CN111859179A

CN111859179A - Method, system and device for recommending boarding points and storage medium

Info

Publication number: CN111859179A
Application number: CN202010321897.3A
Authority: CN
Inventors: 贺明慧; 杨建涛; 束纬寰; 马利
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-04-22
Filing date: 2020-04-22
Publication date: 2020-10-30

Abstract

The embodiment of the application discloses a boarding point recommendation method, a system, a device and a storage medium. The method comprises the following steps: acquiring an original starting point of a user; judging whether the original departure point is a father point POI or not, and recalling at least one child point POI of the father point POI when the original departure point is the father point POI; respectively obtaining corresponding candidate boarding points based on the parent point POI and all the recalled child point POIs, and combining the candidate boarding points of the parent point POI and the candidate boarding points of the child point POIs to obtain a candidate boarding point set; calculating a correlation score of each candidate boarding point in the candidate boarding point set and sorting each candidate boarding point based on the correlation score; and obtaining at least one recommended boarding point based on the sequencing result. According to the method and the device, the child point associated point library of the POI is used as the recall, the recommendation candidate set of the POI with the parent-child relationship attribute can be improved, the recommended boarding point is more accurate and stable, and the user experience is improved.

Description

Method, system and device for recommending boarding points and storage medium

Technical Field

The present application relates to the field of transportation, and in particular, to a pick-up point recommendation method, system, device, and storage medium.

Background

With the rapid development of online taxi taking services and the popularization and use of mobile phones, more and more people use the online taxi taking services on the mobile phones, and the requirement on position recommendation technology is higher and higher. When a planar POI scene is recommended to a pick-up point, usually, candidate pick-up points of a parent point POI and a child point POI of the POI scene are recalled respectively, so that when the parent point POI or the child point POI are searched, the recommended points presented by the parent point POI and the child point POI are not linked. The existence of the problem affects the position recommendation precision and reduces the user position perceptibility and the travel efficiency. Therefore, it is necessary to provide an application of the vehicle-entering point recommendation in a planar POI scene in combination with the parent-child relationship of the POI, so that the vehicle-entering point recommendation is more accurate and stable, and the user experience is improved.

Disclosure of Invention

A first aspect of the present application provides a pick-up point recommendation method. The pick-up point recommendation method comprises the following steps: acquiring an original starting point of a user; judging whether the original departure point is a father point POI or not, and recalling at least one child point POI of the father point POI when the original departure point is the father point POI; respectively obtaining corresponding candidate boarding points based on the parent point POI and all the recalled child point POIs, and combining the candidate boarding points of the parent point POI and the candidate boarding points of the child point POIs to obtain a candidate boarding point set; calculating a correlation score of each candidate boarding point in the candidate boarding point set and sorting each candidate boarding point based on the correlation score; and obtaining at least one recommended boarding point based on the sequencing result.

In some embodiments, the relevance score of the candidate pick-up point is calculated based on at least one of the following feature data: first characteristic data capable of reflecting the distance characteristic from the candidate boarding point to the original departure point; second feature data capable of reflecting a distance feature from the candidate boarding point to the user's current location position; third feature data capable of reflecting a first attribute feature of the parent point POI or child point POI recalling the candidate pick-up point, the first attribute feature at least including: the parent point POI or the child point POI is taken as the frequency of getting-on points in a past period of time; and fourth feature data capable of reflecting second attribute features of the candidate boarding points, the second attribute features at least including: the candidate pick-up point is the frequency of pick-up points over a period of time in the past.

In some embodiments, the method of ranking each of the candidate pick-up points based on the relevance score comprises: obtaining a sequencing model, wherein the sequencing model is a machine learning model; and respectively inputting the characteristic data of each candidate boarding point into the sequencing model, and outputting the sequencing result of each candidate boarding point through the sequencing model.

In some embodiments, the ranking model comprises at least one of the following models: lambda rank model, Lambda Mart model, CNN model, and LTR model.

In some embodiments, the method for obtaining the corresponding candidate boarding point based on the parent point POI or the child point POI includes at least one of the following: recalling all boarding points within a first distance range from the parent point POI or the child point POI as the candidate boarding points; or taking the N boarding points closest to the parent point POI or the child point POI as the candidate boarding points, wherein N is a positive integer.

In some embodiments, the method of recalling at least one child POI of the parent POI comprises: recalling the child POI based on a parent-child relationship attribute; the method for determining the parent-child relationship attribute at least comprises the following steps: the parent point POI and the child point POI have a containing relationship semantically and/or the parent point POI and the child point POI have a containing relationship on a map range.

In some embodiments, the relevance score for the candidate pick-up point is based on the frequency with which the candidate pick-up point is a pick-up point over a period of time and the user's current location position.

A second aspect of the present application provides a pick-up point recommendation system, comprising: the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring an original starting point of a user; the first judging unit is used for judging whether the original departure point is a father point POI or not, and recalling at least one child point POI of the father point POI when the original departure point is the father point POI; the first processing unit is used for respectively obtaining corresponding candidate boarding points based on the parent point POI and all the recalled child point POIs, and combining the candidate boarding points of the parent point POI and the candidate boarding points of the child point POIs to obtain a candidate boarding point set; the first sequencing unit is used for calculating the correlation score of each candidate boarding point in the candidate boarding point set and sequencing each candidate boarding point based on the correlation score; and the first recommending unit is used for obtaining at least one recommended boarding point based on the sequencing result.

In some embodiments, the first ordering unit is to: obtaining a sequencing model, wherein the sequencing model is a machine learning model; and respectively inputting the characteristic data of each candidate boarding point into the sequencing model, and outputting the sequencing result of each candidate boarding point through the sequencing model.

In some embodiments, the method for the first processing unit to obtain the corresponding candidate boarding point based on the parent point POI or the child point POI includes at least one of the following: recalling all boarding points within a first distance range from the parent point POI or the child point POI as the candidate boarding points; or taking the N boarding points closest to the parent point POI or the child point POI as the candidate boarding points, wherein N is a positive integer.

In some embodiments, the method for recalling at least one child POI of the parent POI by the first determining unit includes: recalling the child POI based on a parent-child relationship attribute; the method for determining the parent-child relationship attribute at least comprises the following steps: the parent point POI and the child point POI have a containing relationship semantically and/or the parent point POI and the child point POI have a containing relationship on a map range.

A third aspect of the present application provides a pick-up point recommendation device, including: at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the operations described in some embodiments of the present application.

A fourth aspect of the present application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, perform operations according to some embodiments of the present application.

A fifth aspect of the present application provides a boarding point recommendation method, including: acquiring an original starting point of a user; judging whether the original departure point is a father point POI or not; obtaining at least one candidate boarding point based on the judgment result; obtaining the sequencing result of each candidate boarding point; and recommending at least one recommended boarding point to the user based on the sequencing result.

In some embodiments, the obtaining the ranking result of each candidate pick-up point includes: sending the original departure point and a judgment result of whether the original departure point is a father point POI or not to a server; and receiving at least one candidate boarding point returned by the server and a sequencing result thereof.

A sixth aspect of the present application provides a pick-up point recommendation system, comprising: the second acquisition unit is used for acquiring an original starting point of the user; the second judgment unit is used for judging whether the original departure point is a father point POI or not; the second processing unit is used for obtaining at least one candidate boarding point based on the judgment result; the second sorting unit is used for obtaining sorting results of the candidate boarding points; and the second recommending unit is used for recommending at least one recommended boarding point to the user based on the sorting result.

In some embodiments, the second processing unit further comprises: the sending unit is used for sending the original departure point and a judgment result of whether the original departure point is a father point POI or not to a server; and the receiving unit is used for receiving at least one candidate boarding point returned by the server.

A seventh aspect of the present application provides a pick-up point recommendation device, comprising at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the operations described in some embodiments of the present application.

An eighth aspect of the present application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, perform operations according to some embodiments of the present application.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Wherein:

FIG. 1 is a schematic diagram of an application scenario of a pick-up point recommendation system according to some embodiments of the present application;

FIG. 2 is a schematic block diagram of a pick-up point recommendation system shown in accordance with some embodiments of the present application;

FIG. 3 is an exemplary flow chart of a pick-up point recommendation method according to some embodiments of the present application;

FIG. 4 is a schematic block diagram of a pick-up point recommendation system in accordance with further embodiments of the present application;

FIG. 5 is an exemplary flow chart of a pick-up point recommendation method according to other embodiments of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Although various references are made herein to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a vehicle client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The embodiment of the application can be applied to different traffic service systems, including but not limited to one or a combination of land, river, lake, sea, air and the like. For example, a human powered vehicle, a transportation means, an automobile (e.g., a small car, a bus, a large transportation vehicle, etc.), a rail transportation (e.g., a train, a motor car, a high-speed rail, a subway, etc.), a ship, an airplane, an aircraft, a hot air balloon, an unmanned vehicle, a transportation system to which management and/or distribution is applied, a delivery/reception express, etc., and the like. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these figures. For example, other similar guided user parking systems.

Fig. 1 is a schematic view of an application scenario of a pick-up point recommendation system according to some embodiments of the present application. The pick-up point recommendation system 100 may determine and recommend pick-up points to passengers to guide the passengers in selecting the appropriate pick-up points. The pick-up point recommendation system 100 may be an online service platform for internet services. For example, the pick-up point recommendation system 100 may be an online transportation service platform for a transportation service. In some embodiments, the pick-up recommendation system 100 may be applied to taxi appointment services, such as taxi calls, express calls, special calls, mini-bus calls, car pool, bus service, driver hiring and pick-up services, and the like. In some embodiments, the pick-up point recommendation system 100 may also be applied to designated driving, express delivery, take-away, and the like. The pick-up point recommendation system 100 may be an online service platform including a server 110, a network 120, a user terminal 130, and a storage device 140. The server 110 may include a processing device 112.

In some embodiments, the server 110 may be used to process information and/or data related to determining pick-up point recommendations. The server 110 may be a stand-alone server or a group of servers. The set of servers can be centralized or distributed (e.g., server 110 can be a distributed system). The server 110 may be regional or remote in some embodiments. For example, server 110 may access information and/or data stored in user terminal 130, storage device 140, through network 120. In some embodiments, server 110 may be directly connected to user terminal 130, storage device 140 to access information and/or material stored therein. In some embodiments, the server 110 may execute on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, and the like.

In some embodiments, the server 110 may include a processing device 112. The processing device 112 may process data and/or information related to the service request to perform one or more of the functions described herein. For example, the processing device 112 may receive a car use request signal sent by the user terminal 130 and provide the user with a pick-up point recommendation. In some embodiments, the processing device 112 may include one or more sub-processing devices (e.g., a single core processing device or a multi-core processing device). By way of example only, the processing device 112 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may facilitate the exchange of data and/or information. In some embodiments, one or more components (e.g., server 110, user terminal 130, storage device 140) in the pick-up point recommendation system 100 may send data and/or information to other components in the pick-up point recommendation system 100 via the network 120. In some embodiments, network 120 may be any type of wired or wireless network. For example, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or Internet switching points 120-1, 120-2, …, through which one or more components of the pick-up recommendation system 100 may connect to the network 120 to exchange data and/or information.

In some embodiments, the user may obtain the recommended pick-up point via the user terminal 130. In some embodiments, the user terminal 130 may include one or any combination of a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, an in-vehicle device 130-4, and the like. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, and the like, or any combination thereof. In some embodiments, the smart furniture device may include a smart lighting device, a control device for a smart appliance, a smart monitoring device, or the like, or any combination thereof. In some embodiments, user terminal 130 may include a location-enabled device to determine the location of the user and/or user terminal 130.

Storage device 140 may store data and/or instructions. In some embodiments, storage device 140 may store the profile retrieved from user terminal 130. In some embodiments, storage device 140 may store information and/or instructions for execution or use by server 110 to perform the example methods described herein. In some embodiments, storage device 140 may include mass storage, removable storage, volatile read-and-write memory (e.g., random access memory, RAM), read-only memory (ROM), the like, or any combination thereof. In some embodiments, the storage device 140 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, and the like, or any combination thereof.

In some embodiments, storage device 140 may be connected to network 120 to communicate with one or more components of system 100 (e.g., server 110, user terminal 130, etc.). One or more components of the pick-up point recommendation system 100 may access data or instructions stored in the storage device 140 via the network 120. In some embodiments, the storage device 140 may be directly connected to or in communication with one or more components (e.g., the server 110, the user terminal 130) in the pick-up point recommendation system 100. In some embodiments, the storage device 140 may be part of the server 110.

FIG. 2 is a schematic block diagram of a pick-up point recommendation system shown in accordance with some embodiments of the present application. As shown in fig. 2, in some embodiments, the pick-up point recommendation system 200 may include: a first obtaining unit 201, configured to obtain an original departure point of a user; the first determining unit 203 is configured to determine whether the original departure point is a parent point POI, and recall at least one child point POI of the parent point POI when the original departure point is the parent point POI. The parent POI may refer to one or more sheet areas, such as the university of tokyo post and telecommunications. A sub-point POI may refer to one or more specific points, such as eastern, western, etc. of the beijing post and telecommunications university; the first processing unit 205 is configured to obtain corresponding candidate boarding points based on the parent point POI and all recalled child point POIs, and merge the candidate boarding points of the parent point POI and the candidate boarding points of the child point POI to obtain a candidate boarding point set; a first sorting unit 207, configured to calculate a correlation score of each candidate boarding point in the candidate boarding point set, and sort each candidate boarding point based on the correlation score; and a first recommending unit 209 for obtaining at least one recommended boarding point based on the sorting result.

In some embodiments, the first ordering unit 207 may further be configured to: obtaining a sequencing model, wherein the sequencing model is a machine learning model; and respectively inputting the characteristic data of each candidate boarding point into the sequencing model, and outputting the sequencing result of each candidate boarding point through the sequencing model.

In some embodiments, the method for the first processing unit 205 to obtain the corresponding candidate boarding point based on the parent point POI or the child point POI includes at least one of the following: recalling all boarding points within a first distance range from the parent point POI or the child point POI as the candidate boarding points; or taking the N boarding points closest to the parent point POI or the child point POI as the candidate boarding points, wherein N is a positive integer.

In some embodiments, the method for the first determining unit 203 to recall at least one child POI of the parent POI may include: recalling the child POI based on the parent-child relationship attributes. The method for determining the parent-child relationship attribute at least comprises the following steps: the parent point POI and the child point POI have a containing relationship semantically and/or the parent point POI and the child point POI have a containing relationship on a map range.

In some embodiments, the relevance score for the candidate pick-up point may be based on the frequency with which the candidate pick-up point is a pick-up point over a period of time and the user's current location position.

It should be understood that the system and its elements shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments, the system and its elements may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its elements of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above description of the system and its elements is merely for convenience of description and is not intended to limit the present application to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the first obtaining unit 201, the first judging unit 203, the first processing unit 205, the first sorting unit 207, and the first recommending unit 209 disclosed in fig. 2 may be different units in a system, or may be a unit that implements the functions of two or more units described above. For another example, each unit may share one storage device 140, and each unit may have its own storage device 140. Such variations are within the scope of the present application.

FIG. 3 is an exemplary flow chart diagram illustrating implementation steps of a pick-up point recommendation method according to some embodiments of the present application.

In some embodiments of the present application, a pick-up point recommendation method is provided, which may be performed by the server 110, and the method 300 may include the steps of:

301, acquiring an original starting point of a user; in some embodiments, this step may be performed by the first acquisition unit 201 in the system 200.

In some embodiments, the original starting point may be a query word input by a user, or may be a current position of the user obtained by a positioning technology. For example, when the user inputs a query term at the user terminal 130, the user terminal 130 may transmit the query term to the server 110 via the network 120, and the processing device 112 may receive the query term and process the query term.

In some embodiments, the manner in which the user enters the query term may include, but is not limited to, any combination of one or more of typing, handwriting, selection, voice, scanning, and the like. Specifically, the typing input may include english input, chinese input, and the like depending on the language. The selection input may include selecting a query term from a selection list, and the like. The scan input may include a scan barcode input, a scan two-dimensional code input, a scan text input, a scan picture input, and the like. For example, the query term may be a Chinese character directly handwritten by the user. As another example, the query term may be a word or letter identified from a user scanned picture input. For another example, the query term may be a word or letter recognized from a voice input by the user.

In some embodiments, the current location of the user, which is obtained by the user terminal 130 through the positioning technology, may include latitude and longitude information, and thus an original starting point of the user may be determined according to the latitude and longitude information. In some embodiments, the original starting point may include a starting point name. In some embodiments, the positioning technology may include Global Positioning System (GPS) technology, beidou navigation system technology, global navigation satellite system (GLONASS) technology, galileo positioning system (galileo) technology, quasi-zenith satellite system (QAZZ) technology, base station positioning technology, Wi-Fi positioning technology, and the like, or any combination thereof.

In some embodiments, the user may select an original starting point based on the recording list. For example, the user terminal 130 may provide the user with a list of original starting point records, and the user may click one of the records in the list of records to input the original starting point. In some embodiments, the recording list may be a location information list obtained based on historical input information of a current user.

In some embodiments, the user may input an original starting point based on the map. For example, the user may input the original starting point by dragging the map in the user terminal 130 to make the pin stick to the selected position.

Step 303, judging whether the original departure point is a parent point POI, and recalling at least one child point POI of the parent point POI when the original departure point is the parent point POI; in some embodiments, this step may be performed by the first determination unit 203 in the system 200.

In some embodiments, the parent POI may refer to one or more sheet areas, such as the university of tokyo post and telecommunications. A sub-point POI may refer to one or more specific points, such as eastern, western, etc. of the beijing post and telecommunications university. The first determining unit 203 may determine that the original departure point is a parent point POI or a child point POI by determining an area corresponding to the original departure point on the map. For example, the user's original starting point is "beijing post and telecommunications university" corresponding to one sheet area on the map, and the first judgment unit 203 may determine "beijing post and telecommunications university" as the parent point POI.

In some embodiments, the parent point POI and the child point POI may have a parent-child relationship attribute. The first judgment unit 203 may recall the child POI based on the parent-child relationship attribute. The parent-child relationship attribute may be a contained relationship between a parent point POI and a child point POI, and specifically, may be a relationship that the parent point POI contains the child point POI. A parent POI may have one or more child POIs with which there is a parent-child relationship attribute.

In some embodiments, the first determination unit 203 may determine the parent-child relationship attributes of the parent point POI and the child point POI based on the semantic inclusion relationship between the parent point POI and the child point POI. In some embodiments, the semantically existing inclusion relationship between the parent point POI and the child point POI may refer to a semantically whole-part relationship between the parent point POI and the child point POI. Specifically, the child POI may semantically belong to a part of the parent POI. For example, "east door of Beijing post and telecommunications university" is semantically part of "Beijing post and telecommunications university," Beijing post and telecommunications university "and" east door of Beijing post and telecommunications university "have a parent-child relationship attribute.

In some embodiments, the first determining unit 203 may further determine parent-child relationship attributes of the parent point POI and the child point POI based on the parent point POI and the child point POI having an inclusion relationship on the map range. In some embodiments, the parent point POI and the child point POI have an inclusive relationship on the map range may mean that the position of the child point POI on the map falls within an area range of the parent point POI on the map. For example, a location point of "east door of beijing post and telecommunications university" on the map is within an area range of "beijing post and telecommunications university" on the map, and there are parent-child relationship attributes of "beijing post and telecommunications university" and "east door of beijing post and telecommunications university".

In some embodiments, the first determination unit 203 may further determine one or more child POI of the parent POI based on the parent-child relationship attributes of the parent POI and the child POI. In some embodiments, when the original departure point is a parent point POI, the first determining unit 203 may recall (recall) at least one child point POI of the parent point POI. For example, when the original starting point of the user is "beijing post and telecommunications university", the first judgment unit 203 may determine that "beijing post and telecommunications university" is a parent point POI, and may recall its child point POI "tokyo of beijing post and telecommunications university".

Step 305, respectively obtaining corresponding candidate boarding points based on the parent point POI and all the recalled child point POIs, and combining the candidate boarding points of the parent point POI and the candidate boarding points of the child point POIs to obtain a candidate boarding point set; in some embodiments, this step may be performed by the first processing unit 205 in the system 200.

In some embodiments, the method for obtaining the corresponding candidate boarding points based on the parent point POI or child point POI may include recalling all boarding points within a first distance range from the parent point POI or child point POI as the candidate boarding points. In some embodiments, the method for obtaining corresponding candidate boarding points based on the parent point POI or child point POI may also include using N boarding points closest to the parent point POI or child point POI as the candidate boarding points, where N is a positive integer.

In some embodiments, the first processing unit 205 may recall the candidate boarding point set a of the parent point POI based on the parent point POI. In some embodiments, the first processing unit 205 may obtain the candidate pick-up point set a near the parent point POI according to the historical order behavior data of the parent point POI input by the user and the base point library. In some embodiments, the base point library may be a library of points in a map database including Google maps, high-end maps, hundred degree maps, and the like. In some embodiments, the first processing unit 205 may recall the candidate pick-up point set a, which includes at least one candidate pick-up point, based on the first distance from the parent point POI. For example, all the boarding points within a distance of 50m from the parent point POI may be recalled as the candidate boarding point set a. In some embodiments, the first processing unit 205 may further use N boarding points closest to the parent point POI as the candidate boarding point set a of the parent point POI, where N is a positive integer. For example, the first processing unit 205 may recall the 10 boarding points closest to the parent point POI as the candidate boarding point set a.

In some embodiments, the first processing unit 205 may recall the candidate pick-up point set B of all the sub-point POIs based on at least one sub-point POI obtained in step 303. In some embodiments, the first processing unit 205 may obtain the candidate pick-up point set B near the sub-point POI according to the obtained historical order behavior data of the sub-point POI and the base point library. In some embodiments, the first processing unit 205 may also recall one or more candidate pick-up point sets B based on the first distance from the child point POI. For example, all the boarding points within a distance of 50m from the first child point POI may be recalled, all the boarding points within a distance of 50m from the second child point POI may be recalled, and so on, and the boarding points recalled from all the child point POIs may be regarded as the candidate boarding point set B. In some embodiments, the first processing unit 205 may further use, as its candidate boarding point set B, N nearest to the acquired child point POI, where N is a positive integer. For example, the first processing unit 205 may recall the 5 boarding points closest to the first child point POI, recall the 5 boarding points closest to the second child point POI, and so on, and take the boarding points recalled by all the child point POIs as the candidate boarding point set B.

In some embodiments, the first processing unit 205 may merge the candidate pick-up point set a and the candidate pick-up point set B to obtain the candidate pick-up point set. In some embodiments, the candidate pick set a and the candidate pick set B may be merged by taking the intersection of the two sets, i.e., eliminating duplicate candidate picks.

Step 307, calculating a correlation score of each candidate boarding point in the candidate boarding point set and ranking each candidate boarding point based on the correlation score; in some embodiments, this step may be performed by a first ordering unit 207 in the system 200.

In some embodiments, the correlation score of each of the candidate boarding points may be calculated from at least one of the following correlation characteristic data: first characteristic data, second characteristic data, third characteristic data, and fourth characteristic data.

In some embodiments, the first feature data may be data capable of reflecting a distance feature from the candidate boarding point to the original departure point. In some embodiments, the distance includes, but is not limited to, a spherical distance or a linear distance. The spherical distance may be the length of the shortest connecting line between two points on the spherical surface, that is, the length of a minor arc between the two points of a great circle passing through the two points. In some embodiments, the larger the first feature data, the more the candidate pick-up point deviates from the original departure point. The smaller the first characteristic data is, the less the candidate boarding point deviates from the original departure point.

In some embodiments, the second feature data may be data capable of reflecting a distance feature from the candidate boarding point to the user's current location. In some embodiments, the user's current location may be a location obtained by the positioning system in real time, which may be different from the original starting point of the user when placing an order due to user movement, etc. The larger the second feature data is, the higher the walking cost of the user walking from the current location position to the candidate boarding point is. The larger the second feature data is, the smaller the walking cost of the user walking from the current location position to the candidate boarding point is.

In some embodiments, the third data feature may be a data feature capable of reflecting a first attribute feature of the parent point POI or child point POI recalling the candidate pick-up point. In some embodiments, the first attribute feature comprises at least: the historical popularity of the parent point POI or the child point POI, namely the frequency of the parent point POI or the child point POI as the boarding point in the past period of time. In some embodiments, the frequency of the parent point POI or child point POI being the boarding point in the past period of time may be statistically obtained from historical orders in the past period of time. In some embodiments, the first attribute feature may further include: the number of candidate boarding points recalled by the parent point POI or the child point POI, the historical association times of the parent point POI or the child point POI and the candidate boarding points and the like. In some embodiments, the greater the number of candidate pick-up points recalled by the parent or child POI, the greater the number of candidate pick-up points participating in the ranking. In some embodiments, the more times the parent point POI or child point POI is historically associated with a candidate pick-up point, the higher the popularity weight of the candidate pick-up point, and the higher the score of the candidate pick-up point.

In some embodiments, the fourth data feature may be a data feature capable of reflecting the second attribute feature of the candidate boarding point. In some embodiments, the second attribute feature comprises at least: the historical popularity of the candidate pick-up points, i.e., the frequency with which the candidate pick-up points have been picked up over a past period of time. In some embodiments, the frequency of the candidate pick-up points as pick-up points over a past period of time may be statistically derived from historical orders over a past period of time. In some embodiments, the second attribute feature may further include: global heat, local heat, etc. of the candidate boarding points. In some embodiments, the global heat may be the number of orders in a historical order over a past period of time for which the historical passenger has the candidate pick-up as the actual pick-up. In some embodiments, the local heat may be a number of orders in a historical order over a past period of time that a historical passenger departed from the original departure point and arrived at the candidate pick-up point.

In some embodiments, the method of ranking each of the candidate pick-up points based on the relevance score comprises: and acquiring a sequencing model, wherein the sequencing model is a machine learning model. For example, trained ranking model parameters may be retrieved from the storage device 140 to obtain a ranking model. And respectively inputting the characteristic data of each candidate boarding point into the sequencing model, and outputting the sequencing result of each candidate boarding point through the sequencing model.

In some embodiments, after the user initiates the riding order at the user terminal 130, the first processing unit 205 may determine the set of candidate boarding points based on the original departure point of the order information. The first sorting unit 207 may calculate the above-mentioned related feature data of each candidate boarding point in the candidate boarding point set, input the related feature data of the candidate boarding point set into a trained sorting model, and output a sorting result by using the sorting model. In some embodiments, the ranking model may include a Lambda rank model, a Lambda Mart model, a CNN model, a LTR model, and the like. In some embodiments, the ranking result may be a recommended pick-up point and its score, such as "pick-up point a, 90 points", "pick-up point B, 80 points" …. In some embodiments, the ranking result may also be the TOP point of TOP N with descending score scores.

In some embodiments, the Lambda rank model is an empirical algorithm that directly defines the gradient of the loss function, i.e., the Lambda gradient. In some embodiments, the Lambda Mart model is a combination of Lambda gradient and MART, and is applicable to ranking scenarios, loss function derivation, incremental learning, combining features, feature selection, and the like. In some embodiments, the ranking may also be performed using a CNN (convolutional neural network) model, which is essentially an input to output mapping that is capable of learning a large number of input to output mappings without requiring any precise mathematical expression between inputs and outputs. In some embodiments, the ranking may also be performed using an LTR (learning to rank) model, which is a supervised learning ranking method. The sequencing method comprises the steps of training data acquisition, feature extraction, model training, test data prediction and effect evaluation.

In other embodiments, the ranking model may be further trained by: acquiring a plurality of historical orders; processing each of the plurality of historical orders to obtain a training sample; training the ranking model using a plurality of training samples to obtain a trained ranking model. In some embodiments, the historical orders include historical original departure points and historical boarding points. Specifically, the processing procedure may include: recalling at least one candidate pick-up point based on the historical original departure point; acquiring relevant characteristic data of each candidate boarding point as input data of a training sample; determining a marker for each candidate pick-up point based on whether the distance between the candidate pick-up point and the historical pick-up point is less than a first threshold; and combining the marks of the candidate boarding points to obtain the reference standard of the training sample.

In some embodiments, the model may also be optimized. The optimization process may be a process of adjusting the sequencing model parameters. The process of adjusting the parameters of the sequencing model can be that according to the comparison of the output result of the sequencing model and the marking result, when the deviation of the output sequencing result and the marking result is large, a certain punishment is given to the sequencing model, and when the coincidence degree of the output sequencing result and the marking result is high, a certain reward is given to the sequencing model, so that the final sequencing model is obtained and is used as a boarding point recommendation model. For example, the candidate boarding point a is a negative sample and is marked as "0", after model training, the top five candidate boarding points in the ranking result include the station a, and at this time, a certain penalty needs to be given to the ranking model, and training is continued on the ranking model, so that the tuning effect is achieved. If the first five candidate boarding points in the model ranking result are all positive samples marked as '1', the ranking model has a good training effect and can be put into use.

Step 309, obtaining at least one recommended boarding point based on the sorting result; in some embodiments, this step may be performed by the first recommending unit 209 in the system 200.

In some embodiments, the first recommending unit 209 may use the top N candidate boarding points in the ranking result as the recommended boarding points, may use the candidate boarding points with scores greater than a threshold value as the recommended boarding points, and so on.

In some embodiments, the obtained recommended pick-up points may be presented in a list on a display interface of the user terminal 130.

It should be noted that the above description of the flow is for illustration and description only and does not limit the application scope of the present application. Various modifications and alterations to the flow may occur to those skilled in the art, given the benefit of this disclosure. However, such modifications and variations are intended to be within the scope of the present application.

In other embodiments of the present application, there is provided a pick-up point recommendation device comprising at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the operations described above.

In still other embodiments of the present application, a computer-readable storage medium is provided that stores computer instructions that, when executed by a processor, implement the operations described above.

FIG. 4 is a schematic block diagram of a pick-up point recommendation system in accordance with further embodiments of the present application. As shown in FIG. 4, in some embodiments, the pick-up point recommendation system 400 may include: a second obtaining unit 401, configured to obtain an original starting point of the user. A second determining unit 403, configured to determine whether the original departure point is a parent point POI. And a second processing unit 405, configured to obtain at least one candidate boarding point based on the determination result. A second sorting unit 407, configured to obtain a sorting result of each candidate boarding point. And a second recommending unit 409, configured to recommend at least one recommended boarding point to the user based on the sorting result.

In some embodiments, the second processing unit 405 further comprises: the system comprises a sending unit and a receiving unit, wherein the sending unit is used for sending the original departure point and a judgment result of whether the original departure point is a father point POI or not to a server, and the receiving unit is used for receiving at least one candidate boarding point returned by the server.

It should be noted that the above description of the system and its elements is merely for convenience of description and is not intended to limit the present application to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, having the benefit of the teachings of this system, any combination of the various elements or constituent subsystems may be connected to other elements without departing from such teachings. For example, in some embodiments, the second obtaining unit 401, the second determining unit 403, the second processing unit 405, the second sorting unit 407, and the second recommending unit 409 disclosed in fig. 2 may be different units in a system, or may be a unit that implements functions of two or more units described above. For another example, each unit may share one storage device 140, and each unit may have its own storage device 140. Such variations are within the scope of the present application.

FIG. 5 is an exemplary flowchart illustrating implementation steps of a pick-up point recommendation method according to further embodiments of the present application. In still other embodiments of the present application, a pick-up point recommendation method is provided, which may be performed by the user terminal 130, and the method 500 may include the steps of:

Step 501, acquiring an original starting point of a user; in some embodiments, this step may be performed by the second acquisition unit 401 in the system 400.

In some embodiments, the original starting point may be a query word input by a user, or may be a current position of the user obtained by a positioning technology. In some embodiments, the manner in which the user enters the query term may include, but is not limited to, any combination of one or more of typing, handwriting, selection, voice, scanning, and the like. In some embodiments, the current location of the user, which is obtained by the user terminal 130 through the positioning technology, may include latitude and longitude information, and thus an original starting point of the user may be determined according to the latitude and longitude information. In some embodiments, the user may select an original starting point based on the recording list. For example, the user terminal 130 may provide the user with a list of original starting point records, and the user may click one of the records in the list of records to input the original starting point. In some embodiments, the user may input an original starting point based on the map. For example, the user may input the original starting point by dragging the map in the user terminal 130 to make the pin stick to the selected position.

Step 503, judging whether the original departure point is a father point POI or not; in some embodiments, this step may be performed by the second determination unit 403 in the system 400.

In some embodiments, the parent POI may refer to one or more sheet areas. The sub-point POI may refer to one or more specific points. The second determining unit 403 may determine that the original departure point is a parent point POI or a child point POI by determining an area of the original departure point corresponding to the map. For example, the user's original starting point is "beijing post and telecommunications university", which corresponds to one sheet area on the map, and the second judging unit 403 may determine "beijing post and telecommunications university" as the parent point POI.

In some embodiments, the determination result may be provided by the server. For example, the user terminal 130 sends the original starting point to the server; and receiving a judgment result whether the original starting point returned by the server is the parent point POI or not. In some embodiments, the determination result may also be processed by a processor of the user terminal 130. The process of acquiring the determination result may specifically refer to the related description of fig. 3.

505, obtaining at least one candidate boarding point based on the judgment result; in some embodiments, this step may be performed by the second processing unit 405 in the system 400.

In some embodiments, the second processing unit 405 may obtain at least one candidate boarding point based on the determination result in step 503. In some embodiments, the candidate pick-up point may be provided by the server or may be processed by a processor of the user terminal 130. The candidate boarding point can be a candidate boarding point of a parent point POI or a candidate boarding point of a child point POI. The manner of obtaining at least one candidate pick-up point may be obtained similarly with reference to the related description of step 305 in fig. 3 in this specification, and is not described herein again.

Step 507, obtaining a sequencing result of each candidate boarding point; in some embodiments, this step may be performed by the second sorting unit 407 in the system 400.

In some embodiments, the obtaining the ranking result of each candidate pick-up point may include: and sending the original departure point and a judgment result whether the original departure point is a father point POI or not to a server, and receiving at least one candidate boarding point returned by the server and a sequencing result thereof.

In some embodiments, the candidate pick-up points and the ranking results thereof returned by the server may be provided by the server, or may be implemented by a processor of the user terminal 130. Specifically, the obtaining of the sorting result may be obtained similarly with reference to the related description of step 307 in fig. 3 in this specification, and is not described here again.

509, recommending at least one recommended boarding point to the passenger based on the sequencing result; in some embodiments, this step may be performed by the second recommendation unit 409 in the system 400.

In some embodiments, the second recommending unit 409 may take the top N candidate boarding points in the ranking result as the recommended boarding points, for example, display the top 5 candidate boarding points in the ranking result on the display interface; in another embodiment, candidate pick-up points with scores greater than a threshold may be taken as recommended pick-up points, and the threshold may be 80 points, 90 points, 95 points, and so on.

In some embodiments, the perception of the user on the recommended boarding point may be further improved by adjusting the arrangement, display position, and the like, or any combination thereof, of the recommended boarding point on the display interface.

In some embodiments, the arrangement may include an up-down arrangement and a left-right arrangement. For example, when a user inputs a query word in an input box of a user terminal, the sorted results output by the model are presented to the user in an up-down arrangement. For example, based on the sorting result, the second recommending unit 409 presents it to the user in a left-right arrangement.

In some embodiments, the display modes may include a thumbnail display and a full display. In some embodiments, when the recommended pick-up point information in the ranking result contains a length greater than that of the display box, the recommended pick-up point may be presented to the user terminal in an abbreviated display. For example, when the recommended upper vehicle point information in the ranking result is long, only the front part information of the recommended upper vehicle point may be displayed in the display frame, and the part beyond the display frame may be omitted. In some embodiments, the complete display may be a complete presentation of the ranking results to the user. For example, when the recommended boarding point information in the sorting result is larger than the length of the display frame, the recommended boarding point information can be completely displayed by adjusting the size of the font.

In some embodiments, the display position may include below the input box, above the input box, to the right of the input box, and so on. For example, after the user inputs the query word at the user terminal, the ranking result output based on the ranking model is presented to the user below the input box. For another example, after the user inputs the query term at the user terminal, the query term is presented to the user at the right side of the input box based on the ranking result.

It should be noted that the above description is merely for convenience and should not be taken as limiting the scope of the present application. It will be understood by those skilled in the art that various changes and modifications in form and detail may be made in the implementation of the above-described processes without departing from the principles of the present application. However, such changes and modifications do not depart from the scope of the present application.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) the recalled candidate boarding points are determined by judging the parent-child relationship attributes of the POI, so that the recalled candidate boarding points of the parent POI can contain the recalled candidate boarding points of the child POI, and the coverage rate of the candidate boarding points is improved; (2) the candidate boarding point sets are sorted through the sorting model, so that the recommended boarding points are more accurate and stable, and the user experience is improved.

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

The foregoing describes the present application and/or some other examples. The present application can be modified in various ways in light of the above. The subject matter disclosed herein can be implemented in various forms and examples, and the present application can be applied to a wide variety of applications. All applications, modifications and variations that are claimed in the following claims are within the scope of this application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment," or "one embodiment," or "an alternative embodiment," or "another embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Those skilled in the art will appreciate that various modifications and improvements may be made to the disclosure herein. For example, the different system components described above are implemented by hardware devices, but may also be implemented by software solutions only. For example: the system is installed on an existing server. Further, the location information disclosed herein may be provided via a firmware, firmware/software combination, firmware/hardware combination, or hardware/firmware/software combination.

All or a portion of the software may sometimes communicate over a network, such as the internet or other communication network. Such communication enables loading of software from one computer device or processor to another. For example: from a management server or host computer of the radiation therapy system to a hardware platform of a computer environment, or other computer environment implementing the system, or similar functionality associated with providing information needed to determine wheelchair target structural parameters. Thus, another medium capable of transferring software elements may also be used as a physical connection between local devices, such as optical, electrical, electromagnetic waves, etc., propagating through cables, optical cables, or the air. The physical medium used for the carrier wave, such as an electric, wireless or optical cable or the like, may also be considered as the medium carrying the software. As used herein, unless limited to a tangible "storage" medium, other terms referring to a computer or machine "readable medium" refer to media that participate in the execution of any instructions by a processor.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service using, for example, software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numbers describing attributes, quantities, etc. are used in some embodiments, it being understood that such numbers used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, articles, and the like, cited in this application is hereby incorporated by reference in its entirety. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, embodiments of the present application are not limited to those explicitly described and depicted herein.

Claims

1. A pick-up point recommendation method, the method comprising:

acquiring an original starting point of a user;

judging whether the original departure point is a father point POI or not, and recalling at least one child point POI of the father point POI when the original departure point is the father point POI;

respectively obtaining corresponding candidate boarding points based on the parent point POI and all the recalled child point POIs, and combining the candidate boarding points of the parent point POI and the candidate boarding points of the child point POIs to obtain a candidate boarding point set;

calculating a correlation score of each candidate boarding point in the candidate boarding point set and sorting each candidate boarding point based on the correlation score;

and obtaining at least one recommended boarding point based on the sequencing result.

2. The method of claim 1, wherein the relevance score for the candidate pick-up point is calculated based on at least one of the following feature data:

First characteristic data capable of reflecting the distance characteristic from the candidate boarding point to the original departure point;

second feature data capable of reflecting a distance feature from the candidate boarding point to the user's current location position;

third feature data capable of reflecting a first attribute feature of the parent point POI or child point POI recalling the candidate pick-up point, the first attribute feature at least including: the parent point POI or the child point POI is taken as the frequency of getting-on points in a past period of time;

and fourth feature data capable of reflecting second attribute features of the candidate boarding points, the second attribute features at least including: the candidate pick-up point is the frequency of pick-up points over a period of time in the past.

3. The method of claim 2, wherein the method of ranking each of the candidate pick-up points based on the relevance score comprises:

obtaining a sequencing model, wherein the sequencing model is a machine learning model;

and respectively inputting the characteristic data of each candidate boarding point into the sequencing model, and outputting the sequencing result of each candidate boarding point through the sequencing model.

4. The method of claim 3, wherein the ranking model comprises at least one of: lambda rank model, Lambda Mart model, CNN model, and LTR model.

5. The method according to claim 1, wherein the method for obtaining the corresponding candidate boarding point based on the parent point POI or the child point POI comprises at least one of the following:

recalling all boarding points within a first distance range from the parent point POI or the child point POI as the candidate boarding points; or

And taking N vehicle-entering points closest to the parent point POI or the child point POI as the candidate vehicle-entering points, wherein N is a positive integer.

6. The method according to claim 1, wherein said recalling at least one child POI of said parent POI comprises:

recalling the child POI based on a parent-child relationship attribute;

the method for determining the parent-child relationship attribute at least comprises the following steps: the parent point POI and the child point POI have a containing relationship semantically and/or the parent point POI and the child point POI have a containing relationship on a map range.

7. The method of claim 1, wherein the relevance score for the candidate pick-up point is based on how often the candidate pick-up point is a pick-up point over a period of time and a current location of the user.

8. A pick-up point recommendation system, comprising:

the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring an original starting point of a user;

the first judging unit is used for judging whether the original departure point is a father point POI or not, and recalling at least one child point POI of the father point POI when the original departure point is the father point POI;

the first processing unit is used for respectively obtaining corresponding candidate boarding points based on the parent point POI and all the recalled child point POIs, and combining the candidate boarding points of the parent point POI and the candidate boarding points of the child point POIs to obtain a candidate boarding point set;

the first sequencing unit is used for calculating the correlation score of each candidate boarding point in the candidate boarding point set and sequencing each candidate boarding point based on the correlation score;

and the first recommending unit is used for obtaining at least one recommended boarding point based on the sequencing result.

9. The system of claim 8, wherein the relevance score for the candidate pick-up point is calculated based on at least one of the following feature data:

10. The system of claim 9, wherein the first sequencing unit is configured to:

11. The system of claim 10, wherein the ranking model comprises at least one of: lambda rank model, Lambda Mart model, CNN model, and LTR model.

12. The system of claim 8, wherein the method for the first processing unit to obtain the corresponding candidate boarding point based on the parent point POI or child point POI comprises at least one of:

13. The system according to claim 8, wherein the method for recalling at least one child POI of the parent POI by the first determining unit comprises:

recalling the child POI based on a parent-child relationship attribute;

14. The system of claim 8, wherein the relevance score for the candidate pick-up point is based on the frequency with which the candidate pick-up point is a pick-up point over a period of time and the user's current location.

15. A pick-up point recommendation device, the device comprising at least one processor and at least one memory;

The at least one memory is for storing computer instructions;

the at least one processor is configured to execute at least some of the computer instructions to implement the operations of any of claims 1 to 7.

16. A computer-readable storage medium, characterized in that the storage medium stores computer instructions, which when executed by a processor, implement the operations of any one of claims 1 to 7.