the current time segment of the day according to the number of customer orders of the e-commerce APP in the current time segment of the day obtained by analysis; Wherein, the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day based on the number of customer orders of the e-commerce APP obtained through analysis in the current time segment of the day includes: the smaller the number of customer orders of the e-commerce APP obtained through analysis in the current time segment of the day, the smaller the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day; Among them, the browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing corresponding to each past time segment before the current time segment of the day of the e-commerce APP, including: the number of time segments in each past time segment is proportional to the number of registered customers of the e-commerce APP.

[0008] It can be seen that the present invention has at least the following four key invention points: Invention point A: A deep neural network model is used to analyze the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing of each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP, so as to complete the targeted prediction processing of the number of customer orders of the e-commerce APP in the future time segment; Invention point B: The number of customer orders of the e-commerce APP obtained by analysis in the current time segment of the day is the total number of logistics service personnel assigned by the e-commerce APP to serve the current time segment of the day. The fewer the number of customer orders of the e-commerce APP obtained by analysis in the current time segment of the day, the fewer the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day, thereby achieving full utilization of limited logistics service personnel and dynamic consideration of logistics dispatch requirements of order quantity; Invention point C: when obtaining the total number of customer browsings corresponding to each past time segment before the current time segment of the e-commerce APP on the day, the number of time segments of each past time segment is proportional to the number of registered customers of the e-commerce APP; Invention point D: The deep neural network model that performs parsing of the number of customer orders of the e-commerce APP within the current time segment of the day is a deep neural network after executing multiple learning actions, and the number of learning actions is positively correlated with the types of goods on sale in the e-commerce APP. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1It is a structural block diagram of an intelligent logistics management system according to implementation scheme A of the present invention.

[0010] Figure 2 It is a structural block diagram of an intelligent logistics management system according to implementation scheme B of the present invention.

[0011] Figure 3 It is a structural block diagram of an intelligent logistics management system according to implementation scheme C of the present invention. DETAILED DESCRIPTION

[0012] The implementation scheme of the smart logistics management system of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] Implementation 1 Figure 1 The structure block diagram of the intelligent logistics management system according to the A embodiment of the present invention is shown, and the system includes: A browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing of the e-commerce APP corresponding to each previous time segment before the current time segment of the day; For example, the browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing of each e-commerce APP corresponding to each past time segment before the current time segment of the day, including: selecting a programmable logic device to implement the browsing analysis mechanism, which is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing of each e-commerce APP corresponding to each past time segment before the current time segment of the day; An order collection mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to collect the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the previous day; An information capture mechanism, which is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain various related information of the e-commerce APP, wherein the various related information of the e-commerce APP is the number of registered customers of the e-commerce APP, the types of goods on sale, and the geographical area of ​​the service area; The target mapping device is connected to the browsing analysis mechanism, the order collection mechanism and the information capture mechanism respectively, and is used to analyze the number of customer orders of the e-commerce APP in the current time segment of the day by using a deep neural network model based on the total number of customer browsing of each past time segment corresponding to each previous time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP; A personnel conversion device, connected to the target mapping device, is used to allocate the total number of logistics service personnel to serve the current time segment of the day according to the number of customer orders of the e-commerce APP in the current time segment of the day obtained by analysis; Wherein, the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day based on the number of customer orders of the e-commerce APP obtained through analysis in the current time segment of the day includes: the smaller the number of customer orders of the e-commerce APP obtained through analysis in the current time segment of the day, the smaller the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day; The browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing corresponding to each past time segment before the current time segment of the day of the e-commerce APP, including: the number of time segments of each past time segment is proportional to the number of registered customers of the e-commerce APP; Among them, the deep neural network model is used to analyze the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing of each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP, including: the deep neural network model is a deep neural network after executing multiple learning actions, and the number of learning actions is positively correlated with the type of goods on sale of the e-commerce APP; Among them, the deep neural network model is used to parse the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing times corresponding to each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP. The method also includes: synchronously inputting the total number of customer browsing times corresponding to each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP into the deep neural network model.

[0014] Implementation 2 In order to better manage the e-commerce APP management server, the system described in the present invention also includes: a server monitoring mechanism, which is connected to the e-commerce APP management server and is used to obtain various communication data of the server. The various communication data of the server are one or more of the communication bandwidth of the server, the maximum computing amount per unit time, the content capacity and the remaining power.

[0015] Those skilled in the art may understand that, in order to better manage the server, it is conceivable to monitor communication data not mentioned above, and the above examples are not restrictive.

[0016] Implementation 3 Figure 2 It is a structural block diagram of an intelligent logistics management system according to implementation scheme B of the present invention.

[0017] Compared with implementation scheme A, the smart logistics management system shown in implementation scheme B of the present invention may also include: The power support device is respectively connected to the target mapping device, the browsing analysis mechanism, the order collection mechanism and the information capture mechanism, and is used to provide power distribution support of different working voltages for the target mapping device, the browsing analysis mechanism, the order collection mechanism and the information capture mechanism in a time-sharing manner.

[0018] The power support device can ensure the power supply of the management server and issue an alarm when the power is insufficient.

[0019] Implementation 4 Figure 3 It is a structural block diagram of an intelligent logistics management system according to implementation scheme C of the present invention.

[0020] In order to give an alarm when abnormal power occurs, the system also includes: an instant notification mechanism, which is connected to the power support devices respectively used by the target mapping device, the browsing analysis mechanism, the order collection mechanism and the information capture mechanism, and is used to perform corresponding alarm actions when abnormal power occurs.

[0021] Preferably, the instant notification mechanism is respectively connected to the power support devices respectively adopted by the target mapping device, the browsing analysis mechanism, the order collection mechanism and the information capture mechanism, and is used to execute corresponding alarm actions when abnormal power conditions occur, including: the instant notification mechanism has a built-in power receiving unit, a power judgment unit and a notification execution unit.

[0022] In the instant notification mechanism, the power receiving unit, the power judging unit and the notification executing unit are connected in sequence.

[0023] In addition, in the smart logistics management system, a deep neural network model is used to parse the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing times corresponding to each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment the day before the day, and various related information of the e-commerce APP, and also includes: running the deep neural network model to obtain the number of customer orders of the e-commerce APP in the current time segment of the day output by the deep neural network model.

[0024] The intelligent logistics management system of the present invention is used to address the technical problem in the prior art that e-commerce APPs cannot take into account both the full utilization of limited logistics service personnel and the logistics distribution needs of the order quantity. A deep neural network model is used to intelligently analyze the number of customer orders of the e-commerce APP in the current time segment of the day based on various basic information of the e-commerce APP, and then the total number of logistics service personnel deployed to serve the current time segment of the day is determined, thereby enhancing the intelligence level of the e-commerce APP.

[0025] Having described exemplary embodiments of the present invention, it should also be noted that it will be apparent to those skilled in the art that various modifications may be made without departing from the spirit and scope of the present invention as defined by the metes and bounds of the appended claims.

Claims

1. A smart logistics management system, characterized in that: The system comprises: A browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing of the e-commerce APP corresponding to each previous time segment before the current time segment of the day; An order collection mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to collect the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the previous day; An information capture mechanism, which is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain various related information of the e-commerce APP, wherein the various related information of the e-commerce APP is the number of registered customers of the e-commerce APP, the types of goods on sale, and the geographical area of ​​the service area; The target mapping device is connected to the browsing analysis mechanism, the order collection mechanism and the information capture mechanism respectively, and is used to analyze the number of customer orders of the e-commerce APP in the current time segment of the day by using a deep neural network model based on the total number of customer browsing of each past time segment corresponding to each previous time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP; A personnel conversion device, connected to the target mapping device, is used to allocate the total number of logistics service personnel to serve the current time segment of the day according to the number of customer orders of the e-commerce APP in the current time segment of the day obtained by analysis; Wherein, the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day based on the number of customer orders of the e-commerce APP obtained through analysis in the current time segment of the day includes: the smaller the number of customer orders of the e-commerce APP obtained through analysis in the current time segment of the day, the smaller the total number of logistics service personnel assigned to the e-commerce APP to serve the current time segment of the day; Among them, the browsing analysis mechanism is set in the e-commerce APP management server of the e-commerce operator, and is used to obtain the total number of customer browsing corresponding to each past time segment before the current time segment of the day of the e-commerce APP, including: the number of time segments in each past time segment is proportional to the number of registered customers of the e-commerce APP.

2. The smart logistics management system according to claim 1, characterized in that: The deep neural network model is used to analyze the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing of each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP, including: the deep neural network model is a deep neural network after executing multiple learning actions, and the number of learning actions is positively correlated with the type of goods on sale of the e-commerce APP; Among them, the deep neural network model is used to parse the number of customer orders of the e-commerce APP in the current time segment of the day based on the total number of customer browsing times corresponding to each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP. The method also includes: synchronously inputting the total number of customer browsing times corresponding to each past time segment before the current time segment of the day, the number of customer orders of the e-commerce APP in the same time segment as the current time segment on the day before the day, and various related information of the e-commerce APP into the deep neural network model.

3. The smart logistics management system according to claim 2, characterized in that: The system further comprises: The server monitoring mechanism is connected to the e-commerce APP management server and is used to obtain various communication data of the server. The various communication data of the server are one or more of the communication bandwidth of the server, the maximum computing amount per unit time, the content capacity and the remaining power.

4. The smart logistics management system according to claim 2, characterized in that: The system further comprises: The power support device is respectively connected to the target mapping device, the browsing analysis mechanism, the order collection mechanism and the information capture mechanism, and is used to provide power distribution support of different working voltages for the target mapping device, the browsing analysis mechanism, the order collection mechanism and the information capture mechanism in a time-sharing manner.

5. The smart logistics management system according to claim 4, characterized in that: The system further comprises: The instant notification mechanism is connected to the power support devices respectively used by the target mapping device, the browsing analysis device, the order collection device and the information capture device, and is used to perform corresponding alarm actions when abnormal power conditions occur.

6. The smart logistics management system according to claim 5, characterized in that: The instant notification mechanism is respectively connected to the power support devices respectively used by the target mapping device, the browsing analysis device, the order collection device and the information capture device, and is used to perform corresponding alarm actions when abnormal power conditions occur. The instant notification mechanism includes: a built-in power receiving unit, a power judgment unit and a notification execution unit.

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