CN113905070A - Service providing method and system - Google Patents
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- H04L67/50—Network services
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Abstract
The embodiment of the specification provides a service providing method and 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 which a target user may go to in a preset time period; acquiring related information of a target user; and determining the service provided for the target user through the prediction service computing terminal based on the relevant information of the target user.
Description
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 era. The internet of things is a network which connects sensors, controllers, machines, personnel, objects and the like together in a new mode by utilizing communication technologies such as local networks or the internet and the like to form the connection between people and objects and between objects and realize informatization, remote management control and intellectualization. In the current mainstream internet of things technology, the basic operation idea is that the edge device sends the acquired data to a data center; then the data center carries out operation processing and analysis to obtain an operation instruction, and the operation instruction is issued to the edge device; and finally, the edge equipment executes the operation instruction to obtain the result required by the user.
In this model, the demand for processing power, network bandwidth, available storage space, and other resources available to data centers has grown exponentially over the last several decades, and data centers are under pressure to process large amounts of data. An edge calculation mode is therefore proposed: each edge device of the Internet of things is provided with data acquisition, analysis calculation, communication and the most important intelligence. How to better provide services to users based on edge computing becomes a problem which needs to be solved urgently at present.
Therefore, it is desirable to provide a service providing method and system to better provide services to users and improve user experience.
Disclosure of Invention
One embodiment 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 which the target user may go to in a preset time period; acquiring related information of the target user; and determining the service provided for the target user through the prediction service computing terminal based on the relevant information of the target user.
One embodiment of the present specification provides a service providing system. The system comprises: the system comprises a determining module, an obtaining 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 which the target user may go to in a preset time period; the acquisition module acquires the relevant information of the target user; and the service providing module determines the service provided for the target user through the prediction service computing terminal based on the relevant information of the target user.
One of the embodiments of the present specification provides a service providing apparatus, including a processor, configured to execute a service providing method.
One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes a service providing method.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and like reference numerals refer to like structures throughout.
FIG. 1 is an exemplary diagram of an application scenario of a service providing system in accordance with some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a service provisioning method according to some embodiments of the present description;
FIG. 3 is an exemplary diagram illustrating a deterministic predictive service computation side according to some embodiments of the present description;
FIG. 4 is an exemplary flow diagram illustrating the determination of target data for a forecast service compute, according to some embodiments of the present description;
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 disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the 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 site 150, and a forecast service computing end 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 prediction service computation end 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 prediction service calculation end 160 based on the relevant 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, and the prediction service calculation end 160 calculates the service provided to the user, which may improve the speed of recommendation or query, etc., and improve the user experience of the target user 110.
The server 140 may be used to manage resources and process data and/or information from at least one component of the present system or an external data source (e.g., a cloud data center). Server 140 may execute program instructions based on the data, information, and/or processing results to perform one or more of the functions described herein. In some embodiments, the server 140 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., server 140 can be a distributed system), can be dedicated, or can be serviced by other devices or systems at the same time. In some embodiments, the server 140 may be regional or remote. In some embodiments, the server 140 may be implemented on a cloud platform or provided in a virtual manner. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In some embodiments, the server 140 may determine the prediction service computing side 160 from the 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 forecast service computing end 160 according to the relevant information 130 of the target user. In some embodiments, the server 140 may determine a service site 150 to which the target user 110 may be traveling.
The service site 150 refers to a site that can provide a service to a target user. In some embodiments, the service locations may include various service locations such as dining, leisure, lodging, tourism, and the like.
Prediction service computing side 160 refers to one or more computing devices or software for predicting services.
In some embodiments, the computing end 160 may be any user, such as an individual, a business, or the like, that uses the prediction service. Prediction service computing side 160 may include a terminal or server of a service location that a user may travel to in the future. For example, a computing device 160 used in a service site 150 to which the user 110 will travel in the future. In some embodiments, the prediction service computing side 160 may be one or any combination of mobile device 160-1, tablet computer 160-2, laptop computer 160-3, desktop computer 160-4, or other device having input and/or output capabilities. The above examples are merely illustrative of the breadth of the prediction service computing side 160 device scope and are not limiting thereof.
Fig. 2 is an exemplary flow diagram of a service provisioning method according to some embodiments of the present description.
The target user may refer to an object to be provided with a service. In some embodiments, the target user may be a user who needs to be provided with a service that needs to involve a large amount of computing data. 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 geographic area, a user who has experienced a recommended service of a service location and a user who expresses a complaint of using the recommended service provided by the service location belong to target users of the service location.
The location information of the target user may refer to a 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 a location name. Such as 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 by 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 the Global Positioning System (GPS), the global navigation satellite system (GLONASS), the COMPASS navigation system (COMPASS), the galileo positioning system, the quasi-zenith satellite system (QZSS), wireless fidelity (WiFi) positioning techniques, and the like, or any combination thereof. One or more of the above-described positioning techniques may be used interchangeably in this specification.
The prediction 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 prediction service computing side may include one or any combination of a mobile device, a tablet computer, a laptop computer, a desktop computer, and the like having data processing capabilities.
In some embodiments, the predictive service computing peer may be a terminal or server within a service site (e.g., service site 150) near the location of the target user.
In some embodiments, the processing device (e.g., determination module 510) may select one or more of the plurality of computing devices within a predetermined range centered on the location information as the prediction service computing side 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 peer; for another example, the relationship between the candidate service location and the historical service location and the data information may be processed based on the model to obtain a processing result, and the prediction service calculation end may be determined based on the processing result.
For more description of determining the calculation end of the prediction service based on the location information of the target user, refer to fig. 3 and the related description thereof, which are not described herein again.
The related information of the target user is a general term 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, e.g., accessed services, data generated by applications, etc.
In some embodiments, the relevant information of the target user may also include the type of service that the target user has browsed, is interested in, and/or has used, and the service location that has arrived. 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 user social attributes, living habits, consumer behaviors, and the like, and elements of the feature data include user attributes, user features, and user tags. For example, the user name, sex, age, consumption habit, purchasing ability, hobbies, social networking behavior and other basic information of the target user.
The relevant information of the target user can be used for identifying the identity of the user or determining the computing device and the service type relevant to providing the service for the user. For example, fingerprint data may be used to authenticate a user identity; the user's business data may be used to determine services (e.g., recommended services) to provide to the target user, and so on.
In some embodiments, the processing device (e.g., the obtaining module 520) may obtain the relevant information of the target user by data mining (e.g., collecting fingerprint data of the user through a fingerprint card reader or the like, mining data generated after the target user uses the service, and the like). In some embodiments, the processing device may further obtain the relevant information of the target user by reading from the storage device or the database and calling the relevant data interface.
And step 230, determining the service provided for the user through the forecast service computing end 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 refer to some function or support provided to a user. The services may include query services, recommendation services, translation services, and the like. For example, the target user query provides an analysis report to the target user, pushes a service item list to the target user, reserves service items, queries historical service items, and the like.
In some embodiments, in determining the services provided, the location (e.g., service location 150) where the services are provided to the target user, data related to the service location, and the like may also be determined. Such as location, size, nature of the service location, and authentication means, etc.
In some embodiments, the prediction service computing side may perform computation based on the relevant information of the target user, obtain a service (for example, which may be required by the target user) corresponding to the target user, and push the service to the target user. For example, the prediction service computing end may compute, according to the relevant information of the target user, that the user has a high interest level in a certain service (for example, XX commodity purchase package) in the service location, so as to push details of the service to the target user.
In some embodiments, the prediction service computing side may input the relevant information of the target user into the service prediction model, and output the service pushed to the target user by the service prediction model.
The service prediction model can be trained by a plurality of training samples and labels thereof. Each training sample may include information about a plurality of sample target users, and the label may be a historical service provided to the sample target user. The labels can be obtained through manual labeling or automatic labeling. And performing multiple rounds of training on the initial service prediction model based on a plurality of training samples and labels thereof to obtain the service prediction model.
In some embodiments, the service prediction model may be trained based on a common model training manner, such as a gradient descent method, and the like, which is not limited in this specification.
In some embodiments, the prediction service computing side may also compute services based on the target data. The target data may include at least a portion of the relevant information of the target user. The calculation method may be the same as that of calculating the service based on the relevant information of the target user, and for further description of the target data, reference may be made to fig. 4 and the related description thereof, which are not repeated herein.
It is understood that the prediction service computing side determines that the service provided to the user can be in various application scenarios. For example, application scenarios may include, but are not limited to, authentication, recommendations, queries, analytics, reservations, and the like. Some of these application scenarios are exemplified below.
For example, in some application scenarios, assuming that user a will go to service location X, relevant information (e.g., fingerprint information) of user a is sent to a computing end of service location X to identify the type or identity of user a. If the computing side of the service place X calculates the result that the user A may check the report form, the report and the like of the service place X and need a large amount of analysis, the computing side of the service place X can automatically call the data to generate the analysis result. When the user A is about to get or arrives at the service place X, the terminal (such as a mobile phone, a tablet, a notebook computer and the like) 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 checking the report and analyzing the report can be effectively reduced, and the user experience of the user is improved.
For another example, in still other application scenarios, also assuming that the user a is going to the service site X, the computing side of the service site X may obtain services (e.g., training courses, videos, music, live broadcasts, etc.) that the user a has historically visited. The computing side can load data of the services which are accessed historically in advance, and when the user A goes to the service place X, the computing side is connected with the terminal of the user A to provide the services 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 more accurately provided for the user, and the user experience of the user can be improved.
For another example, in another application scenario, it is assumed that the user a moves to the service location X, and the service accessed by the preference information of the user and the history of the user is sent to the computing end of the service location X, and the computing end calculates in advance the recommended goods that can be recommended 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 the user a will look ahead to the service location X to view a certain item of data, but the item of data needs to perform relatively complicated calculations, such as calculating the historical credit condition of the user, calculating the expected delivery time of goods, and the like. The computing end of the service place X can perform corresponding computation in advance, and when the user A arrives at the service place X, the computed data can be rapidly sent to the user A, 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 made in advance by the service location may be for a certain user or certain specific users (e.g., target users), and not all users may need the service. When the data volume related to the calculated service is large, the service can be calculated and provided in advance, time delay is avoided, and if the data volume is small, the calculation and the service providing can be performed correspondingly when the user arrives at the service place, so that the service is ensured to be performed smoothly and efficiently.
In the embodiment of the present specification, a service that can be provided to a target user is calculated in advance based on relevant information of the target user by a prediction service calculation end, so that not only can the probability that the service is a service in which the target user is interested be improved, but also the time required for providing the service to the target user can be reduced, so that the user can enjoy the service more efficiently, and the user experience of the target user is improved.
FIG. 3 is an exemplary diagram of a deterministic predictive service computation side, according to some embodiments described herein.
And 310, determining candidate service calculation terminals in a preset range with the position information as the center based on the position 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 area of a certain size. For example, the predetermined range may be an area centered on the location information (e.g., centered on a certain coordinate or a certain mall, a cell) and within a radius of a predetermined 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 place within a preset range. For example, as shown in fig. 3, assuming that the position 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 within the dashed line box is the preset range, and the calculation end 301 within the service place 304 within the preset range is the candidate service calculation end.
The candidate service place refers to a place which is located within a preset range and can provide service for the target user. Such as service site 304 and service site 305.
In some embodiments, the processing device (e.g., the determining module 510) may determine, based on the location information of the target user, a candidate service location within a preset range centered on the location information, and then take a computing end within the candidate service location as a candidate service computing end. For example, the processing device may directly determine the service computing end of the candidate service place 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 according to the type of the service computing side, selection according to the equipment load of the service computing side (for example, selecting the candidate service computing side with the equipment load less than 50%), selection according to the location of the service computing side (for example, whether the location is easily reached), and the like.
And step 320, determining a prediction service computing side from the candidate service computing sides. In some embodiments, step 320 may be performed by determination module 510.
The prediction service calculation side refers to a service calculation side for determining calculation of a service provided to a target user. The prediction service computing end may be one or more of a plurality of candidate service computing ends, for example, if the calculation 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 calculation amount required for determining the service is large, a plurality of candidate service computing ends may be selected as the prediction service computing ends at the same time.
The processing device may determine the predicted service computation side from the candidate service computation sides in a variety of ways. For example, the processing device may select one or more of the candidate service computing ends with the lowest device load as the predicted service computing end. For another example, the processing device may select one or more of the candidate service computing ends having the highest computing power as the predicted service computation. The device load and the computing capacity of the candidate service computing end can be obtained by reading the device parameters of the candidate service computing end.
In some embodiments, the processing device may determine the predicted service compute farm from the candidate service compute farms 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 environmental information, and the related information of the target user based on the first prediction model 302 to obtain a processing result.
The historical service location may refer to a location that has provided service to the target user at a historical time. 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 site and the historical service site. Such as one or more of business relationships, distance, travel time.
The business relationships may include similarities 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 historical service location may be used 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 used as the similarity. The same user may refer to the same user, or may refer to users of the same type and/or time period. For example, the category is a restaurant category, the time period is 17:00-18:00, the same user is a restaurant category user and/or a time period is 17:00-18:00 user. The inheritance degree can refer to the inheritance relationship of the candidate service place and the historical service place. E.g., temporal inheritance. The greater the number of identical users with continuity in time, the higher the degree of inheritance.
The distance may include a straight line distance, an actual distance, a spherical distance, and the like.
The travel time may include walking time and/or driving time, etc.
In some embodiments, business relationships may be determined based on the type, historical orders, etc. between candidate service sites and historical service sites. For example, based on the historical order, the number of the candidate service places and the historical service places containing the same user is obtained by adopting a mode of processing the historical order based on manual statistics or a model, and the similarity and/or the inheritance is determined based on the number.
The temporal information may refer to a time period related to business, activities, etc. of the candidate service location. The time information may include weekdays, weekends, holidays, different time periods of the day (e.g., 10: 00 a.m. to 18:00 a.m.), and the like.
The environment information may refer to a surrounding environment of the candidate service place. In some embodiments, the environmental information may include weather environments, traffic environments, and the like. E.g., whether the weather is hot, whether traffic is congested, etc.
For a detailed description of the related information of the target user, reference may be made to the related description of fig. 2, which is not described herein again.
In some embodiments, the processing device may process the keywords to identify the relationship between the candidate service place and the historical service place, the related information of the candidate service place, the time information, the environment information, and 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, to feature vectors, 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 environmental information, and the related information of the target user, based on a first prediction model, and the first prediction model processes the feature vectors to obtain a processing result.
The processing result may refer to a likelihood that the candidate service location may be the predicted service location. In some embodiments, the processing result may be represented by a probability, e.g., 0.75, 0.8, 0.9, etc., indicating a probability that the candidate service location may be the predicted service location. In some embodiments, the processing result may also be represented by 0 or 1, where 1 represents that the candidate service location may be a predicted service location, and 0 represents that the candidate service location cannot be a predicted service location.
In some embodiments, the type of first predictive model may include a neural network model, a deep neural network model, a logistic regression model, or the like.
In some embodiments, the first predictive model may be derived based on a plurality of training samples and label training. Each training sample comprises the relation between a sample candidate service place and a historical service place, the related information of the sample candidate service place, the sample time information, the sample environment information and the related information of a sample target user, and the label is a sample prediction service place. Training data may be obtained based on historical data, and labels may be determined from a plurality of sample candidate service locations by way of manual labeling or automatic labeling.
For the description of the training mode of the first prediction model, reference may be made to the description of the training of other models in this specification, and details are not repeated here.
The processing device may determine a predicted service location from the candidate service locations based on the processing result. The predicted service location may refer to a location determined by prediction through a model for providing a service to a target user.
In some embodiments, the processing device may select, based on the processing result, the candidate service location corresponding to the maximum probability value as the predicted service location.
In some embodiments, the processing device may select a candidate service location corresponding to the processing result of 1 as the predicted service location.
The processing device may determine the predicted service computing side from candidate service computing sides corresponding to the predicted service location. The candidate service calculation end corresponding to the prediction service place can be a service calculation end in the prediction service place.
In some embodiments, the processing device may determine the predicted serving compute node based on the computing power coefficients of the candidate serving compute nodes and the first prediction model. For example only, the manner of determination may be as shown in the embodiments below.
In some embodiments, a processing device may determine a remaining computing power of candidate service computing ends within the predicted service locale.
The remaining computing power may refer to the computing power not used by the service computing side. I.e. the available computing power of the currently serving computing side. The larger the residual computing power of the candidate server computing end is, the more data can be transmitted by the candidate server computing end, and the stronger the computing power is when determining the service.
In some embodiments, the processing device may determine the remaining computing power based on load information of the candidate serving compute farm. The load information may include central processor utilization, memory utilization, average load per unit time, and the like of the candidate service computing side. The processing device may convert the load information, for example, into a load vector, and then obtain the residual computation force by means of a weighted computation.
In some embodiments, the processing device may determine the remaining computing power according to the hardware parameters and the load information of the candidate service computing side. For example, the central processing unit of the candidate service computing end is an 8-core, the total computing power of the central processing unit of the 8-core is 1000, and when the load information shows that the load of the central processing unit is thirty percent, the load information is simply converted, for example, the computing power of 100 is occupied every ten percent, and the remaining computing power of the candidate service computing end can be determined to be 700. It should be understood that the above examples are for illustrative purposes only, and the calculation of the remaining computation force may also consider various types of hardware and their corresponding loads, such as memory, memory occupancy, etc., which is not limited in this embodiment.
The processing device may determine an computation force coefficient based on the remaining computation force.
The computing power coefficient can be a visual representation of the remaining computing power of the service computing side. For example, the calculated force coefficients are 0.1, 1, 3, 5, etc.
In some embodiments, the processing device may calculate the calculated force coefficient according to a certain conversion rule based on the remaining calculated force. For example, the remaining power is 0.1 — the power coefficient. Then the force coefficient for every 100 calculated forces is 1 and the remaining 700 calculated forces are represented as 7. The residual calculated force and the calculated force coefficient can be in a direct proportion relation, and the larger the residual calculated force is, the larger the calculated force coefficient is.
It should be noted that the processing device may calculate the calculation power coefficient based on the remaining calculation power in various ways, such as addition, multiplication, division, square, etc., and this embodiment is not limited thereto.
The processing device may determine the predicted service computation side from candidate service computation sides corresponding to the predicted service location based on the computation force coefficient and a processing result output by the first prediction model.
In some embodiments, the processing device may perform weighted summation on the computation force coefficient and a processing result output by the first prediction model, determine whether a result of the weighted summation satisfies a preset condition, and if so, 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 computation coefficient is 5, the processing result output by the first prediction model is 0.8 (corresponding to the case of outputting the probability value) or 1 (corresponding to the case of outputting 1 or 0), and the weights of the computation coefficient and the processing result are 0.5, respectively, the weighted sum result is 0.5 × 5+0.5 × 0.8 — 2.9, and assuming that the preset threshold is 1 and the weighted sum result is greater than the preset threshold, the candidate service calculation end may be determined as the prediction service calculation end.
In some embodiments, the weight may be assigned according to needs, and the assignment manner of the weight is not limited in this embodiment.
In some embodiments of the present description, the prediction service calculation end is determined from the candidate service calculation ends within the preset range, so that the prediction service calculation end can be determined more accurately, the determined prediction service calculation end can calculate the service provided to the user better, 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 and the prediction accuracy can be further improved by adopting a mode of combining the result of intelligent prediction of the machine learning model and the equipment parameters.
FIG. 4 is an exemplary flow diagram illustrating the determination of target data for a forecast service compute, according to some embodiments of the present description.
And step 410, screening the related information of the target user based on the historical data used by the historical service computing side in computing the service to obtain the target data. In some embodiments, step 410 may be performed by determination module 510.
The historical service computing side refers to a computing side of a service place which provides services for target users. Such as terminals, servers, etc. within the service location.
The historical data may refer to data that has been used to provide a service to a target user or that the target user generated 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 information related to the target user. For example, the target data may be fingerprint data, identity information, historical consumption information, and the like, among others.
In some embodiments, the processing device (e.g., determining module 510) may filter the relevant information of the target user according to the service type, and obtain the target data. For example, the service scenario is a recommendation scenario, and when calculating the recommended content provided to the target user, the age, sex, occupation, character, and the like of the target user are generally considered, and the information of the target user, such as the age, sex, occupation, character, historical consumption, and the like, may be selected from the relevant information of the target user.
In some embodiments, the processing device may predict the target data needed for the recommendation of the target user by venue B by predicting the data used for the recommendation of the target user by venue a. For example, when the user is recommended in the place a, the data of the type 1 and the data of the type 2 are used, and when the service provided to the target user in the place B is calculated, the data of the type 1 and the data of the type 2 are used with a high probability. For example, when a target user inquires an XX-type report at a place a, and when the target user is predicted to go to a place B for data inquiry, the XX-type report is also inquired, so that data required to be used when the place B calculates the XX-type report can be predicted and selected from related information of the target user, and if the inquiry of the report requires permission, fingerprint data in the related information of the target user can be selected for verifying the identity of the target user.
In some embodiments, the processing device may further determine target data to send to the forecast service computing side based on business relationships between the plurality of historical service sites and the forecast service site.
In some embodiments, the processing device may process the business relationship, the related information of the target user, and historical data used by a plurality of historical service computing terminals through a second prediction model to determine the target data. For example, the processing device may input the business relationship, the relevant information of the target user, and the historical data into the second prediction model, and output the target data by the second prediction 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 can be obtained by training a plurality of training samples and labels. The training sample can comprise business relation, relevant information of a target user, historical data used by a historical service calculation end, and a label is corresponding to the target data used really.
For more description of the training of the second prediction model, reference may be made to the training description of other models (e.g., the first prediction model) in this specification, and details thereof are not repeated here.
In some embodiments, the second predictive model may be a graph neural network model. The historical service computing end can comprise computing ends of a plurality of historical service places, and the nodes of the graph neural network model can comprise a prediction service computing end and computing ends of a plurality of historical service places.
In some embodiments, the node characteristics of the prediction service computing side may include relevant information of the target user. The node characteristics of the computation ends of the plurality of history service sites may include history data (e.g., information related to history target users) used when the history provides services to the users, and the like.
In some embodiments, the edges of the graph neural network model may include business relationships between the predicted service sites and the historical service sites, business relationships between the historical service sites.
After the business relationship, the related information of the target user and the historical data used by the plurality of historical service calculation terminals are input into the graph neural network model, the processing result (such as the target data) can be output by the node corresponding to the prediction service calculation terminal of the graph neural network model.
In some embodiments, the processing device (e.g., determination module 510) may send the target data to the prediction service compute farm over a network.
The prediction service computing terminal related in some embodiments of the present description may compute a service based on target data, where the target data is data screened from relevant information of a target user, and perform computation on the target data, so as to improve accuracy in computing the service, and reduce a computation amount required in computing, thereby providing a better service for the target user and improving user experience.
In some embodiments, flow 400 may also include step 430.
And 430, updating or screening the related information of the target user based on the feedback of the target user to the service provided by the historical service computing terminal.
Feedback refers to information presented or submitted by the target user after using the provided service. For example, whether the target user is viewing videos, music, training courses, selecting recommendations, and submitted experiences after using the service.
Updating or filtering may refer to making changes to relevant information of the target user. For example, data in the relevant information of the target user is modified, data in the relevant information of the target user is increased or decreased, and the like. For example, feedback of the target user after using the service provided by the historical service computing terminal may be positive, and the processing device may update the relevant information of the target user based on the feedback of the target user. The processing device can update the times of accessing the service in the related information of the target user, improve the recommendation frequency of the historical service calculation end and the like. For another example, when the feedback is negative, the processing device may filter the same type of history service computing terminals, reduce the recommendation frequency of the same type of history service computing terminals, and the like.
In some embodiments of the present description, the related information of the target user is updated or filtered, so that a more accurate calculation result can be obtained when performing service calculation based on the related information of the target user in the following, accuracy of a service provided for the target user is improved, and user experience is improved.
It should be noted that the above descriptions of the process 200, the process 300, and the process 400 are only for illustration and description, and do not limit the applicable scope of the present specification. Various modifications and changes to flow 200, flow 300, and flow 400 may occur to those skilled in the art upon reading the present specification. However, such modifications and variations are intended to be within the scope of the present description. For example, in flow 300, a candidate service compute is determined while a predicted service compute is determined. For another example, in the process 400, after the relevant information of the target user is updated or filtered, the target data is also 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 determining module 510, an obtaining module 520, and a service providing module 530.
In some embodiments, the determination module 510 may be configured to determine the forecast service computing side based on location information of the target user. The forecast service computing end is computing equipment of a service place which a target user may go to in a preset time period.
In some embodiments, the obtaining module 520 may be configured to obtain the relevant information of the target user.
In some embodiments, the service providing module 530 may be configured to determine, by the prediction service computing side, a service provided to the target user based on the relevant information of the target user.
In some embodiments, determining the forecast service computing side based on the location information of the target user includes: determining candidate service calculation terminals in a preset range with the position information as the 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 in a preset range; and determining a prediction service computing end from the candidate service computing ends.
In some embodiments, determining the predicted service compute farm from the candidate service compute farms may include: processing the relation between the candidate service place and the historical service place, the related information of the candidate service place, the time information, the environmental information and the related information of the target user based on a first prediction model to obtain a processing result, wherein the historical service place is a place which provides service for the target user at the historical time; determining a predicted service location from the candidate service locations based on the processing result; and determining the prediction service computing side from the candidate service computing sides corresponding to the prediction service place.
In some embodiments, the relevant information of the target user is screened based on historical data used by the historical service computing side in computing service; and sending target data to the prediction service computing terminal so that the prediction service computing terminal computes services based on the target data.
It should be understood that the system and its modules shown in FIG. 5 may be implemented in a variety of ways. It should be noted that the above descriptions of the candidate item display and determination system and the modules thereof are only for convenience of description, and the description is not limited to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the determining module 510, the obtaining 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, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose 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 that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server 140 or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
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 which the target user may go to in a preset time period;
acquiring related information of the target user;
and determining the service provided for the target user through the prediction service computing terminal based on the relevant information of the target user.
2. The method of claim 1, wherein determining the forecast service computing side based on the location information of the target user comprises:
determining candidate service computing terminals in a preset range with the position information as the 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 in the preset range;
and determining the prediction service computing side from the candidate service computing sides.
3. The method of claim 2, wherein said determining the predicted service compute node from the candidate service compute nodes comprises:
processing the relation between the candidate service place and a historical service place, the related information of the candidate service place, time information, environment information and the related information of the target user based on a first prediction model to obtain a processing result, wherein the historical service place is a place which provides service for the target user at historical time;
determining a predicted service location from the candidate service locations based on the processing result;
and determining the prediction service computing side from candidate service computing sides corresponding to the prediction service place.
4. The method of claim 3, further comprising:
screening the related information of the target user based on the historical data used by the historical service computing terminal in computing service to obtain target data;
and sending target data to the prediction service computing terminal so that the prediction service computing terminal computes services based on the target data.
5. A service providing system comprises a determining module, an obtaining 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 which the target user may go to in a preset time period;
the acquisition module acquires the relevant information of the target user;
and the service providing module determines the service provided for the target user through the prediction service computing terminal based on the relevant information of the target user.
6. The system of claim 5, the determination module further to:
determining candidate service computing terminals in a preset range with the position information as the 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 in the preset range;
and determining the prediction service computing side from the candidate service computing sides.
7. The method of claim 6, the determination module further to:
processing the relation between the candidate service place and a historical service place, the related information of the candidate service place, time information, environment information and the related information of the target user based on a first prediction model to obtain a processing result, wherein the historical service place is a place which provides service for the target user at historical time;
determining a predicted service location from the candidate service locations based on the processing result;
and determining the prediction service computing side from candidate service computing sides corresponding to the prediction service place.
8. The method of claim 7, the service provisioning module further to:
screening the related information of the target user based on the historical data used by the historical service computing terminal in computing service to obtain target data;
and sending the target data to the prediction service computing terminal so that the prediction service computing terminal computes services based on the target data.
9. A service providing apparatus comprising a processor for executing the service providing method of any one of claims 1 to 4.
10. A computer-readable storage medium storing computer instructions, the computer executing the service providing method according to any one of claims 1 to 4 when the computer reads the computer instructions in the storage medium.
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