CN111260164A

CN111260164A - Transport capacity scheduling method and device

Info

Publication number: CN111260164A
Application number: CN201811457692.7A
Authority: CN
Inventors: 张凌宇; 张玉会; 叶杰平; 张露露
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2018-11-30
Filing date: 2018-11-30
Publication date: 2020-06-09

Abstract

The application provides a capacity scheduling method and a capacity scheduling device, wherein the method comprises the following steps: acquiring historical departure areas of a plurality of first target service providers on line in a future preset time period; acquiring historical order data of each historical departure area in a historical time period corresponding to the future preset time period; and generating scheduling information for scheduling the second target service providers according to the historical order data of each historical departure area and the quantity of the first target service providers departing from the historical departure area in the historical time period corresponding to the future preset time period. The application can balance the transport capacity of a plurality of historical departure areas.

Description

Transport capacity scheduling method and device

Technical Field

The application relates to the technical field of computer application, in particular to a capacity scheduling method and device.

Background

The net appointment vehicle is a novel travel mode, has been selected by more and more users, and more people use the net appointment vehicle driver as the first occupation or the second occupation of the net appointment vehicle driver.

The departure time of the net appointment vehicle as a driver of the first occupation is usually fixed, namely the departure time is longer, and the net appointment vehicle is a main transport capacity undertaker of the net appointment vehicle. The network car booking driver as a second-occupation driver can share great order pressure in the order rush hour, and the network car booking driver is an important supplement of network car booking transport capacity. However, since the departure time of the net appointment driver as the second occupation net appointment is not fixed, and the departure places are more diversified, the problem of unbalanced transport capacity in each area exists.

Disclosure of Invention

In view of this, an object of the embodiments of the present application is to provide a capacity scheduling method and apparatus, which can achieve capacity balance for a plurality of historical departure areas.

In a first aspect, an embodiment of the present application provides a capacity scheduling method, where the method includes:

acquiring historical departure areas of a plurality of first target service providers on line in a future preset time period;

acquiring historical order data of each historical departure area in a historical time period corresponding to the future preset time period;

and generating scheduling information for scheduling the second target service providers according to the historical order data of each historical departure area and the quantity of the first target service providers departing from the historical departure area in the historical time period corresponding to the future preset time period.

In an optional implementation manner, before obtaining the historical departure areas of the plurality of first target service providers that come on line in a future preset time period, the method further includes:

determining the first target service provider from a plurality of service providers.

In an optional implementation manner, the determining the first target service provider from the plurality of service providers includes:

acquiring historical order data of a plurality of service providers; the historical order data comprises order taking time;

for each service provider, grouping the historical orders of the target service provider based on the order receiving time; in each group, the order receiving time difference between any two adjacent historical orders is smaller than a preset time difference threshold value;

detecting whether a target group exists in each group of the service provider; the order receiving time corresponding to the historical order with the earliest order receiving time in the target group falls into the preset time period;

and if the target group exists, determining the service provider as a first target service provider.

In an optional implementation manner, the detecting whether there is a target packet in each packet of the service provider includes:

determining all the groups of which the order taking time corresponding to the historical order with the earliest order taking time falls into the historical time period corresponding to the preset time period from the groups corresponding to the service providing terminal; and if the determined quantity of the historical orders in all the groups reaches a preset percentage of the total quantity of the historical orders of the service provider, or the determined quantity of the historical orders in all the groups meets a preset quantity threshold, determining the service provider as the first target service provider.

In an optional implementation manner, the obtaining the historical departure areas of the plurality of first target service providers who come on line in a preset time period in the future includes:

acquiring historical departure places of a plurality of first target service providers on line in a future preset time period;

clustering historical departure places of a plurality of first target service providing ends to obtain at least one class;

and determining a historical departure area corresponding to each class based on at least one historical departure place contained in the class.

In an optional implementation manner, the obtaining historical departure locations of a plurality of first target service providers who come on line in a preset time period in the future includes:

and determining the order receiving place corresponding to the historical order with the earliest order receiving time in the target group corresponding to each first target service providing end as the historical departure place corresponding to the first target service providing end.

when receiving a order receiving starting instruction of a first target service provider, recording the geographical position of the first service provider;

and determining the geographic position corresponding to the order receiving starting instruction received in the historical time period corresponding to the future preset time period as the historical departure place of the first target service providing end.

In an optional embodiment, the generating, according to the historical order data of the historical departure area and the historical time period corresponding to the preset time period in the future, scheduling information for scheduling the terminal of the second target service provider in the historical departure area includes:

determining the estimated order quantity corresponding to the preset time period according to the historical order data of the historical departure area;

generating an order satisfaction coefficient of the historical departure area in the preset time period according to the estimated order quantity corresponding to the preset time period and the quantity of the first target service providing terminals of the departure area in the historical time period corresponding to the future preset time period;

and generating scheduling information for scheduling the second target service provider according to the order satisfaction coefficients of the historical departure areas.

In an optional implementation manner, before generating scheduling information for scheduling the second target service provider according to the size of the order satisfaction coefficient of each historical departure area, the method further includes:

and determining the service provider which is on line in each historical departure area and is currently in a non-service state as the second target service provider.

In an optional implementation, the clustering the historical departure points of the plurality of first target service providers includes:

taking the historical departure points of all the first target service providing ends as a first cluster, and calculating a cluster center coordinate of the first cluster, wherein the cluster center coordinate is the average longitude and latitude of each historical departure point in the first cluster;

determining a historical departure place which is closest to the cluster center of the first cluster and within a preset distance range with the center as a center to form a second cluster;

taking the second cluster as a new first cluster, returning to the step of calculating the cluster center coordinates of the first cluster until an iteration stop condition is met, taking the finally obtained second cluster as a clustered class, and taking the historical departure place in the second cluster as a historical departure place for completing clustering;

and returning to the step of calculating the cluster center coordinates of the first clusters by taking the historical departure points of the first target service providing ends as the first clusters until the historical departure points of the first target service providing ends are clustered to obtain one or more clustered classes.

randomly selecting K historical departure places from the historical departure places of each first target service provider as initial clustering centers according to the number K of the preset historical departure areas;

for each initial cluster center, performing the following steps:

taking the historical departure place with the distance from the clustering center smaller than a first preset distance and the clustering center as a first cluster, and calculating the cluster center coordinate of the first cluster, wherein the cluster center coordinate is the average longitude and latitude of the historical departure place in the first cluster;

and taking the second cluster as a new first cluster, returning to the step of calculating the cluster center coordinates of the first cluster until an iteration stop condition is met, and taking the finally obtained second cluster as a clustered class.

taking any one historical departure place in the current incomplete clustering historical departure places as a clustering center, and sequentially calculating the distance between each historical departure place of other current incomplete clustering and the clustering center;

dividing historical departure places with a distance smaller than a second preset distance from the clustering center into the same class with the clustering center, and taking all the historical departure places in the class as the historical departure places completing clustering;

and returning to the step of taking any one historical departure place in the historical departure places of the current unfinished clusters as a clustering center and sequentially calculating the distance between each historical departure place of other current unfinished clusters and the clustering center until all the historical departure places finish clustering.

In an alternative embodiment, the determining, based on at least one historical departure location included in each class, a historical departure area corresponding to the class includes:

for each class, determining a historical departure place which is farthest away from the center of the class in the class; and determining the historical departure area corresponding to the type by taking the center of the type as the center of a circle and the distance between the determined historical departure place which is farthest away from the center of the type and the center as the radius.

In a second aspect, an embodiment of the present application further provides a capacity scheduling apparatus, where the apparatus includes:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical departure areas of a plurality of first target service providers on line in a future preset time period;

the second acquisition module is used for acquiring historical order data of each historical departure area in a historical time period corresponding to the future preset time period;

and the generation module is used for generating scheduling information for scheduling the second target service providing terminals according to the historical order data of each historical departure area and the quantity of the first target service providing terminals departing from the historical departure area in the historical time period corresponding to the future preset time period.

In an alternative embodiment, the method further comprises: the first determining module is used for determining the first target service providing end from a plurality of service providing ends before the historical departure areas of the plurality of first target service providing ends which come on line in the future preset time period are obtained.

In an optional implementation manner, the first determining module is configured to determine the first target service provider from a plurality of service providers by:

In an optional implementation manner, the first determining module is configured to detect whether a target packet exists in each packet of the service provider by using the following manner:

In an optional implementation manner, the first obtaining module is configured to obtain historical departure areas of a plurality of first target service providers who come on line in a future preset time period by:

In an optional implementation manner, the first obtaining module is configured to obtain historical departure locations of a plurality of first target service providers who come on line in a future preset time period by:

In an optional implementation manner, the generating module is specifically configured to:

In an alternative embodiment, the method further comprises: and the second determining module is used for determining the service provider which is on line in each historical departure area and is currently in a non-service state as the second target service provider before generating scheduling information for scheduling the second target service provider according to the order satisfaction coefficient of each historical departure area.

In an optional implementation manner, the first obtaining module is configured to cluster historical departure locations of a plurality of first target service providers in the following manner:

for each initial cluster center, performing the following steps:

In an optional implementation manner, the first obtaining module is configured to determine, based on at least one historical departure place included in each class, a historical departure area corresponding to the class by:

In a third aspect, an embodiment of the present application further provides an electronic device, including: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when an electronic device runs, the processor and the memory are communicated through the bus, and the machine-readable instructions are executed by the processor to execute the capacity scheduling method of any one of the first aspect.

In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for scheduling capacity is performed according to any one of the above first aspects.

In the embodiment of the application, historical departure areas of the first target service providers on the line of the future preset time period are dynamically determined, and scheduling information for scheduling the second target service providers is generated based on historical order data of a plurality of historical time periods corresponding to the future preset time period of each historical departure area and the number of the first target service providers departing from the historical departure area in the historical time period corresponding to the future preset time period, so that the future transport capacity balance of the plurality of historical departure areas is realized.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

FIG. 1 is a block diagram of a capacity scheduling system 100 of some embodiments of the present application;

FIG. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester terminal 130, a service provider terminal 140, which may implement the concepts of the present application, according to some embodiments of the present application;

fig. 3 is a flowchart illustrating a capacity scheduling method provided in an embodiment of the present application;

fig. 4 is a flowchart illustrating a specific method for determining a first target service provider from a plurality of service providers in a capacity scheduling method according to an embodiment of the present application;

fig. 5 is a flowchart illustrating a specific method for acquiring multiple historical departure areas of multiple first target service providers on line in a preset time period in the transportation capacity scheduling method according to the embodiment of the present application;

fig. 6 is a flowchart illustrating a first method for clustering historical departure locations in a transportation capacity scheduling method according to an embodiment of the present application;

fig. 7 is a flowchart illustrating a second method for clustering historical departure locations in the capacity scheduling method according to the embodiment of the present application;

fig. 8 is a flowchart illustrating a third method for clustering historical departure locations in the capacity scheduling method according to the embodiment of the present application;

fig. 9 is a flowchart illustrating a specific method for generating scheduling information for scheduling a second target service provider in the capacity scheduling method according to the embodiment of the present application;

fig. 10 shows a schematic diagram of a capacity scheduling apparatus according to a fourth embodiment of the present application.

Detailed Description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

To enable those skilled in the art to utilize the present disclosure, the following embodiments are presented in conjunction with a specific application scenario, "network appointment". It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Although the present application is primarily described in the context of capacity scheduling for network appointments, it should be understood that this is merely one exemplary embodiment. The application can be applied to any other traffic type. For example, the present application may be applied to different transportation system environments, including terrestrial, marine, or airborne, among others, or any combination thereof. The vehicle of the transportation system may include a taxi, a private car, a windmill, a bus, a train, a bullet train, a high speed rail, a subway, a ship, an airplane, a spacecraft, a hot air balloon, or an unmanned vehicle, etc., or any combination thereof. The present application may also include any service system for providing a certain paid service to a user, for example, a system for sending and/or receiving a courier, a service system for a business to a seller. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

The terms "user," "passenger," "requestor," "service person," "service requestor," and "customer" are used interchangeably in this application to refer to an individual, entity, or tool that can request or order a service. The terms "first target service provider," "service provider," and "vendor" are used interchangeably in this application to refer to an individual, entity, or tool that may provide a service. The term "user" in this application may refer to an individual, entity or tool that requests a service, subscribes to a service, provides a service, or facilitates the provision of a service. For example, the user may be a passenger, a driver, an operator, etc., or any combination thereof. In the present application, "passenger" and "passenger terminal" may be used interchangeably, and "driver" and "driver terminal" may be used interchangeably.

The terms "service request" and "order" are used interchangeably herein to refer to a request initiated by a passenger, a service requestor, a first targeted service provider, a service provider, or a supplier, the like, or any combination thereof. Accepting the "service request" or "order" may be a passenger, a service requestor, a first target service provider, a supplier, or the like, or any combination thereof. The service request may be charged or free.

The Positioning technology used in the present application 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 any combination thereof. One or more of the above-described positioning systems may be used interchangeably in this application.

One aspect of the present application relates to a capacity scheduling system. The system can acquire historical departure areas of a plurality of first target service providers on line in a future preset time period, and generates scheduling information for scheduling second target service providers on the basis of historical order data of each historical departure area and the number of the first target service providers departing from the historical departure areas in the historical time period corresponding to the future preset time period.

It is noted that before the application is filed, the capacity is generally scheduled by using the current online service provider to perform instant scheduling. The requirement of transport capacity balance cannot be met due to the fact that transport capacity scheduling information lags. However, the capacity scheduling system provided by the application can realize the capacity balance of a plurality of historical departure areas.

Fig. 1 is a block diagram of a capacity scheduling system 100 of some embodiments of the present application. For example, capacity scheduling system 100 may be an online transportation service platform for transportation services such as taxi cab, designated drive service, express, carpool, bus service, driver rental, or regular service, or any combination thereof. Capacity scheduling system 100 may include one or more of a server 110, a network 120, a service requester terminal 130, a service provider terminal 140, and a database 150, and server 110 may include a processor 112 for performing instruction operations.

In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote to the terminal. For example, the server 110 may access information and/or data stored in the service requester terminal 130, the service provider terminal 140, or the database 150, or any combination thereof, via the network 120. As another example, the server 110 may be directly connected to at least one of the service requester terminal 130, the service provider terminal 140, and the database 150 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof. In some embodiments, the server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 in the present application.

In some embodiments, the server 110 may include a processor 112. Processor 112 may process information and/or data related to the service request to perform one or more of the functions described herein. For example, the processor 112 may determine the target vehicle based on a service request obtained from the service requester terminal 130. In some embodiments, the processor 112 may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, the Processor 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 Physical 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 (Reduced Instruction Set Computing, RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components in capacity scheduling system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, and database 150) may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester terminal 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or combination thereof. Merely by way of example, Network 130 may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a WLAN, a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of capacity scheduling system 100 may connect to network 120 to exchange data and/or information.

In some embodiments, the user of the service requestor terminal 130 may be someone other than the actual demander of the service. For example, the user a of the service requester terminal 130 may use the service requester terminal 130 to initiate a service request for the service actual demander B (for example, the user a may call a car for his friend B), or receive service information or instructions from the server 110. In some embodiments, the user of the service provider terminal 140 may be the actual provider of the service or may be another person than the actual provider of the service. For example, user C of the service provider terminal 140 may use the service provider terminal 140 to receive a service request for services provided by the actual service provider D (e.g., user C may order a first target service provider D employed by itself), and/or information or instructions from the server 110. In some embodiments, "service requester" and "service requester terminal" may be used interchangeably, and "service provider" and "service provider terminal" may be used interchangeably.

In some embodiments, the service requester terminal 130 may comprise a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, etc., or any combination thereof. In some embodiments, the mobile device 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 any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, control devices for smart electrical devices, smart monitoring devices, smart televisions, smart cameras, or walkie-talkies, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, or a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like. In some embodiments, the built-in devices in the motor vehicle may include an on-board computer, an on-board television, and the like. In some embodiments, the service requester terminal 130 may be a device having a location technology for locating the location of the service requester and/or service requester terminal.

In some embodiments, the service provider terminal 140 may be a similar or identical device as the service requestor terminal 130. In some embodiments, the service provider terminal 140 may be a device with location technology for locating the location of the service provider and/or the service provider terminal. In some embodiments, the service requester terminal 130 and/or the service provider terminal 140 may communicate with other locating devices to determine the location of the service requester, service requester terminal 130, service provider, or service provider terminal 140, or any combination thereof. In some embodiments, the service requester terminal 130 and/or the service provider terminal 140 may transmit the location information to the server 110.

Database 150 may store data and/or instructions. In some embodiments, the database 150 may store data obtained from the service requester terminal 130 and/or the service provider terminal 140. In some embodiments, database 150 may store data and/or instructions for the exemplary methods described herein. In some embodiments, database 150 may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, database 150 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.

In some embodiments, a database 150 may be connected to network 120 to communicate with one or more components in capacity scheduling system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, etc.). One or more components in capacity scheduling system 100 may access data or instructions stored in database 150 via network 120. In some embodiments, database 150 may be directly connected to one or more components in capacity scheduling system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, etc.); alternatively, in some embodiments, database 150 may also be part of server 110.

In some embodiments, one or more components in capacity scheduling system 100 (e.g., server 110, service requester terminal 130, service provider terminal 140, etc.) may have access to database 150. In some embodiments, one or more components in capacity scheduling system 100 may read and/or modify information related to a service requester, a service provider, or the public, or any combination thereof, when certain conditions are met. For example, server 110 may read and/or modify information for one or more users after receiving a service request. As another example, the service provider terminal 140 may access information related to the service requester when receiving the service request from the service requester terminal 130, but the service provider terminal 140 may not modify the related information of the service requester.

In some embodiments, the exchange of information by one or more components in capacity scheduling system 100 may be accomplished by requesting a service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or a non-physical product. Tangible products may include food, pharmaceuticals, commodities, chemical products, appliances, clothing, automobiles, homes, or luxury goods, and the like, or any combination thereof. The non-material product may include a service product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include a stand-alone host product, a network product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The internet product may be used in software, programs, or systems of the mobile terminal, etc., or any combination thereof. The mobile terminal may include a tablet, a laptop, a mobile phone, a Personal Digital Assistant (PDA), a smart watch, a Point of sale (POS) device, a vehicle-mounted computer, a vehicle-mounted television, a wearable device, or the like, or any combination thereof. The internet product may be, for example, any software and/or application used in a computer or mobile phone. The software and/or applications may relate to social interaction, shopping, transportation, entertainment time, learning, or investment, or the like, or any combination thereof. In some embodiments, the transportation-related software and/or applications may include travel software and/or applications, vehicle dispatch software and/or applications, mapping software and/or applications, and the like. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a human powered vehicle (e.g., unicycle, bicycle, tricycle, etc.), an automobile (e.g., taxi, bus, privatege, etc.), a train, a subway, a ship, an airplane (e.g., airplane, helicopter, space shuttle, rocket, hot air balloon, etc.), etc., or any combination thereof.

Fig. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester terminal 130, a service provider terminal 140, which may implement the concepts of the present application, according to some embodiments of the present application. For example, the processor 112 may be used on the electronic device 200 and to perform the functions herein.

The electronic device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the capacity scheduling method of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.

For example, the electronic device 200 may include a network port 210 connected to a network, one or more processors 220 for executing program instructions, a communication bus 230, and a different form of storage medium 240, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 200 also includes an Input/Output (I/O) interface 250 between the computer and other Input/Output devices (e.g., keyboard, display screen).

For ease of illustration, only one processor is depicted in the electronic device 200. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.

Fig. 3 shows a flowchart of a capacity scheduling method provided in an embodiment of the present application, where the method includes: s301 to S303.

S301: and acquiring historical departure areas of a plurality of first target service providers on line in a future preset time period.

In particular implementations, not every target service provider may act as the first target service provider. In this embodiment, the first target service provider is a service provider that meets certain requirements.

Therefore, the capacity scheduling method provided by the embodiment of the present application further includes: a first target service provider is determined from a plurality of service providers.

Specifically, referring to fig. 4, an embodiment of the present application further provides a specific method for determining a first target service provider from a plurality of service providers, including:

s401: acquiring historical order data of a plurality of service providers; the historical order data includes order taking time.

Here, the historical order data may be all the historical order data of the service provider, or may be part of the historical order data of the service provider, for example, the obtained historical order data is the historical order data before the current time, such as the historical order data 3 months before the current time.

After receiving the order sent by the passenger from the passenger client, the network car booking platform can acquire the appointed network car booking service providing terminals according to the departure place carried in the order and a certain matching rule, and then pushes the order to the clients of the appointed network car booking service providing terminals. After receiving the order pushed by the network car booking platform through the client, the network car booking service providing end selects whether to take the order or not according to own will; and if the order is received, sending an order receiving request to the online taxi appointment platform through the client. After receiving the order taking request, the network car booking platform distributes the order to the network car booking service provider, records relevant information of the order, such as a starting place, a destination place, order taking time, order completion time, a place where the service provider takes the order and the like, and stores the information in a correlated manner with the network car booking service provider as historical order data of the order.

When the historical order data of each service provider needs to be obtained, all the historical order data of the service provider can be obtained or the historical order data corresponding to the specified date can be obtained only according to the association storage relation between the historical order data and the service provider.

In the historical order data, the order taking time is typically included. The order receiving time refers to the time when the service provider receives the order.

S402: for each service provider, grouping the historical orders of the target service provider based on the order receiving time; in each group, the order receiving time difference between any two adjacent historical orders is smaller than a preset time difference threshold value.

In practice, the acquired historical order data of each service provider includes not only the data of the first order received by the service provider after departure but also the data of other orders received by the service provider after departure, so that the historical orders of the service provider can be grouped according to order receiving time carried in the historical order data in order to be able to screen the data of the first order from all the historical order data, and in each group, the order receiving time difference between any two adjacent historical orders is smaller than the preset time difference threshold value.

The preset time difference threshold may be specifically set based on actual conditions. Generally, the preset time difference threshold may be set based on the average completion time of the order. Here, the order completion time is a length of time required for the service provider to take the order and deliver the passenger to the passenger's destination. For example, if the average completion time of an order is 25 minutes, and the average time that the service provider needs to wait between two adjacent orders is 10 minutes, the preset time difference threshold may be set to 1 hour in consideration of the existence of special situations, such as traffic congestion.

S403: detecting whether a target group exists in each group of the service provider; and the order receiving time corresponding to the historical order with the earliest order receiving time in the target group falls into the preset time period.

It should be noted that there are typically many target packets corresponding to the service provider.

Illustratively, if the preset time period is: 20: 00-20: 30, for example, if a certain service provider is in 30 consecutive days, the ratio of each day is 20: 00-20: 30 are on-line, i.e. 30 packets corresponding to the preset time period will be obtained. If the total number of historical orders of the user in the continuous 30 days is 300, the number of the historical orders in the 30 determined groups is 90, and if the preset percentage is 25, the service provider is determined as the first target provider because the number of the historical orders in all the determined groups reaches 30% of the total number of the historical orders of the service provider, which is higher than the preset percentage.

S404: and if the target group exists, determining the service provider as a first target service provider.

In the above process of determining the first target service provider from the plurality of service providers, in fact, the service provider with a higher probability of departure in the preset time period is determined from the plurality of service providers, and the service provider with the higher probability of departure in the preset time period is determined as the first target service provider.

After the first target service providing end is determined, a plurality of historical departure areas corresponding to the preset time period can be determined according to the departure place of the first target service providing end.

Specifically, referring to fig. 5, the embodiment of the present application obtains a plurality of historical departure areas of a plurality of first target service providers who are on line for a preset time period by using the following steps:

s501: and acquiring historical departure places of a plurality of first target service providers on line in a future preset time period.

The historical departure place of the first target service providing end is the departure place where the first target service providing end is located when the first target service providing end departs, and the departure here means that the first target service providing end starts to enter the network car booking service working state. The area range of the historical departure point is usually fixed for most first target service providers, but the specific departure point is not fixed, and for some first target service providers, the departure point may fall into a plurality of area ranges. For example, a first target service provider a, which is typically the first target service provider for a full-time network appointment, is usually in the morning 9: 00, getting out, and collecting at 10:00 at night, wherein the place of getting out is near home; the first target service provider B is the first target service provider for the part-time network appointment, the departure place is usually after the next shift, for example, at 18:00, and the departure place is near the work unit.

Specifically, an embodiment of the present application further provides a specific method for acquiring historical departure locations of a plurality of first target service providers online in a preset time period, including:

It should be noted here that there may be a plurality of historical departure locations of the first target service provider, or there may be only one historical departure location.

For example, there are 50 target groups corresponding to the first target service provider, and in each group, if all the order taking places corresponding to the historical orders with the earliest order taking time are the place a, the place a is determined as the historical departure place corresponding to the first target service provider.

And if the order taking place corresponding to the historical order with the earliest order taking time comprises the place A and the place B, determining the place A and the place B as the historical departure place corresponding to the first target service provider.

In addition, the order taking place corresponding to the historical order with the earliest order taking time comprises an A place and a B place, but the number of the order taking places which are the target groups of the A place is 45, the number of the target groups of the order taking places which are the B place is 5, and the first target service provider has higher probability of getting out of the vehicle at the A place, so the A place is determined as the historical departure place of the first target service provider.

In addition, another method for obtaining historical departure points of a plurality of first target service providers online in a preset time period is provided in the embodiments of the present application, including:

In a specific implementation, when the above method is used to determine the plurality of historical departure points of each first target service provider, there may be a difference between the departure point and the actual departure point due to the confirmation of the order taking point of the order. For example, when the first target service provider leaves the car near home, the first order taken after leaving the car is 1 or 2 kilometers from the actual leaving location of the first target service provider, which may bring a certain error to subsequent work.

Therefore, in this embodiment, the error is avoided, and after the network appointment platform receives the order receiving starting instruction sent by the first target service provider, the current geographical position of the first target service provider can be acquired from the first target service provider. The geographic location is typically the departure location of the first targeted service provider. The network taxi appointment platform records the current geographic position and taxi-out time of the first target service provider when receiving a taxi order starting instruction sent by the first target service provider, and uses the geographic position and taxi-out time as the historical taxi-out place of the first target service provider and the first target service provider for associated storage.

When a historical departure place of a certain first target service provider is to be obtained, the historical departure place is obtained only by directly based on departure time according to the association storage relationship between the historical departure place and the first target service provider.

S502: and clustering the historical departure places of the first target service providing ends to obtain at least one class.

When the method is specifically realized, clustering a plurality of historical departure places of a plurality of first target service providing ends, namely dividing the historical departure places with relatively close geographic positions into a class, and then determining a historical departure area corresponding to the class based on the historical departure places divided into the same class; in this way, historical departure points corresponding to the plurality of first target service providing terminals are divided into at least one historical departure area. Here, there may be one or more historical departure areas for each first target service provider.

In a specific implementation, any one of the following clustering manners may be adopted to cluster the historical departure location of each first target service provider.

One is as follows: the first method for clustering historical departure places provided by the embodiment of the application is shown in fig. 6, and includes: for each first target service provider, performing the following clustering process:

s601: and calculating cluster center coordinates of the first cluster by taking the historical departure points of all the first target service providing ends as the first cluster, wherein the cluster center coordinates are the average longitude and latitude of all the historical departure points in the first cluster.

S602: and determining the historical departure place within a preset distance range from the center by taking the historical departure place closest to the cluster center of the first cluster as the center to form a second cluster.

S603: detecting whether an iteration stop condition is met; if yes, jumping to S604; if not, jumping to S601;

s604: and taking the finally obtained second cluster as a clustered class, and taking the historical departure place in the second cluster as the historical departure place for completing clustering. Jumping to S605.

S605: and detecting whether the historical departure places of the first target service providing ends are clustered or not. If yes, executing S601; if not, the process is ended.

And finally, obtaining one or more clustered classes.

In the above-described actual embodiment, the historical departure area corresponding to each of the clusters is determined from the historical departure points included in the cluster.

In specific implementation, when clustering is performed for the first time, all historical departure points of all first target service providing ends are not clustered, so that all historical departure points are used as a first cluster when clustering is performed for the first time, and the cluster center coordinates of the first cluster are calculated. And when the clustering is not performed for the first time, removing the historical departure places which have already completed clustering in the previous clustering process, taking all the remaining historical departure places which have not completed clustering as a first cluster, and calculating the cluster center coordinates of the first cluster.

In each iteration cycle, after the cluster center coordinates of the first cluster are obtained through calculation, in order to obtain the historical departure point closest to the cluster center of the first cluster, the distance between the historical departure point and the cluster center in each first cluster can be sequentially calculated, then the historical departure point closest to the cluster center is used as the center, the historical departure point of the second cluster is organized, the second cluster is used as a new first cluster, and the step of calculating the cluster center coordinates of the first cluster is executed again until the iteration stop condition is met.

Here, when selecting the center, the cluster center of the previous cluster is not directly selected as the center of the next iterative cluster, but the longitude and latitude coordinate closest to the average longitude and latitude of the previous cluster is selected as the center. The historical departure place of the first target service providing end has certain practical attributes, the position of the cluster center is not necessarily the historical departure place which is frequently used by the user, and the historical departure place which is actually used by the user of the first target service providing end is selected as the center of the cluster, so that the situation that the position which is not suitable for positioning is excluded, such as an underground garage, a road center and the like, is favorably avoided.

And when the iteration stopping condition is met, taking the second cluster obtained by the last iteration as a clustered class, and taking the historical departure place in the second cluster obtained by the last iteration as the historical departure place for completing clustering. And then, the iteration process is carried out on the historical departure places which are not clustered until all the historical departure places are clustered.

Here, by repeating the iterative clustering process of S602 to S603, the position of the cluster center is continuously updated, and a new second cluster is continuously formed, so that the density of the historical departure points in the new second cluster is increased, the position of the cluster center of the second cluster is closer to the historical departure points mostly used by the first target service provider, and finally, when the iterative condition is satisfied, the historical parking points included in the second cluster are classified into the same class.

The iteration stop condition here includes at least one of the following conditions:

1) the historical parking spots in the second cluster no longer change; 2) the iteration times reach a set time threshold; 3) the moving distance of the cluster center is smaller than a set distance threshold.

In condition 1), the historical parking spots in the second cluster no longer change, indicating that the best cluster has been formed, and the iteration may be stopped. In the condition 2), in order to save the amount of computation, the maximum value of the number of iterations may be set, and if the number of iterations reaches the set number threshold, the iteration of the iteration cycle may be stopped, and the last obtained historical departure point included in the second cluster is regarded as one type. In condition 3), if the moving distance of the cluster center is less than the set distance threshold, it indicates that the current cluster can substantially cover the historical departure place used by most of the machine, and the iteration can be stopped at this time.

For example: the historical departure place is represented by longitude and latitude coordinates under a geographic coordinate system. Its form can be expressed as: m (a, b); where M represents a historical departure point, a represents a longitude coordinate of the historical departure point M, and b represents a latitude coordinate of the historical departure point M. The cluster center coordinates can be expressed as: n (x, y); where N represents the cluster center, x represents the longitude coordinate of the cluster center N, and y represents the latitude coordinate of the cluster center N.

The cluster center coordinates are average longitude and latitude coordinates of historical departure places in the first cluster.

In the current iteration cycle, the historical departure places of the unfinished clusters corresponding to a certain first target service providing end are respectively as follows: m₁(a₁，b₁)、M₂(a₂，b₂)、M₃(a₃，b₃)、M₄(a₄，b₄)、M₅(a₅，b₅) Then from M₁To M₅Is formed as a firstIn a cluster, cluster center coordinates N (x, y) satisfy:

then calculate M separately₁To M₅Respectively, from the cluster center N.

E.g. calculating M₁(a₁，b₁) And cluster center N, then d satisfies:

obtaining all historical departure points M in the first cluster₁To M₅And after the distance between the cluster center and the historical departure point with the minimum distance is taken as the center, and the distances between the other historical departure points except the center in the first cluster and the center are sequentially calculated. The calculation method is similar to the method for calculating the distance between the historical departure point and the cluster center, and is not described in detail herein.

Suppose that M is now₁For the history departure point with the nearest distance from the cluster center, M is added₁As a center, M is calculated in order₂To M₅And the center M₁The distance between them.

If M₂And M₄And the center M₁All the distances between the two are less than the preset distance, then M is added₁、M₂And M₄Forming a second cluster.

It is then detected whether the current second cluster satisfies the iteration stop condition. The iteration stop condition is set as in this example: the number of iterations reaches 3.

If 2 iterations have been performed in the iteration cycle, and the iteration frequency reaches 3 times after the iteration of the cycle is executed, then the current second cluster is used as a clustered class, and the historical departure points in the class include: m₁、M₂And M₄。

If 1 iteration is already performed in the iteration cycle, the iteration frequency reaches to the value after the iteration of the cycle is performedAnd 2 times, taking the current second cluster as a new first cluster, wherein the historical departure place in the new first cluster comprises: m₁、M₂And M₄And returning to the step of calculating the cluster center coordinates of the new first cluster again until the iteration stop condition is met.

At place M of leaving history₁、M₂And M₄After the vehicle is classified into one class, the historical departure place M can be further divided₁、M₂And M₄As the historical departure place for completing the clustering;

because the historical departure place M of the uncompleted clustering still exists at the moment₃And M₅Therefore, the historical departure place M is returned₃And M₅And forming a new first cluster, and calculating the cluster center coordinates of the new first cluster.

Up to M₁To M₅Clustering is completed.

By adopting the clustering method, the number of the classes does not need to be preset, and the clustering method has higher clustering precision.

The second step is as follows: the second method for clustering historical departure locations provided in the embodiment of the present application, shown in fig. 7, includes: for all the first target service providers, executing the following processes:

s701: randomly selecting K historical departure places from the historical departure places of each first target service provider as initial clustering centers according to the number K of the preset historical departure areas; here, K is generally a positive integer greater than or equal to 2.

S702: and regarding each initial clustering center, taking the historical departure place and the clustering center, of which the distance from the clustering center is smaller than a first preset distance, as a first cluster.

S703: and calculating a cluster center coordinate of the first cluster, wherein the cluster center coordinate is the average longitude and latitude of the historical departure place in the first cluster.

S704: and determining the historical departure place within a preset distance range from the center by taking the historical departure place closest to the cluster center of the first cluster as the center to form a second cluster.

S705: detecting whether the current second cluster meets an iteration stop condition; if not, executing S706; if so, S707 is executed.

S706: the second cluster is regarded as a new first cluster, and the process returns to S703.

S707: and taking the finally obtained second cluster as a clustered class.

Finally, a maximum of K classes can be obtained.

In the specific implementation, the number of the initial clustering centers can be specifically set according to actual needs; the setting may be performed according to the number of the historical departure points corresponding to the plurality of first target service providers, and the larger the number of the historical departure points is, the larger the value of K is.

After K is determined, K is selected from historical departure places corresponding to the first target service providing ends to serve as initial clustering centers. And then sequentially calculating the distance between each historical departure place of each initial cluster center and the initial cluster center.

Similar to the embodiment corresponding to fig. 6, the historical departure points are represented by longitude and latitude coordinates under the geographic coordinate system, so that the distance between each historical departure point and each initial clustering center can be calculated by the longitude and latitude coordinates, and the calculation process is not repeated here.

For example, if there are 100 historical departure points corresponding to the plurality of first target service providers, and the value of K is determined to be 5, the 100 initial clustering centers determined from the 100 historical departure points are: m1 to M5.

For M1, the distances between 99 of the 100 historical departure points except for M1 and M1 are calculated in sequence. If the distance between a certain historical departure place and the M1 is smaller than the first preset distance, the historical departure place and the M1 are divided into the same cluster, namely the first cluster. And then according to the average longitude and latitude of all historical departure places in the first cluster, taking the place corresponding to the average longitude and latitude as a cluster center, and taking the average longitude and latitude as a cluster center coordinate.

And then, taking the historical departure point closest to the cluster center as a center, determining the historical departure point within a preset distance range with the center to form a second cluster, taking the second cluster as a new first cluster, returning to the step of calculating the cluster center coordinate of the first cluster until an iteration stop condition is met, and taking the finally obtained second cluster as a clustered class.

Specifically, if a historical departure point selected as an initial cluster center is classified into a certain class in the iterative process, the iterative process is not performed based on the initial cluster center.

And thirdly: the third method for clustering historical departure locations provided in the embodiment of the present application, as shown in fig. 8, includes: aiming at a plurality of first target service providers, the following processes are executed:

s801: taking any one historical departure place in the current incomplete clustering historical departure places as a clustering center, and sequentially calculating the distance between each historical departure place of other current incomplete clustering and the clustering center;

s802: dividing historical departure places with a distance smaller than a second preset distance from the clustering center into the same class with the clustering center, and taking all the historical departure places in the class as the historical departure places completing clustering;

s803: detecting whether a historical departure place with uncompleted clustering exists at present; if yes, the process jumps to S801, and if no, the process ends.

In this third clustering method, assuming that ten historical departure points for completing clustering are available from M1 to M10, the net appointment platform randomly designates one of M1 to M10 as a clustering center.

Assuming that the network appointment platform designates the M5 as a cluster center at this time, distances between nine historical departure points M1 to M4 and M6 to M10 in sequence and the M5 serving as the cluster center are calculated, and the historical departure points with the distances smaller than the second preset distance are classified into the same class as the cluster center M5.

For example, if the distances between M1, M4, M7 and M5 are all smaller than the second preset distance, M1, M4, M7 and M5 are classified into the same class a, and M1, M4, M7 and M5 are used as the current locations where the clustering is completed.

At this time, there are 6 incomplete clustered historical departure points, namely M2, M3, M6 and M8 to M10, so that any one historical departure point is designated as a new clustering center from M2, M3, M6 and M8 to M10, and the distance between the historical departure point of other incomplete clusters and the new clustering center is calculated again.

If the M6 is a new cluster center and the distances between M2, M8, M10 and M6 are all smaller than the second preset distance, then M2, M8, M10 and M6 are classified into the same class B, and M2, M8, M10 and M6 are used as historical departure points for completing clustering.

At this time, the historical departure points of M3 and M9 which are unfinished clusters exist, so that any one of the historical departure points of M3 and M9 is executed as a new cluster, and the distance between the historical departure point of other unfinished clusters and the center of the new cluster is calculated again.

If M3 is the new cluster center and the distance between M9 and M3 is smaller than the second preset distance, then M9 and M3 are classified into the same class C.

Finally, all M1 through M10 complete the clustering and form three classes, respectively: a (M1, M4, M5, M7); b (M2, M6, M8, M10); c (M3, M9).

The third clustering method is simpler and more computationally efficient than the first and second clustering methods, but the accuracy is reduced compared to the two clustering methods.

After clustering a plurality of historical departure places of each first target service providing end to obtain at least one class, the method further comprises the following steps:

s503: and determining a historical departure area corresponding to each class based on at least one historical departure place contained in the class.

Specifically, the following method may be adopted to determine a historical departure area corresponding to each class based on at least one historical departure place included in the class:

for each class, determining a historical departure place which is farthest away from the center of the class in the class; and determining the corresponding historical departure area of the type by taking the center of the type as the center of a circle and the distance between the determined historical departure place and the center as the radius.

In a specific implementation, the historical departure area should include all historical departure points in the class corresponding to the historical departure area, so that for each class, the historical departure point farthest from the center of the class in the class may be determined, and the formed circular area may be determined as the corresponding historical departure area with the determined historical departure point farthest from the center of the class as a radius and the center of the class as a center.

In addition, for each class, all historical departure points included in the class can be connected pairwise, and an area surrounded by the connection lines located at the outermost periphery is used as a historical departure area.

In addition, a rectangular historical departure area can also be formed. For example, rectangular baskets with different sizes are used to match historical departure points in the class, and when a certain rectangular frame can frame all the historical departure points and the area of the rectangular frame is the smallest in all the rectangular frames, the area framed by the current rectangular frame is used as the historical departure area.

In addition, a rectangular historical departure area can also be formed. For example, rectangular baskets with different sizes are used for matching historical departure places in the class, and when a certain rectangular frame can frame all the historical departure places and the rectangular frame is tangent to the other rectangular frames on two sides, the area surrounded by the current rectangular frame is used as the historical departure area.

Next, the above-described S301 is received, and after the historical order data of the historical departure area in the preset time period is acquired, the following S302 is executed.

S302: and acquiring historical order data of the historical departure area in a historical time period corresponding to the future preset time period for each historical departure area.

Here, the historical time period corresponding to the future preset time period is, for example, 13:15 to 13:40 of every day in the history if the current time is 13:00 and the future preset time period is 13:15 to 13: 40.

S303: and generating scheduling information for scheduling the second target service providers according to the historical order data of each historical departure area and the quantity of the first target service providers departing from the historical departure area in the historical time period corresponding to the future preset time period.

In a specific implementation, referring to fig. 9, an embodiment of the present application provides a specific method for generating scheduling information for scheduling a second target service provider, including:

s901: and determining the estimated order quantity corresponding to the preset time period according to the historical order data of the historical departure area.

In specific implementation, when the estimated order quantity corresponding to the preset time period is determined, all historical orders with the departure place in the historical departure area and the order time in the preset time period are determined according to all historical order data of the historical departure area.

And then calculating the average historical order quantity of all historical orders within the preset time period at the order time according to all historical orders within the preset time period at the departure place in the historical departure area, and determining the average historical order quantity as the estimated order quantity.

S902: and generating an order satisfaction coefficient of the historical departure area in the preset time period according to the estimated order quantity corresponding to the preset time period and the quantity of the first target service providing terminals of the departure area in the historical time period corresponding to the future preset time period.

For example, the order satisfaction factor may be a ratio of the estimated number of orders to the number of first target service providers that are departing in the historical departure area for a preset time period.

S903: and generating scheduling information for scheduling the second target service provider according to the order satisfaction coefficients of the historical departure areas.

In order to balance the transport capacity in each historical departure area, the second target service terminal in the historical departure area with the larger order satisfaction coefficient is dispatched to the historical departure area with the smaller order satisfaction coefficient according to the size of the order satisfaction coefficient.

Here, the second target service providing terminal is also selected from a plurality of service providing terminals, and for example, the service providing terminal that has been on-line in each historical departure area and is currently in a non-service state may be determined as the second target service providing terminal.

In the embodiment of the application, historical departure areas are dynamically determined according to the first target service providers on the line of the preset time period, and scheduling information for scheduling the second target service providers is generated based on historical order data of each historical departure area in the preset time period and the number of the first target service providers departing from the historical departure areas in the preset time period, so that transport capacity balance of a plurality of historical departure areas is realized.

Based on the same inventive concept, the embodiment of the present application further provides a capacity scheduling apparatus corresponding to the capacity scheduling method, and as the principle of the apparatus in the embodiment of the present application for solving the problem is similar to the capacity scheduling method described above in the embodiment of the present application, the implementation of the apparatus may refer to the implementation of the method, and repeated details are not described again.

Referring to fig. 10, a capacity scheduling apparatus provided in an embodiment of the present application includes:

the first obtaining module 11 is configured to obtain historical departure areas of a plurality of first target service providers on line in a future preset time period;

a second obtaining module 12, configured to obtain, for each historical departure area, historical order data of the historical departure area in a historical time period corresponding to the future preset time period;

and the generating module 13 is configured to generate scheduling information for scheduling the second target service providers according to the historical order data of each historical departure area and the number of the first target service providers departing in the historical departure area in the historical time period corresponding to the future preset time period.

In an alternative embodiment, the method further comprises: the first determining module 14 is configured to determine a first target service provider from a plurality of service providers before obtaining historical departure areas of the plurality of first target service providers on line in a future preset time period.

In an optional embodiment, the first determining module 14 is configured to determine the first target service provider from a plurality of service providers by:

In an optional implementation manner, the first determining module 14 is configured to detect whether a target packet exists in each packet of the service provider by using the following manner:

In an optional implementation manner, the first obtaining module 11 is configured to obtain historical departure areas of a plurality of first target service providers who come on line in a future preset time period by:

In an optional implementation manner, the first obtaining module 11 is configured to obtain historical departure locations of a plurality of first target service providers who come online in a future preset time period by:

and determining the geographical position corresponding to the order receiving starting instruction received in the historical time period corresponding to the future preset time period as the historical departure place of the first target service providing end.

In an optional implementation manner, the generating module 13 is specifically configured to:

generating an order satisfaction coefficient of the historical departure area in the preset time period according to the estimated order quantity corresponding to the preset time period and the quantity of the first target service providers of the departure area in the historical departure area in the preset time period;

In an alternative embodiment, the method further comprises: and a second determining module 15, configured to determine, before generating scheduling information for scheduling the second target service provider according to the size of the order satisfaction coefficient of each historical departure area, the service provider that is already on-line in each historical departure area and is currently in a non-service state as the second target service provider.

In an optional implementation manner, the first obtaining module 11 is configured to cluster the historical departure locations of the plurality of first target service providers by:

for each initial cluster center, performing the following steps:

In an alternative embodiment, the first obtaining module 11 is configured to determine, based on at least one historical departure place included in each class, a historical departure area corresponding to the class by:

As shown in fig. 2, an embodiment of the present application further provides an electronic device, including: a processor 220, a storage medium and a bus 230, wherein the storage medium stores machine-readable instructions executable by the processor 220, when the electronic device runs, the processor 220 communicates with the storage medium through the bus 230, and the processor 220 executes the machine-readable instructions to execute the steps of the capacity scheduling method provided by the embodiment of the present application.

Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the capacity scheduling method provided in the embodiments of the present application are performed.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A capacity scheduling method, comprising:

2. The method according to claim 1, wherein before acquiring the historical departure areas of the plurality of first target service providers who come on line for a preset period of time in the future, the method further comprises:

3. The method of claim 2, wherein determining the first target service provider from a plurality of service providers comprises:

4. The method of claim 3, wherein the detecting whether the target packet exists in the packets of the service provider comprises:

5. The method of claim 3, wherein the obtaining historical departure areas for the plurality of first target service providers that come online at a predetermined time period in the future comprises:

6. The method of claim 5, wherein the obtaining historical departure locations of the first target service providers that come online at a predetermined time period in the future comprises:

7. The method of claim 5, wherein the obtaining historical departure locations of the first plurality of target service providers that come online at a predetermined time period in the future comprises:

8. The method according to claim 1, wherein generating scheduling information for scheduling the second target service provider terminal according to the historical order data of the historical departure area and the number of the first target service providers departing from the historical departure area in the historical time period corresponding to the preset time period in the future comprises:

9. The method according to claim 8, before generating scheduling information for scheduling the second target service provider according to the size of the order fulfillment coefficient of each historical departure area, further comprising:

10. The method of claim 5, wherein clustering the historical departure locations of the first plurality of target service providers comprises:

11. The method of claim 5, wherein clustering the historical departure locations of the first plurality of target service providers comprises:

for each initial cluster center, performing the following steps:

12. The method of claim 5, wherein clustering the historical departure locations of the first plurality of target service providers comprises:

13. The method of claim 5, wherein determining the historical departure area corresponding to each class based on at least one historical departure location included in the class comprises:

14. A capacity scheduling apparatus, comprising:

15. The apparatus of claim 14, further comprising: the first determining module is used for determining the first target service providing end from a plurality of service providing ends before the historical departure areas of the plurality of first target service providing ends which come on line in the future preset time period are obtained.

16. The apparatus of claim 15, wherein the first determining module is configured to determine the first target service provider from a plurality of service providers by:

17. The apparatus of claim 16, wherein the first determining module is configured to detect whether a target packet exists in each packet of the service provider by:

18. The apparatus of claim 16, wherein the first obtaining module is configured to obtain the historical departure areas of the plurality of first target service providers coming online in a future preset time period by:

19. The apparatus of claim 18, wherein the first obtaining module is configured to obtain historical departure locations of a plurality of first target service providers who come online in a preset time period in the future by:

20. The apparatus of claim 18, wherein the first obtaining module is configured to obtain historical departure locations of a plurality of first target service providers who come online in a preset time period in the future by:

21. The apparatus of claim 14, wherein the generating module is specifically configured to:

22. The apparatus of claim 21, further comprising: and the second determining module is used for determining the service provider which is on line in each historical departure area and is currently in a non-service state as the second target service provider before generating scheduling information for scheduling the second target service provider according to the order satisfaction coefficient of each historical departure area.

23. The apparatus of claim 18, wherein the first obtaining module is configured to cluster historical departure locations of the first target service providers by:

24. The apparatus of claim 18, wherein the first obtaining module is configured to cluster historical departure locations of the first target service providers by:

for each initial cluster center, performing the following steps:

25. The apparatus of claim 18, wherein the first obtaining module is configured to cluster historical departure locations of the first target service providers by:

26. The apparatus according to claim 18, wherein the first obtaining module is configured to determine the historical departure area corresponding to each class based on at least one historical departure location included in the class by:

27. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is operating, the machine readable instructions when executed by the processor performing the capacity scheduling method of any one of claims 1 to 13.

28. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs a capacity scheduling method according to any one of claims 1 to 13.