CN111476389A

CN111476389A - Method and device for pre-estimating order receiving waiting time

Info

Publication number: CN111476389A
Application number: CN201910070116.5A
Authority: CN
Inventors: 肜博辉; 张智标; 窦凯丽
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-01-24
Filing date: 2019-01-24
Publication date: 2020-07-31

Abstract

The application provides a method and a device for estimating the waiting time of order receiving, which comprises the steps of firstly, acquiring characteristic data corresponding to a trip order to be accepted; then, based on the characteristic data, estimating estimated waiting time for waiting when the trip order to be accepted is accepted; and finally, revising the estimated waiting time corresponding to the trip order to be taken on the basis of the deviation information of the estimated waiting time corresponding to the historical trip order and the actual waiting time. According to the technical scheme, the estimated waiting time is revised through the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order, the accuracy of the estimated waiting time and the user experience are improved, and the requirement of a user on the accuracy of the estimated waiting time is met.

Description

Method and device for pre-estimating order receiving waiting time

Technical Field

The application relates to the technical field of network appointment and calculation, in particular to a method and a device for estimating the waiting time of a pick-up order.

Background

When the number of the service request terminals is greater than the number of the service provider terminals, the service request terminals cannot immediately obtain the services provided by the service provider terminals, and the service request terminals enter a waiting mode until idle service provider terminals can provide the services for the idle service provider terminals. At present, an estimated waiting time is generally pushed to a waiting service request end, and the estimated waiting time not only can eliminate the blindness of the service request end in the waiting process, but also is helpful for the service request end to make a decision whether to continue waiting or not.

Although the estimated waiting time is estimated and pushed for the waiting service request end at present, the accuracy of the estimated waiting time is low, the user experience is poor, and the requirement of the user on the accuracy of the estimated waiting time cannot be met.

Disclosure of Invention

In view of this, an object of the embodiments of the present application is to provide a method and an apparatus for estimating an order receiving waiting time, which can revise an estimated waiting time corresponding to a trip order to be taken over according to deviation information between the estimated waiting time corresponding to a historical trip order and an actual waiting time, so as to improve accuracy of the estimated waiting time and user experience.

In a first aspect, an embodiment of the present application provides a method for predicting a call admission waiting duration, including:

acquiring characteristic data corresponding to a trip order to be accepted;

estimating estimated waiting time for waiting when the travel order is taken up based on the characteristic data;

and revising the estimated waiting time of the trip order to be accepted based on the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order.

In a possible embodiment, the estimating, based on the characteristic data, an estimated waiting time for waiting when the travel order is taken over includes:

acquiring first sample data matched with the characteristic data; the first sample data comprises a plurality of historical trip orders, corresponding characteristic data before being taken over and estimated waiting time corresponding to each historical trip order in the first sample data;

and estimating the estimated waiting time for waiting when the trip order to be accepted is accepted based on the first sample data.

In a possible embodiment, the estimating, based on the first sample data, an estimated waiting time for waiting when the trip order to be taken over is taken over includes:

and calculating the average value of the estimated waiting time corresponding to the historical travel orders in the first sample data to obtain the estimated waiting time required for waiting when the travel orders to be accepted are accepted.

In a possible implementation manner, the characteristic data corresponding to the travel order to be taken includes at least one of the following:

the origin of the travel order to be accepted; a destination of the travel order to be taken over; the release time of the travel order to be accepted; the number of service providers in the area where the travel order to be accepted is located; the total number of the trip orders which are not taken over and are in the area where the trip orders to be taken over are located; weather conditions in the area where the travel order to be accepted is located; road conditions in the area where the travel orders to be accepted are located; the speed of the service providing terminal receiving the travel order; the number of travel orders which are released before the travel orders to be taken are released and are not taken in the area where the travel orders to be taken are located.

In a possible embodiment, the method further includes:

determining whether the estimated waiting time corresponding to the trip order to be accepted needs to be revised or not based on the characteristic data corresponding to the trip order to be accepted;

and under the condition that the estimated waiting time corresponding to the trip order to be accepted needs to be revised, revising the estimated waiting time of the trip order to be accepted based on the deviation information.

In a possible implementation manner, the determining, based on the feature data corresponding to the to-be-accepted travel order, whether the estimated waiting time corresponding to the to-be-accepted travel order needs to be revised includes:

and under the condition that the number of the trip orders which are released before the trip orders to be taken over are released and are not taken over in the area where the trip orders to be taken over are located is larger than a preset number, determining that the estimated waiting time corresponding to the trip orders to be taken over needs to be revised.

In one possible embodiment, the historical travel orders are located in an area where the travel orders to be taken over are located.

In a possible embodiment, the historical travel orders include N travel orders with the shortest time interval between the taken time and the current time, where N is a positive integer.

In a possible implementation manner, the deviation information includes a difference value between an estimated waiting time length and an actual waiting time length corresponding to each historical travel order;

the correcting the estimated waiting time of the trip order to be accepted based on the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order comprises the following steps:

calculating the average value of the difference values of the estimated waiting time length and the actual waiting time length corresponding to the historical trip order;

revising the estimated wait time based on the average.

In a possible implementation manner, the deviation information includes a difference between an estimated waiting time length and an actual waiting time length corresponding to each historical travel order, a mean value of the difference, a variance of the difference, and a type of the difference.

In a possible implementation manner, the revising the estimated waiting time of the trip order to be taken over based on the deviation information between the estimated waiting time and the actual waiting time corresponding to the historical trip order includes:

acquiring characteristic data corresponding to the trip order to be accepted and second sample data matched with the deviation information; the second sample data comprises a plurality of historical travel orders, corresponding characteristic data before being taken over, deviation information corresponding to each historical travel order in the second sample data, and a duration revision value corresponding to each historical travel order in the second sample data;

determining a duration revision value corresponding to the trip order to be taken on the basis of the second sample data;

and revising the estimated waiting time of the trip order to be accepted based on the corresponding time revision value of the trip order to be accepted.

In a possible embodiment, the determining, based on the second sample data, a corresponding duration revision value of the travel order to be taken over includes:

and calculating the mean value of the time length revision values corresponding to the historical travel orders in the second sample data to obtain the time length revision value corresponding to the travel orders to be accepted.

In a possible implementation manner, the revising the estimated waiting time of the trip order to be held based on the time revision value corresponding to the trip order to be held includes:

and calculating the sum of the time length revision value corresponding to the trip order to be accepted and the estimated waiting time length of the trip order to be accepted to obtain the target estimated waiting time length of the trip order to be accepted.

In a possible embodiment, the method further includes:

and updating the second sample data according to a preset time interval.

In a second aspect, an embodiment of the present application provides an apparatus for predicting a waiting time for receiving a ticket, including:

the data acquisition module is used for acquiring characteristic data corresponding to the trip order to be accepted;

the waiting time estimation module is used for estimating estimated waiting time for waiting when the travel order is taken up based on the characteristic data;

and the waiting time revision module is used for revising the estimated waiting time of the trip order to be accepted based on the deviation information of the estimated waiting time corresponding to the historical trip order and the actual waiting time.

In one possible implementation, the wait duration estimation module includes:

the first sample acquisition submodule is used for acquiring first sample data matched with the characteristic data; the first sample data comprises a plurality of historical trip orders, corresponding characteristic data before being taken over and estimated waiting time corresponding to each historical trip order in the first sample data;

and the waiting time estimation submodule is used for estimating the estimated waiting time for waiting when the trip order to be taken over is taken over based on the first sample data.

In a possible implementation manner, the waiting duration estimation submodule is specifically configured to:

In a possible embodiment, the above apparatus further comprises:

the revision judging module is used for determining whether the estimated waiting time corresponding to the trip order to be accepted needs to be revised or not based on the characteristic data corresponding to the trip order to be accepted;

the waiting time revision module is further configured to revise the estimated waiting time of the to-be-accepted travel order based on the deviation information under the condition that the estimated waiting time corresponding to the to-be-accepted travel order needs to be revised.

In a possible implementation, the revision decision module is specifically configured to:

the base latency revision module is specifically configured to: and calculating the mean value of the difference value between the estimated waiting time length corresponding to the historical trip order and the actual waiting time length, and revising the estimated waiting time length based on the mean value.

In one possible embodiment, the latency revision module includes:

the second sample acquisition sub-module is used for acquiring characteristic data corresponding to the trip order to be accepted and second sample data matched with the deviation information; the second sample data comprises a plurality of historical travel orders, corresponding characteristic data before being taken over, deviation information corresponding to each historical travel order in the second sample data, and a duration revision value corresponding to each historical travel order in the second sample data;

the revision value determining submodule is used for determining a duration revision value corresponding to the to-be-accepted travel order based on the second sample data;

and the waiting time revision submodule is used for revising the estimated waiting time of the trip order to be accepted based on the time revision value corresponding to the trip order to be accepted.

In a possible implementation, the revision value determination submodule is specifically configured to:

In a possible implementation, the wait duration revision module is specifically configured to:

In a possible embodiment, the above apparatus further comprises:

and the sample data updating module is used for updating the second sample data according to a preset time interval.

In a third aspect, an embodiment of the present application provides an electronic device, including: 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 running, the machine-readable instructions when executed by the processor performing the steps of the first aspect described above, or any possible implementation of the first aspect.

In a fourth aspect, this application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.

The method and the device for estimating the waiting time of the order receiving firstly acquire the characteristic data corresponding to the trip order to be accepted; then, based on the characteristic data, estimating estimated waiting time for waiting when the trip order to be accepted is accepted; and finally, revising the estimated waiting time of the trip order to be taken on the basis of the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order. According to the technical scheme, the estimated waiting time is revised through the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order, the accuracy of the estimated waiting time and the user experience are improved, and the requirement of a user on the accuracy of the estimated waiting time is met.

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 illustrating a system for predicting a waiting time of a pick-up order according to an embodiment of the present disclosure;

fig. 2 illustrates a block diagram of an electronic device provided by an embodiment of the present application;

fig. 3 is a flowchart illustrating a method for predicting a waiting time for receiving an order according to an embodiment of the present disclosure;

fig. 4 is a flowchart illustrating that, in another method for estimating an order pickup waiting time according to the embodiment of the present application, an estimated waiting time for waiting when the travel order is taken is estimated based on the feature data;

fig. 5 is a flowchart illustrating that, in another method for estimating an order taking waiting time according to the embodiment of the present application, based on deviation information between an estimated waiting time corresponding to a historical travel order and an actual waiting time, the estimated waiting time corresponding to the travel order to be taken is revised;

FIG. 6 is a flowchart illustrating steps before correcting the estimated wait time in another method for estimating the waiting time for receiving orders according to the embodiment of the present application;

fig. 7 shows a block diagram of an apparatus for estimating a waiting time of a pick-up order according to an 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 described primarily in the context of a net appointment, it should be understood that this is only 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.

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 "passenger," "service requestor," "user" are used interchangeably in this application to refer to an individual, entity, or tool that can request or order a service. The terms "driver" and "service provider" are used interchangeably in this application to refer to an individual, entity or tool that can provide a service.

One aspect of the present application relates to a system for estimating a call admission wait period. The system can revise the estimated waiting time through the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order, improves the accuracy of the estimated waiting time and the user experience, meets the requirement of the user on the accuracy of the estimated waiting time, and overcomes the defect that the estimated waiting time in the prior art is low in accuracy.

FIG. 1 is a block diagram of an estimated order taking latency 100 according to some embodiments of the present application. For example, the system 100 for estimating the length of the order waiting time may be an online transportation service platform for transportation services such as taxi cab, designated driving service, express bus, carpool, bus service, driver rental, or regular bus service, or any combination thereof. The system 100 for estimating the waiting time of the order receiving may include one or more of a server 110, a network 120, a service request end 130, a service providing end 140 and a database 150, and the server 110 may include a processor for executing 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 130, the service provider 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 130, the service provider 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 220. for example, the Processor 220 may determine the estimated wait duration based on a service request obtained from the service requester 130. in some embodiments, the Processor may include one or more Processing cores (e.g., a single-core Processor (S) or a multi-core Processor (S)), by way of example only, the Processor 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 Device (FPGA), a Field Programmable Gate Array (FPGA), a Field Programmable logic Unit (DSP), or the like.

Network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components in the system 100 (e.g., the server 110, the service requester 130, the service provider 140, and the database 150) that predict the duration of the order waiting period may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester 130 via the network 120.

In some embodiments, one or more components of the system 100 (e.g., the server 110, the service requester 130, the service provider 140, etc.) that predict the wait time for the pick-up order may have access to the database 150. In some embodiments, one or more components in the system 100 that predict order taking latency may read and/or modify information about the service requester, the 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 140 may access information related to the service requester when receiving the service request from the service requester 130, but the service provider 140 may not modify the related information of the service requester 130.

In some embodiments, the exchange of information by one or more components in system 100 that predict the length of order waiting may be accomplished by requesting a service. The object of the service request is the pre-estimated wait time.

Fig. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester 130, a service provider 140, which may implement the concepts of the present application, according to some embodiments of the present application. For example, the processor 220 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 method of estimating the order taking wait time 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 is a flow diagram illustrating a method of predicting order taking latency of some embodiments of the present application. The method for estimating the waiting time of the order receiving is executed on a third-party platform outside a service request end and a service providing end and is used for estimating the estimated waiting time of the waiting of the service providing end for the trip order to be received. Specifically, the method for estimating the receiving waiting time comprises the following steps:

and S310, acquiring characteristic data corresponding to the trip order to be accepted.

The travel orders are travel orders which are not taken over by the service providing terminal, namely travel orders which are taken over by the service providing terminal.

The characteristic data is used for characterizing the order characteristics of the corresponding travel order, and for example, the characteristic data may include at least one of the following items:

The number of the service providers in the area where the travel orders to be accepted are located, and the total number of the travel orders in the area where the travel orders to be accepted are located and are not accepted represent the supply and demand conditions between the current service provider and the service request end, and the supply and demand conditions are key factors influencing the speed of the travel orders. The order speed refers to a speed at which the to-be-accepted travel orders are accepted by the service provider, for example, the order speed is 3 minutes/order, which means that one to-be-accepted travel order is accepted by the service provider every 3 minutes. It can be seen that the order-taking speed has a direct relationship with the estimated waiting time corresponding to the trip order to be accepted, and the larger the order-taking speed is, the shorter the estimated waiting time corresponding to the trip order to be accepted is, and the smaller the order-taking speed is, the longer the estimated waiting time corresponding to the trip order to be accepted is.

The weather condition in the area where the travel order to be taken is located and the road condition in the area where the travel order to be taken is located can also influence the speed of the travel order to a great extent. For example, in severe weather or road congestion, the travel order speed will be seriously reduced, and the estimated waiting time corresponding to the travel order to be accepted will be long.

The origin of the to-be-accepted travel order, the destination of the to-be-accepted travel order, and the release time of the to-be-accepted travel order also influence the travel speed to a certain extent, for example, the origin of the to-be-accepted travel order is remote, the destination of the to-be-accepted travel order is far away, or the release time of the to-be-accepted travel order is in the late night, then the travel speed is reduced, and the estimated waiting time corresponding to the corresponding to-be-accepted travel order is long.

The number of the trip orders which are issued before the trip orders to be taken over and are not taken over in the area where the trip orders to be taken over are located indicates the rank of the trip orders to be taken over, and the later the rank is, the longer the corresponding estimated waiting time is.

In short, all data included in the feature data have an influence on the estimated waiting time, so that a feature data source corresponding to a to-be-accepted travel order needs to be collected in this step.

It should be noted that there may be many methods for determining the area where the travel order to be taken over is located, for example, taking the specific location where the travel order to be taken over is located as the center, and taking the area within the predetermined range as the area where the travel order to be taken over is located; for example, the area within the predetermined range may be used as the area where the travel order to be taken over is located, with the street where the travel order to be taken over is located as the center.

S320, estimating estimated waiting time for waiting when the travel order is taken over based on the characteristic data.

As can be seen from the above statements, the data included in the feature data can affect the length of the estimated waiting time to some extent, so that the step determines the estimated waiting time for waiting when the trip order to be taken is taken according to the feature data.

In specific implementation, the time length corresponding to each data can be determined according to the weight of each data in the characteristic data, and the estimated waiting time length to be waited for taking over the trip order to be taken over is calculated according to the determined time lengths.

Of course, other technical solutions can be used to determine the estimated waiting time corresponding to the trip order to be accepted according to the characteristic data corresponding to the trip order to be accepted.

S330, revising the estimated waiting time of the trip order to be accepted based on the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order.

Here, the historical travel order refers to a travel order that has already been accepted by the service provider, and thus corresponds to an actual waiting time period, and in addition, before the historical travel order is accepted by the service provider, the third-party platform generates an estimated waiting time period based on the corresponding characteristic data. The deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order represents the deviation degree of the estimated waiting time, so that the estimated waiting time is revised based on the deviation information of the estimated waiting time and the actual waiting time corresponding to the historical trip order, and the accuracy of the estimated waiting time can be improved.

Because each region has characteristics of geography and the like, the estimated waiting time of the trip order to be taken is revised by using deviation information corresponding to the historical trip order in the same region, and the accuracy of the estimated waiting time can be further improved.

Since environmental factors such as road conditions and weather change frequently along with the change of time, and the change of the environment greatly affects the waiting time of the trip orders to be taken, the obtained estimated waiting time is directly inaccurate, and in order to avoid the influence of the environmental factors with strong variability on the accuracy of the estimated waiting time, the estimated waiting time can be revised by using the deviation information corresponding to the N historical trip orders with the shortest time interval between the taken time and the current time. N is a positive integer, for example, N may take the value 5.

There are many methods for revising the estimated waiting time of the travel order to be taken over by using the deviation information corresponding to the selected N historical travel orders, for example, revising the estimated waiting time can be realized by using the following substeps: calculating the difference value between the estimated waiting time length and the actual waiting time length corresponding to the N historical travel orders; calculating the average value of the N difference values; revising the estimated wait time based on the average.

In a specific implementation, the modifying the estimated waiting time based on the average value may be: and calculating the sum of the average value and the estimated waiting time, and taking the obtained sum as a revised value of the estimated waiting time.

According to the embodiment, the estimated waiting time obtained through estimation is revised through the deviation information of the estimated waiting time and the actual waiting time corresponding to the selected N latest accepted historical travel orders, the accuracy of the estimated waiting time and the user experience are improved, and the requirement of a user on the accuracy of the estimated waiting time is met

In other embodiments, as shown in fig. 4, in the method for estimating the waiting time for receiving an order, the estimation of the waiting time for waiting when the travel order is received may be implemented by the following steps:

and S410, acquiring first sample data matched with the characteristic data.

Here, the first sample data includes a plurality of historical travel orders, corresponding characteristic data before being accepted, and an estimated waiting time corresponding to each historical travel order in the first sample data. The historical travel orders in the first sample data are travel orders that were published within a first predetermined time period and have been taken over by the service provider.

Here, the first sample data that matches the feature data of the travel order to be taken is sample data that includes feature data similar to or identical to the feature data of the travel order to be taken. Because the feature data corresponding to the historical travel order in the first sample data is similar to or the same as the feature data of the travel order to be taken, the estimated waiting time corresponding to the historical travel order in the first sample data is also similar to or the same as the estimated waiting time corresponding to the travel order to be taken, and therefore the estimated waiting time corresponding to the estimated travel order to be taken needs to be obtained.

And S420, estimating estimated waiting time for waiting when the trip order to be accepted is accepted based on the first sample data.

As can be seen from the above statements, the estimated waiting time corresponding to the historical trip order in the first sample data is similar to or the same as the estimated waiting time corresponding to the trip order to be taken over, so that the estimated waiting time for waiting when the trip order to be taken over is taken over can be estimated based on the estimated waiting time corresponding to the historical trip order in the first sample data.

In specific implementation, many methods may be used to estimate the estimated waiting time for waiting when the to-be-accepted travel order is accepted based on the first sample data, for example, the estimated waiting time may be estimated by the following substeps: and calculating the average value of the estimated waiting time corresponding to the historical travel orders in the first sample data to obtain the estimated waiting time required for waiting when the travel orders to be accepted are accepted.

It should be noted that, the corresponding relationship between the feature data corresponding to the historical travel order in the first sample data and the estimated waiting time may be used as a model, and according to the corresponding relationship in the model, the feature data is determined, and the corresponding estimated waiting time can be obtained.

The characteristic data corresponding to the trip order to be accepted is matched with the data in the model, after the matched first sample data is found, the estimated waiting time in the first sample data can be determined according to the corresponding relation in the model, and then the estimated waiting time corresponding to the trip order to be accepted can be determined by the estimated waiting time in the first sample data.

In other embodiments, the deviation information in step 330 includes differences between the estimated waiting time length and the actual waiting time length corresponding to the N latest accepted historical travel orders, a mean value of the differences, a variance of the differences, and a type of the differences. The type of the difference includes that the difference between the estimated waiting time length and the actual waiting time length is an integer, and the difference between the estimated waiting time length and the actual waiting time length is a negative number.

As shown in fig. 5, in the method for estimating the order taking waiting time, based on the deviation information between the estimated waiting time corresponding to the historical travel order and the actual waiting time, the estimated waiting time corresponding to the travel order to be taken is revised, and the method can be implemented by using the following steps:

and S510, acquiring characteristic data corresponding to the trip order to be accepted and second sample data matched with the deviation information.

The second sample data comprises a plurality of historical travel orders, corresponding characteristic data before being taken over, deviation information corresponding to each historical travel order in the second sample data, and a duration revision value corresponding to each historical travel order in the second sample data. The historical travel orders in the second sample data are travel orders published within a second predetermined time period and these orders have been taken over by the service provider.

Here, the second sample data matched with the feature data corresponding to the travel order to be taken and the deviation information is sample data including feature data similar to or identical to the feature data corresponding to the travel order to be taken and deviation information similar to or identical to the deviation information of the N latest taken historical travel orders.

Since the feature data and the deviation information corresponding to the historical trip order in the second sample data are similar to or the same as the feature data of the trip order to be taken and the deviation information of the N latest taken historical trip orders, the duration revision value corresponding to the historical trip order in the second sample data and the duration revision value for revising the estimated waiting duration corresponding to the trip order to be taken are also similar to or the same as each other, and therefore, the second sample data needs to be obtained when revising the estimated waiting duration corresponding to the trip order to be taken.

It should be noted that, the second predetermined period of time may be later than the first predetermined period of time, and the more new sample data is used to revise the estimated waiting time, the more the accuracy of the revised estimated waiting time can be improved. The second sample data may be updated at predetermined time intervals to improve the accuracy of the revised estimated wait time.

S520, determining a duration revision value corresponding to the trip order to be taken on the basis of the second sample data.

As can be seen from the above statements, the duration revision value corresponding to the historical travel order in the second sample data is similar to or the same as the duration revision value obtained by revising the estimated waiting duration corresponding to the to-be-taken travel order, so that the duration revision value corresponding to the to-be-taken travel order can be determined based on the duration revision value corresponding to the historical travel order in the second sample data.

In specific implementation, the following substeps may be used to determine the time revision value corresponding to the travel order to be taken over: and calculating the mean value of the time length revision values corresponding to the historical travel orders in the second sample data to obtain the time length revision value corresponding to the travel orders to be accepted.

It should be noted that the corresponding relationship between the feature data, the deviation information, and the time length revision value corresponding to the historical travel order in the second sample data may be used as a model, and according to the corresponding relationship in the model, after the feature data and the deviation information are determined, the corresponding time length revision value can be obtained.

The characteristic data corresponding to the trip order to be accepted and the deviation information corresponding to the N latest accepted trip orders are matched with the data in the model, after the matched second sample data is found, the duration revision value in the second sample data can be determined according to the corresponding relation in the model, and then the duration revision value corresponding to the trip order to be accepted can be determined by the duration revision value in the second sample data.

When the model is used for determining the duration revision value corresponding to the to-be-accepted travel order, the feature data corresponding to the to-be-accepted travel order and the deviation information corresponding to the N latest accepted travel orders can be combined to form a feature vector, and the feature vector obtained by combination is combined with the model to determine the duration revision value corresponding to the to-be-accepted travel order.

S530, revising the estimated waiting time corresponding to the trip order to be accepted based on the revised value of the time corresponding to the trip order to be accepted.

In particular implementation, the estimated wait time may be revised using the following substeps: and calculating the sum of the time length revision value corresponding to the to-be-accepted travel order and the estimated waiting time length corresponding to the to-be-accepted travel order to obtain the estimated target waiting time length corresponding to the to-be-accepted travel order.

Of course, other methods may also be used to revise the estimated waiting time corresponding to the to-be-accepted travel order based on the time revision value corresponding to the to-be-accepted travel order. For example, the time length revision value corresponding to the to-be-accepted travel order is multiplied by a preset weight coefficient to obtain a target time length revision value, and then the sum of the target time length revision value and the estimated waiting time length corresponding to the to-be-accepted travel order is calculated to obtain the target estimated waiting time length corresponding to the to-be-accepted travel order.

In other embodiments, as shown in fig. 6, before performing step 330, the method for predicting the receiving order waiting time may further perform the following steps:

s610, determining whether the estimated waiting time corresponding to the to-be-accepted travel order needs to be revised or not based on the characteristic data corresponding to the to-be-accepted travel order.

Here, whether the estimated wait time needs to be revised can be determined using the following sub-steps: and under the condition that the number of the trip orders which are released before the trip orders to be taken over are released and are not taken over in the area where the trip orders to be taken over are located is larger than a preset number, determining that the estimated waiting time corresponding to the trip orders to be taken over needs to be revised.

The step realizes that the travel orders with earlier release time in the travel orders to be accepted are taken as the travel orders to be accepted, the estimated waiting time of which needs to be revised. The travel orders to be taken over with later release time do not need to be revised, and the estimated waiting time after the revision is not accurate enough along with the change of time, environmental factors and the like.

It should be noted here that the travel orders are sorted according to the release time after being released, and the travel orders with the earlier release time are ranked higher.

S620, under the condition that the estimated waiting time corresponding to the to-be-accepted trip order needs to be revised, revising the estimated waiting time corresponding to the to-be-accepted trip order based on the deviation information.

It should be noted that, after the service request end issues the trip order and before the trip order is not received by the service providing end, the service request end will periodically request the third party platform to determine the estimated waiting time of the issued trip order, and after the third party platform receives the request sent by the service request end, the third party platform determines the estimated waiting time according to the method in the above embodiment and sends the determined estimated waiting time to the corresponding service request end.

Fig. 7 is a block diagram illustrating an apparatus for estimating a receiving order waiting time period according to some embodiments of the present application, where the function implemented by the apparatus for estimating the receiving order waiting time period corresponds to the steps executed by the method. The device may be understood as the server or the processor of the server, or may be understood as a component that is independent from the server or the processor and implements the functions of the present application under the control of the server, as shown in the figure, the device for estimating the waiting time of the order receiving may include a data obtaining module 710, a waiting time estimating module 720, and a waiting time revising module 730.

The data obtaining module 710 may be configured to obtain feature data corresponding to a trip order to be taken over.

The waiting duration estimation module 720 may be configured to estimate an estimated waiting duration for waiting when the travel order is taken over based on the feature data.

The waiting duration revising module 730 may be configured to revise the estimated waiting duration corresponding to the to-be-accepted travel order based on the deviation information between the estimated waiting duration corresponding to the historical travel order and the actual waiting duration.

In an implementation, the waiting duration estimation module 720 includes:

a first sample acquisition submodule 7201 configured to acquire first sample data matched with the feature data; the first sample data comprises a plurality of historical trip orders, corresponding characteristic data before being taken over and estimated waiting time corresponding to each historical trip order in the first sample data;

the waiting time duration estimation submodule 7202 is configured to estimate an estimated waiting time duration for waiting when the to-be-accepted travel order is accepted based on the first sample data.

In specific implementation, the waiting duration estimation submodule 7202 is specifically configured to:

In specific implementation, the characteristic data corresponding to the trip order to be accepted includes at least one of the following items:

In specific implementation, the device for predicting the receiving waiting time further comprises:

a revision determining module 740, configured to determine whether an estimated waiting duration corresponding to the to-be-accepted travel order needs to be revised based on the feature data corresponding to the to-be-accepted travel order;

the waiting time revision module 730 is further configured to revise the estimated waiting time corresponding to the to-be-accepted travel order based on the deviation information when the estimated waiting time corresponding to the to-be-accepted travel order needs to be revised.

In particular implementation, the revision determination module 740 is specifically configured to:

In specific implementation, the historical travel order is located in an area where the travel order to be taken over is located.

In specific implementation, the historical travel orders include N travel orders with the shortest time interval between the taken time and the current time, where N is a positive integer.

In specific implementation, the deviation information includes a difference value between an estimated waiting time length corresponding to each historical travel order and an actual waiting time length;

the wait duration revision module 730 is specifically configured to: and calculating the mean value of the difference value between the estimated waiting time length corresponding to the historical trip order and the actual waiting time length, and revising the estimated waiting time length based on the mean value.

In specific implementation, the deviation information includes a difference between an estimated waiting time and an actual waiting time corresponding to each historical travel order, a mean of the differences, a variance of the differences, and a type of the differences.

In specific implementation, the latency revision module 730 includes:

a second sample acquisition sub-module 7301, configured to acquire feature data corresponding to the to-be-accepted travel order and second sample data matched with the deviation information; the second sample data comprises a plurality of historical travel orders, corresponding characteristic data before being taken over, deviation information corresponding to each historical travel order in the second sample data, and a duration revision value corresponding to each historical travel order in the second sample data;

a revision value determining submodule 7302, configured to determine, based on the second sample data, a duration revision value corresponding to the to-be-accepted travel order;

the waiting duration revision submodule 7303 is configured to revise the estimated waiting duration corresponding to the to-be-accepted travel order based on the duration revision value corresponding to the to-be-accepted travel order.

In practical implementation, the revision value determining sub-module 7302 is specifically configured to:

In implementation, the wait duration revision sub-module 7303 is specifically configured to:

and calculating the sum of the time length revision value corresponding to the to-be-accepted travel order and the estimated waiting time length corresponding to the to-be-accepted travel order to obtain the estimated target waiting time length corresponding to the to-be-accepted travel order.

a sample data updating module 750, configured to update the second sample data according to a predetermined time interval.

The wired connections may include connections in the form of L AN, WAN, Bluetooth, ZigBee, or NFC, or the like, or any combination thereof.

The embodiment discloses a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program performs the steps in the method for estimating the receiving order waiting time length of the embodiment.

In the method and the device for predicting the waiting time of the incoming order, the deviation information of the latest taken travel order is used as a factor to select the matched second sample data, which can be regarded as a feedback mechanism, the deviation information and the characteristic data obtained by the feedback mechanism can be matched with the second sample data which is closer to the travel order to be taken, and the predicted waiting time can be corrected more accurately by using the second sample data with high matching degree. Experiments prove that after the feedback mechanism is introduced, the estimation accuracy of the estimated waiting time is improved by more than 20%.

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 method for predicting the waiting time of order receiving is characterized by comprising the following steps:

acquiring characteristic data corresponding to a trip order to be accepted;

and revising the estimated waiting time corresponding to the trip order to be accepted based on the deviation information of the estimated waiting time corresponding to the historical trip order and the actual waiting time.

2. The method according to claim 1, wherein the estimating an estimated waiting time for waiting for taking over the travel order based on the characteristic data comprises:

3. The method according to claim 2, wherein estimating an estimated waiting time for waiting for taking over the to-be-taken over travel order based on the first sample data comprises:

4. The method according to claim 1, wherein the characteristic data corresponding to the travel order to be accepted comprises at least one of the following:

5. The method of claim 4, further comprising:

and under the condition that the estimated waiting time corresponding to the trip order to be accepted needs to be revised, revising the estimated waiting time corresponding to the trip order to be accepted based on the deviation information.

6. The method according to claim 5, wherein the determining whether the estimated waiting time corresponding to the to-be-accepted travel order needs to be revised based on the characteristic data corresponding to the to-be-accepted travel order comprises:

7. The method of claim 1, wherein the historical travel orders are located within an area in which the travel orders to be filled are located.

8. The method according to claim 7, wherein the historical travel orders comprise the travel order with the shortest time interval between the N held times and the current time, wherein N is a positive integer.

9. The method according to claim 8, wherein the deviation information includes a difference between an estimated waiting time and an actual waiting time corresponding to each historical travel order;

the correcting the estimated waiting time corresponding to the trip order to be accepted based on the deviation information of the estimated waiting time corresponding to the historical trip order and the actual waiting time comprises the following steps:

revising the estimated wait time based on the average.

10. The method according to claim 8, wherein the deviation information includes a difference between an estimated waiting time length and an actual waiting time length corresponding to each historical travel order, a mean value of the difference, a variance of the difference, and a type of the difference.

11. The method according to claim 10, wherein the revising of the estimated waiting time corresponding to the to-be-accepted travel order based on the deviation information between the estimated waiting time corresponding to the historical travel order and the actual waiting time comprises:

and revising the estimated waiting time corresponding to the trip order to be accepted based on the revised value of the time corresponding to the trip order to be accepted.

12. The method of claim 11, wherein said determining a corresponding length of time revision value of said to-be-held travel order based on said second sample data comprises:

13. The method according to claim 11, wherein the revising the estimated waiting duration corresponding to the to-be-accepted travel order based on the duration revision value corresponding to the to-be-accepted travel order includes:

14. The method of claim 11, further comprising:

and updating the second sample data according to a preset time interval.

15. An apparatus for predicting a receiving wait time, comprising:

and the waiting time revision module is used for revising the estimated waiting time corresponding to the trip order to be accepted based on the deviation information of the estimated waiting time corresponding to the historical trip order and the actual waiting time.

16. The apparatus of claim 15, wherein the wait duration estimation module comprises:

17. The apparatus according to claim 16, wherein the latency duration estimator module is specifically configured to:

18. The apparatus according to claim 15, wherein the characteristic data corresponding to the travel order to be taken comprises at least one of the following:

19. The apparatus of claim 18, further comprising:

the waiting time revision module is further configured to revise the estimated waiting time corresponding to the to-be-accepted travel order based on the deviation information under the condition that the estimated waiting time corresponding to the to-be-accepted travel order needs to be revised.

20. The apparatus of claim 19, wherein the revision determination module is specifically configured to:

21. The apparatus of claim 15, wherein the historical travel orders are located within an area in which the travel orders to be filled are located.

22. The apparatus of claim 21, wherein the historical travel orders comprise N travel orders with the shortest time interval between the held time and the current time, where N is a positive integer.

23. The apparatus according to claim 22, wherein the deviation information includes a difference between an estimated waiting time and an actual waiting time corresponding to each historical travel order;

the wait duration revision module is specifically configured to: and calculating the mean value of the difference value between the estimated waiting time length corresponding to the historical trip order and the actual waiting time length, and revising the estimated waiting time length based on the mean value.

24. The apparatus according to claim 22, wherein the deviation information includes a difference between an estimated waiting time period and an actual waiting time period corresponding to each historical travel order, a mean value of the difference, a variance of the difference, and a type of the difference.

25. The apparatus of claim 24, wherein the latency length revision module comprises:

and the waiting time revision submodule is used for revising the estimated waiting time corresponding to the trip order to be accepted based on the time revision value corresponding to the trip order to be accepted.

26. The apparatus of claim 25, wherein the revision value determination submodule is further configured to:

27. The apparatus of claim 25, wherein the latency length revision module is specifically configured to:

28. The apparatus of claim 25, further comprising:

29. An electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, the processor and the storage medium communicate via the bus when the electronic device is running, and the processor executes the machine-readable instructions to perform the steps of the method for estimating the waiting time of the order taking as claimed in any one of claims 1 to 14.

30. A computer-readable storage medium, having stored thereon a computer program for performing, when being executed by a processor, the steps of the method for estimating a waiting time for incoming orders as claimed in any one of claims 1 to 14.