CN113905070B - Service providing method and system - Google Patents

Service providing method and system Download PDF

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Publication number
CN113905070B
CN113905070B CN202111153569.8A CN202111153569A CN113905070B CN 113905070 B CN113905070 B CN 113905070B CN 202111153569 A CN202111153569 A CN 202111153569A CN 113905070 B CN113905070 B CN 113905070B
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service
target user
computing
determining
service computing
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CN113905070A (en
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卢国鸣
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Xingrong Shanghai Information Technology Co ltd
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Xingrong Shanghai Information Technology Co ltd
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Priority to CN202111153569.8A priority Critical patent/CN113905070B/en
Priority to CN202311638845.9A priority patent/CN117560400A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/52—Network services specially adapted for the location of the user terminal
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • H04L67/63—Routing a service request depending on the request content or context

Abstract

The embodiment of the specification provides a service providing method and a service providing system. Wherein the method comprises the following steps: determining a prediction service computing end based on the position information of the target user; the prediction service computing end is computing equipment of a service place to which the target user possibly goes in a preset time period; acquiring related information of a target user; and determining the service provided for the target user through the predicted service computing terminal based on the related information of the target user.

Description

Service providing method and system
Technical Field
The present disclosure relates to the field of big data services, and in particular, to a service providing method and system.
Background
The internet of things is an important component of a new generation of information technology and is also an important development stage of the information age. The internet of things is a network which utilizes local network or internet and other communication technologies to link sensors, controllers, machines, personnel, objects and the like together in a new mode to form the connection of people, objects and objects, and realizes informatization, remote management control and intelligence. In the current mainstream internet of things technology, the basic operation thought is that the edge equipment sends collected data to the data center; then the data center performs operation processing analysis to obtain an operation instruction, and the operation instruction is issued to the edge equipment; and finally, the edge equipment executes the operation instruction to obtain a result required by the user.
In this mode, the demands for processing power, network bandwidth, available storage space, and other resources available to data centers have grown exponentially over the last decades, and data centers are under pressure to process vast amounts of data. Thus, an edge calculation mode is proposed: every edge device of the Internet of things is enabled to have data acquisition, analysis, calculation, communication and most important intelligence. How to provide services to users better based on edge computation is a problem that needs to be solved at present.
Therefore, it is desirable to provide a service providing method and system to better provide services to users and enhance user experience.
Disclosure of Invention
One of the embodiments of the present specification provides a service providing method. The service providing method includes: determining a prediction service computing end based on the position information of the target user; the prediction service computing end is computing equipment of a service place to which the target user possibly goes in a preset time period; acquiring related information of the target user; and determining the service provided for the target user through the predicted service computing terminal based on the related information of the target user.
One of the embodiments of the present specification provides a service providing system. The system comprises: the system comprises a determining module, an acquiring module and a service providing module; the determining module is used for determining a prediction service computing end based on the position information of the target user; the prediction service computing end is computing equipment of a service place to which the target user possibly goes in a preset time period; the acquisition module acquires the related information of the target user; and the service providing module determines the service provided for the target user through the predicted service computing terminal based on the related information of the target user.
One of the embodiments of the present specification provides a service providing apparatus including a processor for performing a service providing method.
One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs a service providing method.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein.
FIG. 1 is an exemplary schematic diagram of an application scenario of a service providing system shown in some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a service providing method according to some embodiments of the present description;
FIG. 3 is an exemplary diagram of determining a predictive service computing end according to some embodiments of the present disclosure;
FIG. 4 is an exemplary flow chart for determining target data for a predictive service computing end according to some embodiments of the present disclosure;
fig. 5 is a block diagram of a service providing system according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is an exemplary schematic diagram of an application scenario of a service providing system according to some embodiments of the present description.
As shown in fig. 1, in an application scenario, the service providing system 100 may include a server 140, a service location 150, and a predictive service computing side 160.
In some embodiments, the service providing system 100 may implement service provision by implementing the methods and/or processes disclosed in this specification.
For example, in a typical application scenario, the server 140 may determine the predictive service computing side 160 based on the location information 120 of the target user 110; the server 140 may determine the service provided to the target user through the predicted service computing terminal 160 based on the related information 130 of the target user. In some embodiments, the server 140 may determine data that may be used to calculate the service provided to the target user 110, and send the determined data to the prediction service calculation end 160, where the prediction service calculation end 160 calculates the service provided to the user, so as to increase the speed of recommendation or query, and the user experience of the target user 110.
Server 140 may be used to manage resources and process data and/or information from at least one component of the present system or external data sources (e.g., a cloud data center). Server 140 may execute program instructions to perform one or more of the functions described herein based on such data, information, and/or processing results. In some embodiments, server 140 may be a single server or a group of servers. The server farm may be centralized or distributed (e.g., server 140 may be a distributed system), may be dedicated, or may be serviced concurrently by other devices or systems. In some embodiments, server 140 may be regional or remote. In some embodiments, server 140 may be implemented on a cloud platform or provided in a virtual manner. For example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-layer cloud, or the like, or any combination thereof.
In some embodiments, server 140 may determine predictive service computing end 160 based on location information 120 of the target user. In some embodiments, the server 140 may obtain the relevant information 130 of the target user, and provide the service to the target user 110 through the predictive service computing terminal 160 according to the relevant information 130 of the target user. In some embodiments, server 140 may determine a service location 150 to which target user 110 may be traveling.
Service location 150 refers to a location where a service can be provided to a target user. In some embodiments, the service sites may include various service sites such as dining, leisure, accommodation, travel, and the like.
Predictive services computing end 160 refers to one or more computing devices or software for predictive services.
In some embodiments, the predictive service computing end 160 may be used by any user, such as a person, business, etc. Predictive service computing end 160 may include a terminal or server of a service location to which a user may be going in the future. For example, the user 110 will go to a computing device 160 used in the service site 150 in the future. In some embodiments, predictive service computing end 160 may be one or any combination of mobile device 160-1, tablet computer 160-2, laptop computer 160-3, desktop computer 160-4, and other input and/or output enabled devices. The above examples are only intended to illustrate the breadth of the scope of the predictive service computing end 160 devices and not to limit the scope thereof.
Fig. 2 is an exemplary flow chart of a service providing method according to some embodiments of the present description.
Step 210, determining a prediction service computing end based on the location information of the target user. In some embodiments, step 210 may be performed by determination module 510.
The target user may refer to an object to be serviced. In some embodiments, the target user may be a user who needs to be involved in a large amount of computing data for the service that needs to be provided. In some embodiments, the target user may be a user who has used and/or will likely use certain specific services in the future, within a certain geographic area. For example, in a certain geographical area, a user who has experienced a recommended service of a service location and a user who expressed a appeal of using the recommended service provided by the service location belong to the target user of the service location.
The location information of the target user may refer to the geographic location where the target user is currently located. In some embodiments, the location information of the target user may be represented by location coordinates or location names. For example, GPS location coordinates, latitude and longitude, relative location, location name (e.g., XX cell, XX mall, etc.), etc.
In some embodiments, the location information of the target user may be obtained through a mobile terminal carried with the user. For example, by a positioning device provided in the mobile terminal.
In some embodiments, the location information of the target user may be obtained by a positioning technique. The positioning techniques used in some embodiments of the present description may be based on Global Positioning System (GPS), global navigation satellite system (GLONASS), COMPASS navigation system (COMPASS), galileo positioning system, quasi Zenith Satellite System (QZSS), wireless fidelity (WiFi) positioning techniques, or the like, or any combination thereof. One or more of the above positioning techniques may be used interchangeably throughout this specification.
The predictive service computing side may refer to a computing device that may process data to compute a service that may be provided to a target user. In some embodiments, the predictive service computing end may include one or any combination of mobile devices, tablet computers, laptop computers, desktop computers, and the like, devices having data processing capabilities.
In some embodiments, the predictive service computing end may be a terminal or server within a service venue (e.g., service venue 150) near the location of the target user.
In some embodiments, a processing device (e.g., determination module 510) may select one or more of a plurality of computing devices within a preset range centered on location information as a predictive service computing end based on the location information of the target user. For example, a computing device closest to a destination (e.g., a service location) of a target user may be selected from a plurality of computing devices as a predictive service computing terminal; for another example, the relationship between the candidate service location and the history service location and the data information may be processed based on the model, to obtain a processing result, and the prediction service computing end may be determined based on the processing result.
For more description of determining the predicted service computing end based on the location information of the target user, refer to fig. 3 and the related description thereof, and are not repeated herein.
Step 220, obtaining the related information of the target user. In some embodiments, step 220 may be performed by acquisition module 520.
The relevant information of the target user is a summary of various information related to the target user.
In some embodiments, the relevant information of the target user may include fingerprint data of the target user. The fingerprint data may be used to identify the type or identity of the user.
In some embodiments, the relevant information of the target user may include business data of the user, such as accessed services, data generated by the application, and so forth.
In some embodiments, the relevant information for the target user may also include the types of services that the target user has browsed, interested in, and/or used and the service locales reached. For example, evaluation and feedback of the target user after using the service.
In some embodiments, the relevant information of the target user may also include characteristic data of the target user. The feature data may include products abstracted from information such as social attributes, habits, consumer behaviors, etc. of the user, and the elements thereof include "user attributes, user features, user labels. For example, the user name, gender, age, consumption habits, purchasing power, hobbies, social networking behavior, etc. of the target user.
The relevant information of the target user may be used to identify the user identity or to determine the computing device, service type, associated with providing the service to the user. For example, fingerprint data may be used to authenticate the user identity; the user's business data may be used to determine services (e.g., recommended services) provided to the target user, and so on.
In some embodiments, the processing device (e.g., the acquisition module 520) may acquire relevant information about the target user by way of data mining (e.g., collecting fingerprint data of the user via a fingerprint punch, etc., mining data generated after the target user uses the service, etc.). In some embodiments, the processing device may also obtain the relevant information of the target user by reading from the storage device and the database and calling the relevant data interface.
In step 230, the service provided to the user is determined by the predicted service computing terminal based on the relevant information of the target user. In some embodiments, step 230 may be performed by service providing module 530.
A service may be some function or support that is provided to a user. The services may include query services, recommendation services, translation services, and the like. For example, the target user is queried to provide an analysis report to the target user, push a list of service items to the target user, subscribe to service items, query history service items, and so forth.
In some embodiments, in determining the services provided, a location at which the services are provided to the target user (e.g., service location 150), data related to the service location, etc. may also be determined. Such as location, size, nature of the service site, and the manner of authentication, etc.
In some embodiments, the prediction service computing end may calculate, based on relevant information of the target user, to obtain a service corresponding to the target user (for example, the target user may need to) and push the service to the target user. For example, the prediction service computing end may calculate, according to the relevant information of the target user, that the user has a higher interest level in a certain service (for example, XX commodity purchase package) in the service location, so that details of the service are pushed to the target user.
In some embodiments, the predicted service computing end may input relevant information of the target user into a service prediction model, and the service prediction model outputs the service pushed to the target user.
The service prediction model may be trained from a plurality of training samples and their labels. Each training sample may include information about a plurality of sample target users, and the tag may be a history service provided to the sample target users. The labels can be obtained by manual labeling or automatic labeling. And carrying out multi-round training on the initial service prediction model based on a plurality of training samples and labels thereof, so as to obtain the service prediction model.
In some embodiments, the service prediction model may be trained based on common model training approaches, such as gradient descent methods, and the like, which are not limited in this specification.
In some embodiments, the predictive service computing side may also compute the service based on the target data. The target data may include at least a portion of the relevant information of the target user. The calculation manner may be the same as that of calculating the service based on the related information of the target user, and further description about the target data may be referred to fig. 4 and related description thereof, which will not be repeated here.
It can be appreciated that the predicted service computing end determines that the service provided to the user may be in a variety of application scenarios. For example, application scenarios may include, but are not limited to, authentication, recommendation, query, analysis, reservation, and the like. Some of these application scenarios are illustrated below.
For example, in some application scenarios, it is assumed that user a will be going to service location X, and relevant information (e.g., fingerprint information) of user a is sent to the computing end of service location X to identify the type or identity of user a. If the computing end of the service location X calculates that the user a may view the report, etc. of the service location X, which require a large amount of analysis to obtain the result, the computing end of the service location X may automatically invoke the data to generate the analysis result. When the user a will or reaches the service location X, the terminal (e.g., mobile phone, tablet, notebook computer, etc.) of the user a is automatically connected with the computing terminal to obtain an analysis result. Therefore, the waiting time required by the user A in the process of checking the report and analyzing the report can be effectively reduced, and the user experience of the user is improved.
For another example, in yet other application scenarios, it is also assumed that user a will go to service location X, where the computing end of service location X may obtain services (e.g., training lessons, videos, music, live, etc.) that user a has historically accessed. The computing end can load the data of the service accessed by the history in advance, and when the user A goes to the service location X, the computing end is connected with the terminal of the user A to provide the service for the user A. Therefore, the waiting time required by the user when the service is provided for the user A can be effectively reduced, the targeted service can be provided for the user more accurately, and the user experience of the user can be improved.
For another example, in another application scenario, it is assumed that the user a will go to the service location X, and the user preference information and the service accessed by the user in history are transmitted to the computing end of the service location X, and the computing end calculates in advance a recommended commodity to the user a based on the received data and the characteristics of the service location X itself.
For another example, in the application scenario of data query, it is assumed that user a will go to service location X to view some item of data, but this item of data requires more complex calculations, such as calculating historical credit for the user, calculating estimated delivery time for the good, etc. The computing end of the service place X can perform corresponding computation in advance, and the computed data can be rapidly sent to the user A when the user A arrives at the service place X, so that the query speed of the user is greatly improved, and the user experience of the user is improved.
It should be noted that, the service performed in advance by the service location may be specific to a specific user or users (for example, target users), and not all users may be required. When the data volume related to the calculated service is relatively large, the service can be calculated and provided in advance, so that time delay is avoided, but if the data volume is relatively small, the user can correspondingly wait for the arrival of the user at the service place to calculate and provide the service, and smooth and efficient service is ensured.
In the embodiment of the specification, the service which can be provided for the target user is calculated in advance by the predicted service calculating end based on the related information of the target user, so that the probability that the service is the service which is interested by the target user can be improved, the time required for providing the service for the target user can be reduced, the user can enjoy the service more efficiently, and the user experience of the target user is improved.
FIG. 3 is an exemplary diagram illustrating a determination of a predictive service computing end in accordance with some embodiments of the present disclosure.
Step 310, determining candidate service computing ends within a preset range centering on the location information based on the location information of the target user. In some embodiments, step 310 may be performed by determination module 510.
In some embodiments, the preset range may refer to a predetermined size of area. For example, the preset range may be an area centered on the location information (e.g., centered on a certain coordinate or a certain mall, cell), and within a radius of a preset distance. The preset distance may be 500m, 1000m, 2000m, etc.
The candidate service computing end may refer to a service computing end in a candidate service location within a preset range. For example, as shown in fig. 3, assuming that the location information 120 of the target user is taken as the center and the distance indicated by the arrow is taken as the radius, the size of the area in the dashed box is taken as the preset range, and the computing end 301 in the service location 304 in the preset range is taken as the candidate service computing end.
The candidate service location refers to a location that is located within a preset range and can provide a service to the target user. Such as service location 304 and service location 305.
In some embodiments, the processing device (e.g., the determination module 510) may determine candidate service places within a preset range centered on the location information based on the location information of the target user, and then take the computing end within the candidate service places as the candidate service computing end. For example, the processing device may directly determine the service computing end of the candidate service location within the preset range as the candidate service computing end. For another example, the processing device may screen and determine the candidate service computing end from a preset range through a preset rule. The preset rules may include selection by type of service computing terminal, selection by device load of the service computing terminal (e.g., selection of a candidate service computing terminal with a device load of less than 50%), selection by location of the service computing terminal (e.g., whether the location is easily reached), etc.
Step 320, determining a predicted service computing end from the candidate service computing ends. In some embodiments, step 320 may be performed by determination module 510.
The predicted service computing side refers to a service computing side for determining computation of a service provided to a target user. The prediction service computing end may be one or more of multiple candidate service computing ends, for example, if the computing amount required for determining the service is small, one candidate service computing end may be selected as the prediction service computing end, and if the computing amount required for determining the service is large, multiple candidate service computing ends may be selected to be simultaneously used as the prediction service computing end.
The processing device may determine the predicted service computing end from the candidate service computing ends in a number of ways. For example, the processing device may select one or more of the candidate service computing terminals with the lowest device load as the predictive service computing terminal. For another example, the processing device may select one or more of the candidate service computing ends with the highest computing power as the predictive service computing. The equipment load and the computing capacity of the candidate service computing end can be obtained by reading the equipment parameters of the candidate service computing end.
In some embodiments, the processing device may determine the predicted service computing end from the candidate service computing ends in a manner described in embodiments below.
In some embodiments, the processing device may process the relationship between the candidate service location and the historical service location, the related information of the candidate service location, the time information, the environment information, and the related information of the target user based on the first prediction model 302, to obtain a processing result.
A historic service location may refer to a location that provides services to a target user at historic times. The historical time may be the past 1 day, 1 week, 1 month, etc.
In some embodiments, there may be a relationship between the candidate service locale and the historical service locale. Such as one or more of business relationship, distance, travel time.
The business relationship may include similarity and/or inheritance. The similarity may refer to a degree of similarity between the candidate service location and the historical service location. For example, the number of identical users included in the candidate service location and the history service location may be regarded as the similarity, and the greater the number, the greater the similarity. For another example, the number of the same users may be converted, for example, vector conversion may be performed, and the converted data may be regarded as similarity. The same user may refer to the same user, or may refer to the same type and/or time period of users. For example, the type is a restaurant category, the time period is 17:00-18:00, the same user is a user of the restaurant category and/or the time period is 17:00-18:00. Inheritance may refer to inheritance relationships of candidate service locales and historical service locales. For example, inheritance relationships over time. The greater the number of identical users for which continuity exists in time, the higher the degree of inheritance.
The distance may include a straight line distance, an actual distance, a spherical distance, etc.
The travel time may include walking time and/or driving time, etc.
In some embodiments, business relationships may be determined based on types, historical orders, etc. between candidate service locations and historical service locations. For example, based on the historical order, a manner of manually counting or processing the historical order based on a model is adopted to obtain the number of candidate service places and the historical service places containing the same user, and the similarity and/or the inheritance degree are determined based on the number.
The time information may refer to a time period associated with business, activity, etc. of the candidate service location. The time information may include weekdays, weekends, holidays, different time periods of the day (e.g., 10:00 am to 18:00 pm), etc.
The environmental information may refer to the surrounding environment of the candidate service location. In some embodiments, the environmental information may include weather environments, traffic environments, and the like. Such as whether the weather is hot, whether traffic is jammed, etc.
Details of the relevant information about the target user can be found in the relevant description of fig. 2, and will not be described here again.
In some embodiments, the processing device may process the keyword in the manner of identifying the relationship between the candidate service location and the historical service location, the related information of the candidate service location, the time information, the environment information, the related information of the target user based on the first prediction model to obtain the processing result. In some embodiments, the processing device may further perform vector conversion, for example, conversion into a feature vector, on the basis of the first prediction model, on the relationship between the candidate service location and the historical service location, the related information of the candidate service location, the time information, the environment information, and the related information of the target user, where the first prediction model processes the feature vector to obtain a processing result.
The processing result may refer to a likelihood that the candidate service location may be a predicted service location. In some embodiments, the processing results may be represented with probabilities, representing probabilities that candidate service locations may be predicted service locations, e.g., 0.75, 0.8, 0.9, etc. In some embodiments, the processing result may also be represented by 0 or 1, 1 indicating that the candidate service location may be the predicted service location, and 0 indicating that the candidate service location may not be the predicted service location.
In some embodiments, the type of the first predictive model may include a neural network model, a deep neural network model, a logistic regression model, and the like.
In some embodiments, the first predictive model may be trained based on a plurality of training samples and labels. Each training sample comprises a relation between a sample candidate service place and a historical service place, related information of the sample candidate service place, sample time information, sample environment information and related information of a sample target user, and the label is a sample prediction service place. The training data may be obtained based on historical data and the tag may be determined from a plurality of sample candidate service sites by way of manual labeling or automatic labeling.
For a description of the training manner of the first prediction model, reference may be made to the related description of training of other models in this specification, which is not repeated here.
The processing device may determine a predicted service location from the candidate service locations based on the processing results. The predicted service location may refer to a location for providing a service to a target user determined after prediction by a model.
In some embodiments, the processing device may select, based on the processing result, a candidate service location corresponding to the probability maximum as the predicted service location.
In some embodiments, the processing device may select a candidate service location corresponding to a processing result of 1 as the predicted service location.
The processing device may determine a predicted service computing end from candidate service computing ends corresponding to the predicted service location. The candidate service computing end corresponding to the predicted service location may refer to a service computing end within the predicted service location.
In some embodiments, the processing device may determine the predicted service computing end based on the computational power coefficient of the candidate service computing end and the first prediction model. By way of example only, the manner of determination may be as shown in the embodiments below.
In some embodiments, the processing device may determine a remaining computing power of the candidate service computing end within the predicted service venue.
The remaining computing power may refer to computing power that is not used by the service computing end. I.e. the available computing power of the current service computing end. The larger the residual computing power of the candidate service end computing end is, the more data the candidate service end computing end can transmit, and the stronger the computing power in service determination is.
In some embodiments, the processing device may determine the remaining computing power based on load information of the candidate service computing end. The load information may include a central processor utilization rate, a memory utilization rate, an average load amount per unit time, etc. of the candidate service computing end. The processing device may convert the load information, for example, into a load vector, and then derive the remaining computational power by means of a weighted calculation.
In some embodiments, the processing device may determine the remaining computing power based on the hardware parameters and load information of the candidate service computing end. For example, the central processor of the candidate service computing end is 8 cores, the total computing power of the central processor of the 8 cores is 1000, when the load information shows that the load of the central processor is thirty percent, the central processor is simply converted, for example, every ten percent occupies 100 computing power, and the residual computing power of the candidate service computing end can be determined to be 700. It should be understood that the foregoing examples are for illustrative purposes only, and the remaining computing power may also consider various types of hardware and corresponding loads thereof, such as memory, memory occupancy, etc., which are not limited in this embodiment.
The processing device may determine a computational force coefficient based on the remaining computational force.
The computational force coefficient may be a visual representation of the remaining computational force at the service computing end. For example, the calculation force coefficients are 0.1, 1, 3, 5, and the like.
In some embodiments, the processing device may calculate the computational power coefficient according to a scaling rule based on the remaining computational power. For example, the remaining calculation force is 0.1=calculation force coefficient. Then the corresponding force coefficient for every 100 forces is 1 and the remaining 700 forces can be represented as 7. The residual calculation force and the calculation force coefficient can be in a direct proportion relation, and the larger the residual calculation force is, the larger the calculation force coefficient is.
It should be noted that the processing device may calculate the power coefficient based on the remaining power in various manners, for example, addition, multiplication, division, square, and the like, which is not limited in this embodiment.
The processing device may determine the predicted service computing end from candidate service computing ends corresponding to the predicted service location based on the processing result output by the computing force coefficient and the first prediction model.
In some embodiments, the processing device may perform weighted summation on the calculation force coefficient and the processing result output by the first prediction model, and determine whether the weighted summation result meets a preset condition, and if yes, determine the candidate service computing end as the prediction service computing end.
The preset condition may be that the weighted sum result is greater than a preset threshold, e.g., 1, 2, 5, etc. For example, assuming that the calculation force coefficient is 5, the processing result output by the first prediction model is 0.8 (corresponding to the case of outputting a probability value) or 1 (corresponding to the case of outputting 1 or 0), and the weights of the calculation force coefficient and the processing result are respectively 0.5, the weighted sum result is 0.5×5+0.5×0.8=2.9, assuming that the preset threshold is 1, and the weighted sum result is greater than the preset threshold, the candidate service computing end may be determined as the prediction service computing end.
In some embodiments, the weights may be allocated according to needs, and the allocation manner of the weights is not limited in this embodiment.
According to the method and the device for determining the predicted service calculation end from the candidate service calculation ends in the preset range, which are involved in some embodiments of the present disclosure, the predicted service calculation end can be determined more accurately, the determined predicted service calculation end can be used for better calculating the service provided for the user, and the accuracy of the predicted service can be improved. Meanwhile, the prediction service computing end is comprehensively determined based on the processing result of the first prediction model and the residual computing power of the service computing end, and the subjective influence caused by manual participation can be reduced by adopting a mode of combining the result of intelligent prediction of the machine learning model and the equipment parameters, so that the prediction accuracy can be further improved.
FIG. 4 is an exemplary flow chart for determining target data for a predictive service computing end in accordance with some embodiments herein.
Step 410, screening the relevant information of the target user based on the history data used by the history service computing end in computing the service, and obtaining the target data. In some embodiments, step 410 may be performed by determination module 510.
The history service computing terminal refers to a computing terminal of a service location that has provided a service to a target user. Such as terminals, servers, etc. within the service premises.
The history data may refer to data that has been used to provide a service to a target user or that the target user generates in the course of using the service. Such as identity information of the target user, historical consumption information of the target user, etc.
The target data may be at least a portion of data selected from the relevant information of the target user. For example, the target data may be fingerprint data, identity information, historical consumption information, etc. therein.
In some embodiments, the processing device (e.g., the determination module 510) may filter the relevant information of the target user according to the service type to obtain the target data. For example, the service scenario is a recommended scenario, and when calculating recommended content provided to the target user, the age, sex, occupation, character, etc. of the target user are generally considered, and information such as age, sex, occupation, character, historical consumption, etc. of the target user can be selected from the relevant information of the target user.
In some embodiments, the processing device may predict the target data needed for calculation when site B recommends to the target user by predicting the data used for the recommendation to the target user at site a. For example, when the user is recommended by the location a, the data of the type 1 and the type 2 are used, and then the service provided by the location B for the target user is also used with a high probability in calculation. For example, when the target user inquires a report of XX type in a place A and predicts that the target user inquires data in a place B, the target user also inquires the report of XX type, so that the data required by the place B in calculating the report of XX type can be predicted and selected from relevant information of the target user, and if the report is inquired, fingerprint data in the relevant information of the target user can be selected for verifying the identity of the target user.
In some embodiments, the processing device may also determine the target data to send to the predictive service computing side based on business relationships between the plurality of historical service sites and the predictive service sites.
In some embodiments, the processing device may process the business relationship, the related information of the target user, and the historical data used by the plurality of historical service computing ends through a second prediction model, so as to determine the target data. For example, the processing device may input business relationships, relevant information of the target user, historical data into the second predictive model, and output the target data from the second predictive model.
In some embodiments, the type of the second prediction model may include a neural network model, a deep neural network model, a linear regression model, and the like, and the specific type of the second prediction model is not limited in this embodiment.
The second prediction model may be obtained by training a plurality of training samples and labels. The training samples can comprise business relations, related information of target users and historical data used by a historical service computing end, and the labels correspond to the actually used target data.
For further description of the training of the second predictive model, reference may be made to the training description of other models of the present specification (e.g., the first predictive model), which will not be repeated here.
In some embodiments, the second predictive model may be a graph neural network model. The historical service computation end may include computation ends of a plurality of historical service sites, and the nodes of the graph neural network model may include computation ends of a predictive service computation end and a plurality of historical service sites.
In some embodiments, the node characteristics of the predictive service computing end may include information about the target user. The node characteristics of the computing ends of the plurality of historic service sites may include historical data (e.g., information about the history target user) used when the history provides services to the user, and so on.
In some embodiments, edges of the graph neural network model may include predicting business relationships between service sites and historical service sites, business relationships between historical service sites.
After the business relationship, the related information of the target user, and the history data used by the plurality of history service computing ends are input into the graph neural network model, the node corresponding to the prediction service computing end of the graph neural network model can output the processing result (for example, the target data).
And step 420, the target data is sent to a prediction service computing end, so that the prediction service computing end calculates a service based on the target data. In some embodiments, step 420 may be performed by determination module 510.
In some embodiments, the processing device (e.g., the determination module 510) may send the target data to the predictive service computing side over a network.
The prediction service computing end related in some embodiments of the present disclosure may calculate a service based on target data, where the target data is data screened from relevant information of a target user, and calculate the target data, so that accuracy in calculating the service may be improved, and meanwhile, the amount of calculation required in calculating may be reduced, thereby providing better service for the target user and improving user experience.
In some embodiments, the process 400 may further include step 430.
And 430, updating or screening the related information of the target user based on the feedback of the target user on the service provided by the historical service computing end.
Feedback refers to information that the target user presents or submits after using the provided service. Such as whether the target user is viewing video, music, training lessons, whether recommendations are selected, and post-submission experience with the service, etc.
Updating or filtering may refer to making changes to relevant information of the target user. For example, modifying data in the relevant information of the target user, increasing or decreasing data in the relevant information of the target user, and the like. For example, the feedback of the target user after using the service provided by the historical service computing end can be positive, and the processing device can update the relevant information of the target user based on the feedback of the target user. The processing device may update the number of times of accessing the service in the relevant information of the target user, improve the recommendation frequency of the history service computing end, and so on. For another example, when the feedback is negative, the processing device may filter the same type of history service calculation peer, reduce the recommendation frequency of the same type of history service calculation peer, and so on.
The updating or screening of the related information of the target user in some embodiments of the present disclosure may enable a more accurate calculation result to be obtained when service calculation is performed based on the related information of the target user, improve accuracy of services provided for the target user, and improve user experience.
It should be noted that the above description of the flow 200, the flow 300, and the flow 400 is for illustration and description only, and is not intended to limit the scope of applicability of the present description. Various modifications and changes to flow 200, 300, and 400 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description. For example, in flow 300, a candidate service computing end is determined while a predicted service computing end is determined. For another example, in the process 400, after updating or filtering the related information of the target user, the target data is updated or filtered.
Fig. 5 is a block diagram of a service providing system according to some embodiments of the present description.
In some embodiments, the service providing system 500 may include a determination module 510, an acquisition module 520, and a service providing module 530.
In some embodiments, the determining module 510 may be configured to determine the predictive service computing end based on location information of the target user. The prediction service computing end is computing equipment of a service place to which the target user possibly goes in a preset time period.
In some embodiments, the obtaining module 520 may be configured to obtain information about the target user.
In some embodiments, the service providing module 530 may be configured to determine, based on the information about the target user, a service provided to the target user through the predicted service computing terminal.
In some embodiments, determining the predictive service computing side based on location information of the target user includes: determining a candidate service computing end in a preset range with the position information as a center based on the position information of the target user; the candidate service computing end is a service computing end in a candidate service place within a preset range; and determining a predicted service computing end from the candidate service computing ends.
In some embodiments, determining a predicted service computation from among the candidate service computation may include: processing the relation between the candidate service places and the historical service places, the related information of the candidate service places, the time information, the environment information and the related information of the target user based on the first prediction model to obtain a processing result, wherein the historical service places are places which provide service for the target user in historical time; determining a predicted service location from the candidate service locations based on the processing result; and determining a predicted service calculation end from candidate service calculation ends corresponding to the predicted service places.
In some embodiments, the relevant information of the target user is screened based on the history data used by the history service computing end in computing service; and sending the target data to the prediction service computing end so that the prediction service computing end can calculate the service based on the target data.
It should be understood that the system shown in fig. 5 and its modules may be implemented in a variety of ways. It should be noted that the above description of the candidate display, determination system, and modules thereof is for descriptive convenience only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the determining module 510, the acquiring module 520, and the service providing module 530 disclosed in fig. 5 may be different modules in one system, or may be one module to implement the functions of two or more modules described above. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server 140 or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (6)

1. A service providing method, comprising:
Determining a prediction service computing end based on the position information of the target user; the prediction service computing end is computing equipment of a service place to which the target user possibly goes in a preset time period;
acquiring related information of the target user;
determining a service provided for the target user through the prediction service computing terminal based on the related information of the target user;
the determining a prediction service computing end based on the location information of the target user includes:
determining a candidate service computing end in a preset range centering on the position information based on the position information of the target user; the candidate service computing end is a service computing end in a candidate service place in the preset range;
determining the prediction service computing end from the candidate service computing ends;
wherein the determining the predicted service computing end from the candidate service computing ends includes:
processing the relation between the candidate service places and the historical service places, the related information, the time information, the environment information and the related information of the target user on the basis of a first prediction model to obtain a processing result, wherein the historical service places are places which provide service for the target user in historical time;
Determining a predicted service location from the candidate service locations based on the processing result;
determining the predicted service computing end from candidate service computing ends corresponding to the predicted service places;
the determining the predicted service computing end from the candidate service computing ends corresponding to the predicted service location includes:
determining the residual computing power of a candidate service computing end corresponding to the predicted service place;
determining a computational power coefficient based on the remaining computational power;
and determining the predicted service computing end from candidate service computing ends corresponding to the predicted service places based on the processing results output by the calculation force coefficient and the first prediction model.
2. The method of claim 1, the method further comprising:
based on historical data used by a historical service computing terminal in service computing, screening relevant information of the target user to obtain target data;
and sending the target data to the prediction service computing end so that the prediction service computing end can calculate the service based on the target data.
3. A service providing system comprises a determining module, an acquiring module and a service providing module;
the determining module is used for determining a prediction service computing end based on the position information of the target user; the prediction service computing end is computing equipment of a service place to which the target user possibly goes in a preset time period;
The acquisition module acquires the related information of the target user;
the service providing module determines the service provided for the target user through the predicted service computing terminal based on the related information of the target user;
wherein the determination module is further to:
determining a candidate service computing end in a preset range centering on the position information based on the position information of the target user; the candidate service computing end is a service computing end in a candidate service place in the preset range;
determining the prediction service computing end from the candidate service computing ends;
wherein the determination module is further to:
processing the relation between the candidate service places and the historical service places, the related information, the time information, the environment information and the related information of the target user on the basis of a first prediction model to obtain a processing result, wherein the historical service places are places which provide service for the target user in historical time;
determining a predicted service location from the candidate service locations based on the processing result;
determining the predicted service computing end from candidate service computing ends corresponding to the predicted service places;
Wherein the determination module is further to:
determining the residual computing power of a candidate service computing end corresponding to the predicted service place;
determining a computational power coefficient based on the remaining computational power;
and determining the predicted service computing end from candidate service computing ends corresponding to the predicted service places based on the processing results output by the calculation force coefficient and the first prediction model.
4. The system of claim 3, the service providing module further to:
based on historical data used by a historical service computing terminal in service computing, screening relevant information of the target user to obtain target data;
and sending the target data to the prediction service computing end so that the prediction service computing end can calculate service based on the target data.
5. A service providing apparatus comprising a processor for performing the service providing method of any one of claims 1-2.
6. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the service providing method according to any one of claims 1 to 2.
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