CN110766509A

CN110766509A - Service order processing and takeout order recommending method and device

Info

Publication number: CN110766509A
Application number: CN201910472197.1A
Authority: CN
Inventors: 李爽; 李智锋; 汤恩清; 阳朝霞; 苏昆辉; 赵国旗; 罗振环
Original assignee: Koubei Shanghai Information Technology Co Ltd
Current assignee: Koubei Shanghai Information Technology Co Ltd
Priority date: 2019-05-31
Filing date: 2019-05-31
Publication date: 2020-02-07

Abstract

The application discloses a service order processing method, which comprises the following steps: obtaining service reservation data corresponding to target user information, which is sent by a terminal corresponding to the target user information, wherein the service reservation data comprises information for respectively providing services aiming at the target user information at a plurality of service time points; before any one of the plurality of service time points, performing the following steps: obtaining an impact factor value, wherein the impact factor value is used for determining service data provided for the target user information at a target service time point; obtaining target service data provided aiming at the target user information at the target service time point according to the influence factor value; generating a target service order aiming at the target user information according to the target service data; and pushing the target service order to a computing device of a service provider. By adopting the method, the problem of generating the recommended target service order aiming at the target user information from a large amount of service data is solved.

Description

Service order processing and takeout order recommending method and device

Technical Field

The application relates to the technical field of data processing, in particular to a service order processing method and device, and also relates to a takeout order recommending method and device, and a service booking system.

Background

With the development of the internet, service subscription has become a common way of providing services, such as take-out services. The existing service order processing scheme for booking services by searching and recommending shows more and more disadvantages, a service order is generally generated for timely subscribing services in the existing service order processing scheme, a user is required to decide a target service provider and target service data to be booked from a large amount of service data providing similar services in a short time, for example, in a timely subscribing takeout process, the user logs in a takeout platform, decides which takeout is at a place in a plurality of takeout shops, adds booked takeout dishes into a shopping cart, and places an order to wait for the takeout to arrive. There are the following problems: 1. the active selection increases the difficulty of selection of the user, and the user cannot determine to select the service data which matches the reservation requirement and has higher service quality in a short time through searching. 2. The user needs to repeatedly select the decision-making process every time the user subscribes to the service, and the process is complicated. 3. For takeaway booking, a user may only be able to book takeaway by takeaway providers around the vicinity of the meal served.

Therefore, how to generate a recommended service order for the target user information from the service data is a problem to be solved.

Disclosure of Invention

The application provides a service order processing method, which solves the problem that a recommended target service order is generated aiming at target user information from a large amount of service data.

The application provides a service order processing method, which comprises the following steps:

obtaining service reservation data corresponding to target user information, which is sent by a terminal corresponding to the target user information, wherein the service reservation data comprises information for respectively providing services aiming at the target user information at a plurality of service time points;

before any one of the plurality of service time points, performing the following steps:

obtaining an impact factor value, where the impact factor value is used to determine service data provided for the target user information at a target service time point, where the target service time point is a service time point closest to a current time point among the plurality of service time points;

obtaining target service data provided for the target user information at the target service time point according to the influence factor value; generating a target service order aiming at the target user information according to the target service data; and pushing the target service order to a computing device of a service provider.

Optionally, the obtaining service subscription data corresponding to the target user information sent by the terminal corresponding to the target user information includes: obtaining service reservation plan information corresponding to the target user information;

obtaining the service reservation data from the service reservation plan information.

Optionally, the method further includes: generating a first order not containing target service data for each of the plurality of service time points;

the generating a target service order for the target user information according to the target service data includes: adding the target service data to the first order to generate a second order; and taking the second order as the target service order.

Optionally, the obtaining, according to the impact factor value, target service data provided for the target user information at the target service time point includes: before the target service time point, screening target service data matched with the service reservation data at the target service time point from service data according to the influence factor value; the service data includes information of services that can be provided for the target user information.

Optionally, the screening, according to the impact factor value, target service data matched between the target service time point and the service reservation data from the service data includes:

determining a recommended score of the service data matched with the service reservation data at the target service time point according to the influence factor value; and determining the target service data according to the recommended score.

Optionally, the determining the target service data according to the recommendation score includes: and taking the service data with the highest recommendation score as the target service data.

Optionally, the obtaining the impact factor value includes at least one of the following modes:

calculating the weight of the distance factor by adopting a step function; the distance factor is an influence factor which takes distance information as the matching degree of the service data and the service reservation data, and the distance information is the distance information between the address of the service provided by the service provider and the address of the consumption service corresponding to the target user information;

determining the favorite matching number of user favorite labels on service package label matching, and calculating the weight of favorite label factors by adopting a linear function of the favorite matching number; the preference label factor is an influence factor for determining the matching degree of the service data and the service reservation data by using preference degree evaluation information corresponding to the service data;

calculating the weight of the date-setting factor by using an attenuation function; the date-on-shelf factor is an influence factor for determining the matching degree of the service data and the service reservation data by taking the date-on-shelf of the service data as the date-on-shelf;

calculating the weight of the historical dispatching date factor by adopting a quadratic increasing function; the historical dispatching date factor is an influence factor which takes the historical dispatching date of the service data as the matching degree of the service data and the service reservation data;

calculating the weight of the service package popularity factor by using a logarithmic function; the service package heat factor is an influence factor which takes the dispatching quantity of the service data in a preset time period as the matching degree of the service data and the service reservation data;

calculating the weight of the surprise factor by using a random function; the surprise factor is an influence factor which takes the random degree as the matching degree of the service data and the service reservation data;

the determining a recommended score of the service data matched with the service reservation data at the target service time point according to the impact factor value includes: adding the weight of at least one factor of the distance factor, the preference label factor, the on-shelf date factor, the historical delivery date factor, the service package heat factor and the surprise factor, and taking the sum result as the recommendation score.

Optionally, the method further includes: screening out alternative service data which can be provided for the target user information at the target service time point from service data which can be provided for the target user information according to at least one of distance data, user evaluation data and user preference data;

the obtaining target service data provided for the target user information at the target service time point according to the impact factor value includes: screening target service data provided aiming at the target user information at the target service time point from the alternative service data according to the influence factor value;

the distance data is distance information between an address of a service provided by the service provider and an address of a consumption service corresponding to the target user information; the user evaluation data is historical evaluation information corresponding to the target user information and aiming at the service; the user preference data is preference information corresponding to the target user information.

Optionally, the method further includes: and if the alternative service data is not screened, carrying out alarm prompt.

Optionally, the method further includes: and providing the target service data to a terminal corresponding to the target user information according to the target service order.

Optionally, the method further includes: if the target service data is not obtained, requesting to obtain service intention data aiming at the target user information, and generating the target service order according to the service intention data; the service intention data is service data represented by input information corresponding to target user information.

The application also provides a takeaway order recommendation method, which comprises the following steps:

obtaining takeout appointment data of a user; the takeout reservation data includes information for providing takeout services at a plurality of service time points, respectively;

obtaining takeout service data; the takeout service data is data for representing a takeout service that can be provided to a user;

screening out target service data matched with the takeout reservation data at the target service time point from the takeout service data;

and generating a recommended takeout order corresponding to the target service time point according to the target service data.

Optionally, the method further includes: generating a first order not containing takeout service data for each service time point in the plurality of service time points according to the takeout reservation data;

the generating of the recommended takeout order corresponding to the target service time point according to the target service data includes: adding the target service data into a first order corresponding to the target service time point to generate a second order, wherein the second order is used for providing takeout services represented by the target service data to a user at the target service time point; taking the second order as the take-away order.

Optionally, the obtaining takeout reservation data of the user includes: acquiring takeout subscription information of a user; the takeout subscription information is takeout service reservation plan information subscribed by the user; obtaining the takeaway subscription data from the takeaway subscription information.

Optionally, the generating, according to the takeout reservation data, a first order that does not include takeout service data for each of the plurality of service time points includes: acquiring reservation time information of delivery of the takeout service from the takeout reservation data; determining the plurality of service time points according to the reservation time information, and generating the first order for each of the plurality of service time points.

Optionally, the method further includes: determining order generation time for generating a take-out order according to each service time point in the plurality of service time points; the step of screening out target service data matched with the takeout reservation data at the target service time point from the takeout service data comprises the following steps: and if the current time is the order generation time, screening target service data matched with the takeout reservation data from the takeout service data.

Optionally, the screening out, from the takeout service data, target service data matched with the takeout reservation data at the target service time point includes: screening out alternative service data from the takeout service data according to at least one of distance data, user evaluation data and user preference data; the distance data is distance information between an address providing takeout service and an address consuming the takeout service; the user evaluation data is historical evaluation information of the user for the takeout service; the user preference data is preference information of a user;

and screening the target service data from the alternative service data.

Optionally, the method further includes: and if the alternative service data matched with the takeout reservation data are not screened, carrying out alarm prompt.

Optionally, the method further includes: determining an influence factor of the takeout service data and a weight corresponding to the influence factor; the influence factor is used for determining the matching degree of the takeout service data and the takeout reservation data;

the screening of the target service data from the alternative service data comprises: determining a recommended score of the takeout service data matched with the takeout reservation data in the takeout service data according to the influence factors and the weights corresponding to the influence factors; and determining the target service data according to the recommended score.

Optionally, the determining the target service data according to the recommendation score includes: and taking the takeout service data with the highest recommended score as the target service data.

Optionally, the influence factor includes at least one of the following factors: a distance factor; a favorite label factor; a date on shelf factor; a historical dispatch date factor; a take-out service package popularity factor; a surprise factor;

the determining, according to the influence factor and the weight corresponding to the response factor, a recommended score of takeout service data matched with the takeout reservation data in the takeout service data includes: calculating the weight of the distance factor by adopting a step function; determining the favorite matching quantity of user favorite labels on the takeout service package label matching, and calculating the weight of favorite label factors by adopting a linear function of the favorite matching quantity; calculating the weight of the date-setting factor by using an attenuation function; calculating the weight of the historical dispatching date factor by adopting a quadratic increasing function; calculating the weight of the hot degree factor of the takeaway service package by using a logarithmic function; calculating the weight of the surprise factor by using a random function; and adding the weights of the at least one factor, and taking the addition result as the recommendation score.

Optionally, the method further includes: pushing the take-away order to a computing device of a take-away service provider.

Optionally, the takeout service data is package information that can be provided to the user; the target service data is target package information which is screened from the package information and matched with the takeout appointment data at the target service time point.

The present application also provides a service reservation system, comprising: a client, a service subscription computing device, a computing device of a service provider;

the client is used for obtaining service reservation data corresponding to target user information, and the service reservation data comprises information for respectively providing services aiming at the target user information at a plurality of service time points; providing the service subscription data to a service subscription computing device;

the service subscription computing device is configured to obtain the service subscription data, and execute the following steps before any one of the plurality of service time points:

obtaining target service data provided for the target user information at the target service time point according to the influence factor value;

generating a target service order aiming at the target user information according to the target service data;

pushing the target service order to a computing device of a service provider;

and the computing equipment of the service provider is used for receiving the target service order, providing corresponding service according to target service data contained in the target service order and setting the state of the target service order into a verification and marketing state.

Optionally, the service subscription computing device is further configured to: and generating logistics distribution scheduling data according to the target service order, wherein the logistics distribution scheduling data is used for scheduling delivery resources for providing service for the target user information at the target service time point.

Optionally, the client is further configured to obtain user evaluation data; providing the user ratings data to the service subscription computing device;

the service subscription computing device further to: and obtaining target package information according to the service reservation data and the user evaluation data, and taking the target package information as the target service data.

The present application further provides a service order processing apparatus, including:

a service reservation data obtaining unit configured to obtain service reservation data corresponding to target user information, the service reservation data including information for providing services for the target user information at a plurality of service time points, respectively, and being transmitted from a terminal corresponding to the target user information;

a service order processing unit for performing the following steps before any one of the plurality of service time points:

and pushing the target service order to a computing device of a service provider.

The present application further provides a takeaway order recommending apparatus, including:

a takeout reservation data obtaining unit for obtaining takeout reservation data of the user; the takeout reservation data includes information for providing takeout services at a plurality of service time points, respectively;

a take-away order processing unit for performing the following steps before any one of the plurality of service time points:

Compared with the prior art, the method has the following advantages:

according to the service order processing method and device, service reservation data corresponding to target user information are obtained, and the service reservation data comprise information for respectively providing services for the target user information at a plurality of service time points; and before the target service time point in the plurality of service time points, obtaining target service data provided for the target user information at the target service time point according to the influence factor value, and generating a target service order according to the target service data. Target service data are screened based on the influence factor values, the target service data with higher matching degree of service reservation data corresponding to the target user information can be decided, and the problem of generating a recommended target service order aiming at the target user information is solved.

According to the takeout order recommending method and device, takeout appointment data of a user are obtained; the takeout reservation data includes information for providing takeout services at a plurality of service time points, respectively; screening out target service data matched with the takeout reservation data at the target service time point from the takeout service data before the target service time point in the plurality of service time points; and generating a recommended takeout order corresponding to the target service time point according to the target service data. Target service data are screened from the takeout service data, and a target user indicated by the target user information is not required to make a search decision, so that the difficulty in selection of the target user is avoided, and the problem of generating a recommended takeout order according to the target user information is solved. Furthermore, target service data are screened from the takeout service data based on the influence factors, the target service data with high matching degree with the takeout reservation data corresponding to the target user information can be decided, and the satisfaction degree of the target user on the recommended takeout order can be improved.

The application provides a service reservation system, including: a client, a computing device of a service provider, and a service subscription computing device; the client is used for obtaining service reservation data corresponding to target user information, and the service reservation data comprises information for respectively providing services aiming at the target user information at a plurality of service time points; providing the service subscription data to a service subscription computing device. The service subscription computing device is used for obtaining the service reservation data, and obtaining target service data provided for the target user information at a target service time point in the plurality of service time points according to an influence factor value before the target service time point; and generating a target service order according to the target service data, and pushing the target service order to computing equipment of a service provider. The computing device of the service provider is used for receiving the target service order, providing corresponding service according to target service data contained in the target service order, and then setting the state of the target service order to be a verification and sale state, so that the target service data is recommended without searching and selecting the service data by a target user, and the generated target service order is pushed to the computing device of the service provider providing the service.

Drawings

FIG. 1 is a system environment diagram of a service order processing method according to a first embodiment of the present application;

FIG. 2 is a flowchart illustrating a method for processing a service order according to a first embodiment of the present application;

FIG. 3 is a flow chart of a take out order process provided in a first embodiment of the present application;

FIG. 4 is a process flow diagram of a takeaway order recommendation method provided in a second embodiment of the present application;

FIG. 5 is a schematic view of a service order processing apparatus according to a fourth embodiment of the present application;

fig. 6 is a schematic diagram of a takeaway order recommending apparatus according to a fifth embodiment of the present application;

fig. 7 is a schematic diagram of an electronic device provided herein.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.

The application provides a service order processing method and device and electronic equipment. The application also relates to a takeout order recommending method and device and electronic equipment. The application also relates to a service subscription system. Details are described in the following examples one by one.

For ease of understanding, a system environment in which the service order processing method is actually applied is first given.

Referring to fig. 1, a client 101 is configured to obtain service subscription data corresponding to target user information, where the service subscription data includes information for providing services for the target user information at a plurality of service time points, respectively; providing the service subscription data to a service subscription computing device. For example, the client may be a terminal corresponding to the target user information, and may be capable of receiving input information of the target user indicated by the target user information. As another example, a client may also refer to a client applet or client H5 web page or client APP. For another example, the client 101 includes a configured meal plan module and a feedback evaluation module, where the configured meal plan module is configured to receive configured meal plan data of the target user, and the feedback evaluation module is configured to receive feedback evaluation data of the target user; the configured meal plan data and the feedback evaluation data can interact with the service subscription computing equipment in a http protocol and a json character string mode.

In the figure, the service subscription computing device 102 is configured to obtain the service subscription data, and before any one of the plurality of service time points, perform the following steps: obtaining an impact factor value; obtaining target service data provided aiming at target user information at a target service time point according to the influence factor value; generating a target service order aiming at the target user information according to the target service data; the target service order is pushed to the computing device 103 of the service provider. For example, the service subscription computing device obtains configured meal-ordering plan data of the target user and further obtains feedback evaluation data, performs analysis and decision according to the configured meal-ordering plan data and the feedback evaluation data to obtain a recommended target package, generates a target service order, and sends the target service order to the computing device of the service provider through the message middleware.

In the figure, the computing device 103 of the service provider is configured to receive the target service order and provide a corresponding service according to target service data included in the target service order. For example, the computing device of the service provider is a computing device of a take-out service provider, and includes a package information module, a package tag module, and an order processing module, where the package information module is used to configure a take-out package that can be provided; and the package label module is used for configuring a package label for the take-out package and sending the package label to the service subscription computing device. For example, the package label module selects a label matched with the package information from labels preset by a package management module of the service subscription computing device and marks the label on the package information. The takeout means to provide a takeout service or a delivery of goods, for example, a meal delivery service.

A first embodiment of the present application provides a service order processing method, which may be deployed in a service subscription computing device, and implement recommendation of target service data according to service subscription data corresponding to target user information, generation of a target service order, and pushing of the target service order to a computing device of a service provider. And determining the target service data by using the influence factor value in the process of generating the target service order, so that the target service order with higher matching degree of service reservation data corresponding to the information of the target user can be generated without searching and selecting the target service data by the target user. The method can be used for service subscriptions in a variety of business areas, for example for takeaway subscriptions.

A service order processing method according to a first embodiment is described below with reference to fig. 2 and 3.

The method for generating the service monitoring rule shown in fig. 2 includes: step S201 to step S205.

Step S201, obtaining service reservation data corresponding to target user information sent by a terminal corresponding to the target user information, where the service reservation data includes information for providing services for the target user information at a plurality of service time points, respectively.

In practical application, the target user indicated by the target user information performs service subscription to the service subscription computing device, so as to obtain services provided for the target user information at a plurality of service time points. The service subscription means that: the service subscription computing device obtains service reservation data of a target user, decides service data matched with the service reservation data from the service data through an algorithm, and provides services according to service time points. Wherein the service data may be service data obtained by a service subscription computing device from a computing device of at least one service provider. For example, the takeout subscription as a specific service subscription means that a user can subscribe to a takeout service by a package week, a package month, a package year, or the like, and the service subscription computing device optimizes the takeout recommended to the user by an algorithm from the takeout service data provided by the computing device of the service provider contracted with the platform. Take-out may be provided subsequently in the physical environment according to the scheduled delivery time for the subscription to the take-out service.

In this embodiment, the obtaining of the service reservation data corresponding to the target user information sent by the terminal corresponding to the target user information includes: obtaining service reservation plan information corresponding to the target user information; obtaining the service reservation data from the service reservation plan information. The service reservation plan information is information included in a reservation plan for providing services at a plurality of service time points within a preset time for the target user information. For example, the date, time, address, restaurant preference, restaurant food exclusion, payment method, and the like of the takeaway food delivery for the reserved allocation method such as the package week, the package month, and the package year. Therefore, the service orders of a plurality of service time points can be generated through one service reservation plan according to the target user information, the selection of service data for each service time point is not needed, and the complicated operation flow is avoided. Meanwhile, a service time point for providing a service can be obtained in advance, so that the preparation time for the service can be longer, and therefore, the service which can be subscribed is located in a wider geographical range rather than being limited to the periphery of the address of the consumption service. Preferably, the service subscription data may be sent to the service subscription computing device by the terminal in a form of http protocol or json character string.

Before any one of the plurality of service time points, executing steps S202 to S205 to generate a target service order for the target user information for the target service time point, and pushing the target service order to a computing device of a service provider. In steps S202 to S205, the service subscription computing device determines target service data from the service data by using a recommendation algorithm according to the service reservation data, for example, computing and deciding target package information from package information included in a large amount of takeout service data, generating a target service order by using the target package information, and pushing the target service order to the computing device of the service provider, further, providing services for target user information according to appointed time and place included in the target service order. The following describes steps S202 to S205.

Step S202, obtaining an impact factor value, where the impact factor value is used to determine service data provided for the target user information at a target service time point, and the target service time point is a service time point closest to a current time point in the multiple service time points.

In this embodiment, according to the impact factor value and the service reservation data, calculation is performed for each service data that can be used as a candidate, and target data recommended for target user information is determined. The impact factors include factors for a plurality of dimensions that determine how well service data matches the service subscription data. For example, the user preference data and the tag information are used as influence factors of candidate service data, and the recommended target service data is calculated from the candidate service data according to the weights of different influence factors.

In this embodiment, the obtaining the impact factor value includes at least one of the following modes:

the following processing is performed after obtaining the impact factor value: adding the weight of at least one factor of the distance factor, the preference label factor, the on-shelf date factor, the historical delivery date factor, the service package heat factor and the surprise factor, and taking the sum result as the recommendation score.

Of course, the recommended score may be obtained by adding at least one of the weight of the season factor, the weight of the weather factor, and the weight of the holiday factor to the weight of the season factor, the weight of the weather factor, and the weight of the holiday factor, which are obtained in correspondence with the influence factors such as the season information, the weather information, and the holiday information, respectively.

For example, a step function f(s) is used to calculate a distance factor of the alternative service data, and within a certain range, the closer the distance between the service providing address and the consuming service address corresponding to the alternative service data is, the higher the weight score of the distance factor is. Wherein f(s) can be expressed as: 0< ═ s < ═ 2km, f(s) > 100; 2km < s < ═ 3km, f(s) ═ 60; 3km < s < ═ 4km, f(s) ═ 40; 4km < s < ═ 5km, f(s) ═ 20; s >5km, f(s) -100; s denotes the distance between the service providing address and the consuming service address in km.

For another example, a positive correlation function f (t) is used to calculate a favorite label factor of the alternative service data, where f (t) can be expressed as: (t) kt (k is a constant, k > 0). Obtaining preference degree data fed back by a user for service data after consuming service, such as like or dislike or not evaluation, and adding all tags of the service data into a preference tag group if the preference degree data for the service data is like; and if the like degree data aiming at the service data is dislike, disliking all tags of the service data to the tag group. Wherein t represents the number of the favorite labels corresponding to the currently screened service data, and the more the number of the favorite labels is, the higher the weight score of the favorite label factor is.

For another example, a quadratic increasing function f (d1) is used to calculate the historical dispatch date factor of the alternative service data, wherein d1 represents the actual dispatch time recommended last as the target service data, and the longer the actual dispatch time is from the present, the higher the weight score of the historical dispatch date factor is. As another example, a decay function f (d2) is used to calculate a date on shelf factor for alternative service data, where d2 represents the time the service data was on shelf. The closer the on-shelf date is to the current time, the higher the weight score for the on-shelf date factor.

For another example, a logarithmic function f (h) is used to calculate the service heat factor of the alternative service data, and the logarithmic function is used to make the recommendation as balanced as possible. Where f (h) may be expressed as f (h) ═ klog2(h) (k is a constant), and h denotes the total number of service orders for which the service data was recommended the day before the target service time point.

For another example, a random function is used to calculate a surprise factor of the alternative service data, and a random variable is added to improve the surprise degree of the service data, wherein the random function r is used, and r can be represented as a random number in 1-10.

Step S203, obtaining target service data provided for the target user information at the target service time point according to the impact factor value.

In an implementation manner of this embodiment, the target service data is obtained by the following processing: before the target service time point, screening target service data matched with the service reservation data at the target service time point from service data according to the influence factor value; the service data includes information of services that can be provided for the target user information. Specifically, according to the influence factor value, determining a recommended score of service data matched with the service reservation data at the target service time point; and determining the target service data according to the recommended score. Preferably, the service data with the highest recommendation score is used as the target service data. In practice, the service data may be obtained from a computing device of a service provider that is capable of providing service subscription subscriptions. For example, package information satisfying a quality preset condition is obtained as the service data from a computing device of a service provider that can provide a takeout reservation subscription.

In an implementation manner of this embodiment, first, a preliminary screening is performed on the service data, for example: and screening out alternative service data which can be provided for the target user information at the target service time point from the service data which can be provided for the target user information according to at least one of distance data, user evaluation data and user preference data. Then, according to the influence factor value, screening target service data provided aiming at the target user information at the target service time point from the alternative service data; the distance data is distance information between an address of a service provided by the service provider and an address of a consumption service corresponding to the target user information; the user evaluation data is historical evaluation information for the service corresponding to the target user information and can be provided for the service subscription computing equipment in a http protocol and json character string mode; the user preference data is preference information corresponding to the target user information. Further, if the alternative service data is not screened, an alarm prompt is carried out. According to the method and the device, the recommended target service data is screened from the service data which can be provided, so that the user does not need to select from a large amount of data, and the target service data meeting the quality preset condition can be obtained.

In an implementation manner of this embodiment, screening out, from service data that can be provided for the target user information according to user preference data, alternative service data that can be provided for the target user information at the target service time point includes: matching the label information contained in the user preference data with the label information of the service data, and taking the matched label information as the user preference data. For example, the tag information may be taste information: the taste information of spicy, sweet, salty, light and the like can also be the category information: one kind of food information such as braised pork in brown sauce, grass carp and the like. And further, screening the alternative service data according to user evaluation data. For example, the user evaluation data is: poor evaluation, good evaluation and the like. The service provider's computing device may obtain preset tags from the service subscription computing device, and select corresponding tag tagging service data from the preset tags. Target service data are recommended according to the user preference data and the user evaluation data, the target service data adjusted along with the user preference data and the feedback evaluation data can be obtained, and user experience is improved.

And step S204, generating a target service order aiming at the target user information according to the target service data.

In one embodiment of this embodiment, the following process is included: generating a first order not containing target service data for each of the plurality of service time points; adding the target service data to the first order to generate a second order; and taking the second order as the target service order. Further, if the target service data is not obtained, requesting to obtain service intention data aiming at the target user information, and generating the target service order according to the service intention data; the service intention data is service data represented by input information corresponding to target user information.

Step S205, pushing the target service order to a computing device of a service provider.

In practical application, message middleware is deployed on the service subscription computing device and the computing device of the service provider, and the service subscription computing device sends a message to the computing device of the service provider through the message middleware to push the target service order.

In this embodiment, the method further includes: and providing the target service data to a terminal corresponding to the target user information according to the target service order. For example, the http protocol is adopted, and the transmitted text format is json, and the target service data is provided.

Please refer to fig. 3. The figure shows an order processing flow, using a take order as an example for a specific service order, comprising:

step S301, according to the user distance data, the user preference data and the user evaluation data, screening out alternative package information from package information capable of providing takeout. For example, the screened distance is smaller than a preset distance threshold, the favorite taste of the user does not contain a dietetic restraint, and the evaluation data does not contain poorly evaluated alternative package information.

Step S302, judging whether to screen out the alternative package information.

And step S303, if the alternative package information is not screened out, carrying out alarm prompt to prompt a request to acquire service intention data, and generating a take-out order according to the service intention data.

And step S304, if the alternative package information is screened out, recommending target package information according to the weight value of the influence factor.

And S305, generating a target service order by taking the weight score and the maximum alternative package information as target package information, and pushing the target service order to computing equipment of a service provider.

Thus, the service order processing method provided in this embodiment is described in detail, and the impact factor value is used to determine the target service data in the process of generating the target service order, so that the target service order with a high matching degree with the service reservation data corresponding to the target user information can be recommended to be generated without searching and selecting the target service data by the target user.

Based on the above embodiments, a take-away order recommendation method is provided in the second embodiment of the present application.

For ease of understanding, a system environment for a takeaway order recommendation method is first presented. In the present application, the takeaway order is a specific service order, and the system environment diagram of the recommendation method is similar to fig. 1.

Referring to fig. 1, a client 101 is configured to obtain takeout reservation data corresponding to target user information, where the takeout reservation data includes information that provides takeout services for a target user system at a plurality of service time points, respectively; providing the takeaway reservation data to a service subscription computing device. For example, the client may be a terminal corresponding to the target user information, and may be capable of receiving input information of the target user indicated by the target user information. As another example, a client may also refer to a client applet or client H5 web page or client APP. For another example, the client 101 includes a configured meal plan module and a feedback evaluation module, where the configured meal plan module is configured to receive configured meal plan data of the target user, and the feedback evaluation module is configured to receive feedback evaluation data of the target user; the configured meal plan data and the feedback evaluation data can interact with the service subscription computing equipment in a http protocol and a json character string mode.

In the figure, a service subscription computing device 102 is shown for obtaining takeaway subscription data, and prior to any one of a plurality of service time points, performing the steps of: screening out target service data provided aiming at target user information at a target service time point from the takeout service data; and generating a recommended takeout order corresponding to the target service time point according to the target service data, and pushing the takeout order to computing equipment of a service provider. For example, the service subscription computing device obtains configured meal plan data and feedback evaluation data of the target user, performs analysis and decision according to the configured meal plan data and the feedback evaluation data to obtain a recommended target package, generates a target service order, and sends the target service order to the computing device of the service provider through the message middleware.

In the figure, the computing device 103 of the service provider is configured to receive the takeaway order and provide a corresponding service according to target service data included in the takeaway order. For example, the takeout service data is package information that can be provided, and correspondingly, the target service data is target package information; a package information module of a computing device of a take-away service provider is used for configuring a take-away package that can be provided; and the package label module of the computing equipment of the take-out service provider is used for configuring the package label of the take-out package and sending the package label to the service subscription computing equipment. For another example, the package label module selects a label matched with the package information from labels preset by the package management module of the service subscription computing device and marks the label on the package information.

A second embodiment of the present application provides a takeout order recommendation method, where corresponding to each service time point in a plurality of service time points, target service data matched with takeout reservation data corresponding to target user information is screened from takeout service data, a recommended takeout order is generated, and a target user does not need to search and select the target service data in a process of generating the recommended takeout order.

The method for recommending a take-away order provided by the second embodiment is described below with reference to fig. 4, and for related parts, reference is made to the description of corresponding parts of the first embodiment.

The takeaway order recommendation method shown in fig. 4 includes: step S401 to step S404.

Step S401, obtaining takeout reservation data of a user; the takeout reservation data includes information that provides takeout services at a plurality of service time points, respectively.

In this embodiment, the obtaining takeout reservation data of the user includes: acquiring takeout subscription information of a user; the takeout subscription information is takeout service reservation plan information subscribed by the user; obtaining the takeaway subscription data from the takeaway subscription information. The takeout service appointment plan information is as follows: and the target user indicated by the target user information performs takeout reservation subscription to takeout service subscription computing equipment, and generates reservation plan information for providing takeout service for the target user information at a plurality of service time points. For example, the takeout subscription as a specific service subscription means that a user can subscribe to a takeout service in a package week, package month, package year, or the like, and the service subscription computing device selects a takeout recommended to the user through an algorithm from takeout service data provided by a computing device of a service provider. Take-out may be provided subsequently in the physical environment according to the scheduled delivery time for the subscription to the take-out service. For another example, the takeout service reservation plan information includes: date, time, food delivery point address, food preference, food avoiding, payment mode and other information of take-away food delivery. Therefore, the takeout orders of a plurality of service time points can be generated through one takeout service reservation plan according to the target user information, the takeout order selection does not need to be carried out for each service time point, and the complicated operation flow is avoided. Meanwhile, the service time point for providing takeaway can be obtained in advance, so that the preparation time for takeaway can be longer, and therefore, the geographical range of the takeaway dining point capable of being booked is wider, and the service time point is not limited to the periphery of the dining point. Preferably, the takeout reservation data can be sent to the takeout service subscription computing device by the terminal in a form of a json character string through an http protocol.

Before any one of the service time points, executing steps S402 to S404 to generate a takeout order for the target user information for the target service time point, and pushing the takeout order to a computing device of a service provider. The following describes steps S202 to S205.

Step S402, acquiring takeout service data; the takeout service data is data indicating a takeout service that can be provided to the user.

In this embodiment, the takeout service data is package information that can be provided to the user; the target service data is target package information which is screened from the package information and matched with the takeout appointment data at the target service time point.

The obtaining takeaway service data includes: the service data is obtained from a computing device of a takeaway provider capable of providing a service subscription. For example, package information satisfying a quality preset condition is obtained as the takeout service data from a computing device of a takeout service provider.

Step S403, screening out target service data matching the takeout reservation data at the target service time point from the takeout service data.

In this embodiment, first, the service data is preliminarily screened to obtain alternative service data, and then the target service data is screened from the alternative service data, which specifically includes: screening out alternative service data from the takeout service data according to at least one of distance data, user evaluation data and user preference data; the distance data is distance information between an address providing takeout service and an address consuming the takeout service; the user evaluation data is historical evaluation information of the user for the takeout service; the user preference data is preference information of a user; and screening the target service data from the alternative service data. Further, if the alternative service data matched with the takeout reservation data is not screened, an alarm prompt is carried out.

In an implementation manner of this embodiment, screening out alternative service data that can be provided for the target user information at the target service time point from the takeout service data that can be provided for the target user information according to the user preference data includes: matching the label information contained in the user preference data with the label information of the takeout service data, wherein the matched label information is used as the user preference data. For example, the tag information may be taste information: the taste information of spicy, sweet, salty, light and the like can also be the category information: one kind of food information such as braised pork in brown sauce, grass carp and the like. And further, screening the alternative service data according to user evaluation data. For example, the user evaluation data is: poor evaluation, good evaluation and the like. The service provider's computing device may obtain preset tags from the service subscription computing device, and select corresponding tags from the preset tags to label the takeaway package information. Target package information is recommended according to the user preference data and the user evaluation data, the target package information adjusted along with the user preference data and the feedback evaluation data can be obtained, and user experience is improved.

In an implementation manner of this embodiment, the target service data is screened from the candidate service data according to the impact factor and the weight corresponding to the impact factor. The method specifically comprises the following steps:

determining an influence factor of the takeout service data and a weight corresponding to the influence factor; the influence factor is used for determining the matching degree of the takeout service data and the takeout reservation data;

determining a recommended score of the takeout service data matched with the takeout reservation data in the takeout service data according to the influence factors and the weights corresponding to the influence factors; and determining the target service data according to the recommended score. Specifically, the takeout service data with the highest recommended score is used as the target service data.

Preferably, the influence factor includes at least one of the following factors: a distance factor; a favorite label factor; a date on shelf factor; a historical dispatch date factor; a take-out service package popularity factor; a surprise factor. Wherein, in a preferred embodiment, the recommendation score is obtained by:

calculating the weight of the distance factor by adopting a step function;

determining the favorite matching quantity of user favorite labels on the takeout service package label matching, and calculating the weight of favorite label factors by adopting a linear function of the favorite matching quantity;

calculating the weight of the date-setting factor by using an attenuation function;

calculating the weight of the historical dispatching date factor by adopting a quadratic increasing function;

calculating the weight of the hot degree factor of the takeaway service package by using a logarithmic function;

calculating the weight of the surprise factor by using a random function;

and adding the weights of the at least one factor, and taking the addition result as the recommendation score.

And step S404, generating a recommended takeout order corresponding to the target service time point according to the target service data.

In one embodiment of this embodiment, the following process is included: generating a first order not containing takeout service data for each service time point in the plurality of service time points according to the takeout reservation data; adding the target service data into a first order corresponding to the target service time point to generate a second order, wherein the second order is used for providing takeout services represented by the target service data to a user at the target service time point; taking the second order as the take-away order. Specifically, the first order is generated by the following processing: acquiring reservation time information of delivery of the takeout service from the takeout reservation data; determining the plurality of service time points according to the reservation time information, and generating the first order for each of the plurality of service time points.

In one embodiment of this embodiment, the following process is included: generating the first order for each of the plurality of service time points; adding the target package information into the first order to generate a second order; taking the second order as the take-away order. Further, if the target package information is not obtained, requesting to obtain service intention data aiming at the target user information, and generating the takeout order according to the service intention data; the service intention data is package information represented by input information corresponding to target user information.

In an embodiment of this embodiment, the method further includes: determining order generation time for generating a take-out order according to each service time point in the plurality of service time points; and if the current time is the order generation time, screening target service data matched with the takeout reservation data from the takeout service data. For example, a time point that is earlier than the service time point by a preset time may be used as the order generation time, and if the current time is the order generation time, the recommended target package information is obtained, and the take-out order is generated. Thereby enabling the computing device of the take-away service provider to obtain a reasonable preparation time for the recommended target package information.

In this embodiment, the method further includes: pushing the take-away order to a computing device of a take-away service provider. For example, the take order is pushed by sending a message through messaging middleware.

Thus, the takeout order recommendation method provided in this embodiment is described in detail, and target service data is screened from the takeout service data in the takeout order generation process, so that the takeout order with a high matching degree with the takeout reservation data corresponding to the target user information can be recommended to be generated without the target user searching and selecting the target service data.

Based on the above embodiments, a third embodiment of the present application provides a service subscription system, which has a schematic diagram similar to fig. 1. The system provided by the third embodiment is explained below, and for relevant parts, reference is made to the description of the corresponding parts of the above embodiments.

A service subscription system provided by a third embodiment includes: a client, a service subscription computing device, a computing device of a service provider;

the client is used for obtaining service reservation data corresponding to target user information, and the service reservation data comprises information for respectively providing services aiming at the target user information at a plurality of service time points; providing the service subscription data to a service subscription computing device.

For example, the client may be a terminal corresponding to the target user information, and may be capable of receiving input information of the target user indicated by the target user information. As another example, a client may also refer to a client applet or client H5 web page or client APP. For another example, the client comprises a configured meal ordering plan module and a feedback evaluation module, wherein the configured meal ordering plan module is used for receiving configured meal ordering plan data of the target user, and the feedback evaluation module is used for receiving feedback evaluation data of the target user; the configured meal plan data and the feedback evaluation data can interact with the service subscription computing equipment in a http protocol and a json character string mode.

In this embodiment, the service subscription computing device is further configured to: and generating logistics distribution scheduling data according to the target service order, wherein the logistics distribution scheduling data is used for scheduling delivery resources for providing service for the target user information at the target service time point.

In an implementation manner of this embodiment, the client is further configured to obtain user evaluation data; providing the user ratings data to the service subscription computing device; accordingly, the service subscription computing device is further configured to: and obtaining target package information according to the service reservation data and the user evaluation data, and taking the target package information as the target service data.

For example, the service subscription computing device obtains configured meal-ordering plan data of the target user and further obtains feedback evaluation data, performs analysis and decision according to the configured meal-ordering plan data and the feedback evaluation data to obtain a recommended target package, generates a target service order, and sends the target service order to the computing device of the service provider through the message middleware.

For example, the computing device of the service provider is a computing device of a take-out service provider, and includes a package information module, a package tag module, and an order processing module, where the package information module is used to configure a take-out package that can be provided; and the package label module is used for configuring a package label for the take-out package and sending the package label to the service subscription computing device. For example, the package label module selects a label matched with the package information from labels preset by a package management module of the service subscription computing device and marks the label on the package information.

A fourth embodiment of the present application provides a service order processing apparatus corresponding to the first embodiment. The following describes the apparatus provided in the fourth embodiment with reference to fig. 5.

The service order processing apparatus shown in fig. 5 includes:

a service reservation data obtaining unit 501, configured to obtain service reservation data corresponding to target user information, where the service reservation data includes information that provides services for the target user information at a plurality of service time points, respectively, and is sent by a terminal corresponding to the target user information;

a service order processing unit 502 for performing the following steps before any one of the plurality of service time points:

The service reservation data obtaining unit 501 is specifically configured to obtain service reservation plan information corresponding to the target user information; obtaining the service reservation data from the service reservation plan information.

Wherein the service order processing unit 502 further comprises an order generating subunit, and the order generating subunit is configured to: generating a first order not containing target service data for each of the plurality of service time points; adding the target service data to the first order to generate a second order; and taking the second order as the target service order.

Wherein the service order processing unit 502 further comprises a data filtering subunit, the data filtering subunit is configured to: before the target service time point, screening target service data matched with the service reservation data at the target service time point from service data according to the influence factor value; the service data includes information of services that can be provided for the target user information.

Wherein, the data screening subunit is specifically configured to: determining a recommended score of the service data matched with the service reservation data at the target service time point according to the influence factor value; and determining the target service data according to the recommended score.

Wherein, the data screening subunit is further specifically configured to: and taking the service data with the highest recommendation score as the target service data.

Wherein the service order processing unit 502 further comprises an influence factor value obtaining subunit, configured to perform at least one of the following processes:

correspondingly, the data screening subunit is further specifically configured to: adding the weight of at least one factor of the distance factor, the preference label factor, the on-shelf date factor, the historical delivery date factor, the service package heat factor and the surprise factor, and taking the sum result as the recommendation score.

The service order processing unit 502 is further specifically configured to: screening out alternative service data which can be provided for the target user information at the target service time point from service data which can be provided for the target user information according to at least one of distance data, user evaluation data and user preference data; screening target service data provided aiming at the target user information at the target service time point from the alternative service data according to the influence factor value;

The service order processing unit 502 is further specifically configured to: and if the alternative service data is not screened, carrying out alarm prompt.

The service order processing unit 502 is further specifically configured to: and providing the target service data to a terminal corresponding to the target user information according to the target service order.

The service order processing unit 502 is further specifically configured to: if the target service data is not obtained, requesting to obtain service intention data aiming at the target user information, and generating the target service order according to the service intention data; the service intention data is service data represented by input information corresponding to target user information.

A fifth embodiment of the present application provides a takeaway order recommending apparatus according to the second embodiment. The apparatus provided in the fifth embodiment will be described below with reference to fig. 6.

The takeaway order recommending apparatus shown in fig. 6 includes:

a takeout reservation data obtaining unit 601 configured to obtain takeout reservation data of the user; the takeout reservation data includes information for providing takeout services at a plurality of service time points, respectively;

a take-away order processing unit 602, configured to perform the following steps before any one of the plurality of service time points: obtaining takeout service data; the takeout service data is data for representing a takeout service that can be provided to a user; screening out target service data matched with the takeout reservation data at the target service time point from the takeout service data; and generating a recommended takeout order corresponding to the target service time point according to the target service data.

The takeaway order processing unit 602 is specifically configured to: generating a first order not containing takeout service data for each service time point in the plurality of service time points according to the takeout reservation data; adding the target service data into a first order corresponding to the target service time point to generate a second order, wherein the second order is used for providing takeout services represented by the target service data to a user at the target service time point; taking the second order as the take-away order.

The takeout reservation data obtaining unit 601 is specifically configured to: acquiring takeout subscription information of a user; the takeout subscription information is takeout service reservation plan information subscribed by the user; obtaining the takeaway subscription data from the takeaway subscription information.

The takeaway order processing unit 602 is specifically configured to: acquiring reservation time information of delivery of the takeout service from the takeout reservation data; determining the plurality of service time points according to the reservation time information, and generating the first order for each of the plurality of service time points.

The takeaway order processing unit 602 is specifically configured to: determining order generation time for generating a take-out order according to each service time point in the plurality of service time points; and if the current time is the order generation time, screening target service data matched with the takeout reservation data from the takeout service data.

The takeaway order processing unit 602 includes a data filtering subunit, where the data filtering subunit is configured to: screening out alternative service data from the takeout service data according to at least one of distance data, user evaluation data and user preference data; the distance data is distance information between an address providing takeout service and an address consuming the takeout service; the user evaluation data is historical evaluation information of the user for the takeout service; the user preference data is preference information of a user;

and screening the target service data from the alternative service data.

Wherein the data screening subunit is further configured to: and if the alternative service data matched with the takeout reservation data are not screened, carrying out alarm prompt.

Wherein the take-away order processing unit 602 includes an influence factor obtaining subunit configured to: determining an influence factor of the takeout service data and a weight corresponding to the influence factor; the influence factor is used for determining the matching degree of the takeout service data and the takeout reservation data; correspondingly, the data screening subunit is specifically configured to: determining a recommended score of the takeout service data matched with the takeout reservation data in the takeout service data according to the influence factors and the weights corresponding to the influence factors; and determining the target service data according to the recommended score.

Wherein the data screening subunit is specifically configured to: and taking the takeout service data with the highest recommended score as the target service data.

Wherein the influence factor at least comprises one of the following factors: a distance factor; a favorite label factor; a date on shelf factor; a historical dispatch date factor; a take-out service package popularity factor; a surprise factor;

the influence factor obtaining subunit is specifically configured to: calculating the weight of the distance factor by adopting a step function; determining the favorite matching quantity of user favorite labels on the takeout service package label matching, and calculating the weight of favorite label factors by adopting a linear function of the favorite matching quantity; calculating the weight of the date-setting factor by using an attenuation function; calculating the weight of the historical dispatching date factor by adopting a quadratic increasing function; calculating the weight of the hot degree factor of the takeaway service package by using a logarithmic function; calculating the weight of the surprise factor by using a random function;

the data screening subunit is specifically configured to: and adding the weights of the at least one factor, and taking the addition result as the recommendation score.

The takeaway order processing unit 602 further includes a pushing subunit, where the pushing subunit is configured to: pushing the take-away order to a computing device of a take-away service provider.

The takeout service data is package information which can be provided for a user; the target service data is target package information which is screened from the package information and matched with the takeout appointment data at the target service time point.

A sixth embodiment of the present application provides an electronic device for a service order processing method, corresponding to the first embodiment. Fig. 7 is a schematic view of the electronic device.

The electronic device shown in fig. 7 includes: a memory 701, and a processor 702; the memory 701 is configured to store computer-executable instructions, and the processor 702 is configured to execute the computer-executable instructions to:

Optionally, the processor is further configured to execute the following computer-executable instructions: obtaining service reservation plan information corresponding to the target user information; obtaining the service reservation data from the service reservation plan information.

Optionally, the processor is further configured to execute the following computer-executable instructions: generating a first order not containing target service data for each of the plurality of service time points; adding the target service data to the first order to generate a second order; and taking the second order as the target service order.

Optionally, the processor is further configured to execute the following computer-executable instructions: before the target service time point, screening target service data matched with the service reservation data at the target service time point from service data according to the influence factor value; the service data includes information of services that can be provided for the target user information.

Optionally, the processor is further configured to execute the following computer-executable instructions: determining a recommended score of the service data matched with the service reservation data at the target service time point according to the influence factor value; and determining the target service data according to the recommended score.

Optionally, the processor is further configured to execute the following computer-executable instructions: and taking the service data with the highest recommendation score as the target service data.

Optionally, the processor is further configured to execute the following computer-executable instructions:

adding the weight of at least one factor of the distance factor, the preference label factor, the on-shelf date factor, the historical delivery date factor, the service package heat factor and the surprise factor, and taking the sum result as the recommendation score.

Optionally, the processor is further configured to execute the following computer-executable instructions: screening out alternative service data which can be provided for the target user information at the target service time point from service data which can be provided for the target user information according to at least one of distance data, user evaluation data and user preference data; screening target service data provided aiming at the target user information at the target service time point from the alternative service data according to the influence factor value;

Optionally, the processor is further configured to execute the following computer-executable instructions: and if the alternative service data is not screened, carrying out alarm prompt.

Optionally, the processor is further configured to execute the following computer-executable instructions: and providing the target service data to a terminal corresponding to the target user information according to the target service order.

Optionally, the processor is further configured to execute the following computer-executable instructions: if the target service data is not obtained, requesting to obtain service intention data aiming at the target user information, and generating the target service order according to the service intention data; the service intention data is service data represented by input information corresponding to target user information.

A seventh embodiment of the present application provides an electronic device for the takeaway order recommendation method, corresponding to the second embodiment. The schematic view of the electronic device is similar to fig. 7.

A seventh embodiment provides an electronic device, comprising: a memory, and a processor; the memory is to store computer-executable instructions, and the processor is to execute the computer-executable instructions to:

obtaining takeout service data; the takeout service data is data for representing a takeout service that can be provided to a user; screening out target service data matched with the takeout reservation data at the target service time point from the takeout service data; and generating a recommended takeout order corresponding to the target service time point according to the target service data.

Optionally, the processor is further configured to execute the following computer-executable instructions: generating a first order not containing takeout service data for each service time point in the plurality of service time points according to the takeout reservation data; adding the target service data into a first order corresponding to the target service time point to generate a second order, wherein the second order is used for providing takeout services represented by the target service data to a user at the target service time point; taking the second order as the take-away order.

Optionally, the processor is further configured to execute the following computer-executable instructions: acquiring takeout subscription information of a user; the takeout subscription information is takeout service reservation plan information subscribed by the user;

obtaining the takeaway subscription data from the takeaway subscription information.

Optionally, the processor is further configured to execute the following computer-executable instructions: acquiring reservation time information of delivery of the takeout service from the takeout reservation data; determining the plurality of service time points according to the reservation time information, and generating the first order for each of the plurality of service time points.

Optionally, the processor is further configured to execute the following computer-executable instructions: determining order generation time for generating a take-out order according to each service time point in the plurality of service time points;

and if the current time is the order generation time, screening target service data matched with the takeout reservation data from the takeout service data.

Optionally, the processor is further configured to execute the following computer-executable instructions: screening out alternative service data from the takeout service data according to at least one of distance data, user evaluation data and user preference data; the distance data is distance information between an address providing takeout service and an address consuming the takeout service; the user evaluation data is historical evaluation information of the user for the takeout service; the user preference data is preference information of a user;

and screening the target service data from the alternative service data.

Optionally, the processor is further configured to execute the following computer-executable instructions: and if the alternative service data matched with the takeout reservation data are not screened, carrying out alarm prompt.

Optionally, the processor is further configured to execute the following computer-executable instructions: determining an influence factor of the takeout service data and a weight corresponding to the influence factor; the influence factor is used for determining the matching degree of the takeout service data and the takeout reservation data;

determining a recommended score of the takeout service data matched with the takeout reservation data in the takeout service data according to the influence factors and the weights corresponding to the influence factors; and determining the target service data according to the recommended score.

Optionally, the processor is further configured to execute the following computer-executable instructions: and taking the takeout service data with the highest recommended score as the target service data.

optionally, the processor is further configured to execute the following computer-executable instructions: calculating the weight of the distance factor by adopting a step function; determining the favorite matching quantity of user favorite labels on the takeout service package label matching, and calculating the weight of favorite label factors by adopting a linear function of the favorite matching quantity; calculating the weight of the date-setting factor by using an attenuation function; calculating the weight of the historical dispatching date factor by adopting a quadratic increasing function; calculating the weight of the hot degree factor of the takeaway service package by using a logarithmic function; calculating the weight of the surprise factor by using a random function; and adding the weights of the at least one factor, and taking the addition result as the recommendation score.

Optionally, the processor is further configured to execute the following computer-executable instructions: pushing the take-away order to a computing device of a take-away service provider.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

1. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.

2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the claims that follow.

Claims

1. A service order processing method, comprising:

2. The method of claim 1, wherein obtaining service subscription data corresponding to the target user information sent by the terminal corresponding to the target user information comprises: obtaining service reservation plan information corresponding to the target user information;

3. The method of claim 1, further comprising: generating a first order not containing target service data for each of the plurality of service time points;

4. The method of claim 3, wherein obtaining the target service data provided for the target user information at the target service time point according to the impact factor value comprises: before the target service time point, screening target service data matched with the service reservation data at the target service time point from service data according to the influence factor value; the service data includes information of services that can be provided for the target user information.

5. The method of claim 4, wherein the screening the service data for target service data matching the service subscription data at the target service time point according to the impact factor value comprises:

determining a recommended score of the service data matched with the service reservation data at the target service time point according to the influence factor value;

and determining the target service data according to the recommended score.

6. The method of claim 5, wherein said determining said target service data according to a recommendation score comprises: and taking the service data with the highest recommendation score as the target service data.

7. The method of claim 5, wherein obtaining the impact factor value comprises at least one of:

8. The method of claim 1, further comprising: screening out alternative service data which can be provided for the target user information at the target service time point from service data which can be provided for the target user information according to at least one of distance data, user evaluation data and user preference data;

9. A takeaway order recommendation method, comprising:

10. A service reservation system, comprising: a client, a service subscription computing device, a computing device of a service provider;

pushing the target service order to a computing device of a service provider;