CN111861618A - Boarding point recommendation method and system - Google Patents
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Abstract
The embodiment of the application discloses a boarding point recommendation method which is executed by at least one processor and comprises the steps that a user terminal obtains at least one piece of position information related to a current order and obtains a recommended boarding point determined based on the at least one piece of position information of the current order. Outputting the recommended boarding point on a user terminal, wherein the recommended boarding point is a safe boarding point; and the parking risk value of the safe boarding point is smaller than a preset threshold value. The method and the device can avoid the condition that a driver is subjected to illegal parking punishment when the driver loads the user.
Description
Technical Field
The application relates to the field of network taxi appointment, in particular to a taxi pick-up point recommendation method and system.
Background
At present, users who use net appointment cars to go on a journey are more and more common. In the network car booking service, generally, a user sets a trip starting position and sends a service request, and after receiving the service request, a driver loads the user to the position according to the starting position set by the user. If the starting location is on an illegal road segment, there is a high risk of penalty for drivers stopping at that location. Therefore, there is a need to provide a method for recommending boarding points to avoid the driver from carrying users on the road with higher risk of parking violation, and reduce the penalty of the driver due to the problem of parking violation.
Disclosure of Invention
One embodiment of the application provides a boarding point recommendation method. The method is performed by at least one processor, and the pick-up point recommendation method comprises: the user terminal acquires at least one piece of position information related to the current order; acquiring a recommended boarding point determined based on at least one piece of position information of the current order; outputting the recommended boarding point on a user terminal; wherein the recommended boarding point is a safe boarding point; and the parking risk value of the safe boarding point is smaller than a preset threshold value.
In some embodiments, the method for determining the set of safe boarding points comprises: acquiring illegal parking road section information; when at least one piece of position information of the current order is located in the illegal parking road section, whether the parking risk of a plurality of candidate boarding points related to the current order at the current moment is smaller than the threshold value or not is determined, and the candidate boarding points with the parking risks smaller than the threshold value are added into the safe boarding point set. And when at least one piece of position information of the current order is located on a non-illegal road section, adding a plurality of candidate boarding points related to the current order into the safe boarding point set.
In some embodiments, the parking risk is a ratio of a number of tickets associated with the candidate pick-up point to a total number of orders associated with the candidate pick-up point over a period of time.
In some embodiments, the illegal segment information includes at least one or more of the following combinations: the illegal parking road information with the forbidden parking marks, the road information in the illegal parking ticket, the road information shot by the illegal parking electronic eye and the forbidden parking road information reported by the user.
In some embodiments, the determining of the recommended pick-up point based on the set of safe pick-up points comprises: acquiring characteristic information of the candidate boarding points in the safe boarding point set; and determining the recommended boarding point based on the characteristic information.
In some embodiments, the characteristic information comprises a combination of one or more of: the distance between the candidate getting-on point and the at least one position of the current order, the distance between the candidate getting-on point and the current position of the user, the number of times that the at least one position of the current order is not determined as the recommended getting-on point, the number of times that the at least one position of the current order is determined as the recommended getting-on point, the heat of the at least one position of the current order and the heat of the candidate getting-on point.
In some embodiments, the determining the recommended boarding point based on the characteristic information includes: acquiring a recommendation model; determining the recommended boarding point based on the recommendation model.
In some embodiments, the obtaining a recommendation model includes: obtaining a boarding point in a historical order within a certain time period, and extracting characteristic information of the boarding point; determining relevance scores of the boarding points in the historical orders; training the recommendation model based on the feature information and the relevance score.
In some embodiments, said determining said recommended pick-up point based on said recommendation model comprises: inputting the characteristic information of the candidate boarding points in the safe boarding point set into the recommendation model; determining a ranking of the candidate pick-up points based on the recommendation model; determining the recommended pick-up point based on the ranking.
In some embodiments, the recommendation model is a lambdamard ordering model.
In some embodiments, the method further comprises: acquiring illegal parking road section information; and when the at least one position information of the current order is located on the illegal road section, prompting the user that at least one position of the current order has illegal parking risk on the user terminal.
One of the embodiments of the present application provides a pick-up point recommendation system, including: the terminal acquisition module is used for acquiring at least one piece of position information of the current order; the receiving module is used for acquiring a recommended boarding point determined based on at least one piece of position information of the current order; the output module is used for outputting the recommended boarding points; wherein the recommended boarding point is determined based on a set of safe boarding points; the safe boarding point set is a boarding point set of which the parking risk value is smaller than a preset threshold value.
One of the embodiments of the present application provides a recommended boarding point device, including at least one storage medium and at least one processor; the at least one storage medium is configured to store computer instructions; the at least one processor is configured to execute the computer instructions to implement the recommended pick-up method as previously described.
One embodiment of the present application provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the recommended boarding point method as described above.
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The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, 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 block diagram of a pick-up point recommendation system according to some embodiments of the present application;
FIG. 3 is an exemplary flow chart of a method of recommending pick-up points according to some embodiments of the present application;
FIG. 4 is an exemplary flow diagram of a method of training a recommendation model according to some embodiments of the present application;
FIG. 5 is an exemplary flow chart of another method of recommending pick-up points according to some embodiments of the present application;
FIG. 6 is an exemplary flow chart of a method of recommending pick-up points according to some 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.
It should be understood that "system", "device", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
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.
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, the 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.
Embodiments of the present application may be applied to different transportation systems including, but not limited to, one or a combination of terrestrial, marine, aeronautical, aerospace, and the like. For example, taxis, special cars, tailplanes, buses, designated drives, trains, railcars, high-speed rails, ships, airplanes, hot air balloons, unmanned vehicles, receiving/sending couriers, and the like, employ managed and/or distributed transportation systems. The application scenarios of the different embodiments of the present application include, but are not limited to, one or a combination of several of a web page, a browser plug-in, a client, a customization system, an intra-enterprise analysis system, an artificial intelligence robot, 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.
The terms "passenger", "passenger end", "user terminal", "customer", "demander", "service demander", "consumer", "user demander" and the like are used interchangeably and refer to a party that needs or orders a service, either a person or a tool. Similarly, "driver," "provider," "service provider," "server," and the like, as described herein, are interchangeable and refer to an individual, tool, or other entity that provides a service or assists in providing a service. In addition, a "user" as described herein may be a party that needs or subscribes to a service, or a party that provides or assists in providing a service.
Fig. 1 is a schematic view illustrating an application scenario of a pick-up point recommendation system according to some embodiments of the present application. FIG. 1 is a schematic diagram of an on-demand service system 100 according to some embodiments of the present application. For example, the on-demand service system 100 may be a platform that provides services for transportation services. The on-demand service system 100 may include a server 110, one or more user terminals 120, a storage device 130, a network 150, and an information source 140. The server 110 may include a processing engine 112.
In some embodiments, the server 110 may be a single server or a group of servers. The server farm can be centralized or distributed (e.g., server 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access information and/or data stored in storage device 130, user terminal 120, through network 150. As another example, server 110 may be directly connected to storage device 130, user terminal 120 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, between clouds, multiple clouds, the like, or any combination of the above.
In some embodiments, the server 110 may include a processing engine 112. Processing engine 112 may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processing engine 112 may determine whether the candidate pick-up point is a safe pick-up point, and may also recommend a pick-up point based on the set of safe pick-up points. In some embodiments, processing engine 112 may include one or more processors (e.g., a single-core processor or a multi-core processor). For example only, the processing engine 112 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), a Graphics Processing Unit (GPU), a physical arithmetic processing unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination of the above.
The user terminal 120 may be an individual, tool, or other entity directly associated with the service order, such as a requester of the service order. The user terminal 120 may be a passenger. In this application, "passenger" and "service requester" may be used interchangeably. In some embodiments, the user terminal 120 may include, but is not limited to, a desktop computer 120-1, a laptop computer 120-2, a vehicle mounted built-in device 120-3, a mobile device 120-4, and the like or any combination thereof. The user terminal 120 may send the service request online. For example, the user terminal 120 may send a network appointment order based on the current location and destination. In some embodiments, the in-vehicle built-in device 120-3 may include, but is not limited to, a personal computer, an in-vehicle heads-up display (HUD), an in-vehicle automatic diagnostic system (OBD), and the like, or any combination thereof. In some embodiments, mobile device 120-4 may include, but is not limited to, a smartphone, a Personal Digital Assistant (PDA), a tablet, a palmtop, smart glasses, a smart watch, a wearable device, a virtual display device, a display enhancement device, and the like, or any combination thereof. In some embodiments, the user terminal 120 may send the service order information to one or more devices in the on-demand service system 100. For example, the user terminal 120 may send the service order information to the server 110 for processing. The user terminal 120 may also include one or more of the similar devices described above.
In some embodiments, the storage device 130 may be connected with a network 150 to enable communication with one or more components (e.g., server 110, user terminal 120, etc.) in the on-demand service system 100. One or more components of the on-demand service system 100 may access data or instructions stored in the storage device 130 via the network 150. In some embodiments, the storage device 130 may be directly connected to or in communication with one or more components of the on-demand service system 100 (e.g., the server 110, the user terminal 120, etc.). In some embodiments, storage device 130 may be part of server 110.
The network 150 may facilitate the exchange of information and/or data. In some embodiments, one or more components (e.g., server 110, storage 130, user terminal 120, etc.) in the on-demand service system 100 may send information and/or data to other components in the on-demand service 100 over the network 150. For example, the server 110 may obtain/obtain requests from the user terminal 120 via the network 150. In some embodiments, the network 150 may be any one of, or a combination of, a wired network or a wireless network. For example, network 150 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 of the above. In some embodiments, network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points, such as base stations and/or Internet switching points 150-1, 150-2, and so forth. Through the access point, one or more components of the on-demand service system 100 may connect to the network 150 to exchange data and/or information.
The information source 140 is a source that provides other information to the on-demand service system 100. Information sources 160 may be used to provide information related to services for the system, such as weather conditions, traffic information, legal information, news information, life guide information, and the like. The information source 140 may be in the form of a single central server, or may be in the form of a plurality of servers connected via a network, or may be in the form of a large number of personal devices. When the information source 140 exists as a plurality of personal devices, the devices may upload text, voice, images, videos, etc. to the cloud server in a user-generated content (user-generated content) manner, so that the cloud server communicates with the plurality of personal devices connected thereto to form the information source 140.
FIG. 2 is a block diagram of a pick-up point recommendation system according to some embodiments of the present application. As shown in FIG. 2, the pick-up point recommendation system may include an acquisition module 210, a secure pick-up point set determination module 220, a recommendation module 230, and a training module 240. In some embodiments, the acquisition module 210, the safe pick-up set determination module 220, the recommendation module 230, and the training module 240 may be disposed in the server 110.
The obtaining module 210 may be configured to obtain a plurality of candidate pick-up points related to the current order based on at least one location information of the current order. In some embodiments, the at least one location information of the current order may be a preset boarding location at the time the user sends the service request. For example, the position of the boarding point may be manually added by the user, the position of the user on a map fixed with pins, the current position of the user, the historical boarding point position determined according to the historical order of the user, or the like. In some embodiments, at least one location information of the current order may be determined as a candidate pick-up point, and it may be determined whether the candidate pick-up point is a safe pick-up point. In some embodiments, a plurality of related candidate pick-up points may be determined based on at least one location information of the current order. In some embodiments, a geographic range may be determined based on at least one location information of the current order, and common pick-up locations within the range may be determined as candidate pick-up locations. In some embodiments, the pick-up point associated with the current order may be determined as a candidate pick-up point in the historical order based on the at least one location information of the current order.
The safe boarding point set determination module 220 may be configured to determine whether each candidate boarding point belongs to a safe boarding point at the current time, and if so, add the candidate boarding point into the safe boarding point set. And the parking risk of the safe boarding point is less than a preset threshold value. In some embodiments, the illegal road segment information may include one or a combination of illegal road segment information with forbidden marks, road segment information in illegal parking tickets, road segment information shot by illegal electronic eyes, and forbidden road segment information reported by users. In some embodiments, the safe pick-up point set determining module 220 is further configured to obtain information of the illegal parking road section, determine whether parking risks of a plurality of candidate pick-up points related to the current order at the current time are smaller than the threshold value when at least one position information of the current order is located on the illegal parking road section, and add the candidate pick-up points with the parking risks smaller than the threshold value into the safe pick-up point set. In some embodiments, the safe pick-up point set determining module 220 is further configured to obtain illegal parking section information, and add a plurality of candidate pick-up points related to the current order into the safe pick-up point set when at least one position information of the current order is located on a non-illegal parking section. In some embodiments, the parking risk may be a ratio of a number of tickets associated with the candidate pick-up point to a total number of orders associated with the candidate pick-up point over a period of time. For example, the number of orders with the candidate pick-up point as the starting point and the number of received parking violating tickets in a period of time may be obtained, and the parking risk of the candidate pick-up point may be obtained according to the ratio of the number of received parking violating tickets to the number of orders with the candidate pick-up point as the starting point. If the ratio of the number of the parking violating tickets received at the candidate pick-up point to the number of the orders of the candidate pick-up point is larger, namely the probability that the candidate pick-up point obtains the parking violating tickets is larger, the parking violating risk of the candidate pick-up point is larger. If the probability that the candidate boarding point receives the parking violation ticket is small, the candidate boarding point is relatively safe.
The recommendation module 230 may be configured to determine a recommended pick-up point associated with the current order based on the set of safe pick-up points. In some embodiments, the recommending module 230 is further configured to obtain feature information of the candidate pick-up points in the set of safe pick-up points, and determine the recommended pick-up point based on the feature information. In some embodiments, the characteristic information includes one or more of a distance between a candidate pick-up point and the at least one position of the current order, a distance between the candidate pick-up point and the current position of the user, a number of times that the at least one position of the current order is not determined as a recommended pick-up point, a number of times that the at least one position of the current order is determined as a recommended pick-up point, a heat of the at least one position of the current order, a heat of the candidate pick-up point, and the like. In some embodiments, the recommendation module 230 is further configured to obtain a recommendation model based on which the recommended pick-up point is determined. The recommendation model may be a trained machine learning model. In some embodiments, the recommendation module 230 is further configured to input feature information of the candidate pick-up points in the set of safe pick-up points into the recommendation model, determine a ranking of the candidate pick-up points based on the recommendation model, and determine the recommended pick-up points based on the ranking.
The training module 240 may be configured to obtain a vehicle getting-on point in the historical order within a certain time period, extract characteristic information of the vehicle getting-on point, and determine a relevance score of the vehicle getting-on point in the historical order. Training the recommendation model based on the feature information and the relevance score. In some embodiments, the recommendation model may be a Classification And Regression Tree (CART), an iterative binary Tree three generation (ID 3), a C4.5 algorithm, a random forest algorithm, a deep learning model, a Support Vector Machine (SVM), or other machine model. In some embodiments, the recommendation model may be a lamb damard ordering model. For example, the feature information of the boarding point in the historical order can be used as model input, the relevance score can be used as model output, and the lamb damart model is trained to obtain the recommendation model.
It should be understood that the system and its modules shown in FIG. 2 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules 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 modules 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 descriptions of the candidate item display and determination system and the modules thereof are only for convenience of description, and are not intended to limit the present application within 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 acquisition module 210, the safe pick-up point set determination module 220, the recommendation module 230, and the training module 240 disclosed in fig. 2 may be different modules in a system, or may be a module that implements the functions of two or more of the above modules, for example. For example, the safe boarding point set determination module 220 and the recommendation module 230 may be two modules, or one module may have both functions of determining the safe boarding point set and recommending the boarding point. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present application.
FIG. 3 illustrates an exemplary flow chart of a method for recommending pick-up points according to some embodiments of the present application. As shown in FIG. 3, the method 300 of recommending pick-up points may include:
At step 310, a plurality of candidate pick-up points related to the current order may be obtained based on at least one location information of the current order. In some embodiments, step 310 may be performed by acquisition module 210. In some embodiments, the at least one location information of the current order may be a preset boarding location at the time the user sends the service request. For example, the position of the boarding point may be manually added by the user, the position of the user on a map fixed with pins, the current position of the user, the historical boarding point position determined according to the historical order of the user, or the like. In some embodiments, at least one location information of the current order may be determined as a candidate pick-up point, and it may be determined whether the candidate pick-up point is a safe pick-up point. In some embodiments, a plurality of related candidate pick-up points may be determined based on at least one location information of the current order. In some embodiments, a geographic range may be determined based on at least one location information of the current order, and common pick-up locations within the range may be determined as candidate pick-up locations. For example, according to at least one piece of position information of the current order, the boarding point position which is usually used by most users and is within a range of 500 meters around the position is determined as a candidate boarding point. In some embodiments, the pick-up point associated with the current order may be determined as a candidate pick-up point in the historical order based on the at least one location information of the current order. For example, the location is a preset pick-up point in the service request, but in the historical order, the actual pick-up point of the user may be other nearby pick-up points, and the historical pick-up point related to the location may be determined as a candidate pick-up point. In some embodiments, candidate pick-up points may be determined based on the user's personal habits. For example, the boarding points near the location that are more commonly used by the user are determined as candidate boarding points. In some embodiments, the current order may have multiple pick-up locations entered by the user. The server can acquire a plurality of candidate boarding points related to the boarding point according to the boarding point input by each user, and determine a corresponding safe boarding point set so as to further determine a corresponding recommended boarding point. In some embodiments, a plurality of location information for a current order may be obtained, from which a plurality of corresponding recommended pick-up points are determined.
In step 320, it may be determined whether each of the plurality of candidate pick-up points belongs to a safe pick-up point at the current time. If so, step 330 is performed to add the candidate pick-up point to the set of safe pick-up points. In some embodiments, steps 320 and 330 may be performed by the secure pick-up point set determination module 220. In some embodiments, the safe pick-up point may be a pick-up point where the risk of parking is less than a preset threshold. For example, the safe boarding point can be a boarding point with smaller illegal parking risk so as to prevent a driver from taking passengers on an illegal parking road section and facilitate the driver to take orders. In some embodiments, the parking risk may be a ratio of a number of tickets associated with the candidate pick-up point to a total number of orders associated with the candidate pick-up point over a period of time. For example, the number of orders with the candidate pick-up point as the starting point and the number of received parking violating tickets in a period of time may be obtained, and the parking risk of the candidate pick-up point may be obtained according to the ratio of the number of received parking violating tickets to the number of orders with the candidate pick-up point as the starting point. If the ratio of the number of the parking violating tickets received at the candidate pick-up point to the number of the orders of the candidate pick-up point is larger, namely the probability that the candidate pick-up point obtains the parking violating tickets is larger, the parking violating risk of the candidate pick-up point is larger. If the probability that the candidate boarding point receives the parking violation ticket is small, the candidate boarding point is relatively safe. In some embodiments, a safety threshold may be preset, and a pick-up point with a parking risk less than the threshold may be determined as a safe pick-up point. And combining the plurality of safe boarding points into a safe boarding point set. In some embodiments, the threshold may be a fixed value or determined according to a parking risk ranking, for example, the top 20% of the candidate pick-up points in the ranking are the set of safe pick-up points, or the bottom 20% of the candidate pick-up points in the ranking are the set of safe pick-up points.
In some embodiments, information of the parking violation section may be obtained first, when at least one location information of the current order is located in the parking violation section, it is determined whether parking risks of a plurality of candidate boarding points related to the current order at the current time are less than the threshold, and the candidate boarding points with the parking risks less than the threshold are added to the set of safe boarding points. In some embodiments, the illegal road segment information may include one or a combination of illegal road segment information with forbidden marks, road segment information in illegal parking tickets, road segment information shot by illegal electronic eyes, and forbidden road segment information reported by users. In some embodiments, the parking flags may include a parking sign and a parking yellow line. In some embodiments, the road section information with the stop sign and the stop yellow line may be acquired from the driving image by using the driving image captured by the driving recorder. In some embodiments, parking violation ticket information for a traffic system may be obtained from which parking violation road segment information is obtained. In some embodiments, the position information of the electronic eye monitoring for shooting the illegal activities can be acquired, and the road section information shot by the illegal electronic eye can be acquired. In some embodiments, the illegal segment information may include location information of a forbidden location, road name of the forbidden location, direction of the forbidden road, and the like. In some embodiments, it may be determined whether the location information is on a contra road segment based on at least one location information of the current order. For example, it is determined whether the position is on the illegal road segment according to the positioning coordinates of the position. In some embodiments, when at least one position information of the current order is located in the illegal parking road section, determining parking risks of candidate boarding points related to the position information, eliminating candidates with parking risks larger than a safety threshold value, and adding the candidate boarding points with parking risks smaller than the safety threshold value into a safety boarding point set as safety boarding points. For example, according to a service request sent by a user, getting-on-vehicle point position information set by the user in a current service order, and determining a plurality of candidate getting-on-vehicle points according to the getting-on-vehicle point position information. And judging whether the position is on an illegal parking road section according to the position information of the boarding point set by the user, if the boarding point is on the illegal parking road section, calculating illegal parking risks of each candidate boarding point, and taking the candidate boarding points with small illegal parking risks as safe boarding points to form a safe boarding point set, so that the boarding points are recommended for the user according to the safe boarding point set, the boarding points with small illegal parking risks are obtained, and the driver can conveniently carry the user.
In some embodiments, illegal parking section information may be obtained, and when at least one position information of the current order is located on a non-illegal parking section, a plurality of candidate pick-up points related to the current order are added into the safe pick-up point set. For example, the location information of the vehicle-entering point set by the user in the current service order is obtained, whether the location is on an illegal parking road section is judged according to the location information of the vehicle-entering point set by the user, if the vehicle-entering point is not on the illegal parking road section, the vehicle-entering point is safe, and the vehicle-entering point and the candidate vehicle-entering point related to the vehicle-entering point can be directly determined as the safe vehicle-entering point.
In some embodiments, it may be further determined whether each candidate pick-up point belongs to a safe pick-up point based on the current attributes of the candidate pick-up points at the current time. For example, certain road segments may be allowed to stop for a period of time and not allowed to stop for other periods of time. Or parking is allowed during working days and not allowed during non-working days. When judging whether each candidate boarding point belongs to the safe boarding point, a time factor can be added to judge whether a certain candidate boarding point belongs to the safe boarding point at the current moment. In some embodiments, whether a candidate pick-up point is a safe pick-up point at the current moment can be determined according to time information in a historical parking violation ticket in the traffic system, time information in the forbidden road section information or time information in the forbidden information reported by the user. For example, when determining whether a certain pick-up point is safe, it may be determined whether the pick-up point receives a parking violation ticket at the current time, determine a parking violation risk of the certain pick-up point at the current time by combining the current time and the information of the parking violation road section, and determine whether the pick-up point is a safe pick-up point at the current time based on the parking violation risk at the current time. For another example, if the risk of parking violation is high for a candidate pick-up point, but the pick-up point has not received the penalty ticket within the current time slot, or it can be determined that the pick-up point can park within the current time slot according to the time information in the prohibited road segment information, then it can be determined that the risk of parking violation is very small at the current time, and the pick-up point is a safe pick-up point at the current time. In some embodiments, the time of day may be segmented to obtain the risk of parking violation of the candidate pick-up point in each time period, so as to quickly determine whether the candidate pick-up point is a safe pick-up point at a certain time. For example, the entire day may be divided into several time periods: 4 am to 7 am, 8 am to 11 am, 12 am to 14 pm, 14 pm to 20 pm, 21 pm to 3 am. And calculating the illegal parking risk of the boarding point in each time period. The parking risk of the candidate boarding point in each time period can be obtained by specifically calculating the ratio of the quantity of the orders taking the candidate boarding point as the starting point in each corresponding time in the historical orders according to the quantity of the penalty tickets in the historical traffic information in each time period. In some embodiments, the parking risk of the candidate pick-up point at the current moment can also be determined by establishing a model. For example, the parking risk of the candidate boarding point at the current moment is determined by modeling or machine learning modeling through historical data such as road section information and time information in the historical illegal parking ticket, road section information and time information in the forbidden parking road section information, road section information and time information in the historical manual reporting information, and the like. In some embodiments, the model may be updated periodically or aperiodically, further improving the accuracy of the calculations. For example, no parking violation ticket exists at the historical current moment, the parking violation risk of the boarding point at the current moment is very low, but the parking violation ticket of the current time period recently appears, the model can be updated in a mode of updating historical data, the model parameters are optimized, and the accuracy of model calculation is improved.
At step 340, a recommended pick-up point associated with the current order may be determined based on the set of safe pick-up points. In some embodiments, step 340 may be performed by recommendation module 230. In some embodiments, feature information of the candidate pick-up points in the set of safe pick-up points may be obtained. And determining the recommended boarding point based on the characteristic information. For example, the candidate boarding points are ranked according to the feature information, and the candidate boarding point ranked in the top is determined as the recommended boarding point. In some embodiments, the characteristic information of the candidate pick-up point may include one or more of a distance between the candidate pick-up point and the at least one position of the current order, a distance between the candidate pick-up point and the current position of the user, a number of times that the at least one position of the current order is not determined as a recommended pick-up point, a number of times that the at least one position of the current order is determined as a recommended pick-up point, a heat degree of the at least one position of the current order, and a heat degree of the candidate pick-up point. For example, the relevance score of the candidate boarding point is calculated according to characteristic information such as the distance between the boarding point set by the user in the order and the candidate boarding point, the distance between the candidate boarding point and the current position of the user, the number of times that the boarding point is determined as the recommended boarding point, the number of times that the boarding point appears as the recommended boarding point within a certain time, the number of times that the candidate boarding point appears as the recommended boarding point within a certain time, and the like, the ranking of the candidate boarding points is determined, and the closest candidate boarding point is taken as the recommended boarding point. In some embodiments, relevance scores for candidate pick-up points may be calculated by setting different weight values for different feature information. In some embodiments, the score for the candidate pick-up point may be calculated by modeling or by building a function. In some embodiments, the relevance scores of the candidate pick-up points may also be calculated by training a recommendation model in a machine manner, resulting in a ranking of the candidate pick-up points.
FIG. 4 illustrates an exemplary flow chart of a method of training a recommendation model according to some embodiments of the present application. As shown in FIG. 4, the method 400 of training a recommendation model may include:
At step 420, relevance scores for pick-up points in the historical order may be determined. In some embodiments, step 420 may be performed by training module 240. In some embodiments, the relevance score for a pick-up point may be determined based on the characteristic information of the pick-up point. In some embodiments, the relevance score may be a human-made empirically scoring of the relevance of each pick-up point. For example, the A candidate pick-up point is manually scored 5 points, and the B pick-up point is manually scored 3 points. After manually scoring different candidate pick-up points, the relevance score of each candidate pick-up point is stored as the output of model training.
FIG. 5 illustrates an exemplary flow chart of a method for recommending pick-up points according to some embodiments of the present application. As shown in fig. 5, the method 500 of recommending pick-up points may include:
at step 510, feature information of the candidate boarding points in the set of safe boarding points may be input into the recommendation model. In some embodiments, step 510 may be performed by recommendation module 230. In some embodiments, one piece of location information related to the current order may be acquired, a set of safe boarding points may be determined according to a plurality of candidate boarding points related to the location information, feature information of the candidate boarding points in the set of safe boarding points may be acquired, and the feature information of the candidate boarding points may be input into the recommendation model as input data.
At step 520, a ranking of the candidate pick-up points may be determined based on the recommendation model. In some embodiments, step 520 may be performed by recommendation module 230. In some embodiments, the recommendation model may be a trained machine model. In some embodiments, the recommendation model may be trained by the lamb damard sorting model. In some embodiments, the output of the recommendation model may be the ranking of the candidate pick-up points. For example, the ranking of the candidate pick-up points may be determined based on the relevance scores.
FIG. 6 illustrates an exemplary flow chart of a method for recommending pick-up points according to some embodiments of the present application. As shown in fig. 6, the method 600 for recommending pick-up points may include:
in step 610, the user terminal obtains at least one location information associated with the current order. In some embodiments, step 610 may be performed by a terminal acquisition module. In some embodiments, the user may set a boarding location through the user terminal and send an order request. In some embodiments, the user terminal may be a mobile device, such as a smartphone, a Personal Digital Assistant (PDA), a tablet computer, a handheld game console, smart glasses, a smart watch, a wearable device, a virtual display device, a display enhancement device, or the like. In some embodiments, the user terminal may be a client terminal that sends a service request, and may also include a server terminal that receives an order providing service. In some embodiments, the user terminal may send at least one location information related to the current order to the server. In some embodiments, the user terminal may obtain at least one location information in the service order from a server.
And step 620, acquiring a recommended boarding point determined based on at least one piece of position information of the current order. In some embodiments, step 620 may be performed by the receiving module. In some embodiments, the user terminal may obtain the recommended pick-up point determined by the server. In some embodiments, the server may determine the recommended pick-up point according to at least one location information of the current order, and the specific method may refer to the descriptions of the foregoing flow 300 and flow 500.
In some embodiments, the user terminal or the server may obtain information of the illegal parking road section, and when at least one position information of the current order is located on the illegal parking road section, prompt the user that at least one position of the current order has illegal parking risk on the user terminal. For example, the server may send a short message to prompt the driver that at least one location of the current order is at risk of violation.
And 630, outputting the recommended boarding point on the user terminal. In some embodiments, step 630 may be performed by an output module. In some embodiments, the server may send the determined recommended pick-up point to the user terminal where it is displayed to the user. For example, after the server determines the recommended boarding point, at least one piece of location information set by the user may be updated to the recommended boarding point in the current order, so that the user can take a car according to the recommended boarding point, or so that the driver file recommends the boarding point to pick up a passenger.
The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) the method and the device can reduce the probability of punishment caused by illegal parking when the driver loads the customer; (2) according to the technical scheme, a safe boarding point set can be established, and boarding points are recommended for clients according to the safe boarding point set. 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.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present 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" 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.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
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 format, 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, such as a 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.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals 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.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. 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, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (24)
1. A method for recommending pick-up points, the method being performed by at least one processor, the method comprising:
The user terminal acquires at least one piece of position information related to the current order;
acquiring a recommended boarding point determined based on at least one piece of position information of the current order;
outputting the recommended boarding point on a user terminal;
wherein the recommended boarding point is a safe boarding point; and the parking risk value of the safe boarding point is smaller than a preset threshold value.
2. The method of claim 1, wherein the method of determining the set of safe boarding points comprises:
acquiring illegal parking road section information;
when at least one piece of position information of the current order is located in the illegal parking road section, determining whether the parking risk of a plurality of candidate parking points related to the current order at the current moment is smaller than the threshold value, and adding the candidate parking points with the parking risk smaller than the threshold value into a safe parking point set;
and when at least one piece of position information of the current order is located on a non-illegal road section, adding a plurality of candidate boarding points related to the current order into the safe boarding point set.
3. The method of claim 2,
the illegal road section information at least comprises one or more of the following combinations: the illegal parking road information with the forbidden parking marks, the road information in the illegal parking ticket, the road information shot by the illegal parking electronic eye and the forbidden parking road information reported by the user.
4. The method of claim 2,
the parking risk value is a ratio of the number of tickets related to the candidate pick-up point to the total number of orders related to the candidate pick-up point at the historical current moment in time.
5. The method of claim 2, wherein the recommended pick-up point is determined based on a set of safe pick-up points, comprising:
acquiring characteristic information of the candidate boarding points in the safe boarding point set;
and determining the recommended boarding point based on the characteristic information.
6. The method of claim 5, wherein the characteristic information comprises a combination of one or more of:
the distance between the candidate getting-on point and the at least one position of the current order, the distance between the candidate getting-on point and the current position of the user, the number of times that the at least one position of the current order is not determined as the recommended getting-on point, the number of times that the at least one position of the current order is determined as the recommended getting-on point, the heat of the at least one position of the current order and the heat of the candidate getting-on point.
7. The method of claim 5, wherein said determining the recommended pick-up point based on the characteristic information comprises:
Acquiring a recommendation model;
determining the recommended boarding point based on the recommendation model.
8. The method of claim 7, wherein the obtaining a recommendation model comprises:
obtaining a boarding point in a historical order within a certain time period, and extracting characteristic information of the boarding point;
determining relevance scores of the boarding points in the historical orders;
training the recommendation model based on the feature information and the relevance score.
9. The method of claim 7, wherein said determining the recommended pick-up point based on the recommendation model comprises:
inputting the characteristic information of the candidate boarding points in the safe boarding point set into the recommendation model;
determining a ranking of the candidate pick-up points based on the recommendation model;
determining the recommended pick-up point based on the ranking.
10. The method of claim 7,
the recommendation model is a Lambdamcast sorting model.
11. The method of claim 1, further comprising:
acquiring illegal parking road section information;
when the at least one position information of the current order is located on the illegal road section, prompting a user that at least one position of the current order has illegal parking risk on a user terminal;
The illegal road section information at least comprises one or more of the following combinations: the illegal parking road information with the forbidden parking marks, the road information in the illegal parking ticket, the road information shot by the illegal parking electronic eye and the forbidden parking road information reported by the user.
12. A system for recommending pick-up points, comprising:
the terminal acquisition module is used for acquiring at least one piece of position information of the current order;
the receiving module is used for acquiring a recommended boarding point determined based on at least one piece of position information of the current order;
the output module is used for outputting the recommended boarding points;
wherein the recommended boarding point is determined based on a set of safe boarding points; the safe boarding point set is a boarding point set of which the parking risk value is smaller than a preset threshold value.
13. The system of claim 12, further comprising a secure pick-up point set determination module to:
acquiring illegal parking road section information;
when at least one piece of position information of the current order is located in the illegal parking road section, determining whether the parking risk of a plurality of candidate parking points related to the current order at the current moment is smaller than the threshold value, and adding the candidate parking points with the parking risk smaller than the threshold value into a safe parking point set;
And when at least one piece of position information of the current order is located on a non-illegal road section, adding a plurality of candidate boarding points related to the current order into the safe boarding point set.
14. The system of claim 13,
the illegal road section information at least comprises one or more of the following combinations: the illegal parking road information with the forbidden parking marks, the road information in the illegal parking ticket, the road information shot by the illegal parking electronic eye and the forbidden parking road information reported by the user.
15. The system of claim 13,
the parking risk value is a ratio of the number of tickets related to the candidate pick-up point to the total number of orders related to the candidate pick-up point at the historical current moment in time.
16. The system of claim 13, wherein the recommendation module is further to:
acquiring characteristic information of the candidate boarding points in the safe boarding point set;
and determining the recommended boarding point based on the characteristic information.
17. The system of claim 16, wherein the characteristic information comprises a combination of one or more of:
the distance between the candidate getting-on point and the at least one position of the current order, the distance between the candidate getting-on point and the current position of the user, the number of times that the at least one position of the current order is not determined as the recommended getting-on point, the number of times that the at least one position of the current order is determined as the recommended getting-on point, the heat of the at least one position of the current order and the heat of the candidate getting-on point.
18. The system of claim 16, wherein the recommendation module is further to:
acquiring a recommendation model;
determining the recommended boarding point based on the recommendation model.
19. The system of claim 18, further comprising a training module to:
obtaining a boarding point in a historical order within a certain time period, and extracting characteristic information of the boarding point;
determining relevance scores of the boarding points in the historical orders;
training the recommendation model based on the feature information and the relevance score.
20. The system of claim 18, wherein the recommendation module is further to:
inputting the characteristic information of the candidate boarding points in the safe boarding point set into the recommendation model;
determining a ranking of the candidate pick-up points based on the recommendation model;
determining the recommended pick-up point based on the ranking.
21. The system of claim 18,
the recommendation model is a Lambdamcast sorting model.
22. The system of claim 12,
the output module is further used for acquiring illegal road section information, and when at least one position information of the current order is located on an illegal road section, prompting a user that at least one position of the current order has illegal risks.
23. An apparatus for recommending pick-up points, comprising at least one storage medium and at least one processor;
the at least one storage medium is configured to store computer instructions;
the at least one processor is configured to execute the computer instructions to implement the recommended pick-up method of any of claims 1-11.
24. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the recommended pick-up method of any of claims 1-11.
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| PCT/CN2020/122966 WO2021078216A1 (en) | 2019-10-23 | 2020-10-22 | Pick-up point recommendation method and system |
| US17/660,408 US20220248170A1 (en) | 2019-10-23 | 2022-04-23 | Methods and systems for recommending pick-up points |
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| CN201911012890.7A CN111861618A (en) | 2019-10-23 | 2019-10-23 | Boarding point recommendation method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112652192A (en) * | 2021-01-04 | 2021-04-13 | 北京嘀嘀无限科技发展有限公司 | Method, apparatus, device, medium and program product for determining a restricted parking position |
| CN112650876A (en) * | 2020-12-30 | 2021-04-13 | 北京嘀嘀无限科技发展有限公司 | Image processing method, image processing apparatus, electronic device, storage medium, and program product |
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| CN112991727A (en) * | 2021-02-22 | 2021-06-18 | 北京嘀嘀无限科技发展有限公司 | Target position determining method and device |
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