WO2019128477A1 - Systèmes et procédés d'attribution de demandes de service - Google Patents

Systèmes et procédés d'attribution de demandes de service Download PDF

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Publication number
WO2019128477A1
WO2019128477A1 PCT/CN2018/114239 CN2018114239W WO2019128477A1 WO 2019128477 A1 WO2019128477 A1 WO 2019128477A1 CN 2018114239 W CN2018114239 W CN 2018114239W WO 2019128477 A1 WO2019128477 A1 WO 2019128477A1
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WIPO (PCT)
Prior art keywords
service
period
service provider
request
service request
Prior art date
Application number
PCT/CN2018/114239
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English (en)
Inventor
Junqin LI
Zhe XU
Qingwen GUAN
Chunyang Liu
Dingshui ZHANG
Original Assignee
Beijing Didi Infinity Technology And Development Co., Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Priority claimed from CN201711461004.XA external-priority patent/CN109978193A/zh
Priority claimed from CN201711475425.8A external-priority patent/CN109993328B/zh
Application filed by Beijing Didi Infinity Technology And Development Co., Ltd. filed Critical Beijing Didi Infinity Technology And Development Co., Ltd.
Publication of WO2019128477A1 publication Critical patent/WO2019128477A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/02—Reservations, e.g. for tickets, services or events
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Definitions

  • the present disclosure generally relates to systems and methods for Online-to-Offline services, and in particular, to systems and methods for assigning service requests to service providers.
  • Online-to-Offline services utilizing Internet technology have become increasingly popular.
  • the distance e.g., a pick-up distance
  • the service request may be assigned to a service provider who is the closet to the start location of the service request.
  • this assignment strategy cannot ensure the relatively high-profit value of the service provider.
  • a service provider may correspond to a specific request type (e.g., a special car service request, an ordinary car service request) .
  • the service provider may need to wait for a relatively long time to receive a service request of the specific request type, which may influence the profit value of the service provider. Therefore, it is desirable to provide systems and methods for assigning service requests to service providers efficiently and improving the profit value of the service provider accordingly.
  • An aspect of the present disclosure relates to a system configured to provide Online-to-Offline services.
  • the system may include at least one storage device including a set of instructions and at least one processor in communication with the at least one storage device. When executing the set of instructions, the at least one processor may be configured to cause the system to perform one or more of the following operations.
  • the system may obtain provider information associated with a service provider.
  • the provider information may include at least one of a location of the service provider or a target request type, wherein the location of the service provider may be determined according to Global Positioning System (GPS) data transmitted by a user terminal associated with the service provider.
  • GPS Global Positioning System
  • the system may estimate an assignment parameter associated with the service provider based on the provider information.
  • the system may transmit, via a network, data associated with a service request to the user terminal associated with the service provider based on the assignment parameter, wherein the user terminal, in response to receiving the data associated with the service request, may display at least portion of the received data associated with the service request in a graphic user interface.
  • the system may determine at least one service request based on the location of the service provider, wherein a first distance between a start location of each of the at least one service request and the location of the service provider is less than a distance threshold.
  • the system may estimate a profit value for the each of the at least one service request if the service provider completes the each of the at least one service request.
  • the system may estimate a travel cost of the service provider traveling from the location of the service provider to the start location of the each of the at least one service request based on the first distance.
  • the system may determine a second distance between the start location of the each of the at least one service request and a destination of the each of the at least one service request.
  • the system may estimate a service fee associated with the each of the at least one service request based on the second distance.
  • the system may estimate the profit value for the each of the at least one service request based on the travel cost and the service fee.
  • the system may estimate a travel period of the service provider traveling from the location of the service provider to the start location of the each of the at least one service request.
  • the system may estimate a service period of the service provider traveling from the start location to the destination.
  • the system may estimate a completion time point when the service provider arrives at the destination based on the travel period and the service period.
  • the system may predict a ratio of a number count of available service providers to a number count of candidate service requests within a predetermined distance range of the destination at the completion time point based on a trained prediction model.
  • the system may estimate a waiting period from the completion time point to a time point when a next service request is assigned to the service provider based on the ratio.
  • the system may determine a first unit profit value corresponding to the each of the at least one service request based on the profit value, the travel period, the service period, and the waiting period.
  • the system may assign a first weighting coefficient to the travel period.
  • the system may assign a second weighting coefficient to the service period.
  • the system may assign a third weighting coefficient to the waiting period, wherein the first weighting coefficient is larger than the second weighting coefficient or the third weighting coefficient.
  • the system may determine a total period based on the travel period, the service period, the waiting period, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient.
  • the system may determine the first unit profit value based on the profit value and the total period.
  • the trained prediction model may be trained based on a prediction model training process.
  • the prediction model training process may include determining a plurality of training samples, each of the plurality of training samples including a number count of historical available service providers and a number count of historical service requests to be assigned; obtaining a predetermined preliminary prediction model; and training the predetermined preliminary prediction model based on the plurality of training samples according to a machine learning algorithm.
  • the system may determine whether a service request of the target request type is not assigned to the service provider within a predetermined waiting period. According to a result of the determination that within the predetermined waiting period, a service request of the target request type is not assigned to the service provider, the system may predict an assignment status associated with the target request type at an end of the predetermined waiting period, determine whether to generate an extension period based on the predicted assignment status, and determine at least one service request of a candidate request type based on a result of the determination of not generating the extension period.
  • the assignment status may include a first probability that a service request of the target request type is assigned to the service provider at an end of a first predetermined period.
  • the system may predict the first probability that a service request of the target request type is assigned to the service provider at the end of the first predetermined period based on a first trained prediction model.
  • the system may determine whether the first probability is greater than a first probability threshold.
  • the system may generate the extension period based on a result of the determination that the first probability is greater than the first probability threshold.
  • the first trained prediction model may be trained based on a first training process.
  • the first training process may include obtaining a plurality of first positive samples and a plurality of first negative samples, wherein in each of the plurality of first positive samples, a historical service request of the target request type was assigned to a historical service provider at an end of the first predetermined period, and in each of the plurality of first negative samples, a historical service request of the target request type was not assigned to a historical service provider at the end of the first predetermined period; obtaining a first preliminary prediction model; and training the first preliminary prediction model based on the plurality of first positive samples and the plurality of first negative samples according to a machine learning algorithm.
  • the assignment status may include a first period, at an end of which a second probability that a service request of the target request type is assigned to the service provider reaches a second probability threshold.
  • the system may predict the first period, at the end of which the second probability that a service request of the target request type is assigned to the service provider reaches a second probability threshold based on a second trained prediction model.
  • the system may determine whether the first period is less than a period threshold.
  • the system may generate the extension period based on a result of the determination that the first period is less than the period threshold.
  • the second trained prediction model may be trained based on a second training process.
  • the second training process may include obtaining a plurality of second samples, wherein each of the plurality of second samples includes a historical period, at an end of which a historical service request of the target request type was assigned to a historical service provider; obtaining a second preliminary prediction model; and training the second preliminary prediction model based on the plurality of second samples according to a machine learning algorithm.
  • the system may estimate a second unit profit value associated with the service provider under an assumption that the extension period is generated and a service request of the target request type is assigned to the service provider at an end of the extension period.
  • the system may estimate a third unit profit value associated with the service provider under an assumption that the extension period is not generated and a service request of the candidate type is assigned to the service provider.
  • the system may determine whether the second unit profit value is greater than the third unit profit value.
  • the system may determine to generate the extension period based on a result of the determination that the second unit profit value is greater than the third unit profit value.
  • the system may estimate a first average service fee associated with a service request of the target request type.
  • the system may estimate a first average service period associated with a service request of the target request type.
  • the system may determine an extended waiting period based on the predetermined waiting period and the extension period.
  • the system may determine the second unit profit value based on the first average service fee, the first average service period, and the extended waiting period.
  • the system may estimate a second average service fee associated with a service request of the candidate request type.
  • the system may estimate a second average service period associated with a service request of the candidate request type.
  • the system may estimate a second period, at an end of which a service request of the candidate request type is assigned to the service provider.
  • the system may determine the third unit profit value based on the second average service fee, the second average service period, and the second period.
  • the method may be implemented on a computing device having at least one processor, at least one storage device, and a communication platform connected to a network.
  • the method may include obtaining provider information associated with a service provider, the provider information including at least one of a location of the service provider or a target request type, wherein the location of the service provider is determined according to Global Positioning System (GPS) data transmitted by a user terminal associated with the service provider; estimating an assignment parameter associated with the service provider based on the provider information; and transmitting, via a network, data associated with a service request to the user terminal associated with the service provider based on the assignment parameter, wherein the user terminal, in response to receiving the data associated with the service request, displays at least portion of the received data associated with the service request in a graphic user interface.
  • GPS Global Positioning System
  • estimating the assignment parameter associated with the service provider based on the provider information may include determining at least one service request based on the location of the service provider, wherein a first distance between a start location of each of the at least one service request and the location of the service provider is less than a distance threshold; and estimating a profit value for the each of the at least one service request if the service provider completes the each of the at least one service request.
  • estimating the profit value for the each of the at least one service request may include estimating a travel cost of the service provider traveling from the location of the service provider to the start location of the each of the at least one service request based on the first distance; determining a second distance between the start location of the each of the at least one service request and a destination of the each of the at least one service request; estimating a service fee associated with the each of the at least one service request based on the second distance; and estimating the profit value for the each of the at least one service request based on the travel cost and the service fee.
  • estimating the profit value for the each of the at least one service request may include estimating a travel period of the service provider traveling from the location of the service provider to the start location of the each of the at least one service request; estimating a service period of the service provider traveling from the start location to the destination; estimating a completion time point when the service provider arrives at the destination based on the travel period and the service period; predicting a ratio of a number count of available service providers to a number count of candidate service requests within a predetermined distance range of the destination at the completion time point based on a trained prediction model; estimating a waiting period from the completion time point to a time point when a next service request is assigned to the service provider based on the ratio; and determining a first unit profit value corresponding to the each of the at least one service request based on the profit value, the travel period, the service period, and the waiting period.
  • determining the first unit profit value corresponding to the each of the at least one service request based on the profit value, the travel period, the service period, and the waiting period may include assigning a first weighting coefficient to the travel period; assigning a second weighting coefficient to the service period; assigning a third weighting coefficient to the waiting period, wherein the first weighting coefficient is larger than the second weighting coefficient or the third weighting coefficient; determining a total period based on the travel period, the service period, the waiting period, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient; and determining the first unit profit value based on the profit value and the total period.
  • the trained prediction model may be trained based on a prediction model training process.
  • the prediction model training process may include determining a plurality of training samples, each of the plurality of training samples including a number count of historical available service providers and a number count of historical service requests to be assigned; obtaining a predetermined preliminary prediction model; and training the predetermined preliminary prediction model based on the plurality of training samples according to a machine learning algorithm.
  • estimating the assignment parameter associated with the service provider based on the provider information may include determining whether a service request of the target request type is not assigned to the service provider within a predetermined waiting period; and according to a result of the determination that within the predetermined waiting period, a service request of the target request type is not assigned to the service provider, predicting an assignment status associated with the target request type at an end of the predetermined waiting period, determining whether to generate an extension period based on the predicted assignment status, and determining at least one service request of a candidate request type based on a result of the determination of not generating the extension period.
  • the first trained prediction model may be trained based on a first training process.
  • the first training process may include obtaining a plurality of first positive samples and a plurality of first negative samples, wherein in each of the plurality of first positive samples, a historical service request of the target request type was assigned to a historical service provider at an end of the first predetermined period, and in each of the plurality of first negative samples, a historical service request of the target request type was not assigned to a historical service provider at the end of the first predetermined period; obtaining a first preliminary prediction model; and training the first preliminary prediction model based on the plurality of first positive samples and the plurality of first negative samples according to a machine learning algorithm.
  • the assignment status may include a first period, at an end of which a second probability that a service request of the target request type is assigned to the service provider reaches a second probability threshold.
  • Determining whether to generate the extension period may include predicting the first period, at the end of which the second probability that a service request of the target request type is assigned to the service provider reaches a second probability threshold based on a second trained prediction model; determining whether the first period is less than a period threshold; and generating the extension period based on a result of the determination that the first period is less than the period threshold.
  • the second trained prediction model may be trained based on a second training process.
  • the second training process may include obtaining a plurality of second samples, wherein each of the plurality of second samples includes a historical period, at an end of which a historical service request of the target request type was assigned to a historical service provider; obtaining a second preliminary prediction model; and training the second preliminary prediction model based on the plurality of second samples according to a machine learning algorithm.
  • determining whether to generate an extension period based on the predicted assignment status may include estimating a second unit profit value associated with the service provider under an assumption that the extension period is generated and a service request of the target request type is assigned to the service provider at an end of the extension period; estimating a third unit profit value associated with the service provider under an assumption that the extension period is not generated and a service request of the candidate type is assigned to the service provider; determining whether the second unit profit value is greater than the third unit profit value; and determining to generate the extension period based on a result of the determination that the second unit profit value is greater than the third unit profit value.
  • determining the second unit profit value associated with the service provider may include estimating a first average service fee associated with a service request of the target request type; estimating a first average service period associated with a service request of the target request type; determining an extended waiting period based on the predetermined waiting period and the extension period; and determining the second unit profit value based on the first average service fee, the first average service period, and the extended waiting period.
  • determining the third unit profit value associated with the service provider may include estimating a second average service fee associated with a service request of the candidate request type; estimating a second average service period associated with a service request of the candidate request type; estimating a second period, at an end of which a service request of the candidate request type is assigned to the service provider; and determining the third unit profit value based on the second average service fee, the second average service period, and the second period.
  • a further aspect of the present disclosure relates to a non-transitory computer readable medium including a set of instructions for providing Online-to-Offline services.
  • the set of instructions may direct the at least one processor to effectuate a method.
  • the method may include obtaining provider information associated with a service provider, the provider information including at least one of a location of the service provider or a target request type, wherein the location of the service provider is determined according to Global Positioning System (GPS) data transmitted by a user terminal associated with the service provider; estimating an assignment parameter associated with the service provider based on the provider information; and transmitting, via a network, data associated with a service request to the user terminal associated with the service provider based on the assignment parameter, wherein the user terminal, in response to receiving the data associated with the service request, displays at least portion of the received data associated with the service request in a graphic user interface.
  • GPS Global Positioning System
  • FIG. 1 is a schematic diagram illustrating an exemplary on-demand service system according to some embodiments of the present disclosure
  • FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of a computing device according to some embodiments of the present disclosure
  • FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of a mobile device according to some embodiments of the present disclosure
  • FIG. 4 is a flowchart of an exemplary process for assigning a service request to a service provider according to some embodiments of the present disclosure
  • FIG. 5 is a flowchart of an exemplary process for determining a profit value corresponding to a service request according to some embodiments of the present disclosure
  • FIG. 6 is a flowchart of an exemplary process for determining a unit profit value associated with the service provider according to some embodiments of the present disclosure
  • FIG. 7 is a flowchart of an exemplary process for determining a unit profit value associated with the service provider based on a predetermined prediction model according to some embodiments of the present disclosure
  • FIG. 8 is a flowchart of an exemplary process for assigning the same service request that corresponds to a plurality of maximum profit values of a plurality of service providers to a target service provider according to some embodiments of the present disclosure
  • FIG. 9 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • FIG. 10 is a block diagram illustrating an exemplary profit value prediction module according to some embodiments of the present disclosure.
  • FIG. 11 is a block diagram illustrating an exemplary profit value prediction module according to some embodiments of the present disclosure.
  • FIG. 12 is a block diagram illustrating an exemplary service request assignment module according to some embodiments of the present disclosure.
  • FIG. 13 is a flowchart of an exemplary process for assigning a service request to a service provider according to some embodiments of the present disclosure
  • FIG. 14 is a flowchart of an exemplary process for determining whether to extend a predetermined waiting period according to some embodiments of the present disclosure
  • FIG. 15 is a flowchart of an exemplary process for determining whether to extend a predetermined waiting period according to some embodiments of the present disclosure
  • FIG. 16 is a flowchart of an exemplary process for assigning a service request of a candidate request type to a service provider according to some embodiments of the present disclosure
  • FIG. 17 is a flowchart of an exemplary process for assigning a service request of a candidate request type to a service provider according to some embodiments of the present disclosure
  • FIG. 18 is a flowchart of an exemplary process for determining a first unit profit value according to some embodiments of the present disclosure
  • FIG. 19 is a flowchart of an exemplary process for determining a second unit profit value according to some embodiments of the present disclosure.
  • FIG. 20 is a flowchart of an exemplary process for obtaining a first prediction model according to some embodiments of the present disclosure
  • FIG. 21 is a flowchart of an exemplary process for obtaining a second prediction model according to some embodiments of the present disclosure
  • FIG. 22 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • FIG. 23 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • FIG. 24 is a block diagram illustrating an exemplary extension module according to some embodiments of the present disclosure.
  • FIG. 25 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • FIG. 26 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • FIG. 27 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure.
  • FIG. 28 is a flowchart of an exemplary process for assigning a service request to a service provider according to some embodiments of the present disclosure.
  • the flowcharts used in the present disclosure illustrate operations that systems implement according to some embodiments of the present disclosure. It is to be expressly understood, the operations of the flowchart may be implemented not in order. Conversely, the operations may be implemented in inverted order, or simultaneously. Moreover, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.
  • the systems and methods disclosed in the present disclosure are described primarily regarding on-demand services, it should also be understood that this is only one exemplary embodiment.
  • the systems or methods of the present disclosure may be applied to any other kind of on-demand services.
  • the system or method of the present disclosure may be applied to transportation systems of different environments including land, ocean, aerospace, or the like, or a combination thereof.
  • the vehicle of the transportation systems may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a vessel, an aircraft, a spaceship, a hot-air balloon, a driverless vehicle, or the like, or a combination thereof.
  • the transportation system may also include any transportation system for management and/or distribution, for example, a system for sending and/or receiving an express.
  • the application of the system or method of the present disclosure may include a webpage, a plug-in of a browser, a client terminal, a custom system, an internal analysis system, an artificial intelligence robot, or the like, or a combination thereof.
  • passenger, ” “requester, ” “requestor, ” “service requester, ” “service requestor, ” and “customer” in the present disclosure are used interchangeably to refer to an individual, an entity or a tool that may request or order a service.
  • driver, ” “provider, ” “service provider, ” and “supplier” in the present disclosure are used interchangeably to refer to an individual, an entity or a tool that may provide a service or facilitate the providing of the service.
  • user in the present disclosure refers to an individual, an entity or a tool that may request a service, order a service, provide a service, or facilitate the providing of the service.
  • terms “requester” and “requester terminal” may be used interchangeably
  • terms “provider” and “provider terminal” may be used interchangeably.
  • the terms “request, ” “service, ” “service request, ” and “order” in the present disclosure are used interchangeably to refer to a request that may be initiated by a passenger, a requester, a service requester, a customer, a driver, a provider, a service provider, a supplier, or the like, or a combination thereof.
  • the service request may be accepted by any one of a passenger, a requester, a service requester, a customer, a driver, a provider, a service provider, or a supplier.
  • the service request may be chargeable or free.
  • the positioning technology used in the present disclosure may be based on a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a Galileo positioning system, a quasi-zenith satellite system (QZSS) , a wireless fidelity (WiFi) positioning technology, or the like, or a combination thereof.
  • GPS global positioning system
  • GLONASS global navigation satellite system
  • COMPASS compass navigation system
  • Galileo positioning system Galileo positioning system
  • QZSS quasi-zenith satellite system
  • WiFi wireless fidelity positioning technology
  • An aspect of the present disclosure relates to systems and methods for assigning a service request to a service provider.
  • a system may obtain provider information (e.g., a location of the service provider, a target request type) associated with a service provider.
  • the system may estimate an assignment parameter (e.g., at least one profit value corresponding to at least one service request, an assignment status at the end of a predetermined waiting period) associated with the service provider based on the provider information.
  • the system may further transmit data associated with a service request to a user terminal associated with the service provider based on the assignment parameter via a network. For example, the system may transmit data associated with a service request corresponding to a maximum profit value to the user terminal.
  • the system may transmit data associated with a service request of a candidate request type at the end of the predetermined waiting period in response to that no service request of the target request type is assigned to the service provider. According to the method disclosed in the present disclosure, the service efficiency and user experience of the service provider may be improved.
  • an on-demand service such as online taxi-hailing including taxi-hailing combination services
  • online taxi-hailing including taxi-hailing combination services is a new form of service rooted only in post-Internet era. It provides technical solutions to users and service providers that could raise only in post-Internet era.
  • the pre-Internet era when a passenger hails a taxi on the street, the taxi request and acceptance occur only between the passenger and one taxi driver that sees the passenger. If the passenger hails a taxi through a telephone call, the service request and acceptance may occur only between the passenger and one service provider (e.g., one taxi company or agent) .
  • Online taxi however, allows a user of the service to real-time and automatically distribute a service request to a vast number of individual service providers (e.g., taxi) distance away from the user. It also allows a plurality of service providers to respond to the service request simultaneously and in real-time. Therefore, through the Internet, the online on-demand transportation systems may provide a much more efficient transaction platform for the users and the service providers that may never meet in a traditional pre-Internet transportation service system.
  • FIG. 1 is a schematic diagram illustrating an exemplary on-demand service system according to some embodiments of the present disclosure.
  • the on-demand service system may be a system for Online-to-Offline services.
  • the on-demand service system 100 may be an online transportation service platform for transportation services such as taxi-hailing services, chauffeur services, delivery services, express car services, carpooling services, bus services, driver hiring services, take-out services, shuttle services, or the like, or a combination thereof.
  • the on-demand service system 100 may include a server 110, a network 120, a requester terminal 130, a provider terminal 140, and a storage 150.
  • the server 110 may be a single server or a server group.
  • the server group may be centralized, or distributed (e.g., the server 110 may be a distributed system) .
  • the server 110 may be local or remote.
  • the server 110 may access information and/or data stored in the requester terminal 130, the provider terminal 140, and/or the storage 150 via the network 120.
  • the server 110 may be directly connected to the requester terminal 130, the provider terminal 140, and/or the storage 150 to access stored information and/or data.
  • the server 110 may be implemented on a cloud platform.
  • the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or a combination thereof.
  • the server 110 may be implemented on a computing device 200 having one or more components illustrated in FIG. 2.
  • the server 110 may include a processing engine 112.
  • the processing engine 112 may process information and/or data relating to a service request to perform one or more functions described in the present disclosure. For example, the processing engine 112 may assign a service request to a service provider based on provider information (e.g., a location of the service provider) associated with the service provider.
  • the processing engine 112 may include one or more processing engines (e.g., single-core processing engine (s) or multi-core processor (s) ) .
  • the processing engine 112 may include a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , an application-specific instruction-set processor (ASIP) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a digital signal processor (DSP) , a field programmable gate array (FPGA) , a programmable logic device (PLD) , a controller, a microcontroller unit, a reduced instruction-set computer (RISC) , a microprocessor, or the like, or a combination thereof.
  • CPU central processing unit
  • ASIC application-specific integrated circuit
  • ASIP application-specific instruction-set processor
  • GPU graphics processing unit
  • PPU physics processing unit
  • DSP digital signal processor
  • FPGA field programmable gate array
  • PLD programmable logic device
  • controller a microcontroller unit, a reduced instruction-set computer (RISC) , a microprocessor, or the like, or a combination thereof.
  • the network 120 may facilitate exchange of information and/or data.
  • one or more components of the on-demand service system 100 e.g., the server 110, the requester terminal 130, the provider terminal 140, and/or the storage 150
  • the server 110 may obtain a service request from the requester terminal 130 via the network 120.
  • the network 120 may be any type of wired or wireless network, or a combination thereof.
  • the network 120 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an Internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a public telephone switched network (PSTN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or a combination thereof.
  • the network 120 may include one or more network access points.
  • the network 120 may include wired or wireless network access points such as base stations and/or internet exchange points 120-1, 120-2, ..., through which one or more components of the on-demand service system 100 may be connected to the network 120 to exchange data and/or information.
  • a service requester may be a user of the requester terminal 130.
  • the user of the requester terminal 130 may be someone other than the service requester.
  • a user A of the requester terminal 130 may use the requester terminal 130 to send a service request for a user B or receive a service confirmation and/or information or instructions from the server 110.
  • a service provider may be a user of the provider terminal 140.
  • the user of the provider terminal 140 may be someone other than the service provider.
  • a user C of the provider terminal 140 may use the provider terminal 140 to receive a service request for a user D, and/or information or instructions from the server 110.
  • the requester terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a built-in device in a vehicle 130-4, or the like, or a combination thereof.
  • the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or a combination thereof.
  • the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or a combination thereof.
  • the wearable device may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, a smart clothing, a smart backpack, a smart accessory, or the like, or a combination thereof.
  • the smart mobile device may include a smartphone, a personal digital assistant (PDA) , a gaming device, a navigation device, a point of sale (POS) device, or the like, or a combination thereof.
  • the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or a combination thereof.
  • the virtual reality device and/or the augmented reality device may include a Google Glass TM , an Oculus Rift TM , a Hololens TM , a Gear VR TM , etc.
  • a built-in device in the vehicle 130-4 may include an onboard computer, an onboard television, etc.
  • the requester terminal 130 may be a device with positioning technology for locating the location of the service requester and/or the requester terminal 130.
  • the provider terminal 140 may be similar to, or the same device as the requester terminal 130. In some embodiments, the provider terminal 140 may be a device with positioning technology for locating the location of the service provider and/or the provider terminal 140. In some embodiments, the requester terminal 130 and/or the provider terminal 140 may communicate with another positioning device to determine the location of the service requester, the requester terminal 130, the service provider, and/or the provider terminal 140. In some embodiments, the requester terminal 130 and/or the provider terminal 140 may send positioning information to the server 110.
  • the storage 150 may store data and/or instructions relating to a service request.
  • the storage 150 may store data obtained from the requester terminal 130 and/or the provider terminal 140.
  • the storage 150 may store a service request obtained from the requester terminal 130.
  • the storage 150 may store a predetermined prediction model for predicting a ratio of a number count of available service providers at the destination at a completion time point to a number count of candidate service requests at the completion time point. The start location of each of the candidate service requests may be the destination.
  • the storage 150 may store data and/or instructions that the server 110 may execute or use to perform exemplary methods described in the present disclosure.
  • the storage 150 may store data and/or instructions for assigning a service request to a service provider.
  • the storage 150 may store location information associated with the requester terminal 130 and/or the provider terminal 140.
  • the storage 150 may include a mass storage, removable storage, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or a combination thereof.
  • Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drive, etc.
  • Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc.
  • Exemplary volatile read-and-write memory may include a random access memory (RAM) .
  • Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc.
  • Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc.
  • the storage 150 may be implemented on a cloud platform.
  • the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or a combination thereof.
  • the storage 150 may be connected to the network 120 to communicate with one or more components of the on-demand service system 100 (e.g., the server 110, the requester terminal 130, and/or the provider terminal 140) .
  • One or more components of the on-demand service system 100 may access the data and/or instructions stored in the storage 150 via the network 120.
  • the storage 150 may be directly connected to or communicate with one or more components of the on-demand service system 100 (e.g., the server 110, the requester terminal 130, and/or the provider terminal 140) .
  • the storage 150 may be part of the server 110.
  • one or more components of the on-demand service system 100 may have permissions to access the storage 150.
  • one or more components of the on-demand service system 100 may read and/or modify information relating to the service requester, the service provider, and/or the public when one or more conditions are met.
  • the server 110 may read and/or modify one or more service requesters’ information after the service is completed.
  • the provider terminal 140 may access information relating to the service requester when receiving a service request from the requester terminal 130, but the provider terminal 140 may not modify the relevant information of the service requester.
  • information exchanging of one or more components of the on-demand service system 100 may be achieved by way of requesting a service.
  • the object of the service may be any product.
  • the product may be a tangible product or an immaterial product.
  • the tangible product may include food, medicine, commodity, chemical product, electrical appliance, clothing, car, housing, luxury, or the like, or a combination thereof.
  • the immaterial product may include a servicing product, a financial product, a knowledge product, an internet product, or the like, or a combination thereof.
  • the internet product may include an individual host product, a web product, a mobile internet product, a commercial host product, an embedded product, or the like, or a combination thereof.
  • the mobile internet product may be used in software of a mobile terminal, a program, a system, or the like, or a combination thereof.
  • the mobile terminal may include a tablet computer, a laptop computer, a mobile phone, a personal digital assistant (PDA) , a smartwatch, a point of sale (POS) device, an onboard computer, an onboard television, a wearable device, or the like, or a combination thereof.
  • PDA personal digital assistant
  • POS point of sale
  • the product may be any software and/or application used in the computer or mobile phone.
  • the software and/or application may relate to socializing, shopping, transporting, entertainment, learning, investment, or the like, or a combination thereof.
  • the software and/or application relating to transporting may include a traveling software and/or application, a vehicle scheduling software and/or application, a mapping software and/or application, etc.
  • the vehicle may include a horse, a carriage, a rickshaw (e.g., a wheelbarrow, a bike, a tricycle) , a car (e.g., a taxi, a bus, a private car) , a train, a subway, a vessel, an aircraft (e.g., an airplane, a helicopter, a space shuttle, a rocket, a hot-air balloon) , or the like, or a combination thereof.
  • a traveling software and/or application the vehicle may include a horse, a carriage, a rickshaw (e.g., a wheelbarrow, a bike, a tricycle) , a car (e.g., a taxi, a bus, a private car) , a train, a subway, a vessel, an aircraft (e
  • the element may perform through electrical signals and/or electromagnetic signals.
  • the server 110 may operate logic circuits in its processor to process such task.
  • the server 110 may communicate with the on-demand service system 100 via a wired network, the at least one information exchange port may be physically connected to a cable, which may further transmit the electrical signals to an input port (e.g., an information exchange port) of the requester terminal 130.
  • the at least one information exchange port may be one or more antennas, which may convert the electrical signals to electromagnetic signals.
  • an electronic device such as the requester terminal 130, and/or the server 110
  • a processor thereof when a processor thereof processes an instruction, sends out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals.
  • the processor retrieves or saves data from a storage medium (e.g., the storage 150)
  • the processor may send out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium.
  • the structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device.
  • an electrical signal refers to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.
  • FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of a computing device according to some embodiments of the present disclosure.
  • the server 110, the requester terminal 130, and/or the provider terminal 140 may be implemented on the computing device 200 shown in FIG. 2.
  • the processing engine 112 may be implemented on the computing device 200 and configured to perform functions of the processing engine 112 disclosed in this disclosure.
  • the computing device 200 may be used to implement any component of the on-demand service system 100 as described herein.
  • the processing engine 112 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof.
  • only one such computer is shown, for convenience, the computer functions relating to the on-demand service as described herein may be implemented in a distributed fashion on a number of similar platforms to distribute the processing load.
  • the computing device 200 may include COM ports 250 connected to and from a network connected thereto to facilitate data communications.
  • the computing device 200 may also include a processor (e.g., the processor 220) , in the form of one or more processors (e.g., logic circuits) , for executing program instructions.
  • the processor 220 may include interface circuits and processing circuits therein.
  • the interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process.
  • the processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.
  • the exemplary computing device may further include program storage and data storage of different forms including, for example, a disk 270, and a read-only memory (ROM) 230, or a random-access memory (RAM) 240, for various data files to be processed and/or transmitted by the computing device.
  • the exemplary computing device may also include program instructions stored in the ROM 230, RAM 240, and/or another type of non-transitory storage medium to be executed by the processor 220.
  • the methods and/or processes of the present disclosure may be implemented as the program instructions.
  • the computing device 200 may also include an I/O component 260, supporting input/output between the computer and other components.
  • the computing device 200 may also receive programming and data via network communications.
  • processors 220 are also contemplated; thus, operations and/or method steps performed by one processor 220 as described in the present disclosure may also be jointly or separately performed by the multiple processors.
  • the processor 220 of the computing device 200 executes both step A and step B, it should be understood that step A and step B may also be performed by two different processors 220 jointly or separately in the computing device 200 (e.g., a first processor executes step A and a second processor executes step B, or the first and second processors jointly execute steps A and B) .
  • FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of a mobile device according to some embodiments of the present disclosure.
  • the requester terminal 130 and/or the provider terminal 140 may be implemented on the mobile device 300 shown in FIG. 3.
  • the mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, a mobile operating system (OS) 370, and a storage 390.
  • any other suitable component including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300.
  • the mobile operating system 370 e.g., iOS TM , Android TM , Windows Phone TM , etc.
  • the applications 380 may include a browser or any other suitable mobile apps for receiving and rendering information relating to on-demand services or other information from the on-demand service system 100.
  • User interactions with the information stream may be achieved via the I/O 350 and provided to the processing engine 112 and/or other components of the on-demand service system 100 via the network 120.
  • FIG. 4 is a flowchart of an exemplary process for assigning a service request to a service provider according to some embodiments of the present disclosure.
  • the process 400 may be executed by the on-demand service system 100.
  • the process 400 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 9 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 400.
  • the operations of the illustrated process 400 presented below are intended to be illustrative. In some embodiments, the process 400 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 400 as illustrated in FIG. 4 and described below is not intended to be limiting.
  • a first location of a service provider may be determined.
  • the first location of the service provider may be determined by an onboard positioning device or a positioning device (e.g., a GPS module or chipset) in the provider terminal 40 (e.g., a mobile phone) used by the service provider.
  • a positioning device e.g., a GPS module or chipset
  • the provider terminal 40 e.g., a mobile phone
  • the service provider may be an available service provider waiting for receiving a service request or a service provider who is in a process for providing a service corresponding to a service request and is about to complete the service request.
  • the term “about to complete” refers to that a time interval between a current time point and a predicted completion time point for the service provider to complete a service request is less than a predetermined time interval (e.g., 1 minute, 3 minutes, 5 minutes) .
  • At least one service request may be determined, wherein a first distance (e.g., a road distance, a linear distance) between a start location of each of the at least one service request and the first location is less than a distance threshold.
  • a first distance e.g., a road distance, a linear distance
  • the term “road distance” refers to a length of a route along one or more roads or portions of one or more roads.
  • the distance threshold may be default settings of the on-demand service system 100 or may be adjustable under different situations. For example, during rush hours, the distance threshold may be relatively small as one having ordinary skill in the art would understand, while during non–rush hours, the distance threshold may be relatively large as one having ordinary skill in the art would understand.
  • service requests with a road distance from a start location to the first location less than the distance threshold may be searched for with the first location being a central location.
  • the distance threshold may be expanded, and/or a waiting period may be extended. Further, if still no service request is determined after the extended waiting period, the distance threshold may be further expanded, which can ensure that it is easier to search for a service request with a road distance from a start location to the first location less than the distance threshold (or the expanded distance threshold) .
  • a maximum profit value from at least one profit value corresponding to the at least one service request if the service provider completes the at least one service request may be predicted.
  • a profit value corresponding to the service request if the service provider completes the service request may be determined.
  • the profit value may be determined based on a service fee from a start location to a destination of the service request and a cost from the first location to the start location of the service provider. For example, the profit value may be equal to a result of the service fee minus the cost.
  • a service request that corresponds to the maximum profit value may be assigned to the service provider.
  • the service request that corresponds to the maximum profit value may be assigned to the service provider.
  • the assignment mode in the present disclosure can ensure that a profit value of the service provider is maximized, thereby increasing an overall profit value of service providers and the online transportation service platform.
  • FIG. 5 is a flowchart of an exemplary process for determining a profit value corresponding to a service request according to some embodiments of the present disclosure.
  • the process 500 may be executed by the on-demand service system 100.
  • the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 10 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 500.
  • the operations of the illustrated process 500 presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 500 as illustrated in FIG. 5 and described below is not intended to be limiting.
  • a second distance between the start location of the service request and a destination of the service request may be determined.
  • the first distance or the second distance may be a road distance or a spatial linear distance, which may be set as needed.
  • a cost associated with the service provider may be determined based on the first distance.
  • an amount of fuel consumption per unit distance of the vehicle associated with the service provider may be determined based on a vehicle model of the vehicle.
  • a total amount of fuel consumption of the service provider corresponding to the first distance may be determined based on the first distance and the amount of fuel consumption per unit distance, and the cost may be determined based on the total amount of fuel consumption of the service provider and a price of the fuel.
  • an amount of electric power consumption per unit distance of the vehicle associated with the service provider may be determined based on the vehicle model.
  • a total amount of electric power consumption of the service provider corresponding to the first distance may be determined based on the first distance and the amount of electric power consumption per unit distance, and the cost may be determined based on the total amount of electric power consumption and a price of electricity.
  • the cost may be determined based on a combination of the above two ways. It should be noted that the vehicle associated with the service provider also can be powered by any kind of energy (e.g., electricity, solar energy) and the cost associated with the service provider can be determined accordingly; the above description is not intended to be limiting.
  • energy e.g., electricity, solar energy
  • a service fee associated with the service request may be determined based on the second distance.
  • a service fee per unit distance associated with the service request may be determined based on a current time point and the service fee associated with the service request may be determined based on a product of the second distance and the service fee per unit distance associated with the service request.
  • the service fee per unit distance may vary under different situations. For example, during rush hours (e.g., from 07: 00 a. m. to 09: 00 a.m., from 05: 30 p.m. to 07: 30 p.m. ) , the service fee per unit distance may be relatively large as one having ordinary skill in the art would understand, while during non–rush hours, the service fee per unit distance may be relatively small as one having ordinary skill in the art would understand. As another example, during night time (e.g., from 11: 00 p.m. to 06: 00 a. m.
  • the service fee per unit distance may be relatively large as one having ordinary skill in the art would understand, while during daytime (e.g., from 06: 00 a. m. to 06: 00 p.m. ) , the service fee per unit distance may be relatively small as one having ordinary skill in the art would understand.
  • the service fee per unit distance may also be affected by other factors, such as road condition, weather condition, etc.
  • the profit value corresponding to the service request if the service provider completes the service request may be determined based on the cost and the service fee.
  • the profit value may be determined by subtracting the cost from the service fee.
  • FIG. 6 is a flowchart of an exemplary process for determining a unit profit value associated with the service provider according to some embodiments of the present disclosure.
  • the process 600 may be executed by the on-demand service system 100.
  • the process 600 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 11 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 600.
  • the operations of the illustrated process 600 presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed.
  • operations 602-608 are the same with operations 502-508 illustrated in FIG. 5. For brevity, the descriptions of the operations 602-608 are omitted here.
  • a travel period associated with the service provider traveling from the first location to the start location of the service request and a service period associated with the service provider traveling from the start location to the destination may be determined.
  • the travel period may be determined based on a driving speed of the vehicle associated with the service provider, the first distance, and a road condition from the first location to the start location at the current time point. For example, the travel period may be determined based on a quotient of the first distance divided by the driving speed plus a predicted period required for the service provider to drive through one or more congested road segments from the first location to the start location.
  • the service period may be determined based on a driving speed of the vehicle associated with the service provider, the second distance, and a road condition from the start location to the destination at the current time point. For example, the service period may be determined based on a quotient of the second distance divided by the driving speed plus a predicted period required for the service provider to drive through one or more congested road segments from the start location to the destination.
  • operation 610 may be executed after operation 608. Additionally or alternatively, the order of executing the process 600 may be adjusted as needed under a precondition that operation 610 is executed after operation 602.
  • a completion time point when the service provider arrives at the destination may be determined based on a current time point, the travel period, and the service period.
  • the completion time point may be determined by adding the travel period and the service period to the current time point.
  • a ratio of a number count of available service providers at the destination at the completion time point to a number count of candidate service requests with a start location being the destination at the completion time point may be determined.
  • the term “at the destination” refers to a predetermined distance range (e.g., 500 m, 1 km, 2 km, 3 km) from the destination.
  • start location being the destination refers to that the start location is within a predetermined distance range from the destination.
  • a waiting period associated with the service provider from a time point when the service provider completes the service request (i.e., the completion time point) to a time point when a next service request is assigned to the service provider may be predicted based on the ratio.
  • the ratio of the number count of the available service providers at the destination at the completion time point to the number count of the candidate service requests with the start location being the destination at the completion time point refers to a transport capacity at the destination at the completion time point. If the transport capacity is sufficient; that is, the ratio is greater than a predetermined ratio (which may be set as needed) , it can be determined that the number count of service providers waiting for receiving service requests at the destination at the completion time point is relatively large while the number count of candidate service requests is relatively small, resulting in that the service providers need to wait for a relatively long time for the assignment of a next service request after completing the service request at the destination at the completion time point.
  • a predetermined ratio which may be set as needed
  • the transport capacity is insufficient; that is, the ratio is equal to or smaller than the predetermined ratio, it can be determined that the number count of the service providers waiting for receiving service requests at the destination at the completion time point is relatively small while the number count of the candidate service requests is relatively large, resulting in that the service provider may only need to wait for a short time for the assignment of a next service request after completing the service request at the destination at the completion time point.
  • the waiting period associated with the service provider from the time point when the service provider completes the service request to the time point when a next service request is assigned to the service provider may be predicted.
  • the waiting period may be inversely proportional to the above ratio, where an inverse proportional coefficient may be set as needed.
  • a unit profit value associated with the service provider may be determined based on the profit value, the travel period, the service period, and the waiting period.
  • the profit value corresponding to a service request if the service provider completes the service request may be determined according to the following equation (1) :
  • B refers to the profit value corresponding to the service request
  • C refers to the service fee associated with the service request
  • E refers to the cost associated with the service provider
  • the unit profit value corresponding to the service request may be determined according to the following equation (2) :
  • T 1 refers to the travel period
  • T 2 refers to the service period
  • T 3 refers to the waiting period
  • the unit profit value of the service provider may be determined, further, in 408, a service request that corresponds to a maximum unit profit value may be assigned to the service provider, so that the profit value of the service provider within a certain period can be maximized.
  • a weighting coefficient of the travel period may be greater than at least one of a weight coefficient of the service period or a weight coefficient of the waiting period.
  • weighting coefficients may be set for the travel period, the service period, and the waiting period respectively.
  • the weighting coefficients set for the service period and the waiting period may be 1.
  • the weighting coefficient set for the travel period is may be expressed by K.
  • unit profit value may be determined according to the following equation (3):
  • the value of K may be greater than 1, which may increase the effect of the travel period on the unit profit value based on an inverse correlation between the unit profit value and the travel period.
  • the travel period refers to a period required for the service provider to travel to the start location of the service request
  • the longer the travel period is the higher the probability that a service requester (e.g., a passenger) who initiated the service request cancels the service request may be.
  • the service fee C associated with the profit value B of the service provider may be 0 and only the cost E is left; that is, the profit value B of the service provider may be negative.
  • the unit profit value B ’ will not be negative.
  • a relatively large weighting coefficient may be set for the travel period so that the weighting coefficient of the travel period is greater than the weighting coefficient of the service period and/or the weighting coefficient of the waiting period, which can increase the effect of the travel period on the unit profit value and ensure that the calculation result of the unit profit value is consistent with actual situations.
  • FIG. 7 is a flowchart of an exemplary process for determining a unit profit value associated with the service provider based on a predetermined prediction model according to some embodiments of the present disclosure.
  • the process 700 may be executed by the on-demand service system 100.
  • the process 700 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 11 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 700.
  • the operations of the illustrated process 700 presented below are intended to be illustrative.
  • the process 700 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 700 as illustrated in FIG. 7 and described below is not intended to be limiting. As illustrated, operations 702-712 are the same with operations 602-612 illustrated in FIG. 6 and operations 716-718 are the same with operations 616-618 illustrated in FIG. 6. For brevity, the descriptions of the operations 602-608 and 616-618 are omitted here.
  • the ratio of the number count of available service providers to the number count of candidate service requests may be determined based on a predetermined prediction model.
  • the prediction model may be obtained based on a predetermined training set according to a machine learning algorithm.
  • the predetermined training set may include a plurality of historical ratios; each historical ratio refers to a ratio of a number count of historically available service providers at the destination at a historical time point to a number count of historical candidate service requests with a historical start location being the destination at the historical time point.
  • the historical time point may be obtained in advance as a feature parameter, and data (e.g., historical available service providers, historical candidate service requests, and/or historical ratios) associated with one or more parameters at each of a plurality of historical time points at the destination may be obtained.
  • the training set may be generated based on the obtained feature parameter and the data associated with the one or more parameters.
  • the prediction model may be obtained based on the training set according to a machine learning algorithm.
  • the machine learning algorithm may include at least one of a linear regression algorithm, a regression decision tree algorithm, an iterative decision tree algorithm, or a random forest algorithm.
  • the completion time point when the service provider arrives at the destination may be input into the prediction model as a feature parameter and the ratio of the number count of available service providers to the number count of candidate service requests at the completion time point when the service provider arrives at the destination may be predicted.
  • FIG. 8 is a flowchart of an exemplary process for assigning the same service request that corresponds to a plurality of maximum profit values of a plurality of service providers to a target service provider according to some embodiments of the present disclosure.
  • the process 800 may be executed by the on-demand service system 100.
  • the process 800 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 12 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 800.
  • the operations of the illustrated process 800 presented below are intended to be illustrative. In some embodiments, the process 800 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 800 as illustrated in FIG. 8 and described below is not intended to be limiting.
  • a travel period traveling from a first location of each of the plurality of service providers to a start location of the same service request may be determined.
  • a target service provider corresponding to a minimum travel period may be determined from a plurality of travel periods.
  • the same service request that corresponds to the plurality of maximum profit values may be assigned to the target service provider.
  • ranges associated with the distance threshold used for searching for candidate service requests to be assigned for different service providers may be overlapped, for the plurality of service providers, a plurality of maximum profit values corresponding to the plurality of service providers may correspond to the same service request.
  • a travel period traveling from the first location of each of the plurality of service providers to the start location of the same service request that corresponds to the maximum profit values may be determined and the same service request that corresponds to the maximum profit values may be assigned to a target service provider corresponding to a minimum travel period, which may ensure that the cost for the target service provider to travel from the first location to the start location is as low as possible, and a waiting period for the service requester (e.g., a passenger) to wait to use a service corresponding to the service request is as short as possible.
  • the service requester e.g., a passenger
  • FIG. 9 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • the service request assignment device 900 may include a location determination module 910, a service request determination module 920, a profit value prediction module 930, and a service request assignment module 940.
  • the service request assignment device 900 may be integrated into the server 110.
  • the service request assignment device 900 may be part of the processing engine 112.
  • the location determination module 910 may be configured to determine a first location of a service provider.
  • the service request determination module 920 may be configured to determine at least one service request, wherein a first distance (e.g., a road distance, a linear distance) between a start location of each of the at least one service request and the first location is less than a distance threshold.
  • a first distance e.g., a road distance, a linear distance
  • the profit value prediction module 930 may be configured to predict a maximum profit value from at least one profit value corresponding to the at least one service request if the service provider completes the at least one service request.
  • the service request assignment module 940 may be configured to assign a service request that corresponds to the maximum profit value to the service provider.
  • modules may be found elsewhere in the present disclosure (e.g., FIG. 4 and the description thereof) .
  • the modules in FIG. 9 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a ZigBee
  • NFC Near Field Communication
  • FIG. 10 is a block diagram illustrating an exemplary profit value prediction module according to some embodiments of the present disclosure.
  • the profit value prediction module 930 may include a distance determination sub-module 1010, a cost determination sub-module 1020, a service fee determination sub-module 1030, and a profit value determination sub-module 1040.
  • the distance determination sub-module 1010 may be configured to determine a second distance between a start location of the service request and a destination of the service request.
  • the cost determination sub-module 1020 may be configured to determine a cost associated with the service provider based on the first distance.
  • the service fee determination sub-module 1030 may be configured to determine a service fee associated with the service request based on the second distance.
  • the profit value determination sub-module 1040 may be configured to determine the profit value corresponding to the service request if the service provider completes the service request based on the cost and the service fee.
  • the sub-modules in FIG. 10 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a Bluetooth
  • ZigBee ZigBee
  • NFC Near Field Communication
  • FIG. 11 is a block diagram illustrating an exemplary profit value prediction module according to some embodiments of the present disclosure.
  • the profit value prediction module 930 may further include a period determination sub-module 1110, a time point determination sub-module 1120, a ratio determination sub-module 1130, and a period prediction sub-module 1140.
  • the period determination sub-module 1110 may be configured to determine a travel period associated with the service provider traveling from the first location to the start location of the service request and a service period associated with the service provider traveling from the start location to the destination.
  • the time point determination sub-module 1120 may be configured to determine a completion time point when the service provider arrives at the destination based on a current time point, the travel period, and the service period.
  • the ratio determination sub-module 1130 may be configured to determine a ratio of a number count of available service providers at the destination at the completion time point to a number count of candidate service requests with a start location being the destination at the completion time point.
  • the period prediction sub-module 1140 may be configured to predict a waiting period associated with the service provider from a time point when the service provider completes the service request to a time point when a next service request is assigned to the service provider based on the ratio.
  • the profit value determination sub-module 1040 may be further configured to determine a unit profit value associated with the service provider based on the profit value, the travel period, the service period, and the waiting period.
  • a weighting coefficient of the travel period may be greater than at least one of a weight coefficient of the service period or a weight coefficient of the waiting period.
  • the ratio determination sub-module 1130 may be configured to determine the ratio of the number count of available service providers to the number count of candidate service requests based on a predetermined prediction model.
  • the prediction model may be obtained based on a predetermined training set according to a machine learning algorithm.
  • the predetermined training set may include a plurality of historical ratios; each historical ratio refers to a ratio of a number count of historically available service providers at the destination at a historical time point to a number count of historical candidate service requests with a historical start location being the destination at the historical time point.
  • the sub-modules in FIG. 11 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a Bluetooth
  • ZigBee ZigBee
  • NFC Near Field Communication
  • FIG. 12 is a block diagram illustrating an exemplary service request assignment module according to some embodiments of the present disclosure.
  • the service request assigning module 940 may include a period determination sub-module 1210, a service provider determination sub-module 1220, and an assignment sub-module 1230.
  • the period determination sub-module 1210 may be configured to determine, in response to a plurality of maximum profit values of a plurality of service providers corresponding to the same service request, a travel period traveling from a first location of each of the plurality of service providers to a start location of the same service request.
  • the service provider determination sub-module 1220 may be configured to determine a target service provider corresponding to a minimum travel period from a plurality of travel periods.
  • the assignment sub-module 1230 may be configured to assign the same service request that corresponds to the plurality of maximum profit values to the target service provider.
  • the sub-modules in FIG. 12 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a Bluetooth
  • ZigBee ZigBee
  • NFC Near Field Communication
  • the present disclosure may also provide a computer storage medium including instructions.
  • the instructions may direct the at least one processor to perform a process (e.g., the process 400, the process 500, the process 600, the process 700, and/or the process 800) described elsewhere in the present disclosure.
  • FIG. 13 is a flowchart of an exemplary process for assigning a service request to a service provider according to some embodiments of the present disclosure.
  • the process 1300 may be executed by the on-demand service system 100.
  • the process 1300 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 22 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 1300.
  • the operations of the illustrated process 1300 presented below are intended to be illustrative. In some embodiments, the process 1300 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 1300 as illustrated in FIG. 13 and described below is not intended to be limiting.
  • a service provider that is waiting for a service request of a target request type to be assigned to the service provider may be determined.
  • service requests of different types may be assigned to different service providers.
  • a vehicle model associated with a service provider to whom a service request of the target request type can be assigned may be more advanced than a vehicle model associated with a service provider to whom a service request of a candidate request type may be assigned.
  • a management relationship between the service provider and the type of the service request may be established in advance. It may be easy to identify a service provider to whom a service request of the target request type can be assigned according to the management relationship.
  • the service provider may be determined as a service provider who is waiting for a service request of the target request type to be assigned to the service provider.
  • an assignment status associated with the service provider at the end of the predetermined waiting period may be predicted based on a prediction model.
  • the assignment status may include a relationship between a probability that a service request of the target request type is assigned to the service provider and an extension period.
  • feature data may be collected and input into the prediction model.
  • the feature data may include one or more of a location (e.g., a location of the service provider) , a time (e.g., a current time point, the predetermined waiting period) , a number count of service providers, a number count of service requests of the target request type, or a price multiple of a service fee of a service request of the target request type.
  • a price multiple of a service fee of a service request refers to a price adjustment parameter (e.g., 1.5 times, 2 times) associated with a general price per unit distance of a service request. For example, during rush hours, a service request density within a predetermined region may be relatively high as one having ordinary skill in the art would understand, under this situation, the price adjustment parameter may be determined and applied to a determination of a service fee of a service request.
  • the server 110 may determine the “location” in the feature data according to location information uploaded by an onboard device or location information uploaded by a service provider’s terminal (e.g., the provider terminal 140) .
  • the server 110 may determine the “time” in the feature data in real time according to the system time thereof.
  • the server 110 may determine the number count of service providers in the feature data according to a number count of other service providers near the location of the service provider (e.g., within a predetermined road distance from the location of the service provider) at the time point.
  • the number count of the service providers in the feature data refers to the number count of the service providers to whom a service request of the target request type may be assigned. For example, compared with the service provider to whom a service request of the candidate request type can be assigned, the vehicle model associated with the service provider to whom a service request of the target request type can be assigned may be more advanced and/or the service fee of the service request may be higher.
  • the server 110 may determine the number count of service requests of the target request type in the feature data according to a number count of service requests of the target request type of which the start locations are near the location of the service provider.
  • the term “near” refers to that a distance between the start location of the service request and the location of the service provider is less than a distance threshold.
  • the server 110 may determine the multiple of a service fee of a service request of the target request type in the feature data according to a ratio of the number count of service requests near the location of the service provider to the number count of service requests of the target request type.
  • feature data is not limited to the above types and may be set and adjusted as needed.
  • the prediction model may be obtained based on a machine learning algorithm, such as a supervised machine learning algorithm.
  • an applicable learning algorithm may include a regression algorithm.
  • the regression algorithm belongs to the category of inductive learning.
  • the inductive learning refers to a process for determining a general description of a concept through inductive reasoning based on some examples of the concept.
  • an applicable regression algorithm may include a linear regression algorithm, a regression decision tree algorithm, an iterative decision tree algorithm, or a weighted linear combination algorithm based on a predetermined regression algorithm.
  • the accuracies of prediction results generated by different algorithms may be different, and the computational complexities of different algorithms may also be different. In practical applications, according to specific application requirements, any regression algorithm may be selected to generate the above prediction model for prediction.
  • whether to extend the predetermined waiting period may be determined based on a prediction result.
  • the assignment status associated with the service provider at the end of the predetermined waiting period may be predicted based on the prediction model and the prediction model may be obtained based on a machine learning algorithm using historical data, and therefore, compared with an assignment status associated with the service provider determined based on conditions set according to the subjectivity, the assignment status associated with the service provider determined based on the prediction model and the feature data corresponding to the prediction model may be more accurate. Furthermore, it can ensure that the predetermined waiting period can be accurately extended based on the prediction result and the income of the service provider can be ensured.
  • FIG. 14 is a flowchart of an exemplary process for determining whether to extend a predetermined waiting period according to some embodiments of the present disclosure.
  • the process 1400 may be executed by the on-demand service system 100.
  • the process 1400 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 22 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 1400.
  • the operations of the illustrated process 1400 presented below are intended to be illustrative. In some embodiments, the process 1400 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed.
  • the method may further include the following operations.
  • operation 1402 is the same with operation 1302 illustrated in FIG. 13.
  • the description of operation 1402 may be omitted here.
  • a first probability that a service request of the target request type is assigned to the service provider within a first predetermined period may be predicted based on a first prediction model.
  • the predetermined waiting period may be extended based on a result of the determination that the first probability is greater than a probability threshold (e.g., 0.7, 0.8, 0.85, 0.9) .
  • a probability threshold e.g., 0.7, 0.8, 0.85, 0.9
  • the first predetermined period and/or the probability threshold may be default settings of the on-demand service system 100 or may be adjustable under different situations.
  • FIG. 15 is a flowchart of an exemplary process for determining whether to extend a predetermined waiting period according to some embodiments of the present disclosure.
  • the process 1500 may be executed by the on-demand service system 100.
  • the process 1500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 22 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 1500.
  • the operations of the illustrated process 1500 presented below are intended to be illustrative. In some embodiments, the process 1500 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed.
  • operation 1502 is the same with operation 1302 illustrated in FIG. 13.
  • the description of operation 1502 may be omitted here.
  • a required first period by the end of which a second probability that a service request of the target request type is assigned to the service provider reaches a first predetermined probability may be predicted based on a second prediction model.
  • the predetermined waiting period may be extended based on a result of the determination that the required first period is less than a period threshold.
  • the first predetermined probability and/or the period threshold may be default settings of the on-demand service system 100 or may be adjustable under different situations.
  • the relationship between the probability that a service request of the target request type is assigned to the service provider and the extension period specifically includes a first probability that a service request of the target request type is assigned to the service provider within a first predetermined period or a required first period by the end of which a second probability that a service request of the target request type is assigned to the service provider reaches a first predetermined probability.
  • different prediction models may be used.
  • each relationship may correspond to a prediction result. For example, if the first probability that a service request of the target request type is assigned to the service provider within the first predetermined period is predicted based on the first prediction model, the prediction result may be whether the first probability is greater than the probability threshold. If the required first period by the end of which the second probability that a service request of the target request type is assigned to the service provider reaches the first predetermined probability is predicted based on the second prediction model, the prediction result may be whether the first period is smaller than a period threshold.
  • the predetermined waiting period may be extended, so that a service request of the target request type may be assigned to the service provider when the service provider waits for an extended period after waiting for the predetermined waiting period.
  • the predetermined waiting period may be extended, so that a service request of the target request type may be assigned to the service provider after the service provider waits for an extended period after waiting for the predetermined waiting period.
  • FIG. 16 is a flowchart of an exemplary process for assigning a service request of a candidate request type to a service provider according to some embodiments of the present disclosure.
  • the process 1600 may be executed by the on-demand service system 100.
  • the process 1600 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 23 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 1600.
  • the operations of the illustrated process 1600 presented below are intended to be illustrative. In some embodiments, the process 1600 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed.
  • the method may further include the following operations.
  • operations 1602-1606 are the same with operations 1402-1406 illustrated in FIG. 14.
  • the descriptions of operations 1602-1606 are omitted here.
  • a service request of the candidate request type may be assigned to the service provider in response to that a service request of the target request type is not assigned to the service provider after waiting for an extended waiting period.
  • a service request of the candidate request type may be assigned to the service provider.
  • the full use of the vehicle associated with the service provider can be ensured; on the other hand, an income of the service provider can be ensured, instead of being in a waiting status for a long time.
  • the vehicle model associated with a service request of the target request type may be more advanced than the vehicle model associated with a service request of the candidate request type.
  • the service and the riding condition corresponding to the vehicle associated with a service request of the target request type may also be better.
  • a service request of a type corresponding to the vehicle model associated with the service provider i.e., a service request of the target type
  • a service request of a type corresponding to a relatively lower-level vehicle model i.e., a service request of the candidate type
  • FIG. 17 is a flowchart of an exemplary process for assigning a service request of a candidate request type to a service provider according to some embodiments of the present disclosure.
  • the process 1700 may be executed by the on-demand service system 100.
  • the process 1700 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 24 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 1700.
  • the operations of the illustrated process 1700 presented below are intended to be illustrative.
  • the process 1700 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 1700 as illustrated in FIG. 17 and described below is not intended to be limiting. According to the embodiment illustrated in FIG. 14 or FIG. 15 (for brevity, an exemplary flowchart based on the embodiment illustrated in FIG. 14 is provided in FIG. 17) , the method may further include the following operations. As illustrated, operations 1702 and 1704 are the same with operations 1402 and 1404 illustrated in FIG. 14 respectively. For brevity, descriptions of operations 1702 and 1704 are omitted here.
  • a first unit profit value associated with the service provider under an assumption that the service provider waits for an extended waiting period and a service request of the target request type is assigned to the service provider and a second unit profit value associated with the service provider under an assumption that a service request of the candidate request type is assigned to the service provider may be predicted.
  • the predetermined waiting period may be extended in response to that the first unit profit value is greater than the second unit profit value.
  • a service request of the candidate request type may be assigned to the service provider in response to that the first unit profit value is less than or equal to the second unit profit value.
  • a service request of the target request type is not assigned to the service provider in the predetermined waiting period, if the system still wishes to assign a service request of the target request type to the service provider, it is necessary for the service provider to continue to wait. However, if the system decides to assign a service request of a candidate type to the service provider, it may be immediate for the service provider to receive the service request of the candidate type.
  • a unit profit value of a service request of the candidate request type may be lower than that of a service request of the target request type, considering that waiting for a service request of the target request type may needs a long time, the first unit profit value associated with the service provider under an assumption that the service provider waits for an extended waiting period and a service request of the target request type is assigned to the service provider may be lower than the second unit profit value associated with the service provider under an assumption that a service request of the candidate request type is assigned to the service provider.
  • the first unit profit value under an assumption that the service provider waits for an extended waiting period and a service request of the target request type is assigned to the service provider and the second unit profit value under an assumption that a service request of the candidate request type is assigned to the service provider may be predicted.
  • the service provider can wait for an extended waiting period so that a service request of the target request type may be assigned to the service provider.
  • the first unit profit value is equal to or smaller than the second unit profit value, it may be determined that the unit profit value under an assumption that the service provider waits for an extended waiting period and a service request of the target request type is assigned to the service provider is not greater than the unit profit value under an assumption that a service request of the candidate request type is assigned to the service provider. Therefore, a service request of the candidate request type can be directly assigned to the service provider so as to maximize the profit value of the service provider.
  • the server 110 may transmit prompt information to the service provider.
  • the service provider may choose to wait for a service request of the target request type or to be assigned a service request of the candidate request type, improving the experience of receiving service requests of the service provider.
  • FIG. 18 is a flowchart of an exemplary process for determining a first unit profit value according to some embodiments of the present disclosure.
  • the process 1800 may be executed by the on-demand service system 100.
  • the process 1800 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 24 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 1800.
  • the operations of the illustrated process 1800 presented below are intended to be illustrative. In some embodiments, the process 1800 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 1800 as illustrated in FIG. 18 and described below is not intended to be limiting. According to the embodiment illustrated in FIG. 17, operation 1706 may further include the following operations.
  • a first average service fee and a first average service period associated with a service request of the target request type may be predicted.
  • the first unit profit value may be determined based on the first average service fee, the first average service period, and the extended waiting period.
  • the first average service fee and the first average service period may be predicted respectively according to a corresponding prediction model, respectively. Further, the first unit profit value B 1 may be determined according to the following equation (4) :
  • P 1 refers to the first average service fee
  • t 1 refers to the first average service period
  • T p refers to the extended waiting period
  • a prediction model may be obtained based on historical service fees of historical service request of the target request type according to a machine learning algorithm.
  • a prediction model may be obtained based on historical service periods of historical service request of the target request type according to a machine learning algorithm.
  • FIG. 19 is a flowchart of an exemplary process for determining a second unit profit value according to some embodiments of the present disclosure.
  • the process 1900 may be executed by the on-demand service system 100.
  • the process 1900 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or sub-modules in FIG. 24 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the sub-modules may be configured to perform the process 1900.
  • the operations of the illustrated process 1900 presented below are intended to be illustrative. In some embodiments, the process 1900 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 1900 as illustrated in FIG. 19 and described below is not intended to be limiting. According to the embodiment illustrated in FIG. 17, operation 1706 may further include the following operations.
  • a required second waiting period by an end of which a service request of the candidate request type is assigned to the service provider, a second average service fee associated with a service request of the candidate request type, and a second average service period associated with a service request of the candidate request type may be predicted.
  • the second unit profit value may be determined based on the second average service fee, the second average service period, and the required second waiting period.
  • the second average service fee and the second average service period may be predicted according to a corresponding prediction model, respectively. Further, the second unit profit value may be determined according to the following equation (5) :
  • P 2 refers to the second average service fee
  • t 2 refers to the second average service period
  • T 3 refers to the second period
  • a prediction model may be obtained based on historical service fees of historical service request of the candidate request type according to machine learning algorithm.
  • a prediction model may be obtained based on historical service periods of historical service request of the candidate request type according to machine learning algorithm.
  • FIG. 20 is a flowchart of an exemplary process for obtaining a first prediction model according to some embodiments of the present disclosure.
  • the process 2000 may be executed by the on-demand service system 100.
  • the process 2000 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 25 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 2000.
  • the operations of the illustrated process 2000 presented below are intended to be illustrative. In some embodiments, the process 2000 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 2000 as illustrated in FIG. 20 and described below is not intended to be limiting. According to the embodiment illustrated in FIG. 14, operation 1404 may further include the following operations.
  • a plurality of historical probabilities that historical service requests of the target request type were assigned to historical service providers within the first predetermined period and at least one first historical feature data associated with the plurality of historical probabilities may be obtained.
  • a first training set including the at least one first historical feature data and the plurality of historical probabilities may be determined.
  • the term “historical probability” refers to a historical assignment result of a historical service provider within the first predetermined period. If a historical service request of the target request type was assigned to the historical service provider within the first predetermined period, the “historical probability” may be determined as “1, ” while if no historical service request of the target request type was assigned to the historical service provider within the first predetermined period, the “historical probability” may be determined as “0. ”
  • the first prediction model may be obtained based on the first training set according to a machine learning algorithm.
  • FIG. 21 is a flowchart of an exemplary process for obtaining a second prediction model according to some embodiments of the present disclosure.
  • the process 2100 may be executed by the on-demand service system 100.
  • the process 2100 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 26 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 2100.
  • the operations of the illustrated process 2100 presented below are intended to be illustrative. In some embodiments, the process 2100 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 2100 as illustrated in FIG. 21 and described below is not intended to be limiting. According to the embodiment illustrated in FIG. 15, operation 1504 may further include the following operations.
  • a plurality of historical periods and at least one second historical feature data associated with the plurality of historical periods may be obtained. For each of the plurality of historical periods, by the end of the historical period, a historical probability that a service request of the target request type was assigned to a historical service provider reached the first predetermined probability. Further, a second training set including the at least one second historical feature data and the plurality of historical periods may be determined.
  • the second prediction model may be obtained based on the second training set according to a machine learning algorithm.
  • the first way is to determine whether the first probability that a service request of the target request type is assigned to the service provider within the first predetermined period is greater than the probability threshold.
  • the second way is to predict whether the required first period by the end of which a second probability that a service request of the target request type is assigned to the service provider reaches the first predetermined probability is less than the period threshold.
  • the first prediction model and the second prediction model may be used respectively.
  • the first training set may be determined based on historical data of the past month, two months or more (e.g., half a year or one year) , and the first prediction model may be trained based on the feature data of the first training set.
  • the training process may be a repeated iterative process, and weight coefficients of different feature data may be determined by a large number of data in the training set.
  • a part of training data may be randomly selected from the training data. For example, if there are 500,000 pieces of training data, 100,000 pieces of training data may be randomly selected for the training process.
  • the machine learning algorithm may include at least one of a linear regression algorithm, a regression decision tree algorithm, an iterative decision tree algorithm, or a random forest algorithm.
  • the first historical feature data and/or the second historical feature data may include at least one of a location, a time, a number count of service providers, a number count of service requests of the target request type, or a price multiple of a service fee of a service request of the target request type.
  • the first historical feature data and the second historical feature data may be totally the same, or may be partially the same, or may be totally different.
  • the accuracies of prediction models generated based on the above various training algorithms may be different.
  • the amount of the feature data may be relatively large (ranging from several types described above to dozens) . Therefore, the relationship between the feature data and a predicted multiple of the service fee may be nonlinear. Since the linear regression algorithm is mainly applicable to a linear relationship, the accuracy of the predicted multiple of the service fee may be relatively low if a prediction model generated according to the linear regression algorithm is used.
  • a Rough Set Decision Tree (RDT) algorithm may have many good features, such as a relatively low training time complexity, a relatively fast prediction speed, an easy way for displaying a training model (it is easy to display the obtained decision tree by an image) , etc.
  • RDT Rough Set Decision Tree
  • using a single decision tree may result in an over-fitting problem. Although this situation can be reduced through some methods, such as pruning, the problem still exists.
  • a Gradient Boosting Decision Tree (GBDT) algorithm also known as a Multiple Additive Regression Tree (MART) algorithm, is an iterative decision tree algorithm.
  • the GBDT algorithm may include multiple decision trees, and a final result is obtained based on a combination of conclusions of all decision trees.
  • the GBDT algorithm can be used for almost all regression problems, including linear regression and nonlinear regression. Compared with that the logistic regression can only be used for linear regression, the GBDT may have a wide range of applications.
  • the core of the GBDT algorithm is that each tree learns a residual of a sum of conclusions of previous trees.
  • the residual may be an accumulated amount, wherein the accumulated amount plus a predicted value may be equal to an actual value.
  • the final result of the GBDT algorithm is to generate N (in some embodiments, N can be greater than 100) trees, which may greatly reduce the shortcomings of the single decision tree.
  • the embodiments of the present disclosure may preferably use the GBDT algorithm to generate the predetermined prediction model.
  • FIG. 22 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • the service request assignment device 2200 may include a determination module 2210, a prediction module 2220, and an extension module 2230.
  • the service request assignment device 2200 may be integrated into the server 110.
  • the service request assignment device 2200 may be part of the processing engine 112.
  • the determination module 2210 may be configured to determine a service provider that is waiting for a service request of a target request type to be assigned to the service provider.
  • the prediction module 2220 may be configured to, in response to that a service request of the target request type is not assigned to the service provider within a predetermined waiting period, predict an assignment status associated with the service provider at the end of the predetermined waiting period based on a prediction model.
  • the assignment status may include a relationship between a probability that a service request of the target request type is assigned to the service provider and an extension period.
  • the extension module 2230 may be configured to determine whether to extend the predetermined waiting period based on a prediction result.
  • the prediction module 2220 may predict a first probability that a service request of the target request type is assigned to the service provider within a first predetermined period based on a first prediction model.
  • the extension module 2230 may extend the predetermined waiting period based on a result of the determination that the first probability is greater than a probability threshold.
  • the prediction module 2220 may predict a required first period by the end of which a second probability that a service request of the target request type is assigned to the service provider reaches a first predetermined probability based on a second prediction model.
  • the extension module 2230 may extend the predetermined waiting period based on a result of the determination that the required first period is less than a period threshold.
  • the modules in FIG. 22 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a ZigBee
  • NFC Near Field Communication
  • FIG. 23 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • the service request assignment device 2300 may further include an assignment module 2310.
  • the service request assignment device 2300 may be integrated into the server 110.
  • the service request assignment device 2300 may be part of the processing engine 112.
  • the assignment module 2310 may be configured to assign a service request of a candidate request type to the service provider in response to that a service request of the target request type is not assigned to the service provider after waiting for an extended waiting period.
  • the modules in FIG. 23 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a ZigBee
  • NFC Near Field Communication
  • FIG. 24 is a block diagram illustrating an exemplary extension module according to some embodiments of the present disclosure.
  • the extension module 2230 may include a prediction sub-module 2410, an extension sub-module 2420, and an assignment sub-module 2430.
  • the prediction sub-module 2410 may be configured to predict a first unit profit value associated with the service provider under an assumption that the service provider waits for an extended waiting period, and a service request of the target request type is assigned to the service provider and a second unit profit value associated with the service provider under an assumption that a service request of the candidate request type is assigned to the service provider.
  • the extension sub-module 2420 may be configured to extend the predetermined waiting period in response to that the first unit profit value is greater than the second unit profit value.
  • the assignment sub-module 2430 may be configured to assign a service request of the candidate request type to the service provider in response to that the first unit profit value is less than or equal to the second unit profit value.
  • the prediction sub-module 2410 may be configured to predict a first average service fee and a first average service period associated with a service request of the target request type; and determine the first unit profit value based on the first average service fee, the first average service period, and the extended waiting period.
  • the prediction sub-module 2410 may be configured to predict a required second waiting period by the end of which a service request of the candidate request type is assigned to the service provider, a second average service fee associated with a service request of the candidate request type, and a second average service period associated with a service request of the candidate request type; and determine the second unit profit value based on the second average service fee, the second average service period, and the required second waiting period.
  • the sub-modules in FIG. 24 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a Bluetooth
  • ZigBee ZigBee
  • NFC Near Field Communication
  • FIG. 25 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • the service request assignment device 2500 may further include a first obtaining module 2510 and a first learning module 2520.
  • the service request assignment device 2500 may be integrated into the server 110.
  • the service request assignment device 2500 may be part of the processing engine 112.
  • the first obtaining module 2510 may be configured to obtain a plurality of historical probabilities that historical service requests of the target request type were assigned to historical service providers within the first predetermined period and at least one first historical feature data associated with the plurality of historical probabilities. The first obtaining module 2510 may further determine training data including the at least one first historical feature data and the plurality of historical probabilities as a first training set.
  • the first learning module 2520 may be configured to obtain the first prediction model based on the first training set according to a machine learning algorithm.
  • the modules in FIG. 25 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a ZigBee
  • NFC Near Field Communication
  • FIG. 26 is a block diagram illustrating an exemplary service request assignment device according to some embodiments of the present disclosure.
  • the service request assignment device 2600 may further include a second obtaining module 2610 and a second learning module 2620.
  • the service request assignment device 2600 may be integrated into the server 110.
  • the service request assignment device 2600 may be part of the processing engine 112.
  • the second obtaining module 2610 may be configured to obtain a plurality of historical periods and at least one second historical feature data associated with the plurality of historical periods. For each of the plurality of historical periods, by the end of the historical period, a historical probability that a service request of the target request type was assigned to a historical service provider reached the first predetermined probability. Further, the second obtaining module 2610 may determine training data including the at least one second historical feature data and the plurality of historical periods as a second training set.
  • the second learning module 2620 may be configured to obtain the second prediction model based on the second training set according to a machine learning algorithm.
  • the machine learning algorithm may include at least one of a linear regression algorithm, a regression decision tree algorithm, an iterative decision tree algorithm, or a random forest algorithm.
  • the first historical feature data and/or the second historical feature data may include at least one of a location, a time, a number count of service providers, a number count of service requests of the target request type, or a price multiple of a service fee of a service request of the target request type.
  • the modules in FIG. 26 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • Bluetooth a ZigBee
  • NFC Near Field Communication
  • the present disclosure may also provide a computer storage medium including instructions.
  • the instructions When executing by at least one processor, the instructions may direct the at least one processor to perform a process (e.g., the processes 1300-2100) described elsewhere in the present disclosure.
  • FIG. 27 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure.
  • the processing engine 112 may include an obtaining module 2710, a determination module 2720, and a transmission module 2730.
  • the obtaining module 2710 may be configured to obtain provider information associated with a service provider.
  • the provider information may include a location of the service provider, a target request type, etc. More descriptions of the target request type may be found elsewhere in the present disclosure (e.g., FIG. 13 and the description thereof) .
  • the location of the service provider may be determined according to Global Positioning System (GPS) data determined and transmitted by a user terminal (e.g., the provider terminal 140) associated with the service provider.
  • GPS Global Positioning System
  • the determination module 2720 may be configured to may estimate an assignment parameter associated with the service provider based on the provider information.
  • the assignment parameter may include at least one profit value corresponding to at least one service request, an assignment status of the service provider at the end of a predetermined waiting period, etc. More descriptions of the assignment parameter may be found elsewhere in the present disclosure (e.g., FIG. 28 and the description thereof) .
  • the transmission module 2730 may be configured to may transmit, via a network, data associated with a service request to the user terminal (e.g., the provider terminal 140) associated with the service provider based on the assignment parameter.
  • the data associated with the service request may include a start location of the service request, a destination of the service request, an estimated travel route from the location of the service provider to the start location of the service request, or the like, or a combination thereof.
  • the user terminal in response to receiving the data associated with the service request, the user terminal may display at least portion of the received data associated with the service request in a graphic user interface. For example, the user terminal may display an interface message including one or more user interface elements (e.g., a “confirmation” button, a “refuse” button) on the graphic user interface.
  • the modules in FIG. 27 may be connected to or communicate with each other via a wired connection or a wireless connection.
  • the wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or a combination thereof.
  • the wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or a combination thereof.
  • LAN Local Area Network
  • WAN Wide Area Network
  • NFC Near Field Communication
  • Two or more of the modules may be combined into a single module, and any one of the modules may be divided into two or more units.
  • the processing engine 112 may include a storage module (not shown) configured to store information and/or data (e.g., the provider information, the assignment parameter) associated with the above modules.
  • FIG. 28 is a flowchart of an exemplary process for assigning a service request to a service provider according to some embodiments of the present disclosure.
  • the process 2800 may be executed by the on-demand service system 100.
  • the process 2800 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240.
  • the processor 220 and/or modules in FIG. 27 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 2800.
  • the operations of the illustrated process 2800 presented below are intended to be illustrative. In some embodiments, the process 2800 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 2800 as illustrated in FIG. 28 and described below is not intended to be limiting.
  • the processing engine 112 may obtain provider information associated with a service provider.
  • the provider information may include a location of the service provider, a target request type, etc. More descriptions of the target request type may be found elsewhere in the present disclosure (e.g., FIG. 13 and the description thereof) .
  • the location of the service provider may be determined according to Global Positioning System (GPS) data determined and transmitted by a user terminal (e.g., the provider terminal 140) associated with the service provider.
  • GPS Global Positioning System
  • the processing engine 112 may estimate an assignment parameter associated with the service provider based on the provider information.
  • the assignment parameter may include at least one profit value corresponding to at least one service request, an assignment status of the service provider at the end of a predetermined waiting period, etc.
  • the processing engine 112 may determine at least one service request based on the location of the service provider, wherein a first distance between a start location of each of the at least one service request and the location of the service provider is less than a distance threshold. The processing engine 112 may further estimate a profit value for each of the at least one service request if the service provider completes the each of the at least one service request.
  • the processing engine 112 may estimate a travel cost of the service provider traveling from the location of the service provider to the start location of the each of the at least one service request based on the first distance.
  • the processing engine 112 may also determine a second distance between the start location of the each of the at least one service request and a destination of the each of the at least one service request.
  • the processing engine 112 may further estimate a service fee associated with the each of the at least one service request based on the second distance.
  • the processing engine 112 may estimate the profit value for the each of the at least one service request based on the travel cost and the service fee. More descriptions of the profit value may be found elsewhere in the present disclosure (e.g., FIG. 5 and the description thereof) .
  • the processing engine 112 may further determine a first unit profit value corresponding to the each of the at least one service request. For example, the processing engine 112 may estimate a travel period of the service provider traveling from the location of the service provider to the start location of the each of the at least one service request. The processing engine 112 may estimate a service period of the service provider traveling from the start location to the destination. The processing engine 112 may estimate a completion time point when the service provider arrives at the destination based on the travel period and the service period.
  • the processing engine 112 may predict a ratio of a number count of available service providers to a number count of candidate service requests within a predetermined distance range (e.g., 500 m, 1 km, 2 km, 3 km) of the destination at the completion time point based on a trained prediction model (e.g., the predetermined prediction model described in FIG. 7) .
  • the processing engine 112 may estimate a waiting period from the completion time point to a time point when a next service request is assigned to the service provider based on the ratio. Further, the processing engine 112 may determine the first unit profit value corresponding to the each of the at least one service request based on the profit value, the travel period, the service period, and the waiting period.
  • the processing engine 112 may assign a first weighting coefficient to the travel period, assign a second weighting coefficient to the service period, and assign a third weighting coefficient to the waiting period.
  • the first weighting coefficient may be larger than the second weighting coefficient or the third weighting coefficient.
  • the processing engine 112 may determine the total period based on the travel period, the service period, the waiting period, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient. Further, the processing engine 112 may determine the first unit profit value based on the profit value and the total period. More descriptions of the unit profit value may be found elsewhere in the present disclosure (e.g., FIG. 6, FIG. 7, and the description thereof) .
  • the trained prediction model may be determined based on a prediction model training process.
  • the processing engine 112 may determine a plurality of training samples. Each of the plurality of training samples may include a number count of historically available service providers and a number count of historical service requests to be assigned.
  • the processing engine 112 may obtain a predetermined preliminary prediction model and train the predetermined preliminary prediction model based on the plurality of training samples according to a machine learning algorithm.
  • the processing engine 112 may determine whether a service request of the target request type is not assigned to the service provider within a predetermined waiting period. According to a result of the determination that a service request of the target request type is not assigned to the service provider within the predetermined waiting period, the processing engine 112 may predict an assignment status associated with the target request type at the end of the predetermined waiting period. The processing engine 112 may determine whether to generate an extension period based on the predicted assignment status and determine at least one service request of a candidate request type based on a result of the determination of not generating the extension period.
  • the assignment status may include a first probability that a service request of the target request type is assigned to the service provider at the end of a first predetermined period.
  • the processing engine 112 may generate the extension period based on a result of the determination that the first probability is greater than a first probability threshold (e.g., the probability threshold described in FIG. 14) .
  • the processing engine 112 may predict the first probability based on a first trained prediction model.
  • the first trained prediction model may be trained based on a first training process.
  • the processing engine 112 may obtain a plurality of first positive samples and a plurality of first negative samples, wherein in each of the plurality of first positive samples, a historical service request of the target request type was assigned to a historical service provider at the end of the first predetermined period, and in each of the plurality of first negative samples, a historical service request of the target request type was not assigned to a historical service provider at the end of the first predetermined period.
  • the processing engine 112 may further obtain a first preliminary prediction model and train the first preliminary prediction model based on the plurality of first positive samples and the plurality of first negative samples according to a machine learning algorithm.
  • the assignment status may include a first period (e.g., the required first period described in FIG. 15) , at an end of which a second probability that a service request of the target request type is assigned to the service provider reaches a second probability threshold (e.g., the first predetermined probability described in FIG. 15) .
  • the processing engine 112 may generate the extension period based on a result of the determination that the first period is less than a period threshold.
  • the processing engine 112 may predict the first period based on a second trained prediction model.
  • the second trained prediction model may be trained based on a second training process.
  • the processing engine 112 may obtain a plurality of second samples, wherein each of the plurality of second samples includes a historical period, at the end of which a historical service request of the target request type was assigned to a historical service provider.
  • the processing engine 112 may further obtain a second preliminary prediction model and train the second preliminary prediction model based on the plurality of second samples.
  • the processing engine 112 may estimate a second unit profit value (corresponding to the “first unit profit value” described in FIG. 17 and FIG. 18) associated with the service provider under an assumption that the extension period is generated, and a service request of the target request type is assigned to the service provider at the end of the extension period.
  • the processing engine may also estimate a third unit profit value (corresponding to the “second unit profit value” described in FIG. 17 and FIG. 19) associated with the service provider under an assumption that the extension period is not generated and a service request of the candidate type is assigned to the service provider.
  • the processing engine 112 may determine whether the second unit profit value is greater than the third unit profit and determine to generate the extension period based on a result of the determination that the second unit profit value is greater than the third unit profit value.
  • the processing engine 112 may determine the second unit profit value based on a first average service fee associated with a service request of the target request type, a first average service period associated with a service request of the target request type, and an extended waiting period (e.g., the predetermined waiting period plus the extension period) .
  • the processing engine 112 may determine the third unit profit value based on a second average service fee associated with a service request of the candidate request type, a second average service period associated with a service request of the candidate request type, and a second period (corresponding to the “required second waiting period” described in FIG. 19) at an end of which a service request of the candidate request type is assigned to the service provider.
  • the processing engine 112 may transmit, via a network, data associated with a service request to the user terminal (e.g., the provider terminal 140) associated with the service provider based on the assignment parameter.
  • the data associated with the service request may include a start location of the service request, a destination of the service request, an estimated travel route from the location of the service provider to the start location of the service request, or the like, or a combination thereof.
  • the user terminal in response to receiving the data associated with the service request, may display at least portion of the received data associated with the service request in a graphic user interface. For example, the user terminal may display an interface message including one or more user interface elements (e.g., a “confirmation” button, a “refuse” button) on the graphic user interface.
  • the processing engine 112 may transmit data associated with a service request corresponding to a maximum profit value (or a maximum unit profit value) to the user terminal associated with the service provider.
  • the processing engine 112 may wait for a service request of the target request type and transmit data associated with the service request of the target request type to the user terminal associated with the service provider.
  • the processing engine 112 may transmit data associated with a service request of the target request type corresponding to a maximum profit value (or a maximum unit profit value) to the user terminal associated with the service provider.
  • the processing engine 112 may transmit data associated with a service request of the candidate request type to the user terminal associated with the service provider.
  • the processing engine 112 may transmit data associated with a service request of the candidate request type corresponding to a maximum profit value (or a maximum unit profit value) to the user terminal associated with the service provider.
  • aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “unit, ” “module, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied thereon.
  • a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electromagnetic, optical, or the like, or any suitable combination thereof.
  • a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.
  • Computer program code for carrying out operations for aspects of the present disclosure may be written in a combination of one or more programming languages, including an object-oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran, Perl, COBOL, PHP, ABAP, dynamic programming languages such as Python, Ruby, and Groovy, or other programming languages.
  • 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.
  • 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) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .
  • LAN local area network
  • WAN wide area network
  • SaaS Software as a Service

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Abstract

La présente invention concerne des systèmes et des procédés destinés à assurer des services en ligne à hors ligne. Les systèmes et les procédés peuvent permettre d'obtenir des informations de fournisseur associées à un fournisseur de service. Les informations de fournisseur peuvent comprendre l'emplacement du fournisseur de service ou un type de demande cible, l'emplacement du fournisseur de services pouvant être déterminé selon des données du système mondial de localisation (GPS) transmises par un terminal utilisateur associé au fournisseur de service. Les systèmes et les procédés peuvent permettre d'estimer un paramètre d'attribution associé au fournisseur de service sur la base des informations de fournisseur. Les systèmes et les procédés peuvent permettre de transmettre, par l'intermédiaire d'un réseau, des données associées à une demande de service au terminal utilisateur associé au fournisseur de service selon le paramètre d'attribution, et le terminal utilisateur, en réponse à la réception des données associées à la demande de service, peut afficher dans une interface utilisateur graphique au moins une partie des données associées à la demande de service qui sont reçues.
PCT/CN2018/114239 2017-12-28 2018-11-07 Systèmes et procédés d'attribution de demandes de service WO2019128477A1 (fr)

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