CN113822709A

CN113822709A - Travel data processing method and device and server

Info

Publication number: CN113822709A
Application number: CN202111080121.8A
Authority: CN
Inventors: 朱俊辉; 宋惠玉
Original assignee: Mobai Beijing Information Technology Co Ltd
Current assignee: Mobai Beijing Information Technology Co Ltd
Priority date: 2021-09-15
Filing date: 2021-09-15
Publication date: 2021-12-21

Abstract

The disclosure provides a travel data processing method, a travel data processing device and a server, wherein the method comprises the following steps: acquiring travel data of a target user using a shared vehicle in a historical statistical time period; generating a characteristic vector representing the travel rule of the target user using the shared vehicle in the historical statistical time period according to the travel data; obtaining a predicted place of departure of the target user for using the shared vehicle according to the feature vector and a preset machine learning model; and providing the use service of the shared vehicle to the target user according to the predicted departure place.

Description

Travel data processing method and device and server

Technical Field

The present disclosure relates to the technical field of data processing, and more particularly, to a method and an apparatus for processing trip data, and a server.

Background

At present, the trip through the shared vehicle has become emerging trip mode in the city, can effectively solve city crowd's trip demand to green.

In order to provide better shared vehicle usage services to users, it is often necessary to predict the origin of the user's next use of the shared vehicle.

In the prior art, travel data of a user in a historical statistical time period are generally counted, and a starting point with the largest occurrence frequency in the historical statistical time period is used as a starting point for the user to use a shared vehicle next time.

However, the accuracy of the predicted departure place obtained by the prediction method is low, so that the use service of the shared vehicle cannot be accurately provided for the user, and the user experience is poor.

Disclosure of Invention

An object of the present disclosure is to provide a new technical solution for processing trip data.

According to a first aspect of the present disclosure, a travel data processing method is provided, including:

acquiring travel data of a target user using a shared vehicle in a historical statistical time period;

generating a characteristic vector representing the travel rule of the target user using the shared vehicle in the historical statistical time period according to the travel data;

obtaining a predicted place of departure of the target user for using the shared vehicle according to the feature vector and a preset machine learning model;

and providing the use service of the shared vehicle to the target user according to the predicted departure place.

Optionally, the travel data includes at least one pair of matched latitude and longitude data, the pair of matched latitude data represents a mark point of the shared vehicle used by the target user, and the mark point is a start point or an end point of a corresponding use process;

generating a feature vector representing a travel rule of the target user using the shared vehicle according to the travel data comprises:

generating a time series of the latitude and longitude data;

supplementing the latitude and longitude data lacking in the time sequence;

and generating the feature vector according to the time sequence.

Optionally, the generating the feature vector according to the time series includes:

based on a preset coding algorithm, coding the longitude and latitude data matched in the time sequence to obtain the travel characteristics of the corresponding mark points;

and obtaining the feature vector according to the travel features.

Optionally, the encoding algorithm is a hash encoding algorithm.

Optionally, the providing the service of using the shared vehicle to the target user according to the predicted departure place includes:

acquiring a heat parameter representing the demand heat of the shared vehicle at the prediction departure place;

and providing the target user with a certificate for using the shared vehicle according to the heat parameter.

Optionally, the method further includes:

obtaining historical purchase data of the voucher;

and determining the target user according to the historical purchase data.

determining the number of target users corresponding to any one prediction starting place;

and scheduling the shared vehicles of any one predicted departure place according to the number.

Optionally, the machine learning model is a BERT model.

According to a second aspect of the present disclosure, there is provided a trip data processing apparatus, including:

the data acquisition module is used for acquiring travel data of the target user using the shared vehicle in a historical statistical time period;

the vector generation module is used for generating a characteristic vector representing the travel rule of the target user using the shared vehicle in the historical statistic time period according to the travel data;

the origin prediction module is used for obtaining a predicted origin of the target user using the shared vehicle according to the feature vector and a preset machine learning model;

and the service providing module is used for providing the service of using the shared vehicle for the target user according to the predicted departure place.

According to a third aspect of the present disclosure, there is provided a server comprising a memory for storing an executable computer program and a processor; the processor is configured to run the server under control of the computer program to perform the method according to the first aspect of the disclosure.

According to the method, the characteristic vector representing the travel rule of the target user using the shared vehicle in the historical statistical time period is generated according to the travel data of the target user using the shared vehicle in the historical statistical time period, the predicted departure place of the target user using the shared vehicle is predicted according to the characteristic vector and the preset machine learning model, and the use service of the shared vehicle is provided for the target user according to the predicted departure place, so that the obtained predicted departure place can be more accurate, the use service of the shared vehicle can be better provided for the target user, and the vehicle use experience of the target user is improved.

Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.

Fig. 1 is a block diagram showing an example of a hardware configuration of a vehicle system that can be used to implement an embodiment of the present disclosure.

Fig. 2 shows a flowchart of a travel data processing method according to an embodiment of the present disclosure.

Fig. 3 shows a block schematic diagram of a trip data processing apparatus according to an embodiment of the present disclosure.

Fig. 4 shows a schematic block diagram of a server of an embodiment of the present disclosure.

Detailed Description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

< hardware configuration >

As shown in fig. 1, the vehicle system 100 includes a server 1000, a mobile terminal 2000, a vehicle 3000, and a network 4000.

The server 1000 provides a service point for processes, databases, and communications facilities. The server 1000 may be a unitary server or a distributed server across multiple computers or computer data centers. The server may be of various types, such as, but not limited to, a web server, a news server, a mail server, a message server, an advertisement server, a file server, an application server, an interaction server, a database server, or a proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported or implemented by the server. For example, a server, such as a blade server, a cloud server, etc., or may be a server group consisting of a plurality of servers, which may include one or more of the above types of servers, etc.

In one example, the server 1000 may be as shown in fig. 1, including a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600. Although the server may also include speakers, microphones, and the like, these components are reasonably irrelevant to the present disclosure and are omitted here.

The processor 1100 may be, for example, a central processing unit CPU, a microprocessor MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, an infrared interface, and the like. Communication device 1400 is capable of wired or wireless communication, for example. The display device 1150 is, for example, a liquid crystal display panel, an LED display panel touch display panel, or the like. Input devices 1160 may include, for example, a touch screen, a keyboard, and the like.

In the present embodiment, the mobile terminal 2000 is a server having a communication function and a service processing function. The mobile terminal 2000 may be a mobile terminal such as a mobile phone, a laptop, a tablet, a palmtop, etc. In one example, the mobile terminal 2000 is a device that performs management operations on the vehicle 3000, for example, a mobile phone installed with an Application (APP) that supports operation and management of the vehicle.

As shown in fig. 1, the mobile terminal 2000 may include a processor 2100, a memory 2200, an interface device 2300, a communication device 2400, a display device 2500, an input device 2600, a speaker 2700, a microphone 2800, and the like. The processor 2100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 2200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 2300 includes, for example, a USB interface, a headphone interface, and the like. Communication device 2400 is capable of wired or wireless communication, for example. The display device 2500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 2600 may include, for example, a touch screen, a keyboard, and the like. A user can input/output voice information through the speaker 2700 and the microphone 2800.

The vehicle 3000 is any vehicle that can give the right to share the use by different users in time or separately, for example, a shared bicycle, a shared moped, a shared electric vehicle, a shared vehicle, and the like. The vehicle 3000 may be a bicycle, a tricycle, an electric scooter, a motorcycle, a four-wheeled passenger vehicle, or the like.

As shown in fig. 1, vehicle 3000 may include a processor 3100, a memory 3200, an interface device 3300, a communication device 3400, a display device 3500, an input device 3600, a positioning device 3700, a bluetooth broadcast device 3800, and so forth. The processor 3100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 3200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface 3300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 3400 can perform wired or wireless communication, for example. The output device 3500 may be, for example, a device that outputs a signal, may be a display device such as a liquid crystal display screen or a touch panel, or may be a speaker or the like that outputs voice information or the like. The input device 3600 may include, for example, a touch panel, a keyboard, or the like, and may input voice information through a microphone. The positioning device 3700 is used to provide positioning function, and may be, for example, a GPS positioning module, a beidou positioning module, etc. The bluetooth broadcasting device 3800 is for broadcasting a packet containing own vehicle information by bluetooth.

The network 4000 may be a wireless communication network or a wired communication network, and may be a local area network or a wide area network. In the article management system shown in fig. 1, a vehicle 3000 and a server 1000, and a mobile terminal 2000 and the server 1000 can communicate with each other via a network 4000. The vehicle 3000 may be the same as the server 1000, and the network 4000 through which the mobile terminal 2000 communicates with the server 1000 may be different from each other.

It should be understood that although fig. 1 shows only one server 1000, mobile terminal 2000, vehicle 3000, it is not meant to limit the corresponding number, and multiple servers 1000, mobile terminals 2000, vehicles 3000 may be included in the vehicle system 100.

Taking the vehicle 3000 as an example of a shared bicycle, the vehicle system 100 is a shared bicycle system. The server 1000 is used to provide all the functionality necessary to support shared bicycle use. The mobile terminal 2000 may be a mobile phone on which a shared bicycle application is installed, and the shared bicycle application may help a user to acquire a corresponding function using the vehicle 3000, and the like.

The vehicle system 100 shown in fig. 1 is merely illustrative and is in no way intended to limit the present disclosure, its application, or uses.

Although fig. 1 shows only one server 1000, one mobile terminal 2000 and one vehicle 3000, it should be understood that, in a specific application, the vehicle system 100 may include a plurality of servers 1000, a plurality of mobile terminals 2000 and a plurality of vehicles 3000 according to actual requirements.

In an embodiment of the present disclosure, the memory 1200 of the server 1000 is configured to store instructions for controlling the processor 1100 to operate to execute a travel data processing method provided by an embodiment of the present disclosure.

Although a number of devices are shown for server 1000 in fig. 1, the present disclosure may refer to only some of the devices, for example, server 1000 refers to only memory 1200 and processor 1100.

In an embodiment of the present disclosure, the memory 2200 of the mobile terminal 2000 is configured to store instructions, which are used to control the processor 2100 to operate the mobile terminal 2000 to execute the travel data processing method provided in the embodiment of the present disclosure.

Although a number of devices are shown in fig. 1 for mobile terminal 2000, this disclosure may refer to only some of the devices, for example, mobile terminal 2000 may refer to only memory 2200 and processor 2100.

In the above description, the skilled person can design the instructions according to the disclosed solution of the present disclosure. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.

< method examples >

In the method for processing trip data provided in this embodiment, the method for processing trip data is implemented by a server, and the server may be in various entity forms. For example, the server may be a server, and in particular may be the server 1000 shown in FIG. 1. In one example, the server is an operation center that supports providing services for vehicle operation, management, scheduling, and the like.

As shown in fig. 2, the trip data processing method includes steps S2100 to S2400.

In step S2100, trip data of the target user using the shared vehicle in the historical statistical time period is acquired.

The historical statistical period may be set in advance according to an application scenario or specific requirements, for example, the historical statistical period may be the past 180 days.

The shared vehicle in this embodiment is a transportation device that is released for a user to obtain a use right in modes of time-sharing lease, local lease, and the like, and may be a two-wheeled or three-wheeled bicycle, a power-assisted vehicle, an electric vehicle, or a motor vehicle with four or more wheels.

In this embodiment, the travel data may indicate a marked point of the shared vehicle, which may be a start point or an end point, used by the target user each time within the historical statistical period. Specifically, the travel data may include at least one pair of matched longitude and latitude data, that is, a pair of matched longitude data and latitude data, which represents a position of one marker.

In an embodiment of the present disclosure, the sharing vehicle may be provided with a positioning device, and the positioning device collects position information of the sharing vehicle and reports the position information to the server under the condition that the sharing vehicle is unlocked or locked each time, so that the server associates a start point and an end point of the sharing vehicle in the current use process with a user using the sharing vehicle, so as to obtain travel data of the target user through step S2100.

In another embodiment of the present disclosure, a positioning device is disposed on a mobile terminal of a target user for using a shared vehicle, and when the target user sends a request for using the shared vehicle to the shared vehicle or a server through the mobile terminal, and when the target user sends a request for ending using the shared vehicle to the shared vehicle or the server through the mobile terminal, the positioning device collects position information of the target user and reports the position information to the server, so that the server associates a start point and an end point of the shared vehicle in the current use process with a user using the shared vehicle, so as to obtain travel data of the target user through step S2100.

Step S2200 is that according to the travel data, a characteristic vector representing the travel rule of the shared vehicle used by the target user in the historical statistical time period is generated.

In this embodiment, the feature vector may represent a travel rule of the target user using the shared vehicle in the historical statistical period.

In a typical case, the user may be using the sharing vehicle twice a day, for example, the first time may be using the sharing vehicle from a first location to a second location, and the second time may be using the sharing vehicle from the second location to the first location. Wherein the first location may be a user residence and the second location may be a nearest subway station to the user residence; alternatively, the first location may be the nearest subway station to the user company and the second location may be the user company; alternatively, the first location may be a user's residence and the second location may be a user's company.

In one example, the feature vector may represent a starting point and an ending point of the target user's twice daily use of the shared vehicle within a historical statistical period.

In one embodiment of the present disclosure, generating a feature vector characterizing a law that a target user uses a shared vehicle within a historical statistical period according to travel data may include steps S2210 to S2230 as follows:

step S2210, a time series of latitude and longitude data is generated.

In this embodiment, the time sequence of the longitude and latitude data may be generated according to the time of acquiring the longitude and latitude data and the time sequence.

Step S2220, the latitude and longitude data lacking in the time sequence are supplemented.

In the present embodiment, by supplementing the latitude and longitude data missing in the time series, the latitude and longitude data of the start point and the end point of the shared vehicle used by the target user twice a day in the historical statistical period may be included in the time series. If the target user uses the shared vehicle only once in any day or does not use the shared vehicle, the latitude and longitude data corresponding to the starting point and the ending point of the use process can be supplemented into the set latitude and longitude data. The set latitude and longitude data may be preset according to an application scenario or specific requirements, for example, the set latitude and longitude data may be 0, 0.

In step S2230, a feature vector is generated from the time series.

In one embodiment of the present disclosure, generating the feature vector according to the time series may include steps S2231 to S2232 as follows:

and S2231, based on a preset coding algorithm, coding the longitude and latitude data matched in the time sequence to obtain the travel characteristics of the corresponding mark points.

In this embodiment, the preset encoding algorithm may be preset according to an application scenario or specific requirements. For example, the encoding algorithm may be a hash encoding algorithm.

Based on the hash algorithm, each pair of matched longitude and latitude data can be coded to obtain the travel characteristics of the mark points corresponding to the pair of matched longitude and latitude data.

And step S2232, obtaining a feature vector according to the travel features.

In this embodiment, the feature vector may be generated according to the travel features obtained by encoding each pair of matched longitude and latitude data in the time sequence.

And step S2300, obtaining a predicted departure place of the target user using the shared vehicle according to the feature vector and a preset machine learning model.

The preset machine learning model in this embodiment may be a pre-trained model, and the starting point of the shared vehicle used by the target user for the first time in the next day, that is, the starting point of the predicted departure place, may be predicted according to the feature vector representing the travel rule of the shared vehicle used by the target user in the historical statistical time period.

In one example, the machine learning model may be a BERT model. And predicting the predicted departure place of the target user using the shared vehicle by using the BERT model, so that the final prediction result is more accurate.

In an embodiment of the present disclosure, the feature vector obtained in step S2200 may be input into a BERT model, and an output result of the BERT model is a predicted departure place of the target user using the shared vehicle.

Step S2400 is performed to provide the target user with a service for sharing the vehicle based on the predicted departure point.

In one embodiment of the present disclosure, providing the usage service of the shared vehicle to the target user according to the predicted departure place may include steps S2411 to S2412 as follows:

step S2411, a heat degree parameter indicating the required heat degree of the shared vehicle at the predicted departure place is acquired.

In the present embodiment, the preset area may be divided into a plurality of area units in advance, and the heat parameter of each area unit may be set according to the number of orders generated for using the shared vehicle in each area unit. When the predicted departure point is obtained, the area cell to which the predicted departure point belongs may be specified, and the heat parameter of the area cell to which the predicted departure point belongs may be used as the heat parameter of the predicted departure point.

The preset area is an area where shared vehicle use demands exist, and can be set according to actual vehicle use demands, for example, a certain city or a certain administrative district of a certain city, and the like.

In this embodiment, the preset area may be divided according to a preset division rule to obtain a plurality of corresponding area units, and each area unit has a corresponding geographic location.

For example, the geographical area shape and area of each area unit may be set in advance. Specifically, the area unit is set to be a square geographic area with a preset side length, the preset side length may be set according to specific requirements, for example, the preset side length is 10 meters, the corresponding area unit is a square geographic area with 10 meters by 10 meters, correspondingly, the preset area may be divided into grids along the transverse direction and the longitudinal direction, each grid is an area unit with 10 meters by 10 meters, and the geographic position of the area unit may be geographic coordinate information of the central position of the geographic area, for example, latitude and longitude information of the central position.

Step S2412, providing the target user with a certificate for using the shared vehicle according to the popularity parameter.

In this embodiment, a plurality of certificates for using the shared vehicle may be set in advance, and the purchase price of each certificate may be different. According to the popularity parameter, the target user can be provided with a certificate of using the shared vehicle with a corresponding price for the user to purchase.

In the case where the predicted origin of the target user is different, the price for providing the target user with the credential to use the shared vehicle may also be different.

On the basis of the embodiment, the method may further include a step of acquiring the target user, including:

obtaining historical purchase data using credentials of the shared vehicle; a target user is determined based on the historical purchase data.

The historical purchase data in the present embodiment may be historical purchase data of a plurality of users registered to use the shared vehicle and purchasing a certificate using the shared vehicle in another historical statistical period.

The other historical statistic time period may be the same as or different from the historical statistic time period in the foregoing embodiment, and is not limited herein.

In one example, the target user may be a user who is determined to purchase the credential using the shared vehicle more than a preset threshold number of times according to the historical purchase data. The number threshold may be set in advance according to an application scenario or a specific requirement, and for example, the number threshold may be 5.

In another example, the target user may be a user who is determined to purchase a credential using the shared vehicle in an amount exceeding a preset amount threshold based on historical purchase data. The amount threshold may be preset according to application scenarios or specific requirements, and may be 50 yuan, for example.

By determining the target user through the method of the embodiment, the purchase rate of the certificate using the shared vehicle can be improved.

In another embodiment of the present disclosure, the number of target users may be multiple, and the predicted starting place of different target users may be the same or different. Then, providing the usage service of the shared vehicle to the target user according to the predicted departure place may include steps S2421 to S2422 as follows:

step S2421, determining the number of target users corresponding to any predicted departure place.

And step S2422, dispatching the shared vehicles of the predicted departure place according to the number.

In this embodiment, the number of target users corresponding to the predicted departure place may be compared with the number of shared vehicles currently parked at the predicted departure place, and when the number of target users corresponding to the predicted departure place is greater than the number of shared vehicles currently parked at the predicted departure place, the shared vehicles may be called from other areas to the predicted departure place, so as to meet the user demand for using the shared vehicles at the predicted departure place, and avoid the situation that no vehicles are available.

< apparatus embodiment >

Corresponding to the above method, the present specification also provides a trip data processing device 3000. As shown in fig. 3, the travel data processing apparatus 3000 may include a data acquiring module 3100, a vector generating module 3200, a place of departure predicting module 3300, and a service providing module 3400. The data acquisition module 3100 is configured to acquire travel data of a target user using a shared vehicle within a historical statistics period; the vector generation module 3200 is used for generating a feature vector representing a travel rule of a target user using a shared vehicle in a historical statistical time period according to travel data; the departure place prediction module 3300 is configured to obtain a predicted departure place where the target user uses the shared vehicle according to the feature vector and a preset machine learning model; the service providing module 3400 is configured to provide a service of using the shared vehicle to the target user according to the predicted departure place.

In one embodiment of the present disclosure, the travel data includes at least one pair of matched latitude and longitude data, the pair of matched latitude and longitude data represents a mark point of the target user using the shared vehicle, and the mark point is a start point or an end point of a corresponding use process;

the vector generation module 3200 may specifically be configured to:

generating a time sequence of latitude and longitude data;

supplementing the latitude and longitude data lacking in the time sequence;

feature vectors are generated from the time series.

In one embodiment of the present disclosure, generating the feature vector from the time series includes:

and obtaining a feature vector according to the travel features.

In one embodiment of the present disclosure, the encoding algorithm is a hash encoding algorithm.

In an embodiment of the disclosure, the service providing module 3400 may specifically be configured to:

In an embodiment of the present disclosure, the processing device 3000 for trip data may further include:

means for obtaining historical purchase data for the credential;

means for determining a target user based on historical purchase data.

In one embodiment of the present disclosure, the service providing module 3400 may be further configured to:

and scheduling the shared vehicles of any predicted departure place according to the quantity.

In one embodiment of the present disclosure, the machine learning model is a BERT model.

It will be appreciated by those skilled in the art that the processing means 3000 of travel data may be implemented in various ways. For example, the processing device 3000 of travel data may be implemented by instructing a configuration processor. For example, the instructions may be stored in ROM and read from ROM into a programmable device when the device is started up to implement the processing means 3000 of travel data. For example, the trip data processing apparatus 3000 may be incorporated into a dedicated device (e.g., ASIC). The processing means 3000 for trip data may be divided into units independent of each other, or they may be combined together for implementation. The processing means 3000 for trip data may be implemented by one of the various implementations described above, or may be implemented by a combination of two or more of the various implementations described above.

In this embodiment, the processing device 3000 for trip data may have various implementation forms, for example, the processing device 3000 for trip data may be any functional module running in a software product or an application providing a processing service for trip data, or a peripheral insert, a plug-in, a patch, etc. of the software product or the application, or the software product or the application itself.

< Server embodiment >

In this embodiment, a server 4000 is also provided, as shown in fig. 4, including a memory 4100 and a processor 4200.

The memory 4100 for storing executable instructions; the processor 4200 is configured to control the operation server 4000 according to the instructions to execute the trip data processing method provided in this embodiment.

According to the server, the characteristic vector representing the travel rule of the target user using the shared vehicle in the historical statistical time period is generated according to the travel data of the target user using the shared vehicle in the historical statistical time period, the predicted departure place of the target user using the shared vehicle is predicted according to the characteristic vector and the preset machine learning model, the use service of the shared vehicle is provided for the target user according to the predicted departure place, the obtained predicted departure place can be more accurate, the use service of the shared vehicle can be better provided for the target user, and the vehicle use experience of the target user is improved.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions 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 case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including 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 using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.

Claims

1. A travel data processing method comprises the following steps:

2. The method of claim 1, wherein the travel data comprises at least one pair of matched latitude and longitude data, the pair of matched latitude and longitude data represents a mark point of the target user for using the shared vehicle, and the mark point is a starting point or an end point of a corresponding use process;

generating a time series of the latitude and longitude data;

supplementing the latitude and longitude data lacking in the time sequence;

and generating the feature vector according to the time sequence.

3. The method of claim 2, the generating the feature vector from the time series comprising:

and obtaining the feature vector according to the travel features.

4. The method of claim 3, wherein the encoding algorithm is a hash encoding algorithm.

5. The method of claim 1, the providing the usage service of the shared vehicle to the target user according to the predicted origin comprising:

6. The method of claim 5, further comprising:

obtaining historical purchase data of the voucher;

and determining the target user according to the historical purchase data.

7. The method of claim 1, the providing the usage service of the shared vehicle to the target user according to the predicted origin comprising:

8. The method of claim 1, the machine learning model being a BERT model.

9. A travel data processing apparatus comprising:

10. A server comprising a processor and a memory for storing an executable computer program; the processor is configured to run the server to perform the method according to the control of the computer program as claimed in any one of claims 1 to 8.